import collections
import gzip
import os
import urllib

import numpy
from tensorflow.python.framework import dtypes, random_seed
from tensorflow.python.platform import gfile
from tensorflow.python.util.deprecation import deprecated

UpperCAmelCase_			    =							collections.namedtuple('''_Datasets''', ['''train''', '''validation''', '''test'''])

# CVDF mirror of http://yann.lecun.com/exdb/mnist/
UpperCAmelCase_			    =							'''https://storage.googleapis.com/cvdf-datasets/mnist/'''




def       lowerCAmelCase_     (lowercase:							int		)    ->				Optional[Any]:







 '''simple docstring'''
 _UpperCamelCase:     Dict							= numpy.dtype(numpy.uintaa		).newbyteorder('''>'''		)
 return numpy.frombuffer(bytestream.read(4		)  ,					dtype=lowercase		)[0]




@deprecated(lowercase  ,					'''Please use tf.data to implement this functionality.'''		)
def       lowerCAmelCase_     (lowercase:							Optional[Any]		)    ->				Optional[int]:







 '''simple docstring'''
 print('''Extracting'''  ,					f.name		)
 with gzip.GzipFile(fileobj=lowercase		) as bytestream:
  _UpperCamelCase:     List[str]							= _readaa(lowercase		)
  if magic != 2_051:
   raise ValueError(
       '''Invalid magic number %d in MNIST image file: %s''' % (magic, f.name)		)
  _UpperCamelCase:     str							= _readaa(lowercase		)
  _UpperCamelCase:     Dict							= _readaa(lowercase		)
  _UpperCamelCase:     List[Any]							= _readaa(lowercase		)
  _UpperCamelCase:     str							= bytestream.read(rows * cols * num_images		)
  _UpperCamelCase:     List[str]							= numpy.frombuffer(lowercase  ,					dtype=numpy.uinta		)
  _UpperCamelCase:     List[Any]							= data.reshape(lowercase  ,					lowercase  ,					lowercase  ,					1		)
  return data




@deprecated(lowercase  ,					'''Please use tf.one_hot on tensors.'''		)
def       lowerCAmelCase_     (lowercase:							List[Any]  ,					lowercase:							int		)    ->				Optional[int]:







 '''simple docstring'''
 _UpperCamelCase:     str							= labels_dense.shape[0]
 _UpperCamelCase:     Union[str, Any]							= numpy.arange(lowercase		) * num_classes
 _UpperCamelCase:     Optional[Any]							= numpy.zeros((num_labels, num_classes)		)
 _UpperCamelCase:     Union[str, Any]							= 1
 return labels_one_hot




@deprecated(lowercase  ,					'''Please use tf.data to implement this functionality.'''		)
def       lowerCAmelCase_     (lowercase:							Dict  ,					lowercase:							List[Any]=False  ,					lowercase:							Dict=10		)    ->				Optional[Any]:







 '''simple docstring'''
 print('''Extracting'''  ,					f.name		)
 with gzip.GzipFile(fileobj=lowercase		) as bytestream:
  _UpperCamelCase:     Dict							= _readaa(lowercase		)
  if magic != 2_049:
   raise ValueError(
       '''Invalid magic number %d in MNIST label file: %s''' % (magic, f.name)		)
  _UpperCamelCase:     Union[str, Any]							= _readaa(lowercase		)
  _UpperCamelCase:     Optional[Any]							= bytestream.read(lowercase		)
  _UpperCamelCase:     Optional[int]							= numpy.frombuffer(lowercase  ,					dtype=numpy.uinta		)
  if one_hot:
   return _dense_to_one_hot(lowercase  ,					lowercase		)
  return labels




class  __magic_name__  :





   """simple docstring"""




   @deprecated(
       _lowercase		,  '''Please use alternatives such as official/mnist/_DataSet.py'''
       ''' from tensorflow/models.'''		,  )
   def __init__(	self					:  Dict		,  _lowercase					:  List[str]		,  _lowercase					:  Optional[int]		,  _lowercase					:  Optional[Any]=False		,  _lowercase					:  str=False		,  _lowercase					:  Optional[int]=dtypes.floataa		,  _lowercase					:  Optional[int]=True		,  _lowercase					:  List[str]=None		,  ):



    """simple docstring"""



    _UpperCamelCase:     Tuple							= random_seed.get_seed(_lowercase							)
    # If op level seed is not set, use whatever graph level seed is returned
    numpy.random.seed(seeda if seed is None else seeda							)
    _UpperCamelCase:     Optional[Any]							= dtypes.as_dtype(_lowercase							).base_dtype
    if dtype not in (dtypes.uinta, dtypes.floataa):
     raise TypeError('''Invalid image dtype %r, expected uint8 or float32''' % dtype							)
    if fake_data:
     _UpperCamelCase:     List[Any]							= 10_000
     _UpperCamelCase:     List[str]							= one_hot
    else:
     assert (
         images.shape[0] == labels.shape[0]
     ), f"""images.shape: {images.shape} labels.shape: {labels.shape}"""
     _UpperCamelCase:     List[str]							= images.shape[0]

