
import unittest

import numpy as np

from transformers import MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING, TF_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING
from transformers.pipelines import AudioClassificationPipeline, pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_torchaudio,
    slow,
)

from .test_pipelines_common import ANY




@is_pipeline_test
class 					_SCREAMING_SNAKE_CASE  (      unittest.TestCase ):
      _A						:		str    	=     MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING
      _A						:		Any    	=     TF_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING
      def A_			(			self		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase ):
          snake_case__						=				AudioClassificationPipeline(model=lowerCamelCase		,	feature_extractor=lowerCamelCase )

          # test with a raw waveform
          snake_case__						=				np.zeros((3_40_00,) )
          snake_case__						=				np.zeros((1_40_00,) )
          return audio_classifier, [audioa, audio]
      def A_			(			self		,	lowerCamelCase		,	lowerCamelCase ):
          snake_case__						,					snake_case__						=				examples
          snake_case__						=				audio_classifier(lowerCamelCase )
          # by default a model is initialized with num_labels=2
          self.assertEqual(
              lowerCamelCase		,	[
                  {"score": ANY(lowerCamelCase ), "label": ANY(lowerCamelCase )},
                  {"score": ANY(lowerCamelCase ), "label": ANY(lowerCamelCase )},
              ]		,	)
          snake_case__						=				audio_classifier(lowerCamelCase		,	top_k=1 )
          self.assertEqual(
              lowerCamelCase		,	[
                  {"score": ANY(lowerCamelCase ), "label": ANY(lowerCamelCase )},
              ]		,	)

          self.run_torchaudio(lowerCamelCase )
      @require_torchaudio
      def A_			(			self		,	lowerCamelCase ):
          import datasets

          # test with a local file
          snake_case__						=				datasets.load_dataset("hf-internal-testing/librispeech_asr_dummy"		,	"clean"		,	split="validation" )
          snake_case__						=				dataset[0]["audio"]["array"]
          snake_case__						=				audio_classifier(lowerCamelCase )
          self.assertEqual(
              lowerCamelCase		,	[
                  {"score": ANY(lowerCamelCase ), "label": ANY(lowerCamelCase )},
                  {"score": ANY(lowerCamelCase ), "label": ANY(lowerCamelCase )},
              ]		,	)
      @require_torch
      def A_			(			self ):
          snake_case__						=				"anton-l/wav2vec2-random-tiny-classifier"

          snake_case__						=				pipeline("audio-classification"		,	model=lowerCamelCase )

          snake_case__						=				np.ones((80_00,) )
          snake_case__						=				audio_classifier(lowerCamelCase		,	top_k=4 )

          snake_case__						=				[
              {"score": 0.0_8_4_2, "label": "no"},
              {"score": 0.0_8_3_8, "label": "up"},
              {"score": 0.0_8_3_7, "label": "go"},
              {"score": 0.0_8_3_4, "label": "right"},
          ]
          snake_case__						=				[
              {"score": 0.0_8_4_5, "label": "stop"},
              {"score": 0.0_8_4_4, "label": "on"},
              {"score": 0.0_8_4_1, "label": "right"},
              {"score": 0.0_8_3_4, "label": "left"},
          ]
          self.assertIn(nested_simplify(lowerCamelCase		,	decimals=4 )		,	[EXPECTED_OUTPUT, EXPECTED_OUTPUT_PT_2] )

          snake_case__						=				{"array": np.ones((80_00,) ), "sampling_rate": audio_classifier.feature_extractor.sampling_rate}
          snake_case__						=				audio_classifier(lowerCamelCase		,	top_k=4 )
          self.assertIn(nested_simplify(lowerCamelCase		,	decimals=4 )		,	[EXPECTED_OUTPUT, EXPECTED_OUTPUT_PT_2] )
      @require_torch
      @slow
      def A_			(			self ):
          import datasets

          snake_case__						=				"superb/wav2vec2-base-superb-ks"

          snake_case__						=				pipeline("audio-classification"		,	model=lowerCamelCase )
          snake_case__						=				datasets.load_dataset("anton-l/superb_dummy"		,	"ks"		,	split="test" )

          snake_case__						=				np.array(dataset[3]["speech"]		,	dtype=np.floataa )
          snake_case__						=				audio_classifier(lowerCamelCase		,	top_k=4 )
          self.assertEqual(
              nested_simplify(lowerCamelCase		,	decimals=3 )		,	[
                  {"score": 0.9_8_1, "label": "go"},
                  {"score": 0.0_0_7, "label": "up"},
                  {"score": 0.0_0_6, "label": "_unknown_"},
                  {"score": 0.0_0_1, "label": "down"},
              ]		,	)



