






"""simple docstring"""
import datasets

from .evaluate import evaluate


lowerCamelCase_							      = '''\
@article{hendrycks2021cuad,
      title={CUAD: An Expert-Annotated NLP Dataset for Legal Contract Review},
      author={Dan Hendrycks and Collin Burns and Anya Chen and Spencer Ball},
      journal={arXiv preprint arXiv:2103.06268},
      year={2021}
}
'''

lowerCamelCase_							      = '''
This metric wrap the official scoring script for version 1 of the Contract
Understanding Atticus Dataset (CUAD).
Contract Understanding Atticus Dataset (CUAD) v1 is a corpus of more than 13,000 labels in 510
commercial legal contracts that have been manually labeled to identify 41 categories of important
clauses that lawyers look for when reviewing contracts in connection with corporate transactions.
'''

lowerCamelCase_							      = '''
Computes CUAD scores (EM, F1, AUPR, Precision@80%Recall, and Precision@90%Recall).
Args:
    predictions: List of question-answers dictionaries with the following key-values:
        - \'id\': id of the question-answer pair as given in the references (see below)
        - \'prediction_text\': list of possible texts for the answer, as a list of strings
        depending on a threshold on the confidence probability of each prediction.
    references: List of question-answers dictionaries with the following key-values:
        - \'id\': id of the question-answer pair (see above),
        - \'answers\': a Dict in the CUAD dataset format
            {
                \'text\': list of possible texts for the answer, as a list of strings
                \'answer_start\': list of start positions for the answer, as a list of ints
            }
            Note that answer_start values are not taken into account to compute the metric.
Returns:
    \'exact_match\': Exact match (the normalized answer exactly match the gold answer)
    \'f1\': The F-score of predicted tokens versus the gold answer
    \'aupr\': Area Under the Precision-Recall curve
    \'prec_at_80_recall\': Precision at 80% recall
    \'prec_at_90_recall\': Precision at 90% recall
Examples:
    >>> 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\'}]
    >>> 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\'}]
    >>> cuad_metric = datasets.load_metric("cuad")
    >>> results = cuad_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {\'exact_match\': 100.0, \'f1\': 100.0, \'aupr\': 0.0, \'prec_at_80_recall\': 1.0, \'prec_at_90_recall\': 1.0}
'''







@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION							,		_KWARGS_DESCRIPTION	)
class      UpperCamelCase_			(datasets.Metric	):





	def 	_SCREAMING_SNAKE_CASE ( self   :					str		)		->     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 	_SCREAMING_SNAKE_CASE ( self   :					str      ,      lowerCAmelCase_   :					List[str]      ,      lowerCAmelCase_   :					Optional[int]		)		->     Optional[int]:
				UpperCAmelCase_	:					Optional[Any]     	=   {prediction["id"]: prediction["prediction_text"] for prediction in predictions}
				UpperCAmelCase_	:					Any     	=   [
				    {
				        "paragraphs": [
				            {
				                "qas": [
				                    {
				                        "answers": [{"text": answer_text} for answer_text in ref["answers"]["text"]],
				                        "id": ref["id"],
				                    }
				                    for ref in references
				                ]
				            }
				        ]
				    }
				]
				UpperCAmelCase_	:					Tuple     	=   evaluate(dataset=lowerCAmelCase_      ,      predictions=lowerCAmelCase_		)
				return score











"""simple docstring"""
from argparse import ArgumentParser, Namespace

from ..utils import logging
from . import BaseTransformersCLICommand

def 							snake_case (  A__		):
			return ConvertCommand(
			    args.model_type      ,args.tf_checkpoint      ,args.pytorch_dump_output      ,args.config      ,args.finetuning_task_name		)


lowerCamelCase_							      = '''
transformers can only be used from the commandline to convert TensorFlow models in PyTorch, In that case, it requires
TensorFlow to be installed. Please see https://www.tensorflow.org/install/ for installation instructions.
'''







class      UpperCamelCase_			(__A	):





	@staticmethod
	def 	_SCREAMING_SNAKE_CASE ( lowerCAmelCase_   :					ArgumentParser		)		->     Optional[Any]:
				UpperCAmelCase_	:					Any     	=   parser.add_parser(
				    "convert"      ,      help="CLI tool to run convert model from original author checkpoints to Transformers PyTorch checkpoints."      ,      )
				train_parser.add_argument("--model_type"      ,      type=lowerCAmelCase_      ,      required=lowerCAmelCase_      ,      help="Model's type."		)
				train_parser.add_argument(
				    "--tf_checkpoint"      ,      type=lowerCAmelCase_      ,      required=lowerCAmelCase_      ,      help="TensorFlow checkpoint path or folder."		)
				train_parser.add_argument(
				    "--pytorch_dump_output"      ,      type=lowerCAmelCase_      ,      required=lowerCAmelCase_      ,      help="Path to the PyTorch saved model output."		)
				train_parser.add_argument("--config"      ,      type=lowerCAmelCase_      ,      default=""      ,      help="Configuration file path or folder."		)
				train_parser.add_argument(
				    "--finetuning_task_name"      ,      type=lowerCAmelCase_      ,      default=lowerCAmelCase_      ,      help="Optional fine-tuning task name if the TF model was a finetuned model."      ,      )
				train_parser.set_defaults(func=lowerCAmelCase_		)





	def __init__( self   :					str      ,      lowerCAmelCase_   :					str      ,      lowerCAmelCase_   :					str      ,      lowerCAmelCase_   :					str      ,      lowerCAmelCase_   :					str      ,      lowerCAmelCase_   :					str      ,      *lowerCAmelCase_   :					Tuple      ,      )		->     int:
				UpperCAmelCase_	:					Dict     	=   logging.get_logger("transformers-cli/converting"		)

