




'''simple docstring'''


import shutil
import tempfile
import unittest

from transformers import ClapFeatureExtractor, ClapProcessor, RobertaTokenizer, RobertaTokenizerFast
from transformers.testing_utils import require_sentencepiece, require_torchaudio

from .test_feature_extraction_clap import floats_list





@require_torchaudio
@require_sentencepiece
class  __lowercase    (	unittest.TestCase			):







   def 		UpperCamelCase__      (						self   )					->     Union[str, Any]:
     __a								=       """laion/clap-htsat-unfused"""
     __a								=       tempfile.mkdtemp()







   def 		UpperCamelCase__      (						self   ,				**UpperCamelCase   )					->     Union[str, Any]:
     return RobertaTokenizer.from_pretrained(self.checkpoint   ,				**__a   )







   def 		UpperCamelCase__      (						self   ,				**UpperCamelCase   )					->     str:
     return ClapFeatureExtractor.from_pretrained(self.checkpoint   ,				**__a   )







   def 		UpperCamelCase__      (						self   )					->     Dict:
     shutil.rmtree(self.tmpdirname   )







   def 		UpperCamelCase__      (						self   )					->     Optional[int]:
     __a								=       self.get_tokenizer()
     __a								=       self.get_feature_extractor()

     __a								=       ClapProcessor(tokenizer=__a   ,				feature_extractor=__a   )

     processor.save_pretrained(self.tmpdirname   )
     __a								=       ClapProcessor.from_pretrained(self.tmpdirname   )

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

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







   def 		UpperCamelCase__      (						self   )					->     Any:
     __a								=       ClapProcessor(tokenizer=self.get_tokenizer()   ,				feature_extractor=self.get_feature_extractor()   )
     processor.save_pretrained(self.tmpdirname   )

     __a								=       self.get_tokenizer(bos_token='(BOS)'   ,				eos_token='(EOS)'   )
     __a								=       self.get_feature_extractor(do_normalize=__a   ,				padding_value=1.0   )

     __a								=       ClapProcessor.from_pretrained(
         self.tmpdirname   ,				bos_token='(BOS)'   ,				eos_token='(EOS)'   ,				do_normalize=__a   ,				padding_value=1.0   )

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

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







   def 		UpperCamelCase__      (						self   )					->     Any:
     __a								=       self.get_feature_extractor()
     __a								=       self.get_tokenizer()

     __a								=       ClapProcessor(tokenizer=__a   ,				feature_extractor=__a   )

     __a								=       floats_list((3, 1000)   )

     __a								=       feature_extractor(__a   ,				return_tensors='np'   )
     __a								=       processor(audios=__a   ,				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 		UpperCamelCase__      (						self   )					->     List[str]:
     __a								=       self.get_feature_extractor()
     __a								=       self.get_tokenizer()

     __a								=       ClapProcessor(tokenizer=__a   ,				feature_extractor=__a   )

     __a								=       """This is a test string"""

     __a								=       processor(text=__a   )

     __a								=       tokenizer(__a   )

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







   def 		UpperCamelCase__      (						self   )					->     Dict:
     __a								=       self.get_feature_extractor()
     __a								=       self.get_tokenizer()

     __a								=       ClapProcessor(tokenizer=__a   ,				feature_extractor=__a   )

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

     __a								=       processor.batch_decode(__a   )
     __a								=       tokenizer.batch_decode(__a   )

     self.assertListEqual(__a   ,				__a   )







   def 		UpperCamelCase__      (						self   )					->     Optional[int]:
     __a								=       self.get_feature_extractor()
     __a								=       self.get_tokenizer()

     __a								=       ClapProcessor(tokenizer=__a   ,				feature_extractor=__a   )

     self.assertListEqual(
         processor.model_input_names[2:]   ,				feature_extractor.model_input_names   ,				msg='`processor` and `feature_extractor` model input names do not match'   ,				)









