code: 
  
  
  
  
  
  '''simple docstring'''
  
  
  import itertools
  import random
  import unittest
  
  import numpy as np
  
  from transformers import EncodecFeatureExtractor
  from transformers.testing_utils import require_torch
  from transformers.utils.import_utils import is_torch_available
  
  from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin
  
  
  if is_torch_available():
  				import torch
  
  
  _lowerCamelCase				:	List[str]				    =       random.Random()
  
  
  
  
  def    __a (     UpperCAmelCase       ,			UpperCAmelCase=1.0       ,			UpperCAmelCase=None       ,			UpperCAmelCase=None    )      ->str:
  
  
  
  	"""simple docstring"""
  
  
  
  
  
  
  
  	if rng is None:
  		A							  =      global_rng
  
  	A							  =      []
  	for batch_idx in range(shape[0]    ):
  		values.append([]    )
  		for _ in range(shape[1]    ):
  			values[-1].append(rng.random() * scale    )
  
  	return values
  
  
  
  @require_torch
  class 							__UpperCAmelCase  (       unittest.TestCase						):
  
  
  
  
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		def __init__(self						:      List[str]      ,      _lowerCAmelCase						:      Any      ,      _lowerCAmelCase						:      Union[str, Any]=7      ,      _lowerCAmelCase						:      Optional[int]=400      ,      _lowerCAmelCase						:      str=2000      ,      _lowerCAmelCase						:      List[Any]=1      ,      _lowerCAmelCase						:      Optional[int]=0.0      ,      _lowerCAmelCase						:      List[Any]=2_4000      ,      _lowerCAmelCase						:      List[str]=True      ,      ):
  			A							  =      parent
  			A							  =      batch_size
  			A							  =      min_seq_length
  			A							  =      max_seq_length
  			A							  =      (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
  			A							  =      feature_size
  			A							  =      padding_value
  			A							  =      sampling_rate
  			A							  =      return_attention_mask
  
  
  
  
  
  
  
  		def        A     (self						:      Dict      ):
  			return {
  			    "feature_size": self.feature_size,
  			    "padding_value": self.padding_value,
  			    "sampling_rate": self.sampling_rate,
  			    "return_attention_mask": self.return_attention_mask,
  			}
  
  
  
  
  
  
  
  		def        A     (self						:      List[Any]      ,      _lowerCAmelCase						:      Any=False      ,      _lowerCAmelCase						:      int=False      ):
  			def _flatten(_lowerCAmelCase						:      Optional[int]      ):
  				return list(itertools.chain(*_lowerCAmelCase      )      )
  
  			if equal_length:
  				A							  =      floats_list((self.batch_size, self.max_seq_length)      )
  			else:
  				# make sure that inputs increase in size
  				A							  =      [
  				    _flatten(floats_list((x, self.feature_size)      )      )
  				    for x in range(self.min_seq_length      ,      self.max_seq_length      ,      self.seq_length_diff      )
  				]
  
  			if numpify:
  				A							  =      [np.asarray(_lowerCAmelCase      ) for x in audio_inputs]
  
  			return audio_inputs
  
  
  
  
  
  
  @require_torch
  class 							__UpperCAmelCase  (       A__		,					unittest.TestCase						):
  
  
  
  
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		__lowerCAmelCase 				=    EncodecFeatureExtractor
  
  
  
  
  
  
  
  		def        A     (self						:      Tuple      ):
  			A							  =      EnCodecFeatureExtractionTester(self      )
  
  
  
  
  
  
  
  		def        A     (self						:      Any      ):
  			# Tests that all call wrap to encode_plus and batch_encode_plus
  			A							  =      self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()      )
  			# create three inputs of length 800, 1000, and 1200
  			A							  =      [floats_list((1, x)      )[0] for x in range(800      ,      1400      ,      200      )]
  			A							  =      [np.asarray(_lowerCAmelCase      ) for audio_input in audio_inputs]
  
  			# Test not batched input
  			A							  =      feat_extract(audio_inputs[0]      ,      return_tensors="""np"""      ).input_values
  			A							  =      feat_extract(np_audio_inputs[0]      ,      return_tensors="""np"""      ).input_values
  			self.assertTrue(np.allclose(_lowerCAmelCase      ,      _lowerCAmelCase      ,      atol=1e-3      )      )
  
