



from collections import OrderedDict
from typing import Any, Mapping, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...feature_extraction_utils import FeatureExtractionMixin
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import TensorType, logging


a_   :Tuple        =      logging.get_logger(__name__)

a_   :Union[str, Any]        =      {
    "deepmind/language-perceiver": "https://huggingface.co/deepmind/language-perceiver/resolve/main/config.json",
    # See all Perceiver models at https://huggingface.co/models?filter=perceiver
}




class 					snake_case__						(       UpperCAmelCase_     ):






     """simple docstring"""


     _SCREAMING_SNAKE_CASE						=	'perceiver'






     def __init__(       self  :  List[Any], _snake_case  :  Dict=2_5_6, _snake_case  :  Union[str, Any]=1_2_8_0, _snake_case  :  Dict=7_6_8, _snake_case  :  Dict=1, _snake_case  :  List[str]=2_6, _snake_case  :  int=8, _snake_case  :  List[Any]=8, _snake_case  :  Union[str, Any]=None, _snake_case  :  Optional[int]=None, _snake_case  :  Optional[Any]="kv", _snake_case  :  Dict=1, _snake_case  :  List[str]=1, _snake_case  :  Union[str, Any]="gelu", _snake_case  :  int=0.1, _snake_case  :  Dict=0.0_2, _snake_case  :  Any=1e-12, _snake_case  :  Optional[Any]=True, _snake_case  :  List[Any]=2_6_2, _snake_case  :  str=2_0_4_8, _snake_case  :  Any=5_6, _snake_case  :  int=[3_6_8, 4_9_6], _snake_case  :  Union[str, Any]=1_6, _snake_case  :  Any=1_9_2_0, _snake_case  :  Dict=1_6, _snake_case  :  Dict=[1, 1_6, 2_2_4, 2_2_4], **_snake_case  :  Optional[int], )   ->List[str]:
            super().__init__(**__UpperCAmelCase					)

            snake_case__							:	str   	=	num_latents
            snake_case__							:	int   	=	d_latents
            snake_case__							:	int   	=	d_model
            snake_case__							:	Any   	=	num_blocks
            snake_case__							:	Any   	=	num_self_attends_per_block
            snake_case__							:	Optional[Any]   	=	num_self_attention_heads
            snake_case__							:	Any   	=	num_cross_attention_heads
            snake_case__							:	List[str]   	=	qk_channels
            snake_case__							:	Optional[int]   	=	v_channels
            snake_case__							:	List[str]   	=	cross_attention_shape_for_attention
            snake_case__							:	Optional[Any]   	=	self_attention_widening_factor
            snake_case__							:	Optional[Any]   	=	cross_attention_widening_factor
            snake_case__							:	Tuple   	=	hidden_act
            snake_case__							:	Optional[Any]   	=	attention_probs_dropout_prob
            snake_case__							:	Dict   	=	initializer_range
            snake_case__							:	List[str]   	=	layer_norm_eps
            snake_case__							:	Union[str, Any]   	=	use_query_residual
            # masked language modeling attributes
            snake_case__							:	str   	=	vocab_size
            snake_case__							:	List[str]   	=	max_position_embeddings
            # image classification attributes
            snake_case__							:	Optional[int]   	=	image_size
            # flow attributes
            snake_case__							:	Tuple   	=	train_size
            # multimodal autoencoding attributes
            snake_case__							:	Optional[int]   	=	num_frames
            snake_case__							:	List[Any]   	=	audio_samples_per_frame
            snake_case__							:	int   	=	samples_per_patch
            snake_case__							:	Optional[int]   	=	output_shape




class 					snake_case__						(       UpperCAmelCase_     ):






     """simple docstring"""


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



            if self.task == "multiple-choice":
                   snake_case__							:	Dict   	=	{0: 'batch', 1: 'choice', 2: 'sequence'}
            else:
                   snake_case__							:	Tuple   	=	{0: 'batch', 1: 'sequence'}
            return OrderedDict(
                [
                    ('inputs', dynamic_axis),
                    ('attention_mask', dynamic_axis),
                ]					)






