






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

lowercase  :							Tuple			  =    random.Random()

def      lowerCAmelCase__	(    _a       :			Optional[int]  ,      _a       :			Union[str, Any]=1.0  ,      _a       :			Any=None  ,      _a       :			Optional[Any]=None    ):
		if rng is None:
				snake_case_				:							Union[str, Any]						 =       global_rng

		snake_case_				:							str						 =       []
		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 		UpperCAmelCase_						(	unittest.TestCase					):


						'''simple docstring'''




						def __init__(	self		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE=7		,				_SCREAMING_SNAKE_CASE=400		,				_SCREAMING_SNAKE_CASE=2000		,				_SCREAMING_SNAKE_CASE=10		,				_SCREAMING_SNAKE_CASE=160		,				_SCREAMING_SNAKE_CASE=8		,				_SCREAMING_SNAKE_CASE=0.0		,				_SCREAMING_SNAKE_CASE=4000		,				_SCREAMING_SNAKE_CASE=False		,				_SCREAMING_SNAKE_CASE=True		,				) ->				str:
								snake_case_				:							Union[str, Any]						 =       parent
								snake_case_				:							Any						 =       batch_size
								snake_case_				:							str						 =       min_seq_length
								snake_case_				:							Tuple						 =       max_seq_length
								snake_case_				:							List[str]						 =       (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
								snake_case_				:							List[str]						 =       padding_value
								snake_case_				:							List[Any]						 =       sampling_rate
								snake_case_				:							List[Any]						 =       return_attention_mask
								snake_case_				:							Optional[int]						 =       do_normalize
								snake_case_				:							List[Any]						 =       feature_size
								snake_case_				:							List[str]						 =       chunk_length
								snake_case_				:							Dict						 =       hop_length






						def       _lowerCAmelCase       (	self				) ->				List[Any]:
								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       _lowerCAmelCase       (	self		,				_SCREAMING_SNAKE_CASE=False		,				_SCREAMING_SNAKE_CASE=False				) ->				List[str]:
								def _flatten(_SCREAMING_SNAKE_CASE				):
										return list(itertools.chain(*_SCREAMING_SNAKE_CASE				)				)

								if equal_length:
										snake_case_				:							Optional[Any]						 =       [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_				:							List[str]						 =       [
										    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_				:							Tuple						 =       [np.asarray(_SCREAMING_SNAKE_CASE				) for x in speech_inputs]
								return speech_inputs




@require_torch
@require_torchaudio
class 		UpperCAmelCase_						(	SCREAMING_SNAKE_CASE__					,  unittest.TestCase					):


						'''simple docstring'''




						A     :   Optional[Any]						=					WhisperFeatureExtractor if is_speech_available() else None






						def       _lowerCAmelCase       (	self				) ->				Union[str, Any]:
								snake_case_				:							Tuple						 =       WhisperFeatureExtractionTester(self				)






						def       _lowerCAmelCase       (	self				) ->				Any:
								snake_case_				:							List[Any]						 =       self.feature_extraction_class(**self.feat_extract_dict				)

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

								snake_case_				:							List[Any]						 =       feat_extract_first.to_dict()
								snake_case_				:							Optional[int]						 =       feat_extract_second.to_dict()
								snake_case_				:							str						 =       feat_extract_first.mel_filters
								snake_case_				:							List[str]						 =       feat_extract_second.mel_filters
								self.assertTrue(np.allclose(_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE				)				)
								self.assertEqual(_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE				)






						def       _lowerCAmelCase       (	self				) ->				str:
								snake_case_				:							Tuple						 =       self.feature_extraction_class(**self.feat_extract_dict				)

								with tempfile.TemporaryDirectory() as tmpdirname:
										snake_case_				:							Optional[int]						 =       os.path.join(_SCREAMING_SNAKE_CASE		,				"feat_extract.json"				)
										feat_extract_first.to_json_file(_SCREAMING_SNAKE_CASE				)
										snake_case_				:							Union[str, Any]						 =       self.feature_extraction_class.from_json_file(_SCREAMING_SNAKE_CASE				)

								snake_case_				:							Optional[Any]						 =       feat_extract_first.to_dict()
								snake_case_				:							Any						 =       feat_extract_second.to_dict()
								snake_case_				:							Optional[int]						 =       feat_extract_first.mel_filters
								snake_case_				:							Any						 =       feat_extract_second.mel_filters
								self.assertTrue(np.allclose(_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE				)				)
								self.assertEqual(_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE				)






						def       _lowerCAmelCase       (	self				) ->				Tuple:
								# Tests that all call wrap to encode_plus and batch_encode_plus
								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_				:							str						 =       [floats_list((1, x)				)[0] for x in range(800		,				1400		,				200				)]
								snake_case_				:							Any						 =       [np.asarray(_SCREAMING_SNAKE_CASE				) for speech_input in speech_inputs]