     # Convert shape from [num examples, rows, columns, depth]
     # to [num examples, rows*columns] (assuming depth == 1)
     if reshape:
      assert images.shape[3] == 1
      _UpperCamelCase:     Optional[Any]							= images.reshape(
          images.shape[0]		,  images.shape[1] * images.shape[2]							)
     if dtype == dtypes.floataa:
      # Convert from [0, 255] -> [0.0, 1.0].
      _UpperCamelCase:     Optional[Any]							= images.astype(numpy.floataa							)
      _UpperCamelCase:     str							= numpy.multiply(_lowercase		,  1.0 / 255.0							)
    _UpperCamelCase:     Dict							= images
    _UpperCamelCase:     List[Any]							= labels
    _UpperCamelCase:     int							= 0
    _UpperCamelCase:     str							= 0



   @property
   def   lowerCAmelCase						(	self					:  int							):



    """simple docstring"""



    return self._images



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



    """simple docstring"""



    return self._labels



   @property
   def   lowerCAmelCase						(	self					:  Any							):



    """simple docstring"""



    return self._num_examples



   @property
   def   lowerCAmelCase						(	self					:  Tuple							):



    """simple docstring"""



    return self._epochs_completed



   def   lowerCAmelCase						(	self					:  int		,  _lowercase					:  List[str]		,  _lowercase					:  int=False		,  _lowercase					:  Optional[int]=True							):



    """simple docstring"""



    if fake_data:
     _UpperCamelCase:     int							= [1] * 784
     _UpperCamelCase:     int							= [1] + [0] * 9 if self.one_hot else 0
     return (
         [fake_image for _ in range(_lowercase							)],
         [fake_label for _ in range(_lowercase							)],
     )
    _UpperCamelCase:     int							= self._index_in_epoch
    # Shuffle for the first epoch
    if self._epochs_completed == 0 and start == 0 and shuffle:
     _UpperCamelCase:     Optional[Any]							= numpy.arange(self._num_examples							)
     numpy.random.shuffle(_lowercase							)
     _UpperCamelCase:     Dict							= self.images[perma]
     _UpperCamelCase:     Tuple							= self.labels[perma]
    # Go to the next epoch
    if start + batch_size > self._num_examples:
     # Finished epoch
     self._epochs_completed += 1
     # Get the rest examples in this epoch
     _UpperCamelCase:     Any							= self._num_examples - start
     _UpperCamelCase:     str							= self._images[start : self._num_examples]
     _UpperCamelCase:     Dict							= self._labels[start : self._num_examples]
     # Shuffle the data
     if shuffle:
      _UpperCamelCase:     Optional[Any]							= numpy.arange(self._num_examples							)
      numpy.random.shuffle(_lowercase							)
      _UpperCamelCase:     Tuple							= self.images[perm]
      _UpperCamelCase:     Tuple							= self.labels[perm]
     # Start next epoch
     _UpperCamelCase:     Optional[int]							= 0
     _UpperCamelCase:     Optional[Any]							= batch_size - rest_num_examples
     _UpperCamelCase:     Any							= self._index_in_epoch
     _UpperCamelCase:     Optional[Any]							= self._images[start:end]
     _UpperCamelCase:     Tuple							= self._labels[start:end]
     return (
         numpy.concatenate((images_rest_part, images_new_part)		,  axis=0							),
         numpy.concatenate((labels_rest_part, labels_new_part)		,  axis=0							),
     )
    else:
     self._index_in_epoch += batch_size
     _UpperCamelCase:     Dict							= self._index_in_epoch
     return self._images[start:end], self._labels[start:end]




@deprecated(lowercase  ,					'''Please write your own downloading logic.'''		)
def       lowerCAmelCase_     (lowercase:							Tuple  ,					lowercase:							Dict  ,					lowercase:							Any		)    ->				int:







 '''simple docstring'''
 if not gfile.Exists(lowercase		):
  gfile.MakeDirs(lowercase		)
 _UpperCamelCase:     Any							= os.path.join(lowercase  ,					lowercase		)
 if not gfile.Exists(lowercase		):
  urllib.request.urlretrieve(lowercase  ,					lowercase		)  # noqa: S310
  with gfile.GFile(lowercase		) as f:
   _UpperCamelCase:     List[str]							= f.size()
  print('''Successfully downloaded'''  ,					lowercase  ,					lowercase  ,					'''bytes.'''		)
 return filepath