     @property
     def    lowercase_   (       self  :  Union[str, Any]					)   ->float:
            return 1e-4






     def    lowercase_   (       self  :  Any, _snake_case  :  Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"], _snake_case  :  int = -1, _snake_case  :  int = -1, _snake_case  :  int = -1, _snake_case  :  bool = False, _snake_case  :  Optional[TensorType] = None, _snake_case  :  int = 3, _snake_case  :  int = 4_0, _snake_case  :  int = 4_0, )   ->Mapping[str, Any]:



            if isinstance(__UpperCAmelCase, __UpperCAmelCase					):
                   # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
                   snake_case__							:	Any   	=	compute_effective_axis_dimension(
                       __UpperCAmelCase, fixed_dimension=OnnxConfig.default_fixed_batch, num_token_to_add=0					)
                   # If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
                   snake_case__							:	List[Any]   	=	preprocessor.num_special_tokens_to_add(__UpperCAmelCase					)
                   snake_case__							:	Optional[int]   	=	compute_effective_axis_dimension(
                       __UpperCAmelCase, fixed_dimension=OnnxConfig.default_fixed_sequence, num_token_to_add=__UpperCAmelCase					)
                   # Generate dummy inputs according to compute batch and sequence
                   snake_case__							:	List[str]   	=	[' '.join(['a']					) * seq_length] * batch_size
                   snake_case__							:	List[Any]   	=	dict(preprocessor(__UpperCAmelCase, return_tensors=__UpperCAmelCase					)					)
                   snake_case__							:	Optional[int]   	=	inputs.pop('input_ids'					)
                   return inputs
            elif isinstance(__UpperCAmelCase, __UpperCAmelCase					) and preprocessor.model_input_names[0] == "pixel_values":
                   # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
                   snake_case__							:	Optional[int]   	=	compute_effective_axis_dimension(__UpperCAmelCase, fixed_dimension=OnnxConfig.default_fixed_batch					)
                   snake_case__							:	List[str]   	=	self._generate_dummy_images(__UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase					)
                   snake_case__							:	int   	=	dict(preprocessor(images=__UpperCAmelCase, return_tensors=__UpperCAmelCase					)					)
                   snake_case__							:	Optional[int]   	=	inputs.pop('pixel_values'					)
                   return inputs
            else:
                   raise ValueError(
                       'Unable to generate dummy inputs for the model. Please provide a tokenizer or a preprocessor.'					)







from __future__ import annotations





class      A							:


   def __init__(self     :						Union[str, Any]       ,  __UpperCAmelCase     :						list[list[int]]		)						->				List[str]:
         """simple docstring"""



         UpperCAmelCase__								=  TypeError(
             "Matrices must be formed from a list of zero or more lists containing at "
             "least one and the same number of values, each of which must be of type "
             "int or float."		)
         if len(__UpperCAmelCase		) != 0:
               UpperCAmelCase__								=  len(rows[0]		)
               if cols == 0:
                     raise error
               for row in rows:
                     if len(__UpperCAmelCase		) != cols:
                           raise error
                     for value in row:
                           if not isinstance(__UpperCAmelCase       ,  (int, float)		):
                                 raise error
               UpperCAmelCase__								=  rows
         else:
               UpperCAmelCase__								=  []


   def 			lowercase_      (self     :						Any		)						->				list[list[int]]:
         """simple docstring"""



         return [[row[i] for row in self.rows] for i in range(len(self.rows[0]		)		)]


   @property
   def 			lowercase_      (self     :						Any		)						->				int:
         """simple docstring"""



         return len(self.rows		)


   @property
   def 			lowercase_      (self     :						Union[str, Any]		)						->				int:
         """simple docstring"""



         return len(self.rows[0]		)


   @property
   def 			lowercase_      (self     :						List[Any]		)						->				tuple[int, int]:
         """simple docstring"""



         return (self.num_rows, self.num_columns)


   @property
   def 			lowercase_      (self     :						Tuple		)						->				bool:
         """simple docstring"""



         return self.order[0] == self.order[1]


   def 			lowercase_      (self     :						Any		)						->				Matrix:
         """simple docstring"""



