






from argparse import ArgumentParser, Namespace

from ..utils import logging
from . import BaseTransformersCLICommand






def 					lowerCamelCase__					(_UpperCAmelCase):
			return ConvertCommand(
			    args.model_type				,							args.tf_checkpoint				,							args.pytorch_dump_output				,							args.config				,							args.finetuning_task_name)


a_		:    Any   							=     '\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  SCREAMING_SNAKE_CASE__     (							a)   ->							Union[str, Any]:
						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=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		,       a		,       a		,       a		,       a		,       a		,       *a		,       )   ->							Any:
						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  SCREAMING_SNAKE_CASE__     (							self)   ->							str:
						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():
												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(
									    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

def   __lowercase       (			snake_case,	snake_case			):







				"""simple docstring"""






				print(f'''Vertex\tShortest Distance from vertex {src}'''			)
				for i, d in enumerate(snake_case			):
								print(f'''{i}\t\t{d}'''			)

def   __lowercase       (			snake_case,	snake_case,	snake_case			):







				"""simple docstring"""






				for j in range(snake_case			):
								__magic_name__  ,					__magic_name__  ,					__magic_name__		:Tuple   =   (graph[j][k] for k in ['''src''', '''dst''', '''weight'''])
								if distance[u] != float('''inf'''			) and distance[u] + w < distance[v]:
												return True
				return False

def   __lowercase       (			snake_case,	snake_case,	snake_case,	snake_case			):







				"""simple docstring"""






				__magic_name__		:List[Any]   =   [float('''inf'''			)] * vertex_count
				__magic_name__		:Tuple   =   0.0

				for _ in range(vertex_count - 1			):
								for j in range(snake_case			):
												__magic_name__  ,					__magic_name__  ,					__magic_name__		:Dict   =   (graph[j][k] for k in ['''src''', '''dst''', '''weight'''])

												if distance[u] != float('''inf'''			) and distance[u] + w < distance[v]:
																__magic_name__		:Tuple   =   distance[u] + w

				__magic_name__		:Tuple   =   check_negative_cycle(snake_case,	snake_case,	snake_case			)
				if negative_cycle_exists:
								raise Exception('''Negative cycle found'''			)

				return distance


if __name__ == "__main__":
					import doctest

					doctest.testmod()

					SCREAMING_SNAKE_CASE__   :      Tuple				=		int(input("""Enter number of vertices: """).strip())
					SCREAMING_SNAKE_CASE__   :      Any				=		int(input("""Enter number of edges: """).strip())

					SCREAMING_SNAKE_CASE__   :      list[dict[str, int]]				=		[{} for _ in range(E)]

					for i in range(E):
										print("""Edge """, i + 1)
										SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__   :      Dict				=		(
										    int(x)
										    for x in input("""Enter source, destination, weight: """).strip().split(""" """)
										)
										SCREAMING_SNAKE_CASE__   :      Dict				=		{"""src""": src, """dst""": dest, """weight""": weight}

					SCREAMING_SNAKE_CASE__   :      List[Any]				=		int(input("""\nEnter shortest path source:""").strip())
					SCREAMING_SNAKE_CASE__   :      List[str]				=		bellman_ford(graph, V, E, source)
					print_distance(shortest_distance, 0)






