





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

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




def  __UpperCAmelCase(   lowercase_   ):
       _lowerCamelCase				:  Dict						=		numpy.dtype(numpy.uintaa   ).newbyteorder('''>'''   )
       return numpy.frombuffer(bytestream.read(4   )			,     dtype=UpperCAmelCase__   )[0]




@deprecated(UpperCAmelCase__			,     '''Please use tf.data to implement this functionality.'''   )
def  __UpperCAmelCase(   lowercase_   ):
       print('''Extracting'''			,     f.name   )
       with gzip.GzipFile(fileobj=UpperCAmelCase__   ) as bytestream:
              _lowerCamelCase				:  Tuple						=		_readaa(UpperCAmelCase__   )
              if magic != 20_51:
                     raise ValueError(
                         '''Invalid magic number %d in MNIST image file: %s''' % (magic, f.name)   )
              _lowerCamelCase				:  str						=		_readaa(UpperCAmelCase__   )
              _lowerCamelCase				:  Optional[int]						=		_readaa(UpperCAmelCase__   )
              _lowerCamelCase				:  Optional[int]						=		_readaa(UpperCAmelCase__   )
              _lowerCamelCase				:  List[Any]						=		bytestream.read(rows * cols * num_images   )
              _lowerCamelCase				:  Tuple						=		numpy.frombuffer(UpperCAmelCase__			,     dtype=numpy.uinta   )
              _lowerCamelCase				:  Any						=		data.reshape(UpperCAmelCase__			,     UpperCAmelCase__			,     UpperCAmelCase__			,     1   )
              return data




@deprecated(UpperCAmelCase__			,     '''Please use tf.one_hot on tensors.'''   )
def  __UpperCAmelCase(   lowercase_			,     lowercase_   ):
       _lowerCamelCase				:  List[Any]						=		labels_dense.shape[0]
       _lowerCamelCase				:  Tuple						=		numpy.arange(UpperCAmelCase__   ) * num_classes
       _lowerCamelCase				:  Optional[Any]						=		numpy.zeros((num_labels, num_classes)   )
       _lowerCamelCase				:  Optional[Any]						=		1
       return labels_one_hot




@deprecated(UpperCAmelCase__			,     '''Please use tf.data to implement this functionality.'''   )
def  __UpperCAmelCase(   lowercase_			,     lowercase_=False			,     lowercase_=10   ):
       print('''Extracting'''			,     f.name   )
       with gzip.GzipFile(fileobj=UpperCAmelCase__   ) as bytestream:
              _lowerCamelCase				:  Any						=		_readaa(UpperCAmelCase__   )
              if magic != 20_49:
                     raise ValueError(
                         '''Invalid magic number %d in MNIST label file: %s''' % (magic, f.name)   )
              _lowerCamelCase				:  List[Any]						=		_readaa(UpperCAmelCase__   )
              _lowerCamelCase				:  str						=		bytestream.read(UpperCAmelCase__   )
              _lowerCamelCase				:  Dict						=		numpy.frombuffer(UpperCAmelCase__			,     dtype=numpy.uinta   )
              if one_hot:
                     return _dense_to_one_hot(UpperCAmelCase__			,     UpperCAmelCase__   )
              return labels







class       __A							:




      """simple docstring"""







      @deprecated(
          UpperCamelCase__   ,				'''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"""





             _lowerCamelCase,	_lowerCamelCase				:  List[str]						=		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)
             _lowerCamelCase				:  int						=		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:
                    _lowerCamelCase				:  Optional[int]						=		1_0000
                    _lowerCamelCase				:  Optional[Any]						=		one_hot
             else:
                    assert (
                        images.shape[0] == labels.shape[0]
                    ), F"""images.shape: {images.shape} labels.shape: {labels.shape}"""
                    _lowerCamelCase				:  Tuple						=		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
                           _lowerCamelCase				:  Any						=		images.reshape(
                               images.shape[0]   ,				images.shape[1] * images.shape[2])
                    if dtype == dtypes.floataa:
                           # Convert from [0, 255] -> [0.0, 1.0].
                           _lowerCamelCase				:  Optional[int]						=		images.astype(numpy.floataa)
                           _lowerCamelCase				:  Union[str, Any]						=		numpy.multiply(UpperCamelCase__   ,				1.0 / 255.0)
             _lowerCamelCase				:  Optional[Any]						=		images
             _lowerCamelCase				:  Optional[int]						=		labels
             _lowerCamelCase				:  Any						=		0
             _lowerCamelCase				:  str						=		0


