
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

__lowerCamelCase							=       logging.get_logger(__name__)






class   UpperCAmelCase		(						A_ ):
   A__					:		int						     =     "vision-encoder-decoder"
   A__					:		Tuple						     =     True



   def __init__(self		:					Optional[Any]    ,						**snake_case__		:					Optional[int] )		->	List[Any]:


          '''simple docstring'''


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

          snake_case       :   int							=      kwargs.pop("encoder" )
          snake_case       :   Tuple							=      encoder_config.pop("model_type" )
          snake_case       :   int							=      kwargs.pop("decoder" )
          snake_case       :   Optional[Any]							=      decoder_config.pop("model_type" )

          snake_case       :   Tuple							=      AutoConfig.for_model(snake_case__    ,						**snake_case__ )
          snake_case       :   List[Any]							=      AutoConfig.for_model(snake_case__    ,						**snake_case__ )
          snake_case       :   List[Any]							=      True



   @classmethod
   def 				_SCREAMING_SNAKE_CASE   (cls		:					Optional[Any]    ,						snake_case__		:					PretrainedConfig    ,						snake_case__		:					PretrainedConfig    ,						**snake_case__		:					int )		->	PretrainedConfig:


          '''simple docstring'''


          logger.info("Setting `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config" )
          snake_case       :   List[str]							=      True
          snake_case       :   Optional[Any]							=      True

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



   def 				_SCREAMING_SNAKE_CASE   (self		:					str )		->	Dict:


          '''simple docstring'''


          snake_case       :   Any							=      copy.deepcopy(self.__dict__ )
          snake_case       :   List[Any]							=      self.encoder.to_dict()
          snake_case       :   Tuple							=      self.decoder.to_dict()
          snake_case       :   Union[str, Any]							=      self.__class__.model_type
          return output






class   UpperCAmelCase		(						A_ ):
   A__					:		str						     =     version.parse("1.11" )



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


          '''simple docstring'''


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



   @property
   def 				_SCREAMING_SNAKE_CASE   (self		:					Any )		->	float:


          '''simple docstring'''


          return 1e-4



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


          '''simple docstring'''


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






class   UpperCAmelCase		(						A_ ):



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


          '''simple docstring'''


          snake_case       :   Dict							=      OrderedDict()
          snake_case       :   Optional[Any]							=      {0: "batch", 1: "past_decoder_sequence + sequence"}
          snake_case       :   Tuple							=      {0: "batch", 1: "past_decoder_sequence + sequence"}
          snake_case       :   List[Any]							=      {0: "batch", 1: "encoder_sequence"}

          return common_inputs



   def 				_SCREAMING_SNAKE_CASE   (self		:					Optional[Any]    ,						snake_case__		:					"PreTrainedTokenizerBase"    ,						snake_case__		:					int = -1    ,						snake_case__		:					int = -1    ,						snake_case__		:					bool = False    ,						snake_case__		:					Optional["TensorType"] = None    ,						)		->	Mapping[str, Any]:


          '''simple docstring'''


          import torch

          snake_case       :   str							=      OrderedDict()

          snake_case       :   Dict							=      super().generate_dummy_inputs(
              snake_case__    ,						batch_size=snake_case__    ,						seq_length=snake_case__    ,						is_pair=snake_case__    ,						framework=snake_case__ )

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

          return common_inputs






class   UpperCAmelCase		(						A_ ):



   @property
   def 				_SCREAMING_SNAKE_CASE   (self		:					int )		->	None:


          '''simple docstring'''


          pass



   def 				_SCREAMING_SNAKE_CASE   (self		:					str    ,						snake_case__		:					PretrainedConfig )		->	OnnxConfig:


          '''simple docstring'''


          return VisionEncoderDecoderEncoderOnnxConfig(snake_case__ )



   def 				_SCREAMING_SNAKE_CASE   (self		:					Dict    ,						snake_case__		:					PretrainedConfig    ,						snake_case__		:					PretrainedConfig    ,						snake_case__		:					str = "default" )		->	OnnxConfig:


          '''simple docstring'''


          snake_case       :   Union[str, Any]							=      encoder_config.hidden_size
          return VisionEncoderDecoderDecoderOnnxConfig(snake_case__    ,						snake_case__ )







__lowerCamelCase							=       {
    "joule": 1.0,
    "kilojoule": 10_00,
    "megajoule": 1_00_00_00,
    "gigajoule": 10_00_00_00_00,
    "wattsecond": 1.0,
    "watthour": 36_00,
    "kilowatthour": 3_60_00_00,
    "newtonmeter": 1.0,
    "calorie_nutr": 41_86.8,
    "kilocalorie_nutr": 4_18_68_00.00,
    "electronvolt": 1.602_176_634e-19,
    "britishthermalunit_it": 10_55.0_55_85,
    "footpound": 1.35_5818,
}


def 			UpperCamelCase					(    __lowerCamelCase     : str				,					__lowerCamelCase     : str				,					__lowerCamelCase     : float    ):
       if to_type not in ENERGY_CONVERSION or from_type not in ENERGY_CONVERSION:
              snake_case       :   List[Any]							=      (
                  f"""Incorrect 'from_type' or 'to_type' value: {from_type!r}, {to_type!r}\n"""
                  f"""Valid values are: {', '.join(__lowerCamelCase    )}"""
              )
              raise ValueError(__lowerCamelCase    )
       return value * ENERGY_CONVERSION[from_type] / ENERGY_CONVERSION[to_type]


if __name__ == "__main__":
   import doctest

   doctest.testmod()





