





"""simple docstring"""
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


_snake_case     :		str       						= logging.get_logger(__name__)

_snake_case     :		Union[str, Any]       						= {'vocab_file': 'sentencepiece.bpe.model'}

_snake_case     :		Tuple       						= {
    'vocab_file': {
        'moussaKam/mbarthez': 'https://huggingface.co/moussaKam/mbarthez/resolve/main/sentencepiece.bpe.model',
        'moussaKam/barthez': 'https://huggingface.co/moussaKam/barthez/resolve/main/sentencepiece.bpe.model',
        'moussaKam/barthez-orangesum-title': (
            'https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/sentencepiece.bpe.model'
        ),
    },
}

_snake_case     :		List[str]       						= {
    'moussaKam/mbarthez': 1024,
    'moussaKam/barthez': 1024,
    'moussaKam/barthez-orangesum-title': 1024,
}

_snake_case     :		Any       						= '▁'






class 					_UpperCAmelCase   (	lowercase_ ):
					UpperCamelCase      =							VOCAB_FILES_NAMES
					UpperCamelCase      =							PRETRAINED_VOCAB_FILES_MAP
					UpperCamelCase      =							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					UpperCamelCase      =							['''input_ids''', '''attention_mask''']


					def __init__(					self			:List[Any]     ,       __UpperCamelCase			:List[str]     ,       __UpperCamelCase			:Any="<s>"     ,       __UpperCamelCase			:Dict="</s>"     ,       __UpperCamelCase			:Union[str, Any]="</s>"     ,       __UpperCamelCase			:Optional[Any]="<s>"     ,       __UpperCamelCase			:Tuple="<unk>"     ,       __UpperCamelCase			:List[Any]="<pad>"     ,       __UpperCamelCase			:List[str]="<mask>"     ,       __UpperCamelCase			:Optional[Dict[str, Any]] = None     ,       **__UpperCamelCase			:int     ,       ):
												# Mask token behave like a normal word, i.e. include the space before it
												A		=  AddedToken(__UpperCamelCase     ,       lstrip=__UpperCamelCase     ,       rstrip=__UpperCamelCase		) if isinstance(__UpperCamelCase     ,       __UpperCamelCase		) else mask_token

												A		=  {} if sp_model_kwargs is None else sp_model_kwargs

												super().__init__(
												    bos_token=__UpperCamelCase     ,       eos_token=__UpperCamelCase     ,       unk_token=__UpperCamelCase     ,       sep_token=__UpperCamelCase     ,       cls_token=__UpperCamelCase     ,       pad_token=__UpperCamelCase     ,       mask_token=__UpperCamelCase     ,       sp_model_kwargs=self.sp_model_kwargs     ,       **__UpperCamelCase     ,       )

												A		=  vocab_file
												A		=  spm.SentencePieceProcessor(**self.sp_model_kwargs		)
												self.sp_model.Load(str(__UpperCamelCase		)		)

												A		=  {"<s>": 0, "<pad>": 1, "</s>": 2, "<unk>": 3}

												A		=  len(self.sp_model		) - 1
												A		=  {v: k for k, v in self.fairseq_tokens_to_ids.items()}


					def 	lowerCamelCase       (					self			:Any     ,       __UpperCamelCase			:List[int]     ,       __UpperCamelCase			:Optional[List[int]] = None		):

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


					def 	lowerCamelCase       (					self			:List[Any]     ,       __UpperCamelCase			:List[int]     ,       __UpperCamelCase			:Optional[List[int]] = None     ,       __UpperCamelCase			:bool = False		):
												if already_has_special_tokens:
																			return super().get_special_tokens_mask(
																			    token_ids_a=__UpperCamelCase     ,       token_ids_a=__UpperCamelCase     ,       already_has_special_tokens=__UpperCamelCase		)

												if token_ids_a is None:
																			return [1] + ([0] * len(__UpperCamelCase		)) + [1]
												return [1] + ([0] * len(__UpperCamelCase		)) + [1, 1] + ([0] * len(__UpperCamelCase		)) + [1]


					def 	lowerCamelCase       (					self			:Union[str, Any]     ,       __UpperCamelCase			:List[int]     ,       __UpperCamelCase			:Optional[List[int]] = None		):
												A		=  [self.sep_token_id]
												A		=  [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 + sep + token_ids_a + sep		) * [0]


