






'''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

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

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


def 				lowercase_					(     __A			:							str      )							->   Any:



 """simple docstring"""




 lowercase       : Dict						=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 				lowercase_					(     __A			:							Union[str, Any]      )							->   List[str]:



 """simple docstring"""




 print('''Extracting'''       ,   f.name      )
 with gzip.GzipFile(fileobj=__A      ) as bytestream:
  lowercase       : int						=_readaa(__A      )
  if magic != 2_0_5_1:
   raise ValueError(
       '''Invalid magic number %d in MNIST image file: %s''' % (magic, f.name)      )
  lowercase       : Optional[Any]						=_readaa(__A      )
  lowercase       : int						=_readaa(__A      )
  lowercase       : Dict						=_readaa(__A      )
  lowercase       : Union[str, Any]						=bytestream.read(rows * cols * num_images      )
  lowercase       : Union[str, Any]						=numpy.frombuffer(__A       ,   dtype=numpy.uinta      )
  lowercase       : Tuple						=data.reshape(__A       ,   __A       ,   __A       ,   1      )
  return data


@deprecated(__A       ,   '''Please use tf.one_hot on tensors.'''      )
def 				lowercase_					(     __A			:							Dict       ,   __A			:							Any      )							->   List[str]:



 """simple docstring"""




 lowercase       : str						=labels_dense.shape[0]
 lowercase       : int						=numpy.arange(__A      ) * num_classes
 lowercase       : List[str]						=numpy.zeros((num_labels, num_classes)      )
 lowercase       : Tuple						=1
 return labels_one_hot


@deprecated(__A       ,   '''Please use tf.data to implement this functionality.'''      )
def 				lowercase_					(     __A			:							List[str]       ,   __A			:							Union[str, Any]=False       ,   __A			:							Tuple=1_0      )							->   Optional[int]:



 """simple docstring"""




 print('''Extracting'''       ,   f.name      )
 with gzip.GzipFile(fileobj=__A      ) as bytestream:
  lowercase       : List[Any]						=_readaa(__A      )
  if magic != 2_0_4_9:
   raise ValueError(
       '''Invalid magic number %d in MNIST label file: %s''' % (magic, f.name)      )
  lowercase       : str						=_readaa(__A      )
  lowercase       : Optional[int]						=bytestream.read(__A      )
  lowercase       : List[str]						=numpy.frombuffer(__A       ,   dtype=numpy.uinta      )
  if one_hot:
   return _dense_to_one_hot(__A       ,   __A      )
  return labels
class UpperCAmelCase_       :




      """simple docstring"""




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


       '''simple docstring'''

       lowercase	,		lowercase       : Union[str, Any]						=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       : Optional[int]						=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       : Dict						=1_0000
        lowercase       : Union[str, Any]						=one_hot
       else:
        assert (
            images.shape[0] == labels.shape[0]
        ), f'images.shape: {images.shape} labels.shape: {labels.shape}'
        lowercase       : List[str]						=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       : int						=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       : Tuple						=numpy.multiply(UpperCAmelCase  ,   1.0 / 2_5_5.0		)
       lowercase       : Tuple						=images
       lowercase       : Optional[int]						=labels
       lowercase       : Union[str, Any]						=0
       lowercase       : Any						=0






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


       '''simple docstring'''

       return self._images






      @property
      def 					A__   (			self      :		str		)	->	Dict:


       '''simple docstring'''

       return self._labels






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


       '''simple docstring'''

       return self._num_examples






      @property
      def 					A__   (			self      :		Tuple		)	->	List[Any]:


       '''simple docstring'''

       return self._epochs_completed






      def 					A__   (			self      :		List[str]  ,   UpperCAmelCase      :		int  ,   UpperCAmelCase      :		Union[str, Any]=False  ,   UpperCAmelCase      :		str=True		)	->	int:


