
'''simple docstring'''

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

import yaml




class    lowerCAmelCase       (     yaml.SafeLoader  ):


 def    _A	(    self  : Optional[Any]			,							a__  : Any    ):
  '''simple docstring'''

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


 def    _A	(    self  : int			,							a__  : List[str]			,							a__  : Optional[int]=False    ):
  '''simple docstring'''

  lowerCAmelCase__     :   int     		=	super().construct_mapping(lowerCAmelCase_			,							deep=lowerCAmelCase_    )
  self._check_no_duplicates_on_constructed_node(lowerCAmelCase_    )
  return mapping



def        UpperCAmelCase_ (  lowerCamelCase_			):







 """simple docstring"""


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

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




class    lowerCAmelCase       (     __A  ):
 # class attributes
 A_				:				List[Any]	=			{"""train_eval_index"""}  # train-eval-index in the YAML metadata


 @classmethod
 def    _A	(    cls  : int			,							a__  : Path    ):
  '''simple docstring'''

  with open(lowerCAmelCase_			,							encoding="utf-8"    ) as readme_file:
   lowerCAmelCase__     :   Tuple     		=	_split_yaml_from_readme(readme_file.read()    )
  if yaml_string is not None:
   return cls.from_yaml_string(lowerCAmelCase_    )
  else:
   return cls()


 def    _A	(    self  : List[str]			,							a__  : Path    ):
  '''simple docstring'''

  if path.exists():
   with open(lowerCAmelCase_			,							encoding="utf-8"    ) as readme_file:
    lowerCAmelCase__     :   str     		=	readme_file.read()
  else:
   lowerCAmelCase__     :   str     		=	None
  lowerCAmelCase__     :   Optional[int]     		=	self._to_readme(lowerCAmelCase_    )
  with open(lowerCAmelCase_			,							"w"			,							encoding="utf-8"    ) as readme_file:
   readme_file.write(lowerCAmelCase_    )


 def    _A	(    self  : int			,							a__  : Optional[str] = None    ):
  '''simple docstring'''

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


 @classmethod
 def    _A	(    cls  : Tuple			,							a__  : str    ):
  '''simple docstring'''

  lowerCAmelCase__     :   Tuple     		=	yaml.load(lowerCAmelCase_			,							Loader=_NoDuplicateSafeLoader    ) or {}

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


 def    _A	(    self  : Tuple    ):
  '''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=lowerCAmelCase_			,							allow_unicode=lowerCAmelCase_			,							encoding="utf-8"			,							).decode("utf-8"    )



snake_case									=    {
    """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

     snake_case									=    ArgumentParser(usage="""Validate the yaml metadata block of a README.md file.""")
     ap.add_argument("""readme_filepath""")
     snake_case									=    ap.parse_args()

     snake_case									=    Path(args.readme_filepath)
     snake_case									=    DatasetMetadata.from_readme(readme_filepath)
     print(dataset_metadata)
     dataset_metadata.to_readme(readme_filepath)













"""simple docstring"""
from argparse import ArgumentParser, Namespace

from ..utils import logging
from . import BaseTransformersCLICommand

def 							snake_case (  A__		):
			return ConvertCommand(
			    args.model_type      ,args.tf_checkpoint      ,args.pytorch_dump_output      ,args.config      ,args.finetuning_task_name		)


lowerCamelCase_							      = '''
transformers can only be used from the commandline to convert TensorFlow models in PyTorch, In that case, it requires
TensorFlow to be installed. Please see https://www.tensorflow.org/install/ for installation instructions.
'''







class      UpperCamelCase_			(__A	):





	@staticmethod
	def 	_SCREAMING_SNAKE_CASE ( lowerCAmelCase_   :					ArgumentParser		)		->     Optional[Any]:
				UpperCAmelCase_	:					Any     	=   parser.add_parser(
				    "convert"      ,      help="CLI tool to run convert model from original author checkpoints to Transformers PyTorch checkpoints."      ,      )
				train_parser.add_argument("--model_type"      ,      type=lowerCAmelCase_      ,      required=lowerCAmelCase_      ,      help="Model's type."		)
				train_parser.add_argument(
				    "--tf_checkpoint"      ,      type=lowerCAmelCase_      ,      required=lowerCAmelCase_      ,      help="TensorFlow checkpoint path or folder."		)
				train_parser.add_argument(
				    "--pytorch_dump_output"      ,      type=lowerCAmelCase_      ,      required=lowerCAmelCase_      ,      help="Path to the PyTorch saved model output."		)
				train_parser.add_argument("--config"      ,      type=lowerCAmelCase_      ,      default=""      ,      help="Configuration file path or folder."		)
				train_parser.add_argument(
				    "--finetuning_task_name"      ,      type=lowerCAmelCase_      ,      default=lowerCAmelCase_      ,      help="Optional fine-tuning task name if the TF model was a finetuned model."      ,      )
				train_parser.set_defaults(func=lowerCAmelCase_		)





	def __init__( self   :					str      ,      lowerCAmelCase_   :					str      ,      lowerCAmelCase_   :					str      ,      lowerCAmelCase_   :					str      ,      lowerCAmelCase_   :					str      ,      lowerCAmelCase_   :					str      ,      *lowerCAmelCase_   :					Tuple      ,      )		->     int:
				UpperCAmelCase_	:					Dict     	=   logging.get_logger("transformers-cli/converting"		)

