





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 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				Union[str, Any]     ):
		SCREAMING_SNAKE_CASE__								=   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 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				List[Any]     ):
		print("""Extracting"""       , f.name     )
		with gzip.GzipFile(fileobj=UpperCamelCase__     ) as bytestream:
				SCREAMING_SNAKE_CASE__								=   _readaa(UpperCamelCase__     )
				if magic != 2_051:
						raise ValueError(
						    """Invalid magic number %d in MNIST image file: %s""" % (magic, f.name)     )
				SCREAMING_SNAKE_CASE__								=   _readaa(UpperCamelCase__     )
				SCREAMING_SNAKE_CASE__								=   _readaa(UpperCamelCase__     )
				SCREAMING_SNAKE_CASE__								=   _readaa(UpperCamelCase__     )
				SCREAMING_SNAKE_CASE__								=   bytestream.read(rows * cols * num_images     )
				SCREAMING_SNAKE_CASE__								=   numpy.frombuffer(UpperCamelCase__       , dtype=numpy.uinta     )
				SCREAMING_SNAKE_CASE__								=   data.reshape(UpperCamelCase__       , UpperCamelCase__       , UpperCamelCase__       , 1     )
				return data






@deprecated(UpperCamelCase__       , """Please use tf.one_hot on tensors."""     )
def 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				List[Any]       , UpperCamelCase__:				str     ):
		SCREAMING_SNAKE_CASE__								=   labels_dense.shape[0]
		SCREAMING_SNAKE_CASE__								=   numpy.arange(UpperCamelCase__     ) * num_classes
		SCREAMING_SNAKE_CASE__								=   numpy.zeros((num_labels, num_classes)     )
		SCREAMING_SNAKE_CASE__								=   1
		return labels_one_hot






@deprecated(UpperCamelCase__       , """Please use tf.data to implement this functionality."""     )
def 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				Optional[Any]       , UpperCamelCase__:				Union[str, Any]=False       , UpperCamelCase__:				List[Any]=10     ):
		print("""Extracting"""       , f.name     )
		with gzip.GzipFile(fileobj=UpperCamelCase__     ) as bytestream:
				SCREAMING_SNAKE_CASE__								=   _readaa(UpperCamelCase__     )
				if magic != 2_049:
						raise ValueError(
						    """Invalid magic number %d in MNIST label file: %s""" % (magic, f.name)     )
				SCREAMING_SNAKE_CASE__								=   _readaa(UpperCamelCase__     )
				SCREAMING_SNAKE_CASE__								=   bytestream.read(UpperCamelCase__     )
				SCREAMING_SNAKE_CASE__								=   numpy.frombuffer(UpperCamelCase__       , dtype=numpy.uinta     )
				if one_hot:
						return _dense_to_one_hot(UpperCamelCase__       , UpperCamelCase__     )
				return labels




class 	UpperCamelCase_							:


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



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__								=   random_seed.get_seed(__A		)
									# If op level seed is not set, use whatever graph level seed is returned
									numpy.random.seed(seeda if seed is None else seeda		)
									SCREAMING_SNAKE_CASE__								=   dtypes.as_dtype(__A		).base_dtype
									if dtype not in (dtypes.uinta, dtypes.floataa):
											raise TypeError("""Invalid image dtype %r, expected uint8 or float32""" % dtype		)
									if fake_data:
											SCREAMING_SNAKE_CASE__								=   1_0000
											SCREAMING_SNAKE_CASE__								=   one_hot
									else:
											assert (
											    images.shape[0] == labels.shape[0]
											), f'''images.shape: {images.shape} labels.shape: {labels.shape}'''
											SCREAMING_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
													SCREAMING_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].
													SCREAMING_SNAKE_CASE__								=   images.astype(numpy.floataa		)
													SCREAMING_SNAKE_CASE__								=   numpy.multiply(__A				,					1.0 / 2_5_5.0		)
									SCREAMING_SNAKE_CASE__								=   images
									SCREAMING_SNAKE_CASE__								=   labels
									SCREAMING_SNAKE_CASE__								=   0
									SCREAMING_SNAKE_CASE__								=   0


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



									"""simple docstring"""



									return self._images


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



									"""simple docstring"""



									return self._labels


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



									"""simple docstring"""



									return self._num_examples


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



									"""simple docstring"""



									return self._epochs_completed


							def   _snake_case	(				self     :Union[str, Any]				,					__A     :Union[str, Any]				,					__A     :str=False				,					__A     :str=True		) -> List[str]:



									"""simple docstring"""



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






@deprecated(UpperCamelCase__       , """Please write your own downloading logic."""     )
def 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				List[Any]       , UpperCamelCase__:				Union[str, Any]       , UpperCamelCase__:				List[Any]     ):
		if not gfile.Exists(UpperCamelCase__     ):
				gfile.MakeDirs(UpperCamelCase__     )
		SCREAMING_SNAKE_CASE__								=   os.path.join(UpperCamelCase__       , UpperCamelCase__     )
		if not gfile.Exists(UpperCamelCase__     ):
				urllib.request.urlretrieve(UpperCamelCase__       , UpperCamelCase__     )  # noqa: S310
				with gfile.GFile(UpperCamelCase__     ) as f:
						SCREAMING_SNAKE_CASE__								=   f.size()
				print("""Successfully downloaded"""       , UpperCamelCase__       , UpperCamelCase__       , """bytes."""     )
		return filepath






@deprecated(
    UpperCamelCase__       , """Please use alternatives such as:""" """ tensorflow_datasets.load('mnist')"""     )
def 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				Optional[Any]       , UpperCamelCase__:				Optional[Any]=False       , UpperCamelCase__:				Dict=False       , UpperCamelCase__:				Union[str, Any]=dtypes.floataa       , UpperCamelCase__:				Any=True       , UpperCamelCase__:				List[Any]=5_000       , UpperCamelCase__:				List[Any]=None       , UpperCamelCase__:				List[Any]=DEFAULT_SOURCE_URL       , ):
		if fake_data:

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

				SCREAMING_SNAKE_CASE__								=   fake()
				SCREAMING_SNAKE_CASE__								=   fake()
				SCREAMING_SNAKE_CASE__								=   fake()
				return _Datasets(train=UpperCamelCase__       , validation=UpperCamelCase__       , test=UpperCamelCase__     )

		if not source_url:  # empty string check
				SCREAMING_SNAKE_CASE__								=   DEFAULT_SOURCE_URL

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

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

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

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

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

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

		SCREAMING_SNAKE_CASE__								=   train_images[:validation_size]
		SCREAMING_SNAKE_CASE__								=   train_labels[:validation_size]
		SCREAMING_SNAKE_CASE__								=   train_images[validation_size:]
		SCREAMING_SNAKE_CASE__								=   train_labels[validation_size:]

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

		SCREAMING_SNAKE_CASE__								=   _DataSet(UpperCamelCase__       , UpperCamelCase__       , **UpperCamelCase__     )
		SCREAMING_SNAKE_CASE__								=   _DataSet(UpperCamelCase__       , UpperCamelCase__       , **UpperCamelCase__     )
		SCREAMING_SNAKE_CASE__								=   _DataSet(UpperCamelCase__       , UpperCamelCase__       , **UpperCamelCase__     )

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







'''simple docstring'''



import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
      from .tokenization_xlnet import XLNetTokenizer
else:
      SCREAMING_SNAKE_CASE							=						None


SCREAMING_SNAKE_CASE							=						logging.get_logger(__name__)

SCREAMING_SNAKE_CASE							=						{'vocab_file': 'spiece.model', 'tokenizer_file': 'tokenizer.json'}

SCREAMING_SNAKE_CASE							=						{
    'vocab_file': {
        'xlnet-base-cased': 'https://huggingface.co/xlnet-base-cased/resolve/main/spiece.model',
        'xlnet-large-cased': 'https://huggingface.co/xlnet-large-cased/resolve/main/spiece.model',
    },
    'tokenizer_file': {
        'xlnet-base-cased': 'https://huggingface.co/xlnet-base-cased/resolve/main/tokenizer.json',
        'xlnet-large-cased': 'https://huggingface.co/xlnet-large-cased/resolve/main/tokenizer.json',
    },
}

SCREAMING_SNAKE_CASE							=						{
    'xlnet-base-cased': None,
    'xlnet-large-cased': None,
}

SCREAMING_SNAKE_CASE							=						'▁'

# Segments (not really needed)
SCREAMING_SNAKE_CASE							=						0
SCREAMING_SNAKE_CASE							=						1
SCREAMING_SNAKE_CASE							=						2
SCREAMING_SNAKE_CASE							=						3
SCREAMING_SNAKE_CASE							=						4
class UpperCAmelCase_       (     __A					):




