





'''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

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

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




def 				_lowerCAmelCase  (			lowerCamelCase_     :					Optional[int]			):
			__lowercase			  =							numpy.dtype(numpy.uintaa			).newbyteorder('''>'''			)
			return numpy.frombuffer(bytestream.read(4			)						,    dtype=lowerCamelCase_			)[0]




@deprecated(lowerCamelCase_						,    '''Please use tf.data to implement this functionality.'''			)
def 				_lowerCAmelCase  (			lowerCamelCase_     :					str			):
			print('''Extracting'''						,    f.name			)
			with gzip.GzipFile(fileobj=lowerCamelCase_			) as bytestream:
						__lowercase			  =							_readaa(lowerCamelCase_			)
						if magic != 2_0_5_1:
									raise ValueError(
									    '''Invalid magic number %d in MNIST image file: %s''' % (magic, f.name)			)
						__lowercase			  =							_readaa(lowerCamelCase_			)
						__lowercase			  =							_readaa(lowerCamelCase_			)
						__lowercase			  =							_readaa(lowerCamelCase_			)
						__lowercase			  =							bytestream.read(rows * cols * num_images			)
						__lowercase			  =							numpy.frombuffer(lowerCamelCase_						,    dtype=numpy.uinta			)
						__lowercase			  =							data.reshape(lowerCamelCase_						,    lowerCamelCase_						,    lowerCamelCase_						,    1			)
						return data




@deprecated(lowerCamelCase_						,    '''Please use tf.one_hot on tensors.'''			)
def 				_lowerCAmelCase  (			lowerCamelCase_     :					str						,    lowerCamelCase_     :					Dict			):
			__lowercase			  =							labels_dense.shape[0]
			__lowercase			  =							numpy.arange(lowerCamelCase_			) * num_classes
			__lowercase			  =							numpy.zeros((num_labels, num_classes)			)
			__lowercase			  =							1
			return labels_one_hot




@deprecated(lowerCamelCase_						,    '''Please use tf.data to implement this functionality.'''			)
def 				_lowerCAmelCase  (			lowerCamelCase_     :					int						,    lowerCamelCase_     :					Optional[Any]=False						,    lowerCamelCase_     :					Union[str, Any]=1_0			):
			print('''Extracting'''						,    f.name			)
			with gzip.GzipFile(fileobj=lowerCamelCase_			) as bytestream:
						__lowercase			  =							_readaa(lowerCamelCase_			)
						if magic != 2_0_4_9:
									raise ValueError(
									    '''Invalid magic number %d in MNIST label file: %s''' % (magic, f.name)			)
						__lowercase			  =							_readaa(lowerCamelCase_			)
						__lowercase			  =							bytestream.read(lowerCamelCase_			)
						__lowercase			  =							numpy.frombuffer(lowerCamelCase_						,    dtype=numpy.uinta			)
						if one_hot:
									return _dense_to_one_hot(lowerCamelCase_						,    lowerCamelCase_			)
						return labels






class 							__lowercase    :







	'''simple docstring'''

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







				'''simple docstring'''





				__lowercase  ,						__lowercase			  =							random_seed.get_seed(_lowerCamelCase	)
				# If op level seed is not set, use whatever graph level seed is returned
				numpy.random.seed(seeda if seed is None else seeda	)
				__lowercase			  =							dtypes.as_dtype(_lowerCamelCase	).base_dtype
				if dtype not in (dtypes.uinta, dtypes.floataa):
							raise TypeError('''Invalid image dtype %r, expected uint8 or float32''' % dtype	)
				if fake_data:
							__lowercase			  =							10000
							__lowercase			  =							one_hot
				else:
							assert (
							    images.shape[0] == labels.shape[0]
							), f"images.shape: {images.shape} labels.shape: {labels.shape}"
							__lowercase			  =							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
										__lowercase			  =							images.reshape(
										    images.shape[0]       ,images.shape[1] * images.shape[2]	)
							if dtype == dtypes.floataa:
										# Convert from [0, 255] -> [0.0, 1.0].
										__lowercase			  =							images.astype(numpy.floataa	)
										__lowercase			  =							numpy.multiply(_lowerCamelCase       ,1.0 / 2_5_5.0	)
				__lowercase			  =							images
				__lowercase			  =							labels
				__lowercase			  =							0
				__lowercase			  =							0


	@property
	def   _UpperCAmelCase       (self	)							->							int:







				'''simple docstring'''





				return self._images


	@property
	def   _UpperCAmelCase       (self	)							->							List[str]:







				'''simple docstring'''





				return self._labels


	@property
	def   _UpperCAmelCase       (self	)							->							List[Any]:







				'''simple docstring'''





				return self._num_examples


	@property
	def   _UpperCAmelCase       (self	)							->							str:







				'''simple docstring'''





				return self._epochs_completed


	def   _UpperCAmelCase       (self       ,_lowerCamelCase       ,_lowerCamelCase=False       ,_lowerCamelCase=True	)							->							Dict:







