




"""simple docstring"""


import itertools
import random
import unittest

import numpy as np

from transformers import BatchFeature, SpeechTaFeatureExtractor
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_      			=       random.Random()


def       UpperCAmelCase__ (snake_case__							:      str	,     snake_case__							:      Optional[int]=1.0	,     snake_case__							:      Any=None	,     snake_case__							:      Optional[int]=None					):
  """simple docstring"""

  if rng is None:
    _snake_case							:	Any			=						global_rng

  _snake_case							:	Dict			=						[]
  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       lowercase(  unittest.TestCase				):







 '''simple docstring'''





 def __init__(       self:	Dict,   a_:	str,   a_:	List[str]=7,   a_:	str=400,   a_:	int=2_000,   a_:	Any=1,   a_:	Tuple=0.0,   a_:	Dict=16_000,   a_:	List[Any]=True,   a_:	Optional[Any]=80,   a_:	int=16,   a_:	Any=64,   a_:	int="hann_window",   a_:	int=80,   a_:	List[Any]=7_600,   a_:	Optional[Any]=1E-10,   a_:	Dict=True,   ):




   '''simple docstring'''



   _snake_case							:	Union[str, Any]			=						parent
   _snake_case							:	Any			=						batch_size
   _snake_case							:	List[Any]			=						min_seq_length
   _snake_case							:	str			=						max_seq_length
   _snake_case							:	Optional[int]			=						(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
   _snake_case							:	Dict			=						feature_size
   _snake_case							:	Dict			=						padding_value
   _snake_case							:	Optional[int]			=						sampling_rate
   _snake_case							:	Any			=						do_normalize
   _snake_case							:	List[str]			=						num_mel_bins
   _snake_case							:	Union[str, Any]			=						hop_length
   _snake_case							:	Optional[Any]			=						win_length
   _snake_case							:	Any			=						win_function
   _snake_case							:	Optional[int]			=						fmin
   _snake_case							:	Union[str, Any]			=						fmax
   _snake_case							:	List[Any]			=						mel_floor
   _snake_case							:	Tuple			=						return_attention_mask


 def 				UpperCamelCase_     (       self:	Dict	):




   '''simple docstring'''



   return {
       "feature_size": self.feature_size,
       "padding_value": self.padding_value,
       "sampling_rate": self.sampling_rate,
       "do_normalize": self.do_normalize,
       "num_mel_bins": self.num_mel_bins,
       "hop_length": self.hop_length,
       "win_length": self.win_length,
       "win_function": self.win_function,
       "fmin": self.fmin,
       "fmax": self.fmax,
       "mel_floor": self.mel_floor,
       "return_attention_mask": self.return_attention_mask,
   }


 def 				UpperCamelCase_     (       self:	List[str],   a_:	int=False,   a_:	Any=False	):




   '''simple docstring'''



   def _flatten(a_:	int	):
     return list(itertools.chain(*a_	)	)

   if equal_length:
     _snake_case							:	Dict			=						floats_list((self.batch_size, self.max_seq_length)	)
   else:
     # make sure that inputs increase in size
     _snake_case							:	Dict			=						[
         _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:
     _snake_case							:	Union[str, Any]			=						[np.asarray(a_	) for x in speech_inputs]

   return speech_inputs


 def 				UpperCamelCase_     (       self:	Optional[Any],   a_:	Optional[Any]=False,   a_:	List[str]=False	):




   '''simple docstring'''



   if equal_length:
     _snake_case							:	Optional[Any]			=						[floats_list((self.max_seq_length, self.num_mel_bins)	) for _ in range(self.batch_size	)]
   else:
     # make sure that inputs increase in size
     _snake_case							:	Optional[int]			=						[
         floats_list((x, self.num_mel_bins)	)
         for x in range(self.min_seq_length,   self.max_seq_length,   self.seq_length_diff	)
     ]

   if numpify:
     _snake_case							:	Optional[Any]			=						[np.asarray(a_	) for x in speech_inputs]

   return speech_inputs







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







 '''simple docstring'''





 lowercase__				      =      SpeechTaFeatureExtractor


 def 				UpperCamelCase_     (       self:	Tuple	):




   '''simple docstring'''



   _snake_case							:	Dict			=						SpeechTaFeatureExtractionTester(self	)


 def 				UpperCamelCase_     (       self:	List[str],   a_:	List[str]	):




