code: 
  
  
  '''simple docstring'''
  
  
  
  
  import json
  import os
  from functools import lru_cache
  from typing import List, Optional, Tuple
  
  import regex as re
  
  from ...tokenization_utils import AddedToken, PreTrainedTokenizer
  from ...utils import logging
  
  
  __lowercase  :      Optional[int]      		=				logging.get_logger(__name__)
  
  __lowercase  :      List[str]      		=				{
      """vocab_file""": """vocab.json""",
      """merges_file""": """merges.txt""",
  }
  
  __lowercase  :      str      		=				{
      """vocab_file""": {
          """roberta-base""": """https://huggingface.co/roberta-base/resolve/main/vocab.json""",
          """roberta-large""": """https://huggingface.co/roberta-large/resolve/main/vocab.json""",
          """roberta-large-mnli""": """https://huggingface.co/roberta-large-mnli/resolve/main/vocab.json""",
          """distilroberta-base""": """https://huggingface.co/distilroberta-base/resolve/main/vocab.json""",
          """roberta-base-openai-detector""": """https://huggingface.co/roberta-base-openai-detector/resolve/main/vocab.json""",
          """roberta-large-openai-detector""": (
              """https://huggingface.co/roberta-large-openai-detector/resolve/main/vocab.json"""
          ),
      },
      """merges_file""": {
          """roberta-base""": """https://huggingface.co/roberta-base/resolve/main/merges.txt""",
          """roberta-large""": """https://huggingface.co/roberta-large/resolve/main/merges.txt""",
          """roberta-large-mnli""": """https://huggingface.co/roberta-large-mnli/resolve/main/merges.txt""",
          """distilroberta-base""": """https://huggingface.co/distilroberta-base/resolve/main/merges.txt""",
          """roberta-base-openai-detector""": """https://huggingface.co/roberta-base-openai-detector/resolve/main/merges.txt""",
          """roberta-large-openai-detector""": (
              """https://huggingface.co/roberta-large-openai-detector/resolve/main/merges.txt"""
          ),
      },
  }
  
  __lowercase  :      Dict      		=				{
      """roberta-base""": 5_12,
      """roberta-large""": 5_12,
      """roberta-large-mnli""": 5_12,
      """distilroberta-base""": 5_12,
      """roberta-base-openai-detector""": 5_12,
      """roberta-large-openai-detector""": 5_12,
  }
  
  
  
  
  
  @lru_cache()
  def 							a_       (						)			->int:
  
  
  
  
  
  
  				'''simple docstring'''
  
  				__lowerCamelCase		:      str											=					(
  				    list(range(ord("!"    )							,						ord("~"    ) + 1    )    ) + list(range(ord("¡"    )							,						ord("¬"    ) + 1    )    ) + list(range(ord("®"    )							,						ord("ÿ"    ) + 1    )    )
  				)
  				__lowerCamelCase		:      int											=					bs[:]
  				__lowerCamelCase		:      Optional[int]											=					0
  				for b in range(2**8    ):
  								if b not in bs:
  												bs.append(lowercase    )
  												cs.append(2**8 + n    )
  												n += 1
  				__lowerCamelCase		:      Any											=					[chr(lowercase    ) for n in cs]
  				return dict(zip(lowercase							,						lowercase    )    )
  
  
  
  
  
  def 							a_       (						lowercase    )			->Union[str, Any]:
  
  
  
  
  
  
  				'''simple docstring'''
  
  				__lowerCamelCase		:      str											=					set()
  				__lowerCamelCase		:      Any											=					word[0]
  				for char in word[1:]:
  								pairs.add((prev_char, char)    )
  								__lowerCamelCase		:      Any											=					char
  				return pairs
  
  
  
  
  
  class _snake_case   ( a__   ):
  							"""simple docstring"""
  
  
  
  
  
  							__a												=     VOCAB_FILES_NAMES
  							__a												=     PRETRAINED_VOCAB_FILES_MAP
  							__a												=     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  							__a												=     ['''input_ids''', '''attention_mask''']
  
  
  
  
  
