
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

lowerCamelCase_						: List[Any]   =  random.Random()







def        A__					(						lowerCamelCase     ,    lowerCamelCase=1.0     ,    lowerCamelCase=None     ,    lowerCamelCase=None						)  ->	int:
				if rng is None:
								UpperCamelCase_:							int	=						global_rng

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

			def       lowerCAmelCase__	(       self :       Union[str, 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 :       str						,	snake_case_ :       Union[str, Any]=False						,	snake_case_ :       Tuple=False      ):
							def _flatten(snake_case_ :       Union[str, Any]      ):
											return list(itertools.chain(*snake_case_      )      )

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




@require_torch
@require_torchaudio
class        _UpperCamelCase				(					_A     ,       unittest.TestCase  ):






			'''simple docstring'''

			__UpperCamelCase      :						List[str]					      = WhisperFeatureExtractor if is_speech_available() else None

			def       lowerCAmelCase__	(       self :       Optional[int]      ):
							UpperCamelCase_:							List[str]	=						WhisperFeatureExtractionTester(self      )

			def       lowerCAmelCase__	(       self :       Any      ):
							UpperCamelCase_:							str	=						self.feature_extraction_class(**self.feat_extract_dict      )

							with tempfile.TemporaryDirectory() as tmpdirname:
											UpperCamelCase_:							Dict	=						feat_extract_first.save_pretrained(snake_case_      )[0]
											check_json_file_has_correct_format(snake_case_      )
											UpperCamelCase_:							str	=						self.feature_extraction_class.from_pretrained(snake_case_      )

							UpperCamelCase_:							Union[str, Any]	=						feat_extract_first.to_dict()
							UpperCamelCase_:							Optional[Any]	=						feat_extract_second.to_dict()
							UpperCamelCase_:							Tuple	=						feat_extract_first.mel_filters
							UpperCamelCase_:							Any	=						feat_extract_second.mel_filters
							self.assertTrue(np.allclose(snake_case_						,	snake_case_      )      )
							self.assertEqual(snake_case_						,	snake_case_      )

			def       lowerCAmelCase__	(       self :       Any      ):
							UpperCamelCase_:							Any	=						self.feature_extraction_class(**self.feat_extract_dict      )

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

							UpperCamelCase_:							int	=						feat_extract_first.to_dict()
							UpperCamelCase_:							Any	=						feat_extract_second.to_dict()
							UpperCamelCase_:							Union[str, Any]	=						feat_extract_first.mel_filters
							UpperCamelCase_:							Tuple	=						feat_extract_second.mel_filters
							self.assertTrue(np.allclose(snake_case_						,	snake_case_      )      )
							self.assertEqual(snake_case_						,	snake_case_      )

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

							# Test feature size
							UpperCamelCase_:							List[str]	=						feature_extractor(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
							UpperCamelCase_:							Dict	=						feature_extractor(speech_inputs[0]						,	return_tensors="""np"""      ).input_features
							UpperCamelCase_:							Optional[int]	=						feature_extractor(np_speech_inputs[0]						,	return_tensors="""np"""      ).input_features
							self.assertTrue(np.allclose(snake_case_						,	snake_case_						,	atol=1e-3      )      )

							# Test batched
							UpperCamelCase_:							Union[str, Any]	=						feature_extractor(snake_case_						,	return_tensors="""np"""      ).input_features
							UpperCamelCase_:							Tuple	=						feature_extractor(snake_case_						,	return_tensors="""np"""      ).input_features
							for enc_seq_a, enc_seq_a in zip(snake_case_						,	snake_case_      ):
											self.assertTrue(np.allclose(snake_case_						,	snake_case_						,	atol=1e-3      )      )

