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praateekmahajan/deepseek-ocr-vllm.ipynb

Created October 23, 2025 00:50
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Using `deepseek-ai/DeepSeek-OCR` with vLLM
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deepseek-ocr-vllm.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "36453043",
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"import time\n",
"os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"1\""
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "7671ce13",
"metadata": {},
"outputs": [],
"source": [
"from pathlib import Path\n",
"pdf_directory = \"./pdfs\"\n",
"pdf_files = [str(f.resolve())for f in Path(pdf_directory).glob(\"*.pdf\")][:10]"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "80516b16",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Time taken: 68.44 seconds for 10 files with 275 pages @ 4.02 pages per second\n"
]
}
],
"source": [
"import pymupdf\n",
"from PIL import Image\n",
"import io\n",
"images = []\n",
"dpi = 144\n",
"\n",
"t0 = time.perf_counter()\n",
"for pdf_path in pdf_files:\n",
" pdf_document = pymupdf.open(pdf_path)\n",
"\n",
" zoom = dpi / 72.0\n",
" matrix = pymupdf.Matrix(zoom, zoom)\n",
"\n",
" for page_num in range(pdf_document.page_count):\n",
" page = pdf_document[page_num]\n",
"\n",
" pixmap = page.get_pixmap(matrix=matrix, alpha=False)\n",
" Image.MAX_IMAGE_PIXELS = None\n",
"\n",
" img_data = pixmap.tobytes(\"png\")\n",
" img = Image.open(io.BytesIO(img_data))\n",
" images.append(img)\n",
" \n",
" pdf_document.close()\n",
"\n",
"t1 = time.perf_counter()\n",
"print(f'Time taken: {(t1 - t0):.2f} seconds for {len(pdf_files)} files with {len(images)} pages @ {(len(images) / (t1 - t0)):.2f} pages per second')\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "e226ce0a",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"INFO 10-22 17:04:03 [__init__.py:225] Automatically detected platform cuda.\n",
"INFO 10-22 17:04:06 [utils.py:253] non-default args: {'trust_remote_code': True, 'disable_log_stats': True, 'limit_mm_per_prompt': {'image': 1}, 'model': 'deepseek-ai/DeepSeek-OCR'}\n",
"INFO 10-22 17:04:06 [model.py:667] Resolved architecture: DeepseekOCRForCausalLM\n",
"INFO 10-22 17:04:06 [model.py:1756] Using max model len 8192\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"The argument `trust_remote_code` is to be used with Auto classes. It has no effect here and is ignored.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"INFO 10-22 17:04:07 [scheduler.py:219] Chunked prefill is enabled with max_num_batched_tokens=8192.\n",
"\u001b[1;36m(EngineCore_DP0 pid=2185600)\u001b[0;0m INFO 10-22 17:04:09 [core.py:716] Waiting for init message from front-end.\n",
"\u001b[1;36m(EngineCore_DP0 pid=2185600)\u001b[0;0m INFO 10-22 17:04:09 [core.py:94] Initializing a V1 LLM engine (v0.11.1rc3.dev7+ga0003b56b) with config: model='deepseek-ai/DeepSeek-OCR', speculative_config=None, tokenizer='deepseek-ai/DeepSeek-OCR', skip_tokenizer_init=False, tokenizer_mode=auto, revision=None, tokenizer_revision=None, trust_remote_code=True, dtype=torch.bfloat16, max_seq_len=8192, download_dir=None, load_format=auto, tensor_parallel_size=1, pipeline_parallel_size=1, data_parallel_size=1, disable_custom_all_reduce=False, quantization=None, enforce_eager=False, kv_cache_dtype=auto, device_config=cuda, structured_outputs_config=StructuredOutputsConfig(backend='auto', disable_fallback=False, disable_any_whitespace=False, disable_additional_properties=False, reasoning_parser='', enable_in_reasoning=False), observability_config=ObservabilityConfig(show_hidden_metrics_for_version=None, otlp_traces_endpoint=None, collect_detailed_traces=None), seed=0, served_model_name=deepseek-ai/DeepSeek-OCR, enable_prefix_caching=True, chunked_prefill_enabled=True, pooler_config=None, compilation_config={'level': None, 'mode': 3, 'debug_dump_path': None, 'cache_dir': '', 'backend': 'inductor', 'custom_ops': ['none'], 'splitting_ops': ['vllm::unified_attention', 'vllm::unified_attention_with_output', 'vllm::unified_mla_attention', 'vllm::unified_mla_attention_with_output', 'vllm::mamba_mixer2', 'vllm::mamba_mixer', 'vllm::short_conv', 'vllm::linear_attention', 'vllm::plamo2_mamba_mixer', 'vllm::gdn_attention', 'vllm::sparse_attn_indexer'], 'use_inductor': None, 'compile_sizes': [], 'inductor_compile_config': {'enable_auto_functionalized_v2': False, 'combo_kernels': True, 'benchmark_combo_kernel': True}, 'inductor_passes': {}, 'cudagraph_mode': <CUDAGraphMode.FULL_AND_PIECEWISE: (2, 1)>, 'use_cudagraph': True, 'cudagraph_num_of_warmups': 1, 'cudagraph_capture_sizes': [512, 504, 496, 488, 480, 472, 464, 456, 448, 440, 432, 424, 416, 408, 400, 392, 384, 376, 368, 360, 352, 344, 336, 328, 320, 312, 304, 296, 288, 280, 272, 264, 256, 248, 240, 232, 224, 216, 208, 200, 192, 184, 176, 168, 160, 152, 144, 136, 128, 120, 112, 104, 96, 88, 80, 72, 64, 56, 48, 40, 32, 24, 16, 8, 4, 2, 1], 'cudagraph_copy_inputs': False, 'full_cuda_graph': True, 'cudagraph_specialize_lora': True, 'use_inductor_graph_partition': False, 'pass_config': {}, 'max_capture_size': 512, 'local_cache_dir': None}\n",
"\u001b[1;36m(EngineCore_DP0 pid=2185600)\u001b[0;0m INFO 10-22 17:04:10 [parallel_state.py:1325] rank 0 in world size 1 is assigned as DP rank 0, PP rank 0, TP rank 0, EP rank 0\n",
"[Gloo] Rank 0 is connected to 0 peer ranks. Expected number of connected peer ranks is : 0\n",
"[Gloo] Rank 0 is connected to 0 peer ranks. Expected number of connected peer ranks is : 0\n",
"[Gloo] Rank 0 is connected to 0 peer ranks. Expected number of connected peer ranks is : 0\n",
"[Gloo] Rank 0 is connected to 0 peer ranks. Expected number of connected peer ranks is : 0\n",
"[Gloo] Rank 0 is connected to 0 peer ranks. Expected number of connected peer ranks is : 0\n",
"[Gloo] Rank 0 is connected to 0 peer ranks. Expected number of connected peer ranks is : 0\n",
"\u001b[1;36m(EngineCore_DP0 pid=2185600)\u001b[0;0m INFO 10-22 17:04:14 [gpu_model_runner.py:2860] Starting to load model deepseek-ai/DeepSeek-OCR...\n",
"\u001b[1;36m(EngineCore_DP0 pid=2185600)\u001b[0;0m INFO 10-22 17:04:14 [layer.py:518] MultiHeadAttention attn_backend: _Backend.FLASH_ATTN, use_upstream_fa: False\n",
"\u001b[1;36m(EngineCore_DP0 pid=2185600)\u001b[0;0m INFO 10-22 17:04:14 [cuda.py:403] Using Flash Attention backend on V1 engine.\n",
"\u001b[1;36m(EngineCore_DP0 pid=2185600)\u001b[0;0m WARNING 10-22 17:04:14 [vllm.py:867] `torch.compile` is turned on, but the model deepseek-ai/DeepSeek-OCR does not support it. Please open an issue on GitHub if you want it to be supported.\n",
"\u001b[1;36m(EngineCore_DP0 pid=2185600)\u001b[0;0m WARNING 10-22 17:04:14 [vllm.py:867] `torch.compile` is turned on, but the model deepseek-ai/DeepSeek-OCR does not support it. Please open an issue on GitHub if you want it to be supported.\n",
