

'''simple docstring'''



import itertools
import random
import unittest

import numpy as np

from transformers import is_speech_available
from transformers.testing_utils import require_torch, require_torchaudio

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_speech_available():
  from transformers import SpeechaTextFeatureExtractor

UpperCAmelCase :       Dict			   =     random.Random()







def    a__       (  a__      ,    a__=1.0      ,    a__=None      ,    a__=None						):




     """simple docstring"""


     if rng is None:
          __SCREAMING_SNAKE_CASE     =      global_rng

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

     return values







@require_torch
@require_torchaudio
class 						lowerCAmelCase__   (      unittest.TestCase					):



 """simple docstring"""



 def __init__(		self					:       List[Any]					,							__SCREAMING_SNAKE_CASE					:       Optional[int]					,							__SCREAMING_SNAKE_CASE					:       int=7					,							__SCREAMING_SNAKE_CASE					:       List[str]=400					,							__SCREAMING_SNAKE_CASE					:       Any=2_000					,							__SCREAMING_SNAKE_CASE					:       str=24					,							__SCREAMING_SNAKE_CASE					:       Union[str, Any]=24					,							__SCREAMING_SNAKE_CASE					:       Optional[Any]=0.0					,							__SCREAMING_SNAKE_CASE					:       Optional[Any]=16_000					,							__SCREAMING_SNAKE_CASE					:       List[str]=True					,							__SCREAMING_SNAKE_CASE					:       int=True					,							)  -> Dict:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      parent
      __SCREAMING_SNAKE_CASE     =      batch_size
      __SCREAMING_SNAKE_CASE     =      min_seq_length
      __SCREAMING_SNAKE_CASE     =      max_seq_length
      __SCREAMING_SNAKE_CASE     =      (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
      __SCREAMING_SNAKE_CASE     =      feature_size
      __SCREAMING_SNAKE_CASE     =      num_mel_bins
      __SCREAMING_SNAKE_CASE     =      padding_value
      __SCREAMING_SNAKE_CASE     =      sampling_rate
      __SCREAMING_SNAKE_CASE     =      return_attention_mask
      __SCREAMING_SNAKE_CASE     =      do_normalize
 def       UpperCAmelCase__		(		self					:       Optional[int]			)  -> List[str]:


      """simple docstring"""




      return {
          "feature_size": self.feature_size,
          "num_mel_bins": self.num_mel_bins,
          "padding_value": self.padding_value,
          "sampling_rate": self.sampling_rate,
          "return_attention_mask": self.return_attention_mask,
          "do_normalize": self.do_normalize,
      }


 def       UpperCAmelCase__		(		self					:       Optional[int]					,							__SCREAMING_SNAKE_CASE					:       Any=False					,							__SCREAMING_SNAKE_CASE					:       Optional[Any]=False			)  -> Optional[Any]:


      """simple docstring"""




      def _flatten(__SCREAMING_SNAKE_CASE					:       Optional[int]			):
           return list(itertools.chain(*__SCREAMING_SNAKE_CASE			)			)

      if equal_length:
           __SCREAMING_SNAKE_CASE     =      [floats_list((self.max_seq_length, self.feature_size)			) for _ in range(self.batch_size			)]
      else:
           # make sure that inputs increase in size
           __SCREAMING_SNAKE_CASE     =      [
               floats_list((x, self.feature_size)			)
               for x in range(self.min_seq_length					,							self.max_seq_length					,							self.seq_length_diff			)
           ]
      if numpify:
           __SCREAMING_SNAKE_CASE     =      [np.asarray(__SCREAMING_SNAKE_CASE			) for x in speech_inputs]
      return speech_inputs







@require_torch
@require_torchaudio
class 						lowerCAmelCase__   (      a ,					unittest.TestCase					):



 """simple docstring"""



 lowerCAmelCase__  					=      SpeechaTextFeatureExtractor if is_speech_available() else None
 def       UpperCAmelCase__		(		self					:       Tuple			)  -> str:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      SpeechaTextFeatureExtractionTester(self			)
 def       UpperCAmelCase__		(		self					:       str					,							__SCREAMING_SNAKE_CASE					:       int			)  -> int:


