






from argparse import ArgumentParser, Namespace

from ..utils import logging
from . import BaseTransformersCLICommand




def      lowercase__      (     __snake_case    :							Namespace  ):


					'''simple docstring'''


					return ConvertCommand(
					    args.model_type     ,       args.tf_checkpoint     ,       args.pytorch_dump_output     ,       args.config     ,       args.finetuning_task_name  )


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






class   lowerCamelCase      (_SCREAMING_SNAKE_CASE			):







		'''simple docstring'''



		@staticmethod
		def 					__UpperCAmelCase			(							_UpperCamelCase   )	->							str:
							UpperCAmelCase_    :      Union[str, 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				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase				,		*_UpperCamelCase				,		)	->							str:
							UpperCAmelCase_    :      str            =						logging.get_logger('transformers-cli/converting'   )

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



		def 					__UpperCAmelCase			(							self   )	->							Optional[int]:
							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_    :      Optional[int]            =						self._tf_checkpoint
																	UpperCAmelCase_    :      Optional[int]            =						''
												else:
																	UpperCAmelCase_    :      str            =						self._tf_checkpoint
																	UpperCAmelCase_    :      Union[str, 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]'   )












from __future__ import annotations

lowerCamelCase__ :   Optional[int]					=							[-10, -5, 0, 5, 5.1, 11, 13, 21, 3, 4, -21, -10, -5, -1, 0]
lowerCamelCase__ :   List[Any]					=							[-5, 0, 5, 5.1, 11, 13, 21, -1, 4, -1, -10, -5, -1, 0, -1]

def 					UpperCAmelCase_							(      __UpperCAmelCase						:	list[float]							)					-> list[float]:
      SCREAMING_SNAKE_CASE_							=			[]
      SCREAMING_SNAKE_CASE_							=			len(__UpperCAmelCase							)

      for i in range(__UpperCAmelCase							):
            SCREAMING_SNAKE_CASE_							=			-1
            for j in range(i + 1			,			__UpperCAmelCase							):
                  if arr[i] < arr[j]:
                        SCREAMING_SNAKE_CASE_							=			arr[j]
                        break
            result.append(__UpperCAmelCase							)
      return result

def 					UpperCAmelCase_							(      __UpperCAmelCase						:	list[float]							)					-> list[float]:
      SCREAMING_SNAKE_CASE_							=			[]
      for i, outer in enumerate(__UpperCAmelCase							):
            SCREAMING_SNAKE_CASE_							=			-1
            for inner in arr[i + 1 :]:
                  if outer < inner:
                        SCREAMING_SNAKE_CASE_							=			inner
                        break
            result.append(__UpperCAmelCase							)
      return result

def 					UpperCAmelCase_							(      __UpperCAmelCase						:	list[float]							)					-> list[float]:
      SCREAMING_SNAKE_CASE_							=			len(__UpperCAmelCase							)
      SCREAMING_SNAKE_CASE_							=			[]
      SCREAMING_SNAKE_CASE_							=			[-1] * arr_size

      for index in reversed(range(__UpperCAmelCase							)							):
            if stack:
                  while stack[-1] <= arr[index]:
                        stack.pop()
                        if not stack:
                              break
            if stack:
                  SCREAMING_SNAKE_CASE_							=			stack[-1]
            stack.append(arr[index]							)
      return result


if __name__ == "__main__":
   from doctest import testmod
   from timeit import timeit

   testmod()
   print(next_greatest_element_slow(arr))
   print(next_greatest_element_fast(arr))
   print(next_greatest_element(arr))

   lowerCamelCase__ :   List[str]					=							(
       'from __main__ import arr, next_greatest_element_slow, '
       'next_greatest_element_fast, next_greatest_element'
   )
   print(
       'next_greatest_element_slow():',
       timeit('next_greatest_element_slow(arr)', setup=setup),
   )
   print(
       'next_greatest_element_fast():',
       timeit('next_greatest_element_fast(arr)', setup=setup),
   )
   print(
       '     next_greatest_element():',
       timeit('next_greatest_element(arr)', setup=setup),
   )