"\u001b[1;36m(EngineCore_DP0 pid=2185600)\u001b[0;0m INFO 10-22 17:04:14 [weight_utils.py:419] Using model weights format ['*.safetensors']\n"
]
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "fe6c779f99b64deaa7589061feeeeda7",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Loading safetensors checkpoint shards: 0% Completed | 0/1 [00:00<?, ?it/s]\n"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[1;36m(EngineCore_DP0 pid=2185600)\u001b[0;0m INFO 10-22 17:04:16 [default_loader.py:314] Loading weights took 1.36 seconds\n",
"\u001b[1;36m(EngineCore_DP0 pid=2185600)\u001b[0;0m INFO 10-22 17:04:17 [gpu_model_runner.py:2921] Model loading took 6.2319 GiB and 2.265197 seconds\n",
"\u001b[1;36m(EngineCore_DP0 pid=2185600)\u001b[0;0m INFO 10-22 17:04:17 [gpu_model_runner.py:3693] Encoder cache will be initialized with a budget of 8192 tokens, and profiled with 11 image items of the maximum feature size.\n",
"\u001b[1;36m(EngineCore_DP0 pid=2185600)\u001b[0;0m WARNING 10-22 17:04:18 [fused_moe.py:882] Using default MoE config. Performance might be sub-optimal! Config file not found at ['/raid/praateekm/batch-inference/.venv/lib/python3.12/site-packages/vllm/model_executor/layers/fused_moe/configs/E=64,N=896,device_name=NVIDIA_A100-SXM4-80GB.json']\n",
"\u001b[1;36m(EngineCore_DP0 pid=2185600)\u001b[0;0m INFO 10-22 17:04:21 [gpu_worker.py:316] Available KV cache memory: 63.38 GiB\n",
"\u001b[1;36m(EngineCore_DP0 pid=2185600)\u001b[0;0m INFO 10-22 17:04:21 [kv_cache_utils.py:1201] GPU KV cache size: 1,107,648 tokens\n",
"\u001b[1;36m(EngineCore_DP0 pid=2185600)\u001b[0;0m INFO 10-22 17:04:21 [kv_cache_utils.py:1206] Maximum concurrency for 8,192 tokens per request: 135.21x\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Capturing CUDA graphs (mixed prefill-decode, PIECEWISE): 100%|██████████| 67/67 [00:05<00:00, 11.20it/s]\n",
"Capturing CUDA graphs (decode, FULL): 100%|██████████| 35/35 [00:01<00:00, 22.95it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[1;36m(EngineCore_DP0 pid=2185600)\u001b[0;0m INFO 10-22 17:04:29 [gpu_model_runner.py:3851] Graph capturing finished in 8 secs, took 1.52 GiB\n",
"\u001b[1;36m(EngineCore_DP0 pid=2185600)\u001b[0;0m INFO 10-22 17:04:29 [core.py:238] init engine (profile, create kv cache, warmup model) took 12.78 seconds\n",
"\u001b[1;36m(EngineCore_DP0 pid=2185600)\u001b[0;0m INFO 10-22 17:04:30 [gc_utils.py:40] GC Debug Config. enabled:False,top_objects:-1\n",
"INFO 10-22 17:04:30 [llm.py:343] Supported tasks: ['generate']\n",
"Time taken: 24.95 seconds for LLM initialization\n"
]
}
],
"source": [
"import os\n",
"os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"1\"\n",
"from vllm import LLM, SamplingParams\n",
"\n",
"t0 = time.perf_counter()\n",
"llm = LLM(\n",
" model=\"deepseek-ai/DeepSeek-OCR\",\n",
" trust_remote_code=True,\n",
" limit_mm_per_prompt={\"image\": 1},\n",
" enforce_eager=False, # Use torch.compile instead of eager execution\n",
")\n",
"t1 = time.perf_counter()\n",
"print(f\"Time taken: {(t1 - t0):.2f} seconds for LLM initialization\")\n",
"\n",
"sampling_params = SamplingParams(\n",
" temperature=0.0, # Deterministic for OCR\n",
")"
]
},
{
"cell_type": "markdown",
"id": "69022c73",
"metadata": {},
"source": [
"# Using base64 as input"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "8af62728",
"metadata": {},
"outputs": [],
"source": [
"import base64\n",
"\n",
"def make_ocr_message(\n",
" image: Image.Image,\n",
" prompt: str = \"<image>\\n<|grounding|>Convert the document to markdown.\",\n",
") -> list[dict]:\n",
" \"\"\"Create chat message for OCR processing.\"\"\"\n",
" # Convert to PIL Image if needed\n",
" pil_img = image\n",
"\n",
" # Convert to RGB\n",
" pil_img = pil_img.convert(\"RGB\")\n",
"\n",
" # Convert to base64 data URI\n",
" buf = io.BytesIO()\n",
" pil_img.save(buf, format=\"PNG\")\n",
" data_uri = f\"data:image/png;base64,{base64.b64encode(buf.getvalue()).decode()}\"\n",
"\n",
" # Return message in vLLM format\n",
" return [\n",
" {\n",
" \"role\": \"user\",\n",
" \"content\": [\n",
" {\"type\": \"image_url\", \"image_url\": {\"url\": data_uri}},\n",
" {\"type\": \"text\", \"text\": prompt},\n",
" ],\n",
" }\n",
" ]\n"
]
},
{
"cell_type": "markdown",
"id": "9911cfbd",
"metadata": {},
"source": [
"## Batching"
]
},
{
"cell_type": "code",
"execution_count": 43,
"id": "16a8fc7b",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Processed 1 pages in 0.33s; Preprocess @ 12.21 p/s, Model @ 4.04 p/s, Combined @ 3.03 p/s\n",
"Processed 1 pages in 0.38s; Preprocess @ 5.87 p/s, Model @ 4.79 p/s, Combined @ 2.64 p/s\n",
"Processed 1 pages in 0.28s; Preprocess @ 11.96 p/s, Model @ 5.01 p/s, Combined @ 3.53 p/s\n",
"Processed 1 pages in 0.33s; Preprocess @ 8.30 p/s, Model @ 4.88 p/s, Combined @ 3.07 p/s\n",
"Processed 1 pages in 0.30s; Preprocess @ 9.86 p/s, Model @ 5.02 p/s, Combined @ 3.33 p/s\n",
"Processed 1 pages in 0.39s; Preprocess @ 5.50 p/s, Model @ 4.84 p/s, Combined @ 2.57 p/s\n",
"Processed 1 pages in 0.29s; Preprocess @ 12.00 p/s, Model @ 4.78 p/s, Combined @ 3.42 p/s\n",
"Processed 1 pages in 0.32s; Preprocess @ 8.80 p/s, Model @ 4.88 p/s, Combined @ 3.14 p/s\n",
"Processed 1 pages in 0.32s; Preprocess @ 8.47 p/s, Model @ 4.91 p/s, Combined @ 3.11 p/s\n",
"Processed 1 pages in 0.28s; Preprocess @ 13.42 p/s, Model @ 4.97 p/s, Combined @ 3.63 p/s\n",
"Processed 1 pages in 0.31s; Preprocess @ 9.44 p/s, Model @ 4.91 p/s, Combined @ 3.23 p/s\n",
"Processed 1 pages in 0.30s; Preprocess @ 10.11 p/s, Model @ 4.93 p/s, Combined @ 3.32 p/s\n",
"Processed 1 pages in 0.50s; Preprocess @ 3.56 p/s, Model @ 4.65 p/s, Combined @ 2.02 p/s\n",
"Processed 1 pages in 0.33s; Preprocess @ 8.18 p/s, Model @ 4.74 p/s, Combined @ 3.00 p/s\n",
"Processed 1 pages in 0.34s; Preprocess @ 7.57 p/s, Model @ 4.77 p/s, Combined @ 2.93 p/s\n",
"Processed 1 pages in 0.28s; Preprocess @ 13.05 p/s, Model @ 4.97 p/s, Combined @ 3.60 p/s\n",
"Processed 1 pages in 0.28s; Preprocess @ 13.34 p/s, Model @ 4.99 p/s, Combined @ 3.63 p/s\n",
"Processed 1 pages in 0.44s; Preprocess @ 4.33 p/s, Model @ 4.81 p/s, Combined @ 2.28 p/s\n",
"Processed 1 pages in 0.32s; Preprocess @ 8.43 p/s, Model @ 4.95 p/s, Combined @ 3.12 p/s\n",
"Processed 1 pages in 0.39s; Preprocess @ 5.43 p/s, Model @ 4.77 p/s, Combined @ 2.54 p/s\n",
"Processed 1 pages in 0.29s; Preprocess @ 11.59 p/s, Model @ 5.00 p/s, Combined @ 3.49 p/s\n",
"Processed 1 pages in 0.30s; Preprocess @ 10.67 p/s, Model @ 4.83 p/s, Combined @ 3.32 p/s\n",
"Processed 1 pages in 0.32s; Preprocess @ 8.80 p/s, Model @ 4.95 p/s, Combined @ 3.17 p/s\n",
"Processed 1 pages in 0.29s; Preprocess @ 10.81 p/s, Model @ 4.95 p/s, Combined @ 3.40 p/s\n",
"Processed 1 pages in 0.35s; Preprocess @ 9.79 p/s, Model @ 4.06 p/s, Combined @ 2.87 p/s\n",
