


"""simple docstring"""


import os
import unicodedata
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 SPIECE_UNDERLINE, logging


UpperCamelCase						:				str             =      logging.get_logger(__name__)

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

UpperCamelCase						:				Any             =      {
    "vocab_file": {
        "TsinghuaAI/CPM-Generate": "https://huggingface.co/TsinghuaAI/CPM-Generate/resolve/main/spiece.model",
    }
}



class 			lowerCamelCase__						(   UpperCAmelCase_			):







						def __init__(							self   :    Union[str, Any]			,			_lowercase   :    Union[str, Any]			,			_lowercase   :    List[Any]=False			,			_lowercase   :    Union[str, Any]=True			,			_lowercase   :    Dict=False			,			_lowercase   :    Optional[Any]="<s>"			,			_lowercase   :    Optional[Any]="</s>"			,			_lowercase   :    Union[str, Any]="<unk>"			,			_lowercase   :    Optional[int]="<sep>"			,			_lowercase   :    List[Any]="<pad>"			,			_lowercase   :    str="<cls>"			,			_lowercase   :    Optional[int]="<mask>"			,			_lowercase   :    Dict=["<eop>", "<eod>"]			,			_lowercase   :    Optional[Dict[str, Any]] = None			,			**_lowercase   :    Union[str, Any]			,			):
								A					       = AddedToken(_lowercase			,			lstrip=_lowercase			,			rstrip=_lowercase		) if isinstance(_lowercase			,			_lowercase		) else mask_token

								A					       = {} if sp_model_kwargs is None else sp_model_kwargs

								super().__init__(
								    do_lower_case=_lowercase			,			remove_space=_lowercase			,			keep_accents=_lowercase			,			bos_token=_lowercase			,			eos_token=_lowercase			,			unk_token=_lowercase			,			sep_token=_lowercase			,			pad_token=_lowercase			,			cls_token=_lowercase			,			mask_token=_lowercase			,			additional_special_tokens=_lowercase			,			sp_model_kwargs=self.sp_model_kwargs			,			**_lowercase			,			)

								A					       = 3

								A					       = do_lower_case
								A					       = remove_space
								A					       = keep_accents
								A					       = vocab_file

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

								try:
										import jieba
								except ModuleNotFoundError as error:
										raise error.__class__(
										    'You need to install jieba to use CpmTokenizer or CpmTokenizerFast. '
										    'See https://pypi.org/project/jieba/ for installation.'		)
								A					       = jieba
								A					       = str.maketrans(' \n'			,			'\u2582\u2583'		)







						@property
						# Copied from transformers.models.xlnet.tokenization_xlnet.XLNetTokenizer.vocab_size
						def __a						(							self   :    Any		):
								return len(self.sp_model		)







						def __a						(							self   :    List[str]		):
								A					       = {self.convert_ids_to_tokens(_lowercase		): i for i in range(self.vocab_size		)}
								vocab.update(self.added_tokens_encoder		)
								return vocab







						def __getstate__(							self   :    List[str]		):
								A					       = self.__dict__.copy()
								A					       = None
								return state







						def __setstate__(							self   :    Dict			,			_lowercase   :    Optional[int]		):
								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 __a						(							self   :    Any			,			_lowercase   :    Tuple		):
								if self.remove_space:
										A					       = ' '.join(inputs.strip().split()		)
								else:
										A					       = inputs
								A					       = outputs.replace('``'			,			'"'		).replace('\'\''			,			'"'		)

								if not self.keep_accents:
										A					       = unicodedata.normalize('NFKD'			,			_lowercase		)
										A					       = ''.join([c for c in outputs if not unicodedata.combining(_lowercase		)]		)
								if self.do_lower_case:
										A					       = outputs.lower()

								return outputs







						def __a						(							self   :    Dict			,			_lowercase   :    str		):
								A					       = self.preprocess_text(_lowercase		)
								A					       = self.sp_model.encode(_lowercase			,			out_type=_lowercase		)
								A					       = []
								for piece in pieces:
										if len(_lowercase		) > 1 and piece[-1] == str(','		) and piece[-2].isdigit():
												A					       = self.sp_model.EncodeAsPieces(piece[:-1].replace(_lowercase			,			''		)		)
												if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
														if len(cur_pieces[0]		) == 1:
																A					       = cur_pieces[1:]
														else:
																A					       = cur_pieces[0][1:]
												cur_pieces.append(piece[-1]		)
												new_pieces.extend(_lowercase		)
										else:
												new_pieces.append(_lowercase		)