   '''simple docstring'''



   self.assertTrue(np.all(np.mean(a_,   axis=0	) < 1E-3	)	)
   self.assertTrue(np.all(np.abs(np.var(a_,   axis=0	) - 1	) < 1E-3	)	)


 def 				UpperCamelCase_     (       self:	Dict	):




   '''simple docstring'''



   _snake_case							:	Optional[int]			=						self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()	)
   # create three inputs of length 800, 1000, and 1200
   _snake_case							:	Optional[int]			=						[floats_list((1, x)	)[0] for x in range(800,   1_400,   200	)]
   _snake_case							:	List[str]			=						[np.asarray(a_	) for speech_input in speech_inputs]

   # Test not batched input
   _snake_case							:	Optional[int]			=						feat_extract(speech_inputs[0],   return_tensors="""np"""	).input_values
   _snake_case							:	Dict			=						feat_extract(np_speech_inputs[0],   return_tensors="""np"""	).input_values
   self.assertTrue(np.allclose(a_,   a_,   atol=1E-3	)	)

   # Test batched
   _snake_case							:	str			=						feat_extract(a_,   return_tensors="""np"""	).input_values
   _snake_case							:	Any			=						feat_extract(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 				UpperCamelCase_     (       self:	Tuple	):




   '''simple docstring'''



   _snake_case							:	Optional[Any]			=						self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()	)
   _snake_case							:	List[Any]			=						[floats_list((1, x)	)[0] for x in range(800,   1_400,   200	)]

   _snake_case							:	int			=						["""longest""", """max_length""", """do_not_pad"""]
   _snake_case							:	List[str]			=						[None, 1_600, None]
   for max_length, padding in zip(a_,   a_	):
     _snake_case							:	str			=						feat_extract(a_,   padding=a_,   max_length=a_,   return_tensors="""np"""	)
     _snake_case							:	Dict			=						processed.input_values

     self._check_zero_mean_unit_variance(input_values[0][:800]	)
     self.assertTrue(input_values[0][800:].sum() < 1E-6	)
     self._check_zero_mean_unit_variance(input_values[1][:1_000]	)
     self.assertTrue(input_values[0][1_000:].sum() < 1E-6	)
     self._check_zero_mean_unit_variance(input_values[2][:1_200]	)


 def 				UpperCamelCase_     (       self:	int	):




   '''simple docstring'''



   _snake_case							:	Any			=						self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()	)
   _snake_case							:	Dict			=						range(800,   1_400,   200	)
   _snake_case							:	List[Any]			=						[floats_list((1, x)	)[0] for x in lengths]

   _snake_case							:	Tuple			=						["""longest""", """max_length""", """do_not_pad"""]
   _snake_case							:	Any			=						[None, 1_600, None]

   for max_length, padding in zip(a_,   a_	):
     _snake_case							:	str			=						feat_extract(a_,   max_length=a_,   padding=a_	)
     _snake_case							:	Optional[Any]			=						processed.input_values

     self._check_zero_mean_unit_variance(input_values[0][:800]	)
     self._check_zero_mean_unit_variance(input_values[1][:1_000]	)
     self._check_zero_mean_unit_variance(input_values[2][:1_200]	)


 def 				UpperCamelCase_     (       self:	Dict	):




   '''simple docstring'''



   _snake_case							:	List[Any]			=						self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()	)
   _snake_case							:	Optional[int]			=						[floats_list((1, x)	)[0] for x in range(800,   1_400,   200	)]
   _snake_case							:	List[str]			=						feat_extract(
       a_,   truncation=a_,   max_length=1_000,   padding="""max_length""",   return_tensors="""np"""	)
   _snake_case							:	Dict			=						processed.input_values

   self._check_zero_mean_unit_variance(input_values[0, :800]	)
   self._check_zero_mean_unit_variance(input_values[1]	)
   self._check_zero_mean_unit_variance(input_values[2]	)


 def 				UpperCamelCase_     (       self:	Tuple	):




   '''simple docstring'''



