
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_  :	List[Any] 				=					"""https://storage.googleapis.com/cvdf-datasets/mnist/"""







def     A__   (  lowerCamelCase  )					->		Union[str, Any]:
				UpperCamelCase_:							Optional[int]		=  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  )					->		List[Any]:
				print("""Extracting"""       ,      f.name  )
				with gzip.GzipFile(fileobj=lowerCamelCase  ) as bytestream:
								UpperCamelCase_:							str		=  _readaa(lowerCamelCase  )
								if magic != 20_51:
												raise ValueError(
												    """Invalid magic number %d in MNIST image file: %s""" % (magic, f.name)  )
								UpperCamelCase_:							int		=  _readaa(lowerCamelCase  )
								UpperCamelCase_:							int		=  _readaa(lowerCamelCase  )
								UpperCamelCase_:							Tuple		=  _readaa(lowerCamelCase  )
								UpperCamelCase_:							Dict		=  bytestream.read(rows * cols * num_images  )
								UpperCamelCase_:							Tuple		=  numpy.frombuffer(lowerCamelCase       ,      dtype=numpy.uinta  )
								UpperCamelCase_:							List[str]		=  data.reshape(lowerCamelCase       ,      lowerCamelCase       ,      lowerCamelCase       ,      1  )
								return data







@deprecated(lowerCamelCase       ,      """Please use tf.one_hot on tensors."""  )
def     A__   (  lowerCamelCase       ,      lowerCamelCase  )					->		List[Any]:
				UpperCamelCase_:							Optional[int]		=  labels_dense.shape[0]
				UpperCamelCase_:							Any		=  numpy.arange(lowerCamelCase  ) * num_classes
				UpperCamelCase_:							List[Any]		=  numpy.zeros((num_labels, num_classes)  )
				UpperCamelCase_:							int		=  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_:							Optional[Any]		=  _readaa(lowerCamelCase  )
								if magic != 20_49:
												raise ValueError(
												    """Invalid magic number %d in MNIST label file: %s""" % (magic, f.name)  )
								UpperCamelCase_:							Dict		=  _readaa(lowerCamelCase  )
								UpperCamelCase_:							Optional[Any]		=  bytestream.read(lowerCamelCase  )
								UpperCamelCase_:							int		=  numpy.frombuffer(lowerCamelCase       ,      dtype=numpy.uinta  )
								if one_hot:
												return _dense_to_one_hot(lowerCamelCase       ,      lowerCamelCase  )
								return labels

class     _UpperCamelCase			:






			'''simple docstring'''

			@deprecated(
			    snake_case_  ,		"""Please use alternatives such as official/mnist/_DataSet.py"""
			    """ from tensorflow/models."""  ,		)
			def __init__(    self	:    Union[str, Any]  ,		snake_case_	:    Any  ,		snake_case_	:    Optional[Any]  ,		snake_case_	:    int=False  ,		snake_case_	:    Dict=False  ,		snake_case_	:    List[Any]=dtypes.floataa  ,		snake_case_	:    List[str]=True  ,		snake_case_	:    List[Any]=None  ,		):
							UpperCamelCase_,				UpperCamelCase_:							str		=  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 )
							UpperCamelCase_:							List[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:
											UpperCamelCase_:							Dict		=  1_0000
											UpperCamelCase_:							Optional[Any]		=  one_hot
							else:
											assert (
											    images.shape[0] == labels.shape[0]
											), f'''images.shape: {images.shape} labels.shape: {labels.shape}'''
											UpperCamelCase_:							List[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_:							Any		=  images.astype(numpy.floataa )
															UpperCamelCase_:							Optional[int]		=  numpy.multiply(snake_case_  ,		1.0 / 255.0 )
							UpperCamelCase_:							Tuple		=  images
							UpperCamelCase_:							str		=  labels
							UpperCamelCase_:							Tuple		=  0
							UpperCamelCase_:							str		=  0

			@property
			def  lowerCAmelCase__		(    self	:    List[Any] ):
							return self._images

			@property
			def  lowerCAmelCase__		(    self	:    List[Any] ):
							return self._labels

			@property
			def  lowerCAmelCase__		(    self	:    Union[str, Any] ):
							return self._num_examples

