


'''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


a_  :    Any      =			random.Random()





def __snake_case (				UpperCAmelCase_						:						Tuple ,    UpperCAmelCase_						:						Any=1.0 ,    UpperCAmelCase_						:						Optional[int]=None ,    UpperCAmelCase_						:						List[str]=None		):
 if rng is None:
  lowerCamelCase_								=  global_rng

 lowerCamelCase_								=  []
 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      ,  UpperCamelCase      ,  UpperCamelCase=7      ,  UpperCamelCase=400      ,  UpperCamelCase=2000      ,  UpperCamelCase=1      ,  UpperCamelCase=0.0      ,  UpperCamelCase=2_4000      ,  UpperCamelCase=True      ,  ):







   """simple docstring"""
   lowerCamelCase_								=  parent
   lowerCamelCase_								=  batch_size
   lowerCamelCase_								=  min_seq_length
   lowerCamelCase_								=  max_seq_length
   lowerCamelCase_								=  (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
   lowerCamelCase_								=  feature_size
   lowerCamelCase_								=  padding_value
   lowerCamelCase_								=  sampling_rate
   lowerCamelCase_								=  return_attention_mask






  def   snake_case		(   self					):







   """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   snake_case		(   self      ,  UpperCamelCase=False      ,  UpperCamelCase=False					):







   """simple docstring"""
   def _flatten(UpperCamelCase					):
    return list(itertools.chain(*UpperCamelCase					)					)

   if equal_length:
    lowerCamelCase_								=  floats_list((self.batch_size, self.max_seq_length)					)
   else:
    # make sure that inputs increase in size
    lowerCamelCase_								=  [
        _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_								=  [np.asarray(UpperCamelCase					) for x in audio_inputs]

   return audio_inputs





@require_torch
class 					snake_case   (   lowercase      ,						unittest.TestCase      ):





  """simple docstring"""




  _lowerCamelCase		   =  EncodecFeatureExtractor






  def   snake_case		(   self					):







   """simple docstring"""
   lowerCamelCase_								=  EnCodecFeatureExtractionTester(self					)






  def   snake_case		(   self					):







   """simple docstring"""
   # Tests that all call wrap to encode_plus and batch_encode_plus
   lowerCamelCase_								=  self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()					)
   # create three inputs of length 800, 1000, and 1200
   lowerCamelCase_								=  [floats_list((1, x)					)[0] for x in range(800      ,  1400      ,  200					)]
   lowerCamelCase_								=  [np.asarray(UpperCamelCase					) for audio_input in audio_inputs]

   # Test not batched input
   lowerCamelCase_								=  feat_extract(audio_inputs[0]      ,  return_tensors="np"					).input_values
   lowerCamelCase_								=  feat_extract(np_audio_inputs[0]      ,  return_tensors="np"					).input_values
   self.assertTrue(np.allclose(UpperCamelCase      ,  UpperCamelCase      ,  atol=1e-3					)					)

   # Test batched
   lowerCamelCase_								=  feat_extract(UpperCamelCase      ,  padding=UpperCamelCase      ,  return_tensors="np"					).input_values
   lowerCamelCase_								=  feat_extract(UpperCamelCase      ,  padding=UpperCamelCase      ,  return_tensors="np"					).input_values
   for enc_seq_a, enc_seq_a in zip(UpperCamelCase      ,  UpperCamelCase					):
    self.assertTrue(np.allclose(UpperCamelCase      ,  UpperCamelCase      ,  atol=1e-3					)					)






  def   snake_case		(   self					):







   """simple docstring"""
   lowerCamelCase_								=  self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()					)
   lowerCamelCase_								=  np.random.rand(100					).astype(np.floataa					)
   lowerCamelCase_								=  np_audio_inputs.tolist()

   for inputs in [py_audio_inputs, np_audio_inputs]:
    lowerCamelCase_								=  feature_extractor.pad([{"input_values": inputs}]      ,  return_tensors="np"					)
    self.assertTrue(np_processed.input_values.dtype == np.floataa					)
    lowerCamelCase_								=  feature_extractor.pad([{"input_values": inputs}]      ,  return_tensors="pt"					)
    self.assertTrue(pt_processed.input_values.dtype == torch.floataa					)






  def   snake_case		(   self      ,  UpperCamelCase					):







