


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:
   UpperCAmelCase    =					None


UpperCAmelCase    =					logging.get_logger(__name__)

UpperCAmelCase    =					{"""vocab_file""": """sentencepiece.bpe.model""", """tokenizer_file""": """tokenizer.json"""}

UpperCAmelCase    =					{
    """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""",
    },
}

UpperCAmelCase    =					{
    """camembert-base""": 512,
}

UpperCAmelCase    =					"""▁"""







class lowerCAmelCase_		(				lowerCamelCase__ ):

 '''simple docstring'''

 __snake_case				=						VOCAB_FILES_NAMES
 __snake_case				=						PRETRAINED_VOCAB_FILES_MAP
 __snake_case				=						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 __snake_case				=						["input_ids", "attention_mask"]
 __snake_case				=						CamembertTokenizer



 def __init__(			self	,   _UpperCAmelCase=None	,   _UpperCAmelCase=None	,   _UpperCAmelCase="<s>"	,   _UpperCAmelCase="</s>"	,   _UpperCAmelCase="</s>"	,   _UpperCAmelCase="<s>"	,   _UpperCAmelCase="<unk>"	,   _UpperCAmelCase="<pad>"	,   _UpperCAmelCase="<mask>"	,   _UpperCAmelCase=["<s>NOTUSED", "</s>NOTUSED"]	,   **_UpperCAmelCase	,   ):
       # Mask token behave like a normal word, i.e. include the space before it
       snake_case_ =   AddedToken(_UpperCAmelCase	,   lstrip=_UpperCAmelCase	,   rstrip=_UpperCAmelCase      ) if isinstance(_UpperCAmelCase	,   _UpperCAmelCase      ) else mask_token

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

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



 def 		UpperCamelCase__  (			self	,   _UpperCAmelCase	,   _UpperCAmelCase = None      ):

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



 def 		UpperCamelCase__  (			self	,   _UpperCAmelCase	,   _UpperCAmelCase = None      ):
       snake_case_ =   [self.sep_token_id]
       snake_case_ =   [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	,   _UpperCAmelCase	,   _UpperCAmelCase = None      ):
       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(_UpperCAmelCase      ):
             logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''      )
             return
       snake_case_ =   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      ):
             copyfile(self.vocab_file	,   _UpperCAmelCase      )

       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 lowerCAmelCase_		(				unittest.TestCase ):

 '''simple docstring'''

 __snake_case				=						MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING



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



 def 		UpperCamelCase__  (			self	,   _UpperCAmelCase	,   _UpperCAmelCase      ):
       for example in examples:
             snake_case_ =   video_classifier(_UpperCAmelCase      )

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



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

       snake_case_ =   hf_hub_download(repo_id='''nateraw/video-demo'''	,   filename='''archery.mp4'''	,   repo_type='''dataset'''      )
       snake_case_ =   video_classifier(_UpperCAmelCase	,   top_k=2      )
       self.assertEqual(
           nested_simplify(_UpperCAmelCase	,   decimals=4      )	,   [{'''score''': 0.5_199, '''label''': '''LABEL_0'''}, {'''score''': 0.4_801, '''label''': '''LABEL_1'''}]	,   )

       snake_case_ =   video_classifier(
           [
               video_file_path,
               video_file_path,
           ]	,   top_k=2	,   )
       self.assertEqual(
           nested_simplify(_UpperCAmelCase	,   decimals=4      )	,   [
               [{'''score''': 0.5_199, '''label''': '''LABEL_0'''}, {'''score''': 0.4_801, '''label''': '''LABEL_1'''}],
               [{'''score''': 0.5_199, '''label''': '''LABEL_0'''}, {'''score''': 0.4_801, '''label''': '''LABEL_1'''}],
           ]	,   )





 @require_tf
 def 		UpperCamelCase__  (			self      ):
       pass