


import datasets

from .evaluate import evaluate


__A		='\\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'

__A		='\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'

__A		='\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 						_snake_case   (       datasets.Metric		):




					def 			snake_case__  ( self):
								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 			snake_case__  ( self							,							_lowerCamelCase							,							_lowerCamelCase):
								UpperCAmelCase__					:   int       				=       {prediction['id']: prediction['prediction_text'] for prediction in predictions}
								UpperCAmelCase__					:   Any       				=       [
								    {
								        'paragraphs': [
								            {
								                'qas': [
								                    {
								                        'answers': [{'text': answer_text} for answer_text in ref['answers']['text']],
								                        'id': ref['id'],
								                    }
								                    for ref in references
								                ]
								            }
								        ]
								    }
								]
								UpperCAmelCase__					:   int       				=       evaluate(dataset=_lowerCamelCase							,							predictions=_lowerCamelCase)
								return score



'''simple docstring'''



import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import BeitConfig, BeitForImageClassification, BeitForMaskedImageModeling, BeitImageProcessor
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


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







def       _UpperCamelCase (						UpperCamelCase__	, UpperCamelCase__=False	, UpperCamelCase__=False   ):
			UpperCAmelCase__					:   str       				=       """backbone.""" if is_semantic else """"""

			UpperCAmelCase__					:   Optional[Any]       				=       []
			for i in range(config.num_hidden_layers   ):
						# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
						rename_keys.append((f'''{prefix}blocks.{i}.norm1.weight''', f'''beit.encoder.layer.{i}.layernorm_before.weight''')   )
						rename_keys.append((f'''{prefix}blocks.{i}.norm1.bias''', f'''beit.encoder.layer.{i}.layernorm_before.bias''')   )
						rename_keys.append(
						    (f'''{prefix}blocks.{i}.attn.proj.weight''', f'''beit.encoder.layer.{i}.attention.output.dense.weight''')   )
						rename_keys.append(
						    (f'''{prefix}blocks.{i}.attn.proj.bias''', f'''beit.encoder.layer.{i}.attention.output.dense.bias''')   )
						rename_keys.append((f'''{prefix}blocks.{i}.norm2.weight''', f'''beit.encoder.layer.{i}.layernorm_after.weight''')   )
						rename_keys.append((f'''{prefix}blocks.{i}.norm2.bias''', f'''beit.encoder.layer.{i}.layernorm_after.bias''')   )
						rename_keys.append((f'''{prefix}blocks.{i}.mlp.fc1.weight''', f'''beit.encoder.layer.{i}.intermediate.dense.weight''')   )
						rename_keys.append((f'''{prefix}blocks.{i}.mlp.fc1.bias''', f'''beit.encoder.layer.{i}.intermediate.dense.bias''')   )
						rename_keys.append((f'''{prefix}blocks.{i}.mlp.fc2.weight''', f'''beit.encoder.layer.{i}.output.dense.weight''')   )
						rename_keys.append((f'''{prefix}blocks.{i}.mlp.fc2.bias''', f'''beit.encoder.layer.{i}.output.dense.bias''')   )

			# projection layer + position embeddings
			rename_keys.extend(
			    [
			        (f'''{prefix}cls_token''', """beit.embeddings.cls_token"""),
			        (f'''{prefix}patch_embed.proj.weight''', """beit.embeddings.patch_embeddings.projection.weight"""),
			        (f'''{prefix}patch_embed.proj.bias''', """beit.embeddings.patch_embeddings.projection.bias"""),
			        (f'''{prefix}pos_embed''', """beit.embeddings.position_embeddings"""),
			    ]   )

			if has_lm_head:
						# mask token + layernorm
						rename_keys.extend(
						    [
						        ("""mask_token""", """beit.embeddings.mask_token"""),
						        ("""norm.weight""", """layernorm.weight"""),
						        ("""norm.bias""", """layernorm.bias"""),
						    ]   )
			else:
						# layernorm + classification head
						rename_keys.extend(
						    [
						        ("""fc_norm.weight""", """beit.pooler.layernorm.weight"""),
						        ("""fc_norm.bias""", """beit.pooler.layernorm.bias"""),
						        ("""head.weight""", """classifier.weight"""),
						        ("""head.bias""", """classifier.bias"""),
						    ]   )

