



import itertools
import random
import unittest

import numpy as np

from transformers import WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST, WavaVecaConfig, WavaVecaFeatureExtractor
from transformers.testing_utils import require_torch, slow

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


__lowerCamelCase     :							List[str]  							=				random.Random()

def    lowerCamelCase_(lowerCamelCase_    ,   lowerCamelCase_=1.0    ,   lowerCamelCase_=None    ,   lowerCamelCase_=None    ) ->       List[Any]:
						if rng is None:
												UpperCAmelCase         =     global_rng

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

						return values





class  __magic_name__   (							unittest.TestCase						):






		def __init__(						self      :     Optional[Any]				,		UpperCamelCase__      :     str				,		UpperCamelCase__      :     Tuple=7				,		UpperCamelCase__      :     Optional[Any]=4_00				,		UpperCamelCase__      :     Optional[Any]=20_00				,		UpperCamelCase__      :     Optional[int]=1				,		UpperCamelCase__      :     Union[str, Any]=0.0				,		UpperCamelCase__      :     str=1_60_00				,		UpperCamelCase__      :     str=True				,		UpperCamelCase__      :     int=True				,		)				->					Optional[int]:
								'''simple docstring'''







								UpperCAmelCase         =     parent
								UpperCAmelCase         =     batch_size
								UpperCAmelCase         =     min_seq_length
								UpperCAmelCase         =     max_seq_length
								UpperCAmelCase         =     (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
								UpperCAmelCase         =     feature_size
								UpperCAmelCase         =     padding_value
								UpperCAmelCase         =     sampling_rate
								UpperCAmelCase         =     return_attention_mask
								UpperCAmelCase         =     do_normalize






		def SCREAMING_SNAKE_CASE_			(						self      :     Union[str, Any]      )				->					Union[str, Any]:
								'''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 SCREAMING_SNAKE_CASE_			(						self      :     str				,		UpperCamelCase__      :     Dict=False				,		UpperCamelCase__      :     Optional[Any]=False      )				->					Any:
								'''simple docstring'''







								def _flatten(UpperCamelCase__      :     List[Any]      ):
														return list(itertools.chain(*UpperCamelCase__      )      )

								if equal_length:
														UpperCAmelCase         =     floats_list((self.batch_size, self.max_seq_length)      )
								else:
														# make sure that inputs increase in size
														UpperCAmelCase         =     [
														    _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         =     [np.asarray(UpperCamelCase__      ) for x in speech_inputs]

								return speech_inputs





class  __magic_name__   (							A__,  unittest.TestCase						):
		lowercase      :       str          =WavaVecaFeatureExtractor






		def SCREAMING_SNAKE_CASE_			(						self      :     int      )				->					Optional[Any]:
								'''simple docstring'''







								UpperCAmelCase         =     WavaVecaFeatureExtractionTester(self      )






		def SCREAMING_SNAKE_CASE_			(						self      :     int				,		UpperCamelCase__      :     int      )				->					int:
								'''simple docstring'''







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






		def SCREAMING_SNAKE_CASE_			(						self      :     Any      )				->					Union[str, Any]:
								'''simple docstring'''







								UpperCAmelCase         =     self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()      )
								# create three inputs of length 800, 1000, and 1200
								UpperCAmelCase         =     [floats_list((1, x)      )[0] for x in range(8_00				,		14_00				,		2_00      )]
								UpperCAmelCase         =     [np.asarray(UpperCamelCase__      ) for speech_input in speech_inputs]

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

								# Test batched
								UpperCAmelCase         =     feat_extract(UpperCamelCase__				,		return_tensors="np"      ).input_values
								UpperCAmelCase         =     feat_extract(UpperCamelCase__				,		return_tensors="np"      ).input_values
								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.
								UpperCAmelCase         =     [floats_list((1, x)      )[0] for x in (8_00, 8_00, 8_00)]
								UpperCAmelCase         =     np.asarray(UpperCamelCase__      )
								UpperCAmelCase         =     feat_extract(UpperCamelCase__				,		return_tensors="np"      ).input_values
								UpperCAmelCase         =     feat_extract(UpperCamelCase__				,		return_tensors="np"      ).input_values
								for enc_seq_a, enc_seq_a in zip(UpperCamelCase__				,		UpperCamelCase__      ):
														self.assertTrue(np.allclose(UpperCamelCase__				,		UpperCamelCase__				,		atol=1e-3      )      )






		def SCREAMING_SNAKE_CASE_			(						self      :     List[str]      )				->					Dict:
								'''simple docstring'''







								UpperCAmelCase         =     self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()      )
								UpperCAmelCase         =     [floats_list((1, x)      )[0] for x in range(8_00				,		14_00				,		2_00      )]

								UpperCAmelCase         =     ["longest", "max_length", "do_not_pad"]
								UpperCAmelCase         =     [None, 16_00, None]
								for max_length, padding in zip(UpperCamelCase__				,		UpperCamelCase__      ):
														UpperCAmelCase         =     feat_extract(UpperCamelCase__				,		padding=UpperCamelCase__				,		max_length=UpperCamelCase__				,		return_tensors="np"      )
														UpperCAmelCase         =     processed.input_values

														self._check_zero_mean_unit_variance(input_values[0][:8_00]      )
														self.assertTrue(input_values[0][8_00:].sum() < 1e-6      )
														self._check_zero_mean_unit_variance(input_values[1][:10_00]      )
														self.assertTrue(input_values[0][10_00:].sum() < 1e-6      )
														self._check_zero_mean_unit_variance(input_values[2][:12_00]      )






		def SCREAMING_SNAKE_CASE_			(						self      :     Optional[Any]      )				->					List[str]:
								'''simple docstring'''







