code: 
  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
  
  
  snake_case				:	Optional[Any]							   =						random.Random()
  
  
  
  
  
  
  def 						lowerCAmelCase_		(      _snake_case   :						Tuple   ,     _snake_case   :						List[Any]=1.0   ,     _snake_case   :						Dict=None   ,     _snake_case   :						Optional[Any]=None     )       ->	Any:
  
  
  
  
  
  
  
  	'''simple docstring'''
  
  
  	if rng is None:
  		__magic_name__	:	List[Any]			     =  global_rng
  
  	__magic_name__	:	List[str]			     =  []
  	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       ):
  
  
  
  
  
  
  
  			def __init__(       self		,     _a		,     _a=7		,     _a=400		,     _a=2_000		,     _a=1		,     _a=0.0		,     _a=24_000		,     _a=True		,     ):
  				__magic_name__	:	Tuple			     =  parent
  				__magic_name__	:	List[Any]			     =  batch_size
  				__magic_name__	:	Tuple			     =  min_seq_length
  				__magic_name__	:	List[str]			     =  max_seq_length
  				__magic_name__	:	Union[str, Any]			     =  (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
  				__magic_name__	:	Dict			     =  feature_size
  				__magic_name__	:	List[Any]			     =  padding_value
  				__magic_name__	:	Dict			     =  sampling_rate
  				__magic_name__	:	Optional[Any]			     =  return_attention_mask
  
  
  
  
  
  
  
  			def     SCREAMING_SNAKE_CASE					(       self	):
  				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	):
  				def _flatten(_a	):
  					return list(itertools.chain(*_a	)	)
  
  				if equal_length:
  					__magic_name__	:	Any			     =  floats_list((self.batch_size, self.max_seq_length)	)
  				else:
  					# make sure that inputs increase in size
  					__magic_name__	:	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:
  					__magic_name__	:	Dict			     =  [np.asarray(_a	) for x in audio_inputs]
  
  				return audio_inputs
  
  
  
  
  
  
  
  @require_torch
  class    _snake_case      (  snake_case       ,		unittest.TestCase       ):
  			UpperCamelCase__					   =					EncodecFeatureExtractor
  
  
  
  
  
  
  
  			def     SCREAMING_SNAKE_CASE					(       self	):
  				__magic_name__	:	Any			     =  EnCodecFeatureExtractionTester(self	)
  
  
  
  
  
  
  
  			def     SCREAMING_SNAKE_CASE					(       self	):
  				# Tests that all call wrap to encode_plus and batch_encode_plus
  				__magic_name__	:	List[Any]			     =  self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()	)
  				# create three inputs of length 800, 1000, and 1200
  				__magic_name__	:	Union[str, Any]			     =  [floats_list((1, x)	)[0] for x in range(800		,     1_400		,     200	)]
  				__magic_name__	:	Union[str, Any]			     =  [np.asarray(_a	) for audio_input in audio_inputs]
  
  				# Test not batched input
  				__magic_name__	:	int			     =  feat_extract(audio_inputs[0]		,     return_tensors="np"	).input_values
  				__magic_name__	:	Dict			     =  feat_extract(np_audio_inputs[0]		,     return_tensors="np"	).input_values
  				self.assertTrue(np.allclose(_a		,     _a		,     atol=1e-3	)	)
  
  				# Test batched
  				__magic_name__	:	Union[str, Any]			     =  feat_extract(_a		,     padding=_a		,     return_tensors="np"	).input_values
  				__magic_name__	:	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	):
  				__magic_name__	:	Dict			     =  self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()	)
  				__magic_name__	:	Any			     =  np.random.rand(100	).astype(np.floataa	)
  				__magic_name__	:	Dict			     =  np_audio_inputs.tolist()
  
  				for inputs in [py_audio_inputs, np_audio_inputs]:
  					__magic_name__	:	str			     =  feature_extractor.pad([{"input_values": inputs}]		,     return_tensors="np"	)
  					self.assertTrue(np_processed.input_values.dtype == np.floataa	)
  					__magic_name__	:	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	):
  				from datasets import load_dataset
  
  				__magic_name__	:	List[str]			     =  load_dataset("hf-internal-testing/librispeech_asr_dummy"		,     "clean"		,     split="validation"	)
  				# automatic decoding with librispeech
  				__magic_name__	:	Optional[int]			     =  ds.sort("id"	).select(range(_a	)	)[:num_samples]["audio"]
  
  				return [x["array"] for x in audio_samples]
  
  
  
  
  
