




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

lowerCamelCase	:	Dict				=   collections.namedtuple('_Datasets', ['train', 'validation', 'test'])

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




def 						SCREAMING_SNAKE_CASE__ (   lowercase					)  ->       Union[str, Any]:
   snake_case				:    str           =	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 						SCREAMING_SNAKE_CASE__ (   lowercase					)  ->       str:
   print("""Extracting"""						,f.name					)
   with gzip.GzipFile(fileobj=lowercase					) as bytestream:
      snake_case				:    Any           =	_readaa(lowercase					)
      if magic != 2051:
         raise ValueError(
             """Invalid magic number %d in MNIST image file: %s""" % (magic, f.name)					)
      snake_case				:    Optional[int]           =	_readaa(lowercase					)
      snake_case				:    Union[str, Any]           =	_readaa(lowercase					)
      snake_case				:    Dict           =	_readaa(lowercase					)
      snake_case				:    Dict           =	bytestream.read(rows * cols * num_images					)
      snake_case				:    str           =	numpy.frombuffer(lowercase						,dtype=numpy.uinta					)
      snake_case				:    str           =	data.reshape(lowercase						,lowercase						,lowercase						,1					)
      return data




@deprecated(lowercase						,"""Please use tf.one_hot on tensors."""					)
def 						SCREAMING_SNAKE_CASE__ (   lowercase						,lowercase					)  ->       Optional[int]:
   snake_case				:    int           =	labels_dense.shape[0]
   snake_case				:    List[Any]           =	numpy.arange(lowercase					) * num_classes
   snake_case				:    List[Any]           =	numpy.zeros((num_labels, num_classes)					)
   snake_case				:    Optional[Any]           =	1
   return labels_one_hot




@deprecated(lowercase						,"""Please use tf.data to implement this functionality."""					)
def 						SCREAMING_SNAKE_CASE__ (   lowercase						,lowercase=False						,lowercase=10					)  ->       Tuple:
   print("""Extracting"""						,f.name					)
   with gzip.GzipFile(fileobj=lowercase					) as bytestream:
      snake_case				:    List[Any]           =	_readaa(lowercase					)
      if magic != 2049:
         raise ValueError(
             """Invalid magic number %d in MNIST label file: %s""" % (magic, f.name)					)
      snake_case				:    Dict           =	_readaa(lowercase					)
      snake_case				:    Optional[int]           =	bytestream.read(lowercase					)
      snake_case				:    List[Any]           =	numpy.frombuffer(lowercase						,dtype=numpy.uinta					)
      if one_hot:
         return _dense_to_one_hot(lowercase						,lowercase					)
      return labels




class 						__lowercase		:




      """simple docstring"""





      @deprecated(
          A    ,				"""Please use alternatives such as official/mnist/_DataSet.py"""
          """ from tensorflow/models."""    ,				)
      def __init__(				self    ,				A    ,				A    ,				A=False    ,				A=False    ,				A=dtypes.floataa    ,				A=True    ,				A=None    ,				)		->    int:
         snake_case     , snake_case				:    Tuple           =	random_seed.get_seed(A     )
         # If op level seed is not set, use whatever graph level seed is returned
         numpy.random.seed(seeda if seed is None else seeda     )
         snake_case				:    Any           =	dtypes.as_dtype(A     ).base_dtype
         if dtype not in (dtypes.uinta, dtypes.floataa):
            raise TypeError("""Invalid image dtype %r, expected uint8 or float32""" % dtype     )
         if fake_data:
            snake_case				:    Union[str, Any]           =	1_0_0_0_0
            snake_case				:    int           =	one_hot
         else:
            assert (
                images.shape[0] == labels.shape[0]
            ), f"""images.shape: {images.shape} labels.shape: {labels.shape}"""
            snake_case				:    Optional[int]           =	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
               snake_case				:    List[Any]           =	images.reshape(
                   images.shape[0]    ,				images.shape[1] * images.shape[2]     )
            if dtype == dtypes.floataa:
               # Convert from [0, 255] -> [0.0, 1.0].
               snake_case				:    List[str]           =	images.astype(numpy.floataa     )
               snake_case				:    str           =	numpy.multiply(A    ,				1.0 / 2_55.0     )
         snake_case				:    int           =	images
         snake_case				:    Union[str, Any]           =	labels
         snake_case				:    Optional[int]           =	0
         snake_case				:    Optional[int]           =	0