"Processed 1 pages in 0.34s; Preprocess @ 9.78 p/s, Model @ 4.25 p/s, Combined @ 2.96 p/s\n",
"Processed 1 pages in 0.30s; Preprocess @ 11.07 p/s, Model @ 4.84 p/s, Combined @ 3.37 p/s\n",
"Processed 1 pages in 0.29s; Preprocess @ 11.80 p/s, Model @ 4.82 p/s, Combined @ 3.42 p/s\n",
"Processed 1 pages in 0.28s; Preprocess @ 12.51 p/s, Model @ 4.92 p/s, Combined @ 3.53 p/s\n",
"Processed 1 pages in 0.28s; Preprocess @ 13.48 p/s, Model @ 4.96 p/s, Combined @ 3.63 p/s\n",
"Processed 1 pages in 0.28s; Preprocess @ 12.07 p/s, Model @ 4.98 p/s, Combined @ 3.52 p/s\n",
"Processed 1 pages in 0.29s; Preprocess @ 11.53 p/s, Model @ 4.86 p/s, Combined @ 3.42 p/s\n",
"Processed 1 pages in 0.30s; Preprocess @ 10.74 p/s, Model @ 4.95 p/s, Combined @ 3.39 p/s\n",
"Processed 1 pages in 0.31s; Preprocess @ 9.69 p/s, Model @ 4.87 p/s, Combined @ 3.24 p/s\n",
"Processed 1 pages in 0.30s; Preprocess @ 11.26 p/s, Model @ 4.79 p/s, Combined @ 3.36 p/s\n",
"Processed 1 pages in 0.30s; Preprocess @ 10.48 p/s, Model @ 4.93 p/s, Combined @ 3.35 p/s\n",
"Processed 1 pages in 0.31s; Preprocess @ 9.72 p/s, Model @ 4.91 p/s, Combined @ 3.26 p/s\n",
"Processed 1 pages in 0.27s; Preprocess @ 13.48 p/s, Model @ 5.03 p/s, Combined @ 3.66 p/s\n",
"Processed 1 pages in 0.28s; Preprocess @ 12.57 p/s, Model @ 5.07 p/s, Combined @ 3.61 p/s\n",
"Processed 1 pages in 0.71s; Preprocess @ 2.08 p/s, Model @ 4.47 p/s, Combined @ 1.42 p/s\n",
"Processed 1 pages in 0.71s; Preprocess @ 2.04 p/s, Model @ 4.45 p/s, Combined @ 1.40 p/s\n",
"Processed 1 pages in 0.73s; Preprocess @ 2.01 p/s, Model @ 4.37 p/s, Combined @ 1.38 p/s\n",
"Processed 1 pages in 0.72s; Preprocess @ 2.02 p/s, Model @ 4.49 p/s, Combined @ 1.39 p/s\n",
"Processed 1 pages in 0.73s; Preprocess @ 2.02 p/s, Model @ 4.29 p/s, Combined @ 1.38 p/s\n",
"Processed 1 pages in 0.71s; Preprocess @ 2.05 p/s, Model @ 4.49 p/s, Combined @ 1.41 p/s\n",
"Processed 1 pages in 0.71s; Preprocess @ 2.05 p/s, Model @ 4.46 p/s, Combined @ 1.41 p/s\n",
"Processed 1 pages in 0.72s; Preprocess @ 2.02 p/s, Model @ 4.50 p/s, Combined @ 1.40 p/s\n",
"Processed 1 pages in 0.73s; Preprocess @ 1.98 p/s, Model @ 4.52 p/s, Combined @ 1.37 p/s\n",
"Processed 1 pages in 0.71s; Preprocess @ 2.05 p/s, Model @ 4.60 p/s, Combined @ 1.42 p/s\n",
"Processed 1 pages in 0.72s; Preprocess @ 2.02 p/s, Model @ 4.40 p/s, Combined @ 1.38 p/s\n",
"Processed 1 pages in 0.71s; Preprocess @ 2.03 p/s, Model @ 4.57 p/s, Combined @ 1.41 p/s\n",
"Processed 1 pages in 0.70s; Preprocess @ 2.07 p/s, Model @ 4.59 p/s, Combined @ 1.43 p/s\n",
"Processed 1 pages in 0.71s; Preprocess @ 2.03 p/s, Model @ 4.64 p/s, Combined @ 1.41 p/s\n",
"Processed 1 pages in 0.71s; Preprocess @ 2.05 p/s, Model @ 4.58 p/s, Combined @ 1.42 p/s\n",
"Processed 1 pages in 0.72s; Preprocess @ 1.98 p/s, Model @ 4.61 p/s, Combined @ 1.38 p/s\n",
"Processed 1 pages in 0.72s; Preprocess @ 2.04 p/s, Model @ 4.44 p/s, Combined @ 1.40 p/s\n",
"Processed 1 pages in 0.71s; Preprocess @ 2.05 p/s, Model @ 4.56 p/s, Combined @ 1.42 p/s\n",
"Processed 1 pages in 0.71s; Preprocess @ 2.04 p/s, Model @ 4.60 p/s, Combined @ 1.41 p/s\n",
"Processed 1 pages in 0.71s; Preprocess @ 2.04 p/s, Model @ 4.63 p/s, Combined @ 1.42 p/s\n",
"Processed 1 pages in 0.71s; Preprocess @ 2.07 p/s, Model @ 4.39 p/s, Combined @ 1.41 p/s\n",
"Processed 1 pages in 0.71s; Preprocess @ 2.03 p/s, Model @ 4.60 p/s, Combined @ 1.41 p/s\n",
"Processed 1 pages in 0.72s; Preprocess @ 2.04 p/s, Model @ 4.46 p/s, Combined @ 1.40 p/s\n",
"Processed 1 pages in 0.71s; Preprocess @ 2.05 p/s, Model @ 4.41 p/s, Combined @ 1.40 p/s\n",
"Processed 1 pages in 0.70s; Preprocess @ 2.08 p/s, Model @ 4.51 p/s, Combined @ 1.42 p/s\n",
"Processed 1 pages in 0.70s; Preprocess @ 2.09 p/s, Model @ 4.59 p/s, Combined @ 1.44 p/s\n",
"Processed 1 pages in 0.72s; Preprocess @ 2.04 p/s, Model @ 4.37 p/s, Combined @ 1.39 p/s\n",
"Processed 1 pages in 0.70s; Preprocess @ 2.07 p/s, Model @ 4.53 p/s, Combined @ 1.42 p/s\n",
"Processed 1 pages in 0.70s; Preprocess @ 2.07 p/s, Model @ 4.61 p/s, Combined @ 1.43 p/s\n",
"Processed 1 pages in 0.72s; Preprocess @ 2.02 p/s, Model @ 4.51 p/s, Combined @ 1.40 p/s\n",
"Processed 1 pages in 0.70s; Preprocess @ 2.10 p/s, Model @ 4.50 p/s, Combined @ 1.43 p/s\n",
"Processed 1 pages in 0.71s; Preprocess @ 2.07 p/s, Model @ 4.43 p/s, Combined @ 1.41 p/s\n",
"Processed 1 pages in 0.72s; Preprocess @ 2.00 p/s, Model @ 4.49 p/s, Combined @ 1.38 p/s\n",
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"Processed 1 pages in 0.30s; Preprocess @ 15.68 p/s, Model @ 4.24 p/s, Combined @ 3.34 p/s\n",
"Processed 1 pages in 0.31s; Preprocess @ 14.78 p/s, Model @ 4.14 p/s, Combined @ 3.23 p/s\n",
"Processed 1 pages in 0.27s; Preprocess @ 25.73 p/s, Model @ 4.27 p/s, Combined @ 3.66 p/s\n",
"Processed 1 pages in 0.28s; Preprocess @ 20.30 p/s, Model @ 4.29 p/s, Combined @ 3.54 p/s\n",
"Processed 1 pages in 0.25s; Preprocess @ 25.02 p/s, Model @ 4.66 p/s, Combined @ 3.93 p/s\n",
"Processed 1 pages in 0.29s; Preprocess @ 20.30 p/s, Model @ 4.18 p/s, Combined @ 3.46 p/s\n",
"Processed 1 pages in 0.27s; Preprocess @ 27.28 p/s, Model @ 4.29 p/s, Combined @ 3.70 p/s\n",
"Processed 1 pages in 0.28s; Preprocess @ 25.77 p/s, Model @ 4.23 p/s, Combined @ 3.63 p/s\n",
"Processed 1 pages in 0.30s; Preprocess @ 16.03 p/s, Model @ 4.24 p/s, Combined @ 3.35 p/s\n",
"Processed 1 pages in 0.30s; Preprocess @ 16.09 p/s, Model @ 4.23 p/s, Combined @ 3.35 p/s\n",
"Processed 1 pages in 0.29s; Preprocess @ 18.12 p/s, Model @ 4.21 p/s, Combined @ 3.42 p/s\n",
"Processed 1 pages in 0.29s; Preprocess @ 20.46 p/s, Model @ 4.15 p/s, Combined @ 3.45 p/s\n",
"Processed 1 pages in 0.27s; Preprocess @ 27.33 p/s, Model @ 4.29 p/s, Combined @ 3.71 p/s\n",
"Processed 1 pages in 0.32s; Preprocess @ 13.08 p/s, Model @ 4.09 p/s, Combined @ 3.12 p/s\n",
"Processed 1 pages in 0.30s; Preprocess @ 16.28 p/s, Model @ 4.25 p/s, Combined @ 3.37 p/s\n",
"--------------------\n",
"E2E time taken: 132.19 seconds for 275 pages with throughput of 2.08 pages per second\n",
"(Preprocess @ 52.25% i.e., Model @ 47.72%)\n"
]
}
],
"source": [
"batch_size = 1\n",
"outputs = []\n",
"st = time.perf_counter()\n",
"total_preprocess_time = 0\n",
"total_model_time = 0\n",
"\n",
"for i in range(0, len(images), batch_size):\n",
" batch = images[i:i+batch_size]\n",
" t0 = time.perf_counter()\n",
" messages = [make_ocr_message(image) for image in batch]\n",
" t1 = time.perf_counter()\n",
" outputs = llm.chat(messages, sampling_params, use_tqdm=False)\n",
" t2 = time.perf_counter()\n",
"\n",
" preprocess_time = t1 - t0\n",
" model_time = t2 - t1\n",
" total_time = t2 - t0\n",
" total_preprocess_time += preprocess_time\n",
" total_model_time += model_time\n",
" preprocess_throughput = len(batch) / preprocess_time\n",
" model_throughput = len(batch) / model_time\n",
