




'''simple docstring'''


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


_snake_case : List[str]  				=						logging.get_logger(__name__)

_snake_case : Dict  				=						{
    'vinvino02/glpn-kitti': 'https://huggingface.co/vinvino02/glpn-kitti/resolve/main/config.json',
    # See all GLPN models at https://huggingface.co/models?filter=glpn
}

class      A       (						__A       ):
							lowercase_							     =							'glpn'







							def __init__(    self			:    str				,     lowerCAmelCase_			:    List[Any]=3				,     lowerCAmelCase_			:    Optional[int]=4				,     lowerCAmelCase_			:    Any=[2, 2, 2, 2]				,     lowerCAmelCase_			:    Dict=[8, 4, 2, 1]				,     lowerCAmelCase_			:    Optional[int]=[32, 64, 1_60, 2_56]				,     lowerCAmelCase_			:    Tuple=[7, 3, 3, 3]				,     lowerCAmelCase_			:    Any=[4, 2, 2, 2]				,     lowerCAmelCase_			:    Optional[int]=[1, 2, 5, 8]				,     lowerCAmelCase_			:    Optional[Any]=[4, 4, 4, 4]				,     lowerCAmelCase_			:    Optional[int]="gelu"				,     lowerCAmelCase_			:    str=0.0				,     lowerCAmelCase_			:    List[Any]=0.0				,     lowerCAmelCase_			:    Optional[int]=0.0_2				,     lowerCAmelCase_			:    Union[str, Any]=0.1				,     lowerCAmelCase_			:    List[Any]=1e-6				,     lowerCAmelCase_			:    List[Any]=64				,     lowerCAmelCase_			:    Union[str, Any]=10				,     lowerCAmelCase_			:    Optional[int]=-1				,     **lowerCAmelCase_			:    List[Any]				,     )     ->				List[str]:


														"""simple docstring"""


														super().__init__(**snake_case_   )

														_a      	=							num_channels
														_a      	=							num_encoder_blocks
														_a      	=							depths
														_a      	=							sr_ratios
														_a      	=							hidden_sizes
														_a      	=							patch_sizes
														_a      	=							strides
														_a      	=							mlp_ratios
														_a      	=							num_attention_heads
														_a      	=							hidden_act
														_a      	=							hidden_dropout_prob
														_a      	=							attention_probs_dropout_prob
														_a      	=							initializer_range
														_a      	=							drop_path_rate
														_a      	=							layer_norm_eps
														_a      	=							decoder_hidden_size
														_a      	=							max_depth
														_a      	=							head_in_index


"""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