'''simple docstring'''







import numpy as np
from cva import COLOR_BGR2GRAY, cvtColor, imread
from numpy import array, uinta
from PIL import Image

from digital_image_processing import change_contrast as cc
from digital_image_processing import convert_to_negative as cn
from digital_image_processing import sepia as sp
from digital_image_processing.dithering import burkes as bs
from digital_image_processing.edge_detection import canny
from digital_image_processing.filters import convolve as conv
from digital_image_processing.filters import gaussian_filter as gg
from digital_image_processing.filters import local_binary_pattern as lbp
from digital_image_processing.filters import median_filter as med
from digital_image_processing.filters import sobel_filter as sob
from digital_image_processing.resize import resize as rs

lowercase      :   Any      =       imread(R"""digital_image_processing/image_data/lena_small.jpg""")
lowercase      :   List[str]      =       cvtColor(img, COLOR_BGR2GRAY)



def        lowerCamelCase__				(      ):
     snake_case			:      Optional[int]        =			cn.convert_to_negative(__lowercase				)
     # assert negative_img array for at least one True
     assert negative_img.any()



def        lowerCamelCase__				(      ):
     with Image.open("""digital_image_processing/image_data/lena_small.jpg"""				) as img:
          # Work around assertion for response
          assert str(cc.change_contrast(__lowercase     ,   110				)				).startswith(
              """<PIL.Image.Image image mode=RGB size=100x100 at"""				)



def        lowerCamelCase__				(      ):
     snake_case			:      Union[str, Any]        =			canny.gen_gaussian_kernel(9     ,   sigma=1.4				)
     # Assert ambiguous array
     assert resp.all()



def        lowerCamelCase__				(      ):
     snake_case			:      Optional[Any]        =			imread("""digital_image_processing/image_data/lena_small.jpg"""     ,   0				)
     # assert ambiguous array for all == True
     assert canny_img.all()
     snake_case			:      Any        =			canny.canny(__lowercase				)
     # assert canny array for at least one True
     assert canny_array.any()



def        lowerCamelCase__				(      ):
     assert gg.gaussian_filter(__lowercase     ,   5     ,   sigma=0.9				).all()



def        lowerCamelCase__				(      ):
     # laplace diagonals
     snake_case			:      int        =			array([[0.25, 0.5, 0.25], [0.5, -3, 0.5], [0.25, 0.5, 0.25]]				)
     snake_case			:      Tuple        =			conv.img_convolve(__lowercase     ,   __lowercase				).astype(__lowercase				)
     assert res.any()



def        lowerCamelCase__				(      ):
     assert med.median_filter(__lowercase     ,   3				).any()



def        lowerCamelCase__				(      ):
     snake_case ,		snake_case			:      Dict        =			sob.sobel_filter(__lowercase				)
     assert grad.any() and theta.any()



def        lowerCamelCase__				(      ):
     snake_case			:      int        =			sp.make_sepia(__lowercase     ,   20				)
     assert sepia.all()



def        lowerCamelCase__				(      __lowercase = "digital_image_processing/image_data/lena_small.jpg"				):
     snake_case			:      int        =			bs.Burkes(imread(__lowercase     ,   1				)     ,   120				)
     burkes.process()
     assert burkes.output_img.any()



def        lowerCamelCase__				(      __lowercase = "digital_image_processing/image_data/lena_small.jpg"     ,   ):
     snake_case			:      Dict        =			rs.NearestNeighbour(imread(__lowercase     ,   1				)     ,   400     ,   200				)
     nn.process()
     assert nn.output.any()







def        lowerCamelCase__				(      ):
     snake_case			:      int        =			"""digital_image_processing/image_data/lena.jpg"""

     # Reading the image and converting it to grayscale.
     snake_case			:      Tuple        =			imread(__lowercase     ,   0				)

     # Test for get_neighbors_pixel function() return not None
     snake_case			:      Optional[int]        =			0
     snake_case			:      Tuple        =			0
     snake_case			:      Any        =			image[x_coordinate][y_coordinate]

     snake_case			:      Any        =			lbp.get_neighbors_pixel(
         __lowercase     ,   __lowercase     ,   __lowercase     ,   __lowercase				)

     assert neighbors_pixels is not None

     # Test for local_binary_pattern function()
     # Create a numpy array as the same height and width of read image
     snake_case			:      List[Any]        =			np.zeros((image.shape[0], image.shape[1])				)

     # Iterating through the image and calculating the local binary pattern value
     # for each pixel.
     for i in range(0     ,   image.shape[0]				):
          for j in range(0     ,   image.shape[1]				):
               snake_case			:      Optional[Any]        =			lbp.local_binary_value(__lowercase     ,   __lowercase     ,   __lowercase				)

     assert lbp_image.any()