							# Test 2-D numpy arrays are batched.
							UpperCamelCase_:							List[Any]	=						[floats_list((1, x)      )[0] for x in (800, 800, 800)]
							UpperCamelCase_:							Union[str, Any]	=						np.asarray(snake_case_      )
							UpperCamelCase_:							Optional[int]	=						feature_extractor(snake_case_						,	return_tensors="""np"""      ).input_features
							UpperCamelCase_:							int	=						feature_extractor(snake_case_						,	return_tensors="""np"""      ).input_features
							for enc_seq_a, enc_seq_a in zip(snake_case_						,	snake_case_      ):
											self.assertTrue(np.allclose(snake_case_						,	snake_case_						,	atol=1e-3      )      )

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

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

							UpperCamelCase_:							Optional[int]	=						feature_extractor(snake_case_						,	return_tensors="""np"""      ).input_features
							UpperCamelCase_:							str	=						feature_extractor(snake_case_						,	return_tensors="""np"""      ).input_features
							for enc_seq_a, enc_seq_a in zip(snake_case_						,	snake_case_      ):
											self.assertTrue(np.allclose(snake_case_						,	snake_case_						,	atol=1e-3      )      )

			def       lowerCAmelCase__	(       self :       int      ):
							import torch

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

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

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

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

			def       lowerCAmelCase__	(       self :       Tuple      ):
							# fmt: off
							UpperCamelCase_:							List[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

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




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

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








import cva
import numpy as np

class        _UpperCamelCase				:






			'''simple docstring'''

			def __init__(       self :       Dict						,	snake_case_ :       float						,	snake_case_ :       int      ):

							if k in (0.04, 0.06):
											UpperCamelCase_:							Union[str, Any]	=						k
											UpperCamelCase_:							Union[str, Any]	=						window_size
							else:
											raise ValueError("""invalid k value"""      )

			def __str__(       self :       int      ):
							return str(self.k      )




			def       lowerCAmelCase__	(       self :       Optional[Any]						,	snake_case_ :       str      ):
							UpperCamelCase_:							int	=						cva.imread(snake_case_						,	0      )
							UpperCamelCase_,   UpperCamelCase_:							List[Any]	=						img.shape
							UpperCamelCase_:							list[list[int]]	=						[]
							UpperCamelCase_:							int	=						img.copy()
							UpperCamelCase_:							Any	=						cva.cvtColor(snake_case_						,	cva.COLOR_GRAY2RGB      )
							UpperCamelCase_,   UpperCamelCase_:							List[Any]	=						np.gradient(snake_case_      )
							UpperCamelCase_:							Optional[Any]	=						dx**2
							UpperCamelCase_:							Dict	=						dy**2
							UpperCamelCase_:							Optional[Any]	=						dx * dy
							UpperCamelCase_:							str	=						0.04
							UpperCamelCase_:							int	=						self.window_size // 2
							for y in range(snake_case_						,	h - offset      ):
											for x in range(snake_case_						,	w - offset      ):
															UpperCamelCase_:							List[Any]	=						ixx[
															    y - offset : y + offset + 1, x - offset : x + offset + 1
															].sum()
															UpperCamelCase_:							int	=						iyy[
															    y - offset : y + offset + 1, x - offset : x + offset + 1
															].sum()
															UpperCamelCase_:							List[str]	=						ixy[
															    y - offset : y + offset + 1, x - offset : x + offset + 1
															].sum()

															UpperCamelCase_:							List[str]	=						(wxx * wyy) - (wxy**2)
															UpperCamelCase_:							Optional[int]	=						wxx + wyy
															UpperCamelCase_:							Dict	=						det - k * (trace**2)
															# Can change the value
															if r > 0.5:
																			corner_list.append([x, y, r]      )
																			color_img.itemset((y, x, 0)						,	0      )
																			color_img.itemset((y, x, 1)						,	0      )
																			color_img.itemset((y, x, 2)						,	255      )
							return color_img, corner_list


if __name__ == "__main__":
							lowerCamelCase_						: Optional[Any]         =  HarrisCorner(0.04, 3)
							lowerCamelCase_   ,       lowerCamelCase_						: Any         =  edge_detect.detect("""path_to_image""")
							cva.imwrite("""detect.png""", color_img)