  
  
  							def __init__(   self			,				__A			,				__A			,				__A="replace"			,				__A="<s>"			,				__A="</s>"			,				__A="</s>"			,				__A="<s>"			,				__A="<unk>"			,				__A="<pad>"			,				__A="<mask>"			,				__A=False			,				**__A			,				)     ->List[Any]:
  
  
  
  											"""simple docstring"""
  
  
  
  
  
  
  
  											__lowerCamelCase		:      Tuple											=					AddedToken(__A			,				lstrip=__A			,				rstrip=__A	) if isinstance(__A			,				__A	) else bos_token
  											__lowerCamelCase		:      Optional[Any]											=					AddedToken(__A			,				lstrip=__A			,				rstrip=__A	) if isinstance(__A			,				__A	) else eos_token
  											__lowerCamelCase		:      int											=					AddedToken(__A			,				lstrip=__A			,				rstrip=__A	) if isinstance(__A			,				__A	) else sep_token
  											__lowerCamelCase		:      str											=					AddedToken(__A			,				lstrip=__A			,				rstrip=__A	) if isinstance(__A			,				__A	) else cls_token
  											__lowerCamelCase		:      List[Any]											=					AddedToken(__A			,				lstrip=__A			,				rstrip=__A	) if isinstance(__A			,				__A	) else unk_token
  											__lowerCamelCase		:      Optional[Any]											=					AddedToken(__A			,				lstrip=__A			,				rstrip=__A	) if isinstance(__A			,				__A	) else pad_token
  
  											# Mask token behave like a normal word, i.e. include the space before it
  											__lowerCamelCase		:      Union[str, Any]											=					AddedToken(__A			,				lstrip=__A			,				rstrip=__A	) if isinstance(__A			,				__A	) else mask_token
  
  											super().__init__(
  											    errors=__A			,				bos_token=__A			,				eos_token=__A			,				unk_token=__A			,				sep_token=__A			,				cls_token=__A			,				pad_token=__A			,				mask_token=__A			,				add_prefix_space=__A			,				**__A			,				)
  
  											with open(__A			,				encoding="utf-8"	) as vocab_handle:
  															__lowerCamelCase		:      Optional[Any]											=					json.load(__A	)
  											__lowerCamelCase		:      List[Any]											=					{v: k for k, v in self.encoder.items()}
  											__lowerCamelCase		:      Any											=					errors  # how to handle errors in decoding
  											__lowerCamelCase		:      Optional[Any]											=					bytes_to_unicode()
  											__lowerCamelCase		:      Optional[int]											=					{v: k for k, v in self.byte_encoder.items()}
  											with open(__A			,				encoding="utf-8"	) as merges_handle:
  															__lowerCamelCase		:      List[Any]											=					merges_handle.read().split("\n"	)[1:-1]
  											__lowerCamelCase		:      Union[str, Any]											=					[tuple(merge.split()	) for merge in bpe_merges]
  											__lowerCamelCase		:      Dict											=					dict(zip(__A			,				range(len(__A	)	)	)	)
  											__lowerCamelCase		:      Any											=					{}
  											__lowerCamelCase		:      List[Any]											=					add_prefix_space
  
  											# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
  											__lowerCamelCase		:      int											=					re.compile(r"'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+"	)
  
  
  
  
  
  
  
  							@property
  							def 		__SCREAMING_SNAKE_CASE(   self	)     ->Optional[Any]:
  
  
  
  											"""simple docstring"""
  
  
  
  
  
  
  
  											return len(self.encoder	)
  
  
  
  
  
  
  
  							def 		__SCREAMING_SNAKE_CASE(   self	)     ->Tuple:
  
  
  
  											"""simple docstring"""
  
  
  
  
  
  
  
  											return dict(self.encoder			,				**self.added_tokens_encoder	)
  
  
  
  
  
  
  
  							def 		__SCREAMING_SNAKE_CASE(   self			,				__A	)     ->List[Any]:
  
  
  
  											"""simple docstring"""
  
  
  
  
  
