





"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging


__UpperCamelCase     :						Optional[int]						       =  logging.get_logger(__name__)

__UpperCamelCase     :						Dict						       =  {
    '''microsoft/markuplm-base''': '''https://huggingface.co/microsoft/markuplm-base/resolve/main/config.json''',
    '''microsoft/markuplm-large''': '''https://huggingface.co/microsoft/markuplm-large/resolve/main/config.json''',
}







class       a							(   a__  ):
 snake_case__             =       '''markuplm'''



 def __init__(     self				,       _snake_case=3_05_22				,       _snake_case=7_68				,       _snake_case=12				,       _snake_case=12				,       _snake_case=30_72				,       _snake_case="gelu"				,       _snake_case=0.1				,       _snake_case=0.1				,       _snake_case=5_12				,       _snake_case=2				,       _snake_case=0.02				,       _snake_case=1E-12				,       _snake_case=0				,       _snake_case=0				,       _snake_case=2				,       _snake_case=2_56				,       _snake_case=10_24				,       _snake_case=2_16				,       _snake_case=10_01				,       _snake_case=32				,       _snake_case=50				,       _snake_case="absolute"				,       _snake_case=True				,       _snake_case=None				,       **_snake_case				,       ):







  """simple docstring"""






  super().__init__(
      pad_token_id=_snake_case				,       bos_token_id=_snake_case				,       eos_token_id=_snake_case				,       **_snake_case				,       )
  lowerCAmelCase    				= vocab_size
  lowerCAmelCase    				= hidden_size
  lowerCAmelCase    				= num_hidden_layers
  lowerCAmelCase    				= num_attention_heads
  lowerCAmelCase    				= hidden_act
  lowerCAmelCase    				= intermediate_size
  lowerCAmelCase    				= hidden_dropout_prob
  lowerCAmelCase    				= attention_probs_dropout_prob
  lowerCAmelCase    				= max_position_embeddings
  lowerCAmelCase    				= type_vocab_size
  lowerCAmelCase    				= initializer_range
  lowerCAmelCase    				= layer_norm_eps
  lowerCAmelCase    				= position_embedding_type
  lowerCAmelCase    				= use_cache
  lowerCAmelCase    				= classifier_dropout
  # additional properties
  lowerCAmelCase    				= max_depth
  lowerCAmelCase    				= max_xpath_tag_unit_embeddings
  lowerCAmelCase    				= max_xpath_subs_unit_embeddings
  lowerCAmelCase    				= tag_pad_id
  lowerCAmelCase    				= subs_pad_id
  lowerCAmelCase    				= xpath_unit_hidden_size







'''simple docstring'''
import datasets

from .evaluate import evaluate


lowercase__						       =      "\\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"

lowercase__						       =      "\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"

lowercase__						       =      "\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					):







		"""simple docstring"""

		def       lowerCAmelCase				(      self				:			Optional[int]      )						->			Tuple:






				"""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				:			str  ,	UpperCamelCase__				:			int  ,	UpperCamelCase__				:			str      )						->			Optional[Any]:






				"""simple docstring"""







				snake_case  :       Tuple     			=						{prediction['''id''']: prediction['''prediction_text'''] for prediction in predictions}
				snake_case  :       Dict     			=						[
				    {
				        '''paragraphs''': [
				            {
				                '''qas''': [
				                    {
				                        '''answers''': [{'''text''': answer_text} for answer_text in ref['''answers''']['''text''']],
				                        '''id''': ref['''id'''],
				                    }
				                    for ref in references
				                ]
				            }
				        ]
				    }
				]
				snake_case  :       int     			=						evaluate(dataset=UpperCamelCase__  ,	predictions=UpperCamelCase__      )
				return score