" total_throughput = len(batch) / total_time\n",
" print(f\"Processed {len(batch)} pages in {total_time:.2f}s; Preprocess @ {preprocess_throughput:.2f} p/s, Model @ {model_throughput:.2f} p/s, Combined @ {total_throughput:.2f} p/s\")\n",
" outputs.extend(outputs)\n",
"total_time_taken = time.perf_counter() - st\n",
"print('--' * 10)\n",
"print(f\"E2E time taken: {total_time_taken:.2f} seconds for {len(images)} pages with throughput of {(len(images) / total_time_taken):.2f} pages per second\")\n",
"print(f\"(Preprocess @ {total_preprocess_time*100/total_time_taken:.2f}% i.e., Model @ {total_model_time*100/total_time_taken:.2f}%)\")"
]
},
{
"cell_type": "code",
"execution_count": 39,
"id": "357b749a",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Processed 16 pages in 3.95s; Preprocess @ 8.23 p/s, Model @ 7.96 p/s, Combined @ 4.05 p/s\n",
"Processed 16 pages in 3.64s; Preprocess @ 9.40 p/s, Model @ 8.25 p/s, Combined @ 4.40 p/s\n",
"Processed 16 pages in 7.25s; Preprocess @ 3.14 p/s, Model @ 7.40 p/s, Combined @ 2.21 p/s\n",
"Processed 16 pages in 10.22s; Preprocess @ 2.02 p/s, Model @ 6.93 p/s, Combined @ 1.57 p/s\n",
"Processed 16 pages in 10.29s; Preprocess @ 2.00 p/s, Model @ 6.96 p/s, Combined @ 1.56 p/s\n",
"Processed 16 pages in 10.15s; Preprocess @ 2.03 p/s, Model @ 7.10 p/s, Combined @ 1.58 p/s\n",
"Processed 16 pages in 10.19s; Preprocess @ 2.03 p/s, Model @ 6.98 p/s, Combined @ 1.57 p/s\n",
"Processed 16 pages in 10.22s; Preprocess @ 2.03 p/s, Model @ 6.88 p/s, Combined @ 1.56 p/s\n",
"Processed 16 pages in 10.14s; Preprocess @ 2.04 p/s, Model @ 7.01 p/s, Combined @ 1.58 p/s\n",
"Processed 16 pages in 8.04s; Preprocess @ 2.76 p/s, Model @ 7.16 p/s, Combined @ 1.99 p/s\n",
"Processed 16 pages in 3.09s; Preprocess @ 15.93 p/s, Model @ 7.67 p/s, Combined @ 5.18 p/s\n",
"Processed 16 pages in 3.34s; Preprocess @ 15.49 p/s, Model @ 6.95 p/s, Combined @ 4.80 p/s\n",
"Processed 16 pages in 3.15s; Preprocess @ 14.52 p/s, Model @ 7.82 p/s, Combined @ 5.08 p/s\n",
"Processed 16 pages in 3.74s; Preprocess @ 9.54 p/s, Model @ 7.75 p/s, Combined @ 4.28 p/s\n",
"Processed 16 pages in 3.06s; Preprocess @ 14.56 p/s, Model @ 8.16 p/s, Combined @ 5.23 p/s\n",
"Processed 16 pages in 2.68s; Preprocess @ 19.36 p/s, Model @ 8.62 p/s, Combined @ 5.96 p/s\n",
"Processed 16 pages in 2.60s; Preprocess @ 19.21 p/s, Model @ 9.03 p/s, Combined @ 6.14 p/s\n",
"Processed 3 pages in 0.62s; Preprocess @ 17.18 p/s, Model @ 6.72 p/s, Combined @ 4.83 p/s\n",
"--------------------\n",
"E2E time taken: 106.38 seconds for 275 pages with throughput of 2.58 pages per second\n",
"(Preprocess @ 65.55% i.e., Model @ 34.44%)\n"
]
}
],
"source": [
"batch_size = 16\n",
"outputs = []\n",
"st = time.perf_counter()\n",
"total_preprocess_time = 0\n",
"total_model_time = 0\n",
"\n",
"for i in range(0, len(images), batch_size):\n",
" batch = images[i:i+batch_size]\n",
" t0 = time.perf_counter()\n",
" messages = [make_ocr_message(image) for image in batch]\n",
" t1 = time.perf_counter()\n",
" outputs = llm.chat(messages, sampling_params, use_tqdm=False)\n",
" t2 = time.perf_counter()\n",
"\n",
" preprocess_time = t1 - t0\n",
" model_time = t2 - t1\n",
" total_time = t2 - t0\n",
" total_preprocess_time += preprocess_time\n",
" total_model_time += model_time\n",
" preprocess_throughput = len(batch) / preprocess_time\n",
" model_throughput = len(batch) / model_time\n",
" total_throughput = len(batch) / total_time\n",
" print(f\"Processed {len(batch)} pages in {total_time:.2f}s; Preprocess @ {preprocess_throughput:.2f} p/s, Model @ {model_throughput:.2f} p/s, Combined @ {total_throughput:.2f} p/s\")\n",
" outputs.extend(outputs)\n",
"total_time_taken = time.perf_counter() - st\n",
"print('--' * 10)\n",
"print(f\"E2E time taken: {total_time_taken:.2f} seconds for {len(images)} pages with throughput of {(len(images) / total_time_taken):.2f} pages per second\")\n",
"print(f\"(Preprocess @ {total_preprocess_time*100/total_time_taken:.2f}% i.e., Model @ {total_model_time*100/total_time_taken:.2f}%)\")"
]
},
{
"cell_type": "markdown",
"id": "00ec73a1",
"metadata": {},
"source": [
"## No Batching but using `make_ocr_message`"
]
},
{
"cell_type": "code",
"execution_count": 57,
"id": "590cb264",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Total time: 107.11 seconds with throughput of 2.57 p/s\n",
"Preprocess throughput: 3.97 p/s (64.75%)\n",
"Model throughput: 7.28 p/s (35.25%)\n"
]
}
],
"source": [
"t0 = time.perf_counter()\n",
"messages = [make_ocr_message(image) for image in images]\n",
"t1 = time.perf_counter()\n",
"outputs_base64_image = llm.chat(messages, sampling_params, use_tqdm=False)\n",
"t2 = time.perf_counter()\n",
"\n",
"preprocess_time = t1 - t0\n",
"model_time = t2 - t1\n",
"total_time = t2 - t0\n",
"preprocess_throughput = len(messages) / preprocess_time\n",
"model_throughput = len(messages) / model_time\n",
"total_throughput = len(messages) / total_time\n",
"\n",
"print(f\"Total time: {total_time:.2f} seconds with throughput of {total_throughput:.2f} p/s\")\n",
"print(f\"Preprocess throughput: {preprocess_throughput:.2f} p/s ({(preprocess_time*100/total_time):.2f}%)\")\n",
"print(f\"Model throughput: {model_throughput:.2f} p/s ({(model_time*100/total_time):.2f}%)\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "5cd485b1",
"metadata": {},
"outputs": [],
"source": [
"t0 = time.perf_counter()\n",
"messages = [make_ocr_message(image) for image in images]\n",
"t1 = time.perf_counter()\n",
"outputs_base64_image = llm.chat(messages, sampling_params, use_tqdm=False)\n",
"t2 = time.perf_counter()\n",
"\n",
"preprocess_time = t1 - t0\n",
"model_time = t2 - t1\n",
"total_time = t2 - t0\n",
"preprocess_throughput = len(messages) / preprocess_time\n",
"model_throughput = len(messages) / model_time\n",
"total_throughput = len(messages) / total_time\n",
"\n",
"print(f\"Total time: {total_time:.2f} seconds with throughput of {total_throughput:.2f} p/s\")\n",
"print(f\"Preprocess throughput: {preprocess_throughput:.2f} p/s ({(preprocess_time*100/total_time):.2f}%)\")\n",
"print(f\"Model throughput: {model_throughput:.2f} p/s ({(model_time*100/total_time):.2f}%)\")"
]
},
{
"cell_type": "markdown",
"id": "7a45941e",
"metadata": {},
"source": [
"# Pass Image directly to model"
]
},
{
"cell_type": "code",
"execution_count": 50,
"id": "ff844f4f",
"metadata": {},
"outputs": [],
"source": [
"def process_single_image(image):\n",
" \"\"\"single image\"\"\"\n",
" prompt_in = \"<image>\\n<|grounding|>Convert the document to markdown. \"\n",
" cache_item = {\n",
" \"prompt\": prompt_in,\n",
" \"multi_modal_data\": {\"image\": image},\n",
" }\n",
" return cache_item\n"
]
},
{
"cell_type": "code",
"execution_count": 56,
"id": "266900db",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Total time: 24.48 seconds with throughput of 11.23 p/s\n"
]
}
],
