
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__		:Tuple   =    collections.namedtuple("_Datasets", ["train", "validation", "test"])

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



def        UpperCamelCase    (							lowerCAmelCase__		):
					'''simple docstring'''
					lowercase							=						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        UpperCamelCase    (							lowerCAmelCase__		):
					'''simple docstring'''
					print('''Extracting''' ,			f.name		)
					with gzip.GzipFile(fileobj=lowerCAmelCase__		) as bytestream:
										lowercase							=						_readaa(lowerCAmelCase__		)
										if magic != 2051:
															raise ValueError(
															    '''Invalid magic number %d in MNIST image file: %s''' % (magic, f.name)		)
										lowercase							=						_readaa(lowerCAmelCase__		)
										lowercase							=						_readaa(lowerCAmelCase__		)
										lowercase							=						_readaa(lowerCAmelCase__		)
										lowercase							=						bytestream.read(rows * cols * num_images		)
										lowercase							=						numpy.frombuffer(lowerCAmelCase__ ,			dtype=numpy.uinta		)
										lowercase							=						data.reshape(lowerCAmelCase__ ,			lowerCAmelCase__ ,			lowerCAmelCase__ ,			1		)
										return data



@deprecated(lowerCAmelCase__ ,			'''Please use tf.one_hot on tensors.'''		)
def        UpperCamelCase    (							lowerCAmelCase__ ,			lowerCAmelCase__		):
					'''simple docstring'''
					lowercase							=						labels_dense.shape[0]
					lowercase							=						numpy.arange(lowerCAmelCase__		) * num_classes
					lowercase							=						numpy.zeros((num_labels, num_classes)		)
					lowercase							=						1
					return labels_one_hot



@deprecated(lowerCAmelCase__ ,			'''Please use tf.data to implement this functionality.'''		)
def        UpperCamelCase    (							lowerCAmelCase__ ,			lowerCAmelCase__=False ,			lowerCAmelCase__=10		):
					'''simple docstring'''
					print('''Extracting''' ,			f.name		)
					with gzip.GzipFile(fileobj=lowerCAmelCase__		) as bytestream:
										lowercase							=						_readaa(lowerCAmelCase__		)
										if magic != 2049:
															raise ValueError(
															    '''Invalid magic number %d in MNIST label file: %s''' % (magic, f.name)		)
										lowercase							=						_readaa(lowerCAmelCase__		)
										lowercase							=						bytestream.read(lowerCAmelCase__		)
										lowercase							=						numpy.frombuffer(lowerCAmelCase__ ,			dtype=numpy.uinta		)
										if one_hot:
															return _dense_to_one_hot(lowerCAmelCase__ ,			lowerCAmelCase__		)
										return labels







class 				lowercase		:




							@deprecated(
							    A__   ,'''Please use alternatives such as official/mnist/_DataSet.py'''
							    ''' from tensorflow/models.'''   ,)
							def __init__(				self   ,A__   ,A__   ,A__=False   ,A__=False   ,A__=dtypes.floataa   ,A__=True   ,A__=None   ,):
												lowercase	,						lowercase							=						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)
												lowercase							=						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:
																	lowercase							=						1_0_0_0_0
																	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(A__   ,1.0 / 255.0)
												lowercase							=						images
												lowercase							=						labels
												lowercase							=						0
												lowercase							=						0




							@property
							def  A__      (				self):
												return self._images




							@property
							def  A__      (				self):
												return self._labels




							@property
							def  A__      (				self):
												return self._num_examples




							@property
							def  A__      (				self):
												return self._epochs_completed





							def  A__      (				self   ,A__   ,A__=False   ,A__=True):
												if fake_data:
																	lowercase							=						[1] * 7_8_4
																	lowercase							=						[1] + [0] * 9 if self.one_hot else 0
																	return (
																	    [fake_image for _ in range(A__)],
																	    [fake_label for _ in range(A__)],
																	)
												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(A__)
																	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(A__)
																						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(lowerCAmelCase__ ,			'''Please write your own downloading logic.'''		)
def        UpperCamelCase    (							lowerCAmelCase__ ,			lowerCAmelCase__ ,			lowerCAmelCase__		):
					'''simple docstring'''
					if not gfile.Exists(lowerCAmelCase__		):
										gfile.MakeDirs(lowerCAmelCase__		)
					lowercase							=						os.path.join(lowerCAmelCase__ ,			lowerCAmelCase__		)
					if not gfile.Exists(lowerCAmelCase__		):
										urllib.request.urlretrieve(lowerCAmelCase__ ,			lowerCAmelCase__		)  # noqa: S310
										with gfile.GFile(lowerCAmelCase__		) as f:
															lowercase							=						f.size()
										print('''Successfully downloaded''' ,			lowerCAmelCase__ ,			lowerCAmelCase__ ,			'''bytes.'''		)
					return filepath



