


from dataclasses import dataclass, field
from typing import Optional

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




@dataclass
class        lowerCamelCase      :
	_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 						__SCREAMING_SNAKE_CASE       (					)		->    Union[str, Any]:




						'''simple docstring'''







						__UpperCAmelCase :       Union[str, Any]  						=  HfArgumentParser((ModelArguments,)			)
						((__UpperCAmelCase)						,			) :       Optional[Any]  						=  parser.parse_args_into_dataclasses()

						# Load pretrained model and tokenizer

						# Use explicit specified encoder config
						if model_args.encoder_config_name:
												__UpperCAmelCase :       Union[str, Any]  						=  AutoConfig.from_pretrained(model_args.encoder_config_name			)
						# Use pretrained encoder model's config
						else:
												__UpperCAmelCase :       Tuple  						=  AutoConfig.from_pretrained(model_args.encoder_model_name_or_path			)

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

						# necessary for `from_encoder_decoder_pretrained` when `decoder_config` is passed
						__UpperCAmelCase :       str  						=  True
						__UpperCAmelCase :       Optional[Any]  						=  True

						__UpperCAmelCase :       str  						=  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=lowercase_			,					decoder_config=lowercase_			,					)

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

						# This is necessary to make Flax's generate() work
						__UpperCAmelCase :       Any  						=  decoder_config.eos_token_id
						__UpperCAmelCase :       Dict  						=  decoder_start_token_id
						__UpperCAmelCase :       Optional[int]  						=  pad_token_id

						__UpperCAmelCase :       Any  						=  AutoImageProcessor.from_pretrained(model_args.encoder_model_name_or_path			)

						__UpperCAmelCase :       Optional[Any]  						=  AutoTokenizer.from_pretrained(model_args.decoder_model_name_or_path			)
						__UpperCAmelCase :       Optional[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()





def 						__SCREAMING_SNAKE_CASE       (					lowercase_			)		->    list:




						'''simple docstring'''







						__UpperCAmelCase :       Optional[Any]  						=  int(lowercase_			)
						if n_element < 1:
												__UpperCAmelCase :       str  						=  ValueError('''a should be a positive number'''			)
												raise my_error

						__UpperCAmelCase :       Any  						=  [1]
						__UpperCAmelCase						,			__UpperCAmelCase						,			__UpperCAmelCase :       Any  						=  (0, 0, 0)
						__UpperCAmelCase :       int  						=  1
						while index < n_element:
												while hamming_list[i] * 2 <= hamming_list[-1]:
																		i += 1
												while hamming_list[j] * 3 <= hamming_list[-1]:
																		j += 1
												while hamming_list[k] * 5 <= hamming_list[-1]:
																		k += 1
												hamming_list.append(
												    min(hamming_list[i] * 2			,					hamming_list[j] * 3			,					hamming_list[k] * 5			)			)
												index += 1
						return hamming_list


if __name__ == "__main__":
				lowerCAmelCase							=						input("""Enter the last number (nth term) of the Hamming Number Series: """)
				print("""Formula of Hamming Number Series => 2^i * 3^j * 5^k""")
				lowerCAmelCase							=						hamming(int(n))
				print("""-----------------------------------------------------""")
				print(F'The list with nth numbers is: {hamming_numbers}')
				print("""-----------------------------------------------------""")