"source": [
"t0 = time.perf_counter()\n",
"outputs_direct_image = llm.generate(messages, sampling_params, use_tqdm=False)\n",
"t2 = time.perf_counter()\n",
"\n",
"preprocess_time = t1 - t0\n",
"model_time = t2 - t1\n",
"total_time = t2 - t0\n",
"preprocess_throughput = len(messages) / preprocess_time\n",
"model_throughput = len(messages) / model_time\n",
"total_throughput = len(messages) / total_time\n",
"\n",
"print(f\"Total time: {total_time:.2f} seconds with throughput of {total_throughput:.2f} p/s\")"
]
},
{
"cell_type": "markdown",
"id": "7b5f71d4",
"metadata": {},
"source": [
"## Compare outputs"
]
},
{
"cell_type": "code",
"execution_count": 66,
"id": "bdca0c8f",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"6th INTERNATIONAL YOGA DAY (21-06-2020) --------- 6th INTERNATIONAL YOGA DAY (21- 06- 2020\n",
"3/15/21\n",
"\n",
"image[[105, 115, --------- 3/15/21 \n",
"\n",
"image[[103, 115,\n",
"0. Preamble\n",
"\n",
"text[[217, 161, --------- 0. Preamble \n",
"\n",
"image[[195, 152,\n",
"3/15/21\n",
"\n",
"image[[105, 112, --------- 3/15/21 \n",
"\n",
"image[[104, 115,\n",
"1. Problem\n",
"\n",
"image[[220, 170, 760 --------- 1. Problem \n",
"\n",
"image[[220, 170, 765\n",
"3/15/21\n",
"\n",
"image[[105, 115, --------- 3/15/21 \n",
"\n",
"image[[104, 115,\n",
"3/15/21\n",
"\n",
"title[[144, 140, --------- 3/15/21 \n",
"\n",
"text[[144, 141,\n",
"3/15/21\n",
"\n",
"title[[144, 135, --------- 1. Problem \n",
"\n",
"sub_title[[144, 137,\n",
"3/15/21\n",
"\n",
"title[[144, 135, --------- 3/15/21 \n",
"\n",
"sub_title[[144, 137\n",
"3-day community workshop June 15-17 Contact me if interested \n",
"\n",
"text --------- 3/15/21 \n",
"\n",
"text[[144, 238,\n",
"3/15/21\n",
"\n",
"text[[144, 137, --------- 3/15/21 \n",
"\n",
"image[[150, 180,\n",
"3/15/21\n",
"\n",
"title[[145, 137, --------- 11. V/E/M \n",
"\n",
"table[[144, 167,\n",
"3/15/21\n",
"\n",
"image[[105, 115, --------- 3/15/21 \n",
"\n",
"image[[102, 117,\n",
"3/15/21\n",
"\n",
"text[[142, 137, --------- 3/15/21 \n",
"\n",
"image[[140, 170,\n",
"3/15/21\n",
"\n",
"text[[144, 135, --------- 3/15/21 \n",
"\n",
"image[[104, 115,\n",
"3/15/21\n",
"\n",
"text[[144, 131, --------- 3/15/21 \n",
"\n",
"image[[101, 115,\n",
"3/15/21\n",
"\n",
"text[[144, 137, --------- 3/15/21 \n",
"\n",
"text[[144, 138,\n",
"0.05\n",
"\n",
"image[[150, 175, 510 --------- 3/15/21 \n",
"\n",
"image[[112, 170,\n",
"3/15/21\n",
"\n",
"image[[110, 117, --------- 3/15/21 \n",
"\n",
"image[[105, 115,\n",
"3/15/21\n",
"\n",
"text[[824, 118, --------- 3/15/21 \n",
"\n",
"sub_title[[144, 148\n",
"0.00\n",
"\n",
"text[[80, 98, 760 --------- For office use only Username: Password: Units: Web\n",
"0.00 (plus VAT where applicable). This figure is reviewed annually by the --------- Note: Should ownership of the company signing this application form transfer to another company by\n",
"2021\n",
"\n",
"image[[203, 110, 792, --------- 1 \n",
"\n",
"image[[201, 110, 792,\n",
"1: 1: 1: 1: 1: 1 --------- 3 \n",
"\n",
"image[[202, 110, 792,\n",
"12 Magistrates: 4\n",
"\n",
"text[[238, 220 --------- 5 \n",
"\n",
"sub_title[[247, 170, 490,\n",
"7-25-1. Annual tax levy for construction or improvement of certain buildings --------- 7 \n",
"\n",
"sub_title[[270, 137, 727,\n",
"2000-2019 (More employment/commercial activity → civil litigation, small --------- 9 \n",
"\n",
"image[[200, 110, 795,\n",
"56,735\n",
"\n",
"text[[250, 137, 716 --------- 11 \n",
"\n",
"image[[212, 560, 794,\n",
"500\n",
"\n",
"image[[260, 160, 767, --------- 13 \n",
"\n",
"image[[255, 163, 768,\n",
"15\n",
"\n",
"image[[202, 110, 790, --------- 15 \n",
"\n",
"image[[201, 110, 790,\n",
"77% increase, FY13 to FY20\n",
"\n",
"image[[245 --------- 17 \n",
"\n",
"image[[201, 110, 794,\n",
"19\n",
"\n",
"image[[202, 108, 792, --------- 19 \n",
"\n",
"image[[201, 110, 792,\n",
"21\n",
"\n",
"image[[202, 108, 792, --------- 21 \n",
"\n",
"image[[202, 545, 792,\n",
"23\n",
"\n",
"image[[202, 108, 792, --------- 23 \n",
"\n",
"image[[202, 550, 794,\n",
"6 courtrooms/hearing rooms, including two jury trial courtrooms and two smaller --------- 25 \n",
"\n",
"table[[300, 180, 699,\n",
"Ten Year Projected Need\n",
"\n",
"text[[235, 188, --------- 27 \n",
"\n",
"sub_title[[260, 135, 744,\n",
"29\n",
"\n",
"table[[245, 170, 749, --------- 29 \n",
"\n",
"table[[245, 173, 750,\n",
"1. Property Compliance Specialist 2. Reports to: Director of Housing 3 --------- Title: Property Compliance Specialist Reports to: Director of Housing Department: Housing\n",
"Must have two years' nonprofit experience, demonstrated ability to work effectively with diverse populations --------- 1 \n",
"\n",
"text[[173, 175, 840,\n",
"1. 1. 1. 1. 1. 1 --------- ANNUAL FINANCIAL REPORT \n",
"\n",
"text[[420,\n",
"********* FINANCIAL SECTION *****\n",
"\n",
"text[[68, --------- Financial Information for the following funds and account groups are included in the Annual Financial Report\n",
"2010 EdpCode 2011\n",
"\n",
"text[[92, --------- 2010 EdpCode 2011 \n",
"\n",
"text[[91,\n",
"2010 EdpCode 2011\n",
"\n",
"text[[80, --------- (A) GENERAL Balance Sheet Code Description 2010 Edp\n",
"2010 EdpCode 2011\n",
"\n",
"text[[92, --------- 2010 EdpCode 2011 \n",
"\n",
"text[[91,\n",
"2010 EdpCode 2011\n",
"\n",
"text[[80, --------- 2010 EdpCode 2011 \n",
"\n",
"text[[80,\n",
"2010 EdpCode 2011\n",
"\n",
"text[[95, --------- 2010 EdpCode 2011 \n",
"\n",
"text[[95,\n",
"2010 EdpCode 2011\n",
"\n",
"text[[82, --------- 2010 EdpCode 2011 \n",
"\n",
"text[[82,\n",
"2010 EdpCode 2011\n",
"\n",
"text[[90, --------- 2010 EdpCode 2011 \n",
"\n",
"text[[90,\n",
"2011\n",
"\n",
"text[[80, 118, 168, --------- 2010 EdpCode 2011 \n",
"\n",
"text[[80,\n",
"2011 EdpCode 2012\n",
"\n",
"text[[92, --------- (A) GENERAL \n",
"\n",
"table[[88, 160,\n",
"2011 EdpCode 2012\n",
"\n",
"text[[84, --------- 2011 EdpCode 2012 \n",
"\n",
"text[[84,\n",
"2010 EdpCode 2011\n",
"\n",
"text[[86, --------- 2010 EdpCode 2011 \n",
"\n",
"text[[86,\n",
"2011\n",
"\n",
"text[[85, 118, 301, --------- 2010 EdpCode 2011 \n",
"\n",
"text[[85,\n",
"2010 EdpCode 2011\n",
"\n",
"text[[88, --------- 2010 EdpCode 2011 \n",
"\n",
"text[[88,\n",
"2010 EdpCode 2011\n",
"\n",
"text[[85, --------- (B) GENERAL TOWN-OUTSIDE VG \n",
"\n",
"text\n",
"2010 EdpCode 2011\n",
"\n",
"text[[92, --------- 2010 EdpCode 2011 \n",
"\n",
"text[[93,\n",
"2011 EdpCode 2012\n",
"\n",
"text[[75, --------- (B) GENERAL TOWN-OUTSIDE VG \n",
"\n",
"table\n",
"2011 EdpCode 2012\n",
"\n",
"text[[94, --------- 2011 EdpCode 2012 \n",
"\n",
"text[[92,\n",
"2010 EdpCode 2011\n",
"\n",
"text[[88, --------- 2010 EdpCode 2011 \n",
"\n",
"text[[88,\n",
"2010 EdpCode 2011\n",
"\n",
"text[[90, --------- 2010 EdpCode 2011 \n",
"\n",
"text[[90,\n",
"2010 EdpCode 2011\n",
"\n",
"text[[82, --------- 2010 EdpCode 2011 \n",
"\n",
"text[[84,\n",
