"""simple docstring"""


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

import sentencepiece as spm

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


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


lowerCAmelCase_:					Dict     =					"▁"

lowerCAmelCase_:					str     =					{"vocab_file": "spiece.model"}

lowerCAmelCase_:					Union[str, Any]     =					{
    "vocab_file": {
        "google/reformer-crime-and-punishment": (
            "https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/spiece.model"
        )
    }
}

lowerCAmelCase_:					Any     =					{
    "google/reformer-crime-and-punishment": 5_2_4_2_8_8,
}






class    a__    (   _a	):
   snake_case_							=      VOCAB_FILES_NAMES
   snake_case_							=      PRETRAINED_VOCAB_FILES_MAP
   snake_case_							=      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   snake_case_							=      ["input_ids", "attention_mask"]


   def __init__(     self,				_UpperCAmelCase,				_UpperCAmelCase="</s>",				_UpperCAmelCase="<unk>",				_UpperCAmelCase=[],				_UpperCAmelCase = None,				**_UpperCAmelCase,				):
        '''simple docstring'''



        lowercase__				 =							{} if sp_model_kwargs is None else sp_model_kwargs

        super().__init__(
            eos_token=_UpperCAmelCase,				unk_token=_UpperCAmelCase,				additional_special_tokens=_UpperCAmelCase,				sp_model_kwargs=self.sp_model_kwargs,				**_UpperCAmelCase,				)

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


   @property
   def 	snake_case__	(     self  ):
        '''simple docstring'''



        return self.sp_model.get_piece_size()


   def 	snake_case__	(     self  ):
        '''simple docstring'''



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


   def __getstate__(     self  ):
        '''simple docstring'''



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


   def __setstate__(     self,				_UpperCAmelCase  ):
        '''simple docstring'''



        lowercase__				 =							d

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

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


   def 	snake_case__	(     self,				_UpperCAmelCase  ):
        '''simple docstring'''



        return self.sp_model.encode(_UpperCAmelCase,				out_type=_UpperCAmelCase  )


   def 	snake_case__	(     self,				_UpperCAmelCase  ):
        '''simple docstring'''



        return self.sp_model.piece_to_id(_UpperCAmelCase  )


   def 	snake_case__	(     self,				_UpperCAmelCase  ):
        '''simple docstring'''



        if index < self.sp_model.get_piece_size():
             lowercase__				 =							self.sp_model.IdToPiece(_UpperCAmelCase  )
        return token


   def 	snake_case__	(     self,				_UpperCAmelCase  ):
        '''simple docstring'''



        lowercase__				 =							[]
        lowercase__				 =							""
        for token in tokens:
             # make sure that special tokens are not decoded using sentencepiece model
             if token in self.all_special_tokens:
                  out_string += self.sp_model.decode(_UpperCAmelCase  ) + token
                  lowercase__				 =							[]
             else:
                  current_sub_tokens.append(_UpperCAmelCase  )
        out_string += self.sp_model.decode(_UpperCAmelCase  )
        return out_string.strip()


   def 	snake_case__	(     self,				_UpperCAmelCase,				_UpperCAmelCase = None  ):
        '''simple docstring'''



        if not os.path.isdir(_UpperCAmelCase  ):
             logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''  )
             return
        lowercase__				 =							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:
                  lowercase__				 =							self.sp_model.serialized_model_proto()
                  fi.write(_UpperCAmelCase  )

        return (out_vocab_file,)





"""simple docstring"""


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    a__    (   unittest.TestCase	):
   snake_case_							=      MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING


   def 	snake_case__	(     self,				_UpperCAmelCase,				_UpperCAmelCase,				_UpperCAmelCase  ):
        '''simple docstring'''



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


   def 	snake_case__	(     self,				_UpperCAmelCase,				_UpperCAmelCase  ):
        '''simple docstring'''



        for example in examples:
             lowercase__				 =							video_classifier(_UpperCAmelCase  )

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


   @require_torch
   def 	snake_case__	(     self  ):
        '''simple docstring'''



        lowercase__				 =							"hf-internal-testing/tiny-random-VideoMAEForVideoClassification"
        lowercase__				 =							VideoMAEFeatureExtractor(
            size={"shortest_edge": 10},				crop_size={"height": 10, "width": 10}  )
        lowercase__				 =							pipeline(
            "video-classification",				model=_UpperCAmelCase,				feature_extractor=_UpperCAmelCase,				frame_sampling_rate=4  )

        lowercase__				 =							hf_hub_download(repo_id="nateraw/video-demo",				filename="archery.mp4",				repo_type="dataset"  )
        lowercase__				 =							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"}],				)

        lowercase__				 =							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 	snake_case__	(     self  ):
        '''simple docstring'''



        pass




