



from argparse import ArgumentParser, Namespace

from ..utils import logging
from . import BaseTransformersCLICommand






def   _A					(UpperCamelCase		:   Namespace			)	->Dict:



			'''simple docstring'''







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


_lowercase  				=    '''
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 					__A       (			__lowercase   ):





			@staticmethod
			def 							_snake_case     (__magic_name__					):
						lowerCamelCase__			:   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				,     __magic_name__				,     __magic_name__				,     __magic_name__				,     __magic_name__				,     __magic_name__				,     *__magic_name__				,     ):
						lowerCamelCase__			:   str										=		logging.get_logger("""transformers-cli/converting"""					)

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





			def 							_snake_case     (self					):
						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():
												lowerCamelCase__			:   Union[str, Any]										=		self._tf_checkpoint
												lowerCamelCase__			:   str										=		''''''
									else:
												lowerCamelCase__			:   int										=		self._tf_checkpoint
												lowerCamelCase__			:   str										=		''''''
									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 __future__ import annotations

import matplotlib.pyplot as plt  # type: ignore
import numpy

# initial triangle of Koch snowflake
a__												=						numpy.array([0, 0])
a__												=						numpy.array([0.5, 0.8660254])
a__												=						numpy.array([1, 0])
a__												=						[VECTOR_1, VECTOR_2, VECTOR_3, VECTOR_1]



def   __UpperCAmelCase					(      __a :     list[numpy.ndarray]						,__a :     int      )     ->	list[numpy.ndarray]:

					"""simple docstring"""

					_a      :       Tuple   =						initial_vectors
					for _ in range(__a      ):
										_a      :       int   =						iteration_step(__a      )
					return vectors



def   __UpperCAmelCase					(      __a :     list[numpy.ndarray]      )     ->	list[numpy.ndarray]:

					"""simple docstring"""

					_a      :       Tuple   =						[]
					for i, start_vector in enumerate(vectors[:-1]      ):
										_a      :       str   =						vectors[i + 1]
										new_vectors.append(__a      )
										_a      :       Optional[int]   =						end_vector - start_vector
										new_vectors.append(start_vector + difference_vector / 3      )
										new_vectors.append(
										    start_vector + difference_vector / 3 + rotate(difference_vector / 3						,60      )      )
										new_vectors.append(start_vector + difference_vector * 2 / 3      )
					new_vectors.append(vectors[-1]      )
					return new_vectors



def   __UpperCAmelCase					(      __a :     numpy.ndarray						,__a :     float      )     ->	numpy.ndarray:

					"""simple docstring"""

					_a      :       Tuple   =						numpy.radians(__a      )
					_a ,   _a      :       List[Any]   =						numpy.cos(__a      ), numpy.sin(__a      )
					_a      :       Dict   =						numpy.array(((c, -s), (s, c))      )
					return numpy.dot(__a						,__a      )



def   __UpperCAmelCase					(      __a :     list[numpy.ndarray]      )     ->	None:

					"""simple docstring"""

					_a      :       str   =						plt.gca()
					axes.set_aspect('''equal'''      )

					# matplotlib.pyplot.plot takes a list of all x-coordinates and a list of all
					# y-coordinates as inputs, which are constructed from the vector-list using
					# zip()
					_a ,   _a      :       Optional[int]   =						zip(*__a      )
					plt.plot(__a						,__a      )
					plt.show()


if __name__ == "__main__":
			import doctest

			doctest.testmod()

			a__												=						iterate(INITIAL_VECTORS, 5)
			plot(processed_vectors)



