





import itertools
import os
import random
import tempfile
import unittest

import numpy as np

from transformers import TvltFeatureExtractor, is_datasets_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_torch_available():
						import torch

if is_datasets_available():
						from datasets import load_dataset

_SCREAMING_SNAKE_CASE 		=						random.Random()


def 							SCREAMING_SNAKE_CASE__  (      __a				,	__a=1.0				,	__a=None				,	__a=None				):
	if rng is None:
		snake_case_						:     Union[str, Any]     =		global_rng

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

	return values
class SCREAMING_SNAKE_CASE_  (   unittest.TestCase   ):






						def __init__( self : Optional[int]						,				_A : Any						,				_A : Any=7						,				_A : str=400						,				_A : Tuple=2000						,				_A : List[str]=2048						,				_A : int=128						,				_A : Union[str, Any]=1						,				_A : Union[str, Any]=512						,				_A : Optional[int]=30						,				_A : int=44100						,				)							->      str:







							"""simple docstring"""


							snake_case_						:     Optional[Any]     =		parent
							snake_case_						:     Any     =		batch_size
							snake_case_						:     Union[str, Any]     =		min_seq_length
							snake_case_						:     Any     =		max_seq_length
							snake_case_						:     int     =		(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
							snake_case_						:     int     =		spectrogram_length
							snake_case_						:     int     =		feature_size
							snake_case_						:     List[Any]     =		num_audio_channels
							snake_case_						:     int     =		hop_length
							snake_case_						:     List[str]     =		chunk_length
							snake_case_						:     Optional[Any]     =		sampling_rate






						def UpperCAmelCase_			( self : Union[str, Any]		)							->      Dict:







							"""simple docstring"""


							return {
							    "spectrogram_length": self.spectrogram_length,
							    "feature_size": self.feature_size,
							    "num_audio_channels": self.num_audio_channels,
							    "hop_length": self.hop_length,
							    "chunk_length": self.chunk_length,
							    "sampling_rate": self.sampling_rate,
							}






						def UpperCAmelCase_			( self : Any						,				_A : Optional[Any]=False						,				_A : List[str]=False		)							->      str:







							"""simple docstring"""


							def _flatten(_A : Union[str, Any]		):
								return list(itertools.chain(*_A		)		)

							if equal_length:
								snake_case_						:     Dict     =		[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_						:     int     =		[
								    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
@require_torch
@require_torchaudio
class SCREAMING_SNAKE_CASE_  (   snake_case_    ,			unittest.TestCase   ):
						__magic_name__:     List[str]	 =    TvltFeatureExtractor






						def UpperCAmelCase_			( self : int		)							->      Dict:







							"""simple docstring"""


							snake_case_						:     List[str]     =		TvltFeatureExtractionTester(self		)






						def UpperCAmelCase_			( self : Optional[Any]		)							->      List[Any]:







							"""simple docstring"""


							snake_case_						:     Dict     =		self.feature_extraction_class(**self.feat_extract_dict		)
							self.assertTrue(hasattr(_A						,				'spectrogram_length'		)		)
							self.assertTrue(hasattr(_A						,				'feature_size'		)		)
							self.assertTrue(hasattr(_A						,				'num_audio_channels'		)		)
							self.assertTrue(hasattr(_A						,				'hop_length'		)		)
							self.assertTrue(hasattr(_A						,				'chunk_length'		)		)
							self.assertTrue(hasattr(_A						,				'sampling_rate'		)		)






						def UpperCAmelCase_			( self : str		)							->      Optional[int]:







							"""simple docstring"""


							snake_case_						:     int     =		self.feature_extraction_class(**self.feat_extract_dict		)

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

							snake_case_						:     Tuple     =		feat_extract_first.to_dict()
							snake_case_						:     Dict     =		feat_extract_second.to_dict()
							snake_case_						:     Tuple     =		dict_first.pop('mel_filters'		)
							snake_case_						:     Union[str, Any]     =		dict_second.pop('mel_filters'		)
							self.assertTrue(np.allclose(_A						,				_A		)		)
							self.assertEqual(_A						,				_A		)






						def UpperCAmelCase_			( self : Union[str, Any]		)							->      Tuple:







