



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_								=     random.Random()



def __lowerCAmelCase (     SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_=1.0						,  SCREAMING_SNAKE_CASE_=None						,  SCREAMING_SNAKE_CASE_=None  ):
			if rng is None:
						lowercase__					  =				global_rng

			lowercase__					  =				[]
			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 				_snake_case			(					unittest.TestCase):




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




				def 							A__      (						self					:						Optional[int]						):
							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 							A__      (						self					:						Optional[int],					__lowercase					:						Optional[int]=False,					__lowercase					:						Union[str, Any]=False						):
							def _flatten(__lowercase					:						Any						):
										return list(itertools.chain(*__lowercase						)						)

							if equal_length:
										lowercase__					  =				[floats_list((self.max_seq_length, self.feature_size)						) for _ in range(self.batch_size						)]
							else:
										# make sure that inputs increase in size
										lowercase__					  =				[
										    floats_list((x, self.feature_size)						)
										    for x in range(self.min_seq_length,					self.max_seq_length,					self.seq_length_diff						)
										]
							if numpify:
										lowercase__					  =				[np.asarray(__lowercase						) for x in speech_inputs]
							return speech_inputs
@require_torch
@require_torchaudio
class 				_snake_case			(					lowercase__    ,	unittest.TestCase):
				UpperCamelCase__				:					str     		=WhisperFeatureExtractor if is_speech_available() else None




				def 							A__      (						self					:						int						):
							lowercase__					  =				WhisperFeatureExtractionTester(self						)




				def 							A__      (						self					:						List[Any]						):
							lowercase__					  =				self.feature_extraction_class(**self.feat_extract_dict						)

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

							lowercase__					  =				feat_extract_first.to_dict()
							lowercase__					  =				feat_extract_second.to_dict()
							lowercase__					  =				feat_extract_first.mel_filters
							lowercase__					  =				feat_extract_second.mel_filters
							self.assertTrue(np.allclose(__lowercase,					__lowercase						)						)
							self.assertEqual(__lowercase,					__lowercase						)




				def 							A__      (						self					:						Any						):
							lowercase__					  =				self.feature_extraction_class(**self.feat_extract_dict						)

							with tempfile.TemporaryDirectory() as tmpdirname:
										lowercase__					  =				os.path.join(__lowercase,					"feat_extract.json"						)
										feat_extract_first.to_json_file(__lowercase						)
										lowercase__					  =				self.feature_extraction_class.from_json_file(__lowercase						)

							lowercase__					  =				feat_extract_first.to_dict()
							lowercase__					  =				feat_extract_second.to_dict()
							lowercase__					  =				feat_extract_first.mel_filters
							lowercase__					  =				feat_extract_second.mel_filters
							self.assertTrue(np.allclose(__lowercase,					__lowercase						)						)
							self.assertEqual(__lowercase,					__lowercase						)




				def 							A__      (						self					:						Optional[Any]						):
							# Tests that all call wrap to encode_plus and batch_encode_plus
							lowercase__					  =				self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()						)
							# create three inputs of length 800, 1000, and 1200
							lowercase__					  =				[floats_list((1, x)						)[0] for x in range(800,					1400,					200						)]
							lowercase__					  =				[np.asarray(__lowercase						) for speech_input in speech_inputs]

							# Test feature size
							lowercase__					  =				feature_extractor(__lowercase,					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
							lowercase__					  =				feature_extractor(speech_inputs[0],					return_tensors="np"						).input_features
							lowercase__					  =				feature_extractor(np_speech_inputs[0],					return_tensors="np"						).input_features
							self.assertTrue(np.allclose(__lowercase,					__lowercase,					atol=1e-3						)						)

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

							# Test 2-D numpy arrays are batched.
							lowercase__					  =				[floats_list((1, x)						)[0] for x in (800, 800, 800)]
							lowercase__					  =				np.asarray(__lowercase						)
							lowercase__					  =				feature_extractor(__lowercase,					return_tensors="np"						).input_features
							lowercase__					  =				feature_extractor(__lowercase,					return_tensors="np"						).input_features
							for enc_seq_a, enc_seq_a in zip(__lowercase,					__lowercase						):
										self.assertTrue(np.allclose(__lowercase,					__lowercase,					atol=1e-3						)						)

							# Test truncation required
							lowercase__					  =				[floats_list((1, x)						)[0] for x in range(200,					(feature_extractor.n_samples + 500),					200						)]
							lowercase__					  =				[np.asarray(__lowercase						) for speech_input in speech_inputs]

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

							lowercase__					  =				feature_extractor(__lowercase,					return_tensors="np"						).input_features
							lowercase__					  =				feature_extractor(__lowercase,					return_tensors="np"						).input_features
							for enc_seq_a, enc_seq_a in zip(__lowercase,					__lowercase						):
										self.assertTrue(np.allclose(__lowercase,					__lowercase,					atol=1e-3						)						)




				def 							A__      (						self					:						Dict						):
							import torch

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

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




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

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




				def 							A__      (						self					:						Optional[int]						):
							# fmt: off
							lowercase__					  =				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

							lowercase__					  =				self._load_datasamples(1						)
							lowercase__					  =				WhisperFeatureExtractor()
							lowercase__					  =				feature_extractor(__lowercase,					return_tensors="pt"						).input_features
							self.assertEqual(input_features.shape,					(1, 80, 3000)						)
							self.assertTrue(torch.allclose(input_features[0, 0, :30],					__lowercase,					atol=1e-4						)						)




				def 							A__      (						self					:						Any						):
							lowercase__					  =				self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()						)
							lowercase__					  =				self._load_datasamples(1						)[0]
							lowercase__					  =				((audio - audio.min()) / (audio.max() - audio.min())) * 6_5535  # Rescale to [0, 65535] to show issue
							lowercase__					  =				feat_extract.zero_mean_unit_var_norm([audio],					attention_mask=__lowercase						)[0]

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









import math



def __lowerCAmelCase (     SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_  ):
			if 0 not in (x, y):
						# We use the relation x^y = y*log10(x), where 10 is the base.
						return y * math.logaa(SCREAMING_SNAKE_CASE_  )
			else:
						if x == 0:  # 0 raised to any number is 0
									return 0
						elif y == 0:
									return 1  # any number raised to 0 is 1
			raise AssertionError("This should never happen"  )


if __name__ == "__main__":  # Main function
							# Read two numbers from input and typecast them to int using map function.
							# Here x is the base and y is the power.
							lowercase_								=     """Enter the base and the power separated by a comma: """
							lowercase_				,    lowercase_								=     map(int, input(prompt).split(""","""))
							lowercase_				,    lowercase_								=     map(int, input(prompt).split(""","""))

							# We find the log of each number, using the function res(), which takes two
							# arguments.
							lowercase_								=     res(xa, ya)
							lowercase_								=     res(xa, ya)

							# We check for the largest number
							if resa > resa:
														print("""Largest number is""", xa, """^""", ya)
							elif resa > resa:
														print("""Largest number is""", xa, """^""", ya)
							else:
														print("""Both are equal""")