			@property
			def  lowerCAmelCase__		(    self	:    Tuple ):
							return self._epochs_completed




			def  lowerCAmelCase__		(    self	:    Optional[Any]  ,		snake_case_	:    List[Any]  ,		snake_case_	:    int=False  ,		snake_case_	:    int=True ):
							if fake_data:
											UpperCamelCase_:							Optional[Any]		=  [1] * 784
											UpperCamelCase_:							List[str]		=  [1] + [0] * 9 if self.one_hot else 0
											return (
											    [fake_image for _ in range(snake_case_ )],
											    [fake_label for _ in range(snake_case_ )],
											)
							UpperCamelCase_:							Any		=  self._index_in_epoch
							# Shuffle for the first epoch
							if self._epochs_completed == 0 and start == 0 and shuffle:
											UpperCamelCase_:							int		=  numpy.arange(self._num_examples )
											numpy.random.shuffle(snake_case_ )
											UpperCamelCase_:							Tuple		=  self.images[perma]
											UpperCamelCase_:							str		=  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_:							Dict		=  self._num_examples - start
											UpperCamelCase_:							Tuple		=  self._images[start : self._num_examples]
											UpperCamelCase_:							Any		=  self._labels[start : self._num_examples]
											# Shuffle the data
											if shuffle:
															UpperCamelCase_:							Optional[Any]		=  numpy.arange(self._num_examples )
															numpy.random.shuffle(snake_case_ )
															UpperCamelCase_:							Optional[int]		=  self.images[perm]
															UpperCamelCase_:							Optional[Any]		=  self.labels[perm]
											# Start next epoch
											UpperCamelCase_:							Dict		=  0
											UpperCamelCase_:							Any		=  batch_size - rest_num_examples
											UpperCamelCase_:							Optional[int]		=  self._index_in_epoch
											UpperCamelCase_:							int		=  self._images[start:end]
											UpperCamelCase_:							List[str]		=  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_:							Tuple		=  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  )					->		str:
				if not gfile.Exists(lowerCamelCase  ):
								gfile.MakeDirs(lowerCamelCase  )
				UpperCamelCase_:							List[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_:							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=50_00       ,      lowerCamelCase=None       ,      lowerCamelCase=DEFAULT_SOURCE_URL       ,      )					->		Union[str, Any]:
				if fake_data:

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

								UpperCamelCase_:							Any		=  fake()
								UpperCamelCase_:							Tuple		=  fake()
								UpperCamelCase_:							Union[str, Any]		=  fake()
								return _Datasets(train=lowerCamelCase       ,      validation=lowerCamelCase       ,      test=lowerCamelCase  )

				if not source_url:  # empty string check
								UpperCamelCase_:							Any		=  DEFAULT_SOURCE_URL

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

				UpperCamelCase_:							str		=  _maybe_download(
				    lowerCamelCase       ,      lowerCamelCase       ,      source_url + train_images_file  )
				with gfile.Open(lowerCamelCase       ,      """rb"""  ) as f:
								UpperCamelCase_:							Optional[Any]		=  _extract_images(lowerCamelCase  )

				UpperCamelCase_:							Optional[Any]		=  _maybe_download(
				    lowerCamelCase       ,      lowerCamelCase       ,      source_url + train_labels_file  )
				with gfile.Open(lowerCamelCase       ,      """rb"""  ) as f:
								UpperCamelCase_:							Any		=  _extract_labels(lowerCamelCase       ,      one_hot=lowerCamelCase  )

				UpperCamelCase_:							int		=  _maybe_download(
				    lowerCamelCase       ,      lowerCamelCase       ,      source_url + test_images_file  )
				with gfile.Open(lowerCamelCase       ,      """rb"""  ) as f:
								UpperCamelCase_:							Any		=  _extract_images(lowerCamelCase  )

				UpperCamelCase_:							Union[str, Any]		=  _maybe_download(
				    lowerCamelCase       ,      lowerCamelCase       ,      source_url + test_labels_file  )
				with gfile.Open(lowerCamelCase       ,      """rb"""  ) as f:
								UpperCamelCase_:							Dict		=  _extract_labels(lowerCamelCase       ,      one_hot=lowerCamelCase  )