   _snake_case							:	Dict			=						self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()	)
   _snake_case							:	Optional[Any]			=						[floats_list((1, x)	)[0] for x in range(800,   1_400,   200	)]
   _snake_case							:	Tuple			=						feat_extract(
       a_,   truncation=a_,   max_length=1_000,   padding="""longest""",   return_tensors="""np"""	)
   _snake_case							:	List[str]			=						processed.input_values

   self._check_zero_mean_unit_variance(input_values[0, :800]	)
   self._check_zero_mean_unit_variance(input_values[1, :1_000]	)
   self._check_zero_mean_unit_variance(input_values[2]	)

   # make sure that if max_length < longest -> then pad to max_length
   self.assertTrue(input_values.shape == (3, 1_000)	)

   _snake_case							:	str			=						[floats_list((1, x)	)[0] for x in range(800,   1_400,   200	)]
   _snake_case							:	int			=						feat_extract(
       a_,   truncation=a_,   max_length=2_000,   padding="""longest""",   return_tensors="""np"""	)
   _snake_case							:	List[str]			=						processed.input_values

   self._check_zero_mean_unit_variance(input_values[0, :800]	)
   self._check_zero_mean_unit_variance(input_values[1, :1_000]	)
   self._check_zero_mean_unit_variance(input_values[2]	)

   # make sure that if max_length > longest -> then pad to longest
   self.assertTrue(input_values.shape == (3, 1_200)	)


 def 				UpperCamelCase_     (       self:	int	):




   '''simple docstring'''



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

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


 def 				UpperCamelCase_     (       self:	List[str]	):




   '''simple docstring'''



   _snake_case							:	Dict			=						self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()	)
   # create three inputs of length 800, 1000, and 1200
   _snake_case							:	List[Any]			=						[floats_list((1, x)	)[0] for x in range(800,   1_400,   200	)]
   _snake_case							:	Tuple			=						[np.asarray(a_	) for speech_input in speech_inputs]

   # Test feature size
   _snake_case							:	Tuple			=						feature_extractor(audio_target=a_,   padding=a_,   return_tensors="""np"""	).input_values
   self.assertTrue(input_values.ndim == 3	)
   self.assertTrue(input_values.shape[-1] == feature_extractor.num_mel_bins	)

   # Test not batched input
   _snake_case							:	Any			=						feature_extractor(speech_inputs[0],   return_tensors="""np"""	).input_values
   _snake_case							:	Tuple			=						feature_extractor(np_speech_inputs[0],   return_tensors="""np"""	).input_values
   self.assertTrue(np.allclose(a_,   a_,   atol=1E-3	)	)

   # Test batched
   _snake_case							:	Any			=						feature_extractor(a_,   return_tensors="""np"""	).input_values
   _snake_case							:	Optional[Any]			=						feature_extractor(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	)	)

   # Test 2-D numpy arrays are batched.
   _snake_case							:	Tuple			=						[floats_list((1, x)	)[0] for x in (800, 800, 800)]
   _snake_case							:	Dict			=						np.asarray(a_	)
   _snake_case							:	List[Any]			=						feature_extractor(a_,   return_tensors="""np"""	).input_values
   _snake_case							:	Optional[int]			=						feature_extractor(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 				UpperCamelCase_     (       self:	Tuple	):




   '''simple docstring'''



   _snake_case							:	List[Any]			=						self.feat_extract_tester.prepare_inputs_for_target()
   _snake_case							:	Union[str, Any]			=						self.feature_extraction_class(**self.feat_extract_dict	)
   _snake_case							:	Dict			=						feat_extract.model_input_names[0]

   _snake_case							:	List[Any]			=						BatchFeature({input_name: speech_inputs}	)

   self.assertTrue(all(len(a_	) == len(a_	) for x, y in zip(a_,   processed_features[input_name]	)	)	)

   _snake_case							:	Optional[Any]			=						self.feat_extract_tester.prepare_inputs_for_target(equal_length=a_	)
   _snake_case							:	Tuple			=						BatchFeature({input_name: speech_inputs},   tensor_type="""np"""	)

   _snake_case							:	str			=						processed_features[input_name]

   if len(batch_features_input.shape	) < 3:
     _snake_case							:	int			=						batch_features_input[:, :, None]

   self.assertTrue(
       batch_features_input.shape
       == (self.feat_extract_tester.batch_size, len(speech_inputs[0]	), self.feat_extract_tester.num_mel_bins)	)


 @require_torch
 def 				UpperCamelCase_     (       self:	List[str]	):




   '''simple docstring'''