       '''simple docstring'''

       if fake_data:
        lowercase       : Optional[Any]						=[1] * 784
        lowercase       : Any						=[1] + [0] * 9 if self.one_hot else 0
        return (
            [fake_image for _ in range(UpperCAmelCase		)],
            [fake_label for _ in range(UpperCAmelCase		)],
        )
       lowercase       : List[str]						=self._index_in_epoch
       # Shuffle for the first epoch
       if self._epochs_completed == 0 and start == 0 and shuffle:
        lowercase       : int						=numpy.arange(self._num_examples		)
        numpy.random.shuffle(UpperCAmelCase		)
        lowercase       : Union[str, Any]						=self.images[perma]
        lowercase       : List[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
        lowercase       : Optional[int]						=self._num_examples - start
        lowercase       : Tuple						=self._images[start : self._num_examples]
        lowercase       : Optional[Any]						=self._labels[start : self._num_examples]
        # Shuffle the data
        if shuffle:
         lowercase       : List[str]						=numpy.arange(self._num_examples		)
         numpy.random.shuffle(UpperCAmelCase		)
         lowercase       : Optional[int]						=self.images[perm]
         lowercase       : Union[str, Any]						=self.labels[perm]
        # Start next epoch
        lowercase       : str						=0
        lowercase       : Optional[int]						=batch_size - rest_num_examples
        lowercase       : Optional[int]						=self._index_in_epoch
        lowercase       : str						=self._images[start:end]
        lowercase       : Dict						=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(__A       ,   '''Please write your own downloading logic.'''      )
def 				lowercase_					(     __A			:							Optional[Any]       ,   __A			:							Union[str, Any]       ,   __A			:							Optional[int]      )							->   Dict:



 """simple docstring"""




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





@deprecated(
    __A       ,   '''Please use alternatives such as:''' ''' tensorflow_datasets.load(\'mnist\')'''      )
def 				lowercase_					(     __A			:							Any       ,   __A			:							Union[str, Any]=False       ,   __A			:							Tuple=False       ,   __A			:							Optional[Any]=dtypes.floataa       ,   __A			:							List[Any]=True       ,   __A			:							str=5_0_0_0       ,   __A			:							Any=None       ,   __A			:							Any=DEFAULT_SOURCE_URL       ,   )							->   Dict:



 """simple docstring"""




 if fake_data:

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

  lowercase       : Tuple						=fake()
  lowercase       : Union[str, Any]						=fake()
  lowercase       : int						=fake()
  return _Datasets(train=__A       ,   validation=__A       ,   test=__A      )

 if not source_url:  # empty string check
  lowercase       : List[Any]						=DEFAULT_SOURCE_URL

 lowercase       : List[Any]						='''train-images-idx3-ubyte.gz'''
 lowercase       : List[str]						='''train-labels-idx1-ubyte.gz'''
 lowercase       : List[Any]						='''t10k-images-idx3-ubyte.gz'''
 lowercase       : List[str]						='''t10k-labels-idx1-ubyte.gz'''

 lowercase       : Dict						=_maybe_download(
     __A       ,   __A       ,   source_url + train_images_file      )
 with gfile.Open(__A       ,   '''rb'''      ) as f:
  lowercase       : Union[str, Any]						=_extract_images(__A      )

 lowercase       : Dict						=_maybe_download(
     __A       ,   __A       ,   source_url + train_labels_file      )
 with gfile.Open(__A       ,   '''rb'''      ) as f:
  lowercase       : Tuple						=_extract_labels(__A       ,   one_hot=__A      )

 lowercase       : Any						=_maybe_download(
     __A       ,   __A       ,   source_url + test_images_file      )
 with gfile.Open(__A       ,   '''rb'''      ) as f:
  lowercase       : Dict						=_extract_images(__A      )

 lowercase       : int						=_maybe_download(
     __A       ,   __A       ,   source_url + test_labels_file      )
 with gfile.Open(__A       ,   '''rb'''      ) as f:
  lowercase       : Any						=_extract_labels(__A       ,   one_hot=__A      )

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

 lowercase       : int						=train_images[:validation_size]
 lowercase       : Any						=train_labels[:validation_size]
 lowercase       : List[Any]						=train_images[validation_size:]
 lowercase       : List[Any]						=train_labels[validation_size:]

 lowercase       : int						={'''dtype''': dtype, '''reshape''': reshape, '''seed''': seed}

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

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











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

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


__a   :							List[str]						= "▁"

__a   :							int						= {"vocab_file": "spiece.model"}

__a   :							Any						= {
    "vocab_file": {"google/pegasus-xsum": "https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model"}
}

__a   :							Any						= {
    "google/pegasus-xsum": 512,
}


__a   :							Union[str, Any]						= logging.get_logger(__name__)


class    __lowercase   (						lowercase_	):