								# Test feature size
								snake_case_				:							Dict						 =       feature_extractor(_SCREAMING_SNAKE_CASE		,				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_				:							Tuple						 =       feature_extractor(speech_inputs[0]		,				return_tensors="np"				).input_features
								snake_case_				:							Union[str, Any]						 =       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
								snake_case_				:							Dict						 =       feature_extractor(_SCREAMING_SNAKE_CASE		,				return_tensors="np"				).input_features
								snake_case_				:							List[str]						 =       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.
								snake_case_				:							int						 =       [floats_list((1, x)				)[0] for x in (800, 800, 800)]
								snake_case_				:							Optional[int]						 =       np.asarray(_SCREAMING_SNAKE_CASE				)
								snake_case_				:							int						 =       feature_extractor(_SCREAMING_SNAKE_CASE		,				return_tensors="np"				).input_features
								snake_case_				:							List[Any]						 =       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 truncation required
								snake_case_				:							str						 =       [floats_list((1, x)				)[0] for x in range(200		,				(feature_extractor.n_samples + 500)		,				200				)]
								snake_case_				:							str						 =       [np.asarray(_SCREAMING_SNAKE_CASE				) for speech_input in speech_inputs]

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

								snake_case_				:							Union[str, Any]						 =       feature_extractor(_SCREAMING_SNAKE_CASE		,				return_tensors="np"				).input_features
								snake_case_				:							List[str]						 =       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       _lowerCAmelCase       (	self				) ->				Union[str, Any]:
								import torch

								snake_case_				:							Optional[Any]						 =       self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()				)
								snake_case_				:							str						 =       np.random.rand(100		,				32				).astype(np.floataa				)
								snake_case_				:							Union[str, Any]						 =       np_speech_inputs.tolist()

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






						def       _lowerCAmelCase       (	self		,				_SCREAMING_SNAKE_CASE				) ->				Union[str, Any]:
								snake_case_				:							Optional[Any]						 =       load_dataset("hf-internal-testing/librispeech_asr_dummy"		,				"clean"		,				split="validation"				)
								# automatic decoding with librispeech
								snake_case_				:							int						 =       ds.sort("id"				).select(range(_SCREAMING_SNAKE_CASE				)				)[:num_samples]["audio"]

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






						def       _lowerCAmelCase       (	self				) ->				Any:
								# fmt: off
								snake_case_				:							Any						 =       torch.tensor(
								    [
								        0.1193, -0.0946, -0.1098, -0.0196, 0.0225, -0.0690, -0.1736, 0.0951,
								        0.0971, -0.0817, -0.0702, 0.0162, 0.0260, 0.0017, -0.0192, -0.1678,
								        0.0709, -0.1867, -0.0655, -0.0274, -0.0234, -0.1884, -0.0516, -0.0554,
								        -0.0274, -0.1425, -0.1423, 0.0837, 0.0377, -0.0854
								    ]				)
								# fmt: on

								snake_case_				:							str						 =       self._load_datasamples(1				)
								snake_case_				:							List[Any]						 =       WhisperFeatureExtractor()
								snake_case_				:							Optional[Any]						 =       feature_extractor(_SCREAMING_SNAKE_CASE		,				return_tensors="pt"				).input_features
								self.assertEqual(input_features.shape		,				(1, 80, 3000)				)
								self.assertTrue(torch.allclose(input_features[0, 0, :30]		,				_SCREAMING_SNAKE_CASE		,				atol=1e-4				)				)






						def       _lowerCAmelCase       (	self				) ->				Union[str, Any]:
								snake_case_				:							str						 =       self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()				)
								snake_case_				:							List[Any]						 =       self._load_datasamples(1				)[0]
								snake_case_				:							Optional[Any]						 =       ((audio - audio.min()) / (audio.max() - audio.min())) * 6_5535  # Rescale to [0, 65535] to show issue
								snake_case_				:							int						 =       feat_extract.zero_mean_unit_var_norm([audio]		,				attention_mask=_SCREAMING_SNAKE_CASE				)[0]

								self.assertTrue(np.all(np.mean(_SCREAMING_SNAKE_CASE				) < 1e-3				)				)
								self.assertTrue(np.all(np.abs(np.var(_SCREAMING_SNAKE_CASE				) - 1				) < 1e-3				)				)








def      lowerCAmelCase__	(    _a       :			int    ):
		snake_case_				:							str						 =       1
		for i in range(1  ,      num + 1    ):
				fact *= i
		return fact

def      lowerCAmelCase__	(    _a       :			int    ):
		snake_case_				:							List[str]						 =       0
		while number > 0:
				snake_case_				:							Dict						 =       number % 10
				sum_of_digits += last_digit
				snake_case_				:							List[Any]						 =       number // 10  # Removing the last_digit from the given number
		return sum_of_digits

def      lowerCAmelCase__	(    _a       :			int = 1_00    ):
		snake_case_				:							Optional[Any]						 =       factorial(_a    )
		snake_case_				:							Optional[int]						 =       split_and_add(_a    )
		return result


if __name__ == "__main__":
							print(solution(int(input('''Enter the Number: ''').strip())))
