


"""simple docstring"""


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   _a      (      datasets.Metric ):
    def      _UpperCamelCase       ( self							:					Optional[Any]				):
          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							:					List[Any]							,					SCREAMING_SNAKE_CASE__							:					Any							,					SCREAMING_SNAKE_CASE__							:					Tuple				):
          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=SCREAMING_SNAKE_CASE__							,					predictions=SCREAMING_SNAKE_CASE__				)
          return score







"""simple docstring"""


from __future__ import annotations

import unittest

import numpy as np

from transformers import BlipTextConfig
from transformers.testing_utils import require_tf, slow
from transformers.utils import is_tf_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask


if is_tf_available():
   import tensorflow as tf

   from transformers import TFBlipTextModel
   from transformers.models.blip.modeling_tf_blip import TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST



class   _a      :
    def __init__( self							:					List[str]							,					SCREAMING_SNAKE_CASE__							:					List[str]							,					SCREAMING_SNAKE_CASE__							:					Union[str, Any]=12							,					SCREAMING_SNAKE_CASE__							:					Union[str, Any]=7							,					SCREAMING_SNAKE_CASE__							:					Union[str, Any]=True							,					SCREAMING_SNAKE_CASE__							:					Optional[Any]=True							,					SCREAMING_SNAKE_CASE__							:					str=True							,					SCREAMING_SNAKE_CASE__							:					Any=99							,					SCREAMING_SNAKE_CASE__							:					Optional[Any]=32							,					SCREAMING_SNAKE_CASE__							:					str=32							,					SCREAMING_SNAKE_CASE__							:					Tuple=2							,					SCREAMING_SNAKE_CASE__							:					str=4							,					SCREAMING_SNAKE_CASE__							:					Optional[Any]=37							,					SCREAMING_SNAKE_CASE__							:					Union[str, Any]=0.1							,					SCREAMING_SNAKE_CASE__							:					Optional[Any]=0.1							,					SCREAMING_SNAKE_CASE__							:					str=5_12							,					SCREAMING_SNAKE_CASE__							:					str=0.02							,					SCREAMING_SNAKE_CASE__							:					Union[str, Any]=0							,					SCREAMING_SNAKE_CASE__							:					Any=None							,					):
          lowerCamelCase__           =		parent
          lowerCamelCase__           =		batch_size
          lowerCamelCase__           =		seq_length
          lowerCamelCase__           =		is_training
          lowerCamelCase__           =		use_input_mask
          lowerCamelCase__           =		use_labels
          lowerCamelCase__           =		vocab_size
          lowerCamelCase__           =		hidden_size
          lowerCamelCase__           =		projection_dim
          lowerCamelCase__           =		num_hidden_layers
          lowerCamelCase__           =		num_attention_heads
          lowerCamelCase__           =		intermediate_size
          lowerCamelCase__           =		dropout
          lowerCamelCase__           =		attention_dropout
          lowerCamelCase__           =		max_position_embeddings
          lowerCamelCase__           =		initializer_range
          lowerCamelCase__           =		scope
          lowerCamelCase__           =		bos_token_id
    def      _UpperCamelCase       ( self							:					int				):
          lowerCamelCase__           =		ids_tensor([self.batch_size, self.seq_length]							,					self.vocab_size				)

          lowerCamelCase__           =		None
          if self.use_input_mask:
                lowerCamelCase__           =		random_attention_mask([self.batch_size, self.seq_length]				)

          if input_mask is not None:
                lowerCamelCase__           =		input_mask.numpy()
                lowerCamelCase__    ,  lowerCamelCase__           =		input_mask.shape
                lowerCamelCase__           =		np.random.randint(1							,					seq_length - 1							,					size=(batch_size,)				)
                for batch_idx, start_index in enumerate(SCREAMING_SNAKE_CASE__				):
                      lowerCamelCase__           =		1
                      lowerCamelCase__           =		0

          lowerCamelCase__           =		self.get_config()

