

from collections import Counter
from pathlib import Path
from typing import Optional, Tuple

import yaml



class 						_A     (				yaml.SafeLoader				):





  '''simple docstring'''



  def    snake_case_       (  self   ,SCREAMING_SNAKE_CASE_    ):

      '''simple docstring'''
      snake_case							:		Tuple        =		[self.constructed_objects[key_node] for key_node, _ in node.value]
      snake_case							:		Optional[Any]        =		[tuple(SCREAMING_SNAKE_CASE_    ) if isinstance(SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_    ) else key for key in keys]
      snake_case							:		Optional[int]        =		Counter(SCREAMING_SNAKE_CASE_    )
      snake_case							:		List[Any]        =		[key for key in counter if counter[key] > 1]
      if duplicate_keys:
          raise TypeError(F"""Got duplicate yaml keys: {duplicate_keys}"""    )





  def    snake_case_       (  self   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_=False    ):

      '''simple docstring'''
      snake_case							:		Union[str, Any]        =		super().construct_mapping(SCREAMING_SNAKE_CASE_   ,deep=SCREAMING_SNAKE_CASE_    )
      self._check_no_duplicates_on_constructed_node(SCREAMING_SNAKE_CASE_    )
      return mapping







def       lowercase					(	__A :					str		)				->			Tuple[Optional[str], str]:





    '''simple docstring'''




    snake_case							:		Any        =		list(readme_content.splitlines()		)
    if full_content and full_content[0] == "---" and "---" in full_content[1:]:
        snake_case							:		Tuple        =		full_content[1:].index("""---"""		) + 1
        snake_case							:		str        =		"""\n""".join(full_content[1:sep_idx]		)
        return yamlblock, "\n".join(full_content[sep_idx + 1 :]		)

    return None, "\n".join(__A		)



class 						_A     (				snake_case				):





  '''simple docstring'''



  __lowerCamelCase			:      Any					=   {'''train_eval_index'''}  # train-eval-index in the YAML metadata





  @classmethod
  def    snake_case_       (  cls   ,SCREAMING_SNAKE_CASE_    ):

      '''simple docstring'''
      with open(SCREAMING_SNAKE_CASE_   ,encoding="""utf-8"""    ) as readme_file:
          snake_case      ,		snake_case							:		int        =		_split_yaml_from_readme(readme_file.read()    )
      if yaml_string is not None:
          return cls.from_yaml_string(SCREAMING_SNAKE_CASE_    )
      else:
          return cls()





  def    snake_case_       (  self   ,SCREAMING_SNAKE_CASE_    ):

      '''simple docstring'''
      if path.exists():
          with open(SCREAMING_SNAKE_CASE_   ,encoding="""utf-8"""    ) as readme_file:
              snake_case							:		Union[str, Any]        =		readme_file.read()
      else:
          snake_case							:		Dict        =		None
      snake_case							:		int        =		self._to_readme(SCREAMING_SNAKE_CASE_    )
      with open(SCREAMING_SNAKE_CASE_   ,"""w"""   ,encoding="""utf-8"""    ) as readme_file:
          readme_file.write(SCREAMING_SNAKE_CASE_    )





  def    snake_case_       (  self   ,SCREAMING_SNAKE_CASE_ = None    ):

      '''simple docstring'''
      if readme_content is not None:
          snake_case      ,		snake_case							:		List[str]        =		_split_yaml_from_readme(SCREAMING_SNAKE_CASE_    )
          snake_case							:		Union[str, Any]        =		"""---\n""" + self.to_yaml_string() + """---\n""" + content
      else:
          snake_case							:		Optional[int]        =		"""---\n""" + self.to_yaml_string() + """---\n"""
      return full_content





  @classmethod
  def    snake_case_       (  cls   ,SCREAMING_SNAKE_CASE_    ):

      '''simple docstring'''
      snake_case							:		List[Any]        =		yaml.load(SCREAMING_SNAKE_CASE_   ,Loader=_NoDuplicateSafeLoader    ) or {}

      # Convert the YAML keys to DatasetMetadata fields
      snake_case							:		Dict        =		{
          (key.replace("""-"""   ,"""_"""    ) if key.replace("""-"""   ,"""_"""    ) in cls._FIELDS_WITH_DASHES else key): value
          for key, value in metadata_dict.items()
      }
      return cls(**SCREAMING_SNAKE_CASE_    )





  def    snake_case_       (  self    ):

      '''simple docstring'''
      return yaml.safe_dump(
          {
              (key.replace("""_"""   ,"""-"""    ) if key in self._FIELDS_WITH_DASHES else key): value
              for key, value in self.items()
          }   ,sort_keys=SCREAMING_SNAKE_CASE_   ,allow_unicode=SCREAMING_SNAKE_CASE_   ,encoding="""utf-8"""   ,).decode("""utf-8"""    )







