


from typing import List, Optional, Union

import numpy as np
import tensorflow as tf

from .utils import logging


A      = logging.get_logger(__name__)





def __UpperCAmelCase       (		__A			)						->					List[int]:



       '''simple docstring'''





       if isinstance(lowerCamelCase__ ,						np.ndarray			):
              return list(tensor.shape			)

       UpperCAmelCase__										=     tf.shape(lowerCamelCase__			)

       if tensor.shape == tf.TensorShape(lowerCamelCase__			):
              return dynamic

       UpperCAmelCase__										=     tensor.shape.as_list()

       return [dynamic[i] if s is None else s for i, s in enumerate(lowerCamelCase__			)]





def __UpperCAmelCase       (		__A ,						__A = None ,						__A = None			)						->					tf.Tensor:



       '''simple docstring'''





       return tf.nn.softmax(logits=logits + 1E-9 ,						axis=lowerCamelCase__ ,						name=lowerCamelCase__			)





def __UpperCAmelCase       (		__A ,						__A ,						__A ,						__A=1E-5 ,						__A=-1			)						->					str:



       '''simple docstring'''





       if weight.shape.rank != 1 or bias.shape.rank != 1 or not isinstance(lowerCamelCase__ ,						lowerCamelCase__			):
              raise NotImplementedError("Only 1D weight and bias tensors are supported for now, with only a single axis."			)

       # Get mean and variance on the axis to be normalized
       UpperCAmelCase__										=     tf.nn.moments(lowerCamelCase__ ,						axes=[axis] ,						keepdims=lowerCamelCase__			)

       if axis != -1:
              # Reshape scale and weight to have the same rank as inputs, but with 1 dimensions
              # on every dimension except axis
              UpperCAmelCase__										=     [1] * inputs.shape.rank
              UpperCAmelCase__										=     shape_list(lowerCamelCase__			)[axis]
              UpperCAmelCase__										=     tf.reshape(lowerCamelCase__ ,						lowerCamelCase__			)
              UpperCAmelCase__										=     tf.reshape(lowerCamelCase__ ,						lowerCamelCase__			)

       # Compute layer normalization using the batch_normalization
       # function.
       UpperCAmelCase__										=     tf.nn.batch_normalization(
           lowerCamelCase__ ,						lowerCamelCase__ ,						lowerCamelCase__ ,						offset=lowerCamelCase__ ,						scale=lowerCamelCase__ ,						variance_epsilon=lowerCamelCase__ ,						)
       return outputs





def __UpperCAmelCase       (		__A ,						__A=0 ,						__A=-1			)						->					str:



       '''simple docstring'''





       if end_dim < 0:
              end_dim += input.shape.rank
       if start_dim < 0:
              start_dim += input.shape.rank

       if start_dim == end_dim:
              return input

       UpperCAmelCase__										=     tf.shape(lowerCamelCase__			)
       UpperCAmelCase__										=     tf.math.reduce_prod(in_shape[start_dim : end_dim + 1]			)
       UpperCAmelCase__										=     tf.concat([in_shape[:start_dim], [flattened_dim], in_shape[end_dim + 1 :]] ,						axis=0			)
       return tf.reshape(lowerCamelCase__ ,						lowerCamelCase__			)





def __UpperCAmelCase       (		__A			)						->					tf.Tensor:



       '''simple docstring'''





       if not isinstance(lowerCamelCase__ ,						tf.Tensor			):
              UpperCAmelCase__										=     tf.convert_to_tensor(lowerCamelCase__			)  # Catches stray NumPy inputs
       if encoder_attention_mask.shape.rank == 3:
              UpperCAmelCase__										=     encoder_attention_mask[:, None, :, :]
       if encoder_attention_mask.shape.rank == 2:
              UpperCAmelCase__										=     encoder_attention_mask[:, None, None, :]
       # T5 has a mask that can compare sequence ids, we can simulate this here with this transposition
       # Cf. https://github.com/tensorflow/mesh/blob/8d2465e9bc93129b913b5ccc6a59aa97abd96ec6/mesh_tensorflow
       # /transformer/transformer_layers.py#L270
       # encoder_extended_attention_mask = (encoder_extended_attention_mask ==
       # encoder_extended_attention_mask.transpose(-1, -2))
       UpperCAmelCase__										=     (
           tf.cast(1 ,						encoder_attention_mask.dtype			) - encoder_extended_attention_mask
       ) * encoder_extended_attention_mask.dtype.min

       return encoder_extended_attention_mask





def __UpperCAmelCase       (		__A ,						__A ,						__A = "input_ids"			)						->					None:



       '''simple docstring'''





       tf.debugging.assert_less(
           lowerCamelCase__ ,						tf.cast(lowerCamelCase__ ,						dtype=tensor.dtype			) ,						message=(
               F"""The maximum value of {tensor_name} ({tf.math.reduce_max(lowerCamelCase__			)}) must be smaller than the embedding """
               F"""layer's input dimension ({embed_dim}). The likely cause is some problem at tokenization time."""
           ) ,						)





def __UpperCAmelCase       (		__A ,						__A ,						__A			)						->					str:



       '''simple docstring'''





       UpperCAmelCase__										=     6_4_5_1_2
       # Check that no item in `data` is larger than `HDF5_OBJECT_HEADER_LIMIT`
       # because in that case even chunking the array would not make the saving
       # possible.
       UpperCAmelCase__										=     [x for x in data if len(lowerCamelCase__			) > HDF5_OBJECT_HEADER_LIMIT]

       # Expecting this to never be true.
       if bad_attributes:
              raise RuntimeError(
                  "The following attributes cannot be saved to HDF5 file because "
                  F"""they are larger than {HDF5_OBJECT_HEADER_LIMIT} """
                  F"""bytes: {bad_attributes}"""			)

       UpperCAmelCase__										=     np.asarray(lowerCamelCase__			)

       UpperCAmelCase__										=     1
       UpperCAmelCase__										=     np.array_split(lowerCamelCase__ ,						lowerCamelCase__			)

       # This will never loop forever thanks to the test above.
       while any(x.nbytes > HDF5_OBJECT_HEADER_LIMIT for x in chunked_data			):
              num_chunks += 1
              UpperCAmelCase__										=     np.array_split(lowerCamelCase__ ,						lowerCamelCase__			)

       if num_chunks > 1:
              for chunk_id, chunk_data in enumerate(lowerCamelCase__			):
                     UpperCAmelCase__										=     chunk_data
       else:
              UpperCAmelCase__										=     data





def __UpperCAmelCase       (		__A ,						__A			)						->					Union[str, Any]:



       '''simple docstring'''





       if name in group.attrs:
              UpperCAmelCase__										=     [n.decode("utf8"			) if hasattr(lowerCamelCase__ ,						"decode"			) else n for n in group.attrs[name]]
       else:
              UpperCAmelCase__										=     []
              UpperCAmelCase__										=     0
              while "%s%d" % (name, chunk_id) in group.attrs:
                     data.extend(
                         [n.decode("utf8"			) if hasattr(lowerCamelCase__ ,						"decode"			) else n for n in group.attrs["%s%d" % (name, chunk_id)]]			)
                     chunk_id += 1
       return data





def __UpperCAmelCase       (		__A			)						->					List[Any]:



       '''simple docstring'''





       def _expand_single_ad_tensor(__A			):
              if isinstance(lowerCamelCase__ ,						tf.Tensor			) and t.shape.rank == 1:
                     return tf.expand_dims(lowerCamelCase__ ,						axis=-1			)
              return t

       return tf.nest.map_structure(_expand_single_ad_tensor ,						lowerCamelCase__			)









from __future__ import annotations


class     lowercase__  :






  def __init__(					self      :					int		,						_lowercase      :					list[list[int]] ):




         """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(_lowercase ) != 0:
                UpperCAmelCase__										=     len(rows[0] )
                if cols == 0:
                       raise error
                for row in rows:
                       if len(_lowercase ) != cols:
                              raise error
                       for value in row:
                              if not isinstance(_lowercase		,						(int, float) ):
                                     raise error
                UpperCAmelCase__										=     rows
         else:
                UpperCAmelCase__										=     []






  def 			_UpperCAmelCase  (					self      :					Dict ):




