



'''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

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 a_     (    __UpperCAmelCase					)				->					int:





			"""simple docstring"""







			snake_case:    Tuple      							=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 a_     (    __UpperCAmelCase					)				->					Union[str, Any]:





			"""simple docstring"""







			print('Extracting'				,							f.name					)
			with gzip.GzipFile(fileobj=__UpperCAmelCase					) as bytestream:
						snake_case:    str      							=_readaa(__UpperCAmelCase					)
						if magic != 20_51:
									raise ValueError(
									    'Invalid magic number %d in MNIST image file: %s' % (magic, f.name)					)
						snake_case:    Union[str, Any]      							=_readaa(__UpperCAmelCase					)
						snake_case:    Optional[int]      							=_readaa(__UpperCAmelCase					)
						snake_case:    Dict      							=_readaa(__UpperCAmelCase					)
						snake_case:    Dict      							=bytestream.read(rows * cols * num_images					)
						snake_case:    Optional[Any]      							=numpy.frombuffer(__UpperCAmelCase				,							dtype=numpy.uinta					)
						snake_case:    Optional[Any]      							=data.reshape(__UpperCAmelCase				,							__UpperCAmelCase				,							__UpperCAmelCase				,							1					)
						return data







@deprecated(__UpperCAmelCase				,							'Please use tf.one_hot on tensors.'					)
def a_     (    __UpperCAmelCase				,							__UpperCAmelCase					)				->					List[Any]:





			"""simple docstring"""







			snake_case:    Optional[Any]      							=labels_dense.shape[0]
			snake_case:    Dict      							=numpy.arange(__UpperCAmelCase					) * num_classes
			snake_case:    Optional[Any]      							=numpy.zeros((num_labels, num_classes)					)
			snake_case:    List[str]      							=1
			return labels_one_hot







@deprecated(__UpperCAmelCase				,							'Please use tf.data to implement this functionality.'					)
def a_     (    __UpperCAmelCase				,							__UpperCAmelCase=False				,							__UpperCAmelCase=10					)				->					str:





			"""simple docstring"""







			print('Extracting'				,							f.name					)
			with gzip.GzipFile(fileobj=__UpperCAmelCase					) as bytestream:
						snake_case:    List[Any]      							=_readaa(__UpperCAmelCase					)
						if magic != 20_49:
									raise ValueError(
									    'Invalid magic number %d in MNIST label file: %s' % (magic, f.name)					)
						snake_case:    Optional[Any]      							=_readaa(__UpperCAmelCase					)
						snake_case:    Any      							=bytestream.read(__UpperCAmelCase					)
						snake_case:    Optional[int]      							=numpy.frombuffer(__UpperCAmelCase				,							dtype=numpy.uinta					)
						if one_hot:
									return _dense_to_one_hot(__UpperCAmelCase				,							__UpperCAmelCase					)
						return labels






class 						a_		:


							@deprecated(
							    a_      ,						'Please use alternatives such as official/mnist/_DataSet.py'
							    ' from tensorflow/models.'      ,						)
							def __init__(				self   :						Tuple      ,						a_   :						Optional[int]      ,						a_   :						Tuple      ,						a_   :						str=False      ,						a_   :						Dict=False      ,						a_   :						List[str]=dtypes.floataa      ,						a_   :						List[str]=True      ,						a_   :						Any=None      ,						)			->					List[str]:
										snake_case							,  snake_case:    Any      							=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			)
										snake_case:    Tuple      							=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:
													snake_case:    Tuple      							=1_0_0_0_0
													snake_case:    List[Any]      							=one_hot
										else:
													assert (
													    images.shape[0] == labels.shape[0]
													), F'''images.shape: {images.shape} labels.shape: {labels.shape}'''
													snake_case:    Tuple      							=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
																snake_case:    List[str]      							=images.reshape(
																    images.shape[0]      ,						images.shape[1] * images.shape[2]			)
													if dtype == dtypes.floataa:
																# Convert from [0, 255] -> [0.0, 1.0].
																snake_case:    List[Any]      							=images.astype(numpy.floataa			)
																snake_case:    Optional[int]      							=numpy.multiply(a_      ,						1.0 / 2_5_5.0			)
										snake_case:    str      							=images
										snake_case:    Dict      							=labels
										snake_case:    List[Any]      							=0
										snake_case:    str      							=0


