"""simple docstring"""



import argparse
from pathlib import Path

import requests
import torch
from PIL import Image

from transformers import (
    RobertaTokenizer,
    TrOCRConfig,
    TrOCRForCausalLM,
    TrOCRProcessor,
    VisionEncoderDecoderModel,
    ViTConfig,
    ViTImageProcessor,
    ViTModel,
)
from transformers.utils import logging


logging.set_verbosity_info()
_UpperCamelCase								=		logging.get_logger(__name__)

def  _A(		lowerCAmelCase			,  lowerCAmelCase							):
							A__       :     List[str]								=			[]
							for i in range(encoder_config.num_hidden_layers							):
														# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
														rename_keys.append(
														    (F'''encoder.deit.blocks.{i}.norm1.weight''', F'''encoder.encoder.layer.{i}.layernorm_before.weight''')							)
														rename_keys.append((F'''encoder.deit.blocks.{i}.norm1.bias''', F'''encoder.encoder.layer.{i}.layernorm_before.bias''')							)
														rename_keys.append(
														    (F'''encoder.deit.blocks.{i}.attn.proj.weight''', F'''encoder.encoder.layer.{i}.attention.output.dense.weight''')							)
														rename_keys.append(
														    (F'''encoder.deit.blocks.{i}.attn.proj.bias''', F'''encoder.encoder.layer.{i}.attention.output.dense.bias''')							)
														rename_keys.append(
														    (F'''encoder.deit.blocks.{i}.norm2.weight''', F'''encoder.encoder.layer.{i}.layernorm_after.weight''')							)
														rename_keys.append((F'''encoder.deit.blocks.{i}.norm2.bias''', F'''encoder.encoder.layer.{i}.layernorm_after.bias''')							)
														rename_keys.append(
														    (F'''encoder.deit.blocks.{i}.mlp.fc1.weight''', F'''encoder.encoder.layer.{i}.intermediate.dense.weight''')							)
														rename_keys.append(
														    (F'''encoder.deit.blocks.{i}.mlp.fc1.bias''', F'''encoder.encoder.layer.{i}.intermediate.dense.bias''')							)
														rename_keys.append(
														    (F'''encoder.deit.blocks.{i}.mlp.fc2.weight''', F'''encoder.encoder.layer.{i}.output.dense.weight''')							)
														rename_keys.append((F'''encoder.deit.blocks.{i}.mlp.fc2.bias''', F'''encoder.encoder.layer.{i}.output.dense.bias''')							)

							# cls token, position embeddings and patch embeddings of encoder
							rename_keys.extend(
							    [
							        ("""encoder.deit.cls_token""", """encoder.embeddings.cls_token"""),
							        ("""encoder.deit.pos_embed""", """encoder.embeddings.position_embeddings"""),
							        ("""encoder.deit.patch_embed.proj.weight""", """encoder.embeddings.patch_embeddings.projection.weight"""),
							        ("""encoder.deit.patch_embed.proj.bias""", """encoder.embeddings.patch_embeddings.projection.bias"""),
							        ("""encoder.deit.norm.weight""", """encoder.layernorm.weight"""),
							        ("""encoder.deit.norm.bias""", """encoder.layernorm.bias"""),
							    ]							)

							return rename_keys

def  _A(		lowerCAmelCase			,  lowerCAmelCase							):
							for i in range(encoder_config.num_hidden_layers							):
														# queries, keys and values (only weights, no biases)
														A__       :     Any								=			state_dict.pop(F'''encoder.deit.blocks.{i}.attn.qkv.weight'''							)