"2010 EdpCode 2011\n",
"\n",
"text[[95, --------- 2010 EdpCode 2011 \n",
"\n",
"text[[95,\n",
"2010 EdpCode 2011\n",
"\n",
"text[[73, --------- 2010 EdpCode 2011 \n",
"\n",
"text[[72,\n",
"2010 EdpCode 2011\n",
"\n",
"text[[90, --------- 2010 EdpCode 2011 \n",
"\n",
"text[[88,\n",
"2010 EdpCode 2011\n",
"\n",
"text[[81, --------- 2010 EdpCode 2011 \n",
"\n",
"text[[80,\n",
"2010 EdpCode 2011\n",
"\n",
"text[[95, --------- 2010 EdpCode 2011 \n",
"\n",
"text[[95,\n",
"2010 EdpCode 2011\n",
"\n",
"text[[76, --------- 2010 EdpCode 2011 \n",
"\n",
"text[[75,\n",
"2011 EdpCode 2012\n",
"\n",
"text[[73, --------- 2011 EdpCode 2012 \n",
"\n",
"text[[72,\n",
"2011 EdpCode 2012\n",
"\n",
"text[[92, --------- (DA) HIGHWAY-TOWN- WIDE \n",
"\n",
"table\n",
"2010 EdpCode 2011\n",
"\n",
"text[[84, --------- 2010 EdpCode 2011 \n",
"\n",
"text[[84,\n",
"2010 EdpCode 2011\n",
"\n",
"text[[95, --------- 2010 EdpCode 2011 \n",
"\n",
"text[[97,\n",
"2010 EdpCode 2011\n",
"\n",
"text[[78, --------- 2010 EdpCode 2011 \n",
"\n",
"text[[78,\n",
"2010 EdpCode 2011\n",
"\n",
"text[[92, --------- (DB) HIGHWAY-PART-TOWN \n",
"\n",
"text[[\n",
"2010 EdpCode 2011\n",
"\n",
"text[[81, --------- 2010 EdpCode 2011 \n",
"\n",
"text[[80,\n",
"2011 EdpCode 2012\n",
"\n",
"text[[94, --------- 2011 EdpCode 2012 \n",
"\n",
"text[[92,\n",
"2011 EdpCode 2012\n",
"\n",
"text[[78, --------- 2011 EdpCode 2012 \n",
"\n",
"text[[78,\n",
"2010 EdpCode 2011\n",
"\n",
"text[[92, --------- 2010 EdpCode 2011 \n",
"\n",
"text[[90,\n",
"2010 EdpCode 2011\n",
"\n",
"text[[78, --------- 2010 EdpCode 2011 \n",
"\n",
"text[[78,\n",
"2010 EdpCode 2011\n",
"\n",
"text[[92, --------- 2010 EdpCode 2011 \n",
"\n",
"text[[92,\n",
"2010 EdpCode 2011\n",
"\n",
"text[[75, --------- 2010 EdpCode 2011 \n",
"\n",
"text[[75,\n",
"2010 EdpCode 2011 \n",
"\n",
"text[[92, --------- 2010 EdpCode 2011 \n",
"\n",
"text[[93,\n",
"2010 EdpCode 2011\n",
"\n",
"text[[90, --------- 2010 EdpCode 2011 \n",
"\n",
"text[[90,\n",
"2010 EdpCode 2011\n",
"\n",
"text[[98, --------- 2010 EdpCode 2011 \n",
"\n",
"text[[100,\n",
"2010 EdpCode 2011\n",
"\n",
"text[[90, --------- 2010 EdpCode 2011 \n",
"\n",
"text[[90,\n",
"2010 EdpCode 2011\n",
"\n",
"text[[80, --------- (H) CAPITAL PROJECTS \n",
"\n",
"text[[80,\n",
"2010 EdpCode 2011\n",
"\n",
"text[[97, --------- 2010 EdpCode 2011 \n",
"\n",
"text[[97,\n",
"2010 EdpCode 2011\n",
"\n",
"text[[75, --------- (K) GENERAL FIXED ASSETS \n",
"\n",
"text[[75\n",
"2010 EdpCode 2011\n",
"\n",
"text[[92, --------- 2010 EdpCode 2011 \n",
"\n",
"text[[92,\n",
"2010 EdpCode 2011\n",
"\n",
"text[[72, --------- 2010 EdpCode 2011 \n",
"\n",
"text[[72,\n",
"2010 EdpCode 2011\n",
"\n",
"text[[98, --------- 2010 EdpCode 2011 \n",
"\n",
"text[[97,\n",
"2010 EdpCode 2011\n",
"\n",
"text[[80, --------- 2010 EdpCode 2011 \n",
"\n",
"text[[78,\n",
"2010 EdpCode 2011\n",
"\n",
"text[[92, --------- 2010 EdpCode 2011 \n",
"\n",
"text[[92,\n",
"2010 EdpCode 2011\n",
"\n",
"text[[80, --------- 2010 EdpCode 2011 \n",
"\n",
"text[[80,\n",
"2010 EdpCode 2011\n",
"\n",
"text[[95, --------- 2010 EdpCode 2011 \n",
"\n",
"text[[95,\n",
"2010 EdpCode 2011\n",
"\n",
"text[[80, --------- 2010 EdpCode 2011 \n",
"\n",
"text[[80,\n",
"2010 EdpCode 2011\n",
"\n",
"text[[92, --------- 2010 EdpCode 2011 \n",
"\n",
"text[[92,\n",
"2011 EdpCode 2012 \n",
"\n",
"text[[80, --------- (SS) SEWER \n",
"\n",
"table[[75, 160\n",
"2011 EdpCode 2012\n",
"\n",
"text[[100, --------- 2011 EdpCode 2012 \n",
"\n",
"text[[98,\n",
"2010 EdpCode 2011\n",
"\n",
"text[[80, --------- 2010 EdpCode 2011 \n",
"\n",
"text[[80,\n",
"2010 EdpCode 2011\n",
"\n",
"text[[97, --------- 2010 EdpCode 2011 \n",
"\n",
"text[[100,\n",
"2010 EdpCode 2011\n",
"\n",
"text[[82, --------- 2010 EdpCode 2011 \n",
"\n",
"text[[81,\n",
"2010 EdpCode 2011\n",
"\n",
"text[[90, --------- 2010 EdpCode 2011 \n",
"\n",
"text[[90,\n",
"2010 EdpCode 2011\n",
"\n",
"text[[80, --------- 2010 EdpCode 2011 \n",
"\n",
"text[[80,\n",
"2011 EdpCode 2012 Estimated Revenues Est Rev - Real Property --------- 2011 EdpCode 2012 \n",
"\n",
"text[[88,\n",
"2011 EdpCode 2012\n",
"\n",
"text[[80, --------- 2011 EdpCode 2012 \n",
"\n",
"text[[80,\n",
"2010 EdpCode 2011\n",
"\n",
"text[[90, --------- 2010 EdpCode 2011 \n",
"\n",
"text[[88,\n",
"2010 EdpCode 2011\n",
"\n",
"text[[95, --------- 2010 EdpCode 2011 \n",
"\n",
"text[[98,\n",
"2010 EdpCode 2011\n",
"\n",
"text[[92, --------- 2010 EdpCode 2011 \n",
"\n",
"text[[90,\n",
"2010 EdpCode 2011\n",
"\n",
"text[[80, --------- 2010 EdpCode 2011 \n",
"\n",
"text[[80,\n",
"2010 EdpCode 2011\n",
"\n",
"text[[88, --------- 2010 EdpCode 2011 \n",
"\n",
"text[[88,\n",
"2010 EdpCode 2011\n",
"\n",
"text[[95, --------- 2010 EdpCode 2011 \n",
"\n",
"text[[95,\n",
"2010 EdpCode 2011\n",
"\n",
"text[[92, --------- 2010 EdpCode 2011 \n",
"\n",
"text[[95,\n",
"2010 EdpCode 2011\n",
"\n",
"text[[80, --------- 2010 EdpCode 2011 \n",
"\n",
"text[[80,\n",
"2010 EdpCode 2011\n",
"\n",
"text[[85, --------- 2010 EdpCode 2011 \n",
"\n",
"text[[85,\n",
"2010 EdpCode 2011\n",
"\n",
"text[[80, --------- 2010 EdpCode 2011 \n",
"\n",
"text[[80,\n",
"1. GENERAL Adjustment Reason Account Code A8015 To adjust prior year error ( --------- A) GENERAL Adjustment Reason Account Code A8015 To adjust prior year error\n",
"0.00000004 8/13/1999 1.250 --------- Water and Other Purposes Exempt From Constitutional Debt Limit \n",
"\n",
"table\n",
"0\n",
"\n",
"table[[45, 135, 732, --------- Water and Other Purposes Exempt From Constitutional Debt Limit \n",
"\n",
"<table>\n",
"2008000001\n",
"\n",
"table[[45, 135, --------- Indebtedness Not Exempt From Constitutional Debt Limit \n",
"\n",
"table\n",
"2011000001\n",
"\n",
"table[[48, 135, --------- Indebtedness Not Exempt From Constitutional Debt Limit \n",
"\n",
"table\n",
"0.00\n",
"\n",
"text[[45, 111, 465 --------- Indebtedness Not Exempt From Constitutional Debt Limit \n",
"\n",
"table\n",
"9Z2001 9Z2011 9Z2021 9 --------- TOWN OF Pawling Schedule of Time Deposits and Investments For the\n",
"0.00 0.00 0.00 0.00 --------- TOWN OF Pawling Bank Reconciliation For the Fiscal Year Ending 201\n",
"0.00\n",
"\n",
"text[[426, 72, 663 --------- 1 \n",
"\n",
"text[[365, 152, 722,\n",
"1) Does your municipality have a written procurement policy? 2) Have the --------- 1) Does your municipality have a written procurement policy? Yes 2) Have\n",
"32\n",
"\n",
"table[[90, 95, 940, --------- 1 \n",
"\n",
"table[[88, 93, 940,\n",
"0.00 0.00 0.00 0.00 --------- 1 \n",
"\n",
"table[[81, 85, 767,\n",
"1. Type of Other Post Employment Benefits Plan 2. Annual Required Contribution( --------- 1. Type of Other Post Employment Benefits Plan \n",
"2. Annual Required Contribution(\n",
"0.000000000000000000000000000000000000000000 --------- TOWN OF PAWLING, NEW YORK NOTES TO THE FINANCIAL\n",
