


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[Any]	=      logging.get_logger(__name__)







class     _lowercase				(							lowercase__):


       """simple docstring"""




       A__      		=    "vision-encoder-decoder"
       A__      		=    True





       def __init__(     self							:    int      ,   **__lowerCamelCase							:    List[str] ):


             '''simple docstring'''



             super().__init__(**__lowerCamelCase )
             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}" )

             lowerCamelCase__	:       Dict					=						kwargs.pop("encoder" )
             lowerCamelCase__	:       Union[str, Any]					=						encoder_config.pop("model_type" )
             lowerCamelCase__	:       Tuple					=						kwargs.pop("decoder" )
             lowerCamelCase__	:       List[Any]					=						decoder_config.pop("model_type" )

             lowerCamelCase__	:       Dict					=						AutoConfig.for_model(__lowerCamelCase      ,   **__lowerCamelCase )
             lowerCamelCase__	:       int					=						AutoConfig.for_model(__lowerCamelCase      ,   **__lowerCamelCase )
             lowerCamelCase__	:       Optional[int]					=						True





       @classmethod
       def 		lowerCAmelCase  (     cls							:    Tuple      ,   __lowerCamelCase							:    PretrainedConfig      ,   __lowerCamelCase							:    PretrainedConfig      ,   **__lowerCamelCase							:    int ):


             '''simple docstring'''



             logger.info("Setting `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config" )
             lowerCamelCase__	:       Tuple					=						True
             lowerCamelCase__	:       str					=						True

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





       def 		lowerCAmelCase  (     self							:    Dict ):


             '''simple docstring'''



             lowerCamelCase__	:       str					=						copy.deepcopy(self.__dict__ )
             lowerCamelCase__	:       Dict					=						self.encoder.to_dict()
             lowerCamelCase__	:       List[Any]					=						self.decoder.to_dict()
             lowerCamelCase__	:       Optional[Any]					=						self.__class__.model_type
             return output







class     _lowercase				(							lowercase__):


       """simple docstring"""




       A__      		=    version.parse("1.11")





       @property
       def 		lowerCAmelCase  (     self							:    Dict ):


             '''simple docstring'''



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





       @property
       def 		lowerCAmelCase  (     self							:    Any ):


             '''simple docstring'''



             return 1E-4





       @property
       def 		lowerCAmelCase  (     self							:    str ):


             '''simple docstring'''



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







class     _lowercase				(							lowercase__):


       """simple docstring"""




       @property
       def 		lowerCAmelCase  (     self							:    Tuple ):


             '''simple docstring'''



             lowerCamelCase__	:       List[Any]					=						OrderedDict()
             lowerCamelCase__	:       Optional[Any]					=						{0: "batch", 1: "past_decoder_sequence + sequence"}
             lowerCamelCase__	:       List[str]					=						{0: "batch", 1: "past_decoder_sequence + sequence"}
             lowerCamelCase__	:       Optional[int]					=						{0: "batch", 1: "encoder_sequence"}

             return common_inputs





       def 		lowerCAmelCase  (     self							:    Optional[int]      ,   __lowerCamelCase							:    "PreTrainedTokenizerBase"      ,   __lowerCamelCase							:    int = -1      ,   __lowerCamelCase							:    int = -1      ,   __lowerCamelCase							:    bool = False      ,   __lowerCamelCase							:    Optional["TensorType"] = None      ,   ):


             '''simple docstring'''



             import torch

             lowerCamelCase__	:       str					=						OrderedDict()

             lowerCamelCase__	:       Optional[int]					=						super().generate_dummy_inputs(
                 __lowerCamelCase      ,   batch_size=__lowerCamelCase      ,   seq_length=__lowerCamelCase      ,   is_pair=__lowerCamelCase      ,   framework=__lowerCamelCase )

             lowerCamelCase__					,     lowerCamelCase__	:       Any					=						dummy_input["input_ids"].shape
             lowerCamelCase__	:       Optional[int]					=						(batch, encoder_sequence, self._config.encoder_hidden_size)
             lowerCamelCase__	:       Optional[int]					=						dummy_input.pop("input_ids" )
             lowerCamelCase__	:       Dict					=						dummy_input.pop("attention_mask" )
             lowerCamelCase__	:       Union[str, Any]					=						torch.zeros(__lowerCamelCase )

             return common_inputs







class     _lowercase				(							lowercase__):


       """simple docstring"""




       @property
       def 		lowerCAmelCase  (     self							:    Tuple ):


             '''simple docstring'''



             pass





       def 		lowerCAmelCase  (     self							:    List[Any]      ,   __lowerCamelCase							:    PretrainedConfig ):


             '''simple docstring'''



             return VisionEncoderDecoderEncoderOnnxConfig(__lowerCamelCase )





       def 		lowerCAmelCase  (     self							:    str      ,   __lowerCamelCase							:    PretrainedConfig      ,   __lowerCamelCase							:    PretrainedConfig      ,   __lowerCamelCase							:    str = "default" ):


             '''simple docstring'''



             lowerCamelCase__	:       Union[str, Any]					=						encoder_config.hidden_size
             return VisionEncoderDecoderDecoderOnnxConfig(__lowerCamelCase      ,   __lowerCamelCase )






def    lowercase_	(	_A    :					int		):

      """simple docstring"""




      if not isinstance(_A					,      _A		):
            lowerCamelCase__	:       List[str]					=						F"Input value of [number={number}] must be an integer"
            raise TypeError(_A		)
      if number < 0:
            return False
      lowerCamelCase__	:       Dict					=						number * number
      while number > 0:
            if number % 10 != number_square % 10:
                  return False
            number //= 10
            number_square //= 10
      return True


if __name__ == "__main__":
    import doctest

    doctest.testmod()