      """simple docstring"""




      self.assertTrue(np.all(np.mean(__SCREAMING_SNAKE_CASE					,							axis=0			) < 1E-3			)			)
      self.assertTrue(np.all(np.abs(np.var(__SCREAMING_SNAKE_CASE					,							axis=0			) - 1			) < 1E-3			)			)
 def       UpperCAmelCase__		(		self					:       Optional[Any]			)  -> Any:


      """simple docstring"""




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

      # Test feature size
      __SCREAMING_SNAKE_CASE     =      feature_extractor(__SCREAMING_SNAKE_CASE					,							padding=__SCREAMING_SNAKE_CASE					,							return_tensors="""np"""			).input_features
      self.assertTrue(input_features.ndim == 3			)
      self.assertTrue(input_features.shape[-1] == feature_extractor.feature_size			)

      # Test not batched input
      __SCREAMING_SNAKE_CASE     =      feature_extractor(speech_inputs[0]					,							return_tensors="""np"""			).input_features
      __SCREAMING_SNAKE_CASE     =      feature_extractor(np_speech_inputs[0]					,							return_tensors="""np"""			).input_features
      self.assertTrue(np.allclose(__SCREAMING_SNAKE_CASE					,							__SCREAMING_SNAKE_CASE					,							atol=1E-3			)			)

      # Test batched
      __SCREAMING_SNAKE_CASE     =      feature_extractor(__SCREAMING_SNAKE_CASE					,							return_tensors="""np"""			).input_features
      __SCREAMING_SNAKE_CASE     =      feature_extractor(__SCREAMING_SNAKE_CASE					,							return_tensors="""np"""			).input_features
      for enc_seq_a, enc_seq_a in zip(__SCREAMING_SNAKE_CASE					,							__SCREAMING_SNAKE_CASE			):
           self.assertTrue(np.allclose(__SCREAMING_SNAKE_CASE					,							__SCREAMING_SNAKE_CASE					,							atol=1E-3			)			)

      # Test 2-D numpy arrays are batched.
      __SCREAMING_SNAKE_CASE     =      [floats_list((1, x)			)[0] for x in (800, 800, 800)]
      __SCREAMING_SNAKE_CASE     =      np.asarray(__SCREAMING_SNAKE_CASE			)
      __SCREAMING_SNAKE_CASE     =      feature_extractor(__SCREAMING_SNAKE_CASE					,							return_tensors="""np"""			).input_features
      __SCREAMING_SNAKE_CASE     =      feature_extractor(__SCREAMING_SNAKE_CASE					,							return_tensors="""np"""			).input_features
      for enc_seq_a, enc_seq_a in zip(__SCREAMING_SNAKE_CASE					,							__SCREAMING_SNAKE_CASE			):
           self.assertTrue(np.allclose(__SCREAMING_SNAKE_CASE					,							__SCREAMING_SNAKE_CASE					,							atol=1E-3			)			)
 def       UpperCAmelCase__		(		self					:       Any			)  -> Optional[Any]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()			)
      __SCREAMING_SNAKE_CASE     =      [floats_list((1, x)			)[0] for x in range(800					,							1_400					,							200			)]

      __SCREAMING_SNAKE_CASE     =      ["""longest""", """max_length""", """do_not_pad"""]
      __SCREAMING_SNAKE_CASE     =      [None, 16, None]
      for max_length, padding in zip(__SCREAMING_SNAKE_CASE					,							__SCREAMING_SNAKE_CASE			):
           __SCREAMING_SNAKE_CASE     =      feature_extractor(
               __SCREAMING_SNAKE_CASE					,							padding=__SCREAMING_SNAKE_CASE					,							max_length=__SCREAMING_SNAKE_CASE					,							return_attention_mask=__SCREAMING_SNAKE_CASE			)
           __SCREAMING_SNAKE_CASE     =      inputs.input_features
           __SCREAMING_SNAKE_CASE     =      inputs.attention_mask
           __SCREAMING_SNAKE_CASE     =      [np.sum(__SCREAMING_SNAKE_CASE			) for x in attention_mask]