      @property
      def      UpperCAmelCase				(				self     )		->    int:
         return self._images

      @property
      def      UpperCAmelCase				(				self     )		->    Optional[int]:
         return self._labels

      @property
      def      UpperCAmelCase				(				self     )		->    Optional[int]:
         return self._num_examples

      @property
      def      UpperCAmelCase				(				self     )		->    Tuple:
         return self._epochs_completed

      def      UpperCAmelCase				(				self    ,				A    ,				A=False    ,				A=True     )		->    Any:
         if fake_data:
            snake_case				:    int           =	[1] * 7_8_4
            snake_case				:    List[Any]           =	[1] + [0] * 9 if self.one_hot else 0
            return (
                [fake_image for _ in range(A     )],
                [fake_label for _ in range(A     )],
            )
         snake_case				:    List[Any]           =	self._index_in_epoch
         # Shuffle for the first epoch
         if self._epochs_completed == 0 and start == 0 and shuffle:
            snake_case				:    Optional[int]           =	numpy.arange(self._num_examples     )
            numpy.random.shuffle(A     )
            snake_case				:    List[str]           =	self.images[perma]
            snake_case				:    Union[str, Any]           =	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
            snake_case				:    int           =	self._num_examples - start
            snake_case				:    str           =	self._images[start : self._num_examples]
            snake_case				:    List[str]           =	self._labels[start : self._num_examples]
            # Shuffle the data
            if shuffle:
               snake_case				:    List[Any]           =	numpy.arange(self._num_examples     )
               numpy.random.shuffle(A     )
               snake_case				:    Optional[Any]           =	self.images[perm]
               snake_case				:    List[str]           =	self.labels[perm]
            # Start next epoch
            snake_case				:    List[Any]           =	0
            snake_case				:    Optional[int]           =	batch_size - rest_num_examples
            snake_case				:    List[str]           =	self._index_in_epoch
            snake_case				:    Tuple           =	self._images[start:end]
            snake_case				:    List[Any]           =	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
            snake_case				:    Dict           =	self._index_in_epoch
            return self._images[start:end], self._labels[start:end]




@deprecated(lowercase						,"""Please write your own downloading logic."""					)
def 						SCREAMING_SNAKE_CASE__ (   lowercase						,lowercase						,lowercase					)  ->       str:
   if not gfile.Exists(lowercase					):
      gfile.MakeDirs(lowercase					)
   snake_case				:    Union[str, Any]           =	os.path.join(lowercase						,lowercase					)
   if not gfile.Exists(lowercase					):
      urllib.request.urlretrieve(lowercase						,lowercase					)  # noqa: S310
      with gfile.GFile(lowercase					) as f:
         snake_case				:    Tuple           =	f.size()
      print("""Successfully downloaded"""						,lowercase						,lowercase						,"""bytes."""					)
   return filepath




@deprecated(
    lowercase						,"""Please use alternatives such as:""" """ tensorflow_datasets.load('mnist')"""					)
def 						SCREAMING_SNAKE_CASE__ (   lowercase						,lowercase=False						,lowercase=False						,lowercase=dtypes.floataa						,lowercase=True						,lowercase=5000						,lowercase=None						,lowercase=DEFAULT_SOURCE_URL						,)  ->       int:
   if fake_data:

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

      snake_case				:    Any           =	fake()
      snake_case				:    Union[str, Any]           =	fake()
      snake_case				:    Dict           =	fake()
      return _Datasets(train=lowercase						,validation=lowercase						,test=lowercase					)

   if not source_url:  # empty string check
      snake_case				:    Optional[int]           =	DEFAULT_SOURCE_URL

   snake_case				:    Union[str, Any]           =	"""train-images-idx3-ubyte.gz"""
   snake_case				:    Tuple           =	"""train-labels-idx1-ubyte.gz"""
   snake_case				:    List[Any]           =	"""t10k-images-idx3-ubyte.gz"""
   snake_case				:    Optional[Any]           =	"""t10k-labels-idx1-ubyte.gz"""

   snake_case				:    Dict           =	_maybe_download(
       lowercase						,lowercase						,source_url + train_images_file					)
   with gfile.Open(lowercase						,"""rb"""					) as f:
      snake_case				:    Tuple           =	_extract_images(lowercase					)

   snake_case				:    Dict           =	_maybe_download(
       lowercase						,lowercase						,source_url + train_labels_file					)
   with gfile.Open(lowercase						,"""rb"""					) as f:
      snake_case				:    Optional[int]           =	_extract_labels(lowercase						,one_hot=lowercase					)

   snake_case				:    int           =	_maybe_download(
       lowercase						,lowercase						,source_url + test_images_file					)
   with gfile.Open(lowercase						,"""rb"""					) as f:
      snake_case				:    int           =	_extract_images(lowercase					)

   snake_case				:    Optional[Any]           =	_maybe_download(
       lowercase						,lowercase						,source_url + test_labels_file					)
   with gfile.Open(lowercase						,"""rb"""					) as f:
      snake_case				:    Optional[int]           =	_extract_labels(lowercase						,one_hot=lowercase					)

   if not 0 <= validation_size <= len(lowercase					):
      snake_case				:    Any           =	(
          """Validation size should be between 0 and """
          f"""{len(lowercase					)}. Received: {validation_size}."""
      )
      raise ValueError(lowercase					)

   snake_case				:    str           =	train_images[:validation_size]
   snake_case				:    Dict           =	train_labels[:validation_size]
   snake_case				:    Optional[int]           =	train_images[validation_size:]
   snake_case				:    List[Any]           =	train_labels[validation_size:]

   snake_case				:    List[str]           =	{"""dtype""": dtype, """reshape""": reshape, """seed""": seed}

   snake_case				:    Dict           =	_DataSet(lowercase						,lowercase						,**lowercase					)
   snake_case				:    Dict           =	_DataSet(lowercase						,lowercase						,**lowercase					)
   snake_case				:    Dict           =	_DataSet(lowercase						,lowercase						,**lowercase					)

   return _Datasets(train=lowercase						,validation=lowercase						,test=lowercase					)











import functools




def 						SCREAMING_SNAKE_CASE__ (   lowercase						,lowercase					)  ->       int:

   # Validation
   if not isinstance(lowercase						,lowercase					) or not all(isinstance(lowercase						,lowercase					) for day in days					):
      raise ValueError("""The parameter days should be a list of integers"""					)

   if len(lowercase					) != 3 or not all(isinstance(lowercase						,lowercase					) for cost in costs					):
      raise ValueError("""The parameter costs should be a list of three integers"""					)

   if len(lowercase					) == 0:
      return 0

   if min(lowercase					) <= 0:
      raise ValueError("""All days elements should be greater than 0"""					)

   if max(lowercase					) >= 366:
      raise ValueError("""All days elements should be less than 366"""					)

   snake_case				:    List[str]           =	set(lowercase					)

   @functools.cache
   def dynamic_programming(lowercase					) -> int:
      if index > 365:
         return 0

      if index not in days_set:
         return dynamic_programming(index + 1					)

      return min(
          costs[0] + dynamic_programming(index + 1					)						,costs[1] + dynamic_programming(index + 7					)						,costs[2] + dynamic_programming(index + 30					)						,)

   return dynamic_programming(1					)


if __name__ == "__main__":
       import doctest

       doctest.testmod()