   _snake_case							:	Optional[int]			=						self.feat_extract_tester.prepare_inputs_for_target(equal_length=a_	)
   _snake_case							:	List[Any]			=						self.feature_extraction_class(**self.feat_extract_dict	)
   _snake_case							:	List[Any]			=						feat_extract.model_input_names[0]

   _snake_case							:	Optional[int]			=						BatchFeature({input_name: speech_inputs},   tensor_type="""pt"""	)

   _snake_case							:	List[Any]			=						processed_features[input_name]

   if len(batch_features_input.shape	) < 3:
     _snake_case							:	Tuple			=						batch_features_input[:, :, None]

   self.assertTrue(
       batch_features_input.shape
       == (self.feat_extract_tester.batch_size, len(speech_inputs[0]	), self.feat_extract_tester.num_mel_bins)	)


 @require_torch
 def 				UpperCamelCase_     (       self:	List[Any]	):




   '''simple docstring'''



   _snake_case							:	List[str]			=						self.feature_extraction_class(**self.feat_extract_dict	)
   _snake_case							:	str			=						self.feat_extract_tester.prepare_inputs_for_target()
   _snake_case							:	Dict			=						feat_extract.model_input_names[0]

   _snake_case							:	Optional[Any]			=						BatchFeature({input_name: speech_inputs}	)

   _snake_case							:	str			=						feat_extract.num_mel_bins  # hack!

   _snake_case							:	Any			=						feat_extract.pad(a_,   padding="""longest""",   return_tensors="""np"""	)[input_name]
   _snake_case							:	Optional[Any]			=						feat_extract.pad(a_,   padding="""longest""",   return_tensors="""pt"""	)[input_name]

   self.assertTrue(abs(input_np.astype(np.floataa	).sum() - input_pt.numpy().astype(np.floataa	).sum()	) < 1E-2	)


 def 				UpperCamelCase_     (       self:	int	):




   '''simple docstring'''



   _snake_case							:	Union[str, Any]			=						self.feat_extract_dict
   _snake_case							:	int			=						True
   _snake_case							:	List[str]			=						self.feature_extraction_class(**a_	)
   _snake_case							:	int			=						self.feat_extract_tester.prepare_inputs_for_target()
   _snake_case							:	Any			=						[len(a_	) for x in speech_inputs]
   _snake_case							:	Optional[int]			=						feat_extract.model_input_names[0]

   _snake_case							:	Tuple			=						BatchFeature({input_name: speech_inputs}	)

   _snake_case							:	str			=						feat_extract.num_mel_bins  # hack!

   _snake_case							:	Optional[Any]			=						feat_extract.pad(a_,   padding="""longest""",   return_tensors="""np"""	)
   self.assertIn("""attention_mask""",   a_	)
   self.assertListEqual(list(processed.attention_mask.shape	),   list(processed[input_name].shape[:2]	)	)
   self.assertListEqual(processed.attention_mask.sum(-1	).tolist(),   a_	)


 def 				UpperCamelCase_     (       self:	Dict	):




   '''simple docstring'''



   _snake_case							:	int			=						self.feat_extract_dict
   _snake_case							:	int			=						True
   _snake_case							:	Dict			=						self.feature_extraction_class(**a_	)
   _snake_case							:	Optional[Any]			=						self.feat_extract_tester.prepare_inputs_for_target()
   _snake_case							:	Optional[int]			=						[len(a_	) for x in speech_inputs]
   _snake_case							:	Optional[int]			=						feat_extract.model_input_names[0]

   _snake_case							:	Dict			=						BatchFeature({input_name: speech_inputs}	)
   _snake_case							:	Tuple			=						min(a_	)

   _snake_case							:	Optional[Any]			=						feat_extract.num_mel_bins  # hack!

