


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


SCREAMING_SNAKE_CASE			: str				   =					logging.get_logger(__name__)


SCREAMING_SNAKE_CASE			: Optional[Any]				   =					"▁"

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

SCREAMING_SNAKE_CASE			: Tuple				   =					{
    "vocab_file": {
        "google/reformer-crime-and-punishment": (
            "https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/spiece.model"
        )
    }
}

SCREAMING_SNAKE_CASE			: str				   =					{
    "google/reformer-crime-and-punishment": 524288,
}






class _lowerCamelCase(		_a  ):
       lowercase_					:      Optional[Any]       					=     VOCAB_FILES_NAMES
       lowercase_					:      Dict       					=     PRETRAINED_VOCAB_FILES_MAP
       lowercase_					:      Any       					=     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       lowercase_					:      int       					=     ["""input_ids""", """attention_mask"""]
       def __init__(   self, lowerCamelCase, lowerCamelCase="</s>", lowerCamelCase="<unk>", lowerCamelCase=[], lowerCamelCase = None, **lowerCamelCase, )			->  None:




           """simple docstring"""

           _lowercase			:				Any 						=						{} if sp_model_kwargs is None else sp_model_kwargs

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

           _lowercase			:				List[str] 						=						vocab_file
           _lowercase			:				Dict 						=						spm.SentencePieceProcessor(**self.sp_model_kwargs)
           self.sp_model.Load(lowerCamelCase)
       @property
       def      UpperCamelCase	(   self)			->  Dict:




           """simple docstring"""

           return self.sp_model.get_piece_size()
       def      UpperCamelCase	(   self)			->  Dict[str, int]:




           """simple docstring"""

           _lowercase			:				str 						=						{self.convert_ids_to_tokens(lowerCamelCase): i for i in range(self.vocab_size)}
           vocab.update(self.added_tokens_encoder)
           return vocab
       def __getstate__(   self)			->  Union[str, Any]:




           """simple docstring"""

           _lowercase			:				str 						=						self.__dict__.copy()
           _lowercase			:				Tuple 						=						None
           return state
       def __setstate__(   self, lowerCamelCase)			->  int:




           """simple docstring"""

           _lowercase			:				Union[str, Any] 						=						d

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

           _lowercase			:				str 						=						spm.SentencePieceProcessor(**self.sp_model_kwargs)
           self.sp_model.Load(self.vocab_file)
       def      UpperCamelCase	(   self, lowerCamelCase)			->  List[str]:




           """simple docstring"""

           return self.sp_model.encode(lowerCamelCase, out_type=lowerCamelCase)
       def      UpperCamelCase	(   self, lowerCamelCase)			->  List[Any]:




           """simple docstring"""

           return self.sp_model.piece_to_id(lowerCamelCase)
       def      UpperCamelCase	(   self, lowerCamelCase)			->  Tuple:




           """simple docstring"""

           if index < self.sp_model.get_piece_size():
               _lowercase			:				Any 						=						self.sp_model.IdToPiece(lowerCamelCase)
           return token
       def      UpperCamelCase	(   self, lowerCamelCase)			->  Dict:




           """simple docstring"""

           _lowercase			:				Tuple 						=						[]
           _lowercase			:				Optional[Any] 						=						''
           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(lowerCamelCase) + token
                   _lowercase			:				Tuple 						=						[]
               else:
                   current_sub_tokens.append(lowerCamelCase)
           out_string += self.sp_model.decode(lowerCamelCase)
           return out_string.strip()




       def      UpperCamelCase	(   self, lowerCamelCase, lowerCamelCase = None)			->  Tuple[str]:




           """simple docstring"""

           if not os.path.isdir(lowerCamelCase):
               logger.error(F'''Vocabulary path ({save_directory}) should be a directory''')
               return
           _lowercase			:				Optional[Any] 						=						os.path.join(
               lowerCamelCase, (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'])

