





from argparse import ArgumentParser, Namespace

from ..utils import logging
from . import BaseTransformersCLICommand






def 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				List[Any]     ):
		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_							(  SCREAMING_SNAKE_CASE_     ):


							@staticmethod
							def   _snake_case	(				__A     :str		) -> Optional[int]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   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=snake_case__				,					required=snake_case__				,					help="""Model\'s type."""		)
									train_parser.add_argument(
									    """--tf_checkpoint"""				,					type=snake_case__				,					required=snake_case__				,					help="""TensorFlow checkpoint path or folder."""		)
									train_parser.add_argument(
									    """--pytorch_dump_output"""				,					type=snake_case__				,					required=snake_case__				,					help="""Path to the PyTorch saved model output."""		)
									train_parser.add_argument("""--config"""				,					type=snake_case__				,					default=""""""				,					help="""Configuration file path or folder."""		)
									train_parser.add_argument(
									    """--finetuning_task_name"""				,					type=snake_case__				,					default=snake_case__				,					help="""Optional fine-tuning task name if the TF model was a finetuned model."""				,					)
									train_parser.set_defaults(func=snake_case__		)


							def __init__(				self     :Any				,					__A     :Optional[int]				,					__A     :Dict				,					__A     :Tuple				,					__A     :Union[str, Any]				,					__A     :Tuple				,					*__A     :List[str]				,					) -> str:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   logging.get_logger("""transformers-cli/converting"""		)

									self._logger.info(f'''Loading model {model_type}'''		)
									SCREAMING_SNAKE_CASE__								=   model_type
									SCREAMING_SNAKE_CASE__								=   tf_checkpoint
									SCREAMING_SNAKE_CASE__								=   pytorch_dump_output
									SCREAMING_SNAKE_CASE__								=   config
									SCREAMING_SNAKE_CASE__								=   finetuning_task_name


							def   _snake_case	(				self     :Union[str, Any]		) -> Union[str, Any]:



									"""simple docstring"""



									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(snake_case__		)

											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(snake_case__		)

											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(snake_case__		)

											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(snake_case__		)

											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(snake_case__		)

											if "ckpt" in self._tf_checkpoint.lower():
													SCREAMING_SNAKE_CASE__								=   self._tf_checkpoint
													SCREAMING_SNAKE_CASE__								=   ''
											else:
													SCREAMING_SNAKE_CASE__								=   self._tf_checkpoint
													SCREAMING_SNAKE_CASE__								=   ''
											convert_transfo_xl_checkpoint_to_pytorch(
											    snake_case__				,					self._config				,					self._pytorch_dump_output				,					snake_case__		)
									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(snake_case__		)

											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(snake_case__		)

											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]"""		)



'''simple docstring'''







def       lowercase					(_A       ):







       """simple docstring"""







       _lowerCAmelCase     :				Union[str, Any]       =							len(_A       )
       for i in range(length - 1       ):
              _lowerCAmelCase     :				List[Any]       =							i
              for k in range(i + 1 ,    _A       ):
                     if collection[k] < collection[least]:
                            _lowerCAmelCase     :				List[str]       =							k
              if least != i:
                     _lowerCAmelCase      ,  _lowerCAmelCase     :				Union[str, Any]       =							(collection[i], collection[least])
       return collection


if __name__ == "__main__":
       lowerCAmelCase     :  Optional[Any]											=    input("""Enter numbers separated by a comma:\n""").strip()
       lowerCAmelCase     :  Tuple											=    [int(item) for item in user_input.split(""",""")]
       print(selection_sort(unsorted))