"0.0.0.0.0.0.0.0. --------- NOTE A - Summary of Accounting Policies \n",
"\n",
"text[[129,\n",
"0.0.0.0.0.0.0.0. --------- Summary of Accounting Policies (Continued) \n",
"\n",
"text[[240,\n",
"0.0.0.0.0.0.0.0. --------- Summary of Accounting Policies (Continued) \n",
"\n",
"text[[250,\n",
"0.00\n",
"\n",
"title[[270, 92, 774 --------- 5. EncumbrancesEncumbrance accounting, under which purchase orders,\n",
"2011\n",
"\n",
"title[[256, 123, 760, --------- 7. Pension Plans \n",
"\n",
"sub_title[[196, 264,\n",
"10. Property TaxesThe Town submits an approved budget to the Dutchess County --------- 10. Property Taxes \n",
"\n",
"text[[195, 234,\n",
"2011 consist of the following: \n",
"\n",
"table[[131, --------- 1 \n",
"\n",
"sub_title[[135, 163, 382,\n",
"2011\n",
"\n",
"title[[137, 186, 583, --------- 1. Changes \n",
"\n",
"text[[137, 299, 884\n",
"1. Deficit Fund BalancesThe Town of Pawling Water District No. --------- 1. Deficit Fund Balances \n",
"\n",
"text[[174,\n",
"1. LitigationThe Town is a defendant in various lawsuits and tax certior --------- 1. Litigation \n",
"\n",
"text[[128, 470,\n",
"1P0\n",
"\n",
"text[[358, 172, 961 --------- Minutes of the meeting of Policy, Resources & Finance Committee of Yeovil Town\n",
"2020. \n",
"\n",
"sub_title[[95, 87, --------- 2020. \n",
"\n",
"sub_title[[94, 90,\n",
"0 to recommend the agreed budget and precept to Town Council for adoption. --------- and (2) to recommend the agreed budget and precept to Town Council for\n",
"10/163 WEBSITE UPDATE\n",
"\n",
"text[[92, --------- 10/163 WEBSITE UPDATE \n",
"\n",
"text[[92,\n",
"7:53pm – The member of the press left the meeting and did not --------- 7:53pm - The member of the press left the meeting and did not\n",
"title[[170, 360, 825, 525]] --------- Report on Barriers to Reduce and Reuse Behaviours \n",
"\n",
"text\n",
"2021\n",
"\n",
"title[[40, 123, 498, --------- Research project undertaken May 2021 Queenstown Lakes and Central Otago districts\n",
"3\n",
"\n",
"text[[60, 214, 857, --------- 3 \n",
"\n",
"text[[60, 213, 858,\n",
"0.5cm\n",
"\n",
"sub_title[[40, 105, --------- 1 \n",
"\n",
"sub_title[[42, 101, 226,\n",
"For people to adopt reduce and reuse behaviours such as using a reusable cup for take --------- 1 \n",
"\n",
"sub_title[[52, 174, 309,\n",
"The main reasons that people do not use their own cups/bags/containers --------- - The main reasons that people do not use their own cups/bags/contain\n",
"1,003 participants in total, representing 1.4% of the total --------- 1,003 participants in total, representing \\(1.4\\%\\) of the total\n",
"0-24 years old \n",
"\n",
"image[[77, 170, --------- What is your age? \n",
"\n",
"image[[77, 170,\n",
"0.0.0.0.0.0.0.0. --------- 1 \n",
"\n",
"sub_title[[40, 130, 467,\n",
"0 \"Covid has impacted income so cost is important. Covid also impacts hygiene --------- 1 Most people do not take their own containers for takeaways or deli products,\n",
"1 1 1 1 1 1 1 1 --------- 1 \n",
"\n",
"sub_title[[40, 123, 400,\n",
"2019 2021\n",
"\n",
"image[[48, 103, --------- 100% Always Usually Sometimes Once in a while Thinking of doing Never and unlikely to\n",
"2019 2021\n",
"\n",
"image[[45, 99, --------- 100% Always Usually Sometimes Once in a while Thinking of doing Never and unlikely to\n",
"2019 2021\n",
"\n",
"image[[72, 95, --------- 2019 2021 \n",
"\n",
"image[[72, 95,\n",
"0% 20% 40% 60% 80% 100 --------- 100% 90% 80% 70% 60% 50\n",
"0\n",
"\n",
"image[[70, 94, 870, --------- 0 0 0 0 0 0 0 0\n",
"0% 10% 20% 30% 40% 50 --------- 100% 90% 80% 70% 60% 50\n",
"0% 20% 40% 60% 80% 100 --------- 100% 90% 80% 70% 60% 50\n",
"0 \"I much prefer to have my food supplied in their plastic or cardboard dishes --------- - With regard to taking containers for takeaways and deli items, most people are\n",
"2021\n",
"\n",
"image[[50, 99, 890, --------- 100% 90% 80% 70% 60% 50\n",
"2019 2021\n",
"\n",
"image[[58, 144, --------- 100% 90% 80% 70% 60% 50\n",
"0% 0% 0% 0% 0% 0 --------- 100% 90% 80% 70% 60% 50\n",
"For repair and secondhand options, the barriers are again more complex than with re --------- - For repair and secondhand options, the barriers are again more complex than with\n",
"65% of 35-44 year olds always or usually use a reusable cup --------- 1 \n",
"\n",
"sub_title[[43, 123, 325,\n",
"0.00 0.00 0.00 0.00 --------- 1 \n",
"\n",
"sub_title[[42, 101, 312,\n",
"0% \"Never and Unlikely To\"\n",
"\n",
"image[[48, --------- How often do you... % \"Never and Unlikely To\" \n",
"\n",
"image\n",
"For this question around behaviours to reduce waste, we did not ask what prevented people --------- 1 \n",
"\n",
"sub_title[[41, 130, 193,\n",
"0.0.0.0.0.0.0.0. --------- Other behaviours - across the regions \n",
"\n",
"text[[65, 162\n",
"6 extremely concerned\n",
"\n",
"text[[60, 175, 730 --------- 1. Plastic in our oceans \n",
"2. Amount of single-use packaging being used\n",
"0.0.0.0.0.0.0.0. --------- - There is no distinct difference from the 2019 results for this question,\n",
"0% 20% 40% 60% 80% 100 --------- How concerned are you about... \n",
"\n",
"image[[48, 150\n",
"100% 90% 80% 70% 60% 50 --------- Very or extremely concerned about % of total by region \n",
"\n",
"image[[\n",
"0-24 25-34 35-44 45-54 --------- Does age affect our concern for different environmental issues? \n",
"\n",
"text[[\n",
"0.00 0.00 0.00 0.00 --------- Does income affect our level of concern? \n",
"\n",
"text[[102,\n",
"1. Healthy choices2. Impact on environment and our oceans3. Minimal packaging --------- Factors that influence our consumer choices \n",
"\n",
"text[[40, 174\n",
"When asked about other influences, the common factors mentioned were: \n",
"\n",
"text --------- - When asked about other influences, the common factors mentioned were: \n",
"\n",
"text\n",
"0% of total\n",
"\n",
"image[[58, 140, --------- 100% 90% 80% 70% 60% 50\n",
"0% of total by region\n",
"\n",
"image[[55, 140 --------- 100% 80% 60% 40% 20% 0\n",
"79% of respondents are interested in learning more to help them reduce their waste. --------- Learning more about reducing waste \n",
"\n",
"text[[40, 174,\n",
"0\n",
"\n",
"text[[60, 97, 599, --------- When asked what specific information they would like to learn about, the top topics were\n",
"0\n",
"\n",
"title[[39, 133, 689, --------- 1 \n",
"\n",
"sub_title[[43, 130, 689,\n",
"1s there anything else you would like to tell us?\" \n",
"\n",
"text --------- 1 \n",
"\n",
"sub_title[[40, 130, 350,\n",
"0: 0: 0: 0: 0: 0 --------- Future opportunities and challenges \n",
"\n",
"text[[65, 144,\n",
"Wastebusters: to zero waste and beyond... \n",
"\n",
"image --------- Wastebusters: to zero waste and beyond... \n",
"\n",
"image\n",
"2020 --------- What is Our ‘New Normal’ Like? \n",
"\n",
"text[[188\n",
"image[[42, 168, 323, 812]] --------- image[[42, 164, 324, 816]]\n",
"image[[44, 170, 323, 802]] --------- WEBINAR \n",