     '''simple docstring'''
     SCREAMING_SNAKE_CASE     =					VOCAB_FILES_NAMES

     SCREAMING_SNAKE_CASE     =					VOCAB_FILES_NAMES
     SCREAMING_SNAKE_CASE     =					PRETRAINED_VOCAB_FILES_MAP
     SCREAMING_SNAKE_CASE     =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     SCREAMING_SNAKE_CASE     =					["input_ids", "attention_mask"]




     def __init__(					self							:      List[str]			,						UpperCamelCase_							:      int			,						UpperCamelCase_							:      Dict="<pad>"			,						UpperCamelCase_							:      Dict="</s>"			,						UpperCamelCase_							:      Optional[Any]="<unk>"			,						UpperCamelCase_							:      List[Any]="<mask_2>"			,						UpperCamelCase_							:      List[Any]="<mask_1>"			,						UpperCamelCase_							:      str=None			,						UpperCamelCase_							:      Dict=103			,						UpperCamelCase_							:      Optional[Dict[str, Any]] = None			,						**UpperCamelCase_							:      int			,						):


         """simple docstring"""



         __A       =						offset
         if additional_special_tokens is not None:
             if not isinstance(UpperCamelCase_			,						UpperCamelCase_					):
                 raise TypeError(
                     F"additional_special_tokens should be of type {type(UpperCamelCase_					)}, but is"
                     F" {type(UpperCamelCase_					)}"					)

             __A       =						(
                 ([mask_token_sent] + additional_special_tokens)
                 if mask_token_sent not in additional_special_tokens and mask_token_sent is not None
                 else additional_special_tokens
             )
             # fill additional tokens with ..., <unk_token_102> in case not all additional tokens are already taken
             additional_special_tokens_extended += [
                 F"<unk_{i}>" for i in range(len(UpperCamelCase_					)			,						self.offset - 1					)
             ]

             if len(set(UpperCamelCase_					)					) != len(UpperCamelCase_					):
                 raise ValueError(
                     """Please make sure that the provided additional_special_tokens do not contain an incorrectly"""
                     F" shifted list of <unk_x> tokens. Found {additional_special_tokens_extended}."					)
             __A       =						additional_special_tokens_extended
         else:
             __A       =						[mask_token_sent] if mask_token_sent is not None else []
             additional_special_tokens += [F"<unk_{i}>" for i in range(2			,						self.offset					)]

         __A       =						{} if sp_model_kwargs is None else sp_model_kwargs

         super().__init__(
             eos_token=UpperCamelCase_			,						unk_token=UpperCamelCase_			,						mask_token=UpperCamelCase_			,						pad_token=UpperCamelCase_			,						mask_token_sent=UpperCamelCase_			,						offset=UpperCamelCase_			,						additional_special_tokens=UpperCamelCase_			,						sp_model_kwargs=self.sp_model_kwargs			,						**UpperCamelCase_			,						)
         __A       =						mask_token_sent
         __A       =						vocab_file
         __A       =						spm.SentencePieceProcessor(**self.sp_model_kwargs					)
         self.sp_model.Load(UpperCamelCase_					)

         # add special tokens to encoder dict
         __A       =						{
             0: self.pad_token,
             1: self.eos_token,
         }

         if self.mask_token_sent is not None:
             self.encoder.update(
                 {
                     2: self.mask_token_sent,
                     3: self.mask_token,
                 }					)

         if self.offset > 0:
             # entries 2-104 are only used for pretraining and called <mask_1>, <mask_2>, unk_2, ...unk_102
             # mask_token_sent is already added to list -> so start at 1
             self.encoder.update({i + 3: additional_special_tokens[i] for i in range(1			,						self.offset - 1					)}					)

         __A       =						{v: k for k, v in self.encoder.items()}




     @property
     def 		lowerCAmelCase_  (					self							:      Any					):


         """simple docstring"""



         return len(self.sp_model					) + self.offset




     def 		lowerCAmelCase_  (					self							:      Any					):


         """simple docstring"""



         __A       =						{self.convert_ids_to_tokens(UpperCamelCase_					): i for i in range(self.vocab_size					)}
         vocab.update(self.added_tokens_encoder					)
         return vocab




     def __getstate__(					self							:      Tuple					):