				'''simple docstring'''





				if fake_data:
							__lowercase			  =							[1] * 784
							__lowercase			  =							[1] + [0] * 9 if self.one_hot else 0
							return (
							    [fake_image for _ in range(_lowerCamelCase	)],
							    [fake_label for _ in range(_lowerCamelCase	)],
							)
				__lowercase			  =							self._index_in_epoch
				# Shuffle for the first epoch
				if self._epochs_completed == 0 and start == 0 and shuffle:
							__lowercase			  =							numpy.arange(self._num_examples	)
							numpy.random.shuffle(_lowerCamelCase	)
							__lowercase			  =							self.images[perma]
							__lowercase			  =							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
							__lowercase			  =							self._num_examples - start
							__lowercase			  =							self._images[start : self._num_examples]
							__lowercase			  =							self._labels[start : self._num_examples]
							# Shuffle the data
							if shuffle:
										__lowercase			  =							numpy.arange(self._num_examples	)
										numpy.random.shuffle(_lowerCamelCase	)
										__lowercase			  =							self.images[perm]
										__lowercase			  =							self.labels[perm]
							# Start next epoch
							__lowercase			  =							0
							__lowercase			  =							batch_size - rest_num_examples
							__lowercase			  =							self._index_in_epoch
							__lowercase			  =							self._images[start:end]
							__lowercase			  =							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
							__lowercase			  =							self._index_in_epoch
							return self._images[start:end], self._labels[start:end]





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




@deprecated(
    lowerCamelCase_						,    '''Please use alternatives such as:''' ''' tensorflow_datasets.load(\'mnist\')'''			)
def 				_lowerCAmelCase  (			lowerCamelCase_     :					List[Any]						,    lowerCamelCase_     :					Optional[Any]=False						,    lowerCamelCase_     :					List[Any]=False						,    lowerCamelCase_     :					Union[str, Any]=dtypes.floataa						,    lowerCamelCase_     :					Dict=True						,    lowerCamelCase_     :					str=5_0_0_0						,    lowerCamelCase_     :					Dict=None						,    lowerCamelCase_     :					Union[str, Any]=DEFAULT_SOURCE_URL						,    ):
			if fake_data:

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

						__lowercase			  =							fake()
						__lowercase			  =							fake()
						__lowercase			  =							fake()
						return _Datasets(train=lowerCamelCase_						,    validation=lowerCamelCase_						,    test=lowerCamelCase_			)

			if not source_url:  # empty string check
						__lowercase			  =							DEFAULT_SOURCE_URL

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

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

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

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

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

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

			__lowercase			  =							train_images[:validation_size]
			__lowercase			  =							train_labels[:validation_size]
			__lowercase			  =							train_images[validation_size:]
			__lowercase			  =							train_labels[validation_size:]

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

			__lowercase			  =							_DataSet(lowerCamelCase_						,    lowerCamelCase_						,    **lowerCamelCase_			)
			__lowercase			  =							_DataSet(lowerCamelCase_						,    lowerCamelCase_						,    **lowerCamelCase_			)
			__lowercase			  =							_DataSet(lowerCamelCase_						,    lowerCamelCase_						,    **lowerCamelCase_			)

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












'''simple docstring'''



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


_SCREAMING_SNAKE_CASE   =      {
    '''configuration_blenderbot''': [
        '''BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''BlenderbotConfig''',
        '''BlenderbotOnnxConfig''',
    ],
    '''tokenization_blenderbot''': ['''BlenderbotTokenizer'''],
}

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_SCREAMING_SNAKE_CASE   =      ['''BlenderbotTokenizerFast''']

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_SCREAMING_SNAKE_CASE   =      [
	    '''BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''BlenderbotForCausalLM''',
	    '''BlenderbotForConditionalGeneration''',
	    '''BlenderbotModel''',
	    '''BlenderbotPreTrainedModel''',
	]


try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_SCREAMING_SNAKE_CASE   =      [
	    '''TFBlenderbotForConditionalGeneration''',
	    '''TFBlenderbotModel''',
	    '''TFBlenderbotPreTrainedModel''',
	]


try:
	if not is_flax_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_SCREAMING_SNAKE_CASE   =      [
	    '''FlaxBlenderbotForConditionalGeneration''',
	    '''FlaxBlenderbotModel''',
	    '''FlaxBlenderbotPreTrainedModel''',
	]


if TYPE_CHECKING:
	from .configuration_blenderbot import (
	    BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    BlenderbotConfig,
	    BlenderbotOnnxConfig,
	)
	from .tokenization_blenderbot import BlenderbotTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_blenderbot_fast import BlenderbotTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_blenderbot import (
		    BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    BlenderbotForCausalLM,
		    BlenderbotForConditionalGeneration,
		    BlenderbotModel,
		    BlenderbotPreTrainedModel,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_blenderbot import (
		    TFBlenderbotForConditionalGeneration,
		    TFBlenderbotModel,
		    TFBlenderbotPreTrainedModel,
		)

	try:
		if not is_flax_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_flax_blenderbot import (
		    FlaxBlenderbotForConditionalGeneration,
		    FlaxBlenderbotModel,
		    FlaxBlenderbotPreTrainedModel,
		)

else:
	import sys

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





