



import collections
import os
from typing import List, Optional, Tuple

from transformers.utils import is_jieba_available, requires_backends


if is_jieba_available():
			import jieba

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


lowerCAmelCase__		:					List[str]							=logging.get_logger(__name__)

lowerCAmelCase__		:					str							={'''vocab_file''': '''vocab.txt'''}

lowerCAmelCase__		:					List[Any]							={
    '''vocab_file''': {
        '''openbmb/cpm-ant-10b''': '''https://huggingface.co/openbmb/cpm-ant-10b/blob/main/vocab.txt''',
    },
}

lowerCAmelCase__		:					Optional[Any]							={
    '''openbmb/cpm-ant-10b''': 1024,
}






def      __lowercase       (			a__       )    ->			Optional[Any]:
							__SCREAMING_SNAKE_CASE									= collections.OrderedDict()
							with open(a__							,       'r'							,       encoding='utf-8'       ) as reader:
														__SCREAMING_SNAKE_CASE									= reader.readlines()
							for index, token in enumerate(a__       ):
														__SCREAMING_SNAKE_CASE									= token.rstrip('\n'       )
														__SCREAMING_SNAKE_CASE									= index
							return vocab

class        UpperCAmelCase_  (       UpperCamelCase_	):

						'''simple docstring'''




						def __init__(   self				,							_A				,							_A="<unk>"				,							_A=200		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= vocab
													__SCREAMING_SNAKE_CASE									= unk_token
													__SCREAMING_SNAKE_CASE									= max_input_chars_per_word






						def 	_A     (   self				,							_A		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= list(_A		)
													if len(_A		) > self.max_input_chars_per_word:
																				return [self.unk_token]

													__SCREAMING_SNAKE_CASE									= 0
													__SCREAMING_SNAKE_CASE									= []
													while start < len(_A		):
																				__SCREAMING_SNAKE_CASE									= len(_A		)
																				__SCREAMING_SNAKE_CASE									= None
																				while start < end:
																											__SCREAMING_SNAKE_CASE									= ''.join(chars[start:end]		)
																											if substr in self.vocab:
																																		__SCREAMING_SNAKE_CASE									= substr
																																		break
																											end -= 1
																				if cur_substr is None:
																											sub_tokens.append(self.unk_token		)
																											start += 1
																				else:
																											sub_tokens.append(_A		)
																											__SCREAMING_SNAKE_CASE									= end

													return sub_tokens







class        UpperCAmelCase_  (       UpperCamelCase_	):

						'''simple docstring'''




						UpperCamelCase__      :			Tuple      =  VOCAB_FILES_NAMES
						UpperCamelCase__      :			Any      =  PRETRAINED_VOCAB_FILES_MAP
						UpperCamelCase__      :			List[str]      =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						UpperCamelCase__      :			Any      =  ['''input_ids''', '''attention_mask''']
						UpperCamelCase__      :			Dict      =  False






						def __init__(   self				,							_A				,							_A="<d>"				,							_A="</d>"				,							_A="<s>"				,							_A="</s>"				,							_A="<pad>"				,							_A="<unk>"				,							_A="</n>"				,							_A="</_>"				,							_A="left"				,							**_A				,							):







													'''simple docstring'''

													requires_backends(self				,							['jieba']		)
													super().__init__(
													    bod_token=_A				,							eod_token=_A				,							bos_token=_A				,							eos_token=_A				,							pad_token=_A				,							unk_token=_A				,							line_token=_A				,							space_token=_A				,							padding_side=_A				,							**_A				,							)
													__SCREAMING_SNAKE_CASE									= bod_token
													__SCREAMING_SNAKE_CASE									= eod_token
													__SCREAMING_SNAKE_CASE									= load_vocab(_A		)
													__SCREAMING_SNAKE_CASE									= self.encoder[space_token]
													__SCREAMING_SNAKE_CASE									= self.encoder[line_token]

													del self.encoder[space_token]
													del self.encoder[line_token]

													__SCREAMING_SNAKE_CASE									= collections.OrderedDict(sorted(self.encoder.items()				,							key=lambda _A		: x[1]		)		)
													__SCREAMING_SNAKE_CASE									= {v: k for k, v in self.encoder.items()}

													__SCREAMING_SNAKE_CASE									= WordpieceTokenizer(vocab=self.encoder				,							unk_token=self.unk_token		)






						@property
						def 	_A     (   self		):







