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

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


_lowerCamelCase     					=    logging.get_logger(__name__)

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

_lowerCamelCase     					=    {
    '''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      SCREAMING_SNAKE_CASE__  (			UpperCAmelCase      ):





	'''simple docstring'''



	def __init__(						self			:					Union[str, Any]    ,						lowercase			:					str=None    ,						lowercase			:					Any=None    ,						lowercase			:					Any=False    ,						lowercase			:					List[str]=True    ,						lowercase			:					List[Any]=False    ,						lowercase			:					int="<s>"    ,						lowercase			:					Any="</s>"    ,						lowercase			:					str="<unk>"    ,						lowercase			:					int="<sep>"    ,						lowercase			:					Optional[Any]="<pad>"    ,						lowercase			:					Union[str, Any]="<cls>"    ,						lowercase			:					List[str]="<mask>"    ,						lowercase			:					List[Any]=["<eop>", "<eod>"]    ,						**lowercase			:					Dict    ,						):


				'''simple docstring'''


				_snake_case						=    AddedToken(lowercase    ,						lstrip=lowercase    ,						rstrip=lowercase			) if isinstance(lowercase    ,						lowercase			) else mask_token

				super().__init__(
				    vocab_file=lowercase    ,						tokenizer_file=lowercase    ,						do_lower_case=lowercase    ,						remove_space=lowercase    ,						keep_accents=lowercase    ,						bos_token=lowercase    ,						eos_token=lowercase    ,						unk_token=lowercase    ,						sep_token=lowercase    ,						pad_token=lowercase    ,						cls_token=lowercase    ,						mask_token=lowercase    ,						additional_special_tokens=lowercase    ,						**lowercase    ,						)

				_snake_case						=    3
				_snake_case						=    do_lower_case
				_snake_case						=    remove_space
				_snake_case						=    keep_accents
				_snake_case						=    vocab_file
				_snake_case						=    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.'			)
				_snake_case						=    jieba
				_snake_case						=    str.maketrans(' \n'    ,						'\u2582\u2583'			)



	def 				A							(						self			:					Union[str, Any]    ,						lowercase			:					List[int]    ,						lowercase			:					Optional[List[int]] = None			):


				'''simple docstring'''


				_snake_case						=    [self.sep_token_id]
				_snake_case						=    [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 				A							(						self			:					Any    ,						lowercase			:					List[int]    ,						lowercase			:					Optional[List[int]] = None			):


				'''simple docstring'''


				_snake_case						=    [self.sep_token_id]
				_snake_case						=    [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 				A							(						self			:					List[str]    ,						lowercase			:					str    ,						lowercase			:					Optional[str] = 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(lowercase			):
							logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''			)
							return
				_snake_case						=    os.path.join(
				    lowercase    ,						(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']			)

				if os.path.abspath(self.vocab_file			) != os.path.abspath(lowercase			):
							copyfile(self.vocab_file    ,						lowercase			)

				return (out_vocab_file,)



	def 				A							(						self			:					int    ,						lowercase			:					Dict    ,						*lowercase			:					Optional[Any]    ,						**lowercase			:					Optional[Any]			):


				'''simple docstring'''


				_snake_case						=    [
				    ' '.join([x.translate(self.translator			) for x in self.jieba.cut(lowercase    ,						cut_all=lowercase			)]			)
				    for text in batch_text_or_text_pairs
				]
				return super()._batch_encode_plus(lowercase    ,						*lowercase    ,						**lowercase			)





	def 				A							(						self			:					Any    ,						*lowercase			:					Union[str, Any]    ,						**lowercase			:					Any			):


				'''simple docstring'''


				_snake_case						=    super()._decode(*lowercase    ,						**lowercase			)
				_snake_case						=    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,
)


_lowerCamelCase :       int     					=    {
    '''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:
			_lowerCamelCase :       str     					=    [
			    '''TVLT_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''TvltModel''',
			    '''TvltForPreTraining''',
			    '''TvltForAudioVisualClassification''',
			    '''TvltPreTrainedModel''',
			]

try:
			if not is_vision_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_lowerCamelCase :       str     					=    ['''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

			_lowerCamelCase :       Dict     					=    _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)