
"""simple docstring"""




from typing import List, Optional, Union

import numpy as np
import tensorflow as tf

from .utils import logging


A__   :					int							=   logging.get_logger(__name__)




def  _lowerCAmelCase		(		_UpperCamelCase						):






  """simple docstring"""



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

  _lowercase:			Optional[int]       =     tf.shape(_UpperCamelCase						)

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

  _lowercase:			List[Any]       =     tensor.shape.as_list()

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




def  _lowerCAmelCase		(		_UpperCamelCase ,				_UpperCamelCase = None ,				_UpperCamelCase = None						):






  """simple docstring"""



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




def  _lowerCAmelCase		(		_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
  _lowercase:			str       =     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
    _lowercase:			Dict       =     [1] * inputs.shape.rank
    _lowercase:			List[Any]       =     shape_list(_UpperCamelCase						)[axis]
    _lowercase:			Optional[Any]       =     tf.reshape(_UpperCamelCase ,				_UpperCamelCase						)
    _lowercase:			Dict       =     tf.reshape(_UpperCamelCase ,				_UpperCamelCase						)

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




def  _lowerCAmelCase		(		_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

  _lowercase:			Dict       =     tf.shape(_UpperCamelCase						)
  _lowercase:			List[Any]       =     tf.math.reduce_prod(in_shape[start_dim : end_dim + 1]						)
  _lowercase:			Optional[int]       =     tf.concat([in_shape[:start_dim], [flattened_dim], in_shape[end_dim + 1 :]] ,				axis=0						)
  return tf.reshape(_UpperCamelCase ,				_UpperCamelCase						)




def  _lowerCAmelCase		(		_UpperCamelCase						):






  """simple docstring"""



  if not isinstance(_UpperCamelCase ,				tf.Tensor						):
    _lowercase:			Tuple       =     tf.convert_to_tensor(_UpperCamelCase						)  # Catches stray NumPy inputs
  if encoder_attention_mask.shape.rank == 3:
    _lowercase:			Dict       =     encoder_attention_mask[:, None, :, :]
  if encoder_attention_mask.shape.rank == 2:
    _lowercase:			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))
  _lowercase:			int       =     (
      tf.cast(1 ,				encoder_attention_mask.dtype						) - encoder_extended_attention_mask
  ) * encoder_extended_attention_mask.dtype.min

  return encoder_extended_attention_mask




def  _lowerCAmelCase		(		_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  _lowerCAmelCase		(		_UpperCamelCase ,				_UpperCamelCase ,				_UpperCamelCase						):






  """simple docstring"""



  _lowercase:			Union[str, Any]       =     64_512
  # 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.
  _lowercase:			Optional[int]       =     [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}'''						)

  _lowercase:			Dict       =     np.asarray(_UpperCamelCase						)

  _lowercase:			Optional[Any]       =     1
  _lowercase:			str       =     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
    _lowercase:			Union[str, Any]       =     np.array_split(_UpperCamelCase ,				_UpperCamelCase						)

  if num_chunks > 1:
    for chunk_id, chunk_data in enumerate(_UpperCamelCase						):
      _lowercase:			List[Any]       =     chunk_data
  else:
    _lowercase:			List[str]       =     data




def  _lowerCAmelCase		(		_UpperCamelCase ,				_UpperCamelCase						):






  """simple docstring"""



  if name in group.attrs:
    _lowercase:			Tuple       =     [n.decode('''utf8'''						) if hasattr(_UpperCamelCase ,				'''decode'''						) else n for n in group.attrs[name]]
  else:
    _lowercase:			List[str]       =     []
    _lowercase:			Dict       =     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  _lowerCAmelCase		(		_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						)


"""simple docstring"""




import pandas as pd
from matplotlib import pyplot as plt
from sklearn.linear_model import LinearRegression

# Splitting the dataset into the Training set and Test set
from sklearn.model_selection import train_test_split

# Fitting Polynomial Regression to the dataset
from sklearn.preprocessing import PolynomialFeatures

# Importing the dataset
A__   :					Tuple							=   pd.read_csv(
    'https://s3.us-west-2.amazonaws.com/public.gamelab.fun/dataset/'
    'position_salaries.csv'
)
A__   :					Optional[int]							=   dataset.iloc[:, 1:2].values
A__   :					Union[str, Any]							=   dataset.iloc[:, 2].values


A__							, A__							, A__							, A__   :					Dict							=   train_test_split(X, y, test_size=0.2, random_state=0)


A__   :					Union[str, Any]							=   PolynomialFeatures(degree=4)
A__   :					List[Any]							=   poly_reg.fit_transform(X)
A__   :					Dict							=   LinearRegression()
pol_reg.fit(X_poly, y)




def  _lowerCAmelCase		(		):






  """simple docstring"""



  plt.scatter(_UpperCamelCase ,				_UpperCamelCase ,				color='''red'''						)
  plt.plot(_UpperCamelCase ,				pol_reg.predict(poly_reg.fit_transform(_UpperCamelCase						)						) ,				color='''blue'''						)
  plt.title('''Truth or Bluff (Linear Regression)'''						)
  plt.xlabel('''Position level'''						)
  plt.ylabel('''Salary'''						)
  plt.show()


if __name__ == "__main__":
  viz_polymonial()

  # Predicting a new result with Polymonial Regression
  pol_reg.predict(poly_reg.fit_transform([[5.5]]))
  # output should be 132148.43750003
