



from dataclasses import dataclass, field
from typing import Tuple

from ..utils import cached_property, is_torch_available, is_torch_tpu_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments


if is_torch_available():
			import torch

if is_torch_tpu_available(check_device=False):
			import torch_xla.core.xla_model as xm


lowerCAmelCase__		:					int							=logging.get_logger(__name__)

@dataclass
class        UpperCAmelCase_  (       UpperCamelCase_	):

						'''simple docstring'''




						UpperCamelCase__      :			Optional[int]      =[
						    '''no_inference''',
						    '''no_cuda''',
						    '''no_tpu''',
						    '''no_speed''',
						    '''no_memory''',
						    '''no_env_print''',
						    '''no_multi_process''',
						]






						def __init__(   self				,							**_A		):







													'''simple docstring'''

													for deprecated_arg in self.deprecated_args:
																				if deprecated_arg in kwargs:
																											__SCREAMING_SNAKE_CASE									= deprecated_arg[3:]
																											setattr(self				,							_A				,							not kwargs.pop(_A		)		)
																											logger.warning(
																											    f"""{deprecated_arg} is depreciated. Please use --no_{positive_arg} or"""
																											    f""" {positive_arg}={kwargs[positive_arg]}"""		)

													__SCREAMING_SNAKE_CASE									= kwargs.pop('torchscript'				,							self.torchscript		)
													__SCREAMING_SNAKE_CASE									= kwargs.pop('torch_xla_tpu_print_metrics'				,							self.torch_xla_tpu_print_metrics		)
													__SCREAMING_SNAKE_CASE									= kwargs.pop('fp16_opt_level'				,							self.fpaa_opt_level		)
													super().__init__(**_A		)

						UpperCamelCase__      :			bool      =field(default=UpperCamelCase_   ,   metadata={'''help''': '''Trace the models using torchscript'''}	)
						UpperCamelCase__      :			bool      =field(default=UpperCamelCase_   ,   metadata={'''help''': '''Print Xla/PyTorch tpu metrics'''}	)
						UpperCamelCase__      :			str      =field(
						    default='''O1'''   ,   metadata={
						        '''help''': (
						            '''For fp16: Apex AMP optimization level selected in [\'O0\', \'O1\', \'O2\', and \'O3\']. '''
						            '''See details at https://nvidia.github.io/apex/amp.html'''
						        )
						    }   ,   )






						@cached_property
						def 	_A     (   self		):







													'''simple docstring'''

													requires_backends(self				,							['torch']		)
													logger.info('PyTorch: setting up devices'		)
													if not self.cuda:
																				__SCREAMING_SNAKE_CASE									= torch.device('cpu'		)
																				__SCREAMING_SNAKE_CASE									= 0
													elif is_torch_tpu_available():
																				__SCREAMING_SNAKE_CASE									= xm.xla_device()
																				__SCREAMING_SNAKE_CASE									= 0
													else:
																				__SCREAMING_SNAKE_CASE									= torch.device('cuda' if torch.cuda.is_available() else 'cpu'		)
																				__SCREAMING_SNAKE_CASE									= torch.cuda.device_count()
													return device, n_gpu






						@property
						def 	_A     (   self		):







													'''simple docstring'''

													return is_torch_tpu_available() and self.tpu






						@property
						def 	_A     (   self		):







													'''simple docstring'''

													requires_backends(self				,							['torch']		)
													# TODO(PVP): currently only single GPU is supported
													return torch.cuda.current_device()






						@property
						def 	_A     (   self		):







													'''simple docstring'''

													requires_backends(self				,							['torch']		)
													return self._setup_devices[0]






						@property
						def 	_A     (   self		):







													'''simple docstring'''

													requires_backends(self				,							['torch']		)
													return self._setup_devices[1]






						@property
						def 	_A     (   self		):







													'''simple docstring'''

													return self.n_gpu > 0











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()






