



"""simple docstring"""
from jiwer import compute_measures

import datasets


__a      =				"\\n@inproceedings{inproceedings,\n    author = {Morris, Andrew and Maier, Viktoria and Green, Phil},\n    year = {2004},\n    month = {01},\n    pages = {},\n    title = {From WER and RIL to MER and WIL: improved evaluation measures for connected speech recognition.}\n}\n"

__a      =				"\\nWord error rate (WER) is a common metric of the performance of an automatic speech recognition system.\n\nThe general difficulty of measuring performance lies in the fact that the recognized word sequence can have a different length from the reference word sequence (supposedly the correct one). The WER is derived from the Levenshtein distance, working at the word level instead of the phoneme level. The WER is a valuable tool for comparing different systems as well as for evaluating improvements within one system. This kind of measurement, however, provides no details on the nature of translation errors and further work is therefore required to identify the main source(s) of error and to focus any research effort.\n\nThis problem is solved by first aligning the recognized word sequence with the reference (spoken) word sequence using dynamic string alignment. Examination of this issue is seen through a theory called the power law that states the correlation between perplexity and word error rate.\n\nWord error rate can then be computed as:\n\nWER = (S + D + I) / N = (S + D + I) / (S + D + C)\n\nwhere\n\nS is the number of substitutions,\nD is the number of deletions,\nI is the number of insertions,\nC is the number of correct words,\nN is the number of words in the reference (N=S+D+C).\n\nThis value indicates the average number of errors per reference word. The lower the value, the better the\nperformance of the ASR system with a WER of 0 being a perfect score.\n"

__a      =				"\nCompute WER score of transcribed segments against references.\n\nArgs:\n    references: List of references for each speech input.\n    predictions: List of transcriptions to score.\n    concatenate_texts (bool, default=False): Whether to concatenate all input texts or compute WER iteratively.\n\nReturns:\n    (float): the word error rate\n\nExamples:\n\n    >>> predictions = [\"this is the prediction\", \"there is an other sample\"]\n    >>> references = [\"this is the reference\", \"there is another one\"]\n    >>> wer = datasets.load_metric(\"wer\")\n    >>> wer_score = wer.compute(predictions=predictions, references=references)\n    >>> print(wer_score)\n    0.5\n"
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION    ,						_KWARGS_DESCRIPTION				)
class 				lowerCamelCase  (			datasets.Metric				):






					'''simple docstring'''





					def    lowerCAmelCase_				(       self:							Optional[Any]						)   ->	Optional[Any]:
											return datasets.MetricInfo(
											    description=_DESCRIPTION						,     citation=_CITATION						,     inputs_description=_KWARGS_DESCRIPTION						,     features=datasets.Features(
											        {
											            """predictions""": datasets.Value("""string"""						,     id="""sequence"""						),
											            """references""": datasets.Value("""string"""						,     id="""sequence"""						),
											        }						)						,     codebase_urls=["""https://github.com/jitsi/jiwer/"""]						,     reference_urls=[
											        """https://en.wikipedia.org/wiki/Word_error_rate""",
											    ]						,     )






					def    lowerCAmelCase_				(       self:							int						,     snake_case:							Optional[Any]=None						,     snake_case:							Dict=None						,     snake_case:							Any=False						)   ->	Optional[int]:
											if concatenate_texts:
																	return compute_measures(snake_case						,     snake_case						)["wer"]
											else:
																	snake_case_ :List[str]						=			0
																	snake_case_ :Dict						=			0
																	for prediction, reference in zip(snake_case						,     snake_case						):
																							snake_case_ :List[str]						=			compute_measures(snake_case						,     snake_case						)
																							incorrect += measures["substitutions"] + measures["deletions"] + measures["insertions"]
																							total += measures["substitutions"] + measures["deletions"] + measures["hits"]
																	return incorrect / total


import json
import os
import shutil
import warnings
from argparse import ArgumentParser, Namespace
from pathlib import Path
from typing import List

from ..utils import logging
from . import BaseTransformersCLICommand


try:
      from cookiecutter.main import cookiecutter

      lowercase__       =True
except ImportError:
      lowercase__       =False

lowercase__       =logging.get_logger(__name__)  # pylint: disable=invalid-name




def    __UpperCamelCase     (				lowerCAmelCase__							:				Namespace  ):
 return AddNewModelCommand(args.testing  ,   args.testing_file  ,   path=args.path  )



