





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

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

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





def  __magic_name__  (				lowercase				)				-> int:

							"""simple docstring"""



							lowercase_	:   Any  				=     numpy.dtype(numpy.uintaa				).newbyteorder(""">"""				)
							return numpy.frombuffer(bytestream.read(4				)				,       dtype=lowercase				)[0]





@deprecated(lowercase				,       """Please use tf.data to implement this functionality."""				)
def  __magic_name__  (				lowercase				)				-> List[Any]:

							"""simple docstring"""



							print("""Extracting"""				,       f.name				)
							with gzip.GzipFile(fileobj=lowercase				) as bytestream:
														lowercase_	:   Optional[Any]  				=     _readaa(lowercase				)
														if magic != 2051:
																					raise ValueError(
																					    """Invalid magic number %d in MNIST image file: %s""" % (magic, f.name)				)
														lowercase_	:   Optional[Any]  				=     _readaa(lowercase				)
														lowercase_	:   int  				=     _readaa(lowercase				)
														lowercase_	:   Optional[Any]  				=     _readaa(lowercase				)
														lowercase_	:   Optional[int]  				=     bytestream.read(rows * cols * num_images				)
														lowercase_	:   Optional[int]  				=     numpy.frombuffer(lowercase				,       dtype=numpy.uinta				)
														lowercase_	:   Any  				=     data.reshape(lowercase				,       lowercase				,       lowercase				,       1				)
														return data





@deprecated(lowercase				,       """Please use tf.one_hot on tensors."""				)
def  __magic_name__  (				lowercase				,       lowercase				)				-> List[Any]:

							"""simple docstring"""



							lowercase_	:   Tuple  				=     labels_dense.shape[0]
							lowercase_	:   Dict  				=     numpy.arange(lowercase				) * num_classes
							lowercase_	:   List[Any]  				=     numpy.zeros((num_labels, num_classes)				)
							lowercase_	:   Any  				=     1
							return labels_one_hot





@deprecated(lowercase				,       """Please use tf.data to implement this functionality."""				)
def  __magic_name__  (				lowercase				,       lowercase=False				,       lowercase=10				)				-> int:

							"""simple docstring"""



							print("""Extracting"""				,       f.name				)
							with gzip.GzipFile(fileobj=lowercase				) as bytestream:
														lowercase_	:   Dict  				=     _readaa(lowercase				)
														if magic != 2049:
																					raise ValueError(
																					    """Invalid magic number %d in MNIST label file: %s""" % (magic, f.name)				)
														lowercase_	:   int  				=     _readaa(lowercase				)
														lowercase_	:   Any  				=     bytestream.read(lowercase				)
														lowercase_	:   int  				=     numpy.frombuffer(lowercase				,       dtype=numpy.uinta				)
														if one_hot:
																					return _dense_to_one_hot(lowercase				,       lowercase				)
														return labels
class     UpperCamelCase__					:






		'''simple docstring'''






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






									"""simple docstring"""
									lowercase_				,		lowercase_	:   Optional[int]  				=     random_seed.get_seed(snake_case__  )
									# 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_	:   Optional[Any]  				=     dtypes.as_dtype(snake_case__  ).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_	:   Tuple  				=     1_00_00
																lowercase_	:   Any  				=     one_hot
									else:
																assert (
																    images.shape[0] == labels.shape[0]
																), f"""images.shape: {images.shape} labels.shape: {labels.shape}"""
																lowercase_	:   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
																							lowercase_	:   Dict  				=     images.reshape(
																							    images.shape[0],   images.shape[1] * images.shape[2]  )
																if dtype == dtypes.floataa:
																							# Convert from [0, 255] -> [0.0, 1.0].
																							lowercase_	:   Optional[Any]  				=     images.astype(numpy.floataa  )
																							lowercase_	:   Dict  				=     numpy.multiply(snake_case__,   1.0 / 255.0  )
									lowercase_	:   Union[str, Any]  				=     images
									lowercase_	:   List[Any]  				=     labels
									lowercase_	:   Union[str, Any]  				=     0
									lowercase_	:   int  				=     0