							"""simple docstring"""


							snake_case_						:     Any     =		self.feature_extraction_class(**self.feat_extract_dict		)

							with tempfile.TemporaryDirectory() as tmpdirname:
								snake_case_						:     Union[str, Any]     =		os.path.join(_A						,				'feat_extract.json'		)
								feat_extract_first.to_json_file(_A		)
								snake_case_						:     Dict     =		self.feature_extraction_class.from_json_file(_A		)

							snake_case_						:     Tuple     =		feat_extract_first.to_dict()
							snake_case_						:     str     =		feat_extract_second.to_dict()
							snake_case_						:     List[Any]     =		dict_first.pop('mel_filters'		)
							snake_case_						:     Any     =		dict_second.pop('mel_filters'		)
							self.assertTrue(np.allclose(_A						,				_A		)		)
							self.assertEqual(_A						,				_A		)






						def UpperCAmelCase_			( self : Any		)							->      Optional[int]:







							"""simple docstring"""


							snake_case_						:     int     =		self.feature_extraction_class(**self.feat_extract_dict		)

							# create three inputs of length 800, 1000, and 1200
							snake_case_						:     List[str]     =		[floats_list((1, x)		)[0] for x in range(800						,				1400						,				200		)]
							snake_case_						:     Tuple     =		[np.asarray(_A		) for speech_input in speech_inputs]

							# Test not batched input
							snake_case_						:     List[Any]     =		feature_extractor(np_speech_inputs[0]						,				return_tensors='np'						,				sampling_rate=44100		).audio_values

							self.assertTrue(encoded_audios.ndim == 4		)
							self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size		)
							self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length		)
							self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels		)

							# Test batched
							snake_case_						:     List[Any]     =		feature_extractor(_A						,				return_tensors='np'						,				sampling_rate=44100		).audio_values

							self.assertTrue(encoded_audios.ndim == 4		)
							self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size		)
							self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length		)
							self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels		)

							# Test audio masking
							snake_case_						:     Optional[Any]     =		feature_extractor(
							    _A						,				return_tensors='np'						,				sampling_rate=44100						,				mask_audio=_A		).audio_values

							self.assertTrue(encoded_audios.ndim == 4		)
							self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size		)
							self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length		)
							self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels		)

							# Test 2-D numpy arrays are batched.
							snake_case_						:     Any     =		[floats_list((1, x)		)[0] for x in (800, 800, 800)]
							snake_case_						:     List[Any]     =		np.asarray(_A		)
							snake_case_						:     List[Any]     =		feature_extractor(_A						,				return_tensors='np'						,				sampling_rate=44100		).audio_values
							self.assertTrue(encoded_audios.ndim == 4		)
							self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size		)
							self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length		)
							self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels		)






						def UpperCAmelCase_			( self : int						,				_A : Dict		)							->      Tuple:







							"""simple docstring"""


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

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






						def UpperCAmelCase_			( self : Any		)							->      Optional[int]:







							"""simple docstring"""


							snake_case_						:     Dict     =		self._load_datasamples(1		)
							snake_case_						:     Tuple     =		TvltFeatureExtractor()
							snake_case_						:     Optional[Any]     =		feature_extractor(_A						,				return_tensors='pt'		).audio_values

							self.assertEquals(audio_values.shape						,				(1, 1, 192, 128)		)

							snake_case_						:     Any     =		torch.tensor([[-0.3_0_3_2, -0.2_7_0_8], [-0.4_4_3_4, -0.4_0_0_7]]		)
							self.assertTrue(torch.allclose(audio_values[0, 0, :2, :2]						,				_A						,				atol=1E-4		)		)













def 							SCREAMING_SNAKE_CASE__  (      __a				,	__a				):
	snake_case_						:     Optional[int]     =		int(__a				)

	# Initialize Result
	snake_case_						:     Tuple     =		[]

	# 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__":
						_SCREAMING_SNAKE_CASE 		=						[]
						_SCREAMING_SNAKE_CASE 		=						"""0"""

						if (
						    input("""Do you want to enter your denominations ? (yY/n): """).strip().lower()
						    == "y"
						):
												_SCREAMING_SNAKE_CASE 		=						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()))
												_SCREAMING_SNAKE_CASE 		=						input("""Enter the change you want to make in Indian Currency: """).strip()
						else:
												# All denominations of Indian Currency if user does not enter
												_SCREAMING_SNAKE_CASE 		=						[1, 2, 5, 10, 20, 50, 1_00, 5_00, 20_00]
												_SCREAMING_SNAKE_CASE 		=						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}: ''')
												_SCREAMING_SNAKE_CASE 		=						find_minimum_change(denominations, value)
												# Print result
												for i in range(len(answer)):
																		print(answer[i], end=""" """)

