






import datasets

from .evaluate import evaluate


__lowerCamelCase      :    int   		=   """\
@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      :    List[str]   		=   """
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      :    int   		=   """
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 							SCREAMING_SNAKE_CASE__		(       datasets.Metric		):





     """simple docstring"""






     def  _lowercase  (      self  :						int ):
      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  _lowercase  (      self  :						List[Any]							,	__A  :						Tuple							,	__A  :						str ):
      snake_case__				:   Any     =						{prediction["id"]: prediction["prediction_text"] for prediction in predictions}
      snake_case__				:   Optional[int]     =						[
          {
              "paragraphs": [
                  {
                      "qas": [
                          {
                              "answers": [{"text": answer_text} for answer_text in ref["answers"]["text"]],
                              "id": ref["id"],
                          }
                          for ref in references
                      ]
                  }
              ]
          }
      ]
      snake_case__				:   Any     =						evaluate(dataset=__UpperCamelCase							,	predictions=__UpperCamelCase )
      return score











import argparse
import re
from typing import Dict

import torch
from datasets import Audio, Dataset, load_dataset, load_metric

from transformers import AutoFeatureExtractor, pipeline







def   SCREAMING_SNAKE_CASE       (				snake_case_      :							Dataset  ,						snake_case_      :							Dict[str, str]     ):
	snake_case__				:   Tuple     =						args.log_outputs
	snake_case__				:   Union[str, Any]     =						"_".join(args.dataset.split("/"     ) + [args.config, args.split]     )

	# load metric
	snake_case__				:   List[str]     =						load_metric("wer"     )
	snake_case__				:   List[str]     =						load_metric("cer"     )

	# compute metrics
	snake_case__				:   List[Any]     =						wer.compute(references=result["target"]  ,						predictions=result["prediction"]     )
	snake_case__				:   List[str]     =						cer.compute(references=result["target"]  ,						predictions=result["prediction"]     )

	# print & log results
	snake_case__				:   Dict     =						F'''WER: {wer_result}\nCER: {cer_result}'''
	print(snake_case_     )

	with open(F'''{dataset_id}_eval_results.txt'''  ,						"w"     ) as f:
		f.write(snake_case_     )

	# log all results in text file. Possibly interesting for analysis
	if log_outputs is not None:
		snake_case__				:   Union[str, Any]     =						F'''log_{dataset_id}_predictions.txt'''
		snake_case__				:   int     =						F'''log_{dataset_id}_targets.txt'''

		with open(snake_case_  ,						"w"     ) as p, open(snake_case_  ,						"w"     ) as t:
			# mapping function to write output
			def write_to_file(snake_case_      :							Union[str, Any]  ,						snake_case_      :							Any     ):
				p.write(F'''{i}''' + "\n"     )
				p.write(batch["prediction"] + "\n"     )
				t.write(F'''{i}''' + "\n"     )
				t.write(batch["target"] + "\n"     )

			result.map(snake_case_  ,						with_indices=snake_case_     )







def   SCREAMING_SNAKE_CASE       (				snake_case_      :							str     ):
	snake_case__				:   List[Any]     =						"[,?.!\-\;\:\"“%‘”�—’…–]"  # noqa: W605 IMPORTANT: this should correspond to the chars that were ignored during training

	snake_case__				:   Optional[int]     =						re.sub(snake_case_  ,						""  ,						text.lower()     )

	# In addition, we can normalize the target text, e.g. removing new lines characters etc...
	# note that order is important here!
	snake_case__				:   Optional[Any]     =						["\n\n", "\n", "   ", "  "]

	for t in token_sequences_to_ignore:
		snake_case__				:   Optional[int]     =						" ".join(text.split(snake_case_     )     )

	return text







def   SCREAMING_SNAKE_CASE       (				snake_case_      :							int     ):
	# load dataset
	snake_case__				:   int     =						load_dataset(args.dataset  ,						args.config  ,						split=args.split  ,						use_auth_token=snake_case_     )

	# for testing: only process the first two examples as a test
	# dataset = dataset.select(range(10))

	# load processor
	snake_case__				:   List[str]     =						AutoFeatureExtractor.from_pretrained(args.model_id     )
	snake_case__				:   List[Any]     =						feature_extractor.sampling_rate

	# resample audio
	snake_case__				:   Dict     =						dataset.cast_column("audio"  ,						Audio(sampling_rate=snake_case_     )     )

	# load eval pipeline
	if args.device is None:
		snake_case__				:   int     =						0 if torch.cuda.is_available() else -1
	snake_case__				:   List[str]     =						pipeline("automatic-speech-recognition"  ,						model=args.model_id  ,						device=args.device     )

	# map function to decode audio
	def map_to_pred(snake_case_      :							Any     ):
		snake_case__				:   Union[str, Any]     =						asr(
		    batch["audio"]["array"]  ,						chunk_length_s=args.chunk_length_s  ,						stride_length_s=args.stride_length_s     )

		snake_case__				:   Optional[int]     =						prediction["text"]
		snake_case__				:   Optional[Any]     =						normalize_text(batch["sentence"]     )
		return batch

	# run inference on all examples
	snake_case__				:   Any     =						dataset.map(snake_case_  ,						remove_columns=dataset.column_names     )

	# compute and log_results
	# do not change function below
	log_results(snake_case_  ,						snake_case_     )


if __name__ == "__main__":
	__lowerCamelCase      :    Dict   		=   argparse.ArgumentParser()

	parser.add_argument(
	    """--model_id""", type=str, required=True, help="""Model identifier. Should be loadable with 🤗 Transformers"""
	)
	parser.add_argument(
	    """--dataset""",
	    type=str,
	    required=True,
	    help="""Dataset name to evaluate the `model_id`. Should be loadable with 🤗 Datasets""",
	)
	parser.add_argument(
	    """--config""", type=str, required=True, help="""Config of the dataset. *E.g.* `'en'`  for Common Voice"""
	)
	parser.add_argument("""--split""", type=str, required=True, help="""Split of the dataset. *E.g.* `'test'`""")
	parser.add_argument(
	    """--chunk_length_s""", type=float, default=None, help="""Chunk length in seconds. Defaults to 5 seconds."""
	)
	parser.add_argument(
	    """--stride_length_s""", type=float, default=None, help="""Stride of the audio chunks. Defaults to 1 second."""
	)
	parser.add_argument(
	    """--log_outputs""", action="""store_true""", help="""If defined, write outputs to log file for analysis."""
	)
	parser.add_argument(
	    """--device""",
	    type=int,
	    default=None,
	    help="""The device to run the pipeline on. -1 for CPU (default), 0 for the first GPU and so on.""",
	)
	__lowerCamelCase      :    str   		=   parser.parse_args()

	main(args)