@deprecated(
    lowerCAmelCase__ ,			'''Please use alternatives such as:''' ''' tensorflow_datasets.load(\'mnist\')'''		)
def        UpperCamelCase    (							lowerCAmelCase__ ,			lowerCAmelCase__=False ,			lowerCAmelCase__=False ,			lowerCAmelCase__=dtypes.floataa ,			lowerCAmelCase__=True ,			lowerCAmelCase__=5000 ,			lowerCAmelCase__=None ,			lowerCAmelCase__=DEFAULT_SOURCE_URL ,			):
					'''simple docstring'''
					if fake_data:

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

										lowercase							=						fake()
										lowercase							=						fake()
										lowercase							=						fake()
										return _Datasets(train=lowerCAmelCase__ ,			validation=lowerCAmelCase__ ,			test=lowerCAmelCase__		)

					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(
					    lowerCAmelCase__ ,			lowerCAmelCase__ ,			source_url + train_images_file		)
					with gfile.Open(lowerCAmelCase__ ,			'''rb'''		) as f:
										lowercase							=						_extract_images(lowerCAmelCase__		)

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

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

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

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

					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(lowerCAmelCase__ ,			lowerCAmelCase__ ,			**lowerCAmelCase__		)
					lowercase							=						_DataSet(lowerCAmelCase__ ,			lowerCAmelCase__ ,			**lowerCAmelCase__		)
					lowercase							=						_DataSet(lowerCAmelCase__ ,			lowerCAmelCase__ ,			**lowerCAmelCase__		)

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









"""simple docstring"""






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


_lowercase : Optional[int]      						=						"▁"

_lowercase : Optional[Any]      						=						{"vocab_file": "spiece.model"}

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

_lowercase : Tuple      						=						{
    "google/pegasus-xsum": 5_1_2,
}


_lowercase : Optional[int]      						=						logging.get_logger(__name__)

class        __SCREAMING_SNAKE_CASE	(      lowerCAmelCase_   ):


					'''simple docstring'''





					_a   			=							VOCAB_FILES_NAMES

					_a   			=							VOCAB_FILES_NAMES
					_a   			=							PRETRAINED_VOCAB_FILES_MAP
					_a   			=							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					_a   			=							['input_ids', 'attention_mask']



					def __init__(       self							:  Any,  lowerCamelCase							:  Optional[Any],  lowerCamelCase							:  Any="<pad>",  lowerCamelCase							:  Optional[Any]="</s>",  lowerCamelCase							:  Any="<unk>",  lowerCamelCase							:  Tuple="<mask_2>",  lowerCamelCase							:  int="<mask_1>",  lowerCamelCase							:  Optional[Any]=None,  lowerCamelCase							:  Dict=103,  lowerCamelCase							:  Optional[Dict[str, Any]] = None,  **lowerCamelCase							:  Optional[int],  )->					None:
								lowerCamelCase__		:		Union[str, Any]			=offset
								if additional_special_tokens is not None:
											if not isinstance(lowerCamelCase,  lowerCamelCase     ):
														raise TypeError(
														    F'''additional_special_tokens should be of type {type(lowerCamelCase     )}, but is'''
														    F''' {type(lowerCamelCase     )}'''     )

											lowerCamelCase__		:		Any			=(
											    ([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(lowerCamelCase     ),  self.offset - 1     )
											]

											if len(set(lowerCamelCase     )     ) != len(lowerCamelCase     ):
														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}.'''     )
											lowerCamelCase__		:		Optional[Any]			=additional_special_tokens_extended
								else:
											lowerCamelCase__		:		Tuple			=[mask_token_sent] if mask_token_sent is not None else []
											additional_special_tokens += [F'''<unk_{i}>''' for i in range(2,  self.offset     )]

								lowerCamelCase__		:		Tuple			={} if sp_model_kwargs is None else sp_model_kwargs

								super().__init__(
								    eos_token=lowerCamelCase,  unk_token=lowerCamelCase,  mask_token=lowerCamelCase,  pad_token=lowerCamelCase,  mask_token_sent=lowerCamelCase,  offset=lowerCamelCase,  additional_special_tokens=lowerCamelCase,  sp_model_kwargs=self.sp_model_kwargs,  **lowerCamelCase,  )
								lowerCamelCase__		:		Optional[int]			=mask_token_sent
								lowerCamelCase__		:		Optional[Any]			=vocab_file
								lowerCamelCase__		:		List[str]			=spm.SentencePieceProcessor(**self.sp_model_kwargs     )
								self.sp_model.Load(lowerCamelCase     )