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

				UpperCamelCase_:							Tuple		=  train_images[:validation_size]
				UpperCamelCase_:							Optional[int]		=  train_labels[:validation_size]
				UpperCamelCase_:							List[str]		=  train_images[validation_size:]
				UpperCamelCase_:							Dict		=  train_labels[validation_size:]

				UpperCamelCase_:							Union[str, Any]		=  {"""dtype""": dtype, """reshape""": reshape, """seed""": seed}

				UpperCamelCase_:							int		=  _DataSet(lowerCamelCase       ,      lowerCamelCase       ,      **lowerCamelCase  )
				UpperCamelCase_:							Dict		=  _DataSet(lowerCamelCase       ,      lowerCamelCase       ,      **lowerCamelCase  )
				UpperCamelCase_:							Optional[int]		=  _DataSet(lowerCamelCase       ,      lowerCamelCase       ,      **lowerCamelCase  )

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








import json
import os
import tempfile
import unittest

import numpy as np
from datasets import load_dataset

from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin


if is_torch_available():
							import torch

if is_vision_available():
							from PIL import Image

							from transformers import ImageGPTImageProcessor

class     _UpperCamelCase			(   unittest.TestCase					):






			'''simple docstring'''

			def __init__(    self	:    Optional[int]  ,		snake_case_	:    Dict  ,		snake_case_	:    Tuple=7  ,		snake_case_	:    Optional[Any]=3  ,		snake_case_	:    Dict=18  ,		snake_case_	:    Dict=30  ,		snake_case_	:    Union[str, Any]=400  ,		snake_case_	:    List[Any]=True  ,		snake_case_	:    Any=None  ,		snake_case_	:    List[str]=True  ,		):
							UpperCamelCase_:							Dict		=  size if size is not None else {"""height""": 18, """width""": 18}
							UpperCamelCase_:							Union[str, Any]		=  parent
							UpperCamelCase_:							Tuple		=  batch_size
							UpperCamelCase_:							List[str]		=  num_channels
							UpperCamelCase_:							Optional[int]		=  image_size
							UpperCamelCase_:							Dict		=  min_resolution
							UpperCamelCase_:							Optional[int]		=  max_resolution
							UpperCamelCase_:							str		=  do_resize
							UpperCamelCase_:							Tuple		=  size
							UpperCamelCase_:							Dict		=  do_normalize




			def  lowerCAmelCase__		(    self	:    str ):
							return {
							    # here we create 2 clusters for the sake of simplicity
							    "clusters": np.asarray(
							        [
							            [0.8866_4436_3403_3203, 0.6618_8293_6954_4983, 0.3891_7464_0178_6804],
							            [-0.6042_5591_4688_1104, -0.0_2295_0088_6052_8469, 0.5423_7973_6900_3296],
							        ] ),
							    "do_resize": self.do_resize,
							    "size": self.size,
							    "do_normalize": self.do_normalize,
							}




@require_torch
@require_vision
class     _UpperCamelCase			(   _A       ,    unittest.TestCase					):






			'''simple docstring'''

			__UpperCamelCase :  Optional[int]    =	ImageGPTImageProcessor if is_vision_available() else None

			def  lowerCAmelCase__		(    self	:    List[str] ):
							UpperCamelCase_:							Any		=  ImageGPTImageProcessingTester(self )

			@property
			def  lowerCAmelCase__		(    self	:    Optional[int] ):
							return self.image_processor_tester.prepare_image_processor_dict()

			def  lowerCAmelCase__		(    self	:    Optional[int] ):
							UpperCamelCase_:							List[Any]		=  self.image_processing_class(**self.image_processor_dict )
							self.assertTrue(hasattr(snake_case_  ,		"""clusters""" ) )
							self.assertTrue(hasattr(snake_case_  ,		"""do_resize""" ) )
							self.assertTrue(hasattr(snake_case_  ,		"""size""" ) )
							self.assertTrue(hasattr(snake_case_  ,		"""do_normalize""" ) )

			def  lowerCAmelCase__		(    self	:    int ):
							UpperCamelCase_:							List[Any]		=  self.image_processing_class.from_dict(self.image_processor_dict )
							self.assertEqual(image_processor.size  ,		{"""height""": 18, """width""": 18} )

