
"""simple docstring"""




from dataclasses import dataclass, field
from typing import Optional

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





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







def 							_lowerCamelCase			(			):
		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()

"""simple docstring"""




import doctest
from collections import deque

import numpy as np





class 			snake_case  :

							def __init__(self     ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_    			=			[2, 1, 2, -1]
									SCREAMING_SNAKE_CASE_    			=			[1, 2, 3, 4]







							def _lowercase       (self     ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_    			=			len(self.first_signal     )
									SCREAMING_SNAKE_CASE_    			=			len(self.second_signal     )

									SCREAMING_SNAKE_CASE_    			=			max(SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_     )

									# create a zero matrix of max_length x max_length
									SCREAMING_SNAKE_CASE_    			=			[[0] * max_length for i in range(SCREAMING_SNAKE_CASE_     )]

									# fills the smaller signal with zeros to make both signals of same length
									if length_first_signal < length_second_signal:
											self.first_signal += [0] * (max_length - length_first_signal)
									elif length_first_signal > length_second_signal:
											self.second_signal += [0] * (max_length - length_second_signal)
									for i in range(SCREAMING_SNAKE_CASE_     ):
											SCREAMING_SNAKE_CASE_    			=			deque(self.second_signal     )
											rotated_signal.rotate(SCREAMING_SNAKE_CASE_     )
											for j, item in enumerate(SCREAMING_SNAKE_CASE_     ):
													matrix[i][j] += item

        # multiply the matrix with the first signal
									SCREAMING_SNAKE_CASE_    			=			np.matmul(np.transpose(SCREAMING_SNAKE_CASE_     )							,							np.transpose(self.first_signal     )     )

									# rounding-off to two decimal places
									return [round(SCREAMING_SNAKE_CASE_							,							2     ) for i in final_signal]


if __name__ == "__main__":
						doctest.testmod()