




"""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

a_				:    Union[str, Any]										=     random.Random()





def 						UpperCAmelCase				(			A__:    int							,				A__:    List[str]=1.0							,				A__:    Optional[Any]=None							,				A__:    Any=None     )  ->     Any:
			if rng is None:
						__lowerCamelCase					:	Tuple	       =				global_rng

			__lowerCamelCase					:	Dict	       =				[]
			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 __lowercase(      unittest.TestCase				):




					'''simple docstring'''


					def __init__(    self						,	__a						,	__a=7						,	__a=400						,	__a=2000						,	__a=10						,	__a=160						,	__a=8						,	__a=0.0						,	__a=4000						,	__a=False						,	__a=True						,	):
								__lowerCamelCase					:	Tuple	       =				parent
								__lowerCamelCase					:	Dict	       =				batch_size
								__lowerCamelCase					:	Dict	       =				min_seq_length
								__lowerCamelCase					:	Any	       =				max_seq_length
								__lowerCamelCase					:	Optional[int]	       =				(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
								__lowerCamelCase					:	Dict	       =				padding_value
								__lowerCamelCase					:	Optional[Any]	       =				sampling_rate
								__lowerCamelCase					:	List[str]	       =				return_attention_mask
								__lowerCamelCase					:	List[Any]	       =				do_normalize
								__lowerCamelCase					:	List[Any]	       =				feature_size
								__lowerCamelCase					:	List[str]	       =				chunk_length
								__lowerCamelCase					:	List[str]	       =				hop_length
					def     snake_case_				(    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     snake_case_				(    self						,	__a=False						,	__a=False     ):
								def _flatten(__a     ):
											return list(itertools.chain(*__a     )     )

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



@require_torch
@require_torchaudio
class __lowercase(      lowercase__	,	unittest.TestCase				):




					'''simple docstring'''


					__a						:						List[str]     		= WhisperFeatureExtractor if is_speech_available() else None
					def     snake_case_				(    self     ):
								__lowerCamelCase					:	Optional[Any]	       =				WhisperFeatureExtractionTester(self     )
					def     snake_case_				(    self     ):
								__lowerCamelCase					:	Optional[Any]	       =				self.feature_extraction_class(**self.feat_extract_dict     )

								with tempfile.TemporaryDirectory() as tmpdirname:
											__lowerCamelCase					:	int	       =				feat_extract_first.save_pretrained(__a     )[0]
											check_json_file_has_correct_format(__a     )
											__lowerCamelCase					:	int	       =				self.feature_extraction_class.from_pretrained(__a     )

								__lowerCamelCase					:	int	       =				feat_extract_first.to_dict()
								__lowerCamelCase					:	str	       =				feat_extract_second.to_dict()
								__lowerCamelCase					:	str	       =				feat_extract_first.mel_filters
								__lowerCamelCase					:	List[Any]	       =				feat_extract_second.mel_filters
								self.assertTrue(np.allclose(__a						,	__a     )     )
								self.assertEqual(__a						,	__a     )
					def     snake_case_				(    self     ):
								__lowerCamelCase					:	List[str]	       =				self.feature_extraction_class(**self.feat_extract_dict     )

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

								__lowerCamelCase					:	Union[str, Any]	       =				feat_extract_first.to_dict()
								__lowerCamelCase					:	Union[str, Any]	       =				feat_extract_second.to_dict()
								__lowerCamelCase					:	Dict	       =				feat_extract_first.mel_filters
								__lowerCamelCase					:	Union[str, Any]	       =				feat_extract_second.mel_filters
								self.assertTrue(np.allclose(__a						,	__a     )     )
								self.assertEqual(__a						,	__a     )
					def     snake_case_				(    self     ):
								# Tests that all call wrap to encode_plus and batch_encode_plus
								__lowerCamelCase					:	Optional[Any]	       =				self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()     )
								# create three inputs of length 800, 1000, and 1200
								__lowerCamelCase					:	Union[str, Any]	       =				[floats_list((1, x)     )[0] for x in range(800						,	1400						,	200     )]
								__lowerCamelCase					:	List[Any]	       =				[np.asarray(__a     ) for speech_input in speech_inputs]

								# Test feature size
								__lowerCamelCase					:	Tuple	       =				feature_extractor(__a						,	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
								__lowerCamelCase					:	int	       =				feature_extractor(speech_inputs[0]						,	return_tensors='np'     ).input_features
								__lowerCamelCase					:	Optional[int]	       =				feature_extractor(np_speech_inputs[0]						,	return_tensors='np'     ).input_features
								self.assertTrue(np.allclose(__a						,	__a						,	atol=1E-3     )     )

								# Test batched
								__lowerCamelCase					:	List[str]	       =				feature_extractor(__a						,	return_tensors='np'     ).input_features
								__lowerCamelCase					:	Any	       =				feature_extractor(__a						,	return_tensors='np'     ).input_features
								for enc_seq_a, enc_seq_a in zip(__a						,	__a     ):
											self.assertTrue(np.allclose(__a						,	__a						,	atol=1E-3     )     )

								# Test 2-D numpy arrays are batched.
								__lowerCamelCase					:	Optional[int]	       =				[floats_list((1, x)     )[0] for x in (800, 800, 800)]
								__lowerCamelCase					:	Tuple	       =				np.asarray(__a     )
								__lowerCamelCase					:	Optional[int]	       =				feature_extractor(__a						,	return_tensors='np'     ).input_features
								__lowerCamelCase					:	Optional[int]	       =				feature_extractor(__a						,	return_tensors='np'     ).input_features
								for enc_seq_a, enc_seq_a in zip(__a						,	__a     ):
											self.assertTrue(np.allclose(__a						,	__a						,	atol=1E-3     )     )

