

import argparse
import os

from transformers.utils import direct_transformers_import


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_task_guides.py
__a    :Optional[Any]      =			'src/transformers'
__a    :Tuple      =			'docs/source/en/tasks'

def       __snake_case						( __UpperCamelCase	: List[Any]				,__UpperCamelCase	: Dict				,__UpperCamelCase	: int    ):



      """simple docstring"""

      with open(__UpperCamelCase				,"r"				,encoding="utf-8"				,newline="\n"    ) as f:
            A_						  =				f.readlines()
      # Find the start prompt.
      A_						  =				0
      while not lines[start_index].startswith(__UpperCamelCase    ):
            start_index += 1
      start_index += 1

      A_						  =				start_index
      while not lines[end_index].startswith(__UpperCamelCase    ):
            end_index += 1
      end_index -= 1

      while len(lines[start_index]    ) <= 1:
            start_index += 1
      while len(lines[end_index]    ) <= 1:
            end_index -= 1
      end_index += 1
      return "".join(lines[start_index:end_index]    ), start_index, end_index, lines


# This is to make sure the transformers module imported is the one in the repo.
__a    :List[str]      =			direct_transformers_import(TRANSFORMERS_PATH)

__a    :Optional[Any]      =			{
    'asr.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_CTC_MAPPING_NAMES,
    'audio_classification.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES,
    'language_modeling.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_CAUSAL_LM_MAPPING_NAMES,
    'image_classification.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES,
    'masked_language_modeling.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_MASKED_LM_MAPPING_NAMES,
    'multiple_choice.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES,
    'object_detection.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_OBJECT_DETECTION_MAPPING_NAMES,
    'question_answering.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES,
    'semantic_segmentation.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING_NAMES,
    'sequence_classification.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES,
    'summarization.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES,
    'token_classification.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES,
    'translation.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES,
    'video_classification.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING_NAMES,
    'document_question_answering.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING_NAMES,
    'monocular_depth_estimation.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_DEPTH_ESTIMATION_MAPPING_NAMES,
}

# This list contains model types used in some task guides that are not in `CONFIG_MAPPING_NAMES` (therefore not in any
# `MODEL_MAPPING_NAMES` or any `MODEL_FOR_XXX_MAPPING_NAMES`).
__a    :Optional[Any]      =			{
    'summarization.md': ('nllb',),
    'translation.md': ('nllb',),
}

def       __snake_case						( __UpperCamelCase	: Tuple    ):



      """simple docstring"""

      A_						  =				TASK_GUIDE_TO_MODELS[task_guide]
      A_						  =				SPECIAL_TASK_GUIDE_TO_MODEL_TYPES.get(__UpperCamelCase				,set()    )
      A_						  =				{
          code: name
          for code, name in transformers_module.MODEL_NAMES_MAPPING.items()
          if (code in model_maping_names or code in special_model_types)
      }
      return ", ".join([f'''[{name}](../model_doc/{code})''' for code, name in model_names.items()]    ) + "\n"

def       __snake_case						( __UpperCamelCase	: str				,__UpperCamelCase	: List[str]=False    ):



      """simple docstring"""

      A_  ,     A_  ,     A_  ,     A_						  =				_find_text_in_file(
          filename=os.path.join(__UpperCamelCase				,__UpperCamelCase    )				,start_prompt="<!--This tip is automatically generated by `make fix-copies`, do not fill manually!-->"				,end_prompt="<!--End of the generated tip-->"				,)

      A_						  =				get_model_list_for_task(__UpperCamelCase    )

      if current_list != new_list:
            if overwrite:
                  with open(os.path.join(__UpperCamelCase				,__UpperCamelCase    )				,"w"				,encoding="utf-8"				,newline="\n"    ) as f:
                        f.writelines(lines[:start_index] + [new_list] + lines[end_index:]    )
            else:
                  raise ValueError(
                      f'''The list of models that can be used in the {task_guide} guide needs an update. Run `make fix-copies`'''
                      " to fix this."    )


if __name__ == "__main__":
      __a    :int      =			argparse.ArgumentParser()
      parser.add_argument('--fix_and_overwrite', action='store_true', help='Whether to fix inconsistencies.')
      __a    :Optional[Any]      =			parser.parse_args()

      for task_guide in TASK_GUIDE_TO_MODELS.keys():
            check_model_list_for_task(task_guide, args.fix_and_overwrite)



'''simple docstring'''






from scipy.stats import pearsonr, spearmanr
from sklearn.metrics import fa_score, matthews_corrcoef

import datasets


lowercase				='\\n@inproceedings{wang2019glue,\n  title={{GLUE}: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding},\n  author={Wang, Alex and Singh, Amanpreet and Michael, Julian and Hill, Felix and Levy, Omer and Bowman, Samuel R.},\n  note={In the Proceedings of ICLR.},\n  year={2019}\n}\n'

lowercase				='\\nGLUE, the General Language Understanding Evaluation benchmark\n(https://gluebenchmark.com/) is a collection of resources for training,\nevaluating, and analyzing natural language understanding systems.\n'