         UpperCAmelCase__								=  [
             [0 if column_num != row_num else 1 for column_num in range(self.num_rows		)]
             for row_num in range(self.num_rows		)
         ]
         return Matrix(__UpperCAmelCase		)


   def 			lowercase_      (self     :						int		)						->				int:
         """simple docstring"""



         if not self.is_square:
               return 0
         if self.order == (0, 0):
               return 1
         if self.order == (1, 1):
               return int(self.rows[0][0]		)
         if self.order == (2, 2):
               return int(
                   (self.rows[0][0] * self.rows[1][1])
                   - (self.rows[0][1] * self.rows[1][0])		)
         else:
               return sum(
                   self.rows[0][column] * self.cofactors().rows[0][column]
                   for column in range(self.num_columns		)		)


   def 			lowercase_      (self     :						Tuple		)						->				bool:
         """simple docstring"""



         return bool(self.determinant()		)


   def 			lowercase_      (self     :						Dict       ,  __UpperCAmelCase     :						int       ,  __UpperCAmelCase     :						int		)						->				int:
         """simple docstring"""



         UpperCAmelCase__								=  [
             [
                 self.rows[other_row][other_column]
                 for other_column in range(self.num_columns		)
                 if other_column != column
             ]
             for other_row in range(self.num_rows		)
             if other_row != row
         ]
         return Matrix(__UpperCAmelCase		).determinant()


   def 			lowercase_      (self     :						int       ,  __UpperCAmelCase     :						int       ,  __UpperCAmelCase     :						int		)						->				int:
         """simple docstring"""



         if (row + column) % 2 == 0:
               return self.get_minor(__UpperCAmelCase       ,  __UpperCAmelCase		)
         return -1 * self.get_minor(__UpperCAmelCase       ,  __UpperCAmelCase		)


   def 			lowercase_      (self     :						Union[str, Any]		)						->				Matrix:
         """simple docstring"""



         return Matrix(
             [
                 [self.get_minor(__UpperCAmelCase       ,  __UpperCAmelCase		) for column in range(self.num_columns		)]
                 for row in range(self.num_rows		)
             ]		)


   def 			lowercase_      (self     :						List[str]		)						->				Matrix:
         """simple docstring"""



         return Matrix(
             [
                 [
                     self.minors().rows[row][column]
                     if (row + column) % 2 == 0
                     else self.minors().rows[row][column] * -1
                     for column in range(self.minors().num_columns		)
                 ]
                 for row in range(self.minors().num_rows		)
             ]		)


   def 			lowercase_      (self     :						Optional[Any]		)						->				Matrix:
         """simple docstring"""



         UpperCAmelCase__								=  [
             [self.cofactors().rows[column][row] for column in range(self.num_columns		)]
             for row in range(self.num_rows		)
         ]
         return Matrix(__UpperCAmelCase		)


   def 			lowercase_      (self     :						List[Any]		)						->				Matrix:
         """simple docstring"""



         UpperCAmelCase__								=  self.determinant()
         if not determinant:
               raise TypeError("Only matrices with a non-zero determinant have an inverse"		)
         return self.adjugate() * (1 / determinant)


   def __repr__(self     :						Dict		)						->				str:
         """simple docstring"""



         return str(self.rows		)


   def __str__(self     :						Optional[Any]		)						->				str:
         """simple docstring"""



         if self.num_rows == 0:
               return "[]"
         if self.num_rows == 1:
               return "[[" + ". ".join(str(self.rows[0]		)		) + "]]"
         return (
             "["
             + "\n ".join(
                 [
                     "[" + ". ".join([str(__UpperCAmelCase		) for value in row]		) + ".]"
                     for row in self.rows
                 ]		)
             + "]"
         )


   def 			lowercase_      (self     :						Optional[int]       ,  __UpperCAmelCase     :						list[int]       ,  __UpperCAmelCase     :						int | None = None		)						->				None:
         """simple docstring"""



         UpperCAmelCase__								=  TypeError("Row must be a list containing all ints and/or floats"		)
         if not isinstance(__UpperCAmelCase       ,  __UpperCAmelCase		):
               raise type_error
         for value in row:
               if not isinstance(__UpperCAmelCase       ,  (int, float)		):
                     raise type_error
         if len(__UpperCAmelCase		) != self.num_columns:
               raise ValueError(
                   "Row must be equal in length to the other rows in the matrix"		)
         if position is None:
               self.rows.append(__UpperCAmelCase		)
         else:
               UpperCAmelCase__								=  self.rows[0:position] + [row] + self.rows[position:]


   def 			lowercase_      (self     :						Union[str, Any]       ,  __UpperCAmelCase     :						list[int]       ,  __UpperCAmelCase     :						int | None = None		)						->				None:
         """simple docstring"""