								# add special tokens to encoder dict
								lowerCamelCase__		:		Dict[int, str]			={
								    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     )}     )

								lowerCamelCase__		:		Dict[str, int]			={v: k for k, v in self.encoder.items()}



					@property
					def 		snake_case       (       self							:  Union[str, Any]     )->					int:
								return len(self.sp_model     ) + self.offset



					def 		snake_case       (       self							:  Optional[Any]     )->					Dict[str, int]:
								lowerCamelCase__		:		List[Any]			={self.convert_ids_to_tokens(lowerCamelCase     ): i for i in range(self.vocab_size     )}
								vocab.update(self.added_tokens_encoder     )
								return vocab



					def __getstate__(       self							:  str     )->					List[Any]:
								lowerCamelCase__		:		Optional[Any]			=self.__dict__.copy()
								lowerCamelCase__		:		Optional[int]			=None
								return state



					def __setstate__(       self							:  Dict,  lowerCamelCase							:  int     )->					Optional[Any]:
								lowerCamelCase__		:		Optional[int]			=d

								# for backward compatibility
								if not hasattr(self,  '''sp_model_kwargs'''     ):
											lowerCamelCase__		:		str			={}

								lowerCamelCase__		:		Union[str, Any]			=spm.SentencePieceProcessor(**self.sp_model_kwargs     )
								self.sp_model.Load(self.vocab_file     )



					def 		snake_case       (       self							:  Any,  lowerCamelCase							:  str     )->					List[str]:
								return self.sp_model.encode(lowerCamelCase,  out_type=lowerCamelCase     )



					def 		snake_case       (       self							:  int,  lowerCamelCase							:  str     )->					int:
								if token in self.decoder:
											return self.decoder[token]
								elif token in self.added_tokens_decoder:
											return self.added_tokens_decoder[token]
								lowerCamelCase__		:		Any			=self.sp_model.piece_to_id(lowerCamelCase     )
								return sp_id + self.offset



					def 		snake_case       (       self							:  Tuple,  lowerCamelCase							:  int     )->					str:
								if index in self.encoder:
											return self.encoder[index]
								elif index in self.added_tokens_encoder:
											return self.added_tokens_encoder[index]
								else:
											lowerCamelCase__		:		Any			=self.sp_model.IdToPiece(index - self.offset     )
								return token



					def 		snake_case       (       self							:  List[Any],  lowerCamelCase							:  Optional[int]     )->					Any:
								lowerCamelCase__		:		Optional[int]			=[]
								lowerCamelCase__		:		Tuple			=''''''
								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(lowerCamelCase     ) + token
														lowerCamelCase__		:		str			=[]
											else:
														current_sub_tokens.append(lowerCamelCase     )
								out_string += self.sp_model.decode(lowerCamelCase     )
								return out_string.strip()



					def 		snake_case       (       self							:  Union[str, Any],  lowerCamelCase							:  Union[str, Any]=False     )->					List[str]:
								return 1



					def 		snake_case       (       self							:  Tuple,  lowerCamelCase							:  Optional[int]     )->					Tuple:
								lowerCamelCase__		:		Tuple			=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 		snake_case       (       self							:  Any,  lowerCamelCase							:  List,  lowerCamelCase							:  Optional[List] = None,  lowerCamelCase							:  bool = False     )->					List[int]:
								if already_has_special_tokens:
											return self._special_token_mask(lowerCamelCase     )
								elif token_ids_a is None:
											return self._special_token_mask(lowerCamelCase     ) + [1]
								else:
											return self._special_token_mask(token_ids_a + token_ids_a     ) + [1]



					def 		snake_case       (       self							:  Optional[Any],  lowerCamelCase							:  List[str],  lowerCamelCase							:  Optional[int]=None     )->					List[int]:
								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 		snake_case       (       self							:  Optional[int],  lowerCamelCase							:  str,  lowerCamelCase							:  Optional[str] = None     )->					Tuple[str]:
								if not os.path.isdir(lowerCamelCase     ):
											logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''     )
											return
								lowerCamelCase__		:		List[str]			=os.path.join(
								    lowerCamelCase,  (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']     )

								if os.path.abspath(self.vocab_file     ) != os.path.abspath(lowerCamelCase     ) and os.path.isfile(self.vocab_file     ):
											copyfile(self.vocab_file,  lowerCamelCase     )
								elif not os.path.isfile(self.vocab_file     ):
											with open(lowerCamelCase,  '''wb'''     ) as fi:
														lowerCamelCase__		:		int			=self.sp_model.serialized_model_proto()
														fi.write(lowerCamelCase     )

								return (out_vocab_file,)




