


import itertools
import os
import random
import tempfile
import unittest

import numpy as np
from datasets import load_dataset

from transformers import is_speech_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_speech_available():
   from transformers import WhisperFeatureExtractor

if is_torch_available():
   import torch

UpperCAmelCase      =   random.Random()






def 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE=1.0 , SCREAMING_SNAKE_CASE=None , SCREAMING_SNAKE_CASE=None )->  str:




      """simple docstring"""

      if rng is None:
            snake_case_	=				global_rng

      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		,				_UpperCAmelCase		,				_UpperCAmelCase=7		,				_UpperCAmelCase=4_00		,				_UpperCAmelCase=20_00		,				_UpperCAmelCase=10		,				_UpperCAmelCase=1_60		,				_UpperCAmelCase=8		,				_UpperCAmelCase=0.0		,				_UpperCAmelCase=40_00		,				_UpperCAmelCase=False		,				_UpperCAmelCase=True		,				):
       snake_case_	=				parent
       snake_case_	=				batch_size
       snake_case_	=				min_seq_length
       snake_case_	=				max_seq_length
       snake_case_	=				(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
       snake_case_	=				padding_value
       snake_case_	=				sampling_rate
       snake_case_	=				return_attention_mask
       snake_case_	=				do_normalize
       snake_case_	=				feature_size
       snake_case_	=				chunk_length
       snake_case_	=				hop_length



 def 						UpperCamelCase__					(     self ):
       return {
           "feature_size": self.feature_size,
           "hop_length": self.hop_length,
           "chunk_length": self.chunk_length,
           "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		,				_UpperCAmelCase=False		,				_UpperCAmelCase=False ):
       def _flatten(_UpperCAmelCase ):
             return list(itertools.chain(*_UpperCAmelCase ) )

       if equal_length:
             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
             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:
             snake_case_	=				[np.asarray(_UpperCAmelCase ) for x in speech_inputs]
       return speech_inputs







@require_torch
@require_torchaudio
class lowerCAmelCase_						(			lowerCamelCase__ ,						unittest.TestCase	):

 '''simple docstring'''

 __snake_case												=  WhisperFeatureExtractor if is_speech_available() else None



 def 						UpperCamelCase__					(     self ):
       snake_case_	=				WhisperFeatureExtractionTester(self )



 def 						UpperCamelCase__					(     self ):
       snake_case_	=				self.feature_extraction_class(**self.feat_extract_dict )

       with tempfile.TemporaryDirectory() as tmpdirname:
             snake_case_	=				feat_extract_first.save_pretrained(_UpperCAmelCase )[0]
             check_json_file_has_correct_format(_UpperCAmelCase )
             snake_case_	=				self.feature_extraction_class.from_pretrained(_UpperCAmelCase )

       snake_case_	=				feat_extract_first.to_dict()
       snake_case_	=				feat_extract_second.to_dict()
       snake_case_	=				feat_extract_first.mel_filters
       snake_case_	=				feat_extract_second.mel_filters
       self.assertTrue(np.allclose(_UpperCAmelCase		,				_UpperCAmelCase ) )
       self.assertEqual(_UpperCAmelCase		,				_UpperCAmelCase )



 def 						UpperCamelCase__					(     self ):
       snake_case_	=				self.feature_extraction_class(**self.feat_extract_dict )

       with tempfile.TemporaryDirectory() as tmpdirname:
             snake_case_	=				os.path.join(_UpperCAmelCase		,				'''feat_extract.json''' )
             feat_extract_first.to_json_file(_UpperCAmelCase )
             snake_case_	=				self.feature_extraction_class.from_json_file(_UpperCAmelCase )

       snake_case_	=				feat_extract_first.to_dict()
       snake_case_	=				feat_extract_second.to_dict()
       snake_case_	=				feat_extract_first.mel_filters
       snake_case_	=				feat_extract_second.mel_filters
       self.assertTrue(np.allclose(_UpperCAmelCase		,				_UpperCAmelCase ) )
       self.assertEqual(_UpperCAmelCase		,				_UpperCAmelCase )



 def 						UpperCamelCase__					(     self ):
       # Tests that all call wrap to encode_plus and batch_encode_plus
       snake_case_	=				self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
       # create three inputs of length 800, 1000, and 1200
       snake_case_	=				[floats_list((1, x) )[0] for x in range(8_00		,				14_00		,				2_00 )]
       snake_case_	=				[np.asarray(_UpperCAmelCase ) for speech_input in speech_inputs]

       # Test feature size
       snake_case_	=				feature_extractor(_UpperCAmelCase		,				padding='''max_length'''		,				return_tensors='''np''' ).input_features
       self.assertTrue(input_features.ndim == 3 )
       self.assertTrue(input_features.shape[-1] == feature_extractor.nb_max_frames )
       self.assertTrue(input_features.shape[-2] == feature_extractor.feature_size )