  			# Test batched
  			A							  =      feat_extract(_lowerCAmelCase      ,      padding=_lowerCAmelCase      ,      return_tensors="""np"""      ).input_values
  			A							  =      feat_extract(_lowerCAmelCase      ,      padding=_lowerCAmelCase      ,      return_tensors="""np"""      ).input_values
  			for enc_seq_a, enc_seq_a in zip(_lowerCAmelCase      ,      _lowerCAmelCase      ):
  				self.assertTrue(np.allclose(_lowerCAmelCase      ,      _lowerCAmelCase      ,      atol=1e-3      )      )
  
  
  
  
  
  
  
  		def        A     (self						:      Dict      ):
  			A							  =      self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()      )
  			A							  =      np.random.rand(100      ).astype(np.floataa      )
  			A							  =      np_audio_inputs.tolist()
  
  			for inputs in [py_audio_inputs, np_audio_inputs]:
  				A							  =      feature_extractor.pad([{"""input_values""": inputs}]      ,      return_tensors="""np"""      )
  				self.assertTrue(np_processed.input_values.dtype == np.floataa      )
  				A							  =      feature_extractor.pad([{"""input_values""": inputs}]      ,      return_tensors="""pt"""      )
  				self.assertTrue(pt_processed.input_values.dtype == torch.floataa      )
  
  
  
  
  
  
  
  		def        A     (self						:      Dict      ,      _lowerCAmelCase						:      List[str]      ):
  			from datasets import load_dataset
  
  			A							  =      load_dataset("""hf-internal-testing/librispeech_asr_dummy"""      ,      """clean"""      ,      split="""validation"""      )
  			# automatic decoding with librispeech
  			A							  =      ds.sort("""id"""      ).select(range(_lowerCAmelCase      )      )[:num_samples]["""audio"""]
  
  			return [x["array"] for x in audio_samples]
  
  
  
  
  
  
  
  		def        A     (self						:      int      ):
  			# fmt: off
  			A							  =      torch.tensor(
  			    [2.3_804e-03, 2.0_752e-03, 1.9_836e-03, 2.1_057e-03, 1.6_174e-03,
  			     3.0_518e-04, 9.1_553e-05, 3.3_569e-04, 9.7_656e-04, 1.8_311e-03,
  			     2.0_142e-03, 2.1_057e-03, 1.7_395e-03, 4.5_776e-04, -3.9_673e-04,
  			     4.5_776e-04, 1.0_071e-03, 9.1_553e-05, 4.8_828e-04, 1.1_597e-03,
  			     7.3_242e-04, 9.4_604e-04, 1.8_005e-03, 1.8_311e-03, 8.8_501e-04,
  			     4.2_725e-04, 4.8_828e-04, 7.3_242e-04, 1.0_986e-03, 2.1_057e-03]      )
  			# fmt: on
  			A							  =      self._load_datasamples(1      )
  			A							  =      EncodecFeatureExtractor()
  			A							  =      feature_extractor(_lowerCAmelCase      ,      return_tensors="""pt"""      ).input_values
  			self.assertEquals(input_values.shape      ,      (1, 1, 9_3680)      )
  			self.assertTrue(torch.allclose(input_values[0, 0, :30]      ,      _lowerCAmelCase      ,      atol=1e-6      )      )
  
  
  
  
  
  
  
  		def        A     (self						:      Optional[Any]      ):
  			# fmt: off
  			A							  =      torch.tensor(
  			    [2.3_804e-03, 2.0_752e-03, 1.9_836e-03, 2.1_057e-03, 1.6_174e-03,
  			     3.0_518e-04, 9.1_553e-05, 3.3_569e-04, 9.7_656e-04, 1.8_311e-03,
  			     2.0_142e-03, 2.1_057e-03, 1.7_395e-03, 4.5_776e-04, -3.9_673e-04,
  			     4.5_776e-04, 1.0_071e-03, 9.1_553e-05, 4.8_828e-04, 1.1_597e-03,
  			     7.3_242e-04, 9.4_604e-04, 1.8_005e-03, 1.8_311e-03, 8.8_501e-04,
  			     4.2_725e-04, 4.8_828e-04, 7.3_242e-04, 1.0_986e-03, 2.1_057e-03]      )
  			# fmt: on
  			A							  =      self._load_datasamples(1      )
  			A							  =      [np.tile(input_audio[0][None]      ,      reps=(2, 1)      )]
  			input_audio[0][1] *= 0.5
  			A							  =      EncodecFeatureExtractor(feature_size=2      )
  			A							  =      feature_extractor(_lowerCAmelCase      ,      return_tensors="""pt"""      ).input_values
  			self.assertEquals(input_values.shape      ,      (1, 2, 9_3680)      )
  			self.assertTrue(torch.allclose(input_values[0, 0, :30]      ,      _lowerCAmelCase      ,      atol=1e-6      )      )
  			self.assertTrue(torch.allclose(input_values[0, 1, :30]      ,      EXPECTED_INPUT_VALUES * 0.5      ,      atol=1e-6      )      )
  
