from dataclasses import dataclass, field
from typing import Optional

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





@dataclass
class       lowercase__		:
						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=UpperCamelCase_			,							metadata={"""help""": """Pretrained encoder config name or path if not the same as encoder_model_name"""})
						UpperCamelCase_  =	field(
						    default=UpperCamelCase_			,							metadata={"""help""": """Pretrained decoder config name or path if not the same as decoder_model_name"""})






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

						# Load pretrained model and tokenizer

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

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

						# necessary for `from_encoder_decoder_pretrained` when `decoder_config` is passed
						SCREAMING_SNAKE_CASE							:					str           =							True
						SCREAMING_SNAKE_CASE							:					List[Any]           =							True

						SCREAMING_SNAKE_CASE							:					List[Any]           =							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=_lowercase	,						decoder_config=_lowercase	,						)

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

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

						SCREAMING_SNAKE_CASE							:					int           =							AutoImageProcessor.from_pretrained(model_args.encoder_model_name_or_path		)

						SCREAMING_SNAKE_CASE							:					Tuple           =							AutoTokenizer.from_pretrained(model_args.decoder_model_name_or_path		)
						SCREAMING_SNAKE_CASE							:					Tuple           =							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()






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


__UpperCamelCase	: Tuple    		=  {
    'configuration_ctrl': ['CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP', 'CTRLConfig'],
    'tokenization_ctrl': ['CTRLTokenizer'],
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__UpperCamelCase	: Dict    		=  [
					    'CTRL_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'CTRLForSequenceClassification',
					    'CTRLLMHeadModel',
					    'CTRLModel',
					    'CTRLPreTrainedModel',
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__UpperCamelCase	: Any    		=  [
					    'TF_CTRL_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'TFCTRLForSequenceClassification',
					    'TFCTRLLMHeadModel',
					    'TFCTRLModel',
					    'TFCTRLPreTrainedModel',
					]


if TYPE_CHECKING:
					from .configuration_ctrl import CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP, CTRLConfig
					from .tokenization_ctrl import CTRLTokenizer

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_ctrl import (
										    CTRL_PRETRAINED_MODEL_ARCHIVE_LIST,
										    CTRLForSequenceClassification,
										    CTRLLMHeadModel,
										    CTRLModel,
										    CTRLPreTrainedModel,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_ctrl import (
										    TF_CTRL_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFCTRLForSequenceClassification,
										    TFCTRLLMHeadModel,
										    TFCTRLModel,
										    TFCTRLPreTrainedModel,
										)

else:
					import sys

					__UpperCamelCase	: Optional[Any]    		=  _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





