


'''simple docstring'''






from sklearn.metrics import recall_score

import datasets


__A					:  Dict											=      "\nRecall is the fraction of the positive examples that were correctly labeled by the model as positive. It can be computed with the equation:\nRecall = TP / (TP + FN)\nWhere TP is the true positives and FN is the false negatives.\n"


__A					:  List[Any]											=      "\nArgs:\n- **predictions** (`list` of `int`): The predicted labels.\n- **references** (`list` of `int`): The ground truth labels.\n- **labels** (`list` of `int`): The set of labels to include when `average` is not set to `binary`, and their order when average is `None`. Labels present in the data can be excluded in this input, for example to calculate a multiclass average ignoring a majority negative class, while labels not present in the data will result in 0 components in a macro average. For multilabel targets, labels are column indices. By default, all labels in y_true and y_pred are used in sorted order. Defaults to None.\n- **pos_label** (`int`): The class label to use as the 'positive class' when calculating the recall. Defaults to `1`.\n- **average** (`string`): This parameter is required for multiclass/multilabel targets. If None, the scores for each class are returned. Otherwise, this determines the type of averaging performed on the data. Defaults to `'binary'`.\n    - `'binary'`: Only report results for the class specified by `pos_label`. This is applicable only if the target labels and predictions are binary.\n    - `'micro'`: Calculate metrics globally by counting the total true positives, false negatives, and false positives.\n    - `'macro'`: Calculate metrics for each label, and find their unweighted mean. This does not take label imbalance into account.\n    - `'weighted'`: Calculate metrics for each label, and find their average weighted by support (the number of true instances for each label). This alters `'macro'` to account for label imbalance. Note that it can result in an F-score that is not between precision and recall.\n    - `'samples'`: Calculate metrics for each instance, and find their average (only meaningful for multilabel classification).\n- **sample_weight** (`list` of `float`): Sample weights Defaults to `None`.\n- **zero_division** (): Sets the value to return when there is a zero division. Defaults to .\n    - `'warn'`: If there is a zero division, the return value is `0`, but warnings are also raised.\n    - `0`: If there is a zero division, the return value is `0`.\n    - `1`: If there is a zero division, the return value is `1`.\n\nReturns:\n- **recall** (`float`, or `array` of `float`): Either the general recall score, or the recall scores for individual classes, depending on the values input to `labels` and `average`. Minimum possible value is 0. Maximum possible value is 1. A higher recall means that more of the positive examples have been labeled correctly. Therefore, a higher recall is generally considered better.\n\nExamples:\n\n    Example 1-A simple example with some errors\n        >>> recall_metric = datasets.load_metric('recall')\n        >>> results = recall_metric.compute(references=[0, 0, 1, 1, 1], predictions=[0, 1, 0, 1, 1])\n        >>> print(results)\n        {'recall': 0.6666666666666666}\n\n    Example 2-The same example as Example 1, but with `pos_label=0` instead of the default `pos_label=1`.\n        >>> recall_metric = datasets.load_metric('recall')\n        >>> results = recall_metric.compute(references=[0, 0, 1, 1, 1], predictions=[0, 1, 0, 1, 1], pos_label=0)\n        >>> print(results)\n        {'recall': 0.5}\n\n    Example 3-The same example as Example 1, but with `sample_weight` included.\n        >>> recall_metric = datasets.load_metric('recall')\n        >>> sample_weight = [0.9, 0.2, 0.9, 0.3, 0.8]\n        >>> results = recall_metric.compute(references=[0, 0, 1, 1, 1], predictions=[0, 1, 0, 1, 1], sample_weight=sample_weight)\n        >>> print(results)\n        {'recall': 0.55}\n\n    Example 4-A multiclass example, using different averages.\n        >>> recall_metric = datasets.load_metric('recall')\n        >>> predictions = [0, 2, 1, 0, 0, 1]\n        >>> references = [0, 1, 2, 0, 1, 2]\n        >>> results = recall_metric.compute(predictions=predictions, references=references, average='macro')\n        >>> print(results)\n        {'recall': 0.3333333333333333}\n        >>> results = recall_metric.compute(predictions=predictions, references=references, average='micro')\n        >>> print(results)\n        {'recall': 0.3333333333333333}\n        >>> results = recall_metric.compute(predictions=predictions, references=references, average='weighted')\n        >>> print(results)\n        {'recall': 0.3333333333333333}\n        >>> results = recall_metric.compute(predictions=predictions, references=references, average=None)\n        >>> print(results)\n        {'recall': array([1., 0., 0.])}\n"


