






'''simple docstring'''







from dataclasses import dataclass, field
from typing import Optional

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




@dataclass
class 		UpperCamelCase__    :




		'''simple docstring'''





		_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=a   ,metadata={'help': 'Pretrained encoder config name or path if not the same as encoder_model_name'}			)
		_UpperCamelCase				  =					field(
		    default=a   ,metadata={'help': 'Pretrained decoder config name or path if not the same as decoder_model_name'}			)



def 						A__			(				):
						lowerCamelCase__         =  HfArgumentParser((ModelArguments,)						)
						((lowerCamelCase__)       ,			)         =  parser.parse_args_into_dataclasses()

						# Load pretrained model and tokenizer

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

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

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

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

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

						lowerCamelCase__         =  AutoImageProcessor.from_pretrained(model_args.encoder_model_name_or_path						)

						lowerCamelCase__         =  AutoTokenizer.from_pretrained(model_args.decoder_model_name_or_path						)
						lowerCamelCase__         =  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'''







from manim import *




class 		UpperCamelCase__    (a			):




		'''simple docstring'''





		def 			UpperCamelCase_					(      self       ):
								lowerCamelCase__         =  Rectangle(height=0.5   ,width=0.5       )
								lowerCamelCase__         =  Rectangle(height=0.46   ,width=0.46       ).set_stroke(width=0       )

								lowerCamelCase__         =  [mem.copy() for i in range(6       )]
								lowerCamelCase__         =  [mem.copy() for i in range(6       )]
								lowerCamelCase__         =  VGroup(*_lowerCAmelCase       ).arrange(_lowerCAmelCase   ,buff=0       )
								lowerCamelCase__         =  VGroup(*_lowerCAmelCase       ).arrange(_lowerCAmelCase   ,buff=0       )
								lowerCamelCase__         =  VGroup(_lowerCAmelCase   ,_lowerCAmelCase       ).arrange(_lowerCAmelCase   ,buff=0       )
								lowerCamelCase__         =  Text("""CPU"""   ,font_size=24       )
								lowerCamelCase__         =  Group(_lowerCAmelCase   ,_lowerCAmelCase       ).arrange(_lowerCAmelCase   ,buff=0.5   ,aligned_edge=_lowerCAmelCase       )
								cpu.move_to([-2.5, -0.5, 0]       )
								self.add(_lowerCAmelCase       )

								lowerCamelCase__         =  [mem.copy() for i in range(1       )]
								lowerCamelCase__         =  VGroup(*_lowerCAmelCase       ).arrange(_lowerCAmelCase   ,buff=0       )
								lowerCamelCase__         =  Text("""GPU"""   ,font_size=24       )
								lowerCamelCase__         =  Group(_lowerCAmelCase   ,_lowerCAmelCase       ).arrange(_lowerCAmelCase   ,buff=0.5   ,aligned_edge=_lowerCAmelCase       )
								gpu.align_to(_lowerCAmelCase   ,_lowerCAmelCase       )
								gpu.set_x(gpu.get_x() - 1       )

								self.add(_lowerCAmelCase       )

								lowerCamelCase__         =  [mem.copy() for i in range(6       )]
								lowerCamelCase__         =  VGroup(*_lowerCAmelCase       ).arrange(_lowerCAmelCase   ,buff=0       )

								lowerCamelCase__         =  Text("""Model"""   ,font_size=24       )
								lowerCamelCase__         =  Group(_lowerCAmelCase   ,_lowerCAmelCase       ).arrange(_lowerCAmelCase   ,buff=0.5   ,aligned_edge=_lowerCAmelCase       )
								model.move_to([3, -1.0, 0]       )

								self.play(
								    Create(_lowerCAmelCase   ,run_time=1       )   ,Create(_lowerCAmelCase   ,run_time=1       )   ,Create(_lowerCAmelCase   ,run_time=1       )   ,)

								lowerCamelCase__         =  MarkupText(
								    F'''First, an empty model skeleton is loaded\ninto <span fgcolor=\'{YELLOW}\'>memory</span> without using much RAM.'''   ,font_size=24   ,)

								lowerCamelCase__         =  Square(side_length=2.2       )
								key.move_to([-5, 2, 0]       )

								lowerCamelCase__         =  MarkupText(
								    F'''<b>Key:</b>\n\n<span fgcolor=\'{YELLOW}\'>●</span> Empty Model'''   ,font_size=18   ,)

								key_text.move_to([-5, 2.4, 0]       )

								step_a.move_to([2, 2, 0]       )
								self.play(Write(_lowerCAmelCase   ,run_time=2.5       )   ,Write(_lowerCAmelCase       )   ,Write(_lowerCAmelCase       )       )

								self.add(_lowerCAmelCase       )

								lowerCamelCase__         =  []
								lowerCamelCase__         =  []
								lowerCamelCase__         =  []
								for i, rect in enumerate(_lowerCAmelCase       ):
														lowerCamelCase__         =  Rectangle(height=0.46   ,width=0.46       ).set_stroke(width=0.0       ).set_fill(_lowerCAmelCase   ,opacity=0.7       )
														cpu_target.move_to(_lowerCAmelCase       )
														cpu_target.generate_target()
														lowerCamelCase__         =  0.46 / 4
														lowerCamelCase__         =  0.46 / 3

														if i == 0:
																				cpu_target.target.next_to(cpu_left_col_base[0].get_corner(DOWN + LEFT       )   ,buff=0.02   ,direction=_lowerCAmelCase       )
																				cpu_target.target.set_x(cpu_target.target.get_x() + 0.1       )
														elif i == 3:
																				cpu_target.target.next_to(cpu_targs[0].target   ,direction=_lowerCAmelCase   ,buff=0.0       )
														else:
																				cpu_target.target.next_to(cpu_targs[i - 1].target   ,direction=_lowerCAmelCase   ,buff=0.0       )
														cpu_targs.append(_lowerCAmelCase       )

														first_animations.append(rect.animate(run_time=0.5       ).set_stroke(_lowerCAmelCase       )       )
														second_animations.append(MoveToTarget(_lowerCAmelCase   ,run_time=1.5       )       )

								self.play(*_lowerCAmelCase       )
								self.play(*_lowerCAmelCase       )

								self.wait()

