


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			:      int 							=   logging.get_logger(__name__)

snake_case			:      List[Any] 							=   {"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"}

snake_case			:      List[str] 							=   {
    "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    __lowercase  (       UpperCamelCase_							):

							"""simple docstring"""


							def __init__(    self				, A_=None				, A_=None				, A_=False				, A_=True				, A_=False				, A_="<s>"				, A_="</s>"				, A_="<unk>"				, A_="<sep>"				, A_="<pad>"				, A_="<cls>"				, A_="<mask>"				, A_=["<eop>", "<eod>"]				, **A_				, )->	List[Any]:
											_SCREAMING_SNAKE_CASE      = 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				, )

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





							def 		__magic_name__			(    self				, A_				, A_ = None	)->	List[int]:
											_SCREAMING_SNAKE_CASE      = [self.sep_token_id]
											_SCREAMING_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 		__magic_name__			(    self				, A_				, A_ = None	)->	List[int]:
											_SCREAMING_SNAKE_CASE      = [self.sep_token_id]
											_SCREAMING_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 		__magic_name__			(    self				, A_				, A_ = None	)->	Tuple[str]:


											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
											_SCREAMING_SNAKE_CASE      = 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 		__magic_name__			(    self				, A_				, *A_				, **A_	)->	List[str]:
											_SCREAMING_SNAKE_CASE      = [
											    ''' '''.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 		__magic_name__			(    self				, *A_				, **A_	)->	List[str]:
											_SCREAMING_SNAKE_CASE      = super()._decode(*__UpperCamelCase				, **__UpperCamelCase	)
											_SCREAMING_SNAKE_CASE      = text.replace(' '				, ''	).replace('\u2582'				, ' '	).replace('\u2583'				, '\n'	)
											return text



from typing import TYPE_CHECKING

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


snake_case			:      Optional[int] 							=   {'configuration_vit_hybrid': ['VIT_HYBRID_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ViTHybridConfig']}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							snake_case			:      int 							=   [
							    'VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'ViTHybridForImageClassification',
							    'ViTHybridModel',
							    'ViTHybridPreTrainedModel',
							]

try:
							if not is_vision_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							snake_case			:      Any 							=   ['ViTHybridImageProcessor']


if TYPE_CHECKING:
							from .configuration_vit_hybrid import VIT_HYBRID_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTHybridConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_vit_hybrid import (
														    VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST,
														    ViTHybridForImageClassification,
														    ViTHybridModel,
														    ViTHybridPreTrainedModel,
														)

							try:
														if not is_vision_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .image_processing_vit_hybrid import ViTHybridImageProcessor


else:
							import sys

							snake_case			:      Optional[int] 							=   _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
