


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

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


snake_case__  				=			logging.get_logger(__name__)

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

snake_case__  				=			{
    'vocab_file': {
        'TsinghuaAI/CPM-Generate': 'https://huggingface.co/TsinghuaAI/CPM-Generate/resolve/main/spiece.model',
    },
    'tokenizer_file': {
        'TsinghuaAI/CPM-Generate': 'https://huggingface.co/TsinghuaAI/CPM-Generate/resolve/main/tokenizer.json',
    },
}



class      snake_case__	(   _A	):
				def __init__(	self  ,   _UpperCAmelCase=None  ,   _UpperCAmelCase=None  ,   _UpperCAmelCase=False  ,   _UpperCAmelCase=True  ,   _UpperCAmelCase=False  ,   _UpperCAmelCase="<s>"  ,   _UpperCAmelCase="</s>"  ,   _UpperCAmelCase="<unk>"  ,   _UpperCAmelCase="<sep>"  ,   _UpperCAmelCase="<pad>"  ,   _UpperCAmelCase="<cls>"  ,   _UpperCAmelCase="<mask>"  ,   _UpperCAmelCase=["<eop>", "<eod>"]  ,   **_UpperCAmelCase  ,   ):




										"""simple docstring"""




										__magic_name__					:				int				=   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  ,   )

										__magic_name__					:				Union[str, Any]				=   3
										__magic_name__					:				Dict				=   do_lower_case
										__magic_name__					:				List[Any]				=   remove_space
										__magic_name__					:				Optional[Any]				=   keep_accents
										__magic_name__					:				str				=   vocab_file
										__magic_name__					:				str				=   False if not self.vocab_file else True

										try:
																import jieba
										except ModuleNotFoundError as error:
																raise error.__class__(
																    'You need to install jieba to use CpmTokenizer or CpmTokenizerFast. '
																    'See https://pypi.org/project/jieba/ for installation.'   )
										__magic_name__					:				Tuple				=   jieba
										__magic_name__					:				Dict				=   str.maketrans(' \n'  ,   '\u2582\u2583'   )
				def 			UpperCamelCase_    (	self  ,   _UpperCAmelCase  ,   _UpperCAmelCase = None   ):




										"""simple docstring"""




										__magic_name__					:				List[str]				=   [self.sep_token_id]
										__magic_name__					:				Dict				=   [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 			UpperCamelCase_    (	self  ,   _UpperCAmelCase  ,   _UpperCAmelCase = None   ):




										"""simple docstring"""




										__magic_name__					:				Optional[int]				=   [self.sep_token_id]
										__magic_name__					:				Tuple				=   [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 			UpperCamelCase_    (	self  ,   _UpperCAmelCase  ,   _UpperCAmelCase = None   ):




										"""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
										__magic_name__					:				Any				=   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,)
				def 			UpperCamelCase_    (	self  ,   _UpperCAmelCase  ,   *_UpperCAmelCase  ,   **_UpperCAmelCase   ):




										"""simple docstring"""




										__magic_name__					:				Optional[int]				=   [
										    ' '.join([x.translate(self.translator   ) for x in self.jieba.cut(_UpperCAmelCase  ,   cut_all=_UpperCAmelCase   )]   )
										    for text in batch_text_or_text_pairs
										]
										return super()._batch_encode_plus(_UpperCAmelCase  ,   *_UpperCAmelCase  ,   **_UpperCAmelCase   )





				def 			UpperCamelCase_    (	self  ,   *_UpperCAmelCase  ,   **_UpperCAmelCase   ):




										"""simple docstring"""




										__magic_name__					:				Dict				=   super()._decode(*_UpperCAmelCase  ,   **_UpperCAmelCase   )
										__magic_name__					:				List[str]				=   text.replace(' '  ,   ''   ).replace('\u2582'  ,   ' '   ).replace('\u2583'  ,   '\n'   )
										return text






# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
    is_vision_available,
)


snake_case__  				=			{
    'configuration_tvlt': ['TVLT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'TvltConfig'],
    'feature_extraction_tvlt': ['TvltFeatureExtractor'],
    'processing_tvlt': ['TvltProcessor'],
}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			snake_case__  				=			[
			    'TVLT_PRETRAINED_MODEL_ARCHIVE_LIST',
			    'TvltModel',
			    'TvltForPreTraining',
			    'TvltForAudioVisualClassification',
			    'TvltPreTrainedModel',
			]

try:
			if not is_vision_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			snake_case__  				=			['TvltImageProcessor']


if TYPE_CHECKING:
			from .configuration_tvlt import TVLT_PRETRAINED_CONFIG_ARCHIVE_MAP, TvltConfig
			from .processing_tvlt import TvltProcessor
			from .feature_extraction_tvlt import TvltFeatureExtractor

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tvlt import (
						    TVLT_PRETRAINED_MODEL_ARCHIVE_LIST,
						    TvltForAudioVisualClassification,
						    TvltForPreTraining,
						    TvltModel,
						    TvltPreTrainedModel,
						)

			try:
						if not is_vision_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .image_processing_tvlt import TvltImageProcessor


else:
			import sys

			snake_case__  				=			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