         """simple docstring"""



         __A       =						self.__dict__.copy()
         __A       =						None
         return state




     def __setstate__(					self							:      Tuple			,						UpperCamelCase_							:      Dict					):


         """simple docstring"""



         __A       =						d

         # for backward compatibility
         if not hasattr(self			,						"""sp_model_kwargs"""					):
             __A       =						{}

         __A       =						spm.SentencePieceProcessor(**self.sp_model_kwargs					)
         self.sp_model.Load(self.vocab_file					)




     def 		lowerCAmelCase_  (					self							:      Tuple			,						UpperCamelCase_							:      str					):


         """simple docstring"""



         return self.sp_model.encode(UpperCamelCase_			,						out_type=UpperCamelCase_					)




     def 		lowerCAmelCase_  (					self							:      str			,						UpperCamelCase_							:      str					):


         """simple docstring"""



         if token in self.decoder:
             return self.decoder[token]
         elif token in self.added_tokens_decoder:
             return self.added_tokens_decoder[token]
         __A       =						self.sp_model.piece_to_id(UpperCamelCase_					)
         return sp_id + self.offset




     def 		lowerCAmelCase_  (					self							:      int			,						UpperCamelCase_							:      int					):


         """simple docstring"""



         if index in self.encoder:
             return self.encoder[index]
         elif index in self.added_tokens_encoder:
             return self.added_tokens_encoder[index]
         else:
             __A       =						self.sp_model.IdToPiece(index - self.offset					)
         return token




     def 		lowerCAmelCase_  (					self							:      Optional[int]			,						UpperCamelCase_							:      List[str]					):


         """simple docstring"""



         __A       =						[]
         __A       =						""""""
         for token in tokens:
             # make sure that special tokens are not decoded using sentencepiece model
             if token in self.all_special_tokens:
                 out_string += self.sp_model.decode(UpperCamelCase_					) + token
                 __A       =						[]
             else:
                 current_sub_tokens.append(UpperCamelCase_					)
         out_string += self.sp_model.decode(UpperCamelCase_					)
         return out_string.strip()




     def 		lowerCAmelCase_  (					self							:      Any			,						UpperCamelCase_							:      List[Any]=False					):


         """simple docstring"""



         return 1




     def 		lowerCAmelCase_  (					self							:      Optional[int]			,						UpperCamelCase_							:      Tuple					):


         """simple docstring"""



         __A       =						set(self.all_special_ids					)  # call it once instead of inside list comp
         all_special_ids.remove(self.unk_token_id					)  # <unk> is only sometimes special

         return [1 if x in all_special_ids else 0 for x in seq]




     def 		lowerCAmelCase_  (					self							:      int			,						UpperCamelCase_							:      List			,						UpperCamelCase_							:      Optional[List] = None			,						UpperCamelCase_							:      bool = False					):


         """simple docstring"""



         if already_has_special_tokens:
             return self._special_token_mask(UpperCamelCase_					)
         elif token_ids_a is None:
             return self._special_token_mask(UpperCamelCase_					) + [1]
         else:
             return self._special_token_mask(token_ids_a + token_ids_a					) + [1]




     def 		lowerCAmelCase_  (					self							:      List[Any]			,						UpperCamelCase_							:      str			,						UpperCamelCase_							:      Optional[Any]=None					):


         """simple docstring"""



         if token_ids_a is None:
             return token_ids_a + [self.eos_token_id]
         # We don't expect to process pairs, but leave the pair logic for API consistency
         return token_ids_a + token_ids_a + [self.eos_token_id]





     def 		lowerCAmelCase_  (					self							:      List[Any]			,						UpperCamelCase_							:      str			,						UpperCamelCase_							:      Optional[str] = None					):


         """simple docstring"""



         if not os.path.isdir(UpperCamelCase_					):
             logger.error(F"Vocabulary path ({save_directory}) should be a directory"					)
             return
         __A       =						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_					) and os.path.isfile(self.vocab_file					):
             copyfile(self.vocab_file			,						UpperCamelCase_					)
         elif not os.path.isfile(self.vocab_file					):
             with open(UpperCamelCase_			,						"""wb"""					) as fi:
                 __A       =						self.sp_model.serialized_model_proto()
                 fi.write(UpperCamelCase_					)

         return (out_vocab_file,)




