





"""simple docstring"""




from typing import List, Optional, Union

import numpy as np
import tensorflow as tf

from .utils import logging


lowerCamelCase_     :						List[Any]						=					logging.get_logger(__name__)





def 						UpperCAmelCase__		(     _UpperCAmelCase					):






  """simple docstring"""





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

  A_  :       Tuple										=  tf.shape(__UpperCamelCase					)

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

  A_  :       List[str]										=  tensor.shape.as_list()

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





def 						UpperCAmelCase__		(     _UpperCAmelCase ,					_UpperCAmelCase = None ,					_UpperCAmelCase = None					):






  """simple docstring"""





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





def 						UpperCAmelCase__		(     _UpperCAmelCase ,					_UpperCAmelCase ,					_UpperCAmelCase ,					_UpperCAmelCase=1E-5 ,					_UpperCAmelCase=-1					):






  """simple docstring"""





  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
  A_						,       A_  :       Tuple										=  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
    A_  :       Any										=  [1] * inputs.shape.rank
    A_  :       List[Any]										=  shape_list(__UpperCamelCase					)[axis]
    A_  :       Union[str, Any]										=  tf.reshape(__UpperCamelCase ,					__UpperCamelCase					)
    A_  :       Any										=  tf.reshape(__UpperCamelCase ,					__UpperCamelCase					)

  # Compute layer normalization using the batch_normalization
  # function.
  A_  :       Optional[int]										=  tf.nn.batch_normalization(
      __UpperCamelCase ,					__UpperCamelCase ,					__UpperCamelCase ,					offset=__UpperCamelCase ,					scale=__UpperCamelCase ,					variance_epsilon=__UpperCamelCase ,					)
  return outputs





def 						UpperCAmelCase__		(     _UpperCAmelCase ,					_UpperCAmelCase=0 ,					_UpperCAmelCase=-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_  :       Tuple										=  tf.shape(__UpperCamelCase					)
  A_  :       Any										=  tf.math.reduce_prod(in_shape[start_dim : end_dim + 1]					)
  A_  :       List[str]										=  tf.concat([in_shape[:start_dim], [flattened_dim], in_shape[end_dim + 1 :]] ,					axis=0					)
  return tf.reshape(__UpperCamelCase ,					__UpperCamelCase					)





def 						UpperCAmelCase__		(     _UpperCAmelCase					):






  """simple docstring"""





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

  return encoder_extended_attention_mask





def 						UpperCAmelCase__		(     _UpperCAmelCase ,					_UpperCAmelCase ,					_UpperCAmelCase = "input_ids"					):






  """simple docstring"""





  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 						UpperCAmelCase__		(     _UpperCAmelCase ,					_UpperCAmelCase ,					_UpperCAmelCase					):






  """simple docstring"""





  A_  :       Tuple										=  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.
  A_  :       Tuple										=  [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}"""					)

  A_  :       str										=  np.asarray(__UpperCamelCase					)

  A_  :       Union[str, Any]										=  1
  A_  :       Union[str, Any]										=  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
    A_  :       Union[str, Any]										=  np.array_split(__UpperCamelCase ,					__UpperCamelCase					)

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





def 						UpperCAmelCase__		(     _UpperCAmelCase ,					_UpperCAmelCase					):






  """simple docstring"""





  if name in group.attrs:
    A_  :       Union[str, Any]										=  [n.decode('utf8'					) if hasattr(__UpperCamelCase ,					'decode'					) else n for n in group.attrs[name]]
  else:
    A_  :       Optional[int]										=  []
    A_  :       List[str]										=  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 						UpperCAmelCase__		(     _UpperCAmelCase					):






  """simple docstring"""





  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					)
import numpy as np
import skfuzzy as fuzz

if __name__ == "__main__":
   # Create universe of discourse in Python using linspace ()
   __lowerCamelCase			: List[Any]							   =		np.linspace(start=0, stop=75, num=75, endpoint=True, retstep=False)

   # Create two fuzzy sets by defining any membership function
   # (trapmf(), gbellmf(), gaussmf(), etc).
   __lowerCamelCase			: int							   =		[0, 25, 50]
   __lowerCamelCase			: Tuple							   =		[25, 50, 75]
   __lowerCamelCase			: List[str]							   =		fuzz.membership.trimf(X, abca)
   __lowerCamelCase			: Tuple							   =		fuzz.membership.trimf(X, abca)

   # Compute the different operations using inbuilt functions.
   __lowerCamelCase			: List[str]							   =		np.ones(75)
   __lowerCamelCase			: Tuple							   =		np.zeros((75,))
   # 1. Union = max(µA(x), µB(x))
   __lowerCamelCase			: str							   =		fuzz.fuzzy_or(X, young, X, middle_aged)[1]
   # 2. Intersection = min(µA(x), µB(x))
   __lowerCamelCase			: int							   =		fuzz.fuzzy_and(X, young, X, middle_aged)[1]
   # 3. Complement (A) = (1- min(µA(x))
   __lowerCamelCase			: Union[str, Any]							   =		fuzz.fuzzy_not(young)
   # 4. Difference (A/B) = min(µA(x),(1- µB(x)))
   __lowerCamelCase			: Union[str, Any]							   =		fuzz.fuzzy_and(X, young, X, fuzz.fuzzy_not(middle_aged)[1])[1]
   # 5. Algebraic Sum = [µA(x) + µB(x) – (µA(x) * µB(x))]
   __lowerCamelCase			: List[Any]							   =		young + middle_aged - (young * middle_aged)
   # 6. Algebraic Product = (µA(x) * µB(x))
   __lowerCamelCase			: int							   =		young * middle_aged
   # 7. Bounded Sum = min[1,(µA(x), µB(x))]
   __lowerCamelCase			: int							   =		fuzz.fuzzy_and(X, one, X, young + middle_aged)[1]
   # 8. Bounded difference = min[0,(µA(x), µB(x))]
   __lowerCamelCase			: str							   =		fuzz.fuzzy_or(X, zero, X, young - middle_aged)[1]

   # max-min composition
   # max-product composition

   # Plot each set A, set B and each operation result using plot() and subplot().
   from matplotlib import pyplot as plt

   plt.figure()

   plt.subplot(4, 3, 1)
   plt.plot(X, young)
   plt.title('''Young''')
   plt.grid(True)

   plt.subplot(4, 3, 2)
   plt.plot(X, middle_aged)
   plt.title('''Middle aged''')
   plt.grid(True)

   plt.subplot(4, 3, 3)
   plt.plot(X, union)
   plt.title('''union''')
   plt.grid(True)

   plt.subplot(4, 3, 4)
   plt.plot(X, intersection)
   plt.title('''intersection''')
   plt.grid(True)

   plt.subplot(4, 3, 5)
   plt.plot(X, complement_a)
   plt.title('''complement_a''')
   plt.grid(True)

   plt.subplot(4, 3, 6)
   plt.plot(X, difference)
   plt.title('''difference a/b''')
   plt.grid(True)

   plt.subplot(4, 3, 7)
   plt.plot(X, alg_sum)
   plt.title('''alg_sum''')
   plt.grid(True)

   plt.subplot(4, 3, 8)
   plt.plot(X, alg_product)
   plt.title('''alg_product''')
   plt.grid(True)

   plt.subplot(4, 3, 9)
   plt.plot(X, bdd_sum)
   plt.title('''bdd_sum''')
   plt.grid(True)

   plt.subplot(4, 3, 10)
   plt.plot(X, bdd_difference)
   plt.title('''bdd_difference''')
   plt.grid(True)

   plt.subplots_adjust(hspace=0.5)
   plt.show()