  
  
  											if token in self.cache:
  															return self.cache[token]
  											__lowerCamelCase		:      Union[str, Any]											=					tuple(__A	)
  											__lowerCamelCase		:      List[str]											=					get_pairs(__A	)
  
  											if not pairs:
  															return token
  
  											while True:
  															__lowerCamelCase		:      Dict											=					min(__A			,				key=lambda __A	: self.bpe_ranks.get(__A			,				float("inf"	)	)	)
  															if bigram not in self.bpe_ranks:
  																			break
  															__lowerCamelCase						,					__lowerCamelCase		:      Tuple											=					bigram
  															__lowerCamelCase		:      List[Any]											=					[]
  															__lowerCamelCase		:      Dict											=					0
  															while i < len(__A	):
  																			try:
  																							__lowerCamelCase		:      List[str]											=					word.index(__A			,				__A	)
  																			except ValueError:
  																							new_word.extend(word[i:]	)
  																							break
  																			else:
  																							new_word.extend(word[i:j]	)
  																							__lowerCamelCase		:      Optional[int]											=					j
  
  																			if word[i] == first and i < len(__A	) - 1 and word[i + 1] == second:
  																							new_word.append(first + second	)
  																							i += 2
  																			else:
  																							new_word.append(word[i]	)
  																							i += 1
  															__lowerCamelCase		:      Union[str, Any]											=					tuple(__A	)
  															__lowerCamelCase		:      Optional[Any]											=					new_word
  															if len(__A	) == 1:
  																			break
  															else:
  																			__lowerCamelCase		:      Optional[Any]											=					get_pairs(__A	)
  											__lowerCamelCase		:      Optional[int]											=					" ".join(__A	)
  											__lowerCamelCase		:      Optional[Any]											=					word
  											return word
  
  
  
  
  
  
  
  							def 		__SCREAMING_SNAKE_CASE(   self			,				__A	)     ->List[str]:
  
  
  
  											"""simple docstring"""
  
  
  
  
  
  
  
  											__lowerCamelCase		:      Any											=					[]
  											for token in re.findall(self.pat			,				__A	):
  															__lowerCamelCase		:      Optional[int]											=					"".join(
  															    self.byte_encoder[b] for b in token.encode("utf-8"	)	)  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
  															bpe_tokens.extend(bpe_token for bpe_token in self.bpe(__A	).split(" "	)	)
  											return bpe_tokens
  
  
  
  
  
  
  
  							def 		__SCREAMING_SNAKE_CASE(   self			,				__A	)     ->List[str]:
  
  
  
  											"""simple docstring"""
  
  
  
  
  
  
  
  											return self.encoder.get(__A			,				self.encoder.get(self.unk_token	)	)
  
  
  
  
  
  
  
  							def 		__SCREAMING_SNAKE_CASE(   self			,				__A	)     ->Any:
  
  
  
  											"""simple docstring"""
  
  
  
  
  
  
  
  											return self.decoder.get(__A	)
  
  
  
  
  
  
  
  							def 		__SCREAMING_SNAKE_CASE(   self			,				__A	)     ->Union[str, Any]:
  
  
  
  											"""simple docstring"""
  
  
  
  
  
  
  
  											__lowerCamelCase		:      List[str]											=					"".join(__A	)
  											__lowerCamelCase		:      List[Any]											=					bytearray([self.byte_decoder[c] for c in text]	).decode("utf-8"			,				errors=self.errors	)
  											return text
  
  
  
  
  
  
  
  							def 		__SCREAMING_SNAKE_CASE(   self			,				__A			,				__A = None	)     ->Tuple[str]:
  
  
  
  											"""simple docstring"""
  
  
  
  
  
  
  
  											if not os.path.isdir(__A	):
  															logger.error(f'Vocabulary path ({save_directory}) should be a directory'	)
  															return
  											__lowerCamelCase		:      Optional[Any]											=					os.path.join(
  											    __A			,				(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]	)
  											__lowerCamelCase		:      Any											=					os.path.join(
  											    __A			,				(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"]	)
  
  											with open(__A			,				"w"			,				encoding="utf-8"	) as f:
  															f.write(json.dumps(self.encoder			,				indent=2			,				sort_keys=__A			,				ensure_ascii=__A	) + "\n"	)
  