      """simple docstring"""




      UpperCamelCase_             =      VOCAB_FILES_NAMES
      UpperCamelCase_             =      PRETRAINED_VOCAB_FILES_MAP
      UpperCamelCase_             =      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      UpperCamelCase_             =      '''left'''
      UpperCamelCase_             =      XLNetTokenizer






      def __init__(			self      :		int  ,   UpperCAmelCase      :		Dict=None  ,   UpperCAmelCase      :		str=None  ,   UpperCAmelCase      :		str=False  ,   UpperCAmelCase      :		List[Any]=True  ,   UpperCAmelCase      :		Optional[Any]=False  ,   UpperCAmelCase      :		int="<s>"  ,   UpperCAmelCase      :		Optional[int]="</s>"  ,   UpperCAmelCase      :		str="<unk>"  ,   UpperCAmelCase      :		Optional[Any]="<sep>"  ,   UpperCAmelCase      :		Optional[int]="<pad>"  ,   UpperCAmelCase      :		Optional[Any]="<cls>"  ,   UpperCAmelCase      :		Dict="<mask>"  ,   UpperCAmelCase      :		int=["<eop>", "<eod>"]  ,   **UpperCAmelCase      :		List[Any]  ,   )	->	List[str]:


       '''simple docstring'''

       lowercase       : Dict						=AddedToken(UpperCAmelCase  ,   lstrip=UpperCAmelCase  ,   rstrip=UpperCAmelCase		) if isinstance(UpperCAmelCase  ,   UpperCAmelCase		) else mask_token

       super().__init__(
           vocab_file=UpperCAmelCase  ,   tokenizer_file=UpperCAmelCase  ,   do_lower_case=UpperCAmelCase  ,   remove_space=UpperCAmelCase  ,   keep_accents=UpperCAmelCase  ,   bos_token=UpperCAmelCase  ,   eos_token=UpperCAmelCase  ,   unk_token=UpperCAmelCase  ,   sep_token=UpperCAmelCase  ,   pad_token=UpperCAmelCase  ,   cls_token=UpperCAmelCase  ,   mask_token=UpperCAmelCase  ,   additional_special_tokens=UpperCAmelCase  ,   **UpperCAmelCase  ,   )

       lowercase       : Tuple						=3
       lowercase       : Union[str, Any]						=do_lower_case
       lowercase       : Any						=remove_space
       lowercase       : int						=keep_accents
       lowercase       : int						=vocab_file
       lowercase       : Union[str, Any]						=False if not self.vocab_file else True






      def 					A__   (			self      :		Any  ,   UpperCAmelCase      :		List[int]  ,   UpperCAmelCase      :		Optional[List[int]] = None		)	->	List[int]:


       '''simple docstring'''

       lowercase       : Union[str, Any]						=[self.sep_token_id]
       lowercase       : Optional[Any]						=[self.cls_token_id]
       if token_ids_a is None:
        return token_ids_a + sep + cls
       return token_ids_a + sep + token_ids_a + sep + cls






      def 					A__   (			self      :		str  ,   UpperCAmelCase      :		List[int]  ,   UpperCAmelCase      :		Optional[List[int]] = None		)	->	List[int]:


       '''simple docstring'''

       lowercase       : Optional[int]						=[self.sep_token_id]
       lowercase       : Union[str, Any]						=[2]

       if token_ids_a is None:
        return len(token_ids_a + sep		) * [0] + cls_segment_id
       return len(token_ids_a + sep		) * [0] + len(token_ids_a + sep		) * [1] + cls_segment_id






      def 					A__   (			self      :		str  ,   UpperCAmelCase      :		str  ,   UpperCAmelCase      :		Optional[str] = None		)	->	Tuple[str]:


       '''simple docstring'''

       if not self.can_save_slow_tokenizer:
        raise ValueError(
            '''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
            '''tokenizer.'''		)

       if not os.path.isdir(UpperCAmelCase		):
        logger.error(f'Vocabulary path ({save_directory}) should be a directory'		)
        return
       lowercase       : Dict						=os.path.join(
           UpperCAmelCase  ,   (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']		)

       if os.path.abspath(self.vocab_file		) != os.path.abspath(UpperCAmelCase		):
        copyfile(self.vocab_file  ,   UpperCAmelCase		)

       return (out_vocab_file,)



