




'''simple docstring'''

from dataclasses import dataclass, field
from typing import Optional

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






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







def 		__magic_name__    (	)		->					List[str]:

					'''simple docstring'''

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

					# Load pretrained model and tokenizer

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

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

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

					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=__UpperCAmelCase,      decoder_config=__UpperCAmelCase,      )

					# GPT2 only has bos/eos tokens but not decoder_start/pad tokens
					snake_case_									=				decoder_config.decoder_start_token_id
					snake_case_									=				decoder_config.pad_token_id
					if decoder_start_token_id is None:
										snake_case_									=				decoder_config.bos_token_id
					if pad_token_id is None:
										snake_case_									=				decoder_config.eos_token_id

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

					snake_case_									=				AutoImageProcessor.from_pretrained(model_args.encoder_model_name_or_path	)

					snake_case_									=				AutoTokenizer.from_pretrained(model_args.decoder_model_name_or_path	)
					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()







'''simple docstring'''

from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


a  :				Tuple									=  {
    'configuration_llama': ['LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LlamaConfig'],
}

try:
							if not is_sentencepiece_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							a  :				Optional[Any]									=  ['LlamaTokenizer']

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							a  :				str									=  ['LlamaTokenizerFast']

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							a  :				Optional[Any]									=  [
							    'LlamaForCausalLM',
							    'LlamaModel',
							    'LlamaPreTrainedModel',
							    'LlamaForSequenceClassification',
							]


if TYPE_CHECKING:
							from .configuration_llama import LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP, LlamaConfig

							try:
														if not is_sentencepiece_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_llama import LlamaTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_llama_fast import LlamaTokenizerFast

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_llama import LlamaForCausalLM, LlamaForSequenceClassification, LlamaModel, LlamaPreTrainedModel


else:
							import sys

							a  :				str									=  _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)