  
  
  			def     SCREAMING_SNAKE_CASE					(       self	):
  				# fmt: off
  				__magic_name__	:	Tuple			     =  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
  				__magic_name__	:	Union[str, Any]			     =  self._load_datasamples(1	)
  				__magic_name__	:	Optional[int]			     =  EncodecFeatureExtractor()
  				__magic_name__	:	Optional[Any]			     =  feature_extractor(_a		,     return_tensors="pt"	).input_values
  				self.assertEquals(input_values.shape		,     (1, 1, 93_680)	)
  				self.assertTrue(torch.allclose(input_values[0, 0, :30]		,     _a		,     atol=1e-6	)	)
  
  
  
  
  
  
  
  			def     SCREAMING_SNAKE_CASE					(       self	):
  				# fmt: off
  				__magic_name__	:	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
  				__magic_name__	:	Optional[Any]			     =  self._load_datasamples(1	)
  				__magic_name__	:	str			     =  [np.tile(input_audio[0][None]		,     reps=(2, 1)	)]
  				input_audio[0][1] *= 0.5
  				__magic_name__	:	Any			     =  EncodecFeatureExtractor(feature_size=2	)
  				__magic_name__	:	Optional[Any]			     =  feature_extractor(_a		,     return_tensors="pt"	).input_values
  				self.assertEquals(input_values.shape		,     (1, 2, 93_680)	)
  				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	):
  				__magic_name__	:	Union[str, Any]			     =  self._load_datasamples(2	)
  				# would be easier if the stride was like
  				__magic_name__	:	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.$"		,     ):
  					__magic_name__	:	str			     =  feature_extractor(
  					    _a		,     padding="max_length"		,     truncation=_a		,     return_tensors="pt"	).input_values
  
  				# truncate to chunk
  				__magic_name__	:	Dict			     =  feature_extractor(_a		,     truncation=_a		,     return_tensors="pt"	).input_values
  				self.assertEquals(truncated_outputs.shape		,     (2, 1, 71_520)	)  # 2 chunks
  
  				# force truncate to max_length
  				__magic_name__	:	List[Any]			     =  feature_extractor(
  				    _a		,     truncation=_a		,     max_length=48_000		,     return_tensors="pt"	).input_values
  				self.assertEquals(truncated_outputs.shape		,     (2, 1, 48_000)	)
  
  				# pad to chunk
  				__magic_name__	:	Any			     =  feature_extractor(_a		,     padding=_a		,     return_tensors="pt"	).input_values
  				self.assertEquals(padded_outputs.shape		,     (2, 1, 95_280)	)
  
  				# pad to chunk
  				__magic_name__	:	Union[str, Any]			     =  feature_extractor(_a		,     return_tensors="pt"	).input_values
  				self.assertEquals(truncated_outputs.shape		,     (2, 1, 95_280)	)
  
  				# force pad to max length
  				__magic_name__	:	Tuple			     =  feature_extractor(
  				    _a		,     padding="max_length"		,     max_length=100_000		,     return_tensors="pt"	).input_values
  				self.assertEquals(truncated_outputs.shape		,     (2, 1, 100_000)	)
  
  				# 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.$"		,     ):
  					__magic_name__	:	Optional[Any]			     =  feature_extractor(_a		,     padding=_a		,     return_tensors="pt"	).input_values
  
  				__magic_name__	:	int			     =  feature_extractor(input_audio[0]		,     padding=_a		,     return_tensors="pt"	).input_values
  				self.assertEquals(truncated_outputs.shape		,     (1, 1, 93_680)	)
  
  				# no pad if no chunk_length_s
  				__magic_name__	:	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.$"		,     ):
  					__magic_name__	:	str			     =  feature_extractor(_a		,     padding=_a		,     return_tensors="pt"	).input_values
  
  				__magic_name__	:	Dict			     =  feature_extractor(input_audio[0]		,     padding=_a		,     return_tensors="pt"	).input_values
  				self.assertEquals(truncated_outputs.shape		,     (1, 1, 93_680)	)
  
  				# no pad if no overlap
  				__magic_name__	:	int			     =  2
  				__magic_name__	:	Dict			     =  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.$"		,     ):
  					__magic_name__	:	Union[str, Any]			     =  feature_extractor(_a		,     padding=_a		,     return_tensors="pt"	).input_values
  
  				__magic_name__	:	Optional[int]			     =  feature_extractor(input_audio[0]		,     padding=_a		,     return_tensors="pt"	).input_values
  				self.assertEquals(truncated_outputs.shape		,     (1, 1, 93_680)	)
  
style_context: 
  import os
  from shutil import copyfile
  from typing import List, Optional, Tuple
  
  from ...tokenization_utils import AddedToken
  from ...tokenization_utils_fast import PreTrainedTokenizerFast
  from ...utils import is_sentencepiece_available, logging
  
  
  if is_sentencepiece_available():
  				from .tokenization_xlm_roberta import XLMRobertaTokenizer
  else:
  				snake_case				:	Any							   =						None
  
  
  snake_case				:	Union[str, Any]							   =						logging.get_logger(__name__)
  
