'''simple docstring'''




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

_a   :	Optional[int] 							=   logging.get_logger(__name__)






class 		__A       (__magic_name__   ):
       snake_case       :Any				    =					"vision-encoder-decoder"
       snake_case       :List[str]				    =					True






       def __init__(      self				,     **UpperCamelCase_      ):
              super().__init__(**UpperCamelCase_      )
              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  :				List[Any]       				=		kwargs.pop("encoder"      )
              __UpperCAmelCase  :				int       				=		encoder_config.pop("model_type"      )
              __UpperCAmelCase  :				str       				=		kwargs.pop("decoder"      )
              __UpperCAmelCase  :				Tuple       				=		decoder_config.pop("model_type"      )

              __UpperCAmelCase  :				Optional[int]       				=		AutoConfig.for_model(UpperCamelCase_				,     **UpperCamelCase_      )
              __UpperCAmelCase  :				List[str]       				=		AutoConfig.for_model(UpperCamelCase_				,     **UpperCamelCase_      )
              __UpperCAmelCase  :				List[Any]       				=		True






       @classmethod
       def 			_snake_case						(      cls				,     UpperCamelCase_				,     UpperCamelCase_				,     **UpperCamelCase_      ):
              logger.info("Setting `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config"      )
              __UpperCAmelCase  :				Optional[int]       				=		True
              __UpperCAmelCase  :				Dict       				=		True

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







       def 			_snake_case						(      self      ):
              __UpperCAmelCase  :				List[Any]       				=		copy.deepcopy(self.__dict__      )
              __UpperCAmelCase  :				Optional[int]       				=		self.encoder.to_dict()
              __UpperCAmelCase  :				str       				=		self.decoder.to_dict()
              __UpperCAmelCase  :				int       				=		self.__class__.model_type
              return output






class 		__A       (__magic_name__   ):
       snake_case       :str				    =					version.parse("1.11"   )






       @property
       def 			_snake_case						(      self      ):
              return OrderedDict(
                  [
                      ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
                  ]      )






       @property
       def 			_snake_case						(      self      ):
              return 1E-4







       @property
       def 			_snake_case						(      self      ):
              return OrderedDict({"last_hidden_state": {0: "batch", 1: "encoder_sequence"}}      )






class 		__A       (__magic_name__   ):






       @property
       def 			_snake_case						(      self      ):
              __UpperCAmelCase  :				int       				=		OrderedDict()
              __UpperCAmelCase  :				Tuple       				=		{0: "batch", 1: "past_decoder_sequence + sequence"}
              __UpperCAmelCase  :				Optional[Any]       				=		{0: "batch", 1: "past_decoder_sequence + sequence"}
              __UpperCAmelCase  :				Dict       				=		{0: "batch", 1: "encoder_sequence"}

              return common_inputs







       def 			_snake_case						(      self				,     UpperCamelCase_				,     UpperCamelCase_ = -1				,     UpperCamelCase_ = -1				,     UpperCamelCase_ = False				,     UpperCamelCase_ = None				,     ):
              import torch

              __UpperCAmelCase  :				Optional[int]       				=		OrderedDict()

              __UpperCAmelCase  :				Any       				=		super().generate_dummy_inputs(
                  UpperCamelCase_				,     batch_size=UpperCamelCase_				,     seq_length=UpperCamelCase_				,     is_pair=UpperCamelCase_				,     framework=UpperCamelCase_      )

              __UpperCAmelCase				, __UpperCAmelCase  :				List[str]       				=		dummy_input["input_ids"].shape
              __UpperCAmelCase  :				int       				=		(batch, encoder_sequence, self._config.encoder_hidden_size)
              __UpperCAmelCase  :				int       				=		dummy_input.pop("input_ids"      )
              __UpperCAmelCase  :				Optional[int]       				=		dummy_input.pop("attention_mask"      )
              __UpperCAmelCase  :				Tuple       				=		torch.zeros(UpperCamelCase_      )

              return common_inputs






class 		__A       (__magic_name__   ):






       @property
       def 			_snake_case						(      self      ):
              pass






       def 			_snake_case						(      self				,     UpperCamelCase_      ):
              return VisionEncoderDecoderEncoderOnnxConfig(UpperCamelCase_      )







       def 			_snake_case						(      self				,     UpperCamelCase_				,     UpperCamelCase_				,     UpperCamelCase_ = "default"      ):
              __UpperCAmelCase  :				str       				=		encoder_config.hidden_size
              return VisionEncoderDecoderDecoderOnnxConfig(UpperCamelCase_				,     UpperCamelCase_      )







'''simple docstring'''




from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments

def _lowercase	(      )					->       Dict:





       """simple docstring"""


       __UpperCAmelCase  :				str       				=		HfArgumentParser(lowerCamelCase__						)
       __UpperCAmelCase  :				Optional[Any]       				=		parser.parse_args_into_dataclasses()[0]
       __UpperCAmelCase  :				Any       				=		TensorFlowBenchmark(args=lowerCamelCase__						)
       try:
              __UpperCAmelCase  :				List[Any]       				=		parser.parse_args_into_dataclasses()[0]
       except ValueError as e:
              __UpperCAmelCase  :				str       				=		"Arg --no_{0} is no longer used, please use --no-{0} instead."
              __UpperCAmelCase  :				Tuple       				=		" ".join(str(lowerCamelCase__						).split(" "						)[:-1]						)
              __UpperCAmelCase  :				Any       				=		""
              __UpperCAmelCase  :				List[Any]       				=		eval(str(lowerCamelCase__						).split(" "						)[-1]						)
              __UpperCAmelCase  :				Optional[int]       				=		[]
              for arg in depreciated_args:
                     # arg[2:] removes '--'
                     if arg[2:] in TensorFlowBenchmark.deprecated_args:
                            # arg[5:] removes '--no_'
                            full_error_msg += arg_error_msg.format(arg[5:]						)
                     else:
                            wrong_args.append(lowerCamelCase__						)
              if len(lowerCamelCase__						) > 0:
                     __UpperCAmelCase  :				Union[str, Any]       				=		full_error_msg + begin_error_msg + str(lowerCamelCase__						)
              raise ValueError(lowerCamelCase__						)
       benchmark.run()


if __name__ == "__main__":
   main()






