





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       :     Tuple   =     logging.get_logger(__name__)



class 					A__   (       __snake_case			):
      _UpperCAmelCase	:Optional[int]						    =   'vision-encoder-decoder'
      _UpperCAmelCase	:Any						    =   True





      def __init__( self       ,							**A_   ):
       '''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	:					List[str]       	=   kwargs.pop("encoder"   )
       UpperCamelCase	:					Optional[int]       	=   encoder_config.pop("model_type"   )
       UpperCamelCase	:					str       	=   kwargs.pop("decoder"   )
       UpperCamelCase	:					str       	=   decoder_config.pop("model_type"   )

       UpperCamelCase	:					Optional[int]       	=   AutoConfig.for_model(SCREAMING_SNAKE_CASE_       ,							**SCREAMING_SNAKE_CASE_   )
       UpperCamelCase	:					Any       	=   AutoConfig.for_model(SCREAMING_SNAKE_CASE_       ,							**SCREAMING_SNAKE_CASE_   )
       UpperCamelCase	:					List[Any]       	=   True





      @classmethod
      def        __UpperCamelCase( cls       ,							A_       ,							A_       ,							**A_   ):
       '''simple docstring'''




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

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





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




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



class 					A__   (       __snake_case			):
      _UpperCAmelCase	:Any						    =   version.parse('1.11'			)





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




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





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




       return 1e-4





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




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



class 					A__   (       __snake_case			):





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




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

       return common_inputs





      def        __UpperCamelCase( self       ,							A_       ,							A_ = -1       ,							A_ = -1       ,							A_ = False       ,							A_ = None       ,							):
       '''simple docstring'''




       import torch

       UpperCamelCase	:					Dict       	=   OrderedDict()

       UpperCamelCase	:					Dict       	=   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	:					List[str]       	=   dummy_input["input_ids"].shape
       UpperCamelCase	:					Any       	=   (batch, encoder_sequence, self._config.encoder_hidden_size)
       UpperCamelCase	:					Tuple       	=   dummy_input.pop("input_ids"   )
       UpperCamelCase	:					List[Any]       	=   dummy_input.pop("attention_mask"   )
       UpperCamelCase	:					List[Any]       	=   torch.zeros(SCREAMING_SNAKE_CASE_   )

       return common_inputs



class 					A__   (       __snake_case			):





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




       pass





      def        __UpperCamelCase( self       ,							A_   ):
       '''simple docstring'''




       return VisionEncoderDecoderEncoderOnnxConfig(SCREAMING_SNAKE_CASE_   )





      def        __UpperCamelCase( self       ,							A_       ,							A_       ,							A_ = "default"   ):
       '''simple docstring'''




       UpperCamelCase	:					List[Any]       	=   encoder_config.hidden_size
       return VisionEncoderDecoderDecoderOnnxConfig(SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_   )


from __future__ import annotations

from collections.abc import MutableSequence
class  lowercase__:



	"""simple docstring"""




	def __init__(       self  :			Optional[Any]       ,							SCREAMING_SNAKE_CASE_  :			int       ,							SCREAMING_SNAKE_CASE_  :			MutableSequence[float]       )       ->     None:
					if len(SCREAMING_SNAKE_CASE_       ) != degree + 1:
									raise ValueError(
									    '''The number of coefficients should be equal to the degree + 1.'''       )

					lowercase_       =					list(SCREAMING_SNAKE_CASE_       )
					lowercase_       =					degree
	def __add__(       self  :			Any       ,							SCREAMING_SNAKE_CASE_  :			Polynomial       )       ->     Polynomial:

					if self.degree > polynomial_a.degree:
									lowercase_       =					self.coefficients[:]
									for i in range(polynomial_a.degree + 1       ):
													coefficients[i] += polynomial_a.coefficients[i]
									return Polynomial(self.degree       ,							SCREAMING_SNAKE_CASE_       )
					else:
									lowercase_       =					polynomial_a.coefficients[:]
									for i in range(self.degree + 1       ):
													coefficients[i] += self.coefficients[i]
									return Polynomial(polynomial_a.degree       ,							SCREAMING_SNAKE_CASE_       )
	def __sub__(       self  :			str       ,							SCREAMING_SNAKE_CASE_  :			Polynomial       )       ->     Polynomial:
					return self + polynomial_a * Polynomial(0       ,							[-1]       )
	def __neg__(       self  :			int       )       ->     Polynomial:
					return Polynomial(self.degree       ,							[-c for c in self.coefficients]       )
	def __mul__(       self  :			Any       ,							SCREAMING_SNAKE_CASE_  :			Polynomial       )       ->     Polynomial:
					lowercase_       =					[0] * (self.degree + polynomial_a.degree + 1)
					for i in range(self.degree + 1       ):
									for j in range(polynomial_a.degree + 1       ):
													coefficients[i + j] += (
													    self.coefficients[i] * polynomial_a.coefficients[j]
													)

					return Polynomial(self.degree + polynomial_a.degree       ,							SCREAMING_SNAKE_CASE_       )
	def 						_lowercase  (       self  :			Dict       ,							SCREAMING_SNAKE_CASE_  :			int | float       )       ->     int | float:
					lowercase_       =					0
					for i in range(self.degree + 1       ):
									result += self.coefficients[i] * (substitution**i)
					return result
	def __str__(       self  :			Tuple       )       ->     str:
					lowercase_       =					''''''
					for i in range(self.degree       ,							-1       ,							-1       ):
									if self.coefficients[i] == 0:
													continue
									elif self.coefficients[i] > 0:
													if polynomial:
																	polynomial += " + "
									else:
													polynomial += " - "

									if i == 0:
													polynomial += str(abs(self.coefficients[i]       )       )
									elif i == 1:
													polynomial += str(abs(self.coefficients[i]       )       ) + "x"
									else:
													polynomial += str(abs(self.coefficients[i]       )       ) + "x^" + str(SCREAMING_SNAKE_CASE_       )

					return polynomial
	def __repr__(       self  :			Optional[Any]       )       ->     str:
					return self.__str__()
	def 						_lowercase  (       self  :			int       )       ->     Polynomial:
					lowercase_       =					[0] * self.degree
					for i in range(self.degree       ):
									lowercase_       =					self.coefficients[i + 1] * (i + 1)
					return Polynomial(self.degree - 1       ,							SCREAMING_SNAKE_CASE_       )
	def 						_lowercase  (       self  :			List[str]       ,							SCREAMING_SNAKE_CASE_  :			int | float = 0       )       ->     Polynomial:
					lowercase_       =					[0] * (self.degree + 2)
					lowercase_       =					constant
					for i in range(self.degree + 1       ):
									lowercase_       =					self.coefficients[i] / (i + 1)
					return Polynomial(self.degree + 1       ,							SCREAMING_SNAKE_CASE_       )
	def __eq__(       self  :			str       ,							SCREAMING_SNAKE_CASE_  :			object       )       ->     bool:
					if not isinstance(SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ):
									return False

					if self.degree != polynomial_a.degree:
									return False

					for i in range(self.degree + 1       ):
									if self.coefficients[i] != polynomial_a.coefficients[i]:
													return False

					return True




	def __ne__(       self  :			List[str]       ,							SCREAMING_SNAKE_CASE_  :			object       )       ->     bool:
					return not self.__eq__(SCREAMING_SNAKE_CASE_       )




