

"""simple docstring"""






from typing import List, Optional, Union

import numpy as np
import tensorflow as tf

from .utils import logging


__lowerCamelCase										=		logging.get_logger(__name__)





def 	lowercase      (   __UpperCamelCase						) ->      List[int]:
 if isinstance(__UpperCamelCase      ,   np.ndarray						):
  return list(tensor.shape						)

 __magic_name__     =      tf.shape(__UpperCamelCase						)

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

 __magic_name__     =      tensor.shape.as_list()

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





def 	lowercase      (   __UpperCamelCase      ,   __UpperCamelCase = None      ,   __UpperCamelCase = None						) ->      tf.Tensor:
 return tf.nn.softmax(logits=logits + 1e-9      ,   axis=__UpperCamelCase      ,   name=__UpperCamelCase						)





def 	lowercase      (   __UpperCamelCase      ,   __UpperCamelCase      ,   __UpperCamelCase      ,   __UpperCamelCase=1e-5      ,   __UpperCamelCase=-1						) ->      List[str]:
 # This is a very simplified functional layernorm, designed to duplicate
 # the functionality of PyTorch nn.functional.layer_norm when this is needed to port
 # models in Transformers.

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

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

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





def 	lowercase      (   __UpperCamelCase      ,   __UpperCamelCase=0      ,   __UpperCamelCase=-1						) ->      List[str]:
 # Replicates the behavior of torch.flatten in TF

 # If end_dim or start_dim is negative, count them from the end
 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

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





def 	lowercase      (   __UpperCamelCase						) ->      tf.Tensor:
 if not isinstance(__UpperCamelCase      ,   tf.Tensor						):
  __magic_name__     =      tf.convert_to_tensor(__UpperCamelCase						)  # Catches stray NumPy inputs
 if encoder_attention_mask.shape.rank == 3:
  __magic_name__     =      encoder_attention_mask[:, None, :, :]
 if encoder_attention_mask.shape.rank == 2:
  __magic_name__     =      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))
 __magic_name__     =      (
     tf.cast(1      ,   encoder_attention_mask.dtype						) - encoder_extended_attention_mask
 ) * encoder_extended_attention_mask.dtype.min

 return encoder_extended_attention_mask





def 	lowercase      (   __UpperCamelCase      ,   __UpperCamelCase      ,   __UpperCamelCase = "input_ids"						) ->      None:
 tf.debugging.assert_less(
     __UpperCamelCase      ,   tf.cast(__UpperCamelCase      ,   dtype=tensor.dtype						)      ,   message=(
         f'''The maximum value of {tensor_name} ({tf.math.reduce_max(__UpperCamelCase						)}) must be smaller than the embedding '''
         f'''layer\'s input dimension ({embed_dim}). The likely cause is some problem at tokenization time.'''
     )      ,   )





def 	lowercase      (   __UpperCamelCase      ,   __UpperCamelCase      ,   __UpperCamelCase						) ->      List[Any]:
 __magic_name__     =      64512
 # 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.
 __magic_name__     =      [x for x in data if len(__UpperCamelCase						) > 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}'''						)

 __magic_name__     =      np.asarray(__UpperCamelCase						)

 __magic_name__     =      1
 __magic_name__     =      np.array_split(__UpperCamelCase      ,   __UpperCamelCase						)

 # 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
  __magic_name__     =      np.array_split(__UpperCamelCase      ,   __UpperCamelCase						)

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





def 	lowercase      (   __UpperCamelCase      ,   __UpperCamelCase						) ->      Tuple:
 if name in group.attrs:
  __magic_name__     =      [n.decode('''utf8'''						) if hasattr(__UpperCamelCase      ,   '''decode'''						) else n for n in group.attrs[name]]
 else:
  __magic_name__     =      []
  __magic_name__     =      0
  while "%s%d" % (name, chunk_id) in group.attrs:
   data.extend(
       [n.decode('''utf8'''						) if hasattr(__UpperCamelCase      ,   '''decode'''						) else n for n in group.attrs['''%s%d''' % (name, chunk_id)]]						)
   chunk_id += 1
 return data





def 	lowercase      (   __UpperCamelCase						) ->      Optional[int]:

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

 return tf.nest.map_structure(_expand_single_ad_tensor      ,   __UpperCamelCase						)




"""simple docstring"""






from __future__ import annotations

from random import choice





def 	lowercase      (   __UpperCamelCase						) ->      Any:
 return choice(__UpperCamelCase						)





def 	lowercase      (   __UpperCamelCase      ,   __UpperCamelCase						) ->      int:
 __magic_name__     =      random_pivot(__UpperCamelCase						)

 # partition based on pivot
 # linear time
 __magic_name__     =      [e for e in lst if e < pivot]
 __magic_name__     =      [e for e in lst if e > pivot]

 # if we get lucky, pivot might be the element we want.
 # we can easily see this:
 # small (elements smaller than k)
 # + pivot (kth element)
 # + big (elements larger than k)
 if len(__UpperCamelCase						) == k - 1:
  return pivot
 # pivot is in elements bigger than k
 elif len(__UpperCamelCase						) < k - 1:
  return kth_number(__UpperCamelCase      ,   k - len(__UpperCamelCase						) - 1						)
 # pivot is in elements smaller than k
 else:
  return kth_number(__UpperCamelCase      ,   __UpperCamelCase						)


if __name__ == "__main__":
 import doctest

 doctest.testmod()