														A__       :     Dict								=			in_proj_weight[
														    : encoder_config.hidden_size, :
														]
														A__       :     str								=			in_proj_weight[
														    encoder_config.hidden_size : encoder_config.hidden_size * 2, :
														]
														A__       :     str								=			in_proj_weight[
														    -encoder_config.hidden_size :, :
														]

def  _A(		lowerCAmelCase			,  lowerCAmelCase			,  lowerCAmelCase							):
							A__       :     Tuple								=			dct.pop(lowerCAmelCase							)
							A__       :     List[str]								=			val

def  _A(		lowerCAmelCase							):
							if "handwritten" in checkpoint_url:
														A__       :     str								=			"""https://fki.tic.heia-fr.ch/static/img/a01-122-02-00.jpg"""  # industry
														# url = "https://fki.tic.heia-fr.ch/static/img/a01-122-02-12.jpg" # have
														# url = "https://fki.tic.heia-fr.ch/static/img/a01-122-02-10.jpg" # let
														# url = "https://fki.tic.heia-fr.ch/static/img/a01-122-02.jpg"  #
														# url = "https://fki.tic.heia-fr.ch/static/img/a01-122.jpg"
							elif "printed" in checkpoint_url or "stage1" in checkpoint_url:
														A__       :     Any								=			"""https://www.researchgate.net/profile/Dinh-Sang/publication/338099565/figure/fig8/AS:840413229350922@1577381536857/An-receipt-example-in-the-SROIE-2019-dataset_Q640.jpg"""
							A__       :     Optional[Any]								=			Image.open(requests.get(lowerCAmelCase			,  stream=lowerCAmelCase							).raw							).convert("""RGB"""							)
							return im

@torch.no_grad()
def  _A(		lowerCAmelCase			,  lowerCAmelCase							):
							A__       :     Optional[Any]								=			ViTConfig(image_size=384			,  qkv_bias=lowerCAmelCase							)
							A__       :     List[str]								=			TrOCRConfig()

							# size of the architecture
							if "base" in checkpoint_url:
														A__       :     Any								=			768
							elif "large" in checkpoint_url:
														# use ViT-large encoder
														A__       :     str								=			1024
														A__       :     List[Any]								=			4096
														A__       :     Tuple								=			24
														A__       :     Tuple								=			16
														A__       :     int								=			1024
							else:
														raise ValueError("""Should either find 'base' or 'large' in checkpoint URL"""							)

							# the large-printed + stage1 checkpoints uses sinusoidal position embeddings, no layernorm afterwards
							if "large-printed" in checkpoint_url or "stage1" in checkpoint_url:
														A__       :     Union[str, Any]								=			False
														A__       :     Union[str, Any]								=			"""relu"""
														A__       :     Any								=			1024
														A__       :     Optional[int]								=			True
														A__       :     Union[str, Any]								=			False
														A__       :     Tuple								=			False

							# load HuggingFace model
							A__       :     Any								=			ViTModel(lowerCAmelCase			,  add_pooling_layer=lowerCAmelCase							)
							A__       :     Tuple								=			TrOCRForCausalLM(lowerCAmelCase							)
							A__       :     List[str]								=			VisionEncoderDecoderModel(encoder=lowerCAmelCase			,  decoder=lowerCAmelCase							)
							model.eval()

							# load state_dict of original model, rename some keys
							A__       :     Optional[int]								=			torch.hub.load_state_dict_from_url(lowerCAmelCase			,  map_location="""cpu"""			,  check_hash=lowerCAmelCase							)["""model"""]

							A__       :     int								=			create_rename_keys(lowerCAmelCase			,  lowerCAmelCase							)
							for src, dest in rename_keys:
														rename_key(lowerCAmelCase			,  lowerCAmelCase			,  lowerCAmelCase							)
							read_in_q_k_v(lowerCAmelCase			,  lowerCAmelCase							)

							# remove parameters we don't need
							del state_dict["encoder.deit.head.weight"]
							del state_dict["encoder.deit.head.bias"]
							del state_dict["decoder.version"]

							# add prefix to decoder keys
							for key, val in state_dict.copy().items():
														A__       :     int								=			state_dict.pop(lowerCAmelCase							)
														if key.startswith("""decoder"""							) and "output_projection" not in key:
																					A__       :     List[Any]								=			val
														else:
																					A__       :     Optional[Any]								=			val

