
'''simple docstring'''







import json
import os
import shutil
import tempfile
import unittest

from transformers import is_speech_available, is_torch_available
from transformers.models.speechta import SpeechTaTokenizer
from transformers.testing_utils import get_tests_dir, require_torch
from transformers.utils import FEATURE_EXTRACTOR_NAME


if is_speech_available() and is_torch_available():
						from transformers import SpeechTaFeatureExtractor, SpeechTaProcessor

						from .test_feature_extraction_speechta import floats_list


_snake_case					: List[Any]     			=					get_tests_dir("""fixtures/test_sentencepiece_bpe_char.model""")


@require_torch
class       lowerCAmelCase			(					unittest.TestCase      ):




		def      lowercase							(					self   ):
								_SCREAMING_SNAKE_CASE					=			tempfile.mkdtemp()

								_SCREAMING_SNAKE_CASE					=			SpeechTaTokenizer(UpperCamelCase   )
								tokenizer.save_pretrained(self.tmpdirname   )

								_SCREAMING_SNAKE_CASE					=			{
								    "feature_size": 1,
								    "padding_value": 0.0,
								    "sampling_rate": 16_000,
								    "do_normalize": False,
								    "num_mel_bins": 80,
								    "hop_length": 16,
								    "win_length": 64,
								    "win_function": "hann_window",
								    "fmin": 80,
								    "fmax": 7_600,
								    "mel_floor": 1e-10,
								    "reduction_factor": 2,
								    "return_attention_mask": True,
								}

								_SCREAMING_SNAKE_CASE					=			os.path.join(self.tmpdirname       ,    UpperCamelCase   )
								with open(self.feature_extraction_file       ,    "w"       ,    encoding="utf-8"   ) as fp:
														fp.write(json.dumps(UpperCamelCase   ) + "\n"   )




		def      lowercase							(					self       ,    **UpperCamelCase   ):
								return SpeechTaTokenizer.from_pretrained(self.tmpdirname       ,    **UpperCamelCase   )




		def      lowercase							(					self       ,    **UpperCamelCase   ):
								return SpeechTaFeatureExtractor.from_pretrained(self.tmpdirname       ,    **UpperCamelCase   )




		def      lowercase							(					self   ):
								shutil.rmtree(self.tmpdirname   )




		def      lowercase							(					self   ):
								_SCREAMING_SNAKE_CASE					=			self.get_tokenizer()
								_SCREAMING_SNAKE_CASE					=			self.get_feature_extractor()

								_SCREAMING_SNAKE_CASE					=			SpeechTaProcessor(tokenizer=UpperCamelCase       ,    feature_extractor=UpperCamelCase   )

								processor.save_pretrained(self.tmpdirname   )
								_SCREAMING_SNAKE_CASE					=			SpeechTaProcessor.from_pretrained(self.tmpdirname   )

								self.assertEqual(processor.tokenizer.get_vocab()       ,    tokenizer.get_vocab()   )
								self.assertIsInstance(processor.tokenizer       ,    UpperCamelCase   )

								self.assertEqual(processor.feature_extractor.to_json_string()       ,    feature_extractor.to_json_string()   )
								self.assertIsInstance(processor.feature_extractor       ,    UpperCamelCase   )




		def      lowercase							(					self   ):
								_SCREAMING_SNAKE_CASE					=			SpeechTaProcessor(tokenizer=self.get_tokenizer()       ,    feature_extractor=self.get_feature_extractor()   )
								processor.save_pretrained(self.tmpdirname   )

								_SCREAMING_SNAKE_CASE					=			self.get_tokenizer(bos_token="(BOS)"       ,    eos_token="(EOS)"   )
								_SCREAMING_SNAKE_CASE					=			self.get_feature_extractor(do_normalize=UpperCamelCase       ,    padding_value=1.0   )