		@property
		def 						snake_case__				(							self  )       -> Optional[Any]:






									"""simple docstring"""
									return self._images






		@property
		def 						snake_case__				(							self  )       -> Dict:






									"""simple docstring"""
									return self._labels






		@property
		def 						snake_case__				(							self  )       -> Optional[Any]:






									"""simple docstring"""
									return self._num_examples






		@property
		def 						snake_case__				(							self  )       -> int:






									"""simple docstring"""
									return self._epochs_completed






		def 						snake_case__				(							self,   snake_case__,   snake_case__=False,   snake_case__=True  )       -> int:






									"""simple docstring"""
									if fake_data:
																lowercase_	:   Optional[int]  				=     [1] * 7_84
																lowercase_	:   Optional[Any]  				=     [1] + [0] * 9 if self.one_hot else 0
																return (
																    [fake_image for _ in range(snake_case__  )],
																    [fake_label for _ in range(snake_case__  )],
																)
									lowercase_	:   Optional[int]  				=     self._index_in_epoch
									# Shuffle for the first epoch
									if self._epochs_completed == 0 and start == 0 and shuffle:
																lowercase_	:   Optional[Any]  				=     numpy.arange(self._num_examples  )
																numpy.random.shuffle(snake_case__  )
																lowercase_	:   Any  				=     self.images[perma]
																lowercase_	:   List[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
																lowercase_	:   Tuple  				=     self._num_examples - start
																lowercase_	:   List[Any]  				=     self._images[start : self._num_examples]
																lowercase_	:   Tuple  				=     self._labels[start : self._num_examples]
																# Shuffle the data
																if shuffle:
																							lowercase_	:   Optional[int]  				=     numpy.arange(self._num_examples  )
																							numpy.random.shuffle(snake_case__  )
																							lowercase_	:   Any  				=     self.images[perm]
																							lowercase_	:   List[str]  				=     self.labels[perm]
																# Start next epoch
																lowercase_	:   Any  				=     0
																lowercase_	:   List[str]  				=     batch_size - rest_num_examples
																lowercase_	:   List[str]  				=     self._index_in_epoch
																lowercase_	:   str  				=     self._images[start:end]
																lowercase_	:   Tuple  				=     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_	:   Union[str, Any]  				=     self._index_in_epoch
																return self._images[start:end], self._labels[start:end]





@deprecated(lowercase				,       """Please write your own downloading logic."""				)
def  __magic_name__  (				lowercase				,       lowercase				,       lowercase				)				-> Tuple:

							"""simple docstring"""



							if not gfile.Exists(lowercase				):
														gfile.MakeDirs(lowercase				)
							lowercase_	:   Tuple  				=     os.path.join(lowercase				,       lowercase				)
							if not gfile.Exists(lowercase				):
														urllib.request.urlretrieve(lowercase				,       lowercase				)  # noqa: S310
														with gfile.GFile(lowercase				) as f:
																					lowercase_	:   Optional[int]  				=     f.size()
														print("""Successfully downloaded"""				,       lowercase				,       lowercase				,       """bytes."""				)
							return filepath





@deprecated(
    lowercase				,       """Please use alternatives such as:""" """ tensorflow_datasets.load('mnist')"""				)
def  __magic_name__  (				lowercase				,       lowercase=False				,       lowercase=False				,       lowercase=dtypes.floataa				,       lowercase=True				,       lowercase=5000				,       lowercase=None				,       lowercase=DEFAULT_SOURCE_URL				,       )				-> str:

							"""simple docstring"""



							if fake_data:

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

														lowercase_	:   Dict  				=     fake()
														lowercase_	:   List[Any]  				=     fake()
														lowercase_	:   List[str]  				=     fake()
														return _Datasets(train=lowercase				,       validation=lowercase				,       test=lowercase				)