								# Test truncation required
								__lowerCamelCase					:	int	       =				[floats_list((1, x)     )[0] for x in range(200						,	(feature_extractor.n_samples + 500)						,	200     )]
								__lowerCamelCase					:	List[Any]	       =				[np.asarray(__a     ) for speech_input in speech_inputs]

								__lowerCamelCase					:	Union[str, Any]	       =				[x[: feature_extractor.n_samples] for x in speech_inputs]
								__lowerCamelCase					:	Optional[Any]	       =				[np.asarray(__a     ) for speech_input in speech_inputs_truncated]

								__lowerCamelCase					:	Optional[Any]	       =				feature_extractor(__a						,	return_tensors='np'     ).input_features
								__lowerCamelCase					:	Dict	       =				feature_extractor(__a						,	return_tensors='np'     ).input_features
								for enc_seq_a, enc_seq_a in zip(__a						,	__a     ):
											self.assertTrue(np.allclose(__a						,	__a						,	atol=1E-3     )     )
					def     snake_case_				(    self     ):
								import torch

								__lowerCamelCase					:	int	       =				self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()     )
								__lowerCamelCase					:	int	       =				np.random.rand(100						,	32     ).astype(np.floataa     )
								__lowerCamelCase					:	Any	       =				np_speech_inputs.tolist()

								for inputs in [py_speech_inputs, np_speech_inputs]:
											__lowerCamelCase					:	str	       =				feature_extractor.pad([{'input_features': inputs}]						,	return_tensors='np'     )
											self.assertTrue(np_processed.input_features.dtype == np.floataa     )
											__lowerCamelCase					:	str	       =				feature_extractor.pad([{'input_features': inputs}]						,	return_tensors='pt'     )
											self.assertTrue(pt_processed.input_features.dtype == torch.floataa     )
					def     snake_case_				(    self						,	__a     ):
								__lowerCamelCase					:	Optional[Any]	       =				load_dataset('hf-internal-testing/librispeech_asr_dummy'						,	'clean'						,	split='validation'     )
								# automatic decoding with librispeech
								__lowerCamelCase					:	int	       =				ds.sort('id'     ).select(range(__a     )     )[:num_samples]['audio']

								return [x["array"] for x in speech_samples]
					def     snake_case_				(    self     ):
								# fmt: off
								__lowerCamelCase					:	Optional[Any]	       =				torch.tensor(
								    [
								        0.1_193, -0.0_946, -0.1_098, -0.0_196, 0.0_225, -0.0_690, -0.1_736, 0.0_951,
								        0.0_971, -0.0_817, -0.0_702, 0.0_162, 0.0_260, 0.0_017, -0.0_192, -0.1_678,
								        0.0_709, -0.1_867, -0.0_655, -0.0_274, -0.0_234, -0.1_884, -0.0_516, -0.0_554,
								        -0.0_274, -0.1_425, -0.1_423, 0.0_837, 0.0_377, -0.0_854
								    ]     )
								# fmt: on

								__lowerCamelCase					:	Union[str, Any]	       =				self._load_datasamples(1     )
								__lowerCamelCase					:	Union[str, Any]	       =				WhisperFeatureExtractor()
								__lowerCamelCase					:	Tuple	       =				feature_extractor(__a						,	return_tensors='pt'     ).input_features
								self.assertEqual(input_features.shape						,	(1, 80, 3000)     )
								self.assertTrue(torch.allclose(input_features[0, 0, :30]						,	__a						,	atol=1E-4     )     )





					def     snake_case_				(    self     ):
								__lowerCamelCase					:	Optional[Any]	       =				self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()     )
								__lowerCamelCase					:	Dict	       =				self._load_datasamples(1     )[0]
								__lowerCamelCase					:	int	       =				((audio - audio.min()) / (audio.max() - audio.min())) * 65535  # Rescale to [0, 65535] to show issue
								__lowerCamelCase					:	Optional[Any]	       =				feat_extract.zero_mean_unit_var_norm([audio]						,	attention_mask=__a     )[0]

								self.assertTrue(np.all(np.mean(__a     ) < 1E-3     )     )
								self.assertTrue(np.all(np.abs(np.var(__a     ) - 1     ) < 1E-3     )     )









"""simple docstring"""
from cva import destroyAllWindows, imread, imshow, waitKey





def 						UpperCAmelCase				(			A__:    Tuple     )  ->     Union[str, Any]:
			# getting number of pixels in the image
			__lowerCamelCase       ,      __lowerCamelCase					:	Optional[Any]	       =				img.shape[0], img.shape[1]

			# converting each pixel's color to its negative
			for i in range(A__     ):
						for j in range(A__     ):
									__lowerCamelCase					:	Optional[Any]	       =				[255, 255, 255] - img[i][j]

			return img


if __name__ == "__main__":
					# read original image
					a_				:    Dict										=     imread('''image_data/lena.jpg''', 1)

					# convert to its negative
					a_				:    str										=     convert_to_negative(img)

					# show result image
					imshow('''negative of original image''', img)
					waitKey(0)
					destroyAllWindows()



