






'''simple docstring'''





from argparse import ArgumentParser, Namespace

from ..utils import logging
from . import BaseTransformersCLICommand





def 					lowercase_ (    __snake_case     :							Namespace      )     -> Union[str, Any]:





				'''simple docstring'''


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


__UpperCAmelCase							:   int							=  "\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 					_snake_case						(			__a ):






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






	def __init__(		self					,UpperCamelCase					,UpperCamelCase					,UpperCamelCase					,UpperCamelCase					,UpperCamelCase					,*UpperCamelCase					,)						->    Optional[int]:
					snake_case__      :str         =   logging.get_logger("transformers-cli/converting"       )

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






	def    lowerCAmelCase_							(		self       )						->    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(a_       )

									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(a_       )

									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(a_       )

									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(a_       )

									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(a_       )

									if "ckpt" in self._tf_checkpoint.lower():
													snake_case__      :Tuple         =   self._tf_checkpoint
													snake_case__      :Any         =   """"""
									else:
													snake_case__      :Any         =   self._tf_checkpoint
													snake_case__      :Dict         =   """"""
									convert_transfo_xl_checkpoint_to_pytorch(
									    a_					,self._config					,self._pytorch_dump_output					,a_       )
					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(a_       )

									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(a_       )

									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 typing import Any





def 					lowercase_ (    __snake_case     :							list	,	__snake_case     :							list	,	__snake_case     :							dict	,	__snake_case     :							dict	,	__snake_case     :							dict	,	)     -> list:





				'''simple docstring'''


				_validation(
				    __snake_case	,	__snake_case	,	__snake_case	,	__snake_case	,	__snake_case	,	)
				# Creates data structures and fill initial step
				snake_case__      :dict         =   {}
				snake_case__      :dict         =   {}
				for state in states_space:
								snake_case__      :List[Any]         =   observations_space[0]
								snake_case__      :str         =   (
								    initial_probabilities[state] * emission_probabilities[state][observation]
								)
								snake_case__      :str         =   None

				# Fills the data structure with the probabilities of
				# different transitions and pointers to previous states
				for o in range(1	,	len(__snake_case      )      ):
								snake_case__      :Any         =   observations_space[o]
								snake_case__      :Tuple         =   observations_space[o - 1]
								for state in states_space:
												# Calculates the argmax for probability function
												snake_case__      :Tuple         =   ""
												snake_case__      :Union[str, Any]         =   -1
												for k_state in states_space:
																snake_case__      :int         =   (
																    probabilities[(k_state, prior_observation)]
																    * transition_probabilities[k_state][state]
																    * emission_probabilities[state][observation]
																)
																if probability > max_probability:
																				snake_case__      :str         =   probability
																				snake_case__      :Tuple         =   k_state

            # Update probabilities and pointers dicts
												snake_case__      :List[str]         =   (
												    probabilities[(arg_max, prior_observation)]
												    * transition_probabilities[arg_max][state]
												    * emission_probabilities[state][observation]
												)

												snake_case__      :List[str]         =   arg_max

    # The final observation
				snake_case__      :str         =   observations_space[len(__snake_case      ) - 1]

				# argmax for given final observation
				snake_case__      :Optional[int]         =   ""
				snake_case__      :List[str]         =   -1
				for k_state in states_space:
								snake_case__      :List[str]         =   probabilities[(k_state, final_observation)]
								if probability > max_probability:
												snake_case__      :List[str]         =   probability
												snake_case__      :int         =   k_state
				snake_case__      :Any         =   arg_max

				# Process pointers backwards
				snake_case__      :int         =   last_state
				snake_case__      :List[str]         =   []
				for o in range(len(__snake_case      ) - 1	,	-1	,	-1      ):
								result.append(__snake_case      )
								snake_case__      :List[str]         =   pointers[previous, observations_space[o]]
				result.reverse()

				return result





def 					lowercase_ (    __snake_case     :							Any	,	__snake_case     :							Any	,	__snake_case     :							Any	,	__snake_case     :							Any	,	__snake_case     :							Any	,	)     -> None:





				'''simple docstring'''


				_validate_not_empty(
				    __snake_case	,	__snake_case	,	__snake_case	,	__snake_case	,	__snake_case	,	)
				_validate_lists(__snake_case	,	__snake_case      )
				_validate_dicts(
				    __snake_case	,	__snake_case	,	__snake_case      )





def 					lowercase_ (    __snake_case     :							Any	,	__snake_case     :							Any	,	__snake_case     :							Any	,	__snake_case     :							Any	,	__snake_case     :							Any	,	)     -> None:





				'''simple docstring'''


				if not all(
				    [
				        observations_space,
				        states_space,
				        initial_probabilities,
				        transition_probabilities,
				        emission_probabilities,
				    ]      ):
								raise ValueError("There's an empty parameter"      )





def 					lowercase_ (    __snake_case     :							Any	,	__snake_case     :							Any      )     -> None:





				'''simple docstring'''


				_validate_list(__snake_case	,	"observations_space"      )
				_validate_list(__snake_case	,	"states_space"      )





def 					lowercase_ (    __snake_case     :							Any	,	__snake_case     :							str      )     -> None:





				'''simple docstring'''


				if not isinstance(_object	,	__snake_case      ):
								snake_case__      :Optional[int]         =   F'{var_name} must be a list'
								raise ValueError(__snake_case      )
				else:
								for x in _object:
												if not isinstance(__snake_case	,	__snake_case      ):
																snake_case__      :Any         =   F'{var_name} must be a list of strings'
																raise ValueError(__snake_case      )





def 					lowercase_ (    __snake_case     :							Any	,	__snake_case     :							Any	,	__snake_case     :							Any	,	)     -> None:





				'''simple docstring'''


				_validate_dict(__snake_case	,	"initial_probabilities"	,	__snake_case      )
				_validate_nested_dict(__snake_case	,	"transition_probabilities"      )
				_validate_nested_dict(__snake_case	,	"emission_probabilities"      )





def 					lowercase_ (    __snake_case     :							Any	,	__snake_case     :							str      )     -> None:





				'''simple docstring'''


				_validate_dict(_object	,	__snake_case	,	__snake_case      )
				for x in _object.values():
								_validate_dict(__snake_case	,	__snake_case	,	__snake_case	,	__snake_case      )





def 					lowercase_ (    __snake_case     :							Any	,	__snake_case     :							str	,	__snake_case     :							type	,	__snake_case     :							bool = False      )     -> None:





				'''simple docstring'''


				if not isinstance(_object	,	__snake_case      ):
								snake_case__      :str         =   F'{var_name} must be a dict'
								raise ValueError(__snake_case      )
				if not all(isinstance(__snake_case	,	__snake_case      ) for x in _object      ):
								snake_case__      :List[Any]         =   F'{var_name} all keys must be strings'
								raise ValueError(__snake_case      )
				if not all(isinstance(__snake_case	,	__snake_case      ) for x in _object.values()      ):
								snake_case__      :Optional[int]         =   "nested dictionary " if nested else ""
								snake_case__      :int         =   F'{var_name} {nested_text}all values must be {value_type.__name__}'
								raise ValueError(__snake_case      )


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

	testmod()