							UpperCamelCase_:							Tuple		=  self.image_processing_class.from_dict(self.image_processor_dict  ,		size=42 )
							self.assertEqual(image_processor.size  ,		{"""height""": 42, """width""": 42} )

			def  lowerCAmelCase__		(    self	:    Optional[Any] ):
							UpperCamelCase_:							int		=  self.image_processing_class(**self.image_processor_dict )
							UpperCamelCase_:							Optional[int]		=  json.loads(image_processor.to_json_string() )
							for key, value in self.image_processor_dict.items():
											if key == "clusters":
															self.assertTrue(np.array_equal(snake_case_  ,		obj[key] ) )
											else:
															self.assertEqual(obj[key]  ,		snake_case_ )

			def  lowerCAmelCase__		(    self	:    List[Any] ):
							UpperCamelCase_:							Dict		=  self.image_processing_class(**self.image_processor_dict )

							with tempfile.TemporaryDirectory() as tmpdirname:
											UpperCamelCase_:							int		=  os.path.join(snake_case_  ,		"""image_processor.json""" )
											image_processor_first.to_json_file(snake_case_ )
											UpperCamelCase_:							Any		=  self.image_processing_class.from_json_file(snake_case_ ).to_dict()

							UpperCamelCase_:							str		=  image_processor_first.to_dict()
							for key, value in image_processor_first.items():
											if key == "clusters":
															self.assertTrue(np.array_equal(snake_case_  ,		image_processor_second[key] ) )
											else:
															self.assertEqual(image_processor_first[key]  ,		snake_case_ )

			def  lowerCAmelCase__		(    self	:    Union[str, Any] ):
							UpperCamelCase_:							Optional[Any]		=  self.image_processing_class(**self.image_processor_dict )

							with tempfile.TemporaryDirectory() as tmpdirname:
											image_processor_first.save_pretrained(snake_case_ )
											UpperCamelCase_:							Optional[int]		=  self.image_processing_class.from_pretrained(snake_case_ ).to_dict()

							UpperCamelCase_:							Union[str, Any]		=  image_processor_first.to_dict()
							for key, value in image_processor_first.items():
											if key == "clusters":
															self.assertTrue(np.array_equal(snake_case_  ,		image_processor_second[key] ) )
											else:
															self.assertEqual(image_processor_first[key]  ,		snake_case_ )




			@unittest.skip("""ImageGPT requires clusters at initialization""" )
			def  lowerCAmelCase__		(    self	:    List[Any] ):
							pass







def     A__   (  )					->		Optional[int]:
				UpperCamelCase_:							Optional[int]		=  load_dataset("""hf-internal-testing/fixtures_image_utils"""       ,      split="""test"""  )

				UpperCamelCase_:							Tuple		=  Image.open(dataset[4]["""file"""]  )
				UpperCamelCase_:							Union[str, Any]		=  Image.open(dataset[5]["""file"""]  )

				UpperCamelCase_:							List[str]		=  [imagea, imagea]

				return images





@require_vision
@require_torch
class     _UpperCamelCase			(   unittest.TestCase					):






			'''simple docstring'''

			@slow
			def  lowerCAmelCase__		(    self	:    List[str] ):
							UpperCamelCase_:							List[Any]		=  ImageGPTImageProcessor.from_pretrained("""openai/imagegpt-small""" )

							UpperCamelCase_:							List[str]		=  prepare_images()

							# test non-batched
							UpperCamelCase_:							List[str]		=  image_processing(images[0]  ,		return_tensors="""pt""" )

							self.assertIsInstance(encoding.input_ids  ,		torch.LongTensor )
							self.assertEqual(encoding.input_ids.shape  ,		(1, 1024) )

							UpperCamelCase_:							Union[str, Any]		=  [306, 191, 191]
							self.assertEqual(encoding.input_ids[0, :3].tolist()  ,		snake_case_ )

							# test batched
							UpperCamelCase_:							Optional[int]		=  image_processing(snake_case_  ,		return_tensors="""pt""" )

							self.assertIsInstance(encoding.input_ids  ,		torch.LongTensor )
							self.assertEqual(encoding.input_ids.shape  ,		(2, 1024) )

							UpperCamelCase_:							str		=  [303, 13, 13]
							self.assertEqual(encoding.input_ids[1, -3:].tolist()  ,		snake_case_ )