								_SCREAMING_SNAKE_CASE					=			SpeechTaProcessor.from_pretrained(
								    self.tmpdirname       ,    bos_token="(BOS)"       ,    eos_token="(EOS)"       ,    do_normalize=UpperCamelCase       ,    padding_value=1.0   )

								self.assertEqual(processor.tokenizer.get_vocab()       ,    tokenizer_add_kwargs.get_vocab()   )
								self.assertIsInstance(processor.tokenizer       ,    UpperCamelCase   )

								self.assertEqual(processor.feature_extractor.to_json_string()       ,    feature_extractor_add_kwargs.to_json_string()   )
								self.assertIsInstance(processor.feature_extractor       ,    UpperCamelCase   )




		def      lowercase							(					self   ):
								_SCREAMING_SNAKE_CASE					=			self.get_feature_extractor()
								_SCREAMING_SNAKE_CASE					=			self.get_tokenizer()

								_SCREAMING_SNAKE_CASE					=			SpeechTaProcessor(tokenizer=UpperCamelCase       ,    feature_extractor=UpperCamelCase   )

								_SCREAMING_SNAKE_CASE					=			floats_list((3, 1_000)   )

								_SCREAMING_SNAKE_CASE					=			feature_extractor(audio=UpperCamelCase       ,    return_tensors="np"   )
								_SCREAMING_SNAKE_CASE					=			processor(audio=UpperCamelCase       ,    return_tensors="np"   )

								for key in input_feat_extract.keys():
														self.assertAlmostEqual(input_feat_extract[key].sum()       ,    input_processor[key].sum()       ,    delta=1e-2   )




		def      lowercase							(					self   ):
								_SCREAMING_SNAKE_CASE					=			self.get_feature_extractor()
								_SCREAMING_SNAKE_CASE					=			self.get_tokenizer()

								_SCREAMING_SNAKE_CASE					=			SpeechTaProcessor(tokenizer=UpperCamelCase       ,    feature_extractor=UpperCamelCase   )

								_SCREAMING_SNAKE_CASE					=			floats_list((3, 1_000)   )

								_SCREAMING_SNAKE_CASE					=			feature_extractor(audio_target=UpperCamelCase       ,    return_tensors="np"   )
								_SCREAMING_SNAKE_CASE					=			processor(audio_target=UpperCamelCase       ,    return_tensors="np"   )

								for key in input_feat_extract.keys():
														self.assertAlmostEqual(input_feat_extract[key].sum()       ,    input_processor[key].sum()       ,    delta=1e-2   )




		def      lowercase							(					self   ):
								_SCREAMING_SNAKE_CASE					=			self.get_feature_extractor()
								_SCREAMING_SNAKE_CASE					=			self.get_tokenizer()

								_SCREAMING_SNAKE_CASE					=			SpeechTaProcessor(tokenizer=UpperCamelCase       ,    feature_extractor=UpperCamelCase   )

								_SCREAMING_SNAKE_CASE					=			"This is a test string"

								_SCREAMING_SNAKE_CASE					=			processor(text=UpperCamelCase   )
								_SCREAMING_SNAKE_CASE					=			tokenizer(UpperCamelCase   )

								for key in encoded_tok.keys():
														self.assertListEqual(encoded_tok[key]       ,    encoded_processor[key]   )




		def      lowercase							(					self   ):
								_SCREAMING_SNAKE_CASE					=			self.get_feature_extractor()
								_SCREAMING_SNAKE_CASE					=			self.get_tokenizer()

								_SCREAMING_SNAKE_CASE					=			SpeechTaProcessor(tokenizer=UpperCamelCase       ,    feature_extractor=UpperCamelCase   )

								_SCREAMING_SNAKE_CASE					=			"This is a test string"

								_SCREAMING_SNAKE_CASE					=			processor(text_target=UpperCamelCase   )
								_SCREAMING_SNAKE_CASE					=			tokenizer(UpperCamelCase   )

								for key in encoded_tok.keys():
														self.assertListEqual(encoded_tok[key]       ,    encoded_processor[key]   )




		def      lowercase							(					self   ):
								_SCREAMING_SNAKE_CASE					=			self.get_feature_extractor()
								_SCREAMING_SNAKE_CASE					=			self.get_tokenizer()