  											__lowerCamelCase		:      Tuple											=					0
  											with open(__A			,				"w"			,				encoding="utf-8"	) as writer:
  															writer.write("#version: 0.2\n"	)
  															for bpe_tokens, token_index in sorted(self.bpe_ranks.items()			,				key=lambda __A	: kv[1]	):
  																			if index != token_index:
  																							logger.warning(
  																							    f'Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'
  																							    " Please check that the tokenizer is not corrupted!"	)
  																							__lowerCamelCase		:      Optional[Any]											=					token_index
  																			writer.write(" ".join(__A	) + "\n"	)
  																			index += 1
  
  											return vocab_file, merge_file
  
  
  
  
  
  
  
  							def 		__SCREAMING_SNAKE_CASE(   self			,				__A			,				__A = None	)     ->List[int]:
  
  
  
  											"""simple docstring"""
  
  
  
  
  
  
  
  											if token_ids_a is None:
  															return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
  											__lowerCamelCase		:      List[str]											=					[self.cls_token_id]
  											__lowerCamelCase		:      Any											=					[self.sep_token_id]
  											return cls + token_ids_a + sep + sep + token_ids_a + sep
  
  
  
  
  
  
  
  							def 		__SCREAMING_SNAKE_CASE(   self			,				__A			,				__A = None			,				__A = False	)     ->List[int]:
  
  
  
  											"""simple docstring"""
  
  
  
  
  
  
  
  											if already_has_special_tokens:
  															return super().get_special_tokens_mask(
  															    token_ids_a=__A			,				token_ids_a=__A			,				already_has_special_tokens=__A	)
  
  											if token_ids_a is None:
  															return [1] + ([0] * len(__A	)) + [1]
  											return [1] + ([0] * len(__A	)) + [1, 1] + ([0] * len(__A	)) + [1]
  
  
  
  
  
  
  
  							def 		__SCREAMING_SNAKE_CASE(   self			,				__A			,				__A = None	)     ->List[int]:
  
  
  
  											"""simple docstring"""
  
  
  
  
  
  
  
  											__lowerCamelCase		:      Tuple											=					[self.sep_token_id]
  											__lowerCamelCase		:      Union[str, Any]											=					[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 + sep + token_ids_a + sep	) * [0]
  
  
  
  
  
  
  
  							def 		__SCREAMING_SNAKE_CASE(   self			,				__A			,				__A=False			,				**__A	)     ->Optional[Any]:
  
  
  
  											"""simple docstring"""
  
  
  
  
  
  
  
  											__lowerCamelCase		:      Optional[Any]											=					kwargs.pop("add_prefix_space"			,				self.add_prefix_space	)
  											if (is_split_into_words or add_prefix_space) and (len(__A	) > 0 and not text[0].isspace()):
  															__lowerCamelCase		:      Optional[Any]											=					" " + text
  											return (text, kwargs)
  
  
  
  
  
  
style_context: 
  
  
  '''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
  
  
  __lowercase  :      Any      		=				random.Random()
  
  
  
  
  
  def 							a_       (						lowercase							,						lowercase=1.0							,						lowercase=None							,						lowercase=None    )			->int:
  
  
  
  
  
  
  				'''simple docstring'''
  
  				if rng is None:
  								__lowerCamelCase		:      Any											=					global_rng
  
  				__lowerCamelCase		:      List[Any]											=					[]
  				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 _snake_case   ( unittest.TestCase   ):
  							"""simple docstring"""
  
  
  
  
  
  							def __init__(   self			,				__A			,				__A=7			,				__A=400			,				__A=2000			,				__A=1			,				__A=0.0			,				__A=24000			,				__A=True			,				)     ->Optional[int]:
  
  
  
  											"""simple docstring"""
  
  
  
  
  
  
  
  											__lowerCamelCase		:      Dict											=					parent
  											__lowerCamelCase		:      Tuple											=					batch_size
  											__lowerCamelCase		:      str											=					min_seq_length
  											__lowerCamelCase		:      Optional[Any]											=					max_seq_length
  											__lowerCamelCase		:      int											=					(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
  											__lowerCamelCase		:      List[Any]											=					feature_size
  											__lowerCamelCase		:      int											=					padding_value
  											__lowerCamelCase		:      Optional[Any]											=					sampling_rate
  											__lowerCamelCase		:      List[str]											=					return_attention_mask
  