			return rename_keys







def       _UpperCamelCase (						UpperCamelCase__	, UpperCamelCase__	, UpperCamelCase__=False	, UpperCamelCase__=False   ):
			for i in range(config.num_hidden_layers   ):
						UpperCAmelCase__					:   Optional[Any]       				=       """backbone.""" if is_semantic else """"""
						# queries, keys and values
						UpperCAmelCase__					:   Any       				=       state_dict.pop(f'''{prefix}blocks.{i}.attn.qkv.weight'''   )
						UpperCAmelCase__					:   List[str]       				=       state_dict.pop(f'''{prefix}blocks.{i}.attn.q_bias'''   )
						UpperCAmelCase__					:   int       				=       state_dict.pop(f'''{prefix}blocks.{i}.attn.v_bias'''   )

						UpperCAmelCase__					:   Optional[Any]       				=       in_proj_weight[
						    : config.hidden_size, :
						]
						UpperCAmelCase__					:   Any       				=       q_bias
						UpperCAmelCase__					:   Dict       				=       in_proj_weight[
						    config.hidden_size : config.hidden_size * 2, :
						]
						UpperCAmelCase__					:   Any       				=       in_proj_weight[
						    -config.hidden_size :, :
						]
						UpperCAmelCase__					:   Any       				=       v_bias

						# gamma_1 and gamma_2
						# we call them lambda because otherwise they are renamed when using .from_pretrained
						UpperCAmelCase__					:   Dict       				=       state_dict.pop(f'''{prefix}blocks.{i}.gamma_1'''   )
						UpperCAmelCase__					:   Dict       				=       state_dict.pop(f'''{prefix}blocks.{i}.gamma_2'''   )

						UpperCAmelCase__					:   Union[str, Any]       				=       gamma_a
						UpperCAmelCase__					:   str       				=       gamma_a







def       _UpperCamelCase (						UpperCamelCase__	, UpperCamelCase__	, UpperCamelCase__   ):
			UpperCAmelCase__					:   int       				=       dct.pop(UpperCamelCase__   )
			UpperCAmelCase__					:   Optional[Any]       				=       val







def       _UpperCamelCase (						):
			UpperCAmelCase__					:   Any       				=       """http://images.cocodataset.org/val2017/000000039769.jpg"""
			UpperCAmelCase__					:   Dict       				=       Image.open(requests.get(UpperCamelCase__	, stream=UpperCamelCase__   ).raw   )
			return im







@torch.no_grad()
def       _UpperCamelCase (						UpperCamelCase__	, UpperCamelCase__	, UpperCamelCase__=False   ):
			UpperCAmelCase__					:   Optional[Any]       				=       False if """rvlcdip""" in checkpoint_url else True
			UpperCAmelCase__					:   Any       				=       BeitConfig(use_absolute_position_embeddings=UpperCamelCase__	, use_mask_token=UpperCamelCase__   )

			# size of the architecture
			if "large" in checkpoint_url or "dit-l" in checkpoint_url:
						UpperCAmelCase__					:   Optional[Any]       				=       1_0_2_4
						UpperCAmelCase__					:   Dict       				=       4_0_9_6
						UpperCAmelCase__					:   Any       				=       2_4
						UpperCAmelCase__					:   Tuple       				=       1_6

