

from ...configuration_utils import PretrainedConfig
from ...utils import logging


a__												=						logging.get_logger(__name__)

a__												=						{
    '''EleutherAI/gpt-neox-20b''': '''https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/config.json''',
    # See all GPTNeoX models at https://huggingface.co/models?filter=gpt_neox
}

class 							UpperCAmelCase_						(     __lowercase  ):
							"""simple docstring"""







							UpperCAmelCase__			:		Tuple             =       "gpt_neox"


							def __init__(     self			,				_a=5_0_4_3_2			,				_a=6_1_4_4			,				_a=4_4			,				_a=6_4			,				_a=2_4_5_7_6			,				_a="gelu"			,				_a=0.25			,				_a=1_0_0_0_0			,				_a=0.0			,				_a=0.0			,				_a=0.1			,				_a=2_0_4_8			,				_a=0.02			,				_a=1e-5			,				_a=True			,				_a=0			,				_a=2			,				_a=False			,				_a=True			,				_a=None			,				**_a			,				)       ->       str:
												super().__init__(bos_token_id=_a			,				eos_token_id=_a			,				**_a			)
												_a      :       Union[str, Any]   =						vocab_size
												_a      :       Optional[Any]   =						max_position_embeddings
												_a      :       str   =						hidden_size
												_a      :       Tuple   =						num_hidden_layers
												_a      :       Optional[int]   =						num_attention_heads
												_a      :       int   =						intermediate_size
												_a      :       Dict   =						hidden_act
												_a      :       Union[str, Any]   =						rotary_pct
												_a      :       List[Any]   =						rotary_emb_base
												_a      :       List[Any]   =						attention_dropout
												_a      :       Tuple   =						hidden_dropout
												_a      :       Union[str, Any]   =						classifier_dropout
												_a      :       List[Any]   =						initializer_range
												_a      :       Optional[int]   =						layer_norm_eps
												_a      :       str   =						use_cache
												_a      :       int   =						tie_word_embeddings
												_a      :       Tuple   =						use_parallel_residual
												_a      :       Union[str, Any]   =						rope_scaling
												self._rope_scaling_validation()

												if self.hidden_size % self.num_attention_heads != 0:
																	raise ValueError(
																	    '''The hidden size is not divisble by the number of attention heads! Make sure to update them!'''			)




							def 							__lowercase							(     self			)       ->       List[str]:
												if self.rope_scaling is None:
																	return

												if not isinstance(self.rope_scaling			,				_a			) or len(self.rope_scaling			) != 2:
																	raise ValueError(
																	    '''`rope_scaling` must be a dictionary with with two fields, `name` and `factor`, '''
																	    F"""got {self.rope_scaling}"""			)
												_a      :       Optional[int]   =						self.rope_scaling.get('''type'''			,				_a			)
												_a      :       List[str]   =						self.rope_scaling.get('''factor'''			,				_a			)
												if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
																	raise ValueError(
																	    F"""`rope_scaling`'s name field must be one of ['linear', 'dynamic'], got {rope_scaling_type}"""			)
												if rope_scaling_factor is None or not isinstance(_a			,				_a			) or rope_scaling_factor <= 1.0:
																	raise ValueError(F"""`rope_scaling`'s factor field must be an float > 1, got {rope_scaling_factor}"""			)




"""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





