
'''simple docstring'''
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      UpperCamelCase_				(      A__  ,      A__=1.0  ,      A__=None  ,      A__=None						):
	if rng is None:
		a_      				=						global_rng

	a_      				=						[]
	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   a_ (      unittest.TestCase					):




			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					,   ):
				a_      				=						parent
				a_      				=						batch_size
				a_      				=						min_seq_length
				a_      				=						max_seq_length
				a_      				=						(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
				a_      				=						padding_value
				a_      				=						sampling_rate
				a_      				=						return_attention_mask
				a_      				=						do_normalize
				a_      				=						feature_size
				a_      				=						chunk_length
				a_      				=						hop_length




			def 					lowerCAmelCase__					(       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 					lowerCAmelCase__					(       self					,   UpperCAmelCase=False					,   UpperCAmelCase=False	):
				def _flatten(UpperCAmelCase	):
					return list(itertools.chain(*UpperCAmelCase	)	)

				if equal_length:
					a_      				=						[floats_list((self.max_seq_length, self.feature_size)	) for _ in range(self.batch_size	)]
				else:
					# make sure that inputs increase in size
					a_      				=						[
					    floats_list((x, self.feature_size)	)
					    for x in range(self.min_seq_length					,   self.max_seq_length					,   self.seq_length_diff	)
					]
				if numpify:
					a_      				=						[np.asarray(UpperCAmelCase	) for x in speech_inputs]
				return speech_inputs







@require_torch
@require_torchaudio
class   a_ (      UpperCamelCase__		,  unittest.TestCase					):
			lowerCamelCase__	:			int       =    WhisperFeatureExtractor if is_speech_available() else None




			def 					lowerCAmelCase__					(       self	):
				a_      				=						WhisperFeatureExtractionTester(self	)




			def 					lowerCAmelCase__					(       self	):
				a_      				=						self.feature_extraction_class(**self.feat_extract_dict	)

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

				a_      				=						feat_extract_first.to_dict()
				a_      				=						feat_extract_second.to_dict()
				a_      				=						feat_extract_first.mel_filters
				a_      				=						feat_extract_second.mel_filters
				self.assertTrue(np.allclose(UpperCAmelCase					,   UpperCAmelCase	)	)
				self.assertEqual(UpperCAmelCase					,   UpperCAmelCase	)




			def 					lowerCAmelCase__					(       self	):
				a_      				=						self.feature_extraction_class(**self.feat_extract_dict	)

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

				a_      				=						feat_extract_first.to_dict()
				a_      				=						feat_extract_second.to_dict()
				a_      				=						feat_extract_first.mel_filters
				a_      				=						feat_extract_second.mel_filters
				self.assertTrue(np.allclose(UpperCAmelCase					,   UpperCAmelCase	)	)
				self.assertEqual(UpperCAmelCase					,   UpperCAmelCase	)




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

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

				# Test batched
				a_      				=						feature_extractor(UpperCAmelCase					,   return_tensors="""np"""	).input_features
				a_      				=						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.
				a_      				=						[floats_list((1, x)	)[0] for x in (8_00, 8_00, 8_00)]
				a_      				=						np.asarray(UpperCAmelCase	)
				a_      				=						feature_extractor(UpperCAmelCase					,   return_tensors="""np"""	).input_features
				a_      				=						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
				a_      				=						[floats_list((1, x)	)[0] for x in range(2_00					,   (feature_extractor.n_samples + 5_00)					,   2_00	)]
				a_      				=						[np.asarray(UpperCAmelCase	) for speech_input in speech_inputs]

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

				a_      				=						feature_extractor(UpperCAmelCase					,   return_tensors="""np"""	).input_features
				a_      				=						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 					lowerCAmelCase__					(       self	):
				import torch

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

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




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

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




			def 					lowerCAmelCase__					(       self	):
				# fmt: off
				a_      				=						torch.tensor(
				    [
				        0.11_93, -0.09_46, -0.10_98, -0.01_96, 0.02_25, -0.06_90, -0.17_36, 0.09_51,
				        0.09_71, -0.08_17, -0.07_02, 0.01_62, 0.02_60, 0.00_17, -0.01_92, -0.16_78,
				        0.07_09, -0.18_67, -0.06_55, -0.02_74, -0.02_34, -0.18_84, -0.05_16, -0.05_54,
				        -0.02_74, -0.14_25, -0.14_23, 0.08_37, 0.03_77, -0.08_54
				    ]	)
				# fmt: on

				a_      				=						self._load_datasamples(1	)
				a_      				=						WhisperFeatureExtractor()
				a_      				=						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 					lowerCAmelCase__					(       self	):
				a_      				=						self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()	)
				a_      				=						self._load_datasamples(1	)[0]
				a_      				=						((audio - audio.min()) / (audio.max() - audio.min())) * 6_55_35  # Rescale to [0, 65535] to show issue
				a_      				=						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	)	)


'''simple docstring'''
import math






def      UpperCamelCase_				(      A__						):
	return math.sqrt(A__						) * math.sqrt(A__						) == num






def      UpperCamelCase_				(      A__						):
	a_      				=						0
	a_      				=						n
	while left <= right:
		a_      				=						(left + right) // 2
		if mid**2 == n:
			return True
		elif mid**2 > n:
			a_      				=						mid - 1
		else:
			a_      				=						mid + 1
	return False


if __name__ == "__main__":
		import doctest

		doctest.testmod()