   """simple docstring"""
   from datasets import load_dataset

   lowerCamelCase_								=  load_dataset("hf-internal-testing/librispeech_asr_dummy"      ,  "clean"      ,  split="validation"					)
   # automatic decoding with librispeech
   lowerCamelCase_								=  ds.sort("id"					).select(range(UpperCamelCase					)					)[:num_samples]["audio"]

   return [x["array"] for x in audio_samples]






  def   snake_case		(   self					):







   """simple docstring"""
   # fmt: off
   lowerCamelCase_								=  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_								=  self._load_datasamples(1					)
   lowerCamelCase_								=  EncodecFeatureExtractor()
   lowerCamelCase_								=  feature_extractor(UpperCamelCase      ,  return_tensors="pt"					).input_values
   self.assertEquals(input_values.shape      ,  (1, 1, 9_3680)					)
   self.assertTrue(torch.allclose(input_values[0, 0, :30]      ,  UpperCamelCase      ,  atol=1e-6					)					)






  def   snake_case		(   self					):







   """simple docstring"""
   # fmt: off
   lowerCamelCase_								=  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_								=  self._load_datasamples(1					)
   lowerCamelCase_								=  [np.tile(input_audio[0][None]      ,  reps=(2, 1)					)]
   input_audio[0][1] *= 0.5
   lowerCamelCase_								=  EncodecFeatureExtractor(feature_size=2					)
   lowerCamelCase_								=  feature_extractor(UpperCamelCase      ,  return_tensors="pt"					).input_values
   self.assertEquals(input_values.shape      ,  (1, 2, 9_3680)					)
   self.assertTrue(torch.allclose(input_values[0, 0, :30]      ,  UpperCamelCase      ,  atol=1e-6					)					)
   self.assertTrue(torch.allclose(input_values[0, 1, :30]      ,  EXPECTED_INPUT_VALUES * 0.5      ,  atol=1e-6					)					)







  def   snake_case		(   self					):







   """simple docstring"""
   lowerCamelCase_								=  self._load_datasamples(2					)
   # would be easier if the stride was like
   lowerCamelCase_								=  EncodecFeatureExtractor(feature_size=1      ,  chunk_length_s=1      ,  overlap=0.01					)

   # pad and trunc raise an error ?
   with self.assertRaisesRegex(
       UpperCamelCase      ,  "^Both padding and truncation were set. Make sure you only set one.$"      ,  ):
    lowerCamelCase_								=  feature_extractor(
        UpperCamelCase      ,  padding="max_length"      ,  truncation=UpperCamelCase      ,  return_tensors="pt"					).input_values

   # truncate to chunk
   lowerCamelCase_								=  feature_extractor(UpperCamelCase      ,  truncation=UpperCamelCase      ,  return_tensors="pt"					).input_values
   self.assertEquals(truncated_outputs.shape      ,  (2, 1, 7_1520)					)  # 2 chunks

   # force truncate to max_length
   lowerCamelCase_								=  feature_extractor(
       UpperCamelCase      ,  truncation=UpperCamelCase      ,  max_length=4_8000      ,  return_tensors="pt"					).input_values
   self.assertEquals(truncated_outputs.shape      ,  (2, 1, 4_8000)					)

   # pad to chunk
   lowerCamelCase_								=  feature_extractor(UpperCamelCase      ,  padding=UpperCamelCase      ,  return_tensors="pt"					).input_values
   self.assertEquals(padded_outputs.shape      ,  (2, 1, 9_5280)					)