			# labels
			if "rvlcdip" in checkpoint_url:
						UpperCAmelCase__					:   int       				=       1_6
						UpperCAmelCase__					:   List[str]       				=       """huggingface/label-files"""
						UpperCAmelCase__					:   Optional[Any]       				=       """rvlcdip-id2label.json"""
						UpperCAmelCase__					:   Optional[int]       				=       json.load(open(hf_hub_download(UpperCamelCase__	, UpperCamelCase__	, repo_type="""dataset"""   )	, """r"""   )   )
						UpperCAmelCase__					:   Union[str, Any]       				=       {int(UpperCamelCase__   ): v for k, v in idalabel.items()}
						UpperCAmelCase__					:   Optional[Any]       				=       idalabel
						UpperCAmelCase__					:   List[Any]       				=       {v: k for k, v in idalabel.items()}

			# load state_dict of original model, remove and rename some keys
			UpperCAmelCase__					:   Optional[Any]       				=       torch.hub.load_state_dict_from_url(UpperCamelCase__	, map_location="""cpu"""   )["""model"""]

			UpperCAmelCase__					:   List[str]       				=       create_rename_keys(UpperCamelCase__	, has_lm_head=UpperCamelCase__   )
			for src, dest in rename_keys:
						rename_key(UpperCamelCase__	, UpperCamelCase__	, UpperCamelCase__   )
			read_in_q_k_v(UpperCamelCase__	, UpperCamelCase__	, has_lm_head=UpperCamelCase__   )

			# load HuggingFace model
			UpperCAmelCase__					:   str       				=       BeitForMaskedImageModeling(UpperCamelCase__   ) if has_lm_head else BeitForImageClassification(UpperCamelCase__   )
			model.eval()
			model.load_state_dict(UpperCamelCase__   )

			# Check outputs on an image
			UpperCAmelCase__					:   List[str]       				=       BeitImageProcessor(
			    size=config.image_size	, resample=PILImageResampling.BILINEAR	, do_center_crop=UpperCamelCase__   )
			UpperCAmelCase__					:   List[str]       				=       prepare_img()

			UpperCAmelCase__					:   Optional[Any]       				=       image_processor(images=UpperCamelCase__	, return_tensors="""pt"""   )
			UpperCAmelCase__					:   Optional[Any]       				=       encoding["""pixel_values"""]

			UpperCAmelCase__					:   Optional[Any]       				=       model(UpperCamelCase__   )
			UpperCAmelCase__					:   int       				=       outputs.logits

			# verify logits
			UpperCAmelCase__					:   int       				=       [1, 1_6] if """rvlcdip""" in checkpoint_url else [1, 1_9_6, 8_1_9_2]
			assert logits.shape == torch.Size(UpperCamelCase__   ), "Shape of logits not as expected"

			Path(UpperCamelCase__   ).mkdir(exist_ok=UpperCamelCase__   )
			print(f'''Saving model to {pytorch_dump_folder_path}'''   )
			model.save_pretrained(UpperCamelCase__   )
			print(f'''Saving image processor to {pytorch_dump_folder_path}'''   )
			image_processor.save_pretrained(UpperCamelCase__   )

			if push_to_hub:
						if has_lm_head:
									UpperCAmelCase__					:   Union[str, Any]       				=       """dit-base""" if """base""" in checkpoint_url else """dit-large"""
						else:
									UpperCAmelCase__					:   Tuple       				=       """dit-base-finetuned-rvlcdip""" if """dit-b""" in checkpoint_url else """dit-large-finetuned-rvlcdip"""
						image_processor.push_to_hub(
						    repo_path_or_name=Path(UpperCamelCase__	, UpperCamelCase__   )	, organization="""nielsr"""	, commit_message="""Add image processor"""	, use_temp_dir=UpperCamelCase__	, )
						model.push_to_hub(
						    repo_path_or_name=Path(UpperCamelCase__	, UpperCamelCase__   )	, organization="""nielsr"""	, commit_message="""Add model"""	, use_temp_dir=UpperCamelCase__	, )


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

		parser.add_argument(
		    '--checkpoint_url',
		    default='https://layoutlm.blob.core.windows.net/dit/dit-pts/dit-base-224-p16-500k-62d53a.pth',
		    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.'
		)
		parser.add_argument(
		    '--push_to_hub',
		    action='store_true',
		)
		__A		=parser.parse_args()
		convert_dit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)