





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,
)


__lowerCamelCase	:					List[Any]			  =					{
    """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:
    __lowerCamelCase	:					int			  =					["""CLIPTokenizerFast"""]

try:
    if not is_vision_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __lowerCamelCase	:					Union[str, Any]			  =					["""CLIPFeatureExtractor"""]
    __lowerCamelCase	:					List[Any]			  =					["""CLIPImageProcessor"""]

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

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

try:
    if not is_flax_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __lowerCamelCase	:					str			  =					[
        """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

    __lowerCamelCase	:					List[str]			  =					_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)











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

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



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



@deprecated(_lowerCAmelCase							,       "Please use tf.one_hot on tensors."					)
def 		A_    (					_lowerCAmelCase							,       _lowerCAmelCase					)						->			Dict:
 UpperCamelCase     : List[str]	     =       labels_dense.shape[0]
 UpperCamelCase     : str	     =       numpy.arange(_lowerCAmelCase					) * num_classes
 UpperCamelCase     : Optional[Any]	     =       numpy.zeros((num_labels, num_classes)					)
 UpperCamelCase     : Dict	     =       1
 return labels_one_hot



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



class  A__       :





      @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	,							):
       '''simple docstring'''




       UpperCamelCase     ,   UpperCamelCase     : int	     =       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       )
       UpperCamelCase     : Optional[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:
        UpperCamelCase     : List[str]	     =       1_0000
        UpperCamelCase     : int	     =       one_hot
       else:
        assert (
            images.shape[0] == labels.shape[0]
        ), F"""images.shape: {images.shape} labels.shape: {labels.shape}"""
        UpperCamelCase     : Optional[Any]	     =       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     : int	     =       images.reshape(
             images.shape[0]	,							images.shape[1] * images.shape[2]       )
        if dtype == dtypes.floataa:
         # Convert from [0, 255] -> [0.0, 1.0].
         UpperCamelCase     : str	     =       images.astype(numpy.floataa       )
         UpperCamelCase     : str	     =       numpy.multiply(A_	,							1.0 / 2_55.0       )
       UpperCamelCase     : Optional[int]	     =       images
       UpperCamelCase     : str	     =       labels
       UpperCamelCase     : Optional[Any]	     =       0
       UpperCamelCase     : Optional[int]	     =       0





      @property
      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       return self._images





      @property
      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       return self._labels





      @property
      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       return self._num_examples





      @property
      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       return self._epochs_completed





      def 	__UpperCamelCase(			self	,							A_	,							A_=False	,							A_=True       ):
       '''simple docstring'''




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



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



@deprecated(
    _lowerCAmelCase							,       "Please use alternatives such as:" " tensorflow_datasets.load('mnist')"					)
def 		A_    (					_lowerCAmelCase							,       _lowerCAmelCase=False							,       _lowerCAmelCase=False							,       _lowerCAmelCase=dtypes.floataa							,       _lowerCAmelCase=True							,       _lowerCAmelCase=5000							,       _lowerCAmelCase=None							,       _lowerCAmelCase=DEFAULT_SOURCE_URL							,       )						->			List[str]:
 if fake_data:

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

  UpperCamelCase     : Any	     =       fake()
  UpperCamelCase     : List[str]	     =       fake()
  UpperCamelCase     : Union[str, Any]	     =       fake()
  return _Datasets(train=_lowerCAmelCase							,       validation=_lowerCAmelCase							,       test=_lowerCAmelCase					)

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

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

 UpperCamelCase     : Optional[int]	     =       _maybe_download(
     _lowerCAmelCase							,       _lowerCAmelCase							,       source_url + train_images_file					)
 with gfile.Open(_lowerCAmelCase							,       "rb"					) as f:
  UpperCamelCase     : List[str]	     =       _extract_images(_lowerCAmelCase					)

 UpperCamelCase     : Dict	     =       _maybe_download(
     _lowerCAmelCase							,       _lowerCAmelCase							,       source_url + train_labels_file					)
 with gfile.Open(_lowerCAmelCase							,       "rb"					) as f:
  UpperCamelCase     : List[Any]	     =       _extract_labels(_lowerCAmelCase							,       one_hot=_lowerCAmelCase					)

 UpperCamelCase     : Any	     =       _maybe_download(
     _lowerCAmelCase							,       _lowerCAmelCase							,       source_url + test_images_file					)
 with gfile.Open(_lowerCAmelCase							,       "rb"					) as f:
  UpperCamelCase     : Any	     =       _extract_images(_lowerCAmelCase					)

 UpperCamelCase     : List[str]	     =       _maybe_download(
     _lowerCAmelCase							,       _lowerCAmelCase							,       source_url + test_labels_file					)
 with gfile.Open(_lowerCAmelCase							,       "rb"					) as f:
  UpperCamelCase     : str	     =       _extract_labels(_lowerCAmelCase							,       one_hot=_lowerCAmelCase					)

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

 UpperCamelCase     : str	     =       train_images[:validation_size]
 UpperCamelCase     : int	     =       train_labels[:validation_size]
 UpperCamelCase     : List[str]	     =       train_images[validation_size:]
 UpperCamelCase     : Union[str, Any]	     =       train_labels[validation_size:]

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

 UpperCamelCase     : List[str]	     =       _DataSet(_lowerCAmelCase							,       _lowerCAmelCase							,       **_lowerCAmelCase					)
 UpperCamelCase     : List[str]	     =       _DataSet(_lowerCAmelCase							,       _lowerCAmelCase							,       **_lowerCAmelCase					)
 UpperCamelCase     : Any	     =       _DataSet(_lowerCAmelCase							,       _lowerCAmelCase							,       **_lowerCAmelCase					)

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