



'''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__:   int								=			logging.get_logger(__name__)

UpperCamelCase__:   Tuple								=			{"vocab_file": "spiece.model"}

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

class 				SCREAMING_SNAKE_CASE(							A__			):







	"""simple docstring"""






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

								UpperCAmelCase :       Tuple 							=		{} if sp_model_kwargs is None else sp_model_kwargs

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

								UpperCAmelCase :       Union[str, Any] 							=		3

								UpperCAmelCase :       str 							=		do_lower_case
								UpperCAmelCase :       Optional[Any] 							=		remove_space
								UpperCAmelCase :       str 							=		keep_accents
								UpperCAmelCase :       Optional[Any] 							=		vocab_file

								UpperCAmelCase :       List[Any] 							=		spm.SentencePieceProcessor(**self.sp_model_kwargs		)
								self.sp_model.Load(__snake_case		)

								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.'''		)
								UpperCAmelCase :       List[str] 							=		jieba
								UpperCAmelCase :       Any 							=		str.maketrans(''' \n'''		,			'''\u2582\u2583'''		)







	@property
	# Copied from transformers.models.xlnet.tokenization_xlnet.XLNetTokenizer.vocab_size
	def      A	(						self			:     Tuple		)			->		Dict:
								return len(self.sp_model		)







	def      A	(						self			:     Tuple		)			->		int:
								UpperCAmelCase :       Dict 							=		{self.convert_ids_to_tokens(__snake_case		): i for i in range(self.vocab_size		)}
								vocab.update(self.added_tokens_encoder		)
								return vocab







	def __getstate__(						self			:     Optional[int]		)			->		Any:
								UpperCAmelCase :       int 							=		self.__dict__.copy()
								UpperCAmelCase :       Optional[Any] 							=		None
								return state







	def __setstate__(						self			:     Any		,			__snake_case			:     Union[str, Any]		)			->		Optional[Any]:
								UpperCAmelCase :       List[str] 							=		d

								# for backward compatibility
								if not hasattr(self		,			'''sp_model_kwargs'''		):
															UpperCAmelCase :       Dict 							=		{}

								UpperCAmelCase :       Optional[Any] 							=		spm.SentencePieceProcessor(**self.sp_model_kwargs		)
								self.sp_model.Load(self.vocab_file		)







	def      A	(						self			:     int		,			__snake_case			:     int		)			->		Tuple:
								if self.remove_space:
															UpperCAmelCase :       Dict 							=		''' '''.join(inputs.strip().split()		)
								else:
															UpperCAmelCase :       Any 							=		inputs
								UpperCAmelCase :       List[str] 							=		outputs.replace('''``'''		,			'''"'''		).replace('''\'\''''		,			'''"'''		)

								if not self.keep_accents:
															UpperCAmelCase :       int 							=		unicodedata.normalize('''NFKD'''		,			__snake_case		)
															UpperCAmelCase :       List[str] 							=		''''''.join([c for c in outputs if not unicodedata.combining(__snake_case		)]		)
								if self.do_lower_case:
															UpperCAmelCase :       Union[str, Any] 							=		outputs.lower()

								return outputs







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

								return new_pieces







	def      A	(						self			:     Dict		,			__snake_case			:     Tuple		)			->		List[Any]:
								return self.sp_model.PieceToId(__snake_case		)







	def      A	(						self			:     Any		,			__snake_case			:     str		)			->		List[Any]:
								return self.sp_model.IdToPiece(__snake_case		)







	def      A	(						self			:     str		,			__snake_case			:     Tuple		)			->		Dict:
								UpperCAmelCase :       List[Any] 							=		''''''.join(__snake_case		).replace(__snake_case		,			''' '''		).strip()
								return out_string







	def      A	(						self			:     Union[str, Any]		,			__snake_case			:     List[int]		,			__snake_case			:     Optional[List[int]] = None		)			->		List[int]:
								UpperCAmelCase :       Dict 							=		[self.sep_token_id]
								UpperCAmelCase :       int 							=		[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			:     Union[str, Any]		,			__snake_case			:     List[int]		,			__snake_case			:     Optional[List[int]] = None		,			__snake_case			:     bool = False		)			->		List[int]:

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

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







	def      A	(						self			:     Any		,			__snake_case			:     List[int]		,			__snake_case			:     Optional[List[int]] = None		)			->		List[int]:
								UpperCAmelCase :       Any 							=		[self.sep_token_id]
								UpperCAmelCase :       Union[str, Any] 							=		[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			:     Tuple		,			__snake_case			:     str		,			__snake_case			:     Optional[str] = None		)			->		Tuple[str]:
								if not os.path.isdir(__snake_case		):
															logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""		)
															return
								UpperCAmelCase :       Any 							=		os.path.join(
								    __snake_case		,			(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']		)

								if os.path.abspath(self.vocab_file		) != os.path.abspath(__snake_case		) and os.path.isfile(self.vocab_file		):
															copyfile(self.vocab_file		,			__snake_case		)
								elif not os.path.isfile(self.vocab_file		):
															with open(__snake_case		,			'''wb'''		) as fi:
																						UpperCAmelCase :       Optional[Any] 							=		self.sp_model.serialized_model_proto()
																						fi.write(__snake_case		)

								return (out_vocab_file,)







	def      A	(						self			:     Optional[Any]		,			*__snake_case			:     Any		,			**__snake_case			:     Optional[Any]		)			->		int:
								UpperCAmelCase :       Any 							=		super()._decode(*__snake_case		,			**__snake_case		)
								UpperCAmelCase :       Any 							=		text.replace(''' '''		,			''''''		).replace('''\u2582'''		,			''' '''		).replace('''\u2583'''		,			'''\n'''		)
								return text







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 __snake_case		(      unittest.TestCase     ):
    _a		:						Optional[int]=				MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case		,snake_case		,snake_case      ):



           '''simple docstring'''







           lowercase							:     Any       	=      hf_hub_download(
               repo_id="""nateraw/video-demo"""		,filename="""archery.mp4"""		,repo_type="""dataset"""      )
           lowercase							:     List[str]       	=      VideoClassificationPipeline(model=snake_case		,image_processor=snake_case		,top_k=2      )
           lowercase							:     Dict       	=      [
               example_video_filepath,
               """https://huggingface.co/datasets/nateraw/video-demo/resolve/main/archery.mp4""",
           ]
           return video_classifier, examples
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case		,snake_case      ):



           '''simple docstring'''







           for example in examples:
                  lowercase							:     int       	=      video_classifier(snake_case      )

                  self.assertEqual(
                      snake_case		,[
                          {"""score""": ANY(snake_case      ), """label""": ANY(snake_case      )},
                          {"""score""": ANY(snake_case      ), """label""": ANY(snake_case      )},
                      ]		,)
    @require_torch
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     Any       	=      """hf-internal-testing/tiny-random-VideoMAEForVideoClassification"""
           lowercase							:     str       	=      VideoMAEFeatureExtractor(
               size={"""shortest_edge""": 10}		,crop_size={"""height""": 10, """width""": 10}      )
           lowercase							:     List[Any]       	=      pipeline(
               """video-classification"""		,model=snake_case		,feature_extractor=snake_case		,frame_sampling_rate=4      )

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

           lowercase							:     str       	=      video_classifier(
               [
                   video_file_path,
                   video_file_path,
               ]		,top_k=2		,)
           self.assertEqual(
               nested_simplify(snake_case		,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       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           pass