													'''simple docstring'''

													return self.encoder[self.bod_token]






						@property
						def 	_A     (   self		):







													'''simple docstring'''

													return self.encoder[self.eod_token]






						@property
						def 	_A     (   self		):







													'''simple docstring'''

													return self.encoder["\n"]






						@property
						def 	_A     (   self		):







													'''simple docstring'''

													return len(self.encoder		)






						def 	_A     (   self		):







													'''simple docstring'''

													return dict(self.encoder				,							**self.added_tokens_encoder		)






						def 	_A     (   self				,							_A		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= []
													for x in jieba.cut(_A				,							cut_all=_A		):
																				output_tokens.extend(self.wordpiece_tokenizer.tokenize(_A		)		)
													return output_tokens






						def 	_A     (   self				,							_A				,							**_A		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= [i for i in token_ids if i >= 0]
													__SCREAMING_SNAKE_CASE									= [
													    x for x in token_ids if x != self.pad_token_id and x != self.eos_token_id and x != self.bos_token_id
													]
													return super()._decode(_A				,							**_A		)






						def 	_A     (   self				,							_A		):







													'''simple docstring'''

													return token in self.encoder






						def 	_A     (   self				,							_A		):







													'''simple docstring'''

													return "".join(_A		)






						def 	_A     (   self				,							_A		):







													'''simple docstring'''

													return self.encoder.get(_A				,							self.encoder.get(self.unk_token		)		)






						def 	_A     (   self				,							_A		):







													'''simple docstring'''

													return self.decoder.get(_A				,							self.unk_token		)






						def 	_A     (   self				,							_A				,							_A = None		):







													'''simple docstring'''

													if os.path.isdir(_A		):
																				__SCREAMING_SNAKE_CASE									= os.path.join(
																				    _A				,							(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']		)
													else:
																				__SCREAMING_SNAKE_CASE									= (filename_prefix + '-' if filename_prefix else '') + save_directory
													__SCREAMING_SNAKE_CASE									= 0
													if " " in self.encoder:
																				__SCREAMING_SNAKE_CASE									= self.encoder[' ']
																				del self.encoder[" "]
													if "\n" in self.encoder:
																				__SCREAMING_SNAKE_CASE									= self.encoder['\n']
																				del self.encoder["\n"]
													__SCREAMING_SNAKE_CASE									= collections.OrderedDict(sorted(self.encoder.items()				,							key=lambda _A		: x[1]		)		)
													with open(_A				,							'w'				,							encoding='utf-8'		) as writer:
																				for token, token_index in self.encoder.items():
																											if index != token_index:
																																		logger.warning(
																																		    f"""Saving vocabulary to {vocab_file}: vocabulary indices are not consecutive."""
																																		    ' Please check that the vocabulary is not corrupted!'		)
																																		__SCREAMING_SNAKE_CASE									= token_index
																											writer.write(token + '\n'		)
																											index += 1
													return (vocab_file,)






						def 	_A     (   self				,							_A				,							_A = None		):







													'''simple docstring'''

													if token_ids_a is None:
																				return [self.bos_token_id] + token_ids_a
													return [self.bos_token_id] + token_ids_a + [self.bos_token_id] + token_ids_a






						def 	_A     (   self				,							_A				,							_A = None				,							_A = False		):







													'''simple docstring'''

													if already_has_special_tokens:
																				return super().get_special_tokens_mask(
																				    token_ids_a=_A				,							token_ids_a=_A				,							already_has_special_tokens=_A		)

													if token_ids_a is not None:
																				return [1] + ([0] * len(_A		)) + [1] + ([0] * len(_A		))
													return [1] + ([0] * len(_A		))











from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCAmelCase__		:					List[str]							={
    '''configuration_timesformer''': ['''TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''TimesformerConfig'''],
}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowerCAmelCase__		:					List[str]							=[
			    '''TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''TimesformerModel''',
			    '''TimesformerForVideoClassification''',
			    '''TimesformerPreTrainedModel''',
			]

if TYPE_CHECKING:
			from .configuration_timesformer import TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, TimesformerConfig

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_timesformer import (
						    TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
						    TimesformerForVideoClassification,
						    TimesformerModel,
						    TimesformerPreTrainedModel,
						)

else:
			import sys

			lowerCAmelCase__		:					Union[str, Any]							=_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






