

"""simple docstring"""




import itertools
import random
import unittest

import numpy as np

from transformers import ASTFeatureExtractor
from transformers.testing_utils import require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


__UpperCAmelCase =				random.Random()

if is_torch_available():
      import torch
def   lowerCAmelCase						(					__UpperCamelCase							,    __UpperCamelCase=1.0							,    __UpperCamelCase=None							,    __UpperCamelCase=None      ):
 '''simple docstring'''





 if rng is None:
  UpperCAmelCase__					:  Tuple					   =					global_rng

 UpperCAmelCase__					:  int					   =					[]
 for batch_idx in range(shape[0]      ):
  values.append([]      )
  for _ in range(shape[1]      ):
   values[-1].append(rng.random() * scale      )

 return values







class      __lowercase			(    unittest.TestCase					):

  def __init__(  self		:			Tuple					,A		:			Union[str, Any]					,A		:			Dict=7					,A		:			List[Any]=400					,A		:			Tuple=2_000					,A		:			Dict=1					,A		:			List[str]=0.0					,A		:			Dict=16_000					,A		:			Union[str, Any]=True					,A		:			Union[str, Any]=True					,):






   '''simple docstring'''
   UpperCAmelCase__					:  int					   =					parent
   UpperCAmelCase__					:  Dict					   =					batch_size
   UpperCAmelCase__					:  str					   =					min_seq_length
   UpperCAmelCase__					:  Dict					   =					max_seq_length
   UpperCAmelCase__					:  List[str]					   =					(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
   UpperCAmelCase__					:  Dict					   =					feature_size
   UpperCAmelCase__					:  str					   =					padding_value
   UpperCAmelCase__					:  Tuple					   =					sampling_rate
   UpperCAmelCase__					:  List[Any]					   =					return_attention_mask
   UpperCAmelCase__					:  Optional[int]					   =					do_normalize

  def    __lowercase       (  self		:			Optional[int]    ):






   '''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,
       "do_normalize": self.do_normalize,
   }




  def    __lowercase       (  self		:			Optional[Any]					,A		:			List[Any]=False					,A		:			int=False    ):






   '''simple docstring'''
   def _flatten(A		:			str    ):
    return list(itertools.chain(*__lowercase    )    )

   if equal_length:
    UpperCAmelCase__					:  Optional[Any]					   =					floats_list((self.batch_size, self.max_seq_length)    )
   else:
    # make sure that inputs increase in size
    UpperCAmelCase__					:  Union[str, 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:
    UpperCAmelCase__					:  Optional[int]					   =					[np.asarray(__lowercase    ) for x in speech_inputs]

   return speech_inputs







@require_torch
@require_torchaudio
class      __lowercase			(    lowercase__	,			unittest.TestCase					):
  snake_case_				       =       ASTFeatureExtractor

  def    __lowercase       (  self		:			str    ):






   '''simple docstring'''
   UpperCAmelCase__					:  Tuple					   =					ASTFeatureExtractionTester(self    )

  def    __lowercase       (  self		:			str    ):






   '''simple docstring'''
   # Tests that all call wrap to encode_plus and batch_encode_plus
   UpperCAmelCase__					:  str					   =					self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()    )
   # create three inputs of length 800, 1000, and 1200
   UpperCAmelCase__					:  int					   =					[floats_list((1, x)    )[0] for x in range(800					,1_400					,200    )]
   UpperCAmelCase__					:  Dict					   =					[np.asarray(__lowercase    ) for speech_input in speech_inputs]

   # Test not batched input
   UpperCAmelCase__					:  Dict					   =					feat_extract(speech_inputs[0]					,return_tensors="""np"""    ).input_values
   UpperCAmelCase__					:  str					   =					feat_extract(np_speech_inputs[0]					,return_tensors="""np"""    ).input_values
   self.assertTrue(np.allclose(__lowercase					,__lowercase					,atol=1e-3    )    )

   # Test batched
   UpperCAmelCase__					:  List[str]					   =					feat_extract(__lowercase					,padding=__lowercase					,return_tensors="""np"""    ).input_values
   UpperCAmelCase__					:  List[str]					   =					feat_extract(__lowercase					,padding=__lowercase					,return_tensors="""np"""    ).input_values
   for enc_seq_a, enc_seq_a in zip(__lowercase					,__lowercase    ):
    self.assertTrue(np.allclose(__lowercase					,__lowercase					,atol=1e-3    )    )

