


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

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

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


def 			UpperCAmelCase_				(      UpperCAmelCase__  ):
					lowercase_  							=			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_				(      UpperCAmelCase__  ):
					print("""Extracting"""						,							f.name  )
					with gzip.GzipFile(fileobj=UpperCAmelCase__  ) as bytestream:
										lowercase_  							=			_readaa(UpperCAmelCase__  )
										if magic != 2_0_5_1:
															raise ValueError(
															    """Invalid magic number %d in MNIST image file: %s""" % (magic, f.name)  )
										lowercase_  							=			_readaa(UpperCAmelCase__  )
										lowercase_  							=			_readaa(UpperCAmelCase__  )
										lowercase_  							=			_readaa(UpperCAmelCase__  )
										lowercase_  							=			bytestream.read(rows * cols * num_images  )
										lowercase_  							=			numpy.frombuffer(UpperCAmelCase__						,							dtype=numpy.uinta  )
										lowercase_  							=			data.reshape(UpperCAmelCase__						,							UpperCAmelCase__						,							UpperCAmelCase__						,							1  )
										return data


@deprecated(UpperCAmelCase__						,							"""Please use tf.one_hot on tensors."""  )
def 			UpperCAmelCase_				(      UpperCAmelCase__						,							UpperCAmelCase__  ):
					lowercase_  							=			labels_dense.shape[0]
					lowercase_  							=			numpy.arange(UpperCAmelCase__  ) * num_classes
					lowercase_  							=			numpy.zeros((num_labels, num_classes)  )
					lowercase_  							=			1
					return labels_one_hot


@deprecated(UpperCAmelCase__						,							"""Please use tf.data to implement this functionality."""  )
def 			UpperCAmelCase_				(      UpperCAmelCase__						,							UpperCAmelCase__=False						,							UpperCAmelCase__=1_0  ):
					print("""Extracting"""						,							f.name  )
					with gzip.GzipFile(fileobj=UpperCAmelCase__  ) as bytestream:
										lowercase_  							=			_readaa(UpperCAmelCase__  )
										if magic != 2_0_4_9:
															raise ValueError(
															    """Invalid magic number %d in MNIST label file: %s""" % (magic, f.name)  )
										lowercase_  							=			_readaa(UpperCAmelCase__  )
										lowercase_  							=			bytestream.read(UpperCAmelCase__  )
										lowercase_  							=			numpy.frombuffer(UpperCAmelCase__						,							dtype=numpy.uinta  )
										if one_hot:
															return _dense_to_one_hot(UpperCAmelCase__						,							UpperCAmelCase__  )
										return labels







class        UpperCamelCase__   :

					@deprecated(
					    UpperCamelCase__							,					"""Please use alternatives such as official/mnist/_DataSet.py"""
					    """ from tensorflow/models."""							,					)
					def __init__(     self       :   str							,					UpperCamelCase__       :   Optional[Any]							,					UpperCamelCase__       :   Tuple							,					UpperCamelCase__       :   Union[str, Any]=False							,					UpperCamelCase__       :   List[str]=False							,					UpperCamelCase__       :   Optional[Any]=dtypes.floataa							,					UpperCamelCase__       :   Optional[Any]=True							,					UpperCamelCase__       :   str=None							,					):






										'''simple docstring'''






										lowercase_							,					lowercase_  							=			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       )
										lowercase_  							=			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:
															lowercase_  							=			10_000
															lowercase_  							=			one_hot
										else:
															assert (
															    images.shape[0] == labels.shape[0]
															), F'''images.shape: {images.shape} labels.shape: {labels.shape}'''
															lowercase_  							=			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_  							=			images.reshape(
																				    images.shape[0]							,					images.shape[1] * images.shape[2]       )
															if dtype == dtypes.floataa:
																				# Convert from [0, 255] -> [0.0, 1.0].
																				lowercase_  							=			images.astype(numpy.floataa       )
																				lowercase_  							=			numpy.multiply(UpperCamelCase__							,					1.0 / 255.0       )
										lowercase_  							=			images
										lowercase_  							=			labels
										lowercase_  							=			0
										lowercase_  							=			0

