
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


lowercase  :					List[Any]						    =							logging.get_logger(__name__)

lowercase  :					int						    =							{"""vocab_file""": """spiece.model"""}

lowercase  :					Optional[int]						    =							{
    """vocab_file""": {
        """xlnet-base-cased""": """https://huggingface.co/xlnet-base-cased/resolve/main/spiece.model""",
        """xlnet-large-cased""": """https://huggingface.co/xlnet-large-cased/resolve/main/spiece.model""",
    }
}

lowercase  :					Optional[int]						    =							{
    """xlnet-base-cased""": None,
    """xlnet-large-cased""": None,
}

# Segments (not really needed)
lowercase  :					int						    =							0
lowercase  :					int						    =							1
lowercase  :					Optional[Any]						    =							2
lowercase  :					Tuple						    =							3
lowercase  :					str						    =							4

class __snake_case		(      lowerCAmelCase     ):
    _a		:						Dict=				VOCAB_FILES_NAMES
    _a		:						Optional[Any]=				PRETRAINED_VOCAB_FILES_MAP
    _a		:						int=				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    _a		:						Tuple=				"left"
    def __init__(  self		,snake_case		,snake_case=False		,snake_case=True		,snake_case=False		,snake_case="<s>"		,snake_case="</s>"		,snake_case="<unk>"		,snake_case="<sep>"		,snake_case="<pad>"		,snake_case="<cls>"		,snake_case="<mask>"		,snake_case=["<eop>", "<eod>"]		,snake_case = None		,**snake_case		,):



           '''simple docstring'''







           lowercase							:     List[str]       	=      AddedToken(snake_case		,lstrip=snake_case		,rstrip=snake_case      ) if isinstance(snake_case		,snake_case      ) else mask_token

           lowercase							:     int       	=      {} 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		,)

           lowercase							:     List[Any]       	=      3

           lowercase							:     str       	=      do_lower_case
           lowercase							:     Any       	=      remove_space
           lowercase							:     str       	=      keep_accents
           lowercase							:     Any       	=      vocab_file

           lowercase							:     int       	=      spm.SentencePieceProcessor(**self.sp_model_kwargs      )
           self.sp_model.Load(snake_case      )
    @property
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           return len(self.sp_model      )
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     Tuple       	=      {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      ):



           '''simple docstring'''







           lowercase							:     Optional[int]       	=      self.__dict__.copy()
           lowercase							:     Union[str, Any]       	=      None
           return state
    def __setstate__(  self		,snake_case      ):



           '''simple docstring'''







           lowercase							:     Union[str, Any]       	=      d

           # for backward compatibility
           if not hasattr(self		,"""sp_model_kwargs"""      ):
                  lowercase							:     Optional[int]       	=      {}

           lowercase							:     List[str]       	=      spm.SentencePieceProcessor(**self.sp_model_kwargs      )
           self.sp_model.Load(self.vocab_file      )
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case      ):



           '''simple docstring'''







           if self.remove_space:
                  lowercase							:     Optional[Any]       	=      """ """.join(inputs.strip().split()      )
           else:
                  lowercase							:     Optional[int]       	=      inputs
           lowercase							:     Optional[int]       	=      outputs.replace("""``"""		,"""\""""      ).replace("""''"""		,"""\""""      )

           if not self.keep_accents:
                  lowercase							:     str       	=      unicodedata.normalize("""NFKD"""		,snake_case      )
                  lowercase							:     Optional[Any]       	=      """""".join([c for c in outputs if not unicodedata.combining(snake_case      )]      )
           if self.do_lower_case:
                  lowercase							:     str       	=      outputs.lower()

           return outputs
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case      ):



           '''simple docstring'''







           lowercase							:     Tuple       	=      self.preprocess_text(snake_case      )
           lowercase							:     Any       	=      self.sp_model.encode(snake_case		,out_type=snake_case      )
           lowercase							:     Any       	=      []
           for piece in pieces:
                  if len(snake_case      ) > 1 and piece[-1] == str(""","""      ) and piece[-2].isdigit():
                         lowercase							:     Optional[Any]       	=      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:
                                       lowercase							:     Tuple       	=      cur_pieces[1:]
                                else:
                                       lowercase							:     Optional[Any]       	=      cur_pieces[0][1:]
                         cur_pieces.append(piece[-1]      )
                         new_pieces.extend(snake_case      )
                  else:
                         new_pieces.append(snake_case      )

           return new_pieces
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case      ):



           '''simple docstring'''







           return self.sp_model.PieceToId(snake_case      )
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case      ):



           '''simple docstring'''







           return self.sp_model.IdToPiece(snake_case      )
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case      ):



           '''simple docstring'''







           lowercase							:     Dict       	=      """""".join(snake_case      ).replace(snake_case		,""" """      ).strip()
           return out_string
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case		,snake_case = False		,snake_case = None		,snake_case = True		,**snake_case		,):



           '''simple docstring'''







           lowercase							:     Optional[int]       	=      kwargs.pop("""use_source_tokenizer"""		,snake_case      )

           lowercase							:     Optional[int]       	=      self.convert_ids_to_tokens(snake_case		,skip_special_tokens=snake_case      )

           # To avoid mixing byte-level and unicode for byte-level BPT
           # we need to build string separately for added tokens and byte-level tokens
           # cf. https://github.com/huggingface/transformers/issues/1133
           lowercase							:     Optional[Any]       	=      []
           lowercase							:     Union[str, Any]       	=      []
           for token in filtered_tokens:
                  if skip_special_tokens and token in self.all_special_ids:
                         continue
                  if token in self.added_tokens_encoder:
                         if current_sub_text:
                                sub_texts.append(self.convert_tokens_to_string(snake_case      )      )
                                lowercase							:     Dict       	=      []
                         sub_texts.append(snake_case      )
                  else:
                         current_sub_text.append(snake_case      )
           if current_sub_text:
                  sub_texts.append(self.convert_tokens_to_string(snake_case      )      )

           # Mimic the behavior of the Rust tokenizer:
           # By default, there are no spaces between special tokens
           lowercase							:     List[str]       	=      """""".join(snake_case      )

           lowercase							:     Tuple       	=      (
               clean_up_tokenization_spaces
               if clean_up_tokenization_spaces is not None
               else self.clean_up_tokenization_spaces
           )
           if clean_up_tokenization_spaces:
                  lowercase							:     Optional[Any]       	=      self.clean_up_tokenization(snake_case      )
                  return clean_text
           else:
                  return text
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case		,snake_case = None      ):



           '''simple docstring'''







           lowercase							:     int       	=      [self.sep_token_id]
           lowercase							:     Optional[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       _SCREAMING_SNAKE_CASE       (  self		,snake_case		,snake_case = None		,snake_case = False      ):



           '''simple docstring'''







           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       _SCREAMING_SNAKE_CASE       (  self		,snake_case		,snake_case = None      ):



           '''simple docstring'''







           lowercase							:     int       	=      [self.sep_token_id]
           lowercase							:     List[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       _SCREAMING_SNAKE_CASE       (  self		,snake_case		,snake_case = None      ):



           '''simple docstring'''







           if not os.path.isdir(snake_case      ):
                  logger.error(f"Vocabulary path ({save_directory}) should be a directory"      )
                  return
           lowercase							:     List[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:
                         lowercase							:     Dict       	=      self.sp_model.serialized_model_proto()
                         fi.write(snake_case      )

           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 __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
