





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

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


if is_sentencepiece_available():
							from .tokenization_rembert import RemBertTokenizer
else:
							__lowerCAmelCase :int     =    None

__lowerCAmelCase :Dict     =    logging.get_logger(__name__)
__lowerCAmelCase :Optional[Any]     =    {'vocab_file': 'sentencepiece.model', 'tokenizer_file': 'tokenizer.json'}

__lowerCAmelCase :str     =    {
    'vocab_file': {
        'google/rembert': 'https://huggingface.co/google/rembert/resolve/main/sentencepiece.model',
    },
    'tokenizer_file': {
        'google/rembert': 'https://huggingface.co/google/rembert/resolve/main/tokenizer.json',
    },
}

__lowerCAmelCase :str     =    {
    'google/rembert': 2_56,
}

__lowerCAmelCase :Any     =    '▁'






class        _a(    __A    ):
		lowerCamelCase__     :Optional[int]   				=		VOCAB_FILES_NAMES
		lowerCamelCase__     :List[Any]   				=		PRETRAINED_VOCAB_FILES_MAP
		lowerCamelCase__     :Union[str, Any]   				=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		lowerCamelCase__     :Dict   				=		RemBertTokenizer







		def __init__(    self ,						__snake_case=None ,						__snake_case=None ,						__snake_case=True ,						__snake_case=True ,						__snake_case=False ,						__snake_case="[CLS]" ,						__snake_case="[SEP]" ,						__snake_case="<unk>" ,						__snake_case="[SEP]" ,						__snake_case="<pad>" ,						__snake_case="[CLS]" ,						__snake_case="[MASK]" ,						**__snake_case ,						)      ->      int:


							'''simple docstring'''

							_snake_case       :					str				       =   AddedToken(__snake_case ,						lstrip=__snake_case ,						rstrip=__snake_case				) if isinstance(__snake_case ,						__snake_case				) else mask_token

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

							_snake_case       :					Optional[Any]				       =   do_lower_case
							_snake_case       :					str				       =   remove_space
							_snake_case       :					Tuple				       =   keep_accents
							_snake_case       :					int				       =   vocab_file
							_snake_case       :					Union[str, Any]				       =   False if not self.vocab_file else True







		def        lowercase       (    self ,						__snake_case ,						__snake_case = None				)      ->      List[int]:


							'''simple docstring'''

							_snake_case       :					int				       =   [self.sep_token_id]
							_snake_case       :					int				       =   [self.cls_token_id]
							if token_ids_a is None:
												return cls + token_ids_a + sep
							return cls + token_ids_a + sep + token_ids_a + sep







		def        lowercase       (    self ,						__snake_case ,						__snake_case = None ,						__snake_case = False				)      ->      List[int]:


							'''simple docstring'''

							if already_has_special_tokens:
												if token_ids_a is not None:
																	raise ValueError(
																	    "You should not supply a second sequence if the provided sequence of "
																	    "ids is already formatted with special tokens for the model."				)
												return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

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







		def        lowercase       (    self ,						__snake_case ,						__snake_case = None				)      ->      List[int]:


							'''simple docstring'''

							_snake_case       :					Tuple				       =   [self.sep_token_id]
							_snake_case       :					int				       =   [self.cls_token_id]

							if token_ids_a is None:
												return len(cls + token_ids_a + sep				) * [0]
							return len(cls + token_ids_a + sep				) * [0] + len(token_ids_a + sep				) * [1]







		def        lowercase       (    self ,						__snake_case ,						__snake_case = None				)      ->      Tuple[str]:


							'''simple docstring'''

							if not os.path.isdir(__snake_case				):
												logger.error("Vocabulary path ({}) should be a directory".format(__snake_case				)				)
												return
							_snake_case       :					List[str]				       =   os.path.join(
							    __snake_case ,						(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]				)

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

							return (out_vocab_file,)






from ...configuration_utils import PretrainedConfig
from ...utils import logging


__lowerCAmelCase :Tuple     =    logging.get_logger(__name__)

__lowerCAmelCase :int     =    {
    'facebook/timesformer': 'https://huggingface.co/facebook/timesformer/resolve/main/config.json',
}






class        _a(    __A    ):
		lowerCamelCase__     :Optional[Any]   				=		'timesformer'







		def __init__(    self ,						__snake_case=2_2_4 ,						__snake_case=1_6 ,						__snake_case=3 ,						__snake_case=8 ,						__snake_case=7_6_8 ,						__snake_case=1_2 ,						__snake_case=1_2 ,						__snake_case=3_0_7_2 ,						__snake_case="gelu" ,						__snake_case=0.0 ,						__snake_case=0.0 ,						__snake_case=0.02 ,						__snake_case=1E-6 ,						__snake_case=True ,						__snake_case="divided_space_time" ,						__snake_case=0 ,						**__snake_case ,						)      ->      str:


							'''simple docstring'''

							super().__init__(**__snake_case				)

							_snake_case       :					Optional[Any]				       =   image_size
							_snake_case       :					Optional[int]				       =   patch_size
							_snake_case       :					str				       =   num_channels
							_snake_case       :					Tuple				       =   num_frames

							_snake_case       :					Union[str, Any]				       =   hidden_size
							_snake_case       :					Optional[int]				       =   num_hidden_layers
							_snake_case       :					List[Any]				       =   num_attention_heads
							_snake_case       :					Optional[int]				       =   intermediate_size
							_snake_case       :					List[str]				       =   hidden_act
							_snake_case       :					Optional[int]				       =   hidden_dropout_prob
							_snake_case       :					Optional[Any]				       =   attention_probs_dropout_prob
							_snake_case       :					Dict				       =   initializer_range
							_snake_case       :					Optional[int]				       =   layer_norm_eps
							_snake_case       :					str				       =   qkv_bias

							_snake_case       :					List[str]				       =   attention_type
							_snake_case       :					Optional[int]				       =   drop_path_rate