class 		UpperCamelCase__  (       __lowercase    ):
   @staticmethod
   def       lowerCAmelCase			(snake_case_ :					ArgumentParser   ):
    __a :  List[Any]						=  parser.add_parser('''add-new-model'''   )
    add_new_model_parser.add_argument('''--testing'''					,				action='''store_true'''					,				help='''If in testing mode.'''   )
    add_new_model_parser.add_argument('''--testing_file'''					,				type=snake_case_					,				help='''Configuration file on which to run.'''   )
    add_new_model_parser.add_argument(
        '''--path'''					,				type=snake_case_					,				help='''Path to cookiecutter. Should only be used for testing purposes.'''   )
    add_new_model_parser.set_defaults(func=snake_case_   )
   def __init__(self :					Dict					,				snake_case_ :					bool					,				snake_case_ :					str					,				snake_case_ :					Dict=None					,				*snake_case_ :					Optional[Any]   ):
    __a :  Union[str, Any]						=  testing
    __a :  List[Any]						=  testing_file
    __a :  Any						=  path







   def       lowerCAmelCase			(self :					int   ):
    warnings.warn(
        '''The command `transformers-cli add-new-model` is deprecated and will be removed in v5 of Transformers. '''
        '''It is not actively maintained anymore, so might give a result that won\'t pass all tests and quality '''
        '''checks, you should use `transformers-cli add-new-model-like` instead.'''   )
    if not _has_cookiecutter:
     raise ImportError(
         '''Model creation dependencies are required to use the `add_new_model` command. Install them by running '''
         '''the following at the root of your `transformers` clone:\n\n\t$ pip install -e .[modelcreation]\n'''   )
    # Ensure that there is no other `cookiecutter-template-xxx` directory in the current working directory
    __a :  Union[str, Any]						=  [directory for directory in os.listdir() if '''cookiecutter-template-''' == directory[:2_2]]
    if len(snake_case_   ) > 0:
     raise ValueError(
         '''Several directories starting with `cookiecutter-template-` in current working directory. '''
         '''Please clean your directory by removing all folders starting with `cookiecutter-template-` or '''
         '''change your working directory.'''   )

    __a :  Union[str, Any]						=  (
        Path(snake_case_   ).parent.parent.parent.parent if self._path is None else Path(self._path   ).parent.parent
    )
    __a :  Union[str, Any]						=  path_to_transformer_root / '''templates''' / '''adding_a_new_model'''

    # Execute cookiecutter
    if not self._testing:
     cookiecutter(str(snake_case_   )   )
    else:
     with open(self._testing_file					,				'''r'''   ) as configuration_file:
      __a :  List[Any]						=  json.load(snake_case_   )

     cookiecutter(
         str(path_to_cookiecutter if self._path is None else self._path   )					,				no_input=snake_case_					,				extra_context=snake_case_					,				)

    __a :  List[str]						=  [directory for directory in os.listdir() if '''cookiecutter-template-''' in directory[:2_2]][0]

    # Retrieve configuration
    with open(directory + '''/configuration.json'''					,				'''r'''   ) as configuration_file:
     __a :  Optional[Any]						=  json.load(snake_case_   )

    __a :  str						=  configuration['''lowercase_modelname''']
    __a :  int						=  configuration['''generate_tensorflow_pytorch_and_flax''']
    os.remove(f"{directory}/configuration.json"   )

    __a :  Any						=  '''PyTorch''' in generate_tensorflow_pytorch_and_flax
    __a :  Dict						=  '''TensorFlow''' in generate_tensorflow_pytorch_and_flax
    __a :  Optional[int]						=  '''Flax''' in generate_tensorflow_pytorch_and_flax

    __a :  Dict						=  f"{path_to_transformer_root}/src/transformers/models/{lowercase_model_name}"
    os.makedirs(snake_case_					,				exist_ok=snake_case_   )
    os.makedirs(f"{path_to_transformer_root}/tests/models/{lowercase_model_name}"					,				exist_ok=snake_case_   )

    # Tests require submodules as they have parent imports
    with open(f"{path_to_transformer_root}/tests/models/{lowercase_model_name}/__init__.py"					,				'''w'''   ):
     pass

    shutil.move(
        f"{directory}/__init__.py"					,				f"{model_dir}/__init__.py"					,				)
    shutil.move(
        f"{directory}/configuration_{lowercase_model_name}.py"					,				f"{model_dir}/configuration_{lowercase_model_name}.py"					,				)

    def remove_copy_lines(snake_case_ :					Union[str, Any]   ):
     with open(snake_case_					,				'''r'''   ) as f:
      __a :  Union[str, Any]						=  f.readlines()
     with open(snake_case_					,				'''w'''   ) as f:
      for line in lines:
       if "# Copied from transformers." not in line:
        f.write(snake_case_   )

    if output_pytorch:
     if not self._testing:
      remove_copy_lines(f"{directory}/modeling_{lowercase_model_name}.py"   )

     shutil.move(
         f"{directory}/modeling_{lowercase_model_name}.py"					,				f"{model_dir}/modeling_{lowercase_model_name}.py"					,				)

