
"""simple docstring"""


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

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

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






def    _snake_case					(		snake_case__ :  Optional[int]  ):
	A     = numpy.dtype(numpy.uintaa  ).newbyteorder('>'  )
	return numpy.frombuffer(bytestream.read(4  )						,   dtype=snake_case__  )[0]






@deprecated(snake_case__						,   'Please use tf.data to implement this functionality.'  )
def    _snake_case					(		snake_case__ :  Dict  ):
	print('Extracting'						,   f.name  )
	with gzip.GzipFile(fileobj=snake_case__  ) as bytestream:
		A     = _readaa(snake_case__  )
		if magic != 2051:
			raise ValueError(
			    'Invalid magic number %d in MNIST image file: %s' % (magic, f.name)  )
		A     = _readaa(snake_case__  )
		A     = _readaa(snake_case__  )
		A     = _readaa(snake_case__  )
		A     = bytestream.read(rows * cols * num_images  )
		A     = numpy.frombuffer(snake_case__						,   dtype=numpy.uinta  )
		A     = data.reshape(snake_case__						,   snake_case__						,   snake_case__						,   1  )
		return data






@deprecated(snake_case__						,   'Please use tf.one_hot on tensors.'  )
def    _snake_case					(		snake_case__ :  Tuple						,   snake_case__ :  str  ):
	A     = labels_dense.shape[0]
	A     = numpy.arange(snake_case__  ) * num_classes
	A     = numpy.zeros((num_labels, num_classes)  )
	A     = 1
	return labels_one_hot






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







class 					lowerCAmelCase_					:



						'''simple docstring'''


						@deprecated(
						    A_	,'Please use alternatives such as official/mnist/_DataSet.py'
						    ' from tensorflow/models.'	,)
						def __init__(			self  :   Optional[int]	,A_  :   List[str]	,A_  :   Any	,A_  :   Dict=False	,A_  :   Optional[int]=False	,A_  :   List[str]=dtypes.floataa	,A_  :   Optional[Any]=True	,A_  :   Union[str, Any]=None	,)  -> Optional[Any]:
							A ,   A     = 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   )
							A     = 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:
								A     = 1_0000
								A     = one_hot
							else:
								assert (
								    images.shape[0] == labels.shape[0]
								), F'images.shape: {images.shape} labels.shape: {labels.shape}'
								A     = 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
									A     = images.reshape(
									    images.shape[0]	,images.shape[1] * images.shape[2]   )
								if dtype == dtypes.floataa:
									# Convert from [0, 255] -> [0.0, 1.0].
									A     = images.astype(numpy.floataa   )
									A     = numpy.multiply(A_	,1.0 / 2_55.0   )
							A     = images
							A     = labels
							A     = 0
							A     = 0


						@property
						def     _SCREAMING_SNAKE_CASE   (			self  :   List[Any]   )  -> int:
							return self._images


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


						@property
						def     _SCREAMING_SNAKE_CASE   (			self  :   Optional[Any]   )  -> Dict:
							return self._num_examples


						@property
						def     _SCREAMING_SNAKE_CASE   (			self  :   int   )  -> int:
							return self._epochs_completed




						def     _SCREAMING_SNAKE_CASE   (			self  :   Tuple	,A_  :   Dict	,A_  :   Tuple=False	,A_  :   List[Any]=True   )  -> Union[str, Any]:
							if fake_data:
								A     = [1] * 784
								A     = [1] + [0] * 9 if self.one_hot else 0
								return (
								    [fake_image for _ in range(A_   )],
								    [fake_label for _ in range(A_   )],
								)
							A     = self._index_in_epoch
							# Shuffle for the first epoch
							if self._epochs_completed == 0 and start == 0 and shuffle:
								A     = numpy.arange(self._num_examples   )
								numpy.random.shuffle(A_   )
								A     = self.images[perma]
								A     = 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
								A     = self._num_examples - start
								A     = self._images[start : self._num_examples]
								A     = self._labels[start : self._num_examples]
								# Shuffle the data
								if shuffle:
									A     = numpy.arange(self._num_examples   )
									numpy.random.shuffle(A_   )
									A     = self.images[perm]
									A     = self.labels[perm]
								# Start next epoch
								A     = 0
								A     = batch_size - rest_num_examples
								A     = self._index_in_epoch
								A     = self._images[start:end]
								A     = 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
								A     = self._index_in_epoch
								return self._images[start:end], self._labels[start:end]