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

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

							lowercase_	:   Optional[Any]  				=     _maybe_download(
							    lowercase				,       lowercase				,       source_url + train_images_file				)
							with gfile.Open(lowercase				,       """rb"""				) as f:
														lowercase_	:   List[Any]  				=     _extract_images(lowercase				)

							lowercase_	:   int  				=     _maybe_download(
							    lowercase				,       lowercase				,       source_url + train_labels_file				)
							with gfile.Open(lowercase				,       """rb"""				) as f:
														lowercase_	:   int  				=     _extract_labels(lowercase				,       one_hot=lowercase				)

							lowercase_	:   Dict  				=     _maybe_download(
							    lowercase				,       lowercase				,       source_url + test_images_file				)
							with gfile.Open(lowercase				,       """rb"""				) as f:
														lowercase_	:   int  				=     _extract_images(lowercase				)

							lowercase_	:   List[Any]  				=     _maybe_download(
							    lowercase				,       lowercase				,       source_url + test_labels_file				)
							with gfile.Open(lowercase				,       """rb"""				) as f:
														lowercase_	:   Optional[int]  				=     _extract_labels(lowercase				,       one_hot=lowercase				)

							if not 0 <= validation_size <= len(lowercase				):
														lowercase_	:   Union[str, Any]  				=     (
														    """Validation size should be between 0 and """
														    f"""{len(lowercase				)}. Received: {validation_size}."""
														)
														raise ValueError(lowercase				)

							lowercase_	:   List[str]  				=     train_images[:validation_size]
							lowercase_	:   Any  				=     train_labels[:validation_size]
							lowercase_	:   Optional[Any]  				=     train_images[validation_size:]
							lowercase_	:   Union[str, Any]  				=     train_labels[validation_size:]

							lowercase_	:   Optional[Any]  				=     {"""dtype""": dtype, """reshape""": reshape, """seed""": seed}

							lowercase_	:   Any  				=     _DataSet(lowercase				,       lowercase				,       **lowercase				)
							lowercase_	:   List[str]  				=     _DataSet(lowercase				,       lowercase				,       **lowercase				)
							lowercase_	:   str  				=     _DataSet(lowercase				,       lowercase				,       **lowercase				)

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






from typing import Dict, List, Optional, Union

import numpy as np

from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_valid_image,
    to_numpy_array,
    valid_images,
)
from ...utils import logging


if is_vision_available():
							import PIL

UpperCAmelCase_							=				logging.get_logger(__name__)





def  __magic_name__  (				lowercase				)				-> List[List[ImageInput]]:

							"""simple docstring"""



							if isinstance(lowercase				,       (list, tuple)				) and isinstance(videos[0]				,       (list, tuple)				) and is_valid_image(videos[0][0]				):
														return videos

							elif isinstance(lowercase				,       (list, tuple)				) and is_valid_image(videos[0]				):
														return [videos]

							elif is_valid_image(lowercase				):
														return [[videos]]

							raise ValueError(f"""Could not make batched video from {videos}"""				)




class     UpperCamelCase__					(     lowerCamelCase__				):






		'''simple docstring'''






		__a       :       List[Any]       =	["""pixel_values"""]






		def __init__(							self,   snake_case__ = True,   snake_case__ = None,   snake_case__ = PILImageResampling.BILINEAR,   snake_case__ = True,   snake_case__ = None,   snake_case__ = True,   snake_case__ = 1 / 2_55,   snake_case__ = True,   snake_case__ = True,   snake_case__ = None,   snake_case__ = None,   **snake_case__,   )       -> None:






									"""simple docstring"""
									super().__init__(**snake_case__  )
									lowercase_	:   str  				=     size if size is not None else {"""shortest_edge""": 2_56}
									lowercase_	:   int  				=     get_size_dict(snake_case__,   default_to_square=snake_case__  )
									lowercase_	:   int  				=     crop_size if crop_size is not None else {"""height""": 2_24, """width""": 2_24}
									lowercase_	:   Optional[int]  				=     get_size_dict(snake_case__,   param_name="""crop_size"""  )

									lowercase_	:   List[Any]  				=     do_resize
									lowercase_	:   int  				=     size
									lowercase_	:   int  				=     do_center_crop
									lowercase_	:   Optional[Any]  				=     crop_size
									lowercase_	:   str  				=     resample
									lowercase_	:   Any  				=     do_rescale
									lowercase_	:   Dict  				=     rescale_factor
									lowercase_	:   List[Any]  				=     offset
									lowercase_	:   int  				=     do_normalize
									lowercase_	:   str  				=     image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
									lowercase_	:   Tuple  				=     image_std if image_std is not None else IMAGENET_STANDARD_STD






		def 						snake_case__				(							self,   snake_case__,   snake_case__,   snake_case__ = PILImageResampling.BILINEAR,   snake_case__ = None,   **snake_case__,   )       -> np.ndarray:






									"""simple docstring"""
									lowercase_	:   int  				=     get_size_dict(snake_case__,   default_to_square=snake_case__  )
									if "shortest_edge" in size:
																lowercase_	:   Union[str, Any]  				=     get_resize_output_image_size(snake_case__,   size["""shortest_edge"""],   default_to_square=snake_case__  )
									elif "height" in size and "width" in size:
																lowercase_	:   Dict  				=     (size["""height"""], size["""width"""])
									else:
																raise ValueError(f"""Size must have 'height' and 'width' or 'shortest_edge' as keys. Got {size.keys()}"""  )
									return resize(snake_case__,   size=snake_case__,   resample=snake_case__,   data_format=snake_case__,   **snake_case__  )






		def 						snake_case__				(							self,   snake_case__,   snake_case__,   snake_case__ = None,   **snake_case__,   )       -> np.ndarray:






									"""simple docstring"""
									lowercase_	:   List[Any]  				=     get_size_dict(snake_case__  )
									if "height" not in size or "width" not in size:
																raise ValueError(f"""Size must have 'height' and 'width' as keys. Got {size.keys()}"""  )
									return center_crop(snake_case__,   size=(size["""height"""], size["""width"""]),   data_format=snake_case__,   **snake_case__  )






		def 						snake_case__				(							self,   snake_case__,   snake_case__,   snake_case__ = True,   snake_case__ = None,   **snake_case__,   )       -> str:






									"""simple docstring"""
									lowercase_	:   Dict  				=     image.astype(np.floataa  )
									if offset:
																lowercase_	:   Optional[Any]  				=     image - (scale / 2)
									return rescale(snake_case__,   scale=snake_case__,   data_format=snake_case__,   **snake_case__  )






		def 						snake_case__				(							self,   snake_case__,   snake_case__,   snake_case__,   snake_case__ = None,   **snake_case__,   )       -> np.ndarray:






									"""simple docstring"""
									return normalize(snake_case__,   mean=snake_case__,   std=snake_case__,   data_format=snake_case__,   **snake_case__  )






		def 						snake_case__				(							self,   snake_case__,   snake_case__ = None,   snake_case__ = None,   snake_case__ = None,   snake_case__ = None,   snake_case__ = None,   snake_case__ = None,   snake_case__ = None,   snake_case__ = None,   snake_case__ = None,   snake_case__ = None,   snake_case__ = None,   snake_case__ = ChannelDimension.FIRST,   )       -> np.ndarray:






