


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

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






def       _snake_case				(		snake_case__   :     Dict     ):
	A						   =   numpy.dtype(numpy.uintaa     ).newbyteorder('>'     )
	return numpy.frombuffer(bytestream.read(4     )	,      dtype=snake_case__     )[0]






@deprecated(snake_case__	,      'Please use tf.data to implement this functionality.'     )
def       _snake_case				(		snake_case__   :     Any     ):
	print('Extracting'	,      f.name     )
	with gzip.GzipFile(fileobj=snake_case__     ) as bytestream:
		A						   =   _readaa(snake_case__     )
		if magic != 2051:
			raise ValueError(
			    'Invalid magic number %d in MNIST image file: %s' % (magic, f.name)     )
		A						   =   _readaa(snake_case__     )
		A						   =   _readaa(snake_case__     )
		A						   =   _readaa(snake_case__     )
		A						   =   bytestream.read(rows * cols * num_images     )
		A						   =   numpy.frombuffer(snake_case__	,      dtype=numpy.uinta     )
		A						   =   data.reshape(snake_case__	,      snake_case__	,      snake_case__	,      1     )
		return data






@deprecated(snake_case__	,      'Please use tf.one_hot on tensors.'     )
def       _snake_case				(		snake_case__   :     Dict	,      snake_case__   :     Optional[Any]     ):
	A						   =   labels_dense.shape[0]
	A						   =   numpy.arange(snake_case__     ) * num_classes
	A						   =   numpy.zeros((num_labels, num_classes)     )
	A						   =   1
	return labels_one_hot






@deprecated(snake_case__	,      'Please use tf.data to implement this functionality.'     )
def       _snake_case				(		snake_case__   :     Optional[int]	,      snake_case__   :     Any=False	,      snake_case__   :     List[str]=10     ):
	print('Extracting'	,      f.name     )
	with gzip.GzipFile(fileobj=snake_case__     ) as bytestream:
		A						   =   _readaa(snake_case__     )
		if magic != 2049:
			raise ValueError(
			    'Invalid magic number %d in MNIST label file: %s' % (magic, f.name)     )
		A						   =   _readaa(snake_case__     )
		A						   =   bytestream.read(snake_case__     )
		A						   =   numpy.frombuffer(snake_case__	,      dtype=numpy.uinta     )
		if one_hot:
			return _dense_to_one_hot(snake_case__	,      snake_case__     )
		return labels







class 				lowerCAmelCase_				:



						'''simple docstring'''


						@deprecated(
						    snake_case_  ,'Please use alternatives such as official/mnist/_DataSet.py'
						    ' from tensorflow/models.'  ,)
						def __init__( self     :      Optional[int]  ,A_     :      Union[str, Any]  ,A_     :      str  ,A_     :      Dict=False  ,A_     :      Union[str, Any]=False  ,A_     :      Optional[Any]=dtypes.floataa  ,A_     :      Optional[int]=True  ,A_     :      int=None  ,)     ->   Optional[Any]:
							A						   =   random_seed.get_seed(snake_case_      )
							# If op level seed is not set, use whatever graph level seed is returned
							numpy.random.seed(seeda if seed is None else seeda      )
							A						   =   dtypes.as_dtype(snake_case_      ).base_dtype
							if dtype not in (dtypes.uinta, dtypes.floataa):
								raise TypeError('Invalid image dtype %r, expected uint8 or float32' % dtype      )
							if fake_data:
								A						   =   1_0000
								A						   =   one_hot
							else:
								assert (
								    images.shape[0] == labels.shape[0]
								), F'images.shape: {images.shape} labels.shape: {labels.shape}'
								A						   =   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
									A						   =   images.reshape(
									    images.shape[0]  ,images.shape[1] * images.shape[2]      )
								if dtype == dtypes.floataa:
									# Convert from [0, 255] -> [0.0, 1.0].
									A						   =   images.astype(numpy.floataa      )
									A						   =   numpy.multiply(snake_case_  ,1.0 / 2_55.0      )
							A						   =   images
							A						   =   labels
							A						   =   0
							A						   =   0


						@property
						def 						_SCREAMING_SNAKE_CASE      ( self     :      Any      )     ->   Optional[Any]:
							return self._images


