



from __future__ import annotations

import bisect






def      __lowercase       (			a__							,       a__							,       a__ = 0							,       a__ = -1       )    ->			int:
							if hi < 0:
														__SCREAMING_SNAKE_CASE									= len(a__       )

							while lo < hi:
														__SCREAMING_SNAKE_CASE									= lo + (hi - lo) // 2
														if sorted_collection[mid] < item:
																					__SCREAMING_SNAKE_CASE									= mid + 1
														else:
																					__SCREAMING_SNAKE_CASE									= mid

							return lo






def      __lowercase       (			a__							,       a__							,       a__ = 0							,       a__ = -1       )    ->			int:
							if hi < 0:
														__SCREAMING_SNAKE_CASE									= len(a__       )

							while lo < hi:
														__SCREAMING_SNAKE_CASE									= lo + (hi - lo) // 2
														if sorted_collection[mid] <= item:
																					__SCREAMING_SNAKE_CASE									= mid + 1
														else:
																					__SCREAMING_SNAKE_CASE									= mid

							return lo






def      __lowercase       (			a__							,       a__							,       a__ = 0							,       a__ = -1       )    ->			None:
							sorted_collection.insert(bisect_left(a__							,       a__							,       a__							,       a__       )							,       a__       )






def      __lowercase       (			a__							,       a__							,       a__ = 0							,       a__ = -1       )    ->			None:
							sorted_collection.insert(bisect_right(a__							,       a__							,       a__							,       a__       )							,       a__       )






def      __lowercase       (			a__							,       a__       )    ->			int | None:
							__SCREAMING_SNAKE_CASE									= 0
							__SCREAMING_SNAKE_CASE									= len(a__       ) - 1

							while left <= right:
														__SCREAMING_SNAKE_CASE									= left + (right - left) // 2
														__SCREAMING_SNAKE_CASE									= sorted_collection[midpoint]
														if current_item == item:
																					return midpoint
														elif item < current_item:
																					__SCREAMING_SNAKE_CASE									= midpoint - 1
														else:
																					__SCREAMING_SNAKE_CASE									= midpoint + 1
							return None






def      __lowercase       (			a__							,       a__       )    ->			int | None:
							__SCREAMING_SNAKE_CASE									= bisect.bisect_left(a__							,       a__       )
							if index != len(a__       ) and sorted_collection[index] == item:
														return index
							return None






def      __lowercase       (			a__							,       a__							,       a__							,       a__       )    ->			int | None:
							if right < left:
														return None

							__SCREAMING_SNAKE_CASE									= left + (right - left) // 2

							if sorted_collection[midpoint] == item:
														return midpoint
							elif sorted_collection[midpoint] > item:
														return binary_search_by_recursion(a__							,       a__							,       a__							,       midpoint - 1       )
							else:
														return binary_search_by_recursion(a__							,       a__							,       midpoint + 1							,       a__       )


if __name__ == "__main__":
			lowerCAmelCase__		:					int							=input('''Enter numbers separated by comma:\n''').strip()
			lowerCAmelCase__		:					List[str]							=sorted(int(item) for item in user_input.split(''','''))
			lowerCAmelCase__		:					Optional[int]							=int(input('''Enter a single number to be found in the list:\n'''))
			lowerCAmelCase__		:					Any							=binary_search(collection, target)
			if result is None:
						print(F'''{target} was not found in {collection}.''')
			else:
						print(F'''{target} was found at position {result} in {collection}.''')











from dataclasses import dataclass, field
from typing import Optional

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

@dataclass
class        UpperCAmelCase_  :

						'''simple docstring'''




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







def      __lowercase       (			)    ->			Union[str, Any]:
							__SCREAMING_SNAKE_CASE									= HfArgumentParser((ModelArguments,)       )
							((__SCREAMING_SNAKE_CASE)							,	)									= parser.parse_args_into_dataclasses()

							# Load pretrained model and tokenizer

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

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

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

							__SCREAMING_SNAKE_CASE									= 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=a__							,       decoder_config=a__							,       )

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

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

							__SCREAMING_SNAKE_CASE									= AutoImageProcessor.from_pretrained(model_args.encoder_model_name_or_path       )

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