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

           return (out_vocab_file,)




import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import is_speech_available, is_vision_available
from transformers.testing_utils import require_torch


if is_vision_available():
     from transformers import TvltImageProcessor

if is_speech_available():
     from transformers import TvltFeatureExtractor

from transformers import TvltProcessor




@require_torch
class        _SCREAMING_SNAKE_CASE	(   unittest.TestCase			):
      def A_      (      self			):
          snake_case__    =					"ZinengTang/tvlt-base"
          snake_case__    =					tempfile.mkdtemp()
      def A_      (      self		,						**lowerCamelCase			):
          return TvltImageProcessor.from_pretrained(self.checkpoint		,						**lowerCamelCase			)
      def A_      (      self		,						**lowerCamelCase			):
          return TvltFeatureExtractor.from_pretrained(self.checkpoint		,						**lowerCamelCase			)
      def A_      (      self			):
          shutil.rmtree(self.tmpdirname			)
      def A_      (      self			):
          snake_case__    =					self.get_image_processor()
          snake_case__    =					self.get_feature_extractor()

          snake_case__    =					TvltProcessor(image_processor=lowerCamelCase		,						feature_extractor=lowerCamelCase			)
          processor.save_pretrained(self.tmpdirname			)
          snake_case__    =					TvltProcessor.from_pretrained(self.tmpdirname			)

          self.assertIsInstance(processor.feature_extractor		,						lowerCamelCase			)
          self.assertIsInstance(processor.image_processor		,						lowerCamelCase			)
      def A_      (      self			):
          snake_case__    =					self.get_image_processor()
          snake_case__    =					self.get_feature_extractor()

          snake_case__    =					TvltProcessor(image_processor=lowerCamelCase		,						feature_extractor=lowerCamelCase			)

          snake_case__    =					np.ones([1_20_00]			)

          snake_case__    =					feature_extractor(lowerCamelCase		,						return_tensors="np"			)
          snake_case__    =					processor(audio=lowerCamelCase		,						return_tensors="np"			)

          for key in audio_dict.keys():
              self.assertAlmostEqual(audio_dict[key].sum()		,						input_processor[key].sum()		,						delta=1e-2			)
      def A_      (      self			):
          snake_case__    =					self.get_image_processor()
          snake_case__    =					self.get_feature_extractor()

          snake_case__    =					TvltProcessor(image_processor=lowerCamelCase		,						feature_extractor=lowerCamelCase			)

          snake_case__    =					np.ones([3, 2_24, 2_24]			)

          snake_case__    =					image_processor(lowerCamelCase		,						return_tensors="np"			)
          snake_case__    =					processor(images=lowerCamelCase		,						return_tensors="np"			)

          for key in image_dict.keys():
              self.assertAlmostEqual(image_dict[key].sum()		,						input_processor[key].sum()		,						delta=1e-2			)
      def A_      (      self			):
          snake_case__    =					self.get_image_processor()
          snake_case__    =					self.get_feature_extractor()

          snake_case__    =					TvltProcessor(image_processor=lowerCamelCase		,						feature_extractor=lowerCamelCase			)

          snake_case__    =					np.ones([1_20_00]			)
          snake_case__    =					np.ones([3, 2_24, 2_24]			)

          snake_case__    =					processor(audio=lowerCamelCase		,						images=lowerCamelCase			)

          self.assertListEqual(list(inputs.keys()			)		,						["audio_values", "audio_mask", "pixel_values", "pixel_mask"]			)

          # test if it raises when no input is passed
          with pytest.raises(lowerCamelCase			):
              processor()



      def A_      (      self			):
          snake_case__    =					self.get_image_processor()
          snake_case__    =					self.get_feature_extractor()

          snake_case__    =					TvltProcessor(image_processor=lowerCamelCase		,						feature_extractor=lowerCamelCase			)

          self.assertListEqual(
              processor.model_input_names		,						image_processor.model_input_names + feature_extractor.model_input_names		,						msg="`processor` and `image_processor`+`feature_extractor` model input names do not match"		,						)