  
  
  
  
  
  
  							def 		__SCREAMING_SNAKE_CASE(   self	)     ->Optional[Any]:
  
  
  
  											"""simple docstring"""
  
  
  
  
  
  
  
  											return {
  											    "feature_size": self.feature_size,
  											    "padding_value": self.padding_value,
  											    "sampling_rate": self.sampling_rate,
  											    "return_attention_mask": self.return_attention_mask,
  											}
  
  
  
  
  
  
  
  							def 		__SCREAMING_SNAKE_CASE(   self			,				__A=False			,				__A=False	)     ->int:
  
  
  
  											"""simple docstring"""
  
  
  
  
  
  
  
  											def _flatten(__A	):
  															return list(itertools.chain(*__A	)	)
  
  											if equal_length:
  															__lowerCamelCase		:      Optional[int]											=					floats_list((self.batch_size, self.max_seq_length)	)
  											else:
  															# make sure that inputs increase in size
  															__lowerCamelCase		:      Optional[Any]											=					[
  															    _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:
  															__lowerCamelCase		:      int											=					[np.asarray(__A	) for x in audio_inputs]
  
  											return audio_inputs
  
  
  
  
  
  @require_torch
  class _snake_case   ( a__    ,   unittest.TestCase   ):
  							"""simple docstring"""
  
  
  
  
  
  							__a												=     EncodecFeatureExtractor
  
  
  
  
  
  
  
  							def 		__SCREAMING_SNAKE_CASE(   self	)     ->int:
  
  
  
  											"""simple docstring"""
  
  
  
  
  
  
  
  											__lowerCamelCase		:      str											=					EnCodecFeatureExtractionTester(self	)
  
  
  
  
  
  
  
  							def 		__SCREAMING_SNAKE_CASE(   self	)     ->int:
  
  
  
  											"""simple docstring"""
  
  
  
  
  
  
  
  											__lowerCamelCase		:      Optional[int]											=					self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()	)
  											# create three inputs of length 800, 1000, and 1200
  											__lowerCamelCase		:      Optional[Any]											=					[floats_list((1, x)	)[0] for x in range(800			,				1400			,				200	)]
  											__lowerCamelCase		:      Optional[Any]											=					[np.asarray(__A	) for audio_input in audio_inputs]
  
  											# Test not batched input
  											__lowerCamelCase		:      List[str]											=					feat_extract(audio_inputs[0]			,				return_tensors="np"	).input_values
  											__lowerCamelCase		:      Union[str, Any]											=					feat_extract(np_audio_inputs[0]			,				return_tensors="np"	).input_values
  											self.assertTrue(np.allclose(__A			,				__A			,				atol=1E-3	)	)
  
  											# Test batched
  											__lowerCamelCase		:      Optional[int]											=					feat_extract(__A			,				padding=__A			,				return_tensors="np"	).input_values
  											__lowerCamelCase		:      List[str]											=					feat_extract(__A			,				padding=__A			,				return_tensors="np"	).input_values
  											for enc_seq_a, enc_seq_a in zip(__A			,				__A	):
  															self.assertTrue(np.allclose(__A			,				__A			,				atol=1E-3	)	)
  
  
  
  
  
  
  
  							def 		__SCREAMING_SNAKE_CASE(   self	)     ->List[str]:
  
  
  
  											"""simple docstring"""
  
  
  
  
  
  
  
  											__lowerCamelCase		:      Optional[Any]											=					self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()	)
  											__lowerCamelCase		:      Tuple											=					np.random.rand(100	).astype(np.floataa	)
  											__lowerCamelCase		:      Any											=					np_audio_inputs.tolist()
  
  											for inputs in [py_audio_inputs, np_audio_inputs]:
  															__lowerCamelCase		:      int											=					feature_extractor.pad([{"input_values": inputs}]			,				return_tensors="np"	)
  															self.assertTrue(np_processed.input_values.dtype == np.floataa	)
  															__lowerCamelCase		:      List[Any]											=					feature_extractor.pad([{"input_values": inputs}]			,				return_tensors="pt"	)
  															self.assertTrue(pt_processed.input_values.dtype == torch.floataa	)
  