@deprecated(
    lowercase  ,					'''Please use alternatives such as:''' ''' tensorflow_datasets.load(\'mnist\')'''		)
def       lowerCAmelCase_     (lowercase:							Any  ,					lowercase:							int=False  ,					lowercase:							Dict=False  ,					lowercase:							int=dtypes.floataa  ,					lowercase:							List[Any]=True  ,					lowercase:							Tuple=5_000  ,					lowercase:							Optional[int]=None  ,					lowercase:							Tuple=DEFAULT_SOURCE_URL  ,					)    ->				int:







 '''simple docstring'''
 if fake_data:

  def fake():
   return _DataSet(
       []  ,					[]  ,					fake_data=lowercase  ,					one_hot=lowercase  ,					dtype=lowercase  ,					seed=lowercase		)

  _UpperCamelCase:     Any							= fake()
  _UpperCamelCase:     Dict							= fake()
  _UpperCamelCase:     Optional[int]							= fake()
  return _Datasets(train=lowercase  ,					validation=lowercase  ,					test=lowercase		)

 if not source_url:  # empty string check
  _UpperCamelCase:     str							= DEFAULT_SOURCE_URL

 _UpperCamelCase:     Optional[int]							= '''train-images-idx3-ubyte.gz'''
 _UpperCamelCase:     int							= '''train-labels-idx1-ubyte.gz'''
 _UpperCamelCase:     Optional[Any]							= '''t10k-images-idx3-ubyte.gz'''
 _UpperCamelCase:     List[Any]							= '''t10k-labels-idx1-ubyte.gz'''

 _UpperCamelCase:     str							= _maybe_download(
     lowercase  ,					lowercase  ,					source_url + train_images_file		)
 with gfile.Open(lowercase  ,					'''rb'''		) as f:
  _UpperCamelCase:     List[str]							= _extract_images(lowercase		)

 _UpperCamelCase:     Optional[int]							= _maybe_download(
     lowercase  ,					lowercase  ,					source_url + train_labels_file		)
 with gfile.Open(lowercase  ,					'''rb'''		) as f:
  _UpperCamelCase:     Union[str, Any]							= _extract_labels(lowercase  ,					one_hot=lowercase		)

 _UpperCamelCase:     Tuple							= _maybe_download(
     lowercase  ,					lowercase  ,					source_url + test_images_file		)
 with gfile.Open(lowercase  ,					'''rb'''		) as f:
  _UpperCamelCase:     Union[str, Any]							= _extract_images(lowercase		)

 _UpperCamelCase:     Dict							= _maybe_download(
     lowercase  ,					lowercase  ,					source_url + test_labels_file		)
 with gfile.Open(lowercase  ,					'''rb'''		) as f:
  _UpperCamelCase:     Optional[Any]							= _extract_labels(lowercase  ,					one_hot=lowercase		)

 if not 0 <= validation_size <= len(lowercase		):
  _UpperCamelCase:     Dict							= (
      '''Validation size should be between 0 and '''
      F"""{len(lowercase		)}. Received: {validation_size}."""
  )
  raise ValueError(lowercase		)

 _UpperCamelCase:     Optional[int]							= train_images[:validation_size]
 _UpperCamelCase:     Tuple							= train_labels[:validation_size]
 _UpperCamelCase:     Optional[int]							= train_images[validation_size:]
 _UpperCamelCase:     Union[str, Any]							= train_labels[validation_size:]

 _UpperCamelCase:     Dict							= {'''dtype''': dtype, '''reshape''': reshape, '''seed''': seed}

 _UpperCamelCase:     Optional[Any]							= _DataSet(lowercase  ,					lowercase  ,					**lowercase		)
 _UpperCamelCase:     Dict							= _DataSet(lowercase  ,					lowercase  ,					**lowercase		)
 _UpperCamelCase:     str							= _DataSet(lowercase  ,					lowercase  ,					**lowercase		)

 return _Datasets(train=lowercase  ,					validation=lowercase  ,					test=lowercase		)
from __future__ import annotations

UpperCAmelCase_			    =							1.6_0_2_1E-1_9  # units = C




def       lowerCAmelCase_     (		lowercase:							float  ,					lowercase:							float  ,					lowercase:							float  ,					)    ->				tuple[str, float]:







 '''simple docstring'''
 if (conductivity, electron_conc, mobility).count(0		) != 1:
  raise ValueError('''You cannot supply more or less than 2 values'''		)
 elif conductivity < 0:
  raise ValueError('''Conductivity cannot be negative'''		)
 elif electron_conc < 0:
  raise ValueError('''Electron concentration cannot be negative'''		)
 elif mobility < 0:
  raise ValueError('''mobility cannot be negative'''		)
 elif conductivity == 0:
  return (
      "conductivity",
      mobility * electron_conc * ELECTRON_CHARGE,
  )
 elif electron_conc == 0:
  return (
      "electron_conc",
      conductivity / (mobility * ELECTRON_CHARGE),
  )
 else:
  return (
      "mobility",
      conductivity / (electron_conc * ELECTRON_CHARGE),
  )


if __name__ == "__main__":
  import doctest

  doctest.testmod()