import datasets

from .evaluate import evaluate


snake_case__				= '\\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'

snake_case__				= '\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'

snake_case__				= '\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 		UpperCamelCase_						(					self						) ->				Optional[int]:






           """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 		UpperCamelCase_						(					self				,				A_				,				A_						) ->				Optional[Any]:






           """simple docstring"""
           _lowerCamelCase			    =				{prediction['''id''']: prediction['''prediction_text'''] for prediction in predictions}
           _lowerCamelCase			    =				[
               {
                   '''paragraphs''': [
                       {
                           '''qas''': [
                               {
                                   '''answers''': [{'''text''': answer_text} for answer_text in ref['''answers''']['''text''']],
                                   '''id''': ref['''id'''],
                               }
                               for ref in references
                           ]
                       }
                   ]
               }
           ]
           _lowerCamelCase			    =				evaluate(dataset=A_				,				predictions=A_						)
           return score
import argparse

import torch
from datasets import load_dataset
from donut import DonutModel

from transformers import (
    DonutImageProcessor,
    DonutProcessor,
    DonutSwinConfig,
    DonutSwinModel,
    MBartConfig,
    MBartForCausalLM,
    VisionEncoderDecoderModel,
    XLMRobertaTokenizerFast,
)
def       __magic_name__(  __UpperCAmelCase		)						->    str:



      '''simple docstring'''

      _lowerCamelCase			    =				model.config

      _lowerCamelCase			    =				DonutSwinConfig(
          image_size=original_config.input_size ,						patch_size=4 ,						depths=original_config.encoder_layer ,						num_heads=[4, 8, 16, 32] ,						window_size=original_config.window_size ,						embed_dim=128 ,						)
      _lowerCamelCase			    =				MBartConfig(
          is_decoder=__UpperCAmelCase ,						is_encoder_decoder=__UpperCAmelCase ,						add_cross_attention=__UpperCAmelCase ,						decoder_layers=original_config.decoder_layer ,						max_position_embeddings=original_config.max_position_embeddings ,						vocab_size=len(
              model.decoder.tokenizer		) ,						scale_embedding=__UpperCAmelCase ,						add_final_layer_norm=__UpperCAmelCase ,						)

      return encoder_config, decoder_config
def       __magic_name__(  __UpperCAmelCase		)						->    Tuple:



      '''simple docstring'''

      if "encoder.model" in name:
            _lowerCamelCase			    =				name.replace('''encoder.model''' ,						'''encoder'''		)
      if "decoder.model" in name:
            _lowerCamelCase			    =				name.replace('''decoder.model''' ,						'''decoder'''		)
      if "patch_embed.proj" in name:
            _lowerCamelCase			    =				name.replace('''patch_embed.proj''' ,						'''embeddings.patch_embeddings.projection'''		)
      if "patch_embed.norm" in name:
            _lowerCamelCase			    =				name.replace('''patch_embed.norm''' ,						'''embeddings.norm'''		)
      if name.startswith('''encoder'''		):
            if "layers" in name:
                  _lowerCamelCase			    =				'''encoder.''' + name
            if "attn.proj" in name:
                  _lowerCamelCase			    =				name.replace('''attn.proj''' ,						'''attention.output.dense'''		)
            if "attn" in name and "mask" not in name:
                  _lowerCamelCase			    =				name.replace('''attn''' ,						'''attention.self'''		)
            if "norm1" in name:
                  _lowerCamelCase			    =				name.replace('''norm1''' ,						'''layernorm_before'''		)
            if "norm2" in name:
                  _lowerCamelCase			    =				name.replace('''norm2''' ,						'''layernorm_after'''		)
            if "mlp.fc1" in name:
                  _lowerCamelCase			    =				name.replace('''mlp.fc1''' ,						'''intermediate.dense'''		)
            if "mlp.fc2" in name:
                  _lowerCamelCase			    =				name.replace('''mlp.fc2''' ,						'''output.dense'''		)

            if name == "encoder.norm.weight":
                  _lowerCamelCase			    =				'''encoder.layernorm.weight'''
            if name == "encoder.norm.bias":
                  _lowerCamelCase			    =				'''encoder.layernorm.bias'''

      return name
def       __magic_name__(  __UpperCAmelCase ,						__UpperCAmelCase		)						->    Dict:



      '''simple docstring'''

      for key in orig_state_dict.copy().keys():
            _lowerCamelCase			    =				orig_state_dict.pop(__UpperCAmelCase		)

            if "qkv" in key:
                  _lowerCamelCase			    =				key.split('''.'''		)
                  _lowerCamelCase			    =				int(key_split[3]		)
                  _lowerCamelCase			    =				int(key_split[5]		)
                  _lowerCamelCase			    =				model.encoder.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size

                  if "weight" in key:
                        _lowerCamelCase			    =				val[:dim, :]
                        _lowerCamelCase			    =				val[dim : dim * 2, :]
                        _lowerCamelCase			    =				val[-dim:, :]
                  else:
                        _lowerCamelCase			    =				val[:dim]
                        _lowerCamelCase			    =				val[dim : dim * 2]
                        _lowerCamelCase			    =				val[-dim:]
            elif "attn_mask" in key or key in ["encoder.model.norm.weight", "encoder.model.norm.bias"]:
                  # HuggingFace implementation doesn't use attn_mask buffer
                  # and model doesn't use final LayerNorms for the encoder
                  pass
            else:
                  _lowerCamelCase			    =				val

      return orig_state_dict
def       __magic_name__(  __UpperCAmelCase ,						__UpperCAmelCase=None ,						__UpperCAmelCase=False		)						->    int:



      '''simple docstring'''

      _lowerCamelCase			    =				DonutModel.from_pretrained(__UpperCAmelCase		).eval()

      # load HuggingFace model
      _lowerCamelCase    ,					_lowerCamelCase			    =				get_configs(__UpperCAmelCase		)
      _lowerCamelCase			    =				DonutSwinModel(__UpperCAmelCase		)
      _lowerCamelCase			    =				MBartForCausalLM(__UpperCAmelCase		)
      _lowerCamelCase			    =				VisionEncoderDecoderModel(encoder=__UpperCAmelCase ,						decoder=__UpperCAmelCase		)
      model.eval()

      _lowerCamelCase			    =				original_model.state_dict()
      _lowerCamelCase			    =				convert_state_dict(__UpperCAmelCase ,						__UpperCAmelCase		)
      model.load_state_dict(__UpperCAmelCase		)

      # verify results on scanned document
      _lowerCamelCase			    =				load_dataset('''hf-internal-testing/example-documents'''		)
      _lowerCamelCase			    =				dataset['''test'''][0]['''image'''].convert('''RGB'''		)

      _lowerCamelCase			    =				XLMRobertaTokenizerFast.from_pretrained(__UpperCAmelCase ,						from_slow=__UpperCAmelCase		)
      _lowerCamelCase			    =				DonutImageProcessor(
          do_align_long_axis=original_model.config.align_long_axis ,						size=original_model.config.input_size[::-1]		)
      _lowerCamelCase			    =				DonutProcessor(__UpperCAmelCase ,						__UpperCAmelCase		)
      _lowerCamelCase			    =				processor(__UpperCAmelCase ,						return_tensors='''pt'''		).pixel_values

      if model_name == "naver-clova-ix/donut-base-finetuned-docvqa":
            _lowerCamelCase			    =				'''<s_docvqa><s_question>{user_input}</s_question><s_answer>'''
            _lowerCamelCase			    =				'''When is the coffee break?'''
            _lowerCamelCase			    =				task_prompt.replace('''{user_input}''' ,						__UpperCAmelCase		)
      elif model_name == "naver-clova-ix/donut-base-finetuned-rvlcdip":
            _lowerCamelCase			    =				'''<s_rvlcdip>'''
      elif model_name in [
          "naver-clova-ix/donut-base-finetuned-cord-v1",
          "naver-clova-ix/donut-base-finetuned-cord-v1-2560",
      ]:
            _lowerCamelCase			    =				'''<s_cord>'''
      elif model_name == "naver-clova-ix/donut-base-finetuned-cord-v2":
            _lowerCamelCase			    =				'''s_cord-v2>'''
      elif model_name == "naver-clova-ix/donut-base-finetuned-zhtrainticket":
            _lowerCamelCase			    =				'''<s_zhtrainticket>'''
      elif model_name in ["naver-clova-ix/donut-proto", "naver-clova-ix/donut-base"]:
            # use a random prompt
            _lowerCamelCase			    =				'''hello world'''
      else:
            raise ValueError('''Model name not supported'''		)
      _lowerCamelCase			    =				original_model.decoder.tokenizer(__UpperCAmelCase ,						add_special_tokens=__UpperCAmelCase ,						return_tensors='''pt'''		)[
          '''input_ids'''
      ]

      _lowerCamelCase			    =				original_model.encoder.model.patch_embed(__UpperCAmelCase		)
      _lowerCamelCase    ,					_lowerCamelCase			    =				model.encoder.embeddings(__UpperCAmelCase		)
      assert torch.allclose(__UpperCAmelCase ,						__UpperCAmelCase ,						atol=1E-3		)

      # verify encoder hidden states
      _lowerCamelCase			    =				original_model.encoder(__UpperCAmelCase		)
      _lowerCamelCase			    =				model.encoder(__UpperCAmelCase		).last_hidden_state
      assert torch.allclose(__UpperCAmelCase ,						__UpperCAmelCase ,						atol=1E-2		)

      # verify decoder hidden states
      _lowerCamelCase			    =				original_model(__UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase		).logits
      _lowerCamelCase			    =				model(__UpperCAmelCase ,						decoder_input_ids=__UpperCAmelCase		).logits
      assert torch.allclose(__UpperCAmelCase ,						__UpperCAmelCase ,						atol=1E-3		)
      print('''Looks ok!'''		)

      if pytorch_dump_folder_path is not None:
            print(F'Saving model and processor to {pytorch_dump_folder_path}'		)
            model.save_pretrained(__UpperCAmelCase		)
            processor.save_pretrained(__UpperCAmelCase		)

      if push_to_hub:
            model.push_to_hub('''nielsr/''' + model_name.split('''/'''		)[-1] ,						commit_message='''Update model'''		)
            processor.push_to_hub('''nielsr/''' + model_name.split('''/'''		)[-1] ,						commit_message='''Update model'''		)


if __name__ == "__main__":
     snake_case__				= argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         '--model_name',
         default='naver-clova-ix/donut-base-finetuned-docvqa',
         required=False,
         type=str,
         help='Name of the original model you\'d like to convert.',
     )
     parser.add_argument(
         '--pytorch_dump_folder_path',
         default=None,
         required=False,
         type=str,
         help='Path to the output PyTorch model directory.',
     )
     parser.add_argument(
         '--push_to_hub',
         action='store_true',
         help='Whether or not to push the converted model and processor to the 🤗 hub.',
     )

     snake_case__				= parser.parse_args()
     convert_donut_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)