



"""simple docstring"""







from dataclasses import dataclass, field
from typing import Optional

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






@dataclass
class       UpperCamelCase__			:

			"""simple docstring"""


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



def       _lowerCamelCase				(							)	-> Optional[int]:






	"""simple docstring"""





	A__											=					HfArgumentParser((ModelArguments,)     )
	((A__)							,							)											=					parser.parse_args_into_dataclasses()

	# Load pretrained model and tokenizer

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

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

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

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

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

	A__											=					AutoImageProcessor.from_pretrained(model_args.encoder_model_name_or_path     )

	A__											=					AutoTokenizer.from_pretrained(model_args.decoder_model_name_or_path     )
	A__											=					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 typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCamelCase     =							{"""configuration_wavlm""": ["""WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """WavLMConfig"""]}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			UpperCamelCase     =							[
			    """WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST""",
			    """WavLMForAudioFrameClassification""",
			    """WavLMForCTC""",
			    """WavLMForSequenceClassification""",
			    """WavLMForXVector""",
			    """WavLMModel""",
			    """WavLMPreTrainedModel""",
			]

if TYPE_CHECKING:
			from .configuration_wavlm import WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP, WavLMConfig

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_wavlm import (
						    WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST,
						    WavLMForAudioFrameClassification,
						    WavLMForCTC,
						    WavLMForSequenceClassification,
						    WavLMForXVector,
						    WavLMModel,
						    WavLMPreTrainedModel,
						)

else:
			import sys

			UpperCamelCase     =							_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