      @property
      def 					__snake_case      (		self):



             """simple docstring"""





             return self._images


      @property
      def 					__snake_case      (		self):



             """simple docstring"""





             return self._labels


      @property
      def 					__snake_case      (		self):



             """simple docstring"""





             return self._num_examples


      @property
      def 					__snake_case      (		self):



             """simple docstring"""





             return self._epochs_completed



      def 					__snake_case      (		self   ,				a__   ,				a__=False   ,				a__=True):



             """simple docstring"""





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




@deprecated(UpperCAmelCase__			,     '''Please write your own downloading logic.'''   )
def  __UpperCAmelCase(   lowercase_			,     lowercase_			,     lowercase_   ):
       if not gfile.Exists(UpperCAmelCase__   ):
              gfile.MakeDirs(UpperCAmelCase__   )
       _lowerCamelCase				:  Any						=		os.path.join(UpperCAmelCase__			,     UpperCAmelCase__   )
       if not gfile.Exists(UpperCAmelCase__   ):
              urllib.request.urlretrieve(UpperCAmelCase__			,     UpperCAmelCase__   )  # noqa: S310
              with gfile.GFile(UpperCAmelCase__   ) as f:
                     _lowerCamelCase				:  str						=		f.size()
              print('''Successfully downloaded'''			,     UpperCAmelCase__			,     UpperCAmelCase__			,     '''bytes.'''   )
       return filepath




@deprecated(
    UpperCAmelCase__			,     '''Please use alternatives such as:''' ''' tensorflow_datasets.load(\'mnist\')'''   )
def  __UpperCAmelCase(   lowercase_			,     lowercase_=False			,     lowercase_=False			,     lowercase_=dtypes.floataa			,     lowercase_=True			,     lowercase_=50_00			,     lowercase_=None			,     lowercase_=DEFAULT_SOURCE_URL			,     ):
       if fake_data:

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

              _lowerCamelCase				:  Tuple						=		fake()
              _lowerCamelCase				:  Optional[Any]						=		fake()
              _lowerCamelCase				:  Any						=		fake()
              return _Datasets(train=UpperCAmelCase__			,     validation=UpperCAmelCase__			,     test=UpperCAmelCase__   )

       if not source_url:  # empty string check
              _lowerCamelCase				:  Union[str, Any]						=		DEFAULT_SOURCE_URL

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

       _lowerCamelCase				:  str						=		_maybe_download(
           UpperCAmelCase__			,     UpperCAmelCase__			,     source_url + train_images_file   )
       with gfile.Open(UpperCAmelCase__			,     '''rb'''   ) as f:
              _lowerCamelCase				:  List[Any]						=		_extract_images(UpperCAmelCase__   )

       _lowerCamelCase				:  Tuple						=		_maybe_download(
           UpperCAmelCase__			,     UpperCAmelCase__			,     source_url + train_labels_file   )
       with gfile.Open(UpperCAmelCase__			,     '''rb'''   ) as f:
              _lowerCamelCase				:  Dict						=		_extract_labels(UpperCAmelCase__			,     one_hot=UpperCAmelCase__   )

       _lowerCamelCase				:  Tuple						=		_maybe_download(
           UpperCAmelCase__			,     UpperCAmelCase__			,     source_url + test_images_file   )
       with gfile.Open(UpperCAmelCase__			,     '''rb'''   ) as f:
              _lowerCamelCase				:  Tuple						=		_extract_images(UpperCAmelCase__   )

