






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

a__       =     collections.namedtuple("""_Datasets""", ["""train""", """validation""", """test"""])

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

def _UpperCAmelCase   (	a						:	Optional[Any] ):
							snake_case__	  =       numpy.dtype(numpy.uintaa ).newbyteorder(""">""" )
							return numpy.frombuffer(bytestream.read(4 )  ,					dtype=a )[0]

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

@deprecated(a  ,					"""Please use tf.one_hot on tensors.""" )
def _UpperCAmelCase   (	a						:	str  ,					a						:	Any ):
							snake_case__	  =       labels_dense.shape[0]
							snake_case__	  =       numpy.arange(a ) * num_classes
							snake_case__	  =       numpy.zeros((num_labels, num_classes) )
							snake_case__	  =       1
							return labels_one_hot

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


class    _lowerCAmelCase			:



				"""simple docstring"""



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






											'''simple docstring'''







											snake_case__  , snake_case__	  =       random_seed.get_seed(UpperCamelCase__)
											# 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__	  =       dtypes.as_dtype(UpperCamelCase__).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__	  =       1_0_0_0_0
																		snake_case__	  =       one_hot
											else:
																		assert (
																		    images.shape[0] == labels.shape[0]
																		), F'''images.shape: {images.shape} labels.shape: {labels.shape}'''
																		snake_case__	  =       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__	  =       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__	  =       images.astype(numpy.floataa)
																									snake_case__	  =       numpy.multiply(UpperCamelCase__			,		1.0 / 2_55.0)
											snake_case__	  =       images
											snake_case__	  =       labels
											snake_case__	  =       0
											snake_case__	  =       0



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






											'''simple docstring'''







											return self._images



				@property
				def 					__magic_name__							(      self    :    int):






											'''simple docstring'''







											return self._labels



				@property
				def 					__magic_name__							(      self    :    Dict):






											'''simple docstring'''







											return self._num_examples



				@property
				def 					__magic_name__							(      self    :    str):






											'''simple docstring'''







											return self._epochs_completed



				def 					__magic_name__							(      self    :    List[Any]			,		UpperCamelCase__    :    str			,		UpperCamelCase__    :    Optional[Any]=False			,		UpperCamelCase__    :    Union[str, Any]=True):






											'''simple docstring'''







											if fake_data:
																		snake_case__	  =       [1] * 7_8_4
																		snake_case__	  =       [1] + [0] * 9 if self.one_hot else 0
																		return (
																		    [fake_image for _ in range(UpperCamelCase__)],
																		    [fake_label for _ in range(UpperCamelCase__)],
																		)
											snake_case__	  =       self._index_in_epoch
											# Shuffle for the first epoch
											if self._epochs_completed == 0 and start == 0 and shuffle:
																		snake_case__	  =       numpy.arange(self._num_examples)
																		numpy.random.shuffle(UpperCamelCase__)
																		snake_case__	  =       self.images[perma]
																		snake_case__	  =       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__	  =       self._num_examples - start
																		snake_case__	  =       self._images[start : self._num_examples]
																		snake_case__	  =       self._labels[start : self._num_examples]
																		# Shuffle the data
																		if shuffle:
																									snake_case__	  =       numpy.arange(self._num_examples)
																									numpy.random.shuffle(UpperCamelCase__)
																									snake_case__	  =       self.images[perm]
																									snake_case__	  =       self.labels[perm]
																		# Start next epoch
																		snake_case__	  =       0
																		snake_case__	  =       batch_size - rest_num_examples
																		snake_case__	  =       self._index_in_epoch
																		snake_case__	  =       self._images[start:end]
																		snake_case__	  =       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__	  =       self._index_in_epoch
																		return self._images[start:end], self._labels[start:end]


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



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

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

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

							if not source_url:  # empty string check
														snake_case__	  =       DEFAULT_SOURCE_URL

							snake_case__	  =       """train-images-idx3-ubyte.gz"""
							snake_case__	  =       """train-labels-idx1-ubyte.gz"""
							snake_case__	  =       """t10k-images-idx3-ubyte.gz"""
							snake_case__	  =       """t10k-labels-idx1-ubyte.gz"""

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

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

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

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

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

							snake_case__	  =       train_images[:validation_size]
							snake_case__	  =       train_labels[:validation_size]
							snake_case__	  =       train_images[validation_size:]
							snake_case__	  =       train_labels[validation_size:]

							snake_case__	  =       {"""dtype""": dtype, """reshape""": reshape, """seed""": seed}

							snake_case__	  =       _DataSet(a  ,					a  ,					**a )
							snake_case__	  =       _DataSet(a  ,					a  ,					**a )
							snake_case__	  =       _DataSet(a  ,					a  ,					**a )

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








from typing import TYPE_CHECKING

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


a__       =     {
    """configuration_clip""": [
        """CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """CLIPConfig""",
        """CLIPOnnxConfig""",
        """CLIPTextConfig""",
        """CLIPVisionConfig""",
    ],
    """processing_clip""": ["""CLIPProcessor"""],
    """tokenization_clip""": ["""CLIPTokenizer"""],
}

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a__       =     ["""CLIPTokenizerFast"""]

try:
	if not is_vision_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a__       =     ["""CLIPFeatureExtractor"""]
	a__       =     ["""CLIPImageProcessor"""]

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a__       =     [
	    """CLIP_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """CLIPModel""",
	    """CLIPPreTrainedModel""",
	    """CLIPTextModel""",
	    """CLIPTextModelWithProjection""",
	    """CLIPVisionModel""",
	    """CLIPVisionModelWithProjection""",
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a__       =     [
	    """TF_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """TFCLIPModel""",
	    """TFCLIPPreTrainedModel""",
	    """TFCLIPTextModel""",
	    """TFCLIPVisionModel""",
	]

try:
	if not is_flax_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a__       =     [
	    """FlaxCLIPModel""",
	    """FlaxCLIPPreTrainedModel""",
	    """FlaxCLIPTextModel""",
	    """FlaxCLIPTextPreTrainedModel""",
	    """FlaxCLIPVisionModel""",
	    """FlaxCLIPVisionPreTrainedModel""",
	]


if TYPE_CHECKING:
	from .configuration_clip import (
	    CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    CLIPConfig,
	    CLIPOnnxConfig,
	    CLIPTextConfig,
	    CLIPVisionConfig,
	)
	from .processing_clip import CLIPProcessor
	from .tokenization_clip import CLIPTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_clip_fast import CLIPTokenizerFast

	try:
		if not is_vision_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .feature_extraction_clip import CLIPFeatureExtractor
		from .image_processing_clip import CLIPImageProcessor

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_clip import (
		    CLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
		    CLIPModel,
		    CLIPPreTrainedModel,
		    CLIPTextModel,
		    CLIPTextModelWithProjection,
		    CLIPVisionModel,
		    CLIPVisionModelWithProjection,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_clip import (
		    TF_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFCLIPModel,
		    TFCLIPPreTrainedModel,
		    TFCLIPTextModel,
		    TFCLIPVisionModel,
		)

	try:
		if not is_flax_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_flax_clip import (
		    FlaxCLIPModel,
		    FlaxCLIPPreTrainedModel,
		    FlaxCLIPTextModel,
		    FlaxCLIPTextPreTrainedModel,
		    FlaxCLIPVisionModel,
		    FlaxCLIPVisionPreTrainedModel,
		)


else:
	import sys

	a__       =     _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
