
'''simple docstring'''







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

lowercase    :							Optional[int]      =					collections.namedtuple("_Datasets", ["train", "validation", "test"])

# CVDF mirror of http://yann.lecun.com/exdb/mnist/
lowercase    :							Union[str, Any]      =					"https://storage.googleapis.com/cvdf-datasets/mnist/"







def 				SCREAMING_SNAKE_CASE__	(   __A	)       ->	Dict:
	_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 				SCREAMING_SNAKE_CASE__	(   __A	)       ->	Tuple:
	print('Extracting'			,				f.name	)
	with gzip.GzipFile(fileobj=__A	) as bytestream:
		_snake_case   						=		_readaa(__A	)
		if magic != 2_051:
			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 				SCREAMING_SNAKE_CASE__	(   __A			,				__A	)       ->	List[str]:
	_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 				SCREAMING_SNAKE_CASE__	(   __A			,				__A=False			,				__A=10	)       ->	Optional[int]:
	print('Extracting'			,				f.name	)
	with gzip.GzipFile(fileobj=__A	) as bytestream:
		_snake_case   						=		_readaa(__A	)
		if magic != 2_049:
			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 						__UpperCAmelCase      :


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





								"""simple docstring"""






								_snake_case						, _snake_case   						=		random_seed.get_seed(lowerCAmelCase_  )
								# 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(lowerCAmelCase_  ).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_00_00
									_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(lowerCAmelCase_    ,   1.0 / 255.0  )
								_snake_case   						=		images
								_snake_case   						=		labels
								_snake_case   						=		0
								_snake_case   						=		0


							@property
							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






								return self._images


							@property
							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






								return self._labels


							@property
							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






								return self._num_examples


							@property
							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






								return self._epochs_completed




							def  lowerCamelCase				(       self    ,   lowerCAmelCase_    ,   lowerCAmelCase_=False    ,   lowerCAmelCase_=True  ):





								"""simple docstring"""






								if fake_data:
									_snake_case   						=		[1] * 7_84
									_snake_case   						=		[1] + [0] * 9 if self.one_hot else 0
									return (
									    [fake_image for _ in range(lowerCAmelCase_  )],
									    [fake_label for _ in range(lowerCAmelCase_  )],
									)
								_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(lowerCAmelCase_  )
									_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(lowerCAmelCase_  )
										_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 				SCREAMING_SNAKE_CASE__	(   __A			,				__A			,				__A	)       ->	Dict:
	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 				SCREAMING_SNAKE_CASE__	(   __A			,				__A=False			,				__A=False			,				__A=dtypes.floataa			,				__A=True			,				__A=5_000			,				__A=None			,				__A=DEFAULT_SOURCE_URL			,				)       ->	Union[str, Any]:
	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	)



'''simple docstring'''







import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
					from transformers import AutoModelForSeqaSeqLM, AutoTokenizer


@require_torch
@require_sentencepiece
@require_tokenizers
class 						__UpperCAmelCase      (					unittest.TestCase						):


							@slow
							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






								_snake_case   						=		AutoModelForSeqaSeqLM.from_pretrained('google/mt5-small'    ,   return_dict=lowerCAmelCase_  ).to(lowerCAmelCase_  )
								_snake_case   						=		AutoTokenizer.from_pretrained('google/mt5-small'  )

								_snake_case   						=		tokenizer('Hello there'    ,   return_tensors='pt'  ).input_ids
								_snake_case   						=		tokenizer('Hi I am'    ,   return_tensors='pt'  ).input_ids

								_snake_case   						=		model(input_ids.to(lowerCAmelCase_  )    ,   labels=labels.to(lowerCAmelCase_  )  ).loss
								_snake_case   						=		-(labels.shape[-1] * loss.item())

								_snake_case   						=		-84.9127
								self.assertTrue(abs(mtf_score - EXPECTED_SCORE  ) < 1E-4  )