								return new_pieces







						def __a						(							self   :    int			,			_lowercase   :    Any		):
								return self.sp_model.PieceToId(_lowercase		)







						def __a						(							self   :    Dict			,			_lowercase   :    Dict		):
								return self.sp_model.IdToPiece(_lowercase		)







						def __a						(							self   :    List[str]			,			_lowercase   :    List[Any]		):
								A					       = ''.join(_lowercase		).replace(_lowercase			,			' '		).strip()
								return out_string







						def __a						(							self   :    Dict			,			_lowercase   :    List[int]			,			_lowercase   :    Optional[List[int]] = None		):
								A					       = [self.sep_token_id]
								A					       = [self.cls_token_id]
								if token_ids_a is None:
										return token_ids_a + sep + cls
								return token_ids_a + sep + token_ids_a + sep + cls







						def __a						(							self   :    Optional[Any]			,			_lowercase   :    List[int]			,			_lowercase   :    Optional[List[int]] = None			,			_lowercase   :    bool = False		):

								if already_has_special_tokens:
										return super().get_special_tokens_mask(
										    token_ids_a=_lowercase			,			token_ids_a=_lowercase			,			already_has_special_tokens=_lowercase		)

								if token_ids_a is not None:
										return ([0] * len(_lowercase		)) + [1] + ([0] * len(_lowercase		)) + [1, 1]
								return ([0] * len(_lowercase		)) + [1, 1]







						def __a						(							self   :    Optional[int]			,			_lowercase   :    List[int]			,			_lowercase   :    Optional[List[int]] = None		):
								A					       = [self.sep_token_id]
								A					       = [2]

								if token_ids_a is None:
										return len(token_ids_a + sep		) * [0] + cls_segment_id
								return len(token_ids_a + sep		) * [0] + len(token_ids_a + sep		) * [1] + cls_segment_id







						def __a						(							self   :    int			,			_lowercase   :    str			,			_lowercase   :    Optional[str] = None		):
								if not os.path.isdir(_lowercase		):
										logger.error(f'Vocabulary path ({save_directory}) should be a directory'		)
										return
								A					       = os.path.join(
								    _lowercase			,			(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']		)

								if os.path.abspath(self.vocab_file		) != os.path.abspath(_lowercase		) and os.path.isfile(self.vocab_file		):
										copyfile(self.vocab_file			,			_lowercase		)
								elif not os.path.isfile(self.vocab_file		):
										with open(_lowercase			,			'wb'		) as fi:
												A					       = self.sp_model.serialized_model_proto()
												fi.write(_lowercase		)

								return (out_vocab_file,)







						def __a						(							self   :    Dict			,			*_lowercase   :    Optional[int]			,			**_lowercase   :    Dict		):
								A					       = super()._decode(*_lowercase			,			**_lowercase		)
								A					       = text.replace(' '			,			''		).replace('\u2582'			,			' '		).replace('\u2583'			,			'\n'		)
								return text









"""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 			lowerCamelCase__						(   unittest.TestCase			):
						lowerCAmelCase       =     MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING







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







						def __a						(							self   :    Tuple			,			_lowercase   :    Union[str, Any]			,			_lowercase   :    List[Any]		):
								for example in examples:
										A					       = video_classifier(_lowercase		)

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







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

								A					       = hf_hub_download(repo_id='nateraw/video-demo'			,			filename='archery.mp4'			,			repo_type='dataset'		)
								A					       = video_classifier(_lowercase			,			top_k=2		)
								self.assertEqual(
								    nested_simplify(_lowercase			,			decimals=4		)			,			[{'score': 0.5_1_9_9, 'label': 'LABEL_0'}, {'score': 0.4_8_0_1, 'label': 'LABEL_1'}]			,			)

								A					       = video_classifier(
								    [
								        video_file_path,
								        video_file_path,
								    ]			,			top_k=2			,			)
								self.assertEqual(
								    nested_simplify(_lowercase			,			decimals=4		)			,			[
								        [{'score': 0.5_1_9_9, 'label': 'LABEL_0'}, {'score': 0.4_8_0_1, 'label': 'LABEL_1'}],
								        [{'score': 0.5_1_9_9, 'label': 'LABEL_0'}, {'score': 0.4_8_0_1, 'label': 'LABEL_1'}],
								    ]			,			)







						@require_tf
						def __a						(							self   :    Dict		):
								pass