__lowercase     : Union[str, Any]       =						{
    '''image-classification''': [],
    '''translation''': [],
    '''image-segmentation''': [],
    '''fill-mask''': [],
    '''automatic-speech-recognition''': [],
    '''token-classification''': [],
    '''sentence-similarity''': [],
    '''audio-classification''': [],
    '''question-answering''': [],
    '''summarization''': [],
    '''zero-shot-classification''': [],
    '''table-to-text''': [],
    '''feature-extraction''': [],
    '''other''': [],
    '''multiple-choice''': [],
    '''text-classification''': [],
    '''text-to-image''': [],
    '''text2text-generation''': [],
    '''zero-shot-image-classification''': [],
    '''tabular-classification''': [],
    '''tabular-regression''': [],
    '''image-to-image''': [],
    '''tabular-to-text''': [],
    '''unconditional-image-generation''': [],
    '''text-retrieval''': [],
    '''text-to-speech''': [],
    '''object-detection''': [],
    '''audio-to-audio''': [],
    '''text-generation''': [],
    '''conversational''': [],
    '''table-question-answering''': [],
    '''visual-question-answering''': [],
    '''image-to-text''': [],
    '''reinforcement-learning''': [],
    '''voice-activity-detection''': [],
    '''time-series-forecasting''': [],
    '''document-question-answering''': [],
}


if __name__ == "__main__":
     from argparse import ArgumentParser

     __lowercase     : List[str]       =						ArgumentParser(usage='''Validate the yaml metadata block of a README.md file.''')
     ap.add_argument('''readme_filepath''')
     __lowercase     : Any       =						ap.parse_args()

     __lowercase     : List[Any]       =						Path(args.readme_filepath)
     __lowercase     : str       =						DatasetMetadata.from_readme(readme_filepath)
     print(dataset_metadata)
     dataset_metadata.to_readme(readme_filepath)






from dataclasses import dataclass, field
from typing import Tuple

from ..utils import cached_property, is_tf_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments


if is_tf_available():
    import tensorflow as tf


__A			:					Dict						=			logging.get_logger(__name__)





@dataclass
class 			_SCREAMING_SNAKE_CASE (   __snake_case		):







   '''simple docstring'''




   lowerCamelCase__				=      [
       "no_inference",
       "no_cuda",
       "no_tpu",
       "no_speed",
       "no_memory",
       "no_env_print",
       "no_multi_process",
   ]





   def __init__(       self					:			List[Any]     , **__lowerCamelCase					:			Any			):
       for deprecated_arg in self.deprecated_args:
           if deprecated_arg in kwargs:
               SCREAMING_SNAKE_CASE										=   deprecated_arg[3:]
               SCREAMING_SNAKE_CASE										=   not kwargs.pop(__lowerCamelCase			)
               logger.warning(
                   f"{deprecated_arg} is depreciated. Please use --no-{positive_arg} or"
                   f" {positive_arg}={kwargs[positive_arg]}"			)
       SCREAMING_SNAKE_CASE										=   kwargs.pop("tpu_name"     , self.tpu_name			)
       SCREAMING_SNAKE_CASE										=   kwargs.pop("device_idx"     , self.device_idx			)
       SCREAMING_SNAKE_CASE										=   kwargs.pop("eager_mode"     , self.eager_mode			)
       SCREAMING_SNAKE_CASE										=   kwargs.pop("use_xla"     , self.use_xla			)
       super().__init__(**__lowerCamelCase			)

   lowerCamelCase__				=      field(
       default=__snake_case					,       metadata={"help": "Name of TPU"}					,       )
   lowerCamelCase__				=      field(
       default=0					,       metadata={"help": "CPU / GPU device index. Defaults to 0."}					,       )
   lowerCamelCase__				=      field(default=__snake_case					,       metadata={"help": "Benchmark models in eager model."}		)
   lowerCamelCase__				=      field(
       default=__snake_case					,       metadata={
           "help": "Benchmark models using XLA JIT compilation. Note that `eager_model` has to be set to `False`."
       }					,       )





   @cached_property
   def      _snake_case		(       self					:			Optional[int]			):
       requires_backends(self     , ["tf"]			)
       SCREAMING_SNAKE_CASE										=   None
       if self.tpu:
           try:
               if self.tpu_name:
                   SCREAMING_SNAKE_CASE										=   tf.distribute.cluster_resolver.TPUClusterResolver(self.tpu_name			)
               else:
                   SCREAMING_SNAKE_CASE										=   tf.distribute.cluster_resolver.TPUClusterResolver()
           except ValueError:
               SCREAMING_SNAKE_CASE										=   None
       return tpu





   @cached_property
   def      _snake_case		(       self					:			Any			):
       requires_backends(self     , ["tf"]			)
       if self.is_tpu:
           tf.config.experimental_connect_to_cluster(self._setup_tpu			)
           tf.tpu.experimental.initialize_tpu_system(self._setup_tpu			)

           SCREAMING_SNAKE_CASE										=   tf.distribute.TPUStrategy(self._setup_tpu			)
       else:
           # currently no multi gpu is allowed
           if self.is_gpu:
               # TODO: Currently only single GPU is supported
               tf.config.set_visible_devices(self.gpu_list[self.device_idx]     , "GPU"			)
               SCREAMING_SNAKE_CASE										=   tf.distribute.OneDeviceStrategy(device=f"/gpu:{self.device_idx}"			)
           else:
               tf.config.set_visible_devices([]     , "GPU"			)  # disable GPU
               SCREAMING_SNAKE_CASE										=   tf.distribute.OneDeviceStrategy(device=f"/cpu:{self.device_idx}"			)

       return strategy





   @property
   def      _snake_case		(       self					:			Any			):
       requires_backends(self     , ["tf"]			)
       return self._setup_tpu is not None





   @property
   def      _snake_case		(       self					:			Optional[Any]			):
       requires_backends(self     , ["tf"]			)
       return self._setup_strategy





   @property
   def      _snake_case		(       self					:			List[str]			):
       requires_backends(self     , ["tf"]			)
       return tf.config.list_physical_devices("GPU"			)





   @property
   def      _snake_case		(       self					:			Any			):
       requires_backends(self     , ["tf"]			)
       if self.cuda:
           return len(self.gpu_list			)
       return 0





   @property
   def      _snake_case		(       self					:			Dict			):
       return self.n_gpu > 0