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

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

       # Test 2-D numpy arrays are batched.
       snake_case_	=				[floats_list((1, x) )[0] for x in (8_00, 8_00, 8_00)]
       snake_case_	=				np.asarray(_UpperCAmelCase )
       snake_case_	=				feature_extractor(_UpperCAmelCase		,				return_tensors='''np''' ).input_features
       snake_case_	=				feature_extractor(_UpperCAmelCase		,				return_tensors='''np''' ).input_features
       for enc_seq_a, enc_seq_a in zip(_UpperCAmelCase		,				_UpperCAmelCase ):
             self.assertTrue(np.allclose(_UpperCAmelCase		,				_UpperCAmelCase		,				atol=1E-3 ) )

       # Test truncation required
       snake_case_	=				[floats_list((1, x) )[0] for x in range(2_00		,				(feature_extractor.n_samples + 5_00)		,				2_00 )]
       snake_case_	=				[np.asarray(_UpperCAmelCase ) for speech_input in speech_inputs]

       snake_case_	=				[x[: feature_extractor.n_samples] for x in speech_inputs]
       snake_case_	=				[np.asarray(_UpperCAmelCase ) for speech_input in speech_inputs_truncated]

       snake_case_	=				feature_extractor(_UpperCAmelCase		,				return_tensors='''np''' ).input_features
       snake_case_	=				feature_extractor(_UpperCAmelCase		,				return_tensors='''np''' ).input_features
       for enc_seq_a, enc_seq_a in zip(_UpperCAmelCase		,				_UpperCAmelCase ):
             self.assertTrue(np.allclose(_UpperCAmelCase		,				_UpperCAmelCase		,				atol=1E-3 ) )



 def 						UpperCamelCase__					(     self ):
       import torch

       snake_case_	=				self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
       snake_case_	=				np.random.rand(1_00		,				32 ).astype(np.floataa )
       snake_case_	=				np_speech_inputs.tolist()

       for inputs in [py_speech_inputs, np_speech_inputs]:
             snake_case_	=				feature_extractor.pad([{'''input_features''': inputs}]		,				return_tensors='''np''' )
             self.assertTrue(np_processed.input_features.dtype == np.floataa )
             snake_case_	=				feature_extractor.pad([{'''input_features''': inputs}]		,				return_tensors='''pt''' )
             self.assertTrue(pt_processed.input_features.dtype == torch.floataa )



 def 						UpperCamelCase__					(     self		,				_UpperCAmelCase ):
       snake_case_	=				load_dataset('''hf-internal-testing/librispeech_asr_dummy'''		,				'''clean'''		,				split='''validation''' )
       # automatic decoding with librispeech
       snake_case_	=				ds.sort('''id''' ).select(range(_UpperCAmelCase ) )[:num_samples]['''audio''']

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



 def 						UpperCamelCase__					(     self ):
       # fmt: off
       snake_case_	=				torch.tensor(
           [
               0.1_193, -0.0_946, -0.1_098, -0.0_196, 0.0_225, -0.0_690, -0.1_736, 0.0_951,
               0.0_971, -0.0_817, -0.0_702, 0.0_162, 0.0_260, 0.0_017, -0.0_192, -0.1_678,
               0.0_709, -0.1_867, -0.0_655, -0.0_274, -0.0_234, -0.1_884, -0.0_516, -0.0_554,
               -0.0_274, -0.1_425, -0.1_423, 0.0_837, 0.0_377, -0.0_854
           ] )
       # fmt: on

       snake_case_	=				self._load_datasamples(1 )
       snake_case_	=				WhisperFeatureExtractor()
       snake_case_	=				feature_extractor(_UpperCAmelCase		,				return_tensors='''pt''' ).input_features
       self.assertEqual(input_features.shape		,				(1, 80, 30_00) )
       self.assertTrue(torch.allclose(input_features[0, 0, :30]		,				_UpperCAmelCase		,				atol=1E-4 ) )





 def 						UpperCamelCase__					(     self ):
       snake_case_	=				self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
       snake_case_	=				self._load_datasamples(1 )[0]
       snake_case_	=				((audio - audio.min()) / (audio.max() - audio.min())) * 6_55_35  # Rescale to [0, 65535] to show issue
       snake_case_	=				feat_extract.zero_mean_unit_var_norm([audio]		,				attention_mask=_UpperCAmelCase )[0]

       self.assertTrue(np.all(np.mean(_UpperCAmelCase ) < 1E-3 ) )
       self.assertTrue(np.all(np.abs(np.var(_UpperCAmelCase ) - 1 ) < 1E-3 ) )



def 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )->  list[int]:




      """simple docstring"""

      snake_case_	=				int(SCREAMING_SNAKE_CASE )

      # Initialize Result
      snake_case_	=				[]

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

      return answer


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

   if (
       input("""Do you want to enter your denominations ? (yY/n): """).strip().lower()
       == "y"
   ):
      UpperCAmelCase      =   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      =   input("""Enter the change you want to make in Indian Currency: """).strip()
   else:
      # All denominations of Indian Currency if user does not enter
      UpperCAmelCase      =   [1, 2, 5, 10, 20, 50, 100, 500, 2000]
      UpperCAmelCase      =   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      =   find_minimum_change(denominations, value)
      # Print result
      for i in range(len(answer)):
         print(answer[i], end=""" """)