    # load state dict
							model.load_state_dict(lowerCAmelCase							)

							# Check outputs on an image
							A__       :     Optional[int]								=			ViTImageProcessor(size=encoder_config.image_size							)
							A__       :     List[str]								=			RobertaTokenizer.from_pretrained("""roberta-large"""							)
							A__       :     Union[str, Any]								=			TrOCRProcessor(lowerCAmelCase			,  lowerCAmelCase							)

							A__       :     int								=			processor(images=prepare_img(lowerCAmelCase							)			,  return_tensors="""pt"""							).pixel_values

							# verify logits
							A__       :     Dict								=			torch.tensor([[model.config.decoder.decoder_start_token_id]]							)
							A__       :     List[Any]								=			model(pixel_values=lowerCAmelCase			,  decoder_input_ids=lowerCAmelCase							)
							A__       :     Optional[int]								=			outputs.logits

							A__       :     List[str]								=			torch.Size([1, 1, 5_0265]							)
							if "trocr-base-handwritten" in checkpoint_url:
														A__       :     Optional[int]								=			torch.tensor(
														    [-1.4502, -4.6683, -0.5347, -2.9291, 9.1435, -3.0571, 8.9764, 1.7560, 8.7358, -1.5311]							)
							elif "trocr-large-handwritten" in checkpoint_url:
														A__       :     Optional[int]								=			torch.tensor(
														    [-2.6437, -1.3129, -2.2596, -5.3455, 6.3539, 1.7604, 5.4991, 1.4702, 5.6113, 2.0170]							)
							elif "trocr-base-printed" in checkpoint_url:
														A__       :     Any								=			torch.tensor(
														    [-5.6816, -5.8388, 1.1398, -6.9034, 6.8505, -2.4393, 1.2284, -1.0232, -1.9661, -3.9210]							)
							elif "trocr-large-printed" in checkpoint_url:
														A__       :     Optional[int]								=			torch.tensor(
														    [-6.0162, -7.0959, 4.4155, -5.1063, 7.0468, -3.1631, 2.6466, -0.3081, -0.8106, -1.7535]							)

							if "stage1" not in checkpoint_url:
														assert logits.shape == expected_shape, "Shape of logits not as expected"
														assert torch.allclose(logits[0, 0, :10]			,  lowerCAmelCase			,  atol=1E-3							), "First elements of logits not as expected"

							Path(lowerCAmelCase							).mkdir(exist_ok=lowerCAmelCase							)
							print(F'''Saving model to {pytorch_dump_folder_path}'''							)
							model.save_pretrained(lowerCAmelCase							)
							print(F'''Saving processor to {pytorch_dump_folder_path}'''							)
							processor.save_pretrained(lowerCAmelCase							)


if __name__ == "__main__":
				_UpperCamelCase								=		argparse.ArgumentParser()

				parser.add_argument(
				    "--checkpoint_url",
				    default="https://layoutlm.blob.core.windows.net/trocr/model_zoo/fairseq/trocr-base-handwritten.pt",
				    type=str,
				    help="URL to the original PyTorch checkpoint (.pth file).",
				)
				parser.add_argument(
				    "--pytorch_dump_folder_path", default=None, type=str, help="Path to the folder to output PyTorch model."
				)
				_UpperCamelCase								=		parser.parse_args()
				convert_tr_ocr_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)






"""simple docstring"""



import datasets

from .evaluate import evaluate


_UpperCamelCase								=		"\\n@article{hendrycks2021cuad,\n      title={CUAD: An Expert-Annotated NLP Dataset for Legal Contract Review},\n      author={Dan Hendrycks and Collin Burns and Anya Chen and Spencer Ball},\n      journal={arXiv preprint arXiv:2103.06268},\n      year={2021}\n}\n"

_UpperCamelCase								=		"\nThis metric wrap the official scoring script for version 1 of the Contract\nUnderstanding Atticus Dataset (CUAD).\nContract Understanding Atticus Dataset (CUAD) v1 is a corpus of more than 13,000 labels in 510\ncommercial legal contracts that have been manually labeled to identify 41 categories of important\nclauses that lawyers look for when reviewing contracts in connection with corporate transactions.\n"