__A					:  str											=      "\n@article{scikit-learn, title={Scikit-learn: Machine Learning in {P}ython}, author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V. and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P. and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.}, journal={Journal of Machine Learning Research}, volume={12}, pages={2825--2830}, year={2011}\n"






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







    """simple docstring"""



    def __lowercase   (   self    :  str		)   ->  Dict:
      return datasets.MetricInfo(
          description=_DESCRIPTION    ,						citation=_CITATION    ,						inputs_description=_KWARGS_DESCRIPTION    ,						features=datasets.Features(
              {
                  """predictions""": datasets.Sequence(datasets.Value("""int32"""		)		),
                  """references""": datasets.Sequence(datasets.Value("""int32"""		)		),
              }
              if self.config_name == """multilabel"""
              else {
                  """predictions""": datasets.Value("""int32"""		),
                  """references""": datasets.Value("""int32"""		),
              }		)    ,						reference_urls=["""https://scikit-learn.org/stable/modules/generated/sklearn.metrics.recall_score.html"""]    ,						)





    def __lowercase   (   self    :  Tuple    ,						lowerCamelCase    :  Optional[Any]    ,						lowerCamelCase    :  str    ,						lowerCamelCase    :  Optional[Any]=None    ,						lowerCamelCase    :  Optional[int]=1    ,						lowerCamelCase    :  Union[str, Any]="binary"    ,						lowerCamelCase    :  Any=None    ,						lowerCamelCase    :  str="warn"    ,						)   ->  List[Any]:
      lowerCAmelCase_			:      Optional[int]			=   recall_score(
          lowerCamelCase    ,						lowerCamelCase    ,						labels=lowerCamelCase    ,						pos_label=lowerCamelCase    ,						average=lowerCamelCase    ,						sample_weight=lowerCamelCase    ,						zero_division=lowerCamelCase    ,						)
      return {"recall": float(lowerCamelCase		) if score.size == 1 else score}







'''simple docstring'''






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

  __A					:  List[Any]											=      True
except ImportError:
  __A					:  int											=      False

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







def 				UpperCamelCase_			(    A__					:   Namespace					):
  '''simple docstring'''





  return AddNewModelCommand(args.testing							,    args.testing_file							,    path=args.path					)






class    __snake_case       (   _SCREAMING_SNAKE_CASE):







    """simple docstring"""



    @staticmethod
    def __lowercase   (   lowerCamelCase    :  ArgumentParser		)   ->  int:
      lowerCAmelCase_			:      Optional[int]			=   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=lowerCamelCase    ,						help="""Configuration file on which to run."""		)
      add_new_model_parser.add_argument(
          """--path"""    ,						type=lowerCamelCase    ,						help="""Path to cookiecutter. Should only be used for testing purposes."""		)
      add_new_model_parser.set_defaults(func=lowerCamelCase		)

    def __init__(   self    :  List[str]    ,						lowerCamelCase    :  bool    ,						lowerCamelCase    :  str    ,						lowerCamelCase    :  Any=None    ,						*lowerCamelCase    :  List[str]		)   ->  Optional[Any]:
      lowerCAmelCase_			:      int			=   testing
      lowerCAmelCase_			:      Union[str, Any]			=   testing_file
      lowerCAmelCase_			:      Tuple			=   path





    def __lowercase   (   self    :  Tuple		)   ->  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
      lowerCAmelCase_			:      int			=   [directory for directory in os.listdir() if """cookiecutter-template-""" == directory[:22]]
      if len(lowerCamelCase		) > 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."""		)

      lowerCAmelCase_			:      List[Any]			=   (
          Path(lowerCamelCase		).parent.parent.parent.parent if self._path is None else Path(self._path		).parent.parent
      )
      lowerCAmelCase_			:      Dict			=   path_to_transformer_root / """templates""" / """adding_a_new_model"""