					@property
					def 	lowerCamelCase       (					self			:int		):
												return len(self.sp_model		)


					def 	lowerCamelCase       (					self			:str		):
												A		=  {self.convert_ids_to_tokens(__UpperCamelCase		): i for i in range(self.vocab_size		)}
												vocab.update(self.added_tokens_encoder		)
												return vocab


					def 	lowerCamelCase       (					self			:Dict     ,       __UpperCamelCase			:str		):
												return self.sp_model.encode(__UpperCamelCase     ,       out_type=__UpperCamelCase		)


					def 	lowerCamelCase       (					self			:str     ,       __UpperCamelCase			:Any		):
												if token in self.fairseq_tokens_to_ids:
																			return self.fairseq_tokens_to_ids[token]
												A		=  self.sp_model.PieceToId(__UpperCamelCase		)

												return spm_id if spm_id else self.unk_token_id


					def 	lowerCamelCase       (					self			:Optional[int]     ,       __UpperCamelCase			:Any		):
												if index in self.fairseq_ids_to_tokens:
																			return self.fairseq_ids_to_tokens[index]
												return self.sp_model.IdToPiece(__UpperCamelCase		)


					def 	lowerCamelCase       (					self			:Optional[int]     ,       __UpperCamelCase			:Optional[Any]		):
												A		=  []
												A		=  ""
												A		=  False
												for token in tokens:
																			# make sure that special tokens are not decoded using sentencepiece model
																			if token in self.all_special_tokens:
																										if not prev_is_special:
																																	out_string += " "
																										out_string += self.sp_model.decode(__UpperCamelCase		) + token
																										A		=  True
																										A		=  []
																			else:
																										current_sub_tokens.append(__UpperCamelCase		)
																										A		=  False
												out_string += self.sp_model.decode(__UpperCamelCase		)
												return out_string.strip()


					def __getstate__(					self			:Tuple		):
												A		=  self.__dict__.copy()
												A		=  None
												return state


					def __setstate__(					self			:Dict     ,       __UpperCamelCase			:Union[str, Any]		):
												A		=  d

												# for backward compatibility
												if not hasattr(self     ,       "sp_model_kwargs"		):
																			A		=  {}

												A		=  spm.SentencePieceProcessor(**self.sp_model_kwargs		)
												self.sp_model.Load(self.vocab_file		)


					def 	lowerCamelCase       (					self			:Dict     ,       __UpperCamelCase			:str     ,       __UpperCamelCase			:Optional[str] = None		):
												if not os.path.isdir(__UpperCamelCase		):
																			logger.error(f"Vocabulary path ({save_directory}) should be a directory"		)
																			return
												A		=  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		) and os.path.isfile(self.vocab_file		):
																			copyfile(self.vocab_file     ,       __UpperCamelCase		)
												elif not os.path.isfile(self.vocab_file		):
																			with open(__UpperCamelCase     ,       "wb"		) as fi:
																										A		=  self.sp_model.serialized_model_proto()
																										fi.write(__UpperCamelCase		)

												return (out_vocab_file,)









"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging


_snake_case     :		Optional[int]       						= logging.get_logger(__name__)

_snake_case     :		Optional[int]       						= {
    'google/vivit-b-16x2-kinetics400': (
        'https://huggingface.co/google/vivit-b-16x2-kinetics400/resolve/main/config.json'
    ),
    # See all Vivit models at https://huggingface.co/models?filter=vivit
}






class 					_UpperCAmelCase   (	lowercase_ ):
					UpperCamelCase      =							'''vivit'''


					def __init__(					self			:Optional[Any]     ,       __UpperCamelCase			:Dict=2_24     ,       __UpperCamelCase			:int=32     ,       __UpperCamelCase			:Union[str, Any]=[2, 16, 16]     ,       __UpperCamelCase			:Optional[Any]=3     ,       __UpperCamelCase			:Optional[Any]=7_68     ,       __UpperCamelCase			:Any=12     ,       __UpperCamelCase			:List[str]=12     ,       __UpperCamelCase			:List[str]=30_72     ,       __UpperCamelCase			:Any="gelu_fast"     ,       __UpperCamelCase			:List[Any]=0.0     ,       __UpperCamelCase			:str=0.0     ,       __UpperCamelCase			:Dict=0.02     ,       __UpperCamelCase			:Optional[Any]=1e-06     ,       __UpperCamelCase			:Dict=True     ,       **__UpperCamelCase			:Tuple     ,       ):
												A		=  hidden_size
												A		=  num_hidden_layers
												A		=  num_attention_heads
												A		=  intermediate_size
												A		=  hidden_act
												A		=  hidden_dropout_prob
												A		=  attention_probs_dropout_prob
												A		=  initializer_range
												A		=  layer_norm_eps

												A		=  image_size
												A		=  num_frames
												A		=  tubelet_size
												A		=  num_channels
												A		=  qkv_bias

												super().__init__(**__UpperCamelCase		)