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


							@property
							def    UpperCamelCase     (				self   :						Optional[Any]			)			->					Union[str, Any]:
										return self._labels


							@property
							def    UpperCamelCase     (				self   :						List[str]			)			->					Dict:
										return self._num_examples


							@property
							def    UpperCamelCase     (				self   :						List[str]			)			->					Optional[Any]:
										return self._epochs_completed




							def    UpperCamelCase     (				self   :						Union[str, Any]      ,						a_   :						Tuple      ,						a_   :						int=False      ,						a_   :						Optional[Any]=True			)			->					int:
										if fake_data:
													snake_case:    Tuple      							=[1] * 7_8_4
													snake_case:    Any      							=[1] + [0] * 9 if self.one_hot else 0
													return (
													    [fake_image for _ in range(a_			)],
													    [fake_label for _ in range(a_			)],
													)
										snake_case:    Dict      							=self._index_in_epoch
										# Shuffle for the first epoch
										if self._epochs_completed == 0 and start == 0 and shuffle:
													snake_case:    str      							=numpy.arange(self._num_examples			)
													numpy.random.shuffle(a_			)
													snake_case:    List[Any]      							=self.images[perma]
													snake_case:    Any      							=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
													snake_case:    List[Any]      							=self._num_examples - start
													snake_case:    Any      							=self._images[start : self._num_examples]
													snake_case:    Optional[Any]      							=self._labels[start : self._num_examples]
													# Shuffle the data
													if shuffle:
																snake_case:    Optional[int]      							=numpy.arange(self._num_examples			)
																numpy.random.shuffle(a_			)
																snake_case:    Any      							=self.images[perm]
																snake_case:    Tuple      							=self.labels[perm]
													# Start next epoch
													snake_case:    Optional[int]      							=0
													snake_case:    Dict      							=batch_size - rest_num_examples
													snake_case:    Optional[Any]      							=self._index_in_epoch
													snake_case:    Dict      							=self._images[start:end]
													snake_case:    Tuple      							=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
													snake_case:    Tuple      							=self._index_in_epoch
													return self._images[start:end], self._labels[start:end]







@deprecated(__UpperCAmelCase				,							'Please write your own downloading logic.'					)
def a_     (    __UpperCAmelCase				,							__UpperCAmelCase				,							__UpperCAmelCase					)				->					Dict:





			"""simple docstring"""







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







@deprecated(
    __UpperCAmelCase				,							'Please use alternatives such as:' ' tensorflow_datasets.load(\'mnist\')'					)
def a_     (    __UpperCAmelCase				,							__UpperCAmelCase=False				,							__UpperCAmelCase=False				,							__UpperCAmelCase=dtypes.floataa				,							__UpperCAmelCase=True				,							__UpperCAmelCase=50_00				,							__UpperCAmelCase=None				,							__UpperCAmelCase=DEFAULT_SOURCE_URL				,							)				->					List[Any]:





			"""simple docstring"""







			if fake_data:

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

						snake_case:    List[str]      							=fake()
						snake_case:    List[Any]      							=fake()
						snake_case:    str      							=fake()
						return _Datasets(train=__UpperCAmelCase				,							validation=__UpperCAmelCase				,							test=__UpperCAmelCase					)

			if not source_url:  # empty string check
						snake_case:    Any      							=DEFAULT_SOURCE_URL

			snake_case:    Union[str, Any]      							='train-images-idx3-ubyte.gz'
			snake_case:    int      							='train-labels-idx1-ubyte.gz'
			snake_case:    Tuple      							='t10k-images-idx3-ubyte.gz'
			snake_case:    List[str]      							='t10k-labels-idx1-ubyte.gz'