     shutil.move(
         f"{directory}/test_modeling_{lowercase_model_name}.py"					,				f"{path_to_transformer_root}/tests/models/{lowercase_model_name}/test_modeling_{lowercase_model_name}.py"					,				)
    else:
     os.remove(f"{directory}/modeling_{lowercase_model_name}.py"   )
     os.remove(f"{directory}/test_modeling_{lowercase_model_name}.py"   )

    if output_tensorflow:
     if not self._testing:
      remove_copy_lines(f"{directory}/modeling_tf_{lowercase_model_name}.py"   )

     shutil.move(
         f"{directory}/modeling_tf_{lowercase_model_name}.py"					,				f"{model_dir}/modeling_tf_{lowercase_model_name}.py"					,				)

     shutil.move(
         f"{directory}/test_modeling_tf_{lowercase_model_name}.py"					,				f"{path_to_transformer_root}/tests/models/{lowercase_model_name}/test_modeling_tf_{lowercase_model_name}.py"					,				)
    else:
     os.remove(f"{directory}/modeling_tf_{lowercase_model_name}.py"   )
     os.remove(f"{directory}/test_modeling_tf_{lowercase_model_name}.py"   )

    if output_flax:
     if not self._testing:
      remove_copy_lines(f"{directory}/modeling_flax_{lowercase_model_name}.py"   )

     shutil.move(
         f"{directory}/modeling_flax_{lowercase_model_name}.py"					,				f"{model_dir}/modeling_flax_{lowercase_model_name}.py"					,				)

     shutil.move(
         f"{directory}/test_modeling_flax_{lowercase_model_name}.py"					,				f"{path_to_transformer_root}/tests/models/{lowercase_model_name}/test_modeling_flax_{lowercase_model_name}.py"					,				)
    else:
     os.remove(f"{directory}/modeling_flax_{lowercase_model_name}.py"   )
     os.remove(f"{directory}/test_modeling_flax_{lowercase_model_name}.py"   )

    shutil.move(
        f"{directory}/{lowercase_model_name}.md"					,				f"{path_to_transformer_root}/docs/source/en/model_doc/{lowercase_model_name}.md"					,				)

    shutil.move(
        f"{directory}/tokenization_{lowercase_model_name}.py"					,				f"{model_dir}/tokenization_{lowercase_model_name}.py"					,				)

    shutil.move(
        f"{directory}/tokenization_fast_{lowercase_model_name}.py"					,				f"{model_dir}/tokenization_{lowercase_model_name}_fast.py"					,				)

    from os import fdopen, remove
    from shutil import copymode, move
    from tempfile import mkstemp

    def replace(snake_case_ :					str					,				snake_case_ :					str					,				snake_case_ :					List[str]   ):
     # Create temp file
     __a				,       __a :  Tuple						=  mkstemp()
     __a :  Optional[Any]						=  False
     with fdopen(snake_case_					,				'''w'''   ) as new_file:
      with open(snake_case_   ) as old_file:
       for line in old_file:
        new_file.write(snake_case_   )
        if line_to_copy_below in line:
         __a :  Tuple						=  True
         for line_to_copy in lines_to_copy:
          new_file.write(snake_case_   )

     if not line_found:
      raise ValueError(f"Line {line_to_copy_below} was not found in file."   )

     # Copy the file permissions from the old file to the new file
     copymode(snake_case_					,				snake_case_   )
     # Remove original file
     remove(snake_case_   )
     # Move new file
     move(snake_case_					,				snake_case_   )

    def skip_units(snake_case_ :					Any   ):
     return (
         ("generating PyTorch" in line and not output_pytorch)
         or ("generating TensorFlow" in line and not output_tensorflow)
         or ("generating Flax" in line and not output_flax)
     )

    def replace_in_files(snake_case_ :					int   ):
     with open(snake_case_   ) as datafile:
      __a :  List[Any]						=  []
      __a :  int						=  False
      __a :  Tuple						=  False
      for line in datafile:
       if "# To replace in: " in line and "##" not in line:
        __a :  Optional[Any]						=  line.split('''"'''   )[1]
        __a :  Dict						=  skip_units(snake_case_   )
       elif "# Below: " in line and "##" not in line:
        __a :  str						=  line.split('''"'''   )[1]
        __a :  Any						=  skip_units(snake_case_   )
       elif "# End." in line and "##" not in line:
        if not skip_file and not skip_snippet:
         replace(snake_case_					,				snake_case_					,				snake_case_   )

        __a :  str						=  []
       elif "# Replace with" in line and "##" not in line:
        __a :  Optional[int]						=  []
       elif "##" not in line:
        lines_to_copy.append(snake_case_   )

     remove(snake_case_   )

    replace_in_files(f"{directory}/to_replace_{lowercase_model_name}.py"   )
    os.rmdir(snake_case_   )
