




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

import sentencepiece as spm

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


UpperCamelCase__    			=			logging.get_logger(__name__)

UpperCamelCase__    			=			{"""vocab_file""": """sentencepiece.model"""}

UpperCamelCase__    			=			{
    """vocab_file""": {
        """google/rembert""": """https://huggingface.co/google/rembert/resolve/main/sentencepiece.model""",
    },
}

UpperCamelCase__    			=			{
    """google/rembert""": 256,
}
class 			a__    (					snake_case__      ):
						_a							:    Optional[int]					=    VOCAB_FILES_NAMES
						_a							:    Dict					=    PRETRAINED_VOCAB_FILES_MAP
						_a							:    List[Any]					=    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES






						def __init__(   self		,			_A		,			_A=False		,			_A=True		,			_A=True		,			_A="[CLS]"		,			_A="[SEP]"		,			_A="[UNK]"		,			_A="[SEP]"		,			_A="[PAD]"		,			_A="[CLS]"		,			_A="[MASK]"		,			**_A		,			):




											"""simple docstring"""



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

											__lowerCAmelCase				   =       do_lower_case
											__lowerCAmelCase				   =       remove_space
											__lowerCAmelCase				   =       keep_accents
											__lowerCAmelCase				   =       vocab_file

											__lowerCAmelCase				   =       spm.SentencePieceProcessor()
											self.sp_model.Load(_A			)






						@property
						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											return len(self.sp_model			)






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											__lowerCAmelCase				   =       {self.convert_ids_to_tokens(_A			): i for i in range(self.vocab_size			)}
											vocab.update(self.added_tokens_encoder			)
											return vocab






						def __getstate__(   self			):




											"""simple docstring"""



											__lowerCAmelCase				   =       self.__dict__.copy()
											__lowerCAmelCase				   =       None
											return state






						def __setstate__(   self		,			_A			):




											"""simple docstring"""



											__lowerCAmelCase				   =       d
											__lowerCAmelCase				   =       spm.SentencePieceProcessor()
											self.sp_model.Load(self.vocab_file			)






						def 				__SCREAMING_SNAKE_CASE(   self		,			_A		,			_A=False			):




											"""simple docstring"""



											__lowerCAmelCase				   =       self.sp_model.EncodeAsPieces(_A			)
											return pieces






						def 				__SCREAMING_SNAKE_CASE(   self		,			_A			):




											"""simple docstring"""



											return self.sp_model.PieceToId(_A			)






						def 				__SCREAMING_SNAKE_CASE(   self		,			_A			):




											"""simple docstring"""



											return self.sp_model.IdToPiece(_A			)






						def 				__SCREAMING_SNAKE_CASE(   self		,			_A			):




											"""simple docstring"""



											__lowerCAmelCase				   =       self.sp_model.decode_pieces(_A			)
											return out_string






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




											"""simple docstring"""



											__lowerCAmelCase				   =       [self.sep_token_id]
											__lowerCAmelCase				   =       [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 				__SCREAMING_SNAKE_CASE(   self		,			_A		,			_A = None		,			_A = False			):




											"""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(_A			)) + [1] + ([0] * len(_A			)) + [1]
											return [1] + ([0] * len(_A			)) + [1]






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




											"""simple docstring"""



											__lowerCAmelCase				   =       [self.sep_token_id]
											__lowerCAmelCase				   =       [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 				__SCREAMING_SNAKE_CASE(   self		,			_A		,			_A = None			):




											"""simple docstring"""



											if not os.path.isdir(_A			):
																logger.error("Vocabulary path ({}) should be a directory".format(_A			)			)
																return
											__lowerCAmelCase				   =       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,)











import unittest

from huggingface_hub import hf_hub_download

from transformers import MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING, VideoMAEFeatureExtractor
from transformers.pipelines import VideoClassificationPipeline, pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_decord,
    require_tf,
    require_torch,
    require_torch_or_tf,
    require_vision,
)

from .test_pipelines_common import ANY




@is_pipeline_test
@require_torch_or_tf
@require_vision
@require_decord
class     SCREAMING_SNAKE_CASE__ (						unittest.TestCase      ):
 __SCREAMING_SNAKE_CASE							       =	MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING



 def 				UpperCamelCase			(							self,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase ):
        A__											=						hf_hub_download(
            repo_id='''nateraw/video-demo''',filename='''archery.mp4''',repo_type='''dataset''' )
        A__											=						VideoClassificationPipeline(model=__lowerCamelCase,image_processor=__lowerCamelCase,top_k=2 )
        A__											=						[
            example_video_filepath,
            '''https://huggingface.co/datasets/nateraw/video-demo/resolve/main/archery.mp4''',
        ]
        return video_classifier, examples



 def 				UpperCamelCase			(							self,__lowerCamelCase,__lowerCamelCase ):
        for example in examples:
               A__											=						video_classifier(__lowerCamelCase )

               self.assertEqual(
                   __lowerCamelCase,[
                       {'''score''': ANY(__lowerCamelCase ), '''label''': ANY(__lowerCamelCase )},
                       {'''score''': ANY(__lowerCamelCase ), '''label''': ANY(__lowerCamelCase )},
                   ],)



 @require_torch
 def 				UpperCamelCase			(							self ):
        A__											=						'''hf-internal-testing/tiny-random-VideoMAEForVideoClassification'''
        A__											=						VideoMAEFeatureExtractor(
            size={'''shortest_edge''': 10},crop_size={'''height''': 10, '''width''': 10} )
        A__											=						pipeline(
            '''video-classification''',model=__lowerCamelCase,feature_extractor=__lowerCamelCase,frame_sampling_rate=4 )

        A__											=						hf_hub_download(repo_id='''nateraw/video-demo''',filename='''archery.mp4''',repo_type='''dataset''' )
        A__											=						video_classifier(__lowerCamelCase,top_k=2 )
        self.assertEqual(
            nested_simplify(__lowerCamelCase,decimals=4 ),[{'''score''': 0.5199, '''label''': '''LABEL_0'''}, {'''score''': 0.4801, '''label''': '''LABEL_1'''}],)

        A__											=						video_classifier(
            [
                video_file_path,
                video_file_path,
            ],top_k=2,)
        self.assertEqual(
            nested_simplify(__lowerCamelCase,decimals=4 ),[
                [{'''score''': 0.5199, '''label''': '''LABEL_0'''}, {'''score''': 0.4801, '''label''': '''LABEL_1'''}],
                [{'''score''': 0.5199, '''label''': '''LABEL_0'''}, {'''score''': 0.4801, '''label''': '''LABEL_1'''}],
            ],)




 @require_tf
 def 				UpperCamelCase			(							self ):
        pass