			snake_case:    List[Any]      							=_maybe_download(
			    __UpperCAmelCase				,							__UpperCAmelCase				,							source_url + train_images_file					)
			with gfile.Open(__UpperCAmelCase				,							'rb'					) as f:
						snake_case:    Optional[int]      							=_extract_images(__UpperCAmelCase					)

			snake_case:    Any      							=_maybe_download(
			    __UpperCAmelCase				,							__UpperCAmelCase				,							source_url + train_labels_file					)
			with gfile.Open(__UpperCAmelCase				,							'rb'					) as f:
						snake_case:    Union[str, Any]      							=_extract_labels(__UpperCAmelCase				,							one_hot=__UpperCAmelCase					)

			snake_case:    List[str]      							=_maybe_download(
			    __UpperCAmelCase				,							__UpperCAmelCase				,							source_url + test_images_file					)
			with gfile.Open(__UpperCAmelCase				,							'rb'					) as f:
						snake_case:    str      							=_extract_images(__UpperCAmelCase					)

			snake_case:    List[Any]      							=_maybe_download(
			    __UpperCAmelCase				,							__UpperCAmelCase				,							source_url + test_labels_file					)
			with gfile.Open(__UpperCAmelCase				,							'rb'					) as f:
						snake_case:    List[str]      							=_extract_labels(__UpperCAmelCase				,							one_hot=__UpperCAmelCase					)

			if not 0 <= validation_size <= len(__UpperCAmelCase					):
						snake_case:    Optional[Any]      							=(
						    'Validation size should be between 0 and '
						    f'''{len(__UpperCAmelCase					)}. Received: {validation_size}.'''
						)
						raise ValueError(__UpperCAmelCase					)

			snake_case:    List[str]      							=train_images[:validation_size]
			snake_case:    Dict      							=train_labels[:validation_size]
			snake_case:    Any      							=train_images[validation_size:]
			snake_case:    Optional[int]      							=train_labels[validation_size:]

			snake_case:    str      							={'dtype': dtype, 'reshape': reshape, 'seed': seed}

			snake_case:    str      							=_DataSet(__UpperCAmelCase				,							__UpperCAmelCase				,							**__UpperCAmelCase					)
			snake_case:    List[str]      							=_DataSet(__UpperCAmelCase				,							__UpperCAmelCase				,							**__UpperCAmelCase					)
			snake_case:    Optional[int]      							=_DataSet(__UpperCAmelCase				,							__UpperCAmelCase				,							**__UpperCAmelCase					)

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









'''simple docstring'''

import json
import os
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union

import sentencepiece

from ...tokenization_utils import BatchEncoding, PreTrainedTokenizer
from ...utils import logging


a							   =				logging.get_logger(__name__)

a							   =				'▁'

a							   =				{
    'vocab_file': 'vocab.json',
    'spm_file': 'sentencepiece.bpe.model',
    'tokenizer_config_file': 'tokenizer_config.json',
}

a							   =				{
    'vocab_file': {
        'facebook/m2m100_418M': 'https://huggingface.co/facebook/m2m100_418M/resolve/main/vocab.json',
        'facebook/m2m100_1.2B': 'https://huggingface.co/facebook/m2m100_1.2B/resolve/main/vocab.json',
    },
    'spm_file': {
        'facebook/m2m100_418M': 'https://huggingface.co/facebook/m2m100_418M/resolve/main/sentencepiece.bpe.model',
        'facebook/m2m100_1.2B': 'https://huggingface.co/facebook/m2m100_1.2B/resolve/main/sentencepiece.bpe.model',
    },
    'tokenizer_config_file': {
        'facebook/m2m100_418M': 'https://huggingface.co/facebook/m2m100_418M/resolve/main/tokenizer_config.json',
        'facebook/m2m100_1.2B': 'https://huggingface.co/facebook/m2m100_1.2B/resolve/main/tokenizer_config.json',
    },
}

