



'''simple docstring'''

class      __magic_name__						:







							def __init__(							self :	Tuple		):
									_a					:				str				=							{}







							def 							__lowercase      (							self :	Union[str, Any]		):
									print(self.vertex		)
									for i in self.vertex:
											print(_UpperCAmelCase     ,' -> '     ,' -> '.join([str(_UpperCAmelCase		) for j in self.vertex[i]]		)		)







							def 							__lowercase      (							self :	Union[str, Any]     ,_UpperCAmelCase :	int     ,_UpperCAmelCase :	int		):
									# check if vertex is already present,
									if from_vertex in self.vertex:
											self.vertex[from_vertex].append(_UpperCAmelCase		)
									else:
											# else make a new vertex
											_a					:				Optional[Any]				=							[to_vertex]







							def 							__lowercase      (							self :	Any		):
									# visited array for storing already visited nodes
									_a					:				Union[str, Any]				=							[False] * len(self.vertex		)

									# call the recursive helper function
									for i in range(len(self.vertex		)		):
											if not visited[i]:
													self.dfs_recursive(_UpperCAmelCase     ,_UpperCAmelCase		)







							def 							__lowercase      (							self :	str     ,_UpperCAmelCase :	int     ,_UpperCAmelCase :	list		):
									# mark start vertex as visited
									_a					:				Tuple				=							True

									print(_UpperCAmelCase     ,end=' '		)

									# Recur for all the vertices that are adjacent to this node
									for i in self.vertex:
											if not visited[i]:
													self.dfs_recursive(_UpperCAmelCase     ,_UpperCAmelCase		)


if __name__ == "__main__":
							__lowerCAmelCase						      =       Graph()
							g.add_edge(0, 1)
							g.add_edge(0, 2)
							g.add_edge(1, 2)
							g.add_edge(2, 0)
							g.add_edge(2, 3)
							g.add_edge(3, 3)

							g.print_graph()
							print('''DFS:''')
							g.dfs()

							# OUTPUT:
							# 0  ->  1 -> 2
							# 1  ->  2
							# 2  ->  0 -> 3
							# 3  ->  3
							# DFS:
							#  0 1 2 3









'''simple docstring'''

from dataclasses import dataclass, field
from typing import Optional

from transformers import AutoConfig, AutoImageProcessor, AutoTokenizer, FlaxVisionEncoderDecoderModel, HfArgumentParser

@dataclass
class      __magic_name__						:
							lowerCAmelCase	:   str           = field(
							    metadata={'help': 'The output directory where the model will be written.'}      ,						)
							lowerCAmelCase	:   str           = field(
							    metadata={
							        'help': (
							            'The encoder model checkpoint for weights initialization.'
							            'Don\'t set if you want to train an encoder model from scratch.'
							        )
							    }      ,						)
							lowerCAmelCase	:   str           = field(
							    metadata={
							        'help': (
							            'The decoder model checkpoint for weights initialization.'
							            'Don\'t set if you want to train a decoder model from scratch.'
							        )
							    }      ,						)
							lowerCAmelCase	:   Optional[str]           = field(
							    default=_UpperCamelCase      ,						metadata={'help': 'Pretrained encoder config name or path if not the same as encoder_model_name'}       )
							lowerCAmelCase	:   Optional[str]           = field(
							    default=_UpperCamelCase      ,						metadata={'help': 'Pretrained decoder config name or path if not the same as decoder_model_name'}       )



def 					__lowerCamelCase					(				)      ->				Union[str, Any]:
		_a					:				Any				=							HfArgumentParser((ModelArguments,)  )
		((_a)	, )					:				Dict				=							parser.parse_args_into_dataclasses()

		# Load pretrained model and tokenizer

		# Use explicit specified encoder config
		if model_args.encoder_config_name:
				_a					:				Optional[Any]				=							AutoConfig.from_pretrained(model_args.encoder_config_name  )
		# Use pretrained encoder model's config
		else:
				_a					:				Optional[Any]				=							AutoConfig.from_pretrained(model_args.encoder_model_name_or_path  )

		# Use explicit specified decoder config
		if model_args.decoder_config_name:
				_a					:				List[str]				=							AutoConfig.from_pretrained(model_args.decoder_config_name  )
		# Use pretrained decoder model's config
		else:
				_a					:				Optional[int]				=							AutoConfig.from_pretrained(model_args.decoder_model_name_or_path  )

		# necessary for `from_encoder_decoder_pretrained` when `decoder_config` is passed
		_a					:				List[Any]				=							True
		_a					:				int				=							True

		_a					:				Any				=							FlaxVisionEncoderDecoderModel.from_encoder_decoder_pretrained(
		    encoder_pretrained_model_name_or_path=model_args.encoder_model_name_or_path						,      decoder_pretrained_model_name_or_path=model_args.decoder_model_name_or_path						,      encoder_config=lowerCAmelCase_						,      decoder_config=lowerCAmelCase_						,      )

		# GPT2 only has bos/eos tokens but not decoder_start/pad tokens
		_a					:				List[str]				=							decoder_config.decoder_start_token_id
		_a					:				Optional[int]				=							decoder_config.pad_token_id
		if decoder_start_token_id is None:
				_a					:				Tuple				=							decoder_config.bos_token_id
		if pad_token_id is None:
				_a					:				List[Any]				=							decoder_config.eos_token_id

		# This is necessary to make Flax's generate() work
		_a					:				Any				=							decoder_config.eos_token_id
		_a					:				Tuple				=							decoder_start_token_id
		_a					:				Any				=							pad_token_id

		_a					:				Dict				=							AutoImageProcessor.from_pretrained(model_args.encoder_model_name_or_path  )

		_a					:				Dict				=							AutoTokenizer.from_pretrained(model_args.decoder_model_name_or_path  )
		_a					:				int				=							tokenizer.convert_ids_to_tokens(model.config.pad_token_id  )

		model.save_pretrained(model_args.output_dir  )
		image_processor.save_pretrained(model_args.output_dir  )
		tokenizer.save_pretrained(model_args.output_dir  )


if __name__ == "__main__":
							main()