  
  
  
  
  
  
  		def        A     (self						:      Optional[Any]      ):
  			A							  =      self._load_datasamples(2      )
  			# would be easier if the stride was like
  			A							  =      EncodecFeatureExtractor(feature_size=1      ,      chunk_length_s=1      ,      overlap=0.01      )
  
  			# pad and trunc raise an error ?
  			with self.assertRaisesRegex(
  			    _lowerCAmelCase      ,      """^Both padding and truncation were set. Make sure you only set one.$"""      ,      ):
  				A							  =      feature_extractor(
  				    _lowerCAmelCase      ,      padding="""max_length"""      ,      truncation=_lowerCAmelCase      ,      return_tensors="""pt"""      ).input_values
  
  			# truncate to chunk
  			A							  =      feature_extractor(_lowerCAmelCase      ,      truncation=_lowerCAmelCase      ,      return_tensors="""pt"""      ).input_values
  			self.assertEquals(truncated_outputs.shape      ,      (2, 1, 7_1520)      )  # 2 chunks
  
  			# force truncate to max_length
  			A							  =      feature_extractor(
  			    _lowerCAmelCase      ,      truncation=_lowerCAmelCase      ,      max_length=4_8000      ,      return_tensors="""pt"""      ).input_values
  			self.assertEquals(truncated_outputs.shape      ,      (2, 1, 4_8000)      )
  
  			# pad to chunk
  			A							  =      feature_extractor(_lowerCAmelCase      ,      padding=_lowerCAmelCase      ,      return_tensors="""pt"""      ).input_values
  			self.assertEquals(padded_outputs.shape      ,      (2, 1, 9_5280)      )
  
  			# pad to chunk
  			A							  =      feature_extractor(_lowerCAmelCase      ,      return_tensors="""pt"""      ).input_values
  			self.assertEquals(truncated_outputs.shape      ,      (2, 1, 9_5280)      )
  
  			# force pad to max length
  			A							  =      feature_extractor(
  			    _lowerCAmelCase      ,      padding="""max_length"""      ,      max_length=10_0000      ,      return_tensors="""pt"""      ).input_values
  			self.assertEquals(truncated_outputs.shape      ,      (2, 1, 10_0000)      )
  
  			# force no pad
  			with self.assertRaisesRegex(
  			    _lowerCAmelCase      ,      """^Unable to create tensor, you should probably activate padding with 'padding=True' to have batched tensors with the same length.$"""      ,      ):
  				A							  =      feature_extractor(_lowerCAmelCase      ,      padding=_lowerCAmelCase      ,      return_tensors="""pt"""      ).input_values
  
  			A							  =      feature_extractor(input_audio[0]      ,      padding=_lowerCAmelCase      ,      return_tensors="""pt"""      ).input_values
  			self.assertEquals(truncated_outputs.shape      ,      (1, 1, 9_3680)      )
  
  			# no pad if no chunk_length_s
  			A							  =      None
  			with self.assertRaisesRegex(
  			    _lowerCAmelCase      ,      """^Unable to create tensor, you should probably activate padding with 'padding=True' to have batched tensors with the same length.$"""      ,      ):
  				A							  =      feature_extractor(_lowerCAmelCase      ,      padding=_lowerCAmelCase      ,      return_tensors="""pt"""      ).input_values
  