a							   =				{
    'facebook/m2m100_418M': 1_024,
}

# fmt: off
a							   =				{
    'm2m100': ['af', 'am', 'ar', 'ast', 'az', 'ba', 'be', 'bg', 'bn', 'br', 'bs', 'ca', 'ceb', 'cs', 'cy', 'da', 'de', 'el', 'en', 'es', 'et', 'fa', 'ff', 'fi', 'fr', 'fy', 'ga', 'gd', 'gl', 'gu', 'ha', 'he', 'hi', 'hr', 'ht', 'hu', 'hy', 'id', 'ig', 'ilo', 'is', 'it', 'ja', 'jv', 'ka', 'kk', 'km', 'kn', 'ko', 'lb', 'lg', 'ln', 'lo', 'lt', 'lv', 'mg', 'mk', 'ml', 'mn', 'mr', 'ms', 'my', 'ne', 'nl', 'no', 'ns', 'oc', 'or', 'pa', 'pl', 'ps', 'pt', 'ro', 'ru', 'sd', 'si', 'sk', 'sl', 'so', 'sq', 'sr', 'ss', 'su', 'sv', 'sw', 'ta', 'th', 'tl', 'tn', 'tr', 'uk', 'ur', 'uz', 'vi', 'wo', 'xh', 'yi', 'yo', 'zh', 'zu'],
    'wmt21': ['en', 'ha', 'is', 'ja', 'cs', 'ru', 'zh', 'de']
}






class 						a_		(     snake_case    ):
							UpperCAmelCase			:		int        =				VOCAB_FILES_NAMES
							UpperCAmelCase			:		Tuple        =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							UpperCAmelCase			:		Any        =				PRETRAINED_VOCAB_FILES_MAP
							UpperCAmelCase			:		Tuple        =				["""input_ids""", """attention_mask"""]

							UpperCAmelCase			:		List[int]        =				[]
							UpperCAmelCase			:		List[int]        =				[]


							def __init__(				self   :						int      ,						a_   :						Union[str, Any]      ,						a_   :						Union[str, Any]      ,						a_   :						Optional[Any]=None      ,						a_   :						str=None      ,						a_   :						Tuple="<s>"      ,						a_   :						int="</s>"      ,						a_   :						int="</s>"      ,						a_   :						Any="<pad>"      ,						a_   :						Union[str, Any]="<unk>"      ,						a_   :						Optional[Any]="m2m100"      ,						a_   :						Optional[Dict[str, Any]] = None      ,						a_   :						Dict=8      ,						**a_   :						Union[str, Any]      ,						)			->					None:
										snake_case:    Optional[Any]      							={} if sp_model_kwargs is None else sp_model_kwargs

										snake_case:    Union[str, Any]      							=language_codes
										snake_case:    Optional[int]      							=FAIRSEQ_LANGUAGE_CODES[language_codes]
										snake_case:    Dict      							={lang_code: F'''__{lang_code}__''' for lang_code in fairseq_language_code}

										snake_case:    Optional[int]      							=kwargs.get('additional_special_tokens'      ,						[]			)
										kwargs["additional_special_tokens"] += [
										    self.get_lang_token(a_			)
										    for lang_code in fairseq_language_code
										    if self.get_lang_token(a_			) not in kwargs["additional_special_tokens"]
										]

										super().__init__(
										    src_lang=a_      ,						tgt_lang=a_      ,						bos_token=a_      ,						eos_token=a_      ,						sep_token=a_      ,						unk_token=a_      ,						pad_token=a_      ,						language_codes=a_      ,						sp_model_kwargs=self.sp_model_kwargs      ,						num_madeup_words=a_      ,						**a_      ,						)

										snake_case:    int      							=vocab_file
										snake_case:    int      							=load_json(a_			)
										snake_case:    Tuple      							={v: k for k, v in self.encoder.items()}
										snake_case:    Any      							=spm_file
										snake_case:    int      							=load_spm(a_      ,						self.sp_model_kwargs			)

										snake_case:    List[Any]      							=len(self.encoder			)