					@property
					def   UpperCAmelCase__       (     self       :   Tuple       ):






										'''simple docstring'''






										return self._images

					@property
					def   UpperCAmelCase__       (     self       :   List[Any]       ):






										'''simple docstring'''






										return self._labels

					@property
					def   UpperCAmelCase__       (     self       :   Tuple       ):






										'''simple docstring'''






										return self._num_examples

					@property
					def   UpperCAmelCase__       (     self       :   Tuple       ):






										'''simple docstring'''






										return self._epochs_completed







					def   UpperCAmelCase__       (     self       :   List[Any]							,					UpperCamelCase__       :   Tuple							,					UpperCamelCase__       :   Dict=False							,					UpperCamelCase__       :   int=True       ):






										'''simple docstring'''






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



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



@deprecated(
    UpperCAmelCase__						,							"""Please use alternatives such as:""" """ tensorflow_datasets.load('mnist')"""  )
def 			UpperCAmelCase_				(      UpperCAmelCase__						,							UpperCAmelCase__=False						,							UpperCAmelCase__=False						,							UpperCAmelCase__=dtypes.floataa						,							UpperCAmelCase__=True						,							UpperCAmelCase__=5_0_0_0						,							UpperCAmelCase__=None						,							UpperCAmelCase__=DEFAULT_SOURCE_URL						,							):
					if fake_data:

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

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

					if not source_url:  # empty string check
										lowercase_  							=			DEFAULT_SOURCE_URL

					lowercase_  							=			"""train-images-idx3-ubyte.gz"""
					lowercase_  							=			"""train-labels-idx1-ubyte.gz"""
					lowercase_  							=			"""t10k-images-idx3-ubyte.gz"""
					lowercase_  							=			"""t10k-labels-idx1-ubyte.gz"""

					lowercase_  							=			_maybe_download(
					    UpperCAmelCase__						,							UpperCAmelCase__						,							source_url + train_images_file  )
					with gfile.Open(UpperCAmelCase__						,							"""rb"""  ) as f:
										lowercase_  							=			_extract_images(UpperCAmelCase__  )

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

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

					lowercase_  							=			_maybe_download(
					    UpperCAmelCase__						,							UpperCAmelCase__						,							source_url + test_labels_file  )
					with gfile.Open(UpperCAmelCase__						,							"""rb"""  ) as f:
										lowercase_  							=			_extract_labels(UpperCAmelCase__						,							one_hot=UpperCAmelCase__  )

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

					lowercase_  							=			train_images[:validation_size]
					lowercase_  							=			train_labels[:validation_size]
					lowercase_  							=			train_images[validation_size:]
					lowercase_  							=			train_labels[validation_size:]

					lowercase_  							=			{"""dtype""": dtype, """reshape""": reshape, """seed""": seed}

					lowercase_  							=			_DataSet(UpperCAmelCase__						,							UpperCAmelCase__						,							**UpperCAmelCase__  )
					lowercase_  							=			_DataSet(UpperCAmelCase__						,							UpperCAmelCase__						,							**UpperCAmelCase__  )
					lowercase_  							=			_DataSet(UpperCAmelCase__						,							UpperCAmelCase__						,							**UpperCAmelCase__  )

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






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_speech_available,
    is_torch_available,
)


a					  =     {
    'configuration_trocr': ['TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP', 'TrOCRConfig'],
    'processing_trocr': ['TrOCRProcessor'],
}


try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							a					  =     [
							    'TROCR_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'TrOCRForCausalLM',
							    'TrOCRPreTrainedModel',
							]


if TYPE_CHECKING:
							from .configuration_trocr import TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP, TrOCRConfig
							from .processing_trocr import TrOCRProcessor

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_trocr import TROCR_PRETRAINED_MODEL_ARCHIVE_LIST, TrOCRForCausalLM, TrOCRPreTrainedModel

else:
							import sys

							a					  =     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