       _lowerCamelCase				:  int						=		_maybe_download(
           UpperCAmelCase__			,     UpperCAmelCase__			,     source_url + test_labels_file   )
       with gfile.Open(UpperCAmelCase__			,     '''rb'''   ) as f:
              _lowerCamelCase				:  Any						=		_extract_labels(UpperCAmelCase__			,     one_hot=UpperCAmelCase__   )

       if not 0 <= validation_size <= len(UpperCAmelCase__   ):
              _lowerCamelCase				:  List[Any]						=		(
                  '''Validation size should be between 0 and '''
                  F"""{len(UpperCAmelCase__   )}. Received: {validation_size}."""
              )
              raise ValueError(UpperCAmelCase__   )

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

       _lowerCamelCase				:  Tuple						=		{'''dtype''': dtype, '''reshape''': reshape, '''seed''': seed}

       _lowerCamelCase				:  Union[str, Any]						=		_DataSet(UpperCAmelCase__			,     UpperCAmelCase__			,     **UpperCAmelCase__   )
       _lowerCamelCase				:  str						=		_DataSet(UpperCAmelCase__			,     UpperCAmelCase__			,     **UpperCAmelCase__   )
       _lowerCamelCase				:  Tuple						=		_DataSet(UpperCAmelCase__			,     UpperCAmelCase__			,     **UpperCAmelCase__   )

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




'''simple docstring'''







import warnings
from typing import List

import numpy as np

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import is_flax_available, is_tf_available, is_torch_available


class        A__   (   _snake_case      ):
    lowercase						       =		["image_processor", "tokenizer"]
    lowercase						       =		"OwlViTImageProcessor"
    lowercase						       =		("CLIPTokenizer", "CLIPTokenizerFast")



    def __init__(       self		,					UpperCamelCase__=None		,					UpperCamelCase__=None		,					**UpperCamelCase__							)    ->   str:





           '''simple docstring'''
           A_						       =   None
           if "feature_extractor" in kwargs:
                  warnings.warn(
                      """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
                      """ instead."""		,					UpperCamelCase__		,					)
                  A_						       =   kwargs.pop("""feature_extractor"""							)

           A_						       =   image_processor if image_processor is not None else feature_extractor
           if image_processor is None:
                  raise ValueError("""You need to specify an `image_processor`."""							)
           if tokenizer is None:
                  raise ValueError("""You need to specify a `tokenizer`."""							)

           super().__init__(UpperCamelCase__		,					UpperCamelCase__							)



    def __call__(       self		,					UpperCamelCase__=None		,					UpperCamelCase__=None		,					UpperCamelCase__=None		,					UpperCamelCase__="max_length"		,					UpperCamelCase__="np"		,					**UpperCamelCase__							)    ->   int:





           '''simple docstring'''

           if text is None and query_images is None and images is None:
                  raise ValueError(
                      """You have to specify at least one text or query image or image. All three cannot be none."""							)

           if text is not None:
                  if isinstance(UpperCamelCase__		,					UpperCamelCase__							) or (isinstance(UpperCamelCase__		,					UpperCamelCase__							) and not isinstance(text[0]		,					UpperCamelCase__							)):
                         A_						       =   [self.tokenizer(UpperCamelCase__		,					padding=UpperCamelCase__		,					return_tensors=UpperCamelCase__		,					**UpperCamelCase__							)]

                  elif isinstance(UpperCamelCase__		,					UpperCamelCase__							) and isinstance(text[0]		,					UpperCamelCase__							):
                         A_						       =   []

                         # Maximum number of queries across batch
                         A_						       =   max([len(UpperCamelCase__							) for t in text]							)

                         # Pad all batch samples to max number of text queries
                         for t in text:
                                if len(UpperCamelCase__							) != max_num_queries:
                                       A_						       =   t + [""" """] * (max_num_queries - len(UpperCamelCase__							))