				self._logger.info(f"""Loading model {model_type}"""		)
				UpperCAmelCase_	:					Optional[int]     	=   model_type
				UpperCAmelCase_	:					Union[str, Any]     	=   tf_checkpoint
				UpperCAmelCase_	:					Tuple     	=   pytorch_dump_output
				UpperCAmelCase_	:					Tuple     	=   config
				UpperCAmelCase_	:					List[str]     	=   finetuning_task_name





	def 	_SCREAMING_SNAKE_CASE ( self   :					Tuple		)		->     List[Any]:
				if self._model_type == "albert":
							try:
										from ..models.albert.convert_albert_original_tf_checkpoint_to_pytorch import (
										    convert_tf_checkpoint_to_pytorch,
										)
							except ImportError:
										raise ImportError(lowerCAmelCase_		)

							convert_tf_checkpoint_to_pytorch(self._tf_checkpoint      ,      self._config      ,      self._pytorch_dump_output		)
				elif self._model_type == "bert":
							try:
										from ..models.bert.convert_bert_original_tf_checkpoint_to_pytorch import (
										    convert_tf_checkpoint_to_pytorch,
										)
							except ImportError:
										raise ImportError(lowerCAmelCase_		)

							convert_tf_checkpoint_to_pytorch(self._tf_checkpoint      ,      self._config      ,      self._pytorch_dump_output		)
				elif self._model_type == "funnel":
							try:
										from ..models.funnel.convert_funnel_original_tf_checkpoint_to_pytorch import (
										    convert_tf_checkpoint_to_pytorch,
										)
							except ImportError:
										raise ImportError(lowerCAmelCase_		)

							convert_tf_checkpoint_to_pytorch(self._tf_checkpoint      ,      self._config      ,      self._pytorch_dump_output		)
				elif self._model_type == "t5":
							try:
										from ..models.ta.convert_ta_original_tf_checkpoint_to_pytorch import convert_tf_checkpoint_to_pytorch
							except ImportError:
										raise ImportError(lowerCAmelCase_		)

							convert_tf_checkpoint_to_pytorch(self._tf_checkpoint      ,      self._config      ,      self._pytorch_dump_output		)
				elif self._model_type == "gpt":
							from ..models.openai.convert_openai_original_tf_checkpoint_to_pytorch import (
							    convert_openai_checkpoint_to_pytorch,
							)

							convert_openai_checkpoint_to_pytorch(self._tf_checkpoint      ,      self._config      ,      self._pytorch_dump_output		)
				elif self._model_type == "transfo_xl":
							try:
										from ..models.transfo_xl.convert_transfo_xl_original_tf_checkpoint_to_pytorch import (
										    convert_transfo_xl_checkpoint_to_pytorch,
										)
							except ImportError:
										raise ImportError(lowerCAmelCase_		)

							if "ckpt" in self._tf_checkpoint.lower():
										UpperCAmelCase_	:					Union[str, Any]     	=   self._tf_checkpoint
										UpperCAmelCase_	:					List[str]     	=   ""
							else:
										UpperCAmelCase_	:					Any     	=   self._tf_checkpoint
										UpperCAmelCase_	:					Any     	=   ""
							convert_transfo_xl_checkpoint_to_pytorch(
							    lowerCAmelCase_      ,      self._config      ,      self._pytorch_dump_output      ,      lowerCAmelCase_		)
				elif self._model_type == "gpt2":
							try:
										from ..models.gpta.convert_gpta_original_tf_checkpoint_to_pytorch import (
										    convert_gpta_checkpoint_to_pytorch,
										)
							except ImportError:
										raise ImportError(lowerCAmelCase_		)

							convert_gpta_checkpoint_to_pytorch(self._tf_checkpoint      ,      self._config      ,      self._pytorch_dump_output		)
				elif self._model_type == "xlnet":
							try:
										from ..models.xlnet.convert_xlnet_original_tf_checkpoint_to_pytorch import (
										    convert_xlnet_checkpoint_to_pytorch,
										)
							except ImportError:
										raise ImportError(lowerCAmelCase_		)

							convert_xlnet_checkpoint_to_pytorch(
							    self._tf_checkpoint      ,      self._config      ,      self._pytorch_dump_output      ,      self._finetuning_task_name		)
				elif self._model_type == "xlm":
							from ..models.xlm.convert_xlm_original_pytorch_checkpoint_to_pytorch import (
							    convert_xlm_checkpoint_to_pytorch,
							)

							convert_xlm_checkpoint_to_pytorch(self._tf_checkpoint      ,      self._pytorch_dump_output		)
				elif self._model_type == "lxmert":
							from ..models.lxmert.convert_lxmert_original_tf_checkpoint_to_pytorch import (
							    convert_lxmert_checkpoint_to_pytorch,
							)

							convert_lxmert_checkpoint_to_pytorch(self._tf_checkpoint      ,      self._pytorch_dump_output		)
				elif self._model_type == "rembert":
							from ..models.rembert.convert_rembert_tf_checkpoint_to_pytorch import (
							    convert_rembert_tf_checkpoint_to_pytorch,
							)

							convert_rembert_tf_checkpoint_to_pytorch(self._tf_checkpoint      ,      self._config      ,      self._pytorch_dump_output		)
				else:
							raise ValueError(
							    "--model_type should be selected in the list [bert, gpt, gpt2, t5, transfo_xl, xlnet, xlm, lxmert]"		)