  
  
  
  
  
  
  							def 		__SCREAMING_SNAKE_CASE(   self			,				__A	)     ->int:
  
  
  
  											"""simple docstring"""
  
  
  
  
  
  
  
  											from datasets import load_dataset
  
  											__lowerCamelCase		:      List[Any]											=					load_dataset("hf-internal-testing/librispeech_asr_dummy"			,				"clean"			,				split="validation"	)
  											# automatic decoding with librispeech
  											__lowerCamelCase		:      str											=					ds.sort("id"	).select(range(__A	)	)[:num_samples]["audio"]
  
  											return [x["array"] for x in audio_samples]
  
  
  
  
  
  
  
  							def 		__SCREAMING_SNAKE_CASE(   self	)     ->Tuple:
  
  
  
  											"""simple docstring"""
  
  
  
  
  
  
  
  											__lowerCamelCase		:      Dict											=					torch.tensor(
  											    [2.3804E-03, 2.0752E-03, 1.9836E-03, 2.1057E-03, 1.6174E-03,
  											     3.0518E-04, 9.1553E-05, 3.3569E-04, 9.7656E-04, 1.8311E-03,
  											     2.0142E-03, 2.1057E-03, 1.7395E-03, 4.5776E-04, -3.9673E-04,
  											     4.5776E-04, 1.0071E-03, 9.1553E-05, 4.8828E-04, 1.1597E-03,
  											     7.3242E-04, 9.4604E-04, 1.8005E-03, 1.8311E-03, 8.8501E-04,
  											     4.2725E-04, 4.8828E-04, 7.3242E-04, 1.0986E-03, 2.1057E-03]	)
  											# fmt: on
  											__lowerCamelCase		:      Union[str, Any]											=					self._load_datasamples(1	)
  											__lowerCamelCase		:      str											=					EncodecFeatureExtractor()
  											__lowerCamelCase		:      Optional[int]											=					feature_extractor(__A			,				return_tensors="pt"	).input_values
  											self.assertEquals(input_values.shape			,				(1, 1, 93680)	)
  											self.assertTrue(torch.allclose(input_values[0, 0, :30]			,				__A			,				atol=1E-6	)	)
  
  
  
  
  
  
  
  							def 		__SCREAMING_SNAKE_CASE(   self	)     ->Tuple:
  
  
  
  											"""simple docstring"""
  
  
  
  
  
  
  
  											__lowerCamelCase		:      Optional[Any]											=					torch.tensor(
  											    [2.3804E-03, 2.0752E-03, 1.9836E-03, 2.1057E-03, 1.6174E-03,
  											     3.0518E-04, 9.1553E-05, 3.3569E-04, 9.7656E-04, 1.8311E-03,
  											     2.0142E-03, 2.1057E-03, 1.7395E-03, 4.5776E-04, -3.9673E-04,
  											     4.5776E-04, 1.0071E-03, 9.1553E-05, 4.8828E-04, 1.1597E-03,
  											     7.3242E-04, 9.4604E-04, 1.8005E-03, 1.8311E-03, 8.8501E-04,
  											     4.2725E-04, 4.8828E-04, 7.3242E-04, 1.0986E-03, 2.1057E-03]	)
  											# fmt: on
  											__lowerCamelCase		:      Union[str, Any]											=					self._load_datasamples(1	)
  											__lowerCamelCase		:      str											=					[np.tile(input_audio[0][None]			,				reps=(2, 1)	)]
  											input_audio[0][1] *= 0.5
  											__lowerCamelCase		:      Optional[int]											=					EncodecFeatureExtractor(feature_size=2	)
  											__lowerCamelCase		:      str											=					feature_extractor(__A			,				return_tensors="pt"	).input_values
  											self.assertEquals(input_values.shape			,				(1, 2, 93680)	)
  											self.assertTrue(torch.allclose(input_values[0, 0, :30]			,				__A			,				atol=1E-6	)	)
  											self.assertTrue(torch.allclose(input_values[0, 1, :30]			,				EXPECTED_INPUT_VALUES * 0.5			,				atol=1E-6	)	)
  
  
  
  
  