  snake_case				:	Any							   =						{"vocab_file": "sentencepiece.bpe.model", "tokenizer_file": "tokenizer.json"}
  
  snake_case				:	Optional[Any]							   =						{
      "vocab_file": {
          "xlm-roberta-base": "https://huggingface.co/xlm-roberta-base/resolve/main/sentencepiece.bpe.model",
          "xlm-roberta-large": "https://huggingface.co/xlm-roberta-large/resolve/main/sentencepiece.bpe.model",
          "xlm-roberta-large-finetuned-conll02-dutch": (
              "https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/sentencepiece.bpe.model"
          ),
          "xlm-roberta-large-finetuned-conll02-spanish": (
              "https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/sentencepiece.bpe.model"
          ),
          "xlm-roberta-large-finetuned-conll03-english": (
              "https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/sentencepiece.bpe.model"
          ),
          "xlm-roberta-large-finetuned-conll03-german": (
              "https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/sentencepiece.bpe.model"
          ),
      },
      "tokenizer_file": {
          "xlm-roberta-base": "https://huggingface.co/xlm-roberta-base/resolve/main/tokenizer.json",
          "xlm-roberta-large": "https://huggingface.co/xlm-roberta-large/resolve/main/tokenizer.json",
          "xlm-roberta-large-finetuned-conll02-dutch": (
              "https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/tokenizer.json"
          ),
          "xlm-roberta-large-finetuned-conll02-spanish": (
              "https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/tokenizer.json"
          ),
          "xlm-roberta-large-finetuned-conll03-english": (
              "https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/tokenizer.json"
          ),
          "xlm-roberta-large-finetuned-conll03-german": (
              "https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/tokenizer.json"
          ),
      },
  }
  
  snake_case				:	Optional[int]							   =						{
      "xlm-roberta-base": 512,
      "xlm-roberta-large": 512,
      "xlm-roberta-large-finetuned-conll02-dutch": 512,
      "xlm-roberta-large-finetuned-conll02-spanish": 512,
      "xlm-roberta-large-finetuned-conll03-english": 512,
      "xlm-roberta-large-finetuned-conll03-german": 512,
  }
  
  
  
  
  
  class    _snake_case      (  snake_case       ):
  			UpperCamelCase__					   =					VOCAB_FILES_NAMES
  			UpperCamelCase__					   =					PRETRAINED_VOCAB_FILES_MAP
  			UpperCamelCase__					   =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  			UpperCamelCase__					   =					['input_ids', 'attention_mask']
  			UpperCamelCase__					   =					XLMRobertaTokenizer
  
  
  
  
  
  
  
  			def __init__(       self		,     _a=None		,     _a=None		,     _a="<s>"		,     _a="</s>"		,     _a="</s>"		,     _a="<s>"		,     _a="<unk>"		,     _a="<pad>"		,     _a="<mask>"		,     **_a		,     ):
  				# Mask token behave like a normal word, i.e. include the space before it
  				__magic_name__	:	List[str]			     =  AddedToken(_a		,     lstrip=_a		,     rstrip=_a	) if isinstance(_a		,     _a	) else mask_token
  
  				super().__init__(
  				    _a		,     tokenizer_file=_a		,     bos_token=_a		,     eos_token=_a		,     sep_token=_a		,     cls_token=_a		,     unk_token=_a		,     pad_token=_a		,     mask_token=_a		,     **_a		,     )
  
  				__magic_name__	:	Optional[int]			     =  vocab_file
  				__magic_name__	:	List[str]			     =  False if not self.vocab_file else True
  
  
  
  
  
  
  
  			def     SCREAMING_SNAKE_CASE					(       self		,     _a		,     _a = None	):
  
  				if token_ids_a is None:
  					return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
  				__magic_name__	:	Union[str, Any]			     =  [self.cls_token_id]
  				__magic_name__	:	str			     =  [self.sep_token_id]
  				return cls + token_ids_a + sep + sep + token_ids_a + sep
  
  
  
  
  
  
  
  			def     SCREAMING_SNAKE_CASE					(       self		,     _a		,     _a = None	):
  				__magic_name__	:	List[str]			     =  [self.sep_token_id]
  				__magic_name__	:	List[str]			     =  [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 = None	):
  				if not self.can_save_slow_tokenizer:
  					raise ValueError(
  					    "Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
  					    "tokenizer."	)
  
  				if not os.path.isdir(_a	):
  					logger.error(f'''Vocabulary path ({save_directory}) should be a directory.'''	)
  					return
  				__magic_name__	:	Any			     =  os.path.join(
  				    _a		,     (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]	)
  
  				if os.path.abspath(self.vocab_file	) != os.path.abspath(_a	):
  					copyfile(self.vocab_file		,     _a	)
  
  				return (out_vocab_file,)
  