@deprecated(snake_case__						,   'Please write your own downloading logic.'  )
def    _snake_case					(		snake_case__ :  List[str]						,   snake_case__ :  Optional[int]						,   snake_case__ :  List[Any]  ):
	if not gfile.Exists(snake_case__  ):
		gfile.MakeDirs(snake_case__  )
	A     = os.path.join(snake_case__						,   snake_case__  )
	if not gfile.Exists(snake_case__  ):
		urllib.request.urlretrieve(snake_case__						,   snake_case__  )  # noqa: S310
		with gfile.GFile(snake_case__  ) as f:
			A     = f.size()
		print('Successfully downloaded'						,   snake_case__						,   snake_case__						,   'bytes.'  )
	return filepath






@deprecated(
    snake_case__						,   'Please use alternatives such as:' ' tensorflow_datasets.load(\'mnist\')'  )
def    _snake_case					(		snake_case__ :  List[Any]						,   snake_case__ :  Dict=False						,   snake_case__ :  List[Any]=False						,   snake_case__ :  List[str]=dtypes.floataa						,   snake_case__ :  List[Any]=True						,   snake_case__ :  Any=5000						,   snake_case__ :  Tuple=None						,   snake_case__ :  Optional[int]=DEFAULT_SOURCE_URL						,   ):
	if fake_data:

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

		A     = fake()
		A     = fake()
		A     = fake()
		return _Datasets(train=snake_case__						,   validation=snake_case__						,   test=snake_case__  )

	if not source_url:  # empty string check
		A     = DEFAULT_SOURCE_URL

	A     = 'train-images-idx3-ubyte.gz'
	A     = 'train-labels-idx1-ubyte.gz'
	A     = 't10k-images-idx3-ubyte.gz'
	A     = 't10k-labels-idx1-ubyte.gz'

	A     = _maybe_download(
	    snake_case__						,   snake_case__						,   source_url + train_images_file  )
	with gfile.Open(snake_case__						,   'rb'  ) as f:
		A     = _extract_images(snake_case__  )

	A     = _maybe_download(
	    snake_case__						,   snake_case__						,   source_url + train_labels_file  )
	with gfile.Open(snake_case__						,   'rb'  ) as f:
		A     = _extract_labels(snake_case__						,   one_hot=snake_case__  )

	A     = _maybe_download(
	    snake_case__						,   snake_case__						,   source_url + test_images_file  )
	with gfile.Open(snake_case__						,   'rb'  ) as f:
		A     = _extract_images(snake_case__  )

	A     = _maybe_download(
	    snake_case__						,   snake_case__						,   source_url + test_labels_file  )
	with gfile.Open(snake_case__						,   'rb'  ) as f:
		A     = _extract_labels(snake_case__						,   one_hot=snake_case__  )

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

	A     = train_images[:validation_size]
	A     = train_labels[:validation_size]
	A     = train_images[validation_size:]
	A     = train_labels[validation_size:]

	A     = {'dtype': dtype, 'reshape': reshape, 'seed': seed}

	A     = _DataSet(snake_case__						,   snake_case__						,   **snake_case__  )
	A     = _DataSet(snake_case__						,   snake_case__						,   **snake_case__  )
	A     = _DataSet(snake_case__						,   snake_case__						,   **snake_case__  )

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

"""simple docstring"""


from typing import TYPE_CHECKING

from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_lowercase				 =					{
    '''configuration_mctct''': ['''MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MCTCTConfig'''],
    '''feature_extraction_mctct''': ['''MCTCTFeatureExtractor'''],
    '''processing_mctct''': ['''MCTCTProcessor'''],
}


try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowercase				 =					[
		    '''MCTCT_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''MCTCTForCTC''',
		    '''MCTCTModel''',
		    '''MCTCTPreTrainedModel''',
		]


if TYPE_CHECKING:
		from .configuration_mctct import MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP, MCTCTConfig
		from .feature_extraction_mctct import MCTCTFeatureExtractor
		from .processing_mctct import MCTCTProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_mctct import MCTCT_PRETRAINED_MODEL_ARCHIVE_LIST, MCTCTForCTC, MCTCTModel, MCTCTPreTrainedModel

else:
		import sys

		_lowercase				 =					_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)