
'''simple docstring'''
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_camembert import CamembertTokenizer
else:
						lowerCAmelCase					:    Optional[int]           = None


lowerCAmelCase					:    str           = logging.get_logger(__name__)

lowerCAmelCase					:    Tuple           = {'vocab_file': 'sentencepiece.bpe.model', 'tokenizer_file': 'tokenizer.json'}

lowerCAmelCase					:    Any           = {
    'vocab_file': {
        'camembert-base': 'https://huggingface.co/camembert-base/resolve/main/sentencepiece.bpe.model',
    },
    'tokenizer_file': {
        'camembert-base': 'https://huggingface.co/camembert-base/resolve/main/tokenizer.json',
    },
}

lowerCAmelCase					:    Union[str, Any]           = {
    'camembert-base': 5_12,
}

lowerCAmelCase					:    Tuple           = '▁'


class 			SCREAMING_SNAKE_CASE__      (     snake_case_):
				lowerCAmelCase_     			=							VOCAB_FILES_NAMES
				lowerCAmelCase_     			=							PRETRAINED_VOCAB_FILES_MAP
				lowerCAmelCase_     			=							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				lowerCAmelCase_     			=							["""input_ids""", """attention_mask"""]
				lowerCAmelCase_     			=							CamembertTokenizer







				def __init__(       self						,				A_=None						,				A_=None						,				A_="<s>"						,				A_="</s>"						,				A_="</s>"						,				A_="<s>"						,				A_="<unk>"						,				A_="<pad>"						,				A_="<mask>"						,				A_=["<s>NOTUSED", "</s>NOTUSED"]						,				**A_						,				)->		str:







											'''simple docstring'''






											UpperCamelCase   			=     AddedToken(A_						,				lstrip=A_						,				rstrip=A_     ) if isinstance(A_						,				A_     ) else mask_token

											super().__init__(
											    A_						,				tokenizer_file=A_						,				bos_token=A_						,				eos_token=A_						,				sep_token=A_						,				cls_token=A_						,				unk_token=A_						,				pad_token=A_						,				mask_token=A_						,				additional_special_tokens=A_						,				**A_						,				)

											UpperCamelCase   			=     vocab_file
											UpperCamelCase   			=     False if not self.vocab_file else True







				def 						UpperCAmelCase_    (       self						,				A_						,				A_ = None     )->		List[int]:







											'''simple docstring'''






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







				def 						UpperCAmelCase_    (       self						,				A_						,				A_ = None     )->		List[int]:







											'''simple docstring'''






											UpperCamelCase   			=     [self.sep_token_id]
											UpperCamelCase   			=     [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]







				def 						UpperCAmelCase_    (       self						,				A_						,				A_ = None     )->		Tuple[str]:







											'''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(A_     ):
																		logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''     )
																		return
											UpperCamelCase   			=     os.path.join(
											    A_						,				(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']     )

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

											return (out_vocab_file,)







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


lowercase_   							=  logging.get_logger(__name__)

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





class 					__lowerCAmelCase	(   SCREAMING_SNAKE_CASE				):
  _a		     =    """timesformer"""




  def __init__(							self				,  lowerCAmelCase=224				,  lowerCAmelCase=16				,  lowerCAmelCase=3				,  lowerCAmelCase=8				,  lowerCAmelCase=768				,  lowerCAmelCase=12				,  lowerCAmelCase=12				,  lowerCAmelCase=3_072				,  lowerCAmelCase="gelu"				,  lowerCAmelCase=0.0				,  lowerCAmelCase=0.0				,  lowerCAmelCase=0.02				,  lowerCAmelCase=1e-6				,  lowerCAmelCase=True				,  lowerCAmelCase="divided_space_time"				,  lowerCAmelCase=0				,  **lowerCAmelCase				,  )    ->					Any:
        '''simple docstring'''






        super().__init__(**lowerCAmelCase   )

        _lowercase      			=image_size
        _lowercase      			=patch_size
        _lowercase      			=num_channels
        _lowercase      			=num_frames

        _lowercase      			=hidden_size
        _lowercase      			=num_hidden_layers
        _lowercase      			=num_attention_heads
        _lowercase      			=intermediate_size
        _lowercase      			=hidden_act
        _lowercase      			=hidden_dropout_prob
        _lowercase      			=attention_probs_dropout_prob
        _lowercase      			=initializer_range
        _lowercase      			=layer_norm_eps
        _lowercase      			=qkv_bias

        _lowercase      			=attention_type
        _lowercase      			=drop_path_rate