								_SCREAMING_SNAKE_CASE					=			SpeechTaProcessor(tokenizer=UpperCamelCase       ,    feature_extractor=UpperCamelCase   )

								_SCREAMING_SNAKE_CASE					=			[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

								_SCREAMING_SNAKE_CASE					=			processor.batch_decode(UpperCamelCase   )
								_SCREAMING_SNAKE_CASE					=			tokenizer.batch_decode(UpperCamelCase   )

								self.assertListEqual(UpperCamelCase       ,    UpperCamelCase   )




		def      lowercase							(					self   ):
								_SCREAMING_SNAKE_CASE					=			self.get_feature_extractor()
								_SCREAMING_SNAKE_CASE					=			self.get_tokenizer()

								_SCREAMING_SNAKE_CASE					=			SpeechTaProcessor(tokenizer=UpperCamelCase       ,    feature_extractor=UpperCamelCase   )

								self.assertListEqual(
								    processor.model_input_names       ,    feature_extractor.model_input_names       ,    msg="`processor` and `feature_extractor` model input names do not match"       ,    )

'''simple docstring'''







import cva
import numpy as np

from digital_image_processing.filters.convolve import img_convolve
from digital_image_processing.filters.sobel_filter import sobel_filter

_snake_case					: Union[str, Any]     			=					180



def       _a		(						_SCREAMING_SNAKE_CASE		: Optional[int]						,      _SCREAMING_SNAKE_CASE		: Dict	):
						_SCREAMING_SNAKE_CASE					=			k_size // 2
						_SCREAMING_SNAKE_CASE,				_SCREAMING_SNAKE_CASE					=			np.mgrid[0 - center : k_size - center, 0 - center : k_size - center]
						_SCREAMING_SNAKE_CASE					=			(
						    1
						    / (2 * np.pi * sigma)
						    * np.exp(-(np.square(_SCREAMING_SNAKE_CASE	) + np.square(_SCREAMING_SNAKE_CASE	)) / (2 * np.square(_SCREAMING_SNAKE_CASE	))	)
						)
						return g



def       _a		(						_SCREAMING_SNAKE_CASE		: int						,      _SCREAMING_SNAKE_CASE		: Tuple						,      _SCREAMING_SNAKE_CASE		: Dict	):
						_SCREAMING_SNAKE_CASE					=			np.zeros(_SCREAMING_SNAKE_CASE	)

						for row in range(1						,      image_shape[0] - 1	):
												for col in range(1						,      image_shape[1] - 1	):
																		_SCREAMING_SNAKE_CASE					=			gradient_direction[row, col]

																		if (
																		    0 <= direction < PI / 8
																		    or 15 * PI / 8 <= direction <= 2 * PI
																		    or 7 * PI / 8 <= direction <= 9 * PI / 8
																		):
																								_SCREAMING_SNAKE_CASE					=			sobel_grad[row, col - 1]
																								_SCREAMING_SNAKE_CASE					=			sobel_grad[row, col + 1]
																								if sobel_grad[row, col] >= w and sobel_grad[row, col] >= e:
																														_SCREAMING_SNAKE_CASE					=			sobel_grad[row, col]

																		elif (
																		    PI / 8 <= direction < 3 * PI / 8
																		    or 9 * PI / 8 <= direction < 11 * PI / 8
																		):
																								_SCREAMING_SNAKE_CASE					=			sobel_grad[row + 1, col - 1]
																								_SCREAMING_SNAKE_CASE					=			sobel_grad[row - 1, col + 1]
																								if sobel_grad[row, col] >= sw and sobel_grad[row, col] >= ne:
																														_SCREAMING_SNAKE_CASE					=			sobel_grad[row, col]