                                A_						       =   self.tokenizer(UpperCamelCase__		,					padding=UpperCamelCase__		,					return_tensors=UpperCamelCase__		,					**UpperCamelCase__							)
                                encodings.append(UpperCamelCase__							)
                  else:
                         raise TypeError("""Input text should be a string, a list of strings or a nested list of strings"""							)

                  if return_tensors == "np":
                         A_						       =   np.concatenate([encoding["""input_ids"""] for encoding in encodings]		,					axis=0							)
                         A_						       =   np.concatenate([encoding["""attention_mask"""] for encoding in encodings]		,					axis=0							)

                  elif return_tensors == "jax" and is_flax_available():
                         import jax.numpy as jnp

                         A_						       =   jnp.concatenate([encoding["""input_ids"""] for encoding in encodings]		,					axis=0							)
                         A_						       =   jnp.concatenate([encoding["""attention_mask"""] for encoding in encodings]		,					axis=0							)

                  elif return_tensors == "pt" and is_torch_available():
                         import torch

                         A_						       =   torch.cat([encoding["""input_ids"""] for encoding in encodings]		,					dim=0							)
                         A_						       =   torch.cat([encoding["""attention_mask"""] for encoding in encodings]		,					dim=0							)

                  elif return_tensors == "tf" and is_tf_available():
                         import tensorflow as tf

                         A_						       =   tf.stack([encoding["""input_ids"""] for encoding in encodings]		,					axis=0							)
                         A_						       =   tf.stack([encoding["""attention_mask"""] for encoding in encodings]		,					axis=0							)

                  else:
                         raise ValueError("""Target return tensor type could not be returned"""							)

                  A_						       =   BatchEncoding()
                  A_						       =   input_ids
                  A_						       =   attention_mask

           if query_images is not None:
                  A_						       =   BatchEncoding()
                  A_						       =   self.image_processor(
                      UpperCamelCase__		,					return_tensors=UpperCamelCase__		,					**UpperCamelCase__							).pixel_values
                  A_						       =   query_pixel_values

           if images is not None:
                  A_						       =   self.image_processor(UpperCamelCase__		,					return_tensors=UpperCamelCase__		,					**UpperCamelCase__							)

           if text is not None and images is not None:
                  A_						       =   image_features.pixel_values
                  return encoding
           elif query_images is not None and images is not None:
                  A_						       =   image_features.pixel_values
                  return encoding
           elif text is not None or query_images is not None:
                  return encoding
           else:
                  return BatchEncoding(data=dict(**UpperCamelCase__							)		,					tensor_type=UpperCamelCase__							)



    def        snake_case_      (       self		,					*UpperCamelCase__		,					**UpperCamelCase__							)    ->   int:





           '''simple docstring'''
           return self.image_processor.post_process(*UpperCamelCase__		,					**UpperCamelCase__							)



    def        snake_case_      (       self		,					*UpperCamelCase__		,					**UpperCamelCase__							)    ->   List[Any]:





           '''simple docstring'''
           return self.image_processor.post_process_object_detection(*UpperCamelCase__		,					**UpperCamelCase__							)



    def        snake_case_      (       self		,					*UpperCamelCase__		,					**UpperCamelCase__							)    ->   Tuple:





           '''simple docstring'''
           return self.image_processor.post_process_image_guided_detection(*UpperCamelCase__		,					**UpperCamelCase__							)



    def        snake_case_      (       self		,					*UpperCamelCase__		,					**UpperCamelCase__							)    ->   Any:





           '''simple docstring'''
           return self.tokenizer.batch_decode(*UpperCamelCase__		,					**UpperCamelCase__							)



    def        snake_case_      (       self		,					*UpperCamelCase__		,					**UpperCamelCase__							)    ->   List[str]:





           '''simple docstring'''
           return self.tokenizer.decode(*UpperCamelCase__		,					**UpperCamelCase__							)



    @property
    def        snake_case_      (       self							)    ->   Optional[int]:





           '''simple docstring'''
           warnings.warn(
               """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""		,					UpperCamelCase__		,					)
           return self.image_processor_class







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





           '''simple docstring'''
           warnings.warn(
               """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."""		,					UpperCamelCase__		,					)
           return self.image_processor
