






"""simple docstring"""


from typing import List, Optional, Union

import numpy as np
import tensorflow as tf

from .utils import logging


_UpperCamelCase    :   List[str]   			=						logging.get_logger(__name__)




def       snake_case  (A_				:Union[tf.Tensor, np.ndarray]				):







  '''simple docstring'''

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

  a					:      int    =       tf.shape(A_				)

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

  a					:      Optional[Any]    =       tensor.shape.as_list()

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




def       snake_case  (A_				:tf.Tensor     ,      A_				:Optional[int] = None     ,      A_				:Optional[str] = None				):







  '''simple docstring'''

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




def       snake_case  (A_				:Optional[Any]     ,      A_				:List[Any]     ,      A_				:int     ,      A_				:Dict=1E-5     ,      A_				:Union[str, Any]=-1				):







  '''simple docstring'''

  if weight.shape.rank != 1 or bias.shape.rank != 1 or not isinstance(A_     ,      A_				):
    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
  a					:      List[str]    =       tf.nn.moments(A_     ,      axes=[axis]     ,      keepdims=A_				)

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

  # Compute layer normalization using the batch_normalization
  # function.
  a					:      Union[str, Any]    =       tf.nn.batch_normalization(
      A_     ,      A_     ,      A_     ,      offset=A_     ,      scale=A_     ,      variance_epsilon=A_     ,      )
  return outputs




def       snake_case  (A_				:Tuple     ,      A_				:List[str]=0     ,      A_				:str=-1				):







  '''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

  a					:      str    =       tf.shape(A_				)
  a					:      List[str]    =       tf.math.reduce_prod(in_shape[start_dim : end_dim + 1]				)
  a					:      List[Any]    =       tf.concat([in_shape[:start_dim], [flattened_dim], in_shape[end_dim + 1 :]]     ,      axis=0				)
  return tf.reshape(A_     ,      A_				)




def       snake_case  (A_				:tf.Tensor				):







  '''simple docstring'''

  if not isinstance(A_     ,      tf.Tensor				):
    a					:      List[Any]    =       tf.convert_to_tensor(A_				)  # Catches stray NumPy inputs
  if encoder_attention_mask.shape.rank == 3:
    a					:      List[str]    =       encoder_attention_mask[:, None, :, :]
  if encoder_attention_mask.shape.rank == 2:
    a					:      Optional[int]    =       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))
  a					:      Union[str, Any]    =       (
      tf.cast(1     ,      encoder_attention_mask.dtype				) - encoder_extended_attention_mask
  ) * encoder_extended_attention_mask.dtype.min

  return encoder_extended_attention_mask




def       snake_case  (A_				:tf.Tensor     ,      A_				:int     ,      A_				:str = "input_ids"				):







  '''simple docstring'''

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




def       snake_case  (A_				:Tuple     ,      A_				:Optional[Any]     ,      A_				:Union[str, Any]				):







  '''simple docstring'''

  a					:      Union[str, Any]    =       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.
  a					:      Union[str, Any]    =       [x for x in data if len(A_				) > 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}'''				)

  a					:      Optional[int]    =       np.asarray(A_				)

  a					:      Any    =       1
  a					:      Dict    =       np.array_split(A_     ,      A_				)

  # 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
    a					:      str    =       np.array_split(A_     ,      A_				)

  if num_chunks > 1:
    for chunk_id, chunk_data in enumerate(A_				):
      a					:      Dict    =       chunk_data
  else:
    a					:      Optional[Any]    =       data




def       snake_case  (A_				:Optional[Any]     ,      A_				:Dict				):







  '''simple docstring'''

  if name in group.attrs:
    a					:      Union[str, Any]    =       [n.decode('utf8'				) if hasattr(A_     ,      'decode'				) else n for n in group.attrs[name]]
  else:
    a					:      Optional[Any]    =       []
    a					:      int    =       0
    while "%s%d" % (name, chunk_id) in group.attrs:
      data.extend(
          [n.decode('utf8'				) if hasattr(A_     ,      'decode'				) else n for n in group.attrs['%s%d' % (name, chunk_id)]]				)
      chunk_id += 1
  return data






def       snake_case  (A_				:List[str]				):







  '''simple docstring'''

  def _expand_single_ad_tensor(A_				:List[Any]				):
    if isinstance(A_     ,      tf.Tensor				) and t.shape.rank == 1:
      return tf.expand_dims(A_     ,      axis=-1				)
    return t

  return tf.nest.map_structure(_expand_single_ad_tensor     ,      A_				)












"""simple docstring"""


from __future__ import annotations

import math

import numpy as np
from numpy.linalg import norm




def       snake_case  (A_				:np.ndarray     ,      A_				:np.ndarray				):







  '''simple docstring'''

  return math.sqrt(sum(pow(a - b     ,      2				) for a, b in zip(A_     ,      A_				)				)				)




def       snake_case  (A_				:np.ndarray     ,      A_				:np.ndarray				):







  '''simple docstring'''

  if dataset.ndim != value_array.ndim:
    a					:      Optional[Any]    =       (
        'Wrong input data\'s dimensions... '
        f'''dataset : {dataset.ndim}, value_array : {value_array.ndim}'''
    )
    raise ValueError(A_				)

  try:
    if dataset.shape[1] != value_array.shape[1]:
      a					:      Optional[int]    =       (
          'Wrong input data\'s shape... '
          f'''dataset : {dataset.shape[1]}, value_array : {value_array.shape[1]}'''
      )
      raise ValueError(A_				)
  except IndexError:
    if dataset.ndim != value_array.ndim:
      raise TypeError('Wrong shape'				)

  if dataset.dtype != value_array.dtype:
    a					:      Optional[Any]    =       (
        'Input data have different datatype... '
        f'''dataset : {dataset.dtype}, value_array : {value_array.dtype}'''
    )
    raise TypeError(A_				)

  a					:      Tuple    =       []

  for value in value_array:
    a					:      List[Any]    =       euclidean(A_     ,      dataset[0]				)
    a					:      int    =       dataset[0].tolist()

    for dataset_value in dataset[1:]:
      a					:      Optional[int]    =       euclidean(A_     ,      A_				)

      if dist > temp_dist:
        a					:      List[str]    =       temp_dist
        a					:      int    =       dataset_value.tolist()

    answer.append([vector, dist]				)

  return answer




def       snake_case  (A_				:np.ndarray     ,      A_				:np.ndarray				):







  '''simple docstring'''

  return np.dot(A_     ,      A_				) / (norm(A_				) * norm(A_				))


if __name__ == "__main__":
   import doctest

   doctest.testmod()