  			A							  =      feature_extractor(input_audio[0]      ,      padding=_lowerCAmelCase      ,      return_tensors="""pt"""      ).input_values
  			self.assertEquals(truncated_outputs.shape      ,      (1, 1, 9_3680)      )
  
  			# no pad if no overlap
  			A							  =      2
  			A							  =      None
  			with self.assertRaisesRegex(
  			    _lowerCAmelCase      ,      """^Unable to create tensor, you should probably activate padding with 'padding=True' to have batched tensors with the same length.$"""      ,      ):
  				A							  =      feature_extractor(_lowerCAmelCase      ,      padding=_lowerCAmelCase      ,      return_tensors="""pt"""      ).input_values
  
  			A							  =      feature_extractor(input_audio[0]      ,      padding=_lowerCAmelCase      ,      return_tensors="""pt"""      ).input_values
  			self.assertEquals(truncated_outputs.shape      ,      (1, 1, 9_3680)      )
  
  
style_context: 
  
  
  
  
  
  '''simple docstring'''
  
  
  from typing import Dict, List, Optional, Tuple
  
  from ...tokenization_utils import AddedToken, PreTrainedTokenizer
  from ...utils import logging
  
  
  _lowerCamelCase				:	int				    =       logging.get_logger(__name__)
  
  
  
  class 							__UpperCAmelCase  (       A__						):
  
  
  
  
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		__lowerCAmelCase 				=    ['''input_ids''', '''attention_mask''']
  
  
  
  
  
  
  
  		def __init__(self						:      Tuple      ,      _lowerCAmelCase						:      str="[PAD]"      ,      _lowerCAmelCase						:      Dict="[BOS]"      ,      _lowerCAmelCase						:      Any="[EOS]"      ,      _lowerCAmelCase						:      List[Any]="[MASK]"      ,      _lowerCAmelCase						:      int="[CLS]"      ,      _lowerCAmelCase						:      Optional[Any]="[SEP]"      ,      _lowerCAmelCase						:      List[str]=2048      ,      **_lowerCAmelCase						:      Dict      ,      ):
  			A							  =      AddedToken(_lowerCAmelCase      ,      lstrip=_lowerCAmelCase      ,      rstrip=_lowerCAmelCase      ) if isinstance(_lowerCAmelCase      ,      _lowerCAmelCase      ) else pad_token
  			A							  =      AddedToken(_lowerCAmelCase      ,      lstrip=_lowerCAmelCase      ,      rstrip=_lowerCAmelCase      ) if isinstance(_lowerCAmelCase      ,      _lowerCAmelCase      ) else bos_token
  			A							  =      AddedToken(_lowerCAmelCase      ,      lstrip=_lowerCAmelCase      ,      rstrip=_lowerCAmelCase      ) if isinstance(_lowerCAmelCase      ,      _lowerCAmelCase      ) else eos_token
  			A							  =      AddedToken(_lowerCAmelCase      ,      lstrip=_lowerCAmelCase      ,      rstrip=_lowerCAmelCase      ) if isinstance(_lowerCAmelCase      ,      _lowerCAmelCase      ) else mask_token
  			A							  =      AddedToken(_lowerCAmelCase      ,      lstrip=_lowerCAmelCase      ,      rstrip=_lowerCAmelCase      ) if isinstance(_lowerCAmelCase      ,      _lowerCAmelCase      ) else cls_token
  			A							  =      AddedToken(_lowerCAmelCase      ,      lstrip=_lowerCAmelCase      ,      rstrip=_lowerCAmelCase      ) if isinstance(_lowerCAmelCase      ,      _lowerCAmelCase      ) else sep_token
  
  			super().__init__(
  			    pad_token=_lowerCAmelCase      ,      bos_token=_lowerCAmelCase      ,      eos_token=_lowerCAmelCase      ,      mask_token=_lowerCAmelCase      ,      cls_token=_lowerCAmelCase      ,      sep_token=_lowerCAmelCase      ,      model_max_length=_lowerCAmelCase      ,      **_lowerCAmelCase      ,      )
  
  			A							  =      2**8  # utf is 8 bits
  
  			# define special tokens dict
  			A							  =      {
  			    self.pad_token: 0,
  			    self.bos_token: 1,
  			    self.eos_token: 2,
  			    self.mask_token: 3,
  			    self.cls_token: 4,
  			    self.sep_token: 5,
  			}
  			A							  =      len(self.special_tokens_encoder      )
  			A							  =      {v: k for k, v in self.special_tokens_encoder.items()}
  