										snake_case:    Optional[Any]      							={
										    self.get_lang_token(a_			): self.encoder_size + i for i, lang_code in enumerate(a_			)
										}
										snake_case:    List[str]      							={lang_code: self.encoder_size + i for i, lang_code in enumerate(a_			)}
										snake_case:    int      							={v: k for k, v in self.lang_token_to_id.items()}

										snake_case:    Any      							=src_lang if src_lang is not None else 'en'
										snake_case:    Optional[int]      							=tgt_lang
										snake_case:    Optional[int]      							=self.get_lang_id(self._src_lang			)
										self.set_src_lang_special_tokens(self._src_lang			)

										snake_case:    str      							=num_madeup_words


							@property
							def    UpperCamelCase     (				self   :						int			)			->					int:
										return len(self.encoder			) + len(self.lang_token_to_id			)


							@property
							def    UpperCamelCase     (				self   :						Optional[int]			)			->					str:
										return self._src_lang


							@src_lang.setter
							def    UpperCamelCase     (				self   :						List[Any]      ,						a_   :						str			)			->					None:
										snake_case:    Optional[Any]      							=new_src_lang
										self.set_src_lang_special_tokens(self._src_lang			)


							def    UpperCamelCase     (				self   :						Optional[int]      ,						a_   :						str			)			->					List[str]:
										return self.sp_model.encode(a_      ,						out_type=a_			)


							def    UpperCamelCase     (				self   :						List[Any]      ,						a_   :						Optional[int]			)			->					Union[str, Any]:
										if token in self.lang_token_to_id:
													return self.lang_token_to_id[token]
										return self.encoder.get(a_      ,						self.encoder[self.unk_token]			)


							def    UpperCamelCase     (				self   :						str      ,						a_   :						int			)			->					str:
										if index in self.id_to_lang_token:
													return self.id_to_lang_token[index]
										return self.decoder.get(a_      ,						self.unk_token			)


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


							def    UpperCamelCase     (				self   :						Dict      ,						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_			)

										snake_case:    List[Any]      							=[1] * len(self.prefix_tokens			)
										snake_case:    Optional[int]      							=[1] * len(self.suffix_tokens			)
										if token_ids_a is None:
													return prefix_ones + ([0] * len(a_			)) + suffix_ones
										return prefix_ones + ([0] * len(a_			)) + ([0] * len(a_			)) + suffix_ones


							def    UpperCamelCase     (				self   :						str      ,						a_   :						List[int]      ,						a_   :						Optional[List[int]] = None			)			->					List[int]:
										if token_ids_a is None:
													return self.prefix_tokens + token_ids_a + self.suffix_tokens
										# We don't expect to process pairs, but leave the pair logic for API consistency
										return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens


							def    UpperCamelCase     (				self   :						Any			)			->					Dict:
										snake_case:    List[str]      							={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   :						Union[str, Any]			)			->					Dict:
										snake_case:    Optional[int]      							=self.__dict__.copy()
										snake_case:    List[Any]      							=None
										return state


							def __setstate__(				self   :						int      ,						a_   :						Dict			)			->					None:
										snake_case:    Any      							=d

										# for backward compatibility
										if not hasattr(self      ,						'sp_model_kwargs'			):
													snake_case:    Optional[int]      							={}

										snake_case:    List[str]      							=load_spm(self.spm_file      ,						self.sp_model_kwargs			)


							def    UpperCamelCase     (				self   :						str      ,						a_   :						str      ,						a_   :						Optional[str] = None			)			->					Tuple[str]:
										snake_case:    Tuple      							=Path(a_			)
										if not save_dir.is_dir():
													raise OSError(F'''{save_directory} should be a directory'''			)
										snake_case:    Union[str, Any]      							=save_dir / (
										    (filename_prefix + '-' if filename_prefix else '') + self.vocab_files_names['vocab_file']
										)
										snake_case:    List[Any]      							=save_dir / (
										    (filename_prefix + '-' if filename_prefix else '') + self.vocab_files_names['spm_file']
										)