          return config, input_ids, tf.convert_to_tensor(SCREAMING_SNAKE_CASE__				)
    def      _UpperCamelCase       ( self							:					Any				):
          return BlipTextConfig(
              vocab_size=self.vocab_size							,					hidden_size=self.hidden_size							,					projection_dim=self.projection_dim							,					num_hidden_layers=self.num_hidden_layers							,					num_attention_heads=self.num_attention_heads							,					intermediate_size=self.intermediate_size							,					dropout=self.dropout							,					attention_dropout=self.attention_dropout							,					max_position_embeddings=self.max_position_embeddings							,					initializer_range=self.initializer_range							,					bos_token_id=self.bos_token_id							,					)
    def      _UpperCamelCase       ( self							:					Any							,					SCREAMING_SNAKE_CASE__							:					Any							,					SCREAMING_SNAKE_CASE__							:					List[str]							,					SCREAMING_SNAKE_CASE__							:					List[str]				):
          lowerCamelCase__           =		TFBlipTextModel(config=SCREAMING_SNAKE_CASE__				)
          lowerCamelCase__           =		model(SCREAMING_SNAKE_CASE__							,					attention_mask=SCREAMING_SNAKE_CASE__							,					training=SCREAMING_SNAKE_CASE__				)
          lowerCamelCase__           =		model(SCREAMING_SNAKE_CASE__							,					training=SCREAMING_SNAKE_CASE__				)
          self.parent.assertEqual(result.last_hidden_state.shape							,					(self.batch_size, self.seq_length, self.hidden_size)				)
          self.parent.assertEqual(result.pooler_output.shape							,					(self.batch_size, self.hidden_size)				)


    def      _UpperCamelCase       ( self							:					Dict				):
          lowerCamelCase__           =		self.prepare_config_and_inputs()
          lowerCamelCase__    ,  lowerCamelCase__    ,  lowerCamelCase__           =		config_and_inputs
          lowerCamelCase__           =		{'input_ids': input_ids, 'attention_mask': input_mask}
          return config, inputs_dict





@require_tf
class   _a      (      SCREAMING_SNAKE_CASE_		,		unittest.TestCase ):
    a_    :				str             =    (TFBlipTextModel,) if is_tf_available() else ()
    a_    :				List[str]             =    False
    a_    :				Optional[Any]             =    False
    a_    :				Union[str, Any]             =    False
    def      _UpperCamelCase       ( self							:					str				):
          lowerCamelCase__           =		BlipTextModelTester(self				)
          lowerCamelCase__           =		ConfigTester(self							,					config_class=SCREAMING_SNAKE_CASE__							,					hidden_size=37				)
    def      _UpperCamelCase       ( self							:					Tuple				):
          self.config_tester.run_common_tests()
    def      _UpperCamelCase       ( self							:					str				):
          lowerCamelCase__           =		self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE__				)
    def      _UpperCamelCase       ( self							:					Tuple				):
          pass
    def      _UpperCamelCase       ( self							:					Tuple				):
          pass
    @unittest.skip(reason='Blip does not use inputs_embeds'				)
    def      _UpperCamelCase       ( self							:					List[str]				):
          pass
    @unittest.skip(reason='BlipTextModel has no base class and is not available in MODEL_MAPPING'				)
    def      _UpperCamelCase       ( self							:					Dict				):
          pass
    @unittest.skip(reason='BlipTextModel has no base class and is not available in MODEL_MAPPING'				)
    def      _UpperCamelCase       ( self							:					List[Any]				):
          pass
    @slow
    def      _UpperCamelCase       ( self							:					str				):
          for model_name in TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                lowerCamelCase__           =		TFBlipTextModel.from_pretrained(SCREAMING_SNAKE_CASE__				)
                self.assertIsNotNone(SCREAMING_SNAKE_CASE__				)


    def      _UpperCamelCase       ( self							:					Optional[int]							,					SCREAMING_SNAKE_CASE__							:					List[Any]=True				):
          super().test_pt_tf_model_equivalence(allow_missing_keys=SCREAMING_SNAKE_CASE__				)



