
from dataclasses import dataclass, field
from typing import Optional

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






@dataclass
class        _a:
		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=__A	,   metadata={'help': 'Pretrained encoder config name or path if not the same as encoder_model_name'}    )
		lowerCamelCase__     :Optional[str]   				=		field(
		    default=__A	,   metadata={'help': 'Pretrained decoder config name or path if not the same as decoder_model_name'}    )







def    A				(							):
					_snake_case       :					Optional[int]				       =   HfArgumentParser((ModelArguments,)				)
					(_snake_case )       :					Optional[int]				       =   parser.parse_args_into_dataclasses()

					# Load pretrained model and tokenizer

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

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

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

					_snake_case       :					List[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=UpperCAmelCase  ,  decoder_config=UpperCAmelCase  ,  )

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

					# This is necessary to make Flax's generate() work
					_snake_case       :					Dict				       =   decoder_config.eos_token_id
					_snake_case       :					Tuple				       =   decoder_start_token_id
					_snake_case       :					Tuple				       =   pad_token_id

					_snake_case       :					Dict				       =   AutoImageProcessor.from_pretrained(model_args.encoder_model_name_or_path				)

					_snake_case       :					List[str]				       =   AutoTokenizer.from_pretrained(model_args.decoder_model_name_or_path				)
					_snake_case       :					Optional[Any]				       =   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()






# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import argparse

from .config import config_command_parser
from .config_args import default_config_file, load_config_from_file  # noqa: F401
from .default import default_command_parser
from .update import update_command_parser







def    A				(							UpperCAmelCase=None				):
					_snake_case       :					Union[str, Any]				       =   argparse.ArgumentParser(add_help=UpperCAmelCase  ,  allow_abbrev=UpperCAmelCase				)
					# The main config parser
					_snake_case       :					Tuple				       =   config_command_parser(UpperCAmelCase				)
					# The subparser to add commands to
					_snake_case       :					Any				       =   config_parser.add_subparsers(title="subcommands"  ,  dest="subcommand"				)

					# Then add other parsers with the parent parser
					default_command_parser(UpperCAmelCase  ,  parents=[parent_parser]				)
					update_command_parser(UpperCAmelCase  ,  parents=[parent_parser]				)

					return config_parser







def    A				(							):
					_snake_case       :					str				       =   get_config_parser()
					_snake_case       :					Union[str, Any]				       =   config_parser.parse_args()

					if not hasattr(UpperCAmelCase  ,  "func"				):
										config_parser.print_help()
										exit(1				)

					# Run
					args.func(UpperCAmelCase				)


if __name__ == "__main__":
							main()