lowercase				='\nCompute GLUE evaluation metric associated to each GLUE dataset.\nArgs:\n    predictions: list of predictions to score.\n        Each translation should be tokenized into a list of tokens.\n    references: list of lists of references for each translation.\n        Each reference should be tokenized into a list of tokens.\nReturns: depending on the GLUE subset, one or several of:\n    "accuracy": Accuracy\n    "f1": F1 score\n    "pearson": Pearson Correlation\n    "spearmanr": Spearman Correlation\n    "matthews_correlation": Matthew Correlation\nExamples:\n\n    >>> glue_metric = datasets.load_metric(\'glue\', \'sst2\')  # \'sst2\' or any of ["mnli", "mnli_mismatched", "mnli_matched", "qnli", "rte", "wnli", "hans"]\n    >>> references = [0, 1]\n    >>> predictions = [0, 1]\n    >>> results = glue_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {\'accuracy\': 1.0}\n\n    >>> glue_metric = datasets.load_metric(\'glue\', \'mrpc\')  # \'mrpc\' or \'qqp\'\n    >>> references = [0, 1]\n    >>> predictions = [0, 1]\n    >>> results = glue_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {\'accuracy\': 1.0, \'f1\': 1.0}\n\n    >>> glue_metric = datasets.load_metric(\'glue\', \'stsb\')\n    >>> references = [0., 1., 2., 3., 4., 5.]\n    >>> predictions = [0., 1., 2., 3., 4., 5.]\n    >>> results = glue_metric.compute(predictions=predictions, references=references)\n    >>> print({"pearson": round(results["pearson"], 2), "spearmanr": round(results["spearmanr"], 2)})\n    {\'pearson\': 1.0, \'spearmanr\': 1.0}\n\n    >>> glue_metric = datasets.load_metric(\'glue\', \'cola\')\n    >>> references = [0, 1]\n    >>> predictions = [0, 1]\n    >>> results = glue_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {\'matthews_correlation\': 1.0}\n'





def lowerCamelCase__     (		__lowerCamelCase			:				Optional[Any]			,       __lowerCamelCase			:				str				):







   '''simple docstring'''




   return float((preds == labels).mean()				)





def lowerCamelCase__     (		__lowerCamelCase			:				Tuple			,       __lowerCamelCase			:				Union[str, Any]				):







   '''simple docstring'''




   _UpperCAmelCase  :  Union[str, Any]				       =simple_accuracy(__lowerCamelCase			,       __lowerCamelCase				)
   _UpperCAmelCase  :  List[Any]				       =float(fa_score(y_true=__lowerCamelCase			,       y_pred=__lowerCamelCase				)				)
   return {
       "accuracy": acc,
       "f1": fa,
   }





def lowerCamelCase__     (		__lowerCamelCase			:				str			,       __lowerCamelCase			:				int				):







   '''simple docstring'''




   _UpperCAmelCase  :  Dict				       =float(pearsonr(__lowerCamelCase			,       __lowerCamelCase				)[0]				)
   _UpperCAmelCase  :  int				       =float(spearmanr(__lowerCamelCase			,       __lowerCamelCase				)[0]				)
   return {
       "pearson": pearson_corr,
       "spearmanr": spearman_corr,
   }






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






       def      lowerCAmelCase						(     self)		->    str:




          '''simple docstring'''
          if self.config_name not in [
              "sst2",
              "mnli",
              "mnli_mismatched",
              "mnli_matched",
              "cola",
              "stsb",
              "mrpc",
              "qqp",
              "qnli",
              "rte",
              "wnli",
              "hans",
          ]:
             raise KeyError(
                 'You should supply a configuration name selected in '
                 '["sst2", "mnli", "mnli_mismatched", "mnli_matched", '
                 '"cola", "stsb", "mrpc", "qqp", "qnli", "rte", "wnli", "hans"]')
          return datasets.MetricInfo(
              description=_DESCRIPTION    ,				citation=_CITATION    ,				inputs_description=_KWARGS_DESCRIPTION    ,				features=datasets.Features(
                  {
                      'predictions': datasets.Value('int64' if self.config_name != 'stsb' else 'float32'),
                      'references': datasets.Value('int64' if self.config_name != 'stsb' else 'float32'),
                  })    ,				codebase_urls=[]    ,				reference_urls=[]    ,				format='numpy'    ,				)






       def      lowerCAmelCase						(     self    ,				snake_case    ,				snake_case)		->    int:




          '''simple docstring'''
          if self.config_name == "cola":
             return {"matthews_correlation": matthews_corrcoef(snake_case    ,				snake_case)}
          elif self.config_name == "stsb":
             return pearson_and_spearman(snake_case    ,				snake_case)
          elif self.config_name in ["mrpc", "qqp"]:
             return acc_and_fa(snake_case    ,				snake_case)
          elif self.config_name in ["sst2", "mnli", "mnli_mismatched", "mnli_matched", "qnli", "rte", "wnli", "hans"]:
             return {"accuracy": simple_accuracy(snake_case    ,				snake_case)}
          else:
             raise KeyError(
                 'You should supply a configuration name selected in '
                 '["sst2", "mnli", "mnli_mismatched", "mnli_matched", '
                 '"cola", "stsb", "mrpc", "qqp", "qnli", "rte", "wnli", "hans"]')