   # pad to chunk
   lowerCamelCase_								=  feature_extractor(UpperCamelCase      ,  return_tensors="pt"					).input_values
   self.assertEquals(truncated_outputs.shape      ,  (2, 1, 9_5280)					)

   # force pad to max length
   lowerCamelCase_								=  feature_extractor(
       UpperCamelCase      ,  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(
       UpperCamelCase      ,  "^Unable to create tensor, you should probably activate padding with 'padding=True' to have batched tensors with the same length.$"      ,  ):
    lowerCamelCase_								=  feature_extractor(UpperCamelCase      ,  padding=UpperCamelCase      ,  return_tensors="pt"					).input_values

   lowerCamelCase_								=  feature_extractor(input_audio[0]      ,  padding=UpperCamelCase      ,  return_tensors="pt"					).input_values
   self.assertEquals(truncated_outputs.shape      ,  (1, 1, 9_3680)					)

   # no pad if no chunk_length_s
   lowerCamelCase_								=  None
   with self.assertRaisesRegex(
       UpperCamelCase      ,  "^Unable to create tensor, you should probably activate padding with 'padding=True' to have batched tensors with the same length.$"      ,  ):
    lowerCamelCase_								=  feature_extractor(UpperCamelCase      ,  padding=UpperCamelCase      ,  return_tensors="pt"					).input_values

   lowerCamelCase_								=  feature_extractor(input_audio[0]      ,  padding=UpperCamelCase      ,  return_tensors="pt"					).input_values
   self.assertEquals(truncated_outputs.shape      ,  (1, 1, 9_3680)					)

   # no pad if no overlap
   lowerCamelCase_								=  2
   lowerCamelCase_								=  None
   with self.assertRaisesRegex(
       UpperCamelCase      ,  "^Unable to create tensor, you should probably activate padding with 'padding=True' to have batched tensors with the same length.$"      ,  ):
    lowerCamelCase_								=  feature_extractor(UpperCamelCase      ,  padding=UpperCamelCase      ,  return_tensors="pt"					).input_values

   lowerCamelCase_								=  feature_extractor(input_audio[0]      ,  padding=UpperCamelCase      ,  return_tensors="pt"					).input_values
   self.assertEquals(truncated_outputs.shape      ,  (1, 1, 9_3680)					)







'''simple docstring'''

def __snake_case (				UpperCAmelCase_						:						float		):
 return -num if num < 0 else num





def __snake_case (				UpperCAmelCase_						:						list[int]		):
 if len(UpperCAmelCase_		) == 0:
  raise ValueError("abs_min() arg is an empty sequence"		)
 lowerCamelCase_								=  x[0]
 for i in x:
  if abs_val(UpperCAmelCase_		) < abs_val(UpperCAmelCase_		):
   lowerCamelCase_								=  i
 return j





def __snake_case (				UpperCAmelCase_						:						list[int]		):
 if len(UpperCAmelCase_		) == 0:
  raise ValueError("abs_max() arg is an empty sequence"		)
 lowerCamelCase_								=  x[0]
 for i in x:
  if abs(UpperCAmelCase_		) > abs(UpperCAmelCase_		):
   lowerCamelCase_								=  i
 return j





def __snake_case (				UpperCAmelCase_						:						list[int]		):
 if len(UpperCAmelCase_		) == 0:
  raise ValueError("abs_max_sort() arg is an empty sequence"		)
 return sorted(UpperCAmelCase_ ,    key=UpperCAmelCase_		)[-1]





def __snake_case (				):
 assert abs_val(0		) == 0
 assert abs_val(34		) == 34
 assert abs_val(-100000000000		) == 100000000000

 lowerCamelCase_								=  [-3, -1, 2, -11]
 assert abs_max(UpperCAmelCase_		) == -11
 assert abs_max_sort(UpperCAmelCase_		) == -11
 assert abs_min(UpperCAmelCase_		) == -1


if __name__ == "__main__":
     import doctest

     doctest.testmod()

     test_abs_val()
     print(abs_val(-34))  # --> 34