_UpperCamelCase								=		"\nComputes CUAD scores (EM, F1, AUPR, Precision@80%Recall, and Precision@90%Recall).\nArgs:\n    predictions: List of question-answers dictionaries with the following key-values:\n        - 'id': id of the question-answer pair as given in the references (see below)\n        - 'prediction_text': list of possible texts for the answer, as a list of strings\n        depending on a threshold on the confidence probability of each prediction.\n    references: List of question-answers dictionaries with the following key-values:\n        - 'id': id of the question-answer pair (see above),\n        - 'answers': a Dict in the CUAD dataset format\n            {\n                'text': list of possible texts for the answer, as a list of strings\n                'answer_start': list of start positions for the answer, as a list of ints\n            }\n            Note that answer_start values are not taken into account to compute the metric.\nReturns:\n    'exact_match': Exact match (the normalized answer exactly match the gold answer)\n    'f1': The F-score of predicted tokens versus the gold answer\n    'aupr': Area Under the Precision-Recall curve\n    'prec_at_80_recall': Precision at 80% recall\n    'prec_at_90_recall': Precision at 90% recall\nExamples:\n    >>> predictions = [{'prediction_text': ['The seller:', 'The buyer/End-User: Shenzhen LOHAS Supply Chain Management Co., Ltd.'], 'id': 'LohaCompanyltd_20191209_F-1_EX-10.16_11917878_EX-10.16_Supply Agreement__Parties'}]\n    >>> references = [{'answers': {'answer_start': [143, 49], 'text': ['The seller:', 'The buyer/End-User: Shenzhen LOHAS Supply Chain Management Co., Ltd.']}, 'id': 'LohaCompanyltd_20191209_F-1_EX-10.16_11917878_EX-10.16_Supply Agreement__Parties'}]\n    >>> cuad_metric = datasets.load_metric(\"cuad\")\n    >>> results = cuad_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {'exact_match': 100.0, 'f1': 100.0, 'aupr': 0.0, 'prec_at_80_recall': 1.0, 'prec_at_90_recall': 1.0}\n"
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION							,   _KWARGS_DESCRIPTION     )
class 						__UpperCAmelCase    (datasets.Metric     ):







			'''simple docstring'''





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										return datasets.MetricInfo(
										    description=_DESCRIPTION   ,      citation=_CITATION   ,      inputs_description=_KWARGS_DESCRIPTION   ,      features=datasets.Features(
										        {
										            """predictions""": {
										                """id""": datasets.Value("""string"""							),
										                """prediction_text""": datasets.features.Sequence(datasets.Value("""string"""							)							),
										            },
										            """references""": {
										                """id""": datasets.Value("""string"""							),
										                """answers""": datasets.features.Sequence(
										                    {
										                        """text""": datasets.Value("""string"""							),
										                        """answer_start""": datasets.Value("""int32"""							),
										                    }							),
										            },
										        }							)   ,      codebase_urls=["""https://www.atticusprojectai.org/cuad"""]   ,      reference_urls=["""https://www.atticusprojectai.org/cuad"""]   ,      )





			def 		lowerCamelCase					(			self   ,      snake_case_   ,      snake_case_							):







										'''simple docstring'''


										A__       :     str								=			{prediction["""id"""]: prediction["""prediction_text"""] for prediction in predictions}
										A__       :     Optional[Any]								=			[
										    {
										        """paragraphs""": [
										            {
										                """qas""": [
										                    {
										                        """answers""": [{"""text""": answer_text} for answer_text in ref["""answers"""]["""text"""]],
										                        """id""": ref["""id"""],
										                    }
										                    for ref in references
										                ]
										            }
										        ]
										    }
										]
										A__       :     Optional[Any]								=			evaluate(dataset=snake_case_   ,      predictions=snake_case_							)
										return score





