


from __future__ import annotations

from collections.abc import MutableSequence



class        a					:





     def __init__(    self					:				Tuple    ,	SCREAMING_SNAKE_CASE_					:				int    ,	SCREAMING_SNAKE_CASE_					:				MutableSequence[float]							):
        if len(SCREAMING_SNAKE_CASE_							) != degree + 1:
           raise ValueError(
               """The number of coefficients should be equal to the degree + 1."""							)

        __lowerCamelCase:				list[float]     =	list(SCREAMING_SNAKE_CASE_							)
        __lowerCamelCase:				str     =	degree





     def __add__(    self					:				Optional[int]    ,	SCREAMING_SNAKE_CASE_					:				Polynomial							):

        if self.degree > polynomial_a.degree:
           __lowerCamelCase:				Dict     =	self.coefficients[:]
           for i in range(polynomial_a.degree + 1							):
              coefficients[i] += polynomial_a.coefficients[i]
           return Polynomial(self.degree    ,	SCREAMING_SNAKE_CASE_							)
        else:
           __lowerCamelCase:				Union[str, Any]     =	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					:				Dict    ,	SCREAMING_SNAKE_CASE_					:				Polynomial							):
        return self + polynomial_a * Polynomial(0    ,	[-1]							)





     def __neg__(    self					:				Optional[Any]							):
        return Polynomial(self.degree    ,	[-c for c in self.coefficients]							)





     def __mul__(    self					:				List[Any]    ,	SCREAMING_SNAKE_CASE_					:				Polynomial							):
        __lowerCamelCase:				list[float]     =	[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  SCREAMING_SNAKE_CASE__       (    self					:				Any    ,	SCREAMING_SNAKE_CASE_					:				int | float							):
        __lowerCamelCase:				int | float     =	0
        for i in range(self.degree + 1							):
           result += self.coefficients[i] * (substitution**i)
        return result





     def __str__(    self					:				Any							):
        __lowerCamelCase:				Dict     =	""""""
        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					:				Union[str, Any]							):
        return self.__str__()





     def  SCREAMING_SNAKE_CASE__       (    self					:				Any							):
        __lowerCamelCase:				list[float]     =	[0] * self.degree
        for i in range(self.degree							):
           __lowerCamelCase:				str     =	self.coefficients[i + 1] * (i + 1)
        return Polynomial(self.degree - 1    ,	SCREAMING_SNAKE_CASE_							)





     def  SCREAMING_SNAKE_CASE__       (    self					:				Tuple    ,	SCREAMING_SNAKE_CASE_					:				int | float = 0							):
        __lowerCamelCase:				list[float]     =	[0] * (self.degree + 2)
        __lowerCamelCase:				Union[str, Any]     =	constant
        for i in range(self.degree + 1							):
           __lowerCamelCase:				Dict     =	self.coefficients[i] / (i + 1)
        return Polynomial(self.degree + 1    ,	SCREAMING_SNAKE_CASE_							)





     def __eq__(    self					:				str    ,	SCREAMING_SNAKE_CASE_					:				object							):
        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					:				str    ,	SCREAMING_SNAKE_CASE_					:				object							):
        return not self.__eq__(SCREAMING_SNAKE_CASE_							)






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


_A		:				List[Any]									=       {
    '''configuration_convbert''': ['''CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ConvBertConfig''', '''ConvBertOnnxConfig'''],
    '''tokenization_convbert''': ['''ConvBertTokenizer'''],
}

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _A		:				int									=       ['''ConvBertTokenizerFast''']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _A		:				List[Any]									=       [
       '''CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''ConvBertForMaskedLM''',
       '''ConvBertForMultipleChoice''',
       '''ConvBertForQuestionAnswering''',
       '''ConvBertForSequenceClassification''',
       '''ConvBertForTokenClassification''',
       '''ConvBertLayer''',
       '''ConvBertModel''',
       '''ConvBertPreTrainedModel''',
       '''load_tf_weights_in_convbert''',
   ]


try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _A		:				List[Any]									=       [
       '''TF_CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''TFConvBertForMaskedLM''',
       '''TFConvBertForMultipleChoice''',
       '''TFConvBertForQuestionAnswering''',
       '''TFConvBertForSequenceClassification''',
       '''TFConvBertForTokenClassification''',
       '''TFConvBertLayer''',
       '''TFConvBertModel''',
       '''TFConvBertPreTrainedModel''',
   ]


if TYPE_CHECKING:
   from .configuration_convbert import CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, ConvBertConfig, ConvBertOnnxConfig
   from .tokenization_convbert import ConvBertTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_convbert_fast import ConvBertTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_convbert import (
          CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
          ConvBertForMaskedLM,
          ConvBertForMultipleChoice,
          ConvBertForQuestionAnswering,
          ConvBertForSequenceClassification,
          ConvBertForTokenClassification,
          ConvBertLayer,
          ConvBertModel,
          ConvBertPreTrainedModel,
          load_tf_weights_in_convbert,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_convbert import (
          TF_CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFConvBertForMaskedLM,
          TFConvBertForMultipleChoice,
          TFConvBertForQuestionAnswering,
          TFConvBertForSequenceClassification,
          TFConvBertForTokenClassification,
          TFConvBertLayer,
          TFConvBertModel,
          TFConvBertPreTrainedModel,
      )


else:
   import sys

   _A		:				int									=       _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