																		elif (
																		    3 * PI / 8 <= direction < 5 * PI / 8
																		    or 11 * PI / 8 <= direction < 13 * PI / 8
																		):
																								_SCREAMING_SNAKE_CASE					=			sobel_grad[row - 1, col]
																								_SCREAMING_SNAKE_CASE					=			sobel_grad[row + 1, col]
																								if sobel_grad[row, col] >= n and sobel_grad[row, col] >= s:
																														_SCREAMING_SNAKE_CASE					=			sobel_grad[row, col]

																		elif (
																		    5 * PI / 8 <= direction < 7 * PI / 8
																		    or 13 * PI / 8 <= direction < 15 * PI / 8
																		):
																								_SCREAMING_SNAKE_CASE					=			sobel_grad[row - 1, col - 1]
																								_SCREAMING_SNAKE_CASE					=			sobel_grad[row + 1, col + 1]
																								if sobel_grad[row, col] >= nw and sobel_grad[row, col] >= se:
																														_SCREAMING_SNAKE_CASE					=			sobel_grad[row, col]

						return destination



def       _a		(						_SCREAMING_SNAKE_CASE		: List[str]						,      _SCREAMING_SNAKE_CASE		: int						,      _SCREAMING_SNAKE_CASE		: List[str]						,      _SCREAMING_SNAKE_CASE		: Any						,      _SCREAMING_SNAKE_CASE		: Any						,      _SCREAMING_SNAKE_CASE		: List[str]	):
						for row in range(1						,      image_shape[0] - 1	):
												for col in range(1						,      image_shape[1] - 1	):
																		if destination[row, col] >= threshold_high:
																								_SCREAMING_SNAKE_CASE					=			strong
																		elif destination[row, col] <= threshold_low:
																								_SCREAMING_SNAKE_CASE					=			0
																		else:
																								_SCREAMING_SNAKE_CASE					=			weak



def       _a		(						_SCREAMING_SNAKE_CASE		: List[Any]						,      _SCREAMING_SNAKE_CASE		: Dict						,      _SCREAMING_SNAKE_CASE		: Dict						,      _SCREAMING_SNAKE_CASE		: Any	):
						for row in range(1						,      image_shape[0]	):
												for col in range(1						,      image_shape[1]	):
																		if destination[row, col] == weak:
																								if 255 in (
																								    destination[row, col + 1],
																								    destination[row, col - 1],
																								    destination[row - 1, col],
																								    destination[row + 1, col],
																								    destination[row - 1, col - 1],
																								    destination[row + 1, col - 1],
																								    destination[row - 1, col + 1],
																								    destination[row + 1, col + 1],
																								):
																														_SCREAMING_SNAKE_CASE					=			strong
																								else:
																														_SCREAMING_SNAKE_CASE					=			0



def       _a		(						_SCREAMING_SNAKE_CASE		: Dict						,      _SCREAMING_SNAKE_CASE		: List[str]=15						,      _SCREAMING_SNAKE_CASE		: List[Any]=30						,      _SCREAMING_SNAKE_CASE		: Optional[int]=128						,      _SCREAMING_SNAKE_CASE		: List[Any]=255	):
						# gaussian_filter
						_SCREAMING_SNAKE_CASE					=			img_convolve(_SCREAMING_SNAKE_CASE						,      gen_gaussian_kernel(9						,      sigma=1.4	)	)
						# get the gradient and degree by sobel_filter
						_SCREAMING_SNAKE_CASE,				_SCREAMING_SNAKE_CASE					=			sobel_filter(_SCREAMING_SNAKE_CASE	)
						_SCREAMING_SNAKE_CASE					=			PI + np.radadeg(_SCREAMING_SNAKE_CASE	)

						_SCREAMING_SNAKE_CASE					=			suppress_non_maximum(image.shape						,      _SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE	)

						detect_high_low_threshold(
						    image.shape						,      _SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE	)

						track_edge(image.shape						,      _SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE	)

						return destination


if __name__ == "__main__":
						# read original image in gray mode
						_snake_case					: Any     			=					cva.imread(R"""../image_data/lena.jpg""", 0)
						# canny edge detection
						_snake_case					: int     			=					canny(lena)
						cva.imshow("""canny""", canny_destination)
						cva.waitKey(0)