



from dataclasses import dataclass, field
from typing import Optional

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

@dataclass
class   _A   :


						'''simple docstring'''





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



def    snake_case_				(    ):
	__lowercase		       =			HfArgumentParser((ModelArguments,)   )
	((__lowercase)       ,    )		       =			parser.parse_args_into_dataclasses()

	# Load pretrained model and tokenizer

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

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

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

	__lowercase		       =			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=_SCREAMING_SNAKE_CASE  ,	decoder_config=_SCREAMING_SNAKE_CASE  ,	)

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

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

	__lowercase		       =			AutoImageProcessor.from_pretrained(model_args.encoder_model_name_or_path   )

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








from __future__ import annotations

from collections import namedtuple
from dataclasses import dataclass

@dataclass
class   _A   :


						'''simple docstring'''





						_snake_case			:     int
						_snake_case			:     TreeNode | None      							=      None
						_snake_case			:     TreeNode | None      							=      None

snake_case__					:     Dict    		=   namedtuple("""CoinsDistribResult""", """moves excess""")



def    snake_case_				(    _SCREAMING_SNAKE_CASE   ):

	if root is None:
		return 0

	# Validation
	def count_nodes(_SCREAMING_SNAKE_CASE   ) -> int:
		if node is None:
			return 0

		return count_nodes(node.left   ) + count_nodes(node.right   ) + 1

	def count_coins(_SCREAMING_SNAKE_CASE   ) -> int:
		if node is None:
			return 0

		return count_coins(node.left   ) + count_coins(node.right   ) + node.data

	if count_nodes(_SCREAMING_SNAKE_CASE   ) != count_coins(_SCREAMING_SNAKE_CASE   ):
		raise ValueError("The nodes number should be same as the number of coins"   )

	# Main calculation
	def get_distrib(_SCREAMING_SNAKE_CASE   ) -> CoinsDistribResult:

		if node is None:
			return CoinsDistribResult(0  ,	1   )

		__lowercase       ,    __lowercase		       =			get_distrib(node.left   )
		__lowercase       ,    __lowercase		       =			get_distrib(node.right   )

		__lowercase		       =			1 - left_distrib_excess
		__lowercase		       =			1 - right_distrib_excess

		__lowercase		       =			(
		    left_distrib_moves
		    + right_distrib_moves
		    + abs(_SCREAMING_SNAKE_CASE   )
		    + abs(_SCREAMING_SNAKE_CASE   )
		)
		__lowercase		       =			node.data - coins_to_left - coins_to_right

		return CoinsDistribResult(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE   )

	return get_distrib(_SCREAMING_SNAKE_CASE   )[0]


if __name__ == "__main__":
	import doctest

	doctest.testmod()