           self._check_zero_mean_unit_variance(input_features[0][: fbank_feat_lengths[0]]			)
           self._check_zero_mean_unit_variance(input_features[1][: fbank_feat_lengths[1]]			)
           self._check_zero_mean_unit_variance(input_features[2][: fbank_feat_lengths[2]]			)
 def       UpperCAmelCase__		(		self					:       Union[str, Any]			)  -> Optional[int]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()			)
      __SCREAMING_SNAKE_CASE     =      [floats_list((1, x)			)[0] for x in range(800					,							1_400					,							200			)]

      __SCREAMING_SNAKE_CASE     =      ["""longest""", """max_length""", """do_not_pad"""]
      __SCREAMING_SNAKE_CASE     =      [None, 16, None]
      for max_length, padding in zip(__SCREAMING_SNAKE_CASE					,							__SCREAMING_SNAKE_CASE			):
           __SCREAMING_SNAKE_CASE     =      feature_extractor(
               __SCREAMING_SNAKE_CASE					,							max_length=__SCREAMING_SNAKE_CASE					,							padding=__SCREAMING_SNAKE_CASE					,							return_tensors="""np"""					,							return_attention_mask=__SCREAMING_SNAKE_CASE			)
           __SCREAMING_SNAKE_CASE     =      inputs.input_features
           __SCREAMING_SNAKE_CASE     =      inputs.attention_mask
           __SCREAMING_SNAKE_CASE     =      [np.sum(__SCREAMING_SNAKE_CASE			) for x in attention_mask]

           self._check_zero_mean_unit_variance(input_features[0][: fbank_feat_lengths[0]]			)
           self.assertTrue(input_features[0][fbank_feat_lengths[0] :].sum() < 1E-6			)
           self._check_zero_mean_unit_variance(input_features[1][: fbank_feat_lengths[1]]			)
           self.assertTrue(input_features[0][fbank_feat_lengths[1] :].sum() < 1E-6			)
           self._check_zero_mean_unit_variance(input_features[2][: fbank_feat_lengths[2]]			)
 def       UpperCAmelCase__		(		self					:       Any			)  -> Dict:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()			)
      __SCREAMING_SNAKE_CASE     =      [floats_list((1, x)			)[0] for x in range(800					,							1_400					,							200			)]
      __SCREAMING_SNAKE_CASE     =      feature_extractor(
          __SCREAMING_SNAKE_CASE					,							padding="""max_length"""					,							max_length=4					,							truncation=__SCREAMING_SNAKE_CASE					,							return_tensors="""np"""					,							return_attention_mask=__SCREAMING_SNAKE_CASE					,							)
      __SCREAMING_SNAKE_CASE     =      inputs.input_features
      __SCREAMING_SNAKE_CASE     =      inputs.attention_mask
      __SCREAMING_SNAKE_CASE     =      np.sum(attention_mask == 1					,							axis=1			)

      self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]]			)
      self._check_zero_mean_unit_variance(input_features[1]			)
      self._check_zero_mean_unit_variance(input_features[2]			)
 def       UpperCAmelCase__		(		self					:       Any			)  -> Optional[Any]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()			)
      __SCREAMING_SNAKE_CASE     =      [floats_list((1, x)			)[0] for x in range(800					,							1_400					,							200			)]
      __SCREAMING_SNAKE_CASE     =      feature_extractor(
          __SCREAMING_SNAKE_CASE					,							padding="""longest"""					,							max_length=4					,							truncation=__SCREAMING_SNAKE_CASE					,							return_tensors="""np"""					,							return_attention_mask=__SCREAMING_SNAKE_CASE					,							)
      __SCREAMING_SNAKE_CASE     =      inputs.input_features
      __SCREAMING_SNAKE_CASE     =      inputs.attention_mask
      __SCREAMING_SNAKE_CASE     =      np.sum(attention_mask == 1					,							axis=1			)

      self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]]			)
      self._check_zero_mean_unit_variance(input_features[1, : fbank_feat_lengths[1]]			)
      self._check_zero_mean_unit_variance(input_features[2]			)