								UpperCAmelCase         =     self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()      )
								UpperCAmelCase         =     range(8_00				,		14_00				,		2_00      )
								UpperCAmelCase         =     [floats_list((1, x)      )[0] for x in lengths]

								UpperCAmelCase         =     ["longest", "max_length", "do_not_pad"]
								UpperCAmelCase         =     [None, 16_00, None]

								for max_length, padding in zip(UpperCamelCase__				,		UpperCamelCase__      ):
														UpperCAmelCase         =     feat_extract(UpperCamelCase__				,		max_length=UpperCamelCase__				,		padding=UpperCamelCase__      )
														UpperCAmelCase         =     processed.input_values

														self._check_zero_mean_unit_variance(input_values[0][:8_00]      )
														self._check_zero_mean_unit_variance(input_values[1][:10_00]      )
														self._check_zero_mean_unit_variance(input_values[2][:12_00]      )






		def SCREAMING_SNAKE_CASE_			(						self      :     str      )				->					Optional[int]:
								'''simple docstring'''







								UpperCAmelCase         =     self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()      )
								UpperCAmelCase         =     [floats_list((1, x)      )[0] for x in range(8_00				,		14_00				,		2_00      )]
								UpperCAmelCase         =     feat_extract(
								    UpperCamelCase__				,		truncation=UpperCamelCase__				,		max_length=10_00				,		padding="max_length"				,		return_tensors="np"      )
								UpperCAmelCase         =     processed.input_values

								self._check_zero_mean_unit_variance(input_values[0, :8_00]      )
								self._check_zero_mean_unit_variance(input_values[1]      )
								self._check_zero_mean_unit_variance(input_values[2]      )






		def SCREAMING_SNAKE_CASE_			(						self      :     Optional[int]      )				->					int:
								'''simple docstring'''







								UpperCAmelCase         =     self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()      )
								UpperCAmelCase         =     [floats_list((1, x)      )[0] for x in range(8_00				,		14_00				,		2_00      )]
								UpperCAmelCase         =     feat_extract(
								    UpperCamelCase__				,		truncation=UpperCamelCase__				,		max_length=10_00				,		padding="longest"				,		return_tensors="np"      )
								UpperCAmelCase         =     processed.input_values

								self._check_zero_mean_unit_variance(input_values[0, :8_00]      )
								self._check_zero_mean_unit_variance(input_values[1, :10_00]      )
								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, 10_00)      )

								UpperCAmelCase         =     [floats_list((1, x)      )[0] for x in range(8_00				,		14_00				,		2_00      )]
								UpperCAmelCase         =     feat_extract(
								    UpperCamelCase__				,		truncation=UpperCamelCase__				,		max_length=20_00				,		padding="longest"				,		return_tensors="np"      )
								UpperCAmelCase         =     processed.input_values

								self._check_zero_mean_unit_variance(input_values[0, :8_00]      )
								self._check_zero_mean_unit_variance(input_values[1, :10_00]      )
								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, 12_00)      )






		@require_torch
		def SCREAMING_SNAKE_CASE_			(						self      :     Optional[Any]      )				->					Union[str, Any]:
								'''simple docstring'''







								import torch

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

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







		@slow
		@require_torch
		def SCREAMING_SNAKE_CASE_			(						self      :     Any      )				->					Union[str, Any]:
								'''simple docstring'''







								for model_id in WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST:
														UpperCAmelCase         =     WavaVecaConfig.from_pretrained(UpperCamelCase__      )
														UpperCAmelCase         =     WavaVecaFeatureExtractor.from_pretrained(UpperCamelCase__      )

														# only "layer" feature extraction norm should make use of
														# attention_mask
														self.assertEqual(feat_extract.return_attention_mask				,		config.feat_extract_norm == "layer"      )








def    lowerCamelCase_(lowerCamelCase_    ,   lowerCamelCase_    ) ->       list[int]:
						UpperCAmelCase         =     int(lowerCamelCase_    )

						# Initialize Result
						UpperCAmelCase         =     []

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

						return answer


# Driver Code
if __name__ == "__main__":
		__lowerCamelCase     :							Optional[int]  							=				[]
		__lowerCamelCase     :							Dict  							=				"0"

		if (
		    input("Do you want to enter your denominations ? (yY/n): ").strip().lower()
		    == "y"
		):
				__lowerCamelCase     :							List[Any]  							=				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()))
				__lowerCamelCase     :							str  							=				input("Enter the change you want to make in Indian Currency: ").strip()
		else:
				# All denominations of Indian Currency if user does not enter
				__lowerCamelCase     :							Optional[int]  							=				[1, 2, 5, 10, 20, 50, 100, 500, 2_000]
				__lowerCamelCase     :							Optional[int]  							=				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}: ''')
				__lowerCamelCase     :							Union[str, Any]  							=				find_minimum_change(denominations, value)
				# Print result
				for i in range(len(answer)):
						print(answer[i], end=" ")