         """simple docstring"""







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






  @property
  def 			_UpperCAmelCase  (					self      :					str ):




         """simple docstring"""







         return len(self.rows )






  @property
  def 			_UpperCAmelCase  (					self      :					Optional[Any] ):




         """simple docstring"""







         return len(self.rows[0] )






  @property
  def 			_UpperCAmelCase  (					self      :					Optional[Any] ):




         """simple docstring"""







         return (self.num_rows, self.num_columns)






  @property
  def 			_UpperCAmelCase  (					self      :					Tuple ):




         """simple docstring"""







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






  def 			_UpperCAmelCase  (					self      :					Optional[Any] ):




         """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(_lowercase )






  def 			_UpperCAmelCase  (					self      :					Tuple ):




         """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 			_UpperCAmelCase  (					self      :					Any ):




         """simple docstring"""







         return bool(self.determinant() )






  def 			_UpperCAmelCase  (					self      :					Optional[Any]		,						_lowercase      :					int		,						_lowercase      :					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(_lowercase ).determinant()






  def 			_UpperCAmelCase  (					self      :					str		,						_lowercase      :					int		,						_lowercase      :					int ):




         """simple docstring"""







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






  def 			_UpperCAmelCase  (					self      :					Optional[Any] ):




         """simple docstring"""







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






  def 			_UpperCAmelCase  (					self      :					List[str] ):




         """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 			_UpperCAmelCase  (					self      :					str ):




         """simple docstring"""







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






  def 			_UpperCAmelCase  (					self      :					List[str] ):




         """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 ):




         """simple docstring"""







         return str(self.rows )






  def __str__(					self      :					Tuple ):




         """simple docstring"""







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






  def 			_UpperCAmelCase  (					self      :					Any		,						_lowercase      :					list[int]		,						_lowercase      :					int | None = None ):




         """simple docstring"""







         UpperCAmelCase__										=     TypeError("Row must be a list containing all ints and/or floats" )
         if not isinstance(_lowercase		,						_lowercase ):
                raise type_error
         for value in row:
                if not isinstance(_lowercase		,						(int, float) ):
                       raise type_error
         if len(_lowercase ) != 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(_lowercase )
         else:
                UpperCAmelCase__										=     self.rows[0:position] + [row] + self.rows[position:]






  def 			_UpperCAmelCase  (					self      :					Optional[Any]		,						_lowercase      :					list[int]		,						_lowercase      :					int | None = None ):




         """simple docstring"""







         UpperCAmelCase__										=     TypeError(
             "Column must be a list containing all ints and/or floats" )
         if not isinstance(_lowercase		,						_lowercase ):
                raise type_error
         for value in column:
                if not isinstance(_lowercase		,						(int, float) ):
                       raise type_error
         if len(_lowercase ) != 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      :					Dict		,						_lowercase      :					object ):




         """simple docstring"""







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






  def __ne__(					self      :					List[Any]		,						_lowercase      :					object ):




         """simple docstring"""







         return not self == other






  def __neg__(					self      :					int ):




         """simple docstring"""







         return self * -1






  def __add__(					self      :					List[Any]		,						_lowercase      :					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[int]		,						_lowercase      :					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      :					int		,						_lowercase      :					Matrix | int | float ):




         """simple docstring"""







         if isinstance(_lowercase		,						(int, float) ):
                return Matrix(
                    [[int(element * other ) for element in row] for row in self.rows] )
         elif isinstance(_lowercase		,						_lowercase ):
                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(_lowercase		,						_lowercase ) 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      :					Optional[Any]		,						_lowercase      :					int ):




         """simple docstring"""







         if not isinstance(_lowercase		,						_lowercase ):
                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 			_UpperCAmelCase  (					cls      :					List[Any]		,						_lowercase      :					list[int]		,						_lowercase      :					list[int] ):




         """simple docstring"""







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


if __name__ == "__main__":
    import doctest

    doctest.testmod()