									"""simple docstring"""
									if do_resize and size is None or resample is None:
																raise ValueError("""Size and resample must be specified if do_resize is True."""  )

									if do_center_crop and crop_size is None:
																raise ValueError("""Crop size must be specified if do_center_crop is True."""  )

									if do_rescale and rescale_factor is None:
																raise ValueError("""Rescale factor must be specified if do_rescale is True."""  )

									if do_normalize and (image_mean is None or image_std is None):
																raise ValueError("""Image mean and std must be specified if do_normalize is True."""  )

									if offset and not do_rescale:
																raise ValueError("""For offset, do_rescale must also be set to True."""  )

									# All transformations expect numpy arrays.
									lowercase_	:   Optional[int]  				=     to_numpy_array(snake_case__  )

									if do_resize:
																lowercase_	:   Dict  				=     self.resize(image=snake_case__,   size=snake_case__,   resample=snake_case__  )

									if do_center_crop:
																lowercase_	:   Optional[int]  				=     self.center_crop(snake_case__,   size=snake_case__  )

									if do_rescale:
																lowercase_	:   Dict  				=     self.rescale(image=snake_case__,   scale=snake_case__,   offset=snake_case__  )

									if do_normalize:
																lowercase_	:   Optional[Any]  				=     self.normalize(image=snake_case__,   mean=snake_case__,   std=snake_case__  )

									lowercase_	:   Tuple  				=     to_channel_dimension_format(snake_case__,   snake_case__  )
									return image






		def 						snake_case__				(							self,   snake_case__,   snake_case__ = None,   snake_case__ = None,   snake_case__ = None,   snake_case__ = None,   snake_case__ = None,   snake_case__ = None,   snake_case__ = None,   snake_case__ = None,   snake_case__ = None,   snake_case__ = None,   snake_case__ = None,   snake_case__ = None,   snake_case__ = ChannelDimension.FIRST,   **snake_case__,   )       -> PIL.Image.Image:






									"""simple docstring"""
									lowercase_	:   Any  				=     do_resize if do_resize is not None else self.do_resize
									lowercase_	:   str  				=     resample if resample is not None else self.resample
									lowercase_	:   List[str]  				=     do_center_crop if do_center_crop is not None else self.do_center_crop
									lowercase_	:   Optional[Any]  				=     do_rescale if do_rescale is not None else self.do_rescale
									lowercase_	:   str  				=     rescale_factor if rescale_factor is not None else self.rescale_factor
									lowercase_	:   List[Any]  				=     offset if offset is not None else self.offset
									lowercase_	:   Optional[Any]  				=     do_normalize if do_normalize is not None else self.do_normalize
									lowercase_	:   Any  				=     image_mean if image_mean is not None else self.image_mean
									lowercase_	:   Optional[Any]  				=     image_std if image_std is not None else self.image_std

									lowercase_	:   Optional[Any]  				=     size if size is not None else self.size
									lowercase_	:   Dict  				=     get_size_dict(snake_case__,   default_to_square=snake_case__  )
									lowercase_	:   Union[str, Any]  				=     crop_size if crop_size is not None else self.crop_size
									lowercase_	:   Tuple  				=     get_size_dict(snake_case__,   param_name="""crop_size"""  )

									if not valid_images(snake_case__  ):
																raise ValueError(
																    """Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
																    """torch.Tensor, tf.Tensor or jax.ndarray."""  )

									lowercase_	:   Optional[Any]  				=     make_batched(snake_case__  )

									lowercase_	:   Optional[Any]  				=     [
									    [
									        self._preprocess_image(
									            image=snake_case__,   do_resize=snake_case__,   size=snake_case__,   resample=snake_case__,   do_center_crop=snake_case__,   crop_size=snake_case__,   do_rescale=snake_case__,   rescale_factor=snake_case__,   offset=snake_case__,   do_normalize=snake_case__,   image_mean=snake_case__,   image_std=snake_case__,   data_format=snake_case__,   )
									        for img in video
									    ]
									    for video in videos
									]

									lowercase_	:   List[str]  				=     {"""pixel_values""": videos}
									return BatchFeature(data=snake_case__,   tensor_type=snake_case__  )