         UpperCAmelCase__								=  TypeError(
             "Column must be a list containing all ints and/or floats"		)
         if not isinstance(__UpperCAmelCase       ,  __UpperCAmelCase		):
               raise type_error
         for value in column:
               if not isinstance(__UpperCAmelCase       ,  (int, float)		):
                     raise type_error
         if len(__UpperCAmelCase		) != self.num_rows:
               raise ValueError(
                   "Column must be equal in length to the other columns in the matrix"		)
         if position is None:
               UpperCAmelCase__								=  [self.rows[i] + [column[i]] for i in range(self.num_rows		)]
         else:
               UpperCAmelCase__								=  [
                   self.rows[i][0:position] + [column[i]] + self.rows[i][position:]
                   for i in range(self.num_rows		)
               ]


   def __eq__(self     :						Any       ,  __UpperCAmelCase     :						object		)						->				bool:
         """simple docstring"""



         if not isinstance(__UpperCAmelCase       ,  __UpperCAmelCase		):
               return NotImplemented
         return self.rows == other.rows


   def __ne__(self     :						int       ,  __UpperCAmelCase     :						object		)						->				bool:
         """simple docstring"""



         return not self == other


   def __neg__(self     :						Dict		)						->				Matrix:
         """simple docstring"""



         return self * -1


   def __add__(self     :						Dict       ,  __UpperCAmelCase     :						Matrix		)						->				Matrix:
         """simple docstring"""



         if self.order != other.order:
               raise ValueError("Addition requires matrices of the same order"		)
         return Matrix(
             [
                 [self.rows[i][j] + other.rows[i][j] for j in range(self.num_columns		)]
                 for i in range(self.num_rows		)
             ]		)


   def __sub__(self     :						Optional[Any]       ,  __UpperCAmelCase     :						Matrix		)						->				Matrix:
         """simple docstring"""



         if self.order != other.order:
               raise ValueError("Subtraction requires matrices of the same order"		)
         return Matrix(
             [
                 [self.rows[i][j] - other.rows[i][j] for j in range(self.num_columns		)]
                 for i in range(self.num_rows		)
             ]		)


   def __mul__(self     :						Tuple       ,  __UpperCAmelCase     :						Matrix | int | float		)						->				Matrix:
         """simple docstring"""



         if isinstance(__UpperCAmelCase       ,  (int, float)		):
               return Matrix(
                   [[int(element * other		) for element in row] for row in self.rows]		)
         elif isinstance(__UpperCAmelCase       ,  __UpperCAmelCase		):
               if self.num_columns != other.num_rows:
                     raise ValueError(
                         "The number of columns in the first matrix must "
                         "be equal to the number of rows in the second"		)
               return Matrix(
                   [
                       [Matrix.dot_product(__UpperCAmelCase       ,  __UpperCAmelCase		) for column in other.columns()]
                       for row in self.rows
                   ]		)
         else:
               raise TypeError(
                   "A Matrix can only be multiplied by an int, float, or another matrix"		)


   def __pow__(self     :						List[Any]       ,  __UpperCAmelCase     :						int		)						->				Matrix:
         """simple docstring"""



         if not isinstance(__UpperCAmelCase       ,  __UpperCAmelCase		):
               raise TypeError("A Matrix can only be raised to the power of an int"		)
         if not self.is_square:
               raise ValueError("Only square matrices can be raised to a power"		)
         if other == 0:
               return self.identity()
         if other < 0:
               if self.is_invertable():
                     return self.inverse() ** (-other)
               raise ValueError(
                   "Only invertable matrices can be raised to a negative power"		)
         UpperCAmelCase__								=  self
         for _ in range(other - 1		):
               result *= self
         return result





   @classmethod
   def 			lowercase_      (cls     :						Dict       ,  __UpperCAmelCase     :						list[int]       ,  __UpperCAmelCase     :						list[int]		)						->				int:
         """simple docstring"""



         return sum(row[i] * column[i] for i in range(len(__UpperCAmelCase		)		)		)


if __name__ == "__main__":
      import doctest

      doctest.testmod()