										save_json(self.encoder      ,						a_			)

										if os.path.abspath(self.spm_file			) != os.path.abspath(a_			) and os.path.isfile(self.spm_file			):
													copyfile(self.spm_file      ,						a_			)
										elif not os.path.isfile(self.spm_file			):
													with open(a_      ,						'wb'			) as fi:
																snake_case:    Any      							=self.sp_model.serialized_model_proto()
																fi.write(a_			)

										return (str(a_			), str(a_			))


							def    UpperCamelCase     (				self   :						Optional[Any]      ,						a_   :						List[str]      ,						a_   :						str = "en"      ,						a_   :						Optional[List[str]] = None      ,						a_   :						str = "ro"      ,						**a_   :						List[str]      ,						)			->					BatchEncoding:
										snake_case:    List[str]      							=src_lang
										snake_case:    Optional[int]      							=tgt_lang
										self.set_src_lang_special_tokens(self.src_lang			)
										return super().prepare_seqaseq_batch(a_      ,						a_      ,						**a_			)


							def    UpperCamelCase     (				self   :						Union[str, Any]      ,						a_   :						int      ,						a_   :						Optional[str]      ,						a_   :						Optional[str]      ,						**a_   :						Union[str, Any]			)			->					Any:
										if src_lang is None or tgt_lang is None:
													raise ValueError('Translation requires a `src_lang` and a `tgt_lang` for this model'			)
										snake_case:    List[str]      							=src_lang
										snake_case:    List[str]      							=self(a_      ,						add_special_tokens=a_      ,						**a_			)
										snake_case:    List[Any]      							=self.get_lang_id(a_			)
										snake_case:    List[str]      							=tgt_lang_id
										return inputs


							def    UpperCamelCase     (				self   :						str			)			->					List[str]:
										self.set_src_lang_special_tokens(self.src_lang			)


							def    UpperCamelCase     (				self   :						Dict			)			->					Tuple:
										self.set_tgt_lang_special_tokens(self.tgt_lang			)


							def    UpperCamelCase     (				self   :						List[str]      ,						a_   :						str			)			->					None:
										snake_case:    Optional[Any]      							=self.get_lang_token(a_			)
										snake_case:    Optional[Any]      							=self.lang_token_to_id[lang_token]
										snake_case:    Optional[Any]      							=[self.cur_lang_id]
										snake_case:    Optional[int]      							=[self.eos_token_id]


							def    UpperCamelCase     (				self   :						Optional[int]      ,						a_   :						str			)			->					None:
										snake_case:    Dict      							=self.get_lang_token(a_			)
										snake_case:    Tuple      							=self.lang_token_to_id[lang_token]
										snake_case:    Optional[int]      							=[self.cur_lang_id]
										snake_case:    List[str]      							=[self.eos_token_id]


							def    UpperCamelCase     (				self   :						Union[str, Any]      ,						a_   :						str			)			->					str:
										return self.lang_code_to_token[lang]




							def    UpperCamelCase     (				self   :						int      ,						a_   :						str			)			->					int:
										snake_case:    Union[str, Any]      							=self.get_lang_token(a_			)
										return self.lang_token_to_id[lang_token]







def a_     (    __UpperCAmelCase				,							__UpperCAmelCase					)				->					sentencepiece.SentencePieceProcessor:





			"""simple docstring"""







			snake_case:    Dict      							=sentencepiece.SentencePieceProcessor(**__UpperCAmelCase					)
			spm.Load(str(__UpperCAmelCase					)					)
			return spm







def a_     (    __UpperCAmelCase					)				->					Union[Dict, List]:





			"""simple docstring"""







			with open(__UpperCAmelCase				,							'r'					) as f:
						return json.load(__UpperCAmelCase					)







def a_     (    __UpperCAmelCase				,							__UpperCAmelCase					)				->					None:





			"""simple docstring"""







			with open(__UpperCAmelCase				,							'w'					) as f:
						json.dump(__UpperCAmelCase				,							__UpperCAmelCase				,							indent=2					)




