






'''simple docstring'''


from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
from numpy import divide, inta, multiply, ravel, sort, zeros_like







def 						__magic_name__(			_A    ,   _A=3       ):
						'''simple docstring'''





						UpperCamelCase__       =			int(mask / 2       )
						# copy image size
						UpperCamelCase__       =			zeros_like(_A       )
						for i in range(_A    ,   gray_img.shape[0] - bd       ):
												for j in range(_A    ,   gray_img.shape[1] - bd       ):
																		# get mask according with mask
																		UpperCamelCase__       =			ravel(gray_img[i - bd : i + bd + 1, j - bd : j + bd + 1]       )
																		# calculate mask median
																		UpperCamelCase__       =			sort(_A       )[inta(divide((multiply(_A    ,   _A       ))    ,   2       ) + 1       )]
																		UpperCamelCase__       =			median
						return median_img


if __name__ == "__main__":
					# read original image
					lowerCamelCase_					:			Optional[int]     						=				imread('''../image_data/lena.jpg''')
					# turn image in gray scale value
					lowerCamelCase_					:			Union[str, Any]     						=				cvtColor(img, COLOR_BGR2GRAY)

					# get values with two different mask size
					lowerCamelCase_					:			List[Any]     						=				median_filter(gray, 3)
					lowerCamelCase_					:			Optional[int]     						=				median_filter(gray, 5)

					# show result images
					imshow('''median filter with 3x3 mask''', medianaxa)
					imshow('''median filter with 5x5 mask''', medianaxa)
					waitKey(0)








'''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


lowerCamelCase_					:			List[str]     						=				get_tests_dir('''fixtures/test_sentencepiece_bpe_char.model''')
@require_torch
class  _SCREAMING_SNAKE_CASE				(		unittest.TestCase						):


			'''simple docstring'''





			def 					A   ( self				:    Union[str, Any]      )				->     Union[str, Any]:

									'''simple docstring'''







									UpperCamelCase__       =			tempfile.mkdtemp()

									UpperCamelCase__       =			SpeechTaTokenizer(lowercase      )
									tokenizer.save_pretrained(self.tmpdirname      )

									UpperCamelCase__       =			{
									    """feature_size""": 1,
									    """padding_value""": 0.0,
									    """sampling_rate""": 1_6_0_0_0,
									    """do_normalize""": False,
									    """num_mel_bins""": 8_0,
									    """hop_length""": 1_6,
									    """win_length""": 6_4,
									    """win_function""": """hann_window""",
									    """fmin""": 8_0,
									    """fmax""": 7_6_0_0,
									    """mel_floor""": 1e-10,
									    """reduction_factor""": 2,
									    """return_attention_mask""": True,
									}

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






			def 					A   ( self				:    Union[str, Any]			,   **lowercase				:    List[Any]      )				->     Any:

									'''simple docstring'''







									return SpeechTaTokenizer.from_pretrained(self.tmpdirname			,   **lowercase      )






			def 					A   ( self				:    str			,   **lowercase				:    Tuple      )				->     Optional[Any]:

									'''simple docstring'''







									return SpeechTaFeatureExtractor.from_pretrained(self.tmpdirname			,   **lowercase      )






			def 					A   ( self				:    Union[str, Any]      )				->     str:

									'''simple docstring'''







									shutil.rmtree(self.tmpdirname      )






			def 					A   ( self				:    List[Any]      )				->     Optional[int]:

									'''simple docstring'''







									UpperCamelCase__       =			self.get_tokenizer()
									UpperCamelCase__       =			self.get_feature_extractor()

									UpperCamelCase__       =			SpeechTaProcessor(tokenizer=lowercase			,   feature_extractor=lowercase      )

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

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

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






			def 					A   ( self				:    Tuple      )				->     Union[str, Any]:

									'''simple docstring'''







									UpperCamelCase__       =			SpeechTaProcessor(tokenizer=self.get_tokenizer()			,   feature_extractor=self.get_feature_extractor()      )
									processor.save_pretrained(self.tmpdirname      )

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

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

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

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






			def 					A   ( self				:    List[Any]      )				->     Dict:

									'''simple docstring'''







									UpperCamelCase__       =			self.get_feature_extractor()
									UpperCamelCase__       =			self.get_tokenizer()

									UpperCamelCase__       =			SpeechTaProcessor(tokenizer=lowercase			,   feature_extractor=lowercase      )

									UpperCamelCase__       =			floats_list((3, 1_0_0_0)      )

									UpperCamelCase__       =			feature_extractor(audio=lowercase			,   return_tensors="""np"""      )
									UpperCamelCase__       =			processor(audio=lowercase			,   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 					A   ( self				:    List[str]      )				->     Optional[Any]:

									'''simple docstring'''







									UpperCamelCase__       =			self.get_feature_extractor()
									UpperCamelCase__       =			self.get_tokenizer()

									UpperCamelCase__       =			SpeechTaProcessor(tokenizer=lowercase			,   feature_extractor=lowercase      )

									UpperCamelCase__       =			floats_list((3, 1_0_0_0)      )

									UpperCamelCase__       =			feature_extractor(audio_target=lowercase			,   return_tensors="""np"""      )
									UpperCamelCase__       =			processor(audio_target=lowercase			,   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 					A   ( self				:    Optional[Any]      )				->     Optional[Any]:

									'''simple docstring'''







									UpperCamelCase__       =			self.get_feature_extractor()
									UpperCamelCase__       =			self.get_tokenizer()

									UpperCamelCase__       =			SpeechTaProcessor(tokenizer=lowercase			,   feature_extractor=lowercase      )

									UpperCamelCase__       =			"""This is a test string"""

									UpperCamelCase__       =			processor(text=lowercase      )
									UpperCamelCase__       =			tokenizer(lowercase      )

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






			def 					A   ( self				:    List[Any]      )				->     int:

									'''simple docstring'''







									UpperCamelCase__       =			self.get_feature_extractor()
									UpperCamelCase__       =			self.get_tokenizer()

									UpperCamelCase__       =			SpeechTaProcessor(tokenizer=lowercase			,   feature_extractor=lowercase      )

									UpperCamelCase__       =			"""This is a test string"""

									UpperCamelCase__       =			processor(text_target=lowercase      )
									UpperCamelCase__       =			tokenizer(lowercase      )

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






			def 					A   ( self				:    Any      )				->     Tuple:

									'''simple docstring'''







									UpperCamelCase__       =			self.get_feature_extractor()
									UpperCamelCase__       =			self.get_tokenizer()

									UpperCamelCase__       =			SpeechTaProcessor(tokenizer=lowercase			,   feature_extractor=lowercase      )

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

									UpperCamelCase__       =			processor.batch_decode(lowercase      )
									UpperCamelCase__       =			tokenizer.batch_decode(lowercase      )

									self.assertListEqual(lowercase			,   lowercase      )







			def 					A   ( self				:    List[Any]      )				->     List[str]:

									'''simple docstring'''







									UpperCamelCase__       =			self.get_feature_extractor()
									UpperCamelCase__       =			self.get_tokenizer()

									UpperCamelCase__       =			SpeechTaProcessor(tokenizer=lowercase			,   feature_extractor=lowercase      )

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