"""simple docstring"""






from dataclasses import dataclass, field
from typing import Optional

from transformers import AutoConfig, AutoImageProcessor, AutoTokenizer, FlaxVisionEncoderDecoderModel, HfArgumentParser
@dataclass
class 				snake_case       :




					"""simple docstring"""

					snake_case__				    = field(
					    metadata={"help": "The output directory where the model will be written."}      ,    )
					snake_case__				    = 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__				    = 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__				    = field(
					    default=_UpperCamelCase      ,    metadata={"help": "Pretrained encoder config name or path if not the same as encoder_model_name"}				)
					snake_case__				    = field(
					    default=_UpperCamelCase      ,    metadata={"help": "Pretrained decoder config name or path if not the same as decoder_model_name"}				)






def   a_						(					):
				UpperCAmelCase__             =							HfArgumentParser((ModelArguments,)       )
				((UpperCAmelCase__	)      ,		)             =							parser.parse_args_into_dataclasses()

				# Load pretrained model and tokenizer

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

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

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

				UpperCAmelCase__             =							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
				UpperCAmelCase__             =							decoder_config.decoder_start_token_id
				UpperCAmelCase__             =							decoder_config.pad_token_id
				if decoder_start_token_id is None:
								UpperCAmelCase__             =							decoder_config.bos_token_id
				if pad_token_id is None:
								UpperCAmelCase__             =							decoder_config.eos_token_id

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

				UpperCAmelCase__             =							AutoImageProcessor.from_pretrained(model_args.encoder_model_name_or_path       )

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

import math

def   a_    ( lowerCAmelCase_      :			int,	lowerCAmelCase_      :			int,	lowerCAmelCase_      :			bool,	lowerCAmelCase_      :			list[int],	lowerCAmelCase_      :			float	):

   if depth < 0:
      raise ValueError('Depth cannot be less than 0'	)

   if len(lowerCAmelCase_	) == 0:
      raise ValueError('Scores cannot be empty'	)

   if depth == height:
      return scores[node_index]

   if is_max:
      return max(
          minimax(depth + 1,	node_index * 2,	lowerCAmelCase_,	lowerCAmelCase_,	lowerCAmelCase_	),	minimax(depth + 1,	node_index * 2 + 1,	lowerCAmelCase_,	lowerCAmelCase_,	lowerCAmelCase_	),	)

   return min(
       minimax(depth + 1,	node_index * 2,	lowerCAmelCase_,	lowerCAmelCase_,	lowerCAmelCase_	),	minimax(depth + 1,	node_index * 2 + 1,	lowerCAmelCase_,	lowerCAmelCase_,	lowerCAmelCase_	),	)

def   a_    ( ):
   __lowerCAmelCase								=	[90, 23, 6, 33, 21, 65, 123, 3_4423]
   __lowerCAmelCase								=	math.log(len(lowerCAmelCase_	),	2	)
   print('Optimal value : ',	end=''	)
   print(minimax(0,	0,	lowerCAmelCase_,	lowerCAmelCase_,	lowerCAmelCase_	)	)


if __name__ == "__main__":
     import doctest

     doctest.testmod()
     main()