  
  
  
  
  
  
  		def        A     (self						:      Tuple      ):
  			A							  =      self.special_tokens_encoder.copy()
  			vocab.update(self.added_tokens_encoder      )
  			for i in range(self._utf_vocab_size      ):
  				A							  =      chr(_lowerCAmelCase      )
  				A							  =      i + len(self.special_tokens_encoder      )
  			return vocab
  
  
  
  
  
  
  
  		@property
  		def        A     (self						:      Tuple      ):
  			return self._utf_vocab_size + self._num_special_tokens
  
  
  
  
  
  
  
  		def        A     (self						:      Union[str, Any]      ,      _lowerCAmelCase						:      List[int]      ,      _lowerCAmelCase						:      Optional[List[int]] = None      ,      _lowerCAmelCase						:      bool = False      ):
  			if already_has_special_tokens:
  				return super().get_special_tokens_mask(
  				    token_ids_a=_lowerCAmelCase      ,      token_ids_a=_lowerCAmelCase      ,      already_has_special_tokens=_lowerCAmelCase      )
  
  			# normal case: some special tokens
  			if token_ids_a is None:
  				return [1] + [0] * len(_lowerCAmelCase      ) + [1]
  			return [1] + ([0] * len(_lowerCAmelCase      )) + [1] + ([0] * len(_lowerCAmelCase      )) + [1]
  
  
  
  
  
  
  
  		def        A     (self						:      List[Any]      ,      _lowerCAmelCase						:      List[int]      ,      _lowerCAmelCase						:      Optional[List[int]] = None      ):
  			if token_ids_a is None:
  				return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
  			else:
  				return [self.cls_token_id] + token_ids_a + [self.sep_token_id] + token_ids_a + [self.sep_token_id]
  
  
  
  
  
  
  
  		def        A     (self						:      str      ,      _lowerCAmelCase						:      str      ):
  			A							  =      [chr(_lowerCAmelCase      ) for i in text.encode("""utf-8"""      )]
  			return tokens
  
  
  
  
  
  
  
  		def        A     (self						:      int      ,      _lowerCAmelCase						:      List[str]      ):
  			if token in self.special_tokens_encoder:
  				A							  =      self.special_tokens_encoder[token]
  			elif token in self.added_tokens_encoder:
  				A							  =      self.added_tokens_encoder[token]
  			elif len(_lowerCAmelCase      ) != 1:
  				A							  =      self.unk_token_id
  			else:
  				A							  =      ord(_lowerCAmelCase      ) + self._num_special_tokens
  			return token_id
  
  
  
  
  
  
  
  		def        A     (self						:      Optional[int]      ,      _lowerCAmelCase						:      Any      ):
  			if index in self.special_tokens_decoder:
  				A							  =      self.special_tokens_decoder[index]
  			elif index in self.added_tokens_decoder:
  				A							  =      self.added_tokens_decoder[index]
  			else:
  				A							  =      chr(index - self._num_special_tokens      )
  			return token
  
  
  
  
  
  
  
  		def        A     (self						:      Dict      ,      _lowerCAmelCase						:      List[str]      ):
  			A							  =      b""""""
  			for token in tokens:
  				if token in self.special_tokens_decoder:
  					A							  =      self.special_tokens_decoder[token].encode("""utf-8"""      )
  				elif token in self.added_tokens_decoder:
  					A							  =      self.special_tokens_decoder[token].encode("""utf-8"""      )
  				elif token in self.special_tokens_encoder:
  					A							  =      token.encode("""utf-8"""      )
  				elif token in self.added_tokens_encoder:
  					A							  =      token.encode("""utf-8"""      )
  				else:
  					A							  =      bytes([ord(_lowerCAmelCase      )]      )
  				bstring += tok_string
  			A							  =      bstring.decode("""utf-8"""      ,      errors="""replace"""      )
  			return string
  
  
  
  
  
  
  
  		def        A     (self						:      Tuple      ,      _lowerCAmelCase						:      str      ,      _lowerCAmelCase						:      Optional[str] = None      ):
  			return ()
  
  