				self._logger.info(f"""Loading model {model_type}"""		)
				UpperCAmelCase_	:					Optional[int]     	=   model_type
				UpperCAmelCase_	:					Union[str, Any]     	=   tf_checkpoint
				UpperCAmelCase_	:					Tuple     	=   pytorch_dump_output
				UpperCAmelCase_	:					Tuple     	=   config
				UpperCAmelCase_	:					List[str]     	=   finetuning_task_name





	def 	_SCREAMING_SNAKE_CASE ( self   :					Tuple		)		->     List[Any]:
				if self._model_type == "albert":
							try:
										from ..models.albert.convert_albert_original_tf_checkpoint_to_pytorch import (
										    convert_tf_checkpoint_to_pytorch,
										)
							except ImportError:
										raise ImportError(lowerCAmelCase_		)

							convert_tf_checkpoint_to_pytorch(self._tf_checkpoint      ,      self._config      ,      self._pytorch_dump_output		)
				elif self._model_type == "bert":
							try:
										from ..models.bert.convert_bert_original_tf_checkpoint_to_pytorch import (
										    convert_tf_checkpoint_to_pytorch,
										)
							except ImportError:
										raise ImportError(lowerCAmelCase_		)

							convert_tf_checkpoint_to_pytorch(self._tf_checkpoint      ,      self._config      ,      self._pytorch_dump_output		)
				elif self._model_type == "funnel":
							try:
										from ..models.funnel.convert_funnel_original_tf_checkpoint_to_pytorch import (
										    convert_tf_checkpoint_to_pytorch,
										)
							except ImportError:
										raise ImportError(lowerCAmelCase_		)

							convert_tf_checkpoint_to_pytorch(self._tf_checkpoint      ,      self._config      ,      self._pytorch_dump_output		)
				elif self._model_type == "t5":
							try:
										from ..models.ta.convert_ta_original_tf_checkpoint_to_pytorch import convert_tf_checkpoint_to_pytorch
							except ImportError:
										raise ImportError(lowerCAmelCase_		)

							convert_tf_checkpoint_to_pytorch(self._tf_checkpoint      ,      self._config      ,      self._pytorch_dump_output		)
				elif self._model_type == "gpt":
							from ..models.openai.convert_openai_original_tf_checkpoint_to_pytorch import (
							    convert_openai_checkpoint_to_pytorch,
							)

							convert_openai_checkpoint_to_pytorch(self._tf_checkpoint      ,      self._config      ,      self._pytorch_dump_output		)
				elif self._model_type == "transfo_xl":
							try:
										from ..models.transfo_xl.convert_transfo_xl_original_tf_checkpoint_to_pytorch import (
										    convert_transfo_xl_checkpoint_to_pytorch,
										)
							except ImportError:
										raise ImportError(lowerCAmelCase_		)

							if "ckpt" in self._tf_checkpoint.lower():
										UpperCAmelCase_	:					Union[str, Any]     	=   self._tf_checkpoint
										UpperCAmelCase_	:					List[str]     	=   ""
							else:
										UpperCAmelCase_	:					Any     	=   self._tf_checkpoint
										UpperCAmelCase_	:					Any     	=   ""
							convert_transfo_xl_checkpoint_to_pytorch(
							    lowerCAmelCase_      ,      self._config      ,      self._pytorch_dump_output      ,      lowerCAmelCase_		)
				elif self._model_type == "gpt2":
							try:
										from ..models.gpta.convert_gpta_original_tf_checkpoint_to_pytorch import (
										    convert_gpta_checkpoint_to_pytorch,
										)
							except ImportError:
										raise ImportError(lowerCAmelCase_		)

							convert_gpta_checkpoint_to_pytorch(self._tf_checkpoint      ,      self._config      ,      self._pytorch_dump_output		)
				elif self._model_type == "xlnet":
							try:
										from ..models.xlnet.convert_xlnet_original_tf_checkpoint_to_pytorch import (
										    convert_xlnet_checkpoint_to_pytorch,
										)
							except ImportError:
										raise ImportError(lowerCAmelCase_		)

							convert_xlnet_checkpoint_to_pytorch(
							    self._tf_checkpoint      ,      self._config      ,      self._pytorch_dump_output      ,      self._finetuning_task_name		)
				elif self._model_type == "xlm":
							from ..models.xlm.convert_xlm_original_pytorch_checkpoint_to_pytorch import (
							    convert_xlm_checkpoint_to_pytorch,
							)

							convert_xlm_checkpoint_to_pytorch(self._tf_checkpoint      ,      self._pytorch_dump_output		)
				elif self._model_type == "lxmert":
							from ..models.lxmert.convert_lxmert_original_tf_checkpoint_to_pytorch import (
							    convert_lxmert_checkpoint_to_pytorch,
							)

							convert_lxmert_checkpoint_to_pytorch(self._tf_checkpoint      ,      self._pytorch_dump_output		)
				elif self._model_type == "rembert":
							from ..models.rembert.convert_rembert_tf_checkpoint_to_pytorch import (
							    convert_rembert_tf_checkpoint_to_pytorch,
							)

							convert_rembert_tf_checkpoint_to_pytorch(self._tf_checkpoint      ,      self._config      ,      self._pytorch_dump_output		)
				else:
							raise ValueError(
							    "--model_type should be selected in the list [bert, gpt, gpt2, t5, transfo_xl, xlnet, xlm, lxmert]"		)