						@property
						def 						_SCREAMING_SNAKE_CASE      ( self     :      Tuple      )     ->   Tuple:
							return self._labels


						@property
						def 						_SCREAMING_SNAKE_CASE      ( self     :      Union[str, Any]      )     ->   int:
							return self._num_examples


						@property
						def 						_SCREAMING_SNAKE_CASE      ( self     :      Optional[Any]      )     ->   int:
							return self._epochs_completed




						def 						_SCREAMING_SNAKE_CASE      ( self     :      Tuple  ,A_     :      str  ,A_     :      List[Any]=False  ,A_     :      Union[str, Any]=True      )     ->   List[str]:
							if fake_data:
								A						   =   [1] * 784
								A						   =   [1] + [0] * 9 if self.one_hot else 0
								return (
								    [fake_image for _ in range(snake_case_      )],
								    [fake_label for _ in range(snake_case_      )],
								)
							A						   =   self._index_in_epoch
							# Shuffle for the first epoch
							if self._epochs_completed == 0 and start == 0 and shuffle:
								A						   =   numpy.arange(self._num_examples      )
								numpy.random.shuffle(snake_case_      )
								A						   =   self.images[perma]
								A						   =   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
								A						   =   self._num_examples - start
								A						   =   self._images[start : self._num_examples]
								A						   =   self._labels[start : self._num_examples]
								# Shuffle the data
								if shuffle:
									A						   =   numpy.arange(self._num_examples      )
									numpy.random.shuffle(snake_case_      )
									A						   =   self.images[perm]
									A						   =   self.labels[perm]
								# Start next epoch
								A						   =   0
								A						   =   batch_size - rest_num_examples
								A						   =   self._index_in_epoch
								A						   =   self._images[start:end]
								A						   =   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
								A						   =   self._index_in_epoch
								return self._images[start:end], self._labels[start:end]






@deprecated(snake_case__	,      'Please write your own downloading logic.'     )
def       _snake_case				(		snake_case__   :     List[Any]	,      snake_case__   :     int	,      snake_case__   :     Any     ):







	if not gfile.Exists(snake_case__     ):
		gfile.MakeDirs(snake_case__     )
	A						   =   os.path.join(snake_case__	,      snake_case__     )
	if not gfile.Exists(snake_case__     ):
		urllib.request.urlretrieve(snake_case__	,      snake_case__     )  # noqa: S310
		with gfile.GFile(snake_case__     ) as f:
			A						   =   f.size()
		print('Successfully downloaded'	,      snake_case__	,      snake_case__	,      'bytes.'     )
	return filepath






@deprecated(
    snake_case__	,      'Please use alternatives such as:' ' tensorflow_datasets.load(\'mnist\')'     )
def       _snake_case				(		snake_case__   :     Optional[Any]	,      snake_case__   :     Optional[Any]=False	,      snake_case__   :     Tuple=False	,      snake_case__   :     str=dtypes.floataa	,      snake_case__   :     Union[str, Any]=True	,      snake_case__   :     Union[str, Any]=5000	,      snake_case__   :     Union[str, Any]=None	,      snake_case__   :     Optional[int]=DEFAULT_SOURCE_URL	,      ):







	if fake_data:

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

		A						   =   fake()
		A						   =   fake()
		A						   =   fake()
		return _Datasets(train=snake_case__	,      validation=snake_case__	,      test=snake_case__     )

	if not source_url:  # empty string check
		A						   =   DEFAULT_SOURCE_URL

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

	A						   =   _maybe_download(
	    snake_case__	,      snake_case__	,      source_url + train_images_file     )
	with gfile.Open(snake_case__	,      'rb'     ) as f:
		A						   =   _extract_images(snake_case__     )

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

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

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

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

	A						   =   train_images[:validation_size]
	A						   =   train_labels[:validation_size]
	A						   =   train_images[validation_size:]
	A						   =   train_labels[validation_size:]

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

	A						   =   _DataSet(snake_case__	,      snake_case__	,      **snake_case__     )
	A						   =   _DataSet(snake_case__	,      snake_case__	,      **snake_case__     )
	A						   =   _DataSet(snake_case__	,      snake_case__	,      **snake_case__     )

