"""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

lowerCAmelCase_:					Optional[Any]     =					logging.get_logger(__name__)






class    a__    (   _a	):
   snake_case_							=      "vision-encoder-decoder"
   snake_case_							=      True


   def __init__(     self,				**_UpperCAmelCase  ):
        '''simple docstring'''



        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}'''  )

        lowercase__				 =							kwargs.pop("encoder"  )
        lowercase__				 =							encoder_config.pop("model_type"  )
        lowercase__				 =							kwargs.pop("decoder"  )
        lowercase__				 =							decoder_config.pop("model_type"  )

        lowercase__				 =							AutoConfig.for_model(_UpperCAmelCase,				**_UpperCAmelCase  )
        lowercase__				 =							AutoConfig.for_model(_UpperCAmelCase,				**_UpperCAmelCase  )
        lowercase__				 =							True


   @classmethod
   def 	snake_case__	(     cls,				_UpperCAmelCase,				_UpperCAmelCase,				**_UpperCAmelCase  ):
        '''simple docstring'''



        logger.info("Setting `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config"  )
        lowercase__				 =							True
        lowercase__				 =							True

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


   def 	snake_case__	(     self  ):
        '''simple docstring'''



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






class    a__    (   _a	):
   snake_case_							=      version.parse("1.11"	)


   @property
   def 	snake_case__	(     self  ):
        '''simple docstring'''



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


   @property
   def 	snake_case__	(     self  ):
        '''simple docstring'''



        return 1E-4


   @property
   def 	snake_case__	(     self  ):
        '''simple docstring'''



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






class    a__    (   _a	):


   @property
   def 	snake_case__	(     self  ):
        '''simple docstring'''



        lowercase__				 =							OrderedDict()
        lowercase__				 =							{0: "batch", 1: "past_decoder_sequence + sequence"}
        lowercase__				 =							{0: "batch", 1: "past_decoder_sequence + sequence"}
        lowercase__				 =							{0: "batch", 1: "encoder_sequence"}

        return common_inputs


   def 	snake_case__	(     self,				_UpperCAmelCase,				_UpperCAmelCase = -1,				_UpperCAmelCase = -1,				_UpperCAmelCase = False,				_UpperCAmelCase = None,				):
        '''simple docstring'''



        import torch

        lowercase__				 =							OrderedDict()

        lowercase__				 =							super().generate_dummy_inputs(
            _UpperCAmelCase,				batch_size=_UpperCAmelCase,				seq_length=_UpperCAmelCase,				is_pair=_UpperCAmelCase,				framework=_UpperCAmelCase  )

        lowercase__ ,						lowercase__				 =							dummy_input["input_ids"].shape
        lowercase__				 =							(batch, encoder_sequence, self._config.encoder_hidden_size)
        lowercase__				 =							dummy_input.pop("input_ids"  )
        lowercase__				 =							dummy_input.pop("attention_mask"  )
        lowercase__				 =							torch.zeros(_UpperCAmelCase  )

        return common_inputs






class    a__    (   _a	):


   @property
   def 	snake_case__	(     self  ):
        '''simple docstring'''



        pass


   def 	snake_case__	(     self,				_UpperCAmelCase  ):
        '''simple docstring'''



        return VisionEncoderDecoderEncoderOnnxConfig(_UpperCAmelCase  )


   def 	snake_case__	(     self,				_UpperCAmelCase,				_UpperCAmelCase,				_UpperCAmelCase = "default"  ):
        '''simple docstring'''



        lowercase__				 =							encoder_config.hidden_size
        return VisionEncoderDecoderDecoderOnnxConfig(_UpperCAmelCase,				_UpperCAmelCase  )





"""simple docstring"""


from sympy import diff, lambdify, symbols
from sympy.functions import *  # noqa: F403







def 					__a   (		A    ,				A    ,				A = "x"    ,				A = 10**-10    ,				A = 1    ,				):






     '''simple docstring'''



     lowercase__				 =							symbols(A      )
     lowercase__				 =							lambdify(A    ,				A      )
     lowercase__				 =							lambdify(A    ,				diff(A    ,				A      )      )

     lowercase__				 =							starting_point

     while True:
          if diff_function(A      ) != 0:
               lowercase__				 =							prev_guess - multiplicity * func(A      ) / diff_function(
                   A      )
          else:
               raise ZeroDivisionError("Could not find root"      ) from None

          # Precision is checked by comparing the difference of consecutive guesses
          if abs(next_guess - prev_guess      ) < precision:
               return next_guess

          lowercase__				 =							next_guess


# Let's Execute
if __name__ == "__main__":
 # Find root of trigonometric function
 # Find value of pi
 print(F'The root of sin(x) = 0 is {newton_raphson("sin(x)", 2)}')

 # Find root of polynomial
 # Find fourth Root of 5
 print(F'The root of x**4 - 5 = 0 is {newton_raphson("x**4 -5", 0.4 +5j)}')

 # Find value of e
 print(
     "The root of log(y) - 1 = 0 is ",
     F'{newton_raphson("log(y) - 1", 2, variable="y")}',
 )

 # Exponential Roots
 print(
     "The root of exp(x) - 1 = 0 is",
     F'{newton_raphson("exp(x) - 1", 1_0, precision=0.005)}',
 )

 # Find root of cos(x)
 print(F'The root of cos(x) = 0 is {newton_raphson("cos(x)", 0)}')




