
import datasets

from .evaluate import evaluate


SCREAMING_SNAKE_CASE							:List[str]							 =				'\\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'

SCREAMING_SNAKE_CASE							:Optional[int]							 =				'\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'

SCREAMING_SNAKE_CASE							:Optional[Any]							 =				'\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					: Tuple ):
					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					: Dict							,A					: Optional[int]							,A					: Dict ):
					__A   			= {prediction["id"]: prediction["prediction_text"] for prediction in predictions}
					__A   			= [
					    {
					        "paragraphs": [
					            {
					                "qas": [
					                    {
					                        "answers": [{"text": answer_text} for answer_text in ref["answers"]["text"]],
					                        "id": ref["id"],
					                    }
					                    for ref in references
					                ]
					            }
					        ]
					    }
					]
					__A   			= evaluate(dataset=A							,predictions=A )
					return score


from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


SCREAMING_SNAKE_CASE							:Optional[Any]							 =				logging.get_logger(__name__)

SCREAMING_SNAKE_CASE							:List[Any]							 =				{
    'hustvl/yolos-small': 'https://huggingface.co/hustvl/yolos-small/resolve/main/config.json',
    # See all YOLOS models at https://huggingface.co/models?filter=yolos
}





class 							UpperCAmelCase      (  __SCREAMING_SNAKE_CASE  ):




	'''simple docstring'''





	snake_case_					    =     "yolos"






	def __init__(       self					: Any							,A					: Optional[Any]=7_68							,A					: Dict=12							,A					: Any=12							,A					: str=30_72							,A					: Any="gelu"							,A					: str=0.0							,A					: List[str]=0.0							,A					: Dict=0.02							,A					: int=1E-12							,A					: Tuple=[5_12, 8_64]							,A					: List[Any]=16							,A					: str=3							,A					: str=True							,A					: Any=1_00							,A					: Dict=True							,A					: Dict=False							,A					: Tuple=1							,A					: Union[str, Any]=5							,A					: Optional[Any]=2							,A					: Union[str, Any]=5							,A					: int=2							,A					: int=0.1							,**A					: List[str]							,):
					super().__init__(**A )

					__A   			= hidden_size
					__A   			= num_hidden_layers
					__A   			= num_attention_heads
					__A   			= intermediate_size
					__A   			= hidden_act
					__A   			= hidden_dropout_prob
					__A   			= attention_probs_dropout_prob
					__A   			= initializer_range
					__A   			= layer_norm_eps
					__A   			= image_size
					__A   			= patch_size
					__A   			= num_channels
					__A   			= qkv_bias
					__A   			= num_detection_tokens
					__A   			= use_mid_position_embeddings
					__A   			= auxiliary_loss
					# Hungarian matcher
					__A   			= class_cost
					__A   			= bbox_cost
					__A   			= giou_cost
					# Loss coefficients
					__A   			= bbox_loss_coefficient
					__A   			= giou_loss_coefficient
					__A   			= eos_coefficient





class 							UpperCAmelCase      (  __SCREAMING_SNAKE_CASE  ):




	'''simple docstring'''





	snake_case_					    =     version.parse("1.11"  )






	@property
	def 				UpperCamelCase_  (       self					: str ):
					return OrderedDict(
					    [
					        ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
					    ] )






	@property
	def 				UpperCamelCase_  (       self					: List[Any] ):
					return 1E-4







	@property
	def 				UpperCamelCase_  (       self					: Optional[Any] ):
					return 12
