

"""simple docstring"""



import numpy as np
from cva import COLOR_BGR2GRAY, CV_8UC3, cvtColor, filteraD, imread, imshow, waitKey





def  _snake_case     (    UpperCAmelCase_  :					int					,   UpperCAmelCase_  :					int					,   UpperCAmelCase_  :					int					,   UpperCAmelCase_  :					int					,   UpperCAmelCase_  :					int					,   UpperCAmelCase_  :					int			):

     # prepare kernel
     # the kernel size have to be odd
     if (ksize % 2) == 0:
          A__										=  ksize + 1
     A__										=  np.zeros((ksize, ksize)					,   dtype=np.floataa			)

     # each value
     for y in range(UpperCAmelCase_			):
          for x in range(UpperCAmelCase_			):
               # distance from center
               A__										=  x - ksize // 2
               A__										=  y - ksize // 2

               # degree to radiant
               A__										=  theta / 180 * np.pi
               A__										=  np.cos(_theta			)
               A__										=  np.sin(_theta			)

               # get kernel x
               A__										=  cos_theta * px + sin_theta * py

               # get kernel y
               A__										=  -sin_theta * px + cos_theta * py

               # fill kernel
               A__										=  np.exp(
                   -(_x**2 + gamma**2 * _y**2) / (2 * sigma**2)			) * np.cos(2 * np.pi * _x / lambd + psi			)

     return gabor


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 # read original image
 SCREAMING_SNAKE_CASE_					:	List[Any]							  =						imread('../image_data/lena.jpg')
 # turn image in gray scale value
 SCREAMING_SNAKE_CASE_					:	List[Any]							  =						cvtColor(img, COLOR_BGR2GRAY)

 # Apply multiple Kernel to detect edges
 SCREAMING_SNAKE_CASE_					:	Tuple							  =						np.zeros(gray.shape[:2])
 for theta in [0, 3_0, 6_0, 9_0, 1_2_0, 1_5_0]:
  SCREAMING_SNAKE_CASE_					:	Optional[Any]							  =						gabor_filter_kernel(1_0, 8, theta, 1_0, 0, 0)
  out += filteraD(gray, CV_8UC3, kernel_aa)
 SCREAMING_SNAKE_CASE_					:	int							  =						out / out.max() * 2_5_5
 SCREAMING_SNAKE_CASE_					:	Tuple							  =						out.astype(np.uinta)

 imshow('Original', gray)
 imshow('Gabor filter with 20x20 mask and 6 directions', out)

 waitKey(0)









"""simple docstring"""



import unittest

from transformers import (
    MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    TextaTextGenerationPipeline,
    