   _snake_case							:	str			=						feat_extract.pad(
       a_,   padding="""max_length""",   max_length=a_,   truncation=a_,   return_tensors="""np"""	)
   self.assertIn("""attention_mask""",   a_	)
   self.assertListEqual(
       list(processed_pad.attention_mask.shape	),   [processed_pad[input_name].shape[0], max_length]	)
   self.assertListEqual(
       processed_pad.attention_mask[:, :max_length].sum(-1	).tolist(),   [max_length for x in speech_inputs]	)


 def 				UpperCamelCase_     (       self:	Dict,   a_:	Any	):




   '''simple docstring'''



   from datasets import load_dataset

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

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


 def 				UpperCamelCase_     (       self:	Dict	):




   '''simple docstring'''



   _snake_case							:	Union[str, Any]			=						torch.tensor(
       [2.38_04E-03, 2.07_52E-03, 1.98_36E-03, 2.10_57E-03, 1.61_74E-03,
        3.05_18E-04, 9.15_53E-05, 3.35_69E-04, 9.76_56E-04, 1.83_11E-03,
        2.01_42E-03, 2.10_57E-03, 1.73_95E-03, 4.57_76E-04, -3.96_73E-04,
        4.57_76E-04, 1.00_71E-03, 9.15_53E-05, 4.88_28E-04, 1.15_97E-03,
        7.32_42E-04, 9.46_04E-04, 1.80_05E-03, 1.83_11E-03, 8.85_01E-04,
        4.27_25E-04, 4.88_28E-04, 7.32_42E-04, 1.09_86E-03, 2.10_57E-03]	)
   # fmt: on

   _snake_case							:	List[str]			=						self._load_datasamples(1	)
   _snake_case							:	Tuple			=						SpeechTaFeatureExtractor()
   _snake_case							:	Dict			=						feature_extractor(a_,   return_tensors="""pt"""	).input_values
   self.assertEquals(input_values.shape,   (1, 93_680)	)
   self.assertTrue(torch.allclose(input_values[0, :30],   a_,   atol=1E-6	)	)


 def 				UpperCamelCase_     (       self:	Union[str, Any]	):




   '''simple docstring'''



   _snake_case							:	Optional[int]			=						torch.tensor(
       [-2.6_870, -3.0_104, -3.1_356, -3.5_352, -3.0_044, -3.0_353, -3.4_719, -3.6_777,
        -3.1_520, -2.9_435, -2.6_553, -2.8_795, -2.9_944, -2.5_921, -3.0_279, -3.0_386,
        -3.0_864, -3.1_291, -3.2_353, -2.7_444, -2.6_831, -2.7_287, -3.1_761, -3.1_571,
        -3.2_726, -3.0_582, -3.1_007, -3.4_533, -3.4_695, -3.0_998]	)
   # fmt: on

   _snake_case							:	Tuple			=						self._load_datasamples(1	)
   _snake_case							:	Dict			=						SpeechTaFeatureExtractor()
   _snake_case							:	str			=						feature_extractor(audio_target=a_,   return_tensors="""pt"""	).input_values
   self.assertEquals(input_values.shape,   (1, 366, 80)	)
   self.assertTrue(torch.allclose(input_values[0, 0, :30],   a_,   atol=1E-4	)	)








"""simple docstring"""


def       UpperCAmelCase__ (snake_case__							:      list[int]	,     snake_case__							:      str					):
  """simple docstring"""

  _snake_case							:	str			=						int(snake_case__					)

  # Initialize Result
  _snake_case							:	str			=						[]

  # Traverse through all denomination
  for denomination in reversed(snake_case__					):
    # Find denominations
    while int(snake_case__					) >= int(snake_case__					):
      total_value -= int(snake_case__					)
      answer.append(snake_case__					)  # Append the "answers" array

  return answer


# Driver Code
if __name__ == "__main__":
     A_      			=       []
     A_      			=       '''0'''

     if (
         input('''Do you want to enter your denominations ? (yY/n): ''').strip().lower()
         == "y"
     ):
          A_      			=       int(input('''Enter the number of denominations you want to add: ''').strip())

          for i in range(0, n):
               denominations.append(int(input(F'''Denomination {i}: ''').strip()))
          A_      			=       input('''Enter the change you want to make in Indian Currency: ''').strip()
     else:
          # All denominations of Indian Currency if user does not enter
          A_      			=       [1, 2, 5, 10, 20, 50, 1_00, 5_00, 20_00]
          A_      			=       input('''Enter the change you want to make: ''').strip()

     if int(value) == 0 or int(value) < 0:
          print('''The total value cannot be zero or negative.''')

     else:
          print(F'''Following is minimal change for {value}: ''')
          A_      			=       find_minimum_change(denominations, value)
          # Print result
          for i in range(len(answer)):
               print(answer[i], end=''' ''')