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

"""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							   =				logging.get_logger(__name__)

_lowercase							   =				{'''vocab_file''': '''spm_char.model'''}

_lowercase							   =				{
    '''vocab_file''': {
        '''microsoft/speecht5_asr''': '''https://huggingface.co/microsoft/speecht5_asr/resolve/main/spm_char.model''',
        '''microsoft/speecht5_tts''': '''https://huggingface.co/microsoft/speecht5_tts/resolve/main/spm_char.model''',
        '''microsoft/speecht5_vc''': '''https://huggingface.co/microsoft/speecht5_vc/resolve/main/spm_char.model''',
    }
}

_lowercase							   =				{
    '''microsoft/speecht5_asr''': 10_24,
    '''microsoft/speecht5_tts''': 10_24,
    '''microsoft/speecht5_vc''': 10_24,
}







class 				lowerCAmelCase_				(      _lowercase    ):



						'''simple docstring'''


						_lowerCamelCase:    Optional[Any]      =    VOCAB_FILES_NAMES
						_lowerCamelCase:    List[Any]      =    PRETRAINED_VOCAB_FILES_MAP
						_lowerCamelCase:    str      =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						_lowerCamelCase:    Tuple      =    ['''input_ids''', '''attention_mask''']


						def __init__( self     :      List[str]  ,A_     :      int  ,A_     :      List[str]="<s>"  ,A_     :      Optional[Any]="</s>"  ,A_     :      Optional[Any]="<unk>"  ,A_     :      str="<pad>"  ,A_     :      Optional[Dict[str, Any]] = None  ,**A_     :      List[str]  ,)     ->   None:
							A						   =   {} if sp_model_kwargs is None else sp_model_kwargs

							super().__init__(
							    bos_token=A_  ,eos_token=A_  ,unk_token=A_  ,pad_token=A_  ,sp_model_kwargs=self.sp_model_kwargs  ,**A_  ,)

							A						   =   vocab_file

							A						   =   spm.SentencePieceProcessor(**self.sp_model_kwargs      )
							self.sp_model.Load(A_      )


						@property
						def 						_SCREAMING_SNAKE_CASE      ( self     :      Any      )     ->   str:
							return self.sp_model.get_piece_size()


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Optional[int]      )     ->   Optional[Any]:
							A						   =   {self.convert_ids_to_tokens(A_      ): i for i in range(self.vocab_size      )}
							vocab.update(self.added_tokens_encoder      )
							return vocab


						def __getstate__( self     :      str      )     ->   Any:
							A						   =   self.__dict__.copy()
							A						   =   None
							return state


						def __setstate__( self     :      Optional[int]  ,A_     :      str      )     ->   Tuple:
							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 						_SCREAMING_SNAKE_CASE      ( self     :      str  ,A_     :      str      )     ->   List[str]:
							return self.sp_model.encode(A_  ,out_type=A_      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      List[str]  ,A_     :      Union[str, Any]      )     ->   Union[str, Any]:
							return self.sp_model.piece_to_id(A_      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Optional[Any]  ,A_     :      Dict      )     ->   List[Any]:
							A						   =   self.sp_model.IdToPiece(A_      )
							return token


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Tuple  ,A_     :      Optional[Any]      )     ->   List[str]:
							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(A_      ) + token
									A						   =   []
								else:
									current_sub_tokens.append(A_      )
							out_string += self.sp_model.decode(A_      )
							return out_string.strip()


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Dict  ,A_     :      Dict  ,A_     :      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 						_SCREAMING_SNAKE_CASE      ( self     :      Optional[Any]  ,A_     :      List[int]  ,A_     :      Optional[List[int]] = None  ,A_     :      bool = False      )     ->   List[int]:
							if already_has_special_tokens:
								return super().get_special_tokens_mask(
								    token_ids_a=A_  ,token_ids_a=A_  ,already_has_special_tokens=A_      )

							A						   =   [1]
							if token_ids_a is None:
								return ([0] * len(A_      )) + suffix_ones
							return ([0] * len(A_      )) + ([0] * len(A_      )) + suffix_ones




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

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

							return (out_vocab_file,)