
import copy
from typing import TYPE_CHECKING, Any, Mapping, Optional, OrderedDict

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto.configuration_auto import AutoConfig


if TYPE_CHECKING:
				from ... import PreTrainedTokenizerBase, TensorType

lowercase__							:							str									=			logging.get_logger(__name__)






class        SCREAMING_SNAKE_CASE__    (							SCREAMING_SNAKE_CASE_    ):

							"""simple docstring"""






							_snake_case     		=  'vision-encoder-decoder'
							_snake_case     		=  True



							def __init__(  self     ,	**SCREAMING_SNAKE_CASE_			)->   int:





													'''simple docstring'''






													super().__init__(**SCREAMING_SNAKE_CASE_			)
													if "encoder" not in kwargs or "decoder" not in kwargs:
																			raise ValueError(
																			    F"A configuraton of type {self.model_type} cannot be instantiated because "
																			    F"not both `encoder` and `decoder` sub-configurations are passed, but only {kwargs}"			)

													__UpperCamelCase       =			kwargs.pop('''encoder'''			)
													__UpperCamelCase       =			encoder_config.pop('''model_type'''			)
													__UpperCamelCase       =			kwargs.pop('''decoder'''			)
													__UpperCamelCase       =			decoder_config.pop('''model_type'''			)

													__UpperCamelCase       =			AutoConfig.for_model(SCREAMING_SNAKE_CASE_     ,	**SCREAMING_SNAKE_CASE_			)
													__UpperCamelCase       =			AutoConfig.for_model(SCREAMING_SNAKE_CASE_     ,	**SCREAMING_SNAKE_CASE_			)
													__UpperCamelCase       =			True



							@classmethod
							def     A__		(  cls     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	**SCREAMING_SNAKE_CASE_			)->   PretrainedConfig:





													'''simple docstring'''






													logger.info('''Setting `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config'''			)
													__UpperCamelCase       =			True
													__UpperCamelCase       =			True

													return cls(encoder=encoder_config.to_dict()     ,	decoder=decoder_config.to_dict()     ,	**SCREAMING_SNAKE_CASE_			)



							def     A__		(  self			)->   Any:





													'''simple docstring'''






													__UpperCamelCase       =			copy.deepcopy(self.__dict__			)
													__UpperCamelCase       =			self.encoder.to_dict()
													__UpperCamelCase       =			self.decoder.to_dict()
													__UpperCamelCase       =			self.__class__.model_type
													return output






class        SCREAMING_SNAKE_CASE__    (							SCREAMING_SNAKE_CASE_    ):

							"""simple docstring"""






							_snake_case     		=  version.parse('1.11'    )



							@property
							def     A__		(  self			)->   Mapping[str, Mapping[int, str]]:





													'''simple docstring'''






													return OrderedDict(
													    [
													        ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
													    ]			)



							@property
							def     A__		(  self			)->   float:





													'''simple docstring'''






													return 1E-4



							@property
							def     A__		(  self			)->   Mapping[str, Mapping[int, str]]:





													'''simple docstring'''






													return OrderedDict({'''last_hidden_state''': {0: '''batch''', 1: '''encoder_sequence'''}}			)






class        SCREAMING_SNAKE_CASE__    (							SCREAMING_SNAKE_CASE_    ):

							"""simple docstring"""






							@property
							def     A__		(  self			)->   Mapping[str, Mapping[int, str]]:





													'''simple docstring'''






													__UpperCamelCase       =			OrderedDict()
													__UpperCamelCase       =			{0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
													__UpperCamelCase       =			{0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
													__UpperCamelCase       =			{0: '''batch''', 1: '''encoder_sequence'''}

													return common_inputs



							def     A__		(  self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_ = -1     ,	SCREAMING_SNAKE_CASE_ = -1     ,	SCREAMING_SNAKE_CASE_ = False     ,	SCREAMING_SNAKE_CASE_ = None     ,	)->   Mapping[str, Any]:





													'''simple docstring'''






													import torch

													__UpperCamelCase       =			OrderedDict()

													__UpperCamelCase       =			super().generate_dummy_inputs(
													    SCREAMING_SNAKE_CASE_     ,	batch_size=SCREAMING_SNAKE_CASE_     ,	seq_length=SCREAMING_SNAKE_CASE_     ,	is_pair=SCREAMING_SNAKE_CASE_     ,	framework=SCREAMING_SNAKE_CASE_			)

													__UpperCamelCase  ,							__UpperCamelCase       =			dummy_input['''input_ids'''].shape
													__UpperCamelCase       =			(batch, encoder_sequence, self._config.encoder_hidden_size)
													__UpperCamelCase       =			dummy_input.pop('''input_ids'''			)
													__UpperCamelCase       =			dummy_input.pop('''attention_mask'''			)
													__UpperCamelCase       =			torch.zeros(SCREAMING_SNAKE_CASE_			)

													return common_inputs






class        SCREAMING_SNAKE_CASE__    (							SCREAMING_SNAKE_CASE_    ):

							"""simple docstring"""






							@property
							def     A__		(  self			)->   None:





													'''simple docstring'''






													pass



							def     A__		(  self     ,	SCREAMING_SNAKE_CASE_			)->   OnnxConfig:





													'''simple docstring'''






													return VisionEncoderDecoderEncoderOnnxConfig(SCREAMING_SNAKE_CASE_			)



							def     A__		(  self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_ = "default"			)->   OnnxConfig:





													'''simple docstring'''






													__UpperCamelCase       =			encoder_config.hidden_size
													return VisionEncoderDecoderDecoderOnnxConfig(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_			)






import fire

from transformers import AutoConfig, AutoModelForSeqaSeqLM, AutoTokenizer




def 	A_ (							snake_case							:   str ,      snake_case							:   str ,      **snake_case							:   List[str] )      ->    Dict:



						'''simple docstring'''

						__UpperCamelCase       =			AutoConfig.from_pretrained(snake_case ,      **snake_case )
						__UpperCamelCase       =			AutoModelForSeqaSeqLM.from_config(snake_case )
						model.save_pretrained(snake_case )
						AutoTokenizer.from_pretrained(snake_case ).save_pretrained(snake_case )
						return model


if __name__ == "__main__":
				fire.Fire(save_randomly_initialized_version)