  
  
  							def 		__SCREAMING_SNAKE_CASE(   self	)     ->Optional[Any]:
  
  
  
  											"""simple docstring"""
  
  
  
  
  
  
  
  											__lowerCamelCase		:      str											=					self._load_datasamples(2	)
  											# would be easier if the stride was like
  											__lowerCamelCase		:      List[Any]											=					EncodecFeatureExtractor(feature_size=1			,				chunk_length_s=1			,				overlap=0.01	)
  
  											# pad and trunc raise an error ?
  											with self.assertRaisesRegex(
  											    __A			,				"^Both padding and truncation were set. Make sure you only set one.$"			,				):
  															__lowerCamelCase		:      Dict											=					feature_extractor(
  															    __A			,				padding="max_length"			,				truncation=__A			,				return_tensors="pt"	).input_values
  
  											# truncate to chunk
  											__lowerCamelCase		:      Optional[Any]											=					feature_extractor(__A			,				truncation=__A			,				return_tensors="pt"	).input_values
  											self.assertEquals(truncated_outputs.shape			,				(2, 1, 71520)	)  # 2 chunks
  
  											# force truncate to max_length
  											__lowerCamelCase		:      List[str]											=					feature_extractor(
  											    __A			,				truncation=__A			,				max_length=48000			,				return_tensors="pt"	).input_values
  											self.assertEquals(truncated_outputs.shape			,				(2, 1, 48000)	)
  
  											# pad to chunk
  											__lowerCamelCase		:      List[str]											=					feature_extractor(__A			,				padding=__A			,				return_tensors="pt"	).input_values
  											self.assertEquals(padded_outputs.shape			,				(2, 1, 95280)	)
  
  											# pad to chunk
  											__lowerCamelCase		:      Optional[Any]											=					feature_extractor(__A			,				return_tensors="pt"	).input_values
  											self.assertEquals(truncated_outputs.shape			,				(2, 1, 95280)	)
  
  											# force pad to max length
  											__lowerCamelCase		:      int											=					feature_extractor(
  											    __A			,				padding="max_length"			,				max_length=100000			,				return_tensors="pt"	).input_values
  											self.assertEquals(truncated_outputs.shape			,				(2, 1, 100000)	)
  
  											# force no pad
  											with self.assertRaisesRegex(
  											    __A			,				"^Unable to create tensor, you should probably activate padding with 'padding=True' to have batched tensors with the same length.$"			,				):
  															__lowerCamelCase		:      Optional[Any]											=					feature_extractor(__A			,				padding=__A			,				return_tensors="pt"	).input_values
  
  											__lowerCamelCase		:      int											=					feature_extractor(input_audio[0]			,				padding=__A			,				return_tensors="pt"	).input_values
  											self.assertEquals(truncated_outputs.shape			,				(1, 1, 93680)	)
  
  											# no pad if no chunk_length_s
  											__lowerCamelCase		:      Any											=					None
  											with self.assertRaisesRegex(
  											    __A			,				"^Unable to create tensor, you should probably activate padding with 'padding=True' to have batched tensors with the same length.$"			,				):
  															__lowerCamelCase		:      int											=					feature_extractor(__A			,				padding=__A			,				return_tensors="pt"	).input_values
  
  											__lowerCamelCase		:      Any											=					feature_extractor(input_audio[0]			,				padding=__A			,				return_tensors="pt"	).input_values
  											self.assertEquals(truncated_outputs.shape			,				(1, 1, 93680)	)
  
  											# no pad if no overlap
  											__lowerCamelCase		:      List[Any]											=					2
  											__lowerCamelCase		:      Any											=					None
  											with self.assertRaisesRegex(
  											    __A			,				"^Unable to create tensor, you should probably activate padding with 'padding=True' to have batched tensors with the same length.$"			,				):
  															__lowerCamelCase		:      Optional[int]											=					feature_extractor(__A			,				padding=__A			,				return_tensors="pt"	).input_values
  
  											__lowerCamelCase		:      List[str]											=					feature_extractor(input_audio[0]			,				padding=__A			,				return_tensors="pt"	).input_values
  											self.assertEquals(truncated_outputs.shape			,				(1, 1, 93680)	)
  
  
  
  
  
  