      # make sure that if max_length < longest -> then pad to max_length
      self.assertEqual(input_features.shape					,							(3, 4, 24)			)

      __SCREAMING_SNAKE_CASE     =      [floats_list((1, x)			)[0] for x in range(800					,							1_400					,							200			)]
      __SCREAMING_SNAKE_CASE     =      feature_extractor(
          __SCREAMING_SNAKE_CASE					,							padding="""longest"""					,							max_length=16					,							truncation=__SCREAMING_SNAKE_CASE					,							return_tensors="""np"""					,							return_attention_mask=__SCREAMING_SNAKE_CASE					,							)
      __SCREAMING_SNAKE_CASE     =      inputs.input_features
      __SCREAMING_SNAKE_CASE     =      inputs.attention_mask
      __SCREAMING_SNAKE_CASE     =      np.sum(attention_mask == 1					,							axis=1			)

      self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]]			)
      self._check_zero_mean_unit_variance(input_features[1, : fbank_feat_lengths[1]]			)
      self._check_zero_mean_unit_variance(input_features[2]			)

      # make sure that if max_length < longest -> then pad to max_length
      self.assertEqual(input_features.shape					,							(3, 6, 24)			)
 def       UpperCAmelCase__		(		self					:       str			)  -> Dict:


      """simple docstring"""




      import torch

      __SCREAMING_SNAKE_CASE     =      self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()			)
      __SCREAMING_SNAKE_CASE     =      np.random.rand(100					,							32			).astype(np.floataa			)
      __SCREAMING_SNAKE_CASE     =      np_speech_inputs.tolist()

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


      """simple docstring"""




      from datasets import load_dataset

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

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


 def       UpperCAmelCase__		(		self					:       List[Any]			)  -> int:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      np.array([
          -1.5745, -1.7713, -1.7020, -1.6069, -1.2250, -1.1105, -0.9072, -0.8241,
          -1.2310, -0.8098, -0.3320, -0.4101, -0.7985, -0.4996, -0.8213, -0.9128,
          -1.0420, -1.1286, -1.0440, -0.7999, -0.8405, -1.2275, -1.5443, -1.4625,
      ]			)
      # fmt: on

      __SCREAMING_SNAKE_CASE     =      self._load_datasamples(1			)
      __SCREAMING_SNAKE_CASE     =      self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()			)
      __SCREAMING_SNAKE_CASE     =      feature_extractor(__SCREAMING_SNAKE_CASE					,							return_tensors="""pt"""			).input_features
      self.assertEquals(input_features.shape					,							(1, 584, 24)			)
      self.assertTrue(np.allclose(input_features[0, 0, :30]					,							__SCREAMING_SNAKE_CASE					,							atol=1E-4			)			)









'''simple docstring'''



def    a__       (  a__      ,    a__						):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE     =      int(a__						)

     # Initialize Result
     __SCREAMING_SNAKE_CASE     =      []

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

     return answer


# Driver Code
if __name__ == "__main__":
  UpperCAmelCase :       Dict			   =     []
  UpperCAmelCase :       List[str]			   =     '0'

  if (
      input('Do you want to enter your denominations ? (yY/n): ').strip().lower()
      == "y"
  ):
    UpperCAmelCase :       List[str]			   =     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()))
    UpperCAmelCase :       str			   =     input('Enter the change you want to make in Indian Currency: ').strip()
  else:
    # All denominations of Indian Currency if user does not enter
    UpperCAmelCase :       int			   =     [1, 2, 5, 1_0, 2_0, 5_0, 1_0_0, 5_0_0, 2_0_0_0]
    UpperCAmelCase :       Any			   =     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}: """)
    UpperCAmelCase :       Any			   =     find_minimum_change(denominations, value)
    # Print result
    for i in range(len(answer)):
      print(answer[i], end=' ')






