


"""simple docstring"""






from dataclasses import dataclass, field
from typing import Optional

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





@dataclass
class      lowerCamelCase__   :







					"""simple docstring"""


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






def      lowerCamelCase       (     )		->       Dict:

							'''simple docstring'''



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

							# Load pretrained model and tokenizer

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

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

							# necessary for `from_encoder_decoder_pretrained` when `decoder_config` is passed
							__UpperCAmelCase   :						Dict      							=					True
							__UpperCAmelCase   :						str      							=					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=_UpperCamelCase       ,			decoder_config=_UpperCamelCase       ,			)

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

							# This is necessary to make Flax's generate() work
							__UpperCAmelCase   :						List[str]      							=					decoder_config.eos_token_id
							__UpperCAmelCase   :						List[str]      							=					decoder_start_token_id
							__UpperCAmelCase   :						Tuple      							=					pad_token_id

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

							__UpperCAmelCase   :						Dict      							=					AutoTokenizer.from_pretrained(model_args.decoder_model_name_or_path							)
							__UpperCAmelCase   :						Dict      							=					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"""






# Copyright 2023 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.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


UpperCAmelCase    :     Union[str, Any]   						=		{'configuration_mra': ['MRA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MraConfig']}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase    :     int   						=		[
							    'MRA_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'MraForMaskedLM',
							    'MraForMultipleChoice',
							    'MraForQuestionAnswering',
							    'MraForSequenceClassification',
							    'MraForTokenClassification',
							    'MraLayer',
							    'MraModel',
							    'MraPreTrainedModel',
							]


if TYPE_CHECKING:
							from .configuration_mra import MRA_PRETRAINED_CONFIG_ARCHIVE_MAP, MraConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_mra import (
														    MRA_PRETRAINED_MODEL_ARCHIVE_LIST,
														    MraForMaskedLM,
														    MraForMultipleChoice,
														    MraForQuestionAnswering,
														    MraForSequenceClassification,
														    MraForTokenClassification,
														    MraLayer,
														    MraModel,
														    MraPreTrainedModel,
														)
else:
							import sys

							UpperCAmelCase    :     Tuple   						=		_LazyModule(__name__, globals()['__file__'], _import_structure)