   # Test 2-D numpy arrays are batched.
   UpperCAmelCase__					:  Optional[Any]					   =					[floats_list((1, x)    )[0] for x in (800, 800, 800)]
   UpperCAmelCase__					:  str					   =					np.asarray(__lowercase    )
   UpperCAmelCase__					:  Optional[Any]					   =					feat_extract(__lowercase					,return_tensors="""np"""    ).input_values
   UpperCAmelCase__					:  Union[str, Any]					   =					feat_extract(__lowercase					,return_tensors="""np"""    ).input_values
   for enc_seq_a, enc_seq_a in zip(__lowercase					,__lowercase    ):
    self.assertTrue(np.allclose(__lowercase					,__lowercase					,atol=1e-3    )    )

  @require_torch
  def    __lowercase       (  self		:			Dict    ):






   '''simple docstring'''
   import torch

   UpperCAmelCase__					:  Union[str, Any]					   =					self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()    )
   UpperCAmelCase__					:  Optional[int]					   =					np.random.rand(100    ).astype(np.floataa    )
   UpperCAmelCase__					:  Union[str, Any]					   =					np_speech_inputs.tolist()

   for inputs in [py_speech_inputs, np_speech_inputs]:
    UpperCAmelCase__					:  Union[str, Any]					   =					feature_extractor.pad([{"""input_values""": inputs}]					,return_tensors="""np"""    )
    self.assertTrue(np_processed.input_values.dtype == np.floataa    )
    UpperCAmelCase__					:  Union[str, Any]					   =					feature_extractor.pad([{"""input_values""": inputs}]					,return_tensors="""pt"""    )
    self.assertTrue(pt_processed.input_values.dtype == torch.floataa    )

  def    __lowercase       (  self		:			List[str]					,A		:			List[Any]    ):






   '''simple docstring'''
   from datasets import load_dataset

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

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




  @require_torch
  def    __lowercase       (  self		:			str    ):






   '''simple docstring'''
   # fmt: off
   UpperCAmelCase__					:  Optional[int]					   =					torch.tensor(
       [-0.9_8_9_4, -1.2_7_7_6, -0.9_0_6_6, -1.2_7_7_6, -0.9_3_4_9, -1.2_6_0_9, -1.0_3_8_6, -1.2_7_7_6,
        -1.1_5_6_1, -1.2_7_7_6, -1.2_0_5_2, -1.2_7_2_3, -1.2_1_9_0, -1.2_1_3_2, -1.2_7_7_6, -1.1_1_3_3,
        -1.1_9_5_3, -1.1_3_4_3, -1.1_5_8_4, -1.2_2_0_3, -1.1_7_7_0, -1.2_4_7_4, -1.2_3_8_1, -1.1_9_3_6,
        -0.9_2_7_0, -0.8_3_1_7, -0.8_0_4_9, -0.7_7_0_6, -0.7_5_6_5, -0.7_8_6_9]    )
   # fmt: on

   UpperCAmelCase__					:  Any					   =					self._load_datasamples(1    )
   UpperCAmelCase__					:  Optional[int]					   =					ASTFeatureExtractor()
   UpperCAmelCase__					:  Union[str, Any]					   =					feature_extractor(__lowercase					,return_tensors="""pt"""    ).input_values
   self.assertEquals(input_values.shape					,(1, 1_024, 128)    )
   self.assertTrue(torch.allclose(input_values[0, 0, :30]					,__lowercase					,atol=1e-4    )    )








"""simple docstring"""




from __future__ import annotations
def   lowerCAmelCase						(					__UpperCamelCase							,    __UpperCamelCase							,    __UpperCamelCase      ):
 '''simple docstring'''





 UpperCAmelCase__					:  Dict					   =					list(range(len(__UpperCamelCase      )      )      )
 UpperCAmelCase__					:  Union[str, Any]					   =					[v / w for v, w in zip(__UpperCamelCase							,    __UpperCamelCase      )]
 index.sort(key=lambda __UpperCamelCase      : ratio[i]							,    reverse=__UpperCamelCase      )

 UpperCAmelCase__					:  float					   =					0
 UpperCAmelCase__					:  list[float]					   =					[0] * len(__UpperCamelCase      )
 for i in index:
  if weight[i] <= capacity:
   UpperCAmelCase__					:  Optional[Any]					   =					1
   max_value += value[i]
   capacity -= weight[i]
  else:
   UpperCAmelCase__					:  Union[str, Any]					   =					capacity / weight[i]
   max_value += value[i] * capacity / weight[i]
   break

 return max_value, fractions


if __name__ == "__main__":
      import doctest

      doctest.testmod()





