



"""simple docstring"""







from dataclasses import dataclass, field
from typing import Optional

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






@dataclass
class       UpperCamelCase__			:

			"""simple docstring"""


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



def       _lowerCamelCase				(							)	-> Optional[int]:






	"""simple docstring"""





	A__											=					HfArgumentParser((ModelArguments,)     )
	((A__)							,							)											=					parser.parse_args_into_dataclasses()

	# Load pretrained model and tokenizer

	# Use explicit specified encoder config
	if model_args.encoder_config_name:
		A__											=					AutoConfig.from_pretrained(model_args.encoder_config_name     )
	# Use pretrained encoder model's config
	else:
		A__											=					AutoConfig.from_pretrained(model_args.encoder_model_name_or_path     )

	# Use explicit specified decoder config
	if model_args.decoder_config_name:
		A__											=					AutoConfig.from_pretrained(model_args.decoder_config_name     )
	# Use pretrained decoder model's config
	else:
		A__											=					AutoConfig.from_pretrained(model_args.decoder_model_name_or_path     )

	# necessary for `from_encoder_decoder_pretrained` when `decoder_config` is passed
	A__											=					True
	A__											=					True

	A__											=					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=UpperCAmelCase_,	decoder_config=UpperCAmelCase_,	)

	# GPT2 only has bos/eos tokens but not decoder_start/pad tokens
	A__											=					decoder_config.decoder_start_token_id
	A__											=					decoder_config.pad_token_id
	if decoder_start_token_id is None:
		A__											=					decoder_config.bos_token_id
	if pad_token_id is None:
		A__											=					decoder_config.eos_token_id

	# This is necessary to make Flax's generate() work
	A__											=					decoder_config.eos_token_id
	A__											=					decoder_start_token_id
	A__											=					pad_token_id

	A__											=					AutoImageProcessor.from_pretrained(model_args.encoder_model_name_or_path     )

	A__											=					AutoTokenizer.from_pretrained(model_args.decoder_model_name_or_path     )
	A__											=					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()








"""simple docstring"""




from collections import defaultdict





def       a_ (	lowercase__ :int     ):
			__lowerCamelCase          =      1
			__lowerCamelCase          =      True
			for v in tree[start]:
						if v not in visited:
									ret += dfs(lowercase__     )
			if ret % 2 == 0:
						cuts.append(lowercase__     )
			return ret





def       a_ (	):
			dfs(1     )


if __name__ == "__main__":
					__magic_name__  , __magic_name__						: Tuple    =				1_0, 9
					__magic_name__						: Tuple    =				defaultdict(list)
					__magic_name__						: dict[int, bool]    =				{}
					__magic_name__						: list[int]    =				[]
					__magic_name__						: List[str]    =				0
					__magic_name__						: Tuple    =				[(2, 1), (3, 1), (4, 3), (5, 2), (6, 1), (7, 2), (8, 6), (9, 8), (1_0, 8)]
					for u, v in edges:
										tree[u].append(v)
										tree[v].append(u)
					even_tree()
					print(len(cuts) - 1)