      @require_tf
      @unittest.skip("Audio classification is not implemented for TF" )
      def A_			(			self ):
          pass





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

import sentencepiece as spm

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


__magic_name__            =  logging.get_logger(__name__)

__magic_name__            =  {'''vocab_file''': '''sentencepiece.model'''}

__magic_name__            =  {
    '''vocab_file''': {
        '''google/rembert''': '''https://huggingface.co/google/rembert/resolve/main/sentencepiece.model''',
    },
}

__magic_name__            =  {
    '''google/rembert''': 256,
}




class 					_SCREAMING_SNAKE_CASE  (      __UpperCamelCase ):
      _A						:		Optional[Any]    	=     VOCAB_FILES_NAMES
      _A						:		Optional[int]    	=     PRETRAINED_VOCAB_FILES_MAP
      _A						:		Any    	=     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      def __init__(			self		,	lowerCamelCase		,	lowerCamelCase=False		,	lowerCamelCase=True		,	lowerCamelCase=True		,	lowerCamelCase="[CLS]"		,	lowerCamelCase="[SEP]"		,	lowerCamelCase="[UNK]"		,	lowerCamelCase="[SEP]"		,	lowerCamelCase="[PAD]"		,	lowerCamelCase="[CLS]"		,	lowerCamelCase="[MASK]"		,	**lowerCamelCase		,	):
          super().__init__(
              do_lower_case=lowerCamelCase		,	remove_space=lowerCamelCase		,	keep_accents=lowerCamelCase		,	bos_token=lowerCamelCase		,	eos_token=lowerCamelCase		,	unk_token=lowerCamelCase		,	sep_token=lowerCamelCase		,	pad_token=lowerCamelCase		,	cls_token=lowerCamelCase		,	mask_token=lowerCamelCase		,	**lowerCamelCase		,	)

          snake_case__						=				do_lower_case
          snake_case__						=				remove_space
          snake_case__						=				keep_accents
          snake_case__						=				vocab_file

          snake_case__						=				spm.SentencePieceProcessor()
          self.sp_model.Load(lowerCamelCase )
      @property
      def A_			(			self ):
          return len(self.sp_model )
      def A_			(			self ):
          snake_case__						=				{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 ):
          snake_case__						=				self.__dict__.copy()
          snake_case__						=				None
          return state
      def __setstate__(			self		,	lowerCamelCase ):
          snake_case__						=				d
          snake_case__						=				spm.SentencePieceProcessor()
          self.sp_model.Load(self.vocab_file )
      def A_			(			self		,	lowerCamelCase		,	lowerCamelCase=False ):
          snake_case__						=				self.sp_model.EncodeAsPieces(lowerCamelCase )
          return pieces
      def A_			(			self		,	lowerCamelCase ):
          return self.sp_model.PieceToId(lowerCamelCase )
      def A_			(			self		,	lowerCamelCase ):
          return self.sp_model.IdToPiece(lowerCamelCase )
      def A_			(			self		,	lowerCamelCase ):
          snake_case__						=				self.sp_model.decode_pieces(lowerCamelCase )
          return out_string
      def A_			(			self		,	lowerCamelCase		,	lowerCamelCase = None ):
          snake_case__						=				[self.sep_token_id]
          snake_case__						=				[self.cls_token_id]
          if token_ids_a is None:
              return cls + token_ids_a + sep
          return cls + token_ids_a + sep + token_ids_a + sep
      def A_			(			self		,	lowerCamelCase		,	lowerCamelCase = None		,	lowerCamelCase = False ):

          if already_has_special_tokens:
              if token_ids_a is not None:
                  raise ValueError(
                      "You should not supply a second sequence if the provided sequence of "
                      "ids is already formatted with special tokens for the model." )
              return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

          if token_ids_a is not None:
              return [1] + ([0] * len(lowerCamelCase )) + [1] + ([0] * len(lowerCamelCase )) + [1]
          return [1] + ([0] * len(lowerCamelCase )) + [1]
      def A_			(			self		,	lowerCamelCase		,	lowerCamelCase = None ):
          snake_case__						=				[self.sep_token_id]
          snake_case__						=				[self.cls_token_id]

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



      def A_			(			self		,	lowerCamelCase		,	lowerCamelCase = None ):
          if not os.path.isdir(lowerCamelCase ):
              logger.error("Vocabulary path ({}) should be a directory".format(lowerCamelCase ) )
              return
          snake_case__						=				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 ):
              copyfile(self.vocab_file		,	lowerCamelCase )

          return (out_vocab_file,)