      # Execute cookiecutter
      if not self._testing:
        cookiecutter(str(lowerCamelCase		)		)
      else:
        with open(self._testing_file    ,						"""r"""		) as configuration_file:
          lowerCAmelCase_			:      Tuple			=   json.load(lowerCamelCase		)

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

      lowerCAmelCase_			:      List[str]			=   [directory for directory in os.listdir() if """cookiecutter-template-""" in directory[:22]][0]

      # Retrieve configuration
      with open(directory + """/configuration.json"""    ,						"""r"""		) as configuration_file:
        lowerCAmelCase_			:      Tuple			=   json.load(lowerCamelCase		)

      lowerCAmelCase_			:      str			=   configuration["""lowercase_modelname"""]
      lowerCAmelCase_			:      List[str]			=   configuration["""generate_tensorflow_pytorch_and_flax"""]
      os.remove(F'{directory}/configuration.json'		)

      lowerCAmelCase_			:      Dict			=   """PyTorch""" in generate_tensorflow_pytorch_and_flax
      lowerCAmelCase_			:      Optional[int]			=   """TensorFlow""" in generate_tensorflow_pytorch_and_flax
      lowerCAmelCase_			:      List[str]			=   """Flax""" in generate_tensorflow_pytorch_and_flax

      lowerCAmelCase_			:      Union[str, Any]			=   F'{path_to_transformer_root}/src/transformers/models/{lowercase_model_name}'
      os.makedirs(lowerCamelCase    ,						exist_ok=lowerCamelCase		)
      os.makedirs(F'{path_to_transformer_root}/tests/models/{lowercase_model_name}'    ,						exist_ok=lowerCamelCase		)

      # 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(lowerCamelCase    :  Any		):
        with open(lowerCamelCase    ,						"""r"""		) as f:
          lowerCAmelCase_			:      List[str]			=   f.readlines()
        with open(lowerCamelCase    ,						"""w"""		) as f:
          for line in lines:
            if "# Copied from transformers." not in line:
              f.write(lowerCamelCase		)

      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(lowerCamelCase    :  str    ,						lowerCamelCase    :  str    ,						lowerCamelCase    :  List[str]		):
        # Create temp file
        lowerCAmelCase_,							lowerCAmelCase_			:      int			=   mkstemp()
        lowerCAmelCase_			:      List[Any]			=   False
        with fdopen(lowerCamelCase    ,						"""w"""		) as new_file:
          with open(lowerCamelCase		) as old_file:
            for line in old_file:
              new_file.write(lowerCamelCase		)
              if line_to_copy_below in line:
                lowerCAmelCase_			:      List[str]			=   True
                for line_to_copy in lines_to_copy:
                  new_file.write(lowerCamelCase		)

        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(lowerCamelCase    ,						lowerCamelCase		)
        # Remove original file
        remove(lowerCamelCase		)
        # Move new file
        move(lowerCamelCase    ,						lowerCamelCase		)

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

              lowerCAmelCase_			:      Dict			=   []
            elif "# Replace with" in line and "##" not in line:
              lowerCAmelCase_			:      int			=   []
            elif "##" not in line:
              lines_to_copy.append(lowerCamelCase		)

        remove(lowerCamelCase		)

      replace_in_files(F'{directory}/to_replace_{lowercase_model_name}.py'		)
      os.rmdir(lowerCamelCase		)