"\n",
"title[[355, 530, 770\n",
"1\n",
"\n",
"title[[135, 212, 799, --------- Understanding a New Normal: Thinking Through Process \n",
"\n",
"text[[248,\n",
"0 20 40 60\n",
"\n",
"image_caption[[150 --------- Need to Understand Local Human Psyche Impacting New Normal... \n",
"\n",
"image\n",
"Does your business have an online sales component? \n",
"\n",
"image[[150 --------- Need to Understand Business Needs and Issues Impacting New Normal... \n",
"\n",
"image\n",
"1/3 workforce working remotely \n",
"\n",
"text[[170, 555 --------- Need to Understand Trends Impacting New Normal \n",
"\n",
"text[[133,\n",
"0.5\n",
"\n",
"title[[133, 212, 867 --------- Summary: Be Thoughtful in Recovery – No Time to Simply Follow – Be Strategic\n",
"image[[43, 170, 323, 818]] --------- image[[44, 168, 323, 820]]\n",
"title[[135, 205, 819, 266]] --------- The Facts – City of Birmingham \n",
"\n",
"text[[75, 295\n",
"Mayor Randall L. Woodfin \n",
"\n",
"text[[123, 355 --------- 1 \n",
"\n",
"sub_title[[144, 205, 830,\n",
"0.00\n",
"\n",
"image[[88, 220, 905 --------- Economic Crisis \n",
"\n",
"image[[87, 270, 905,\n",
"2020\n",
"\n",
"text[[80, 380, 300, --------- image[[42, 167, 310, 380]]\n",
"image[[45, 171, 323, 825]] --------- image[[45, 170, 325, 825]]\n",
"image[[83, 170, 380, 728]] --------- image[[81, 170, 380, 730]]\n",
"image[[78, 310, 308, 640]] --------- Revenue Impacts of COVID 19 \n",
"\n",
"image[[345, 305\n",
"image[[150, 188, 820, 727]] --------- SOURCE OF AUDIENCE, PRE-COVID \n",
"\n",
"image[[305\n",
"image[[71, 200, 472, 575]] --------- image[[68, 200, 473, 581]]\n",
"0:00:00\n",
"\n",
"image[[66, 198, --------- image[[66, 197, 380, 490]]\n",
"0.0\n",
"\n",
"title[[270, 373, 716 --------- PANEL DISCUSSION \n",
"\n",
"text[[290, 506, 697\n",
"title[[87, 371, 900, 430]] --------- AudeNCE QUESTION AND ANSWER \n",
"\n",
"text[[231,\n",
"title[[135, 222, 415, 285]] --------- sub_title[[134, 222, 416, 285\n",
"Upcoming Webinars \n",
"\n",
"text[[135, 421, --------- sub_title[[135, 209, 454, 272\n",
"image[[42, 170, 323, 810]] --------- image[[42, 169, 325, 816]]\n",
"2019: “an important question—which no one seems interested in addressing— --------- text[[15, 20, 149, 55]]\n",
"1. KREDITO SVARBA --------- I. KREDITO SVARBA\n",
"2017.01.11 www.ekonomika.org 4 --------- sub_title[[336, 10, 657, 70\n",
"2013): “analysts have found that over long periods of time there has --------- sub_title[[55, 110, 612, 157\n",
"2000), Essays on the Great Depression, p. 24, on Irving --------- Bernanke (2000), Essays on the Great Depression, p. 24\n",
"1. KREDITAS KREDITUI NELYGUS --------- title[[119, 356, 875, 580]]\n",
"30 sk. “Money-Capital and Real Capital” iškryė: --------- - **Marx’as (Capital, 30 sk. “Money-Capital\n",
"ší funkciné kredito krypčiu diferencijacija ší --------- sub_title[[265, 12, 728, 70\n",
"2011. 11. 1. 2011. 11. --------- sub_title[[55, 115, 930, 225\n",
"Richard Werner, Michael Hupson, Dirk Bezemer, Thomas Pal --------- sub_title[[264, 10, 729, 70\n",
"‡ˆ‰Š‹ŒŽ --------- sub_title[[264, 10, 728, 68\n",
"(GERO/BLOGO) KREDITO POVEIKIS --------- title[[180, 352, 816, 580]]\n",
"2017.01.11 www.ekonomika.org \n",
"\n",
"image --------- image[[12, 230, 985, 515]]\n",
"\u0007Kreditas gamybai vystyti koreliuo --------- sub_title[[180, 10, 810, 75\n",
"1. jmonė skolinasi investicijoms, algoms ir --------- sub_title[[64, 6, 930, 72\n",
"http://voxeu.org/article/great-mortgaging \n",
"\n",
"image --------- \"The great mortgaging\" \n",
"\n",
"text[[57, 250\n",
"\u0007Kreditas vartojimui: \n",
"\n",
"text[[ --------- sub_title[[63, 11, 930, 75\n",
"2012): \n",
"\n",
"text[[110, 150, 960 --------- Kapitalo pelno „ekonomika“ \n",
"\n",
"sub_title[[\n",
"2012): \n",
"\n",
"text[[108, 183, 936 --------- „ Bezemer (2012): \n",
"\n",
"text[[108\n",
"1. 2. 3. 4. 5. 6 --------- title[[77, 355, 914, 580]]\n",
"Taigi, bendrojo kredito augimo poveikio e ---------  Taigi, bendrojo kredito augimo pove\n",
" Regresija: \n",
"\n",
"equation[[157, --------- sub_title[[70, 6, 925, 75\n",
"2000): volatility of growth tends to decrease and then increase with increasing financial depth --------- - Easterly et al (2000): volatility of growth tends to decrease and\n",
"2011), 'Too Much Finance?', www.imf.org/external/p --------- sub_title[[70, 9, 921, 75\n",
"2011) 'The Real Effects of Debt', http://www.bis.org --------- - Cecchetti et al (2011) ‘The Real Effects of Debt\n",
"0.0\n",
"\n",
"title[[120, 352, 875 --------- title[[120, 357, 872, 580]]\n",
"2017.01.11 www.ekonomika.org 28 --------- image[[20, 180, 976, 400]]\n",
"2017.01.11 www.ekonomika.org 29 --------- sub_title[[328, 10, 670, 70\n",
"ikkij buma/reformos/bu back \n",
"\n",
"image --------- title[[10, 8, 988, 78]]\n",
"būsto kainų bumas\n",
"\n",
"text[[102, --------- title[[180, 10, 820, 72]]\n",
"2014 m. rugpjūčio 5 d. 10: --------- title[[135, 12, 858, 75]]\n",
"0.5inflationavimas\n",
"\n",
"text[[55, --------- 1 \n",
"\n",
"text[[55, 321, 975,\n",
"text[[58, 108, 970, 247]] --------- - Viral V. Acharya@Tim Eisert@Christian Eufinger,\n",
"VI. FINANSIALIZACIJA IR NELYGYBĖ --------- title[[88, 355, 905, 580]]\n",
"0.0\n",
"\n",
"image[[216, 220, 783 --------- title[[241, 12, 753, 75]]\n",
"2013, Financialization, Palgrave. --------- image[[331, 160, 728, 730]]\n",
"5: the share of bank credit to the FIRE sector and rising inequality, --------- Figure 5: the share of bank credit to the FIRE sector and rising\n",
"RENTIER, RENTIER, RENTIER, R --------- 7. RENTA, RENTIER, PALUKANY ESMÉ\n",
"0.0\n",
"\n",
"text[[108, 370, 348 --------- sub_title[[69, 6, 930, 75\n",
"title[[175, 10, 825, 78]] --------- sub_title[[175, 10, 822, 75\n",
"0.0.0.0.0.0.0.0. --------- - There is, however, a second, much more fundamental inference from our argument\n",
"2014: What level of real interest rates is fair? Post Keynesians argue --------- - Koudis (2014): What level of real interest rates is fair\n",
"Ulrich’as Bindseil’is (ECB) savo k --------- sub_title[[138, 7, 857, 75\n",
"1. 2. 3. 4. 5. 6 --------- sub_title[[15, 345, 978, 440\n",
"21 pav. PFI paskolų privačiajam ne finans --------- 20 pav. PFĮ paskolų privačiajam ne finans\n",
"Ką matome/jaučiame? Tai, kad LT privatus se --------- sub_title[[54, 110, 925, 195\n",
"Mismatch count: 258\n",
"Match count: 17\n"
]
}
],
"source": [
"mismatch_count = 0\n",
"match_count = 0\n",
"for o1, o2 in zip(outputs_base64_image, outputs_direct_image):\n",
" for o1_o, o2_o in zip(o1.outputs, o2.outputs):\n",
" if o1_o.text != o2_o.text:\n",
" print(o1_o.text.strip(), \" --------- \", o2_o.text.strip())\n",
" mismatch_count += 1\n",
" else:\n",
" match_count += 1\n",
" \n",
"print(f\"Mismatch count: {mismatch_count}\")\n",
"print(f\"Match count: {match_count}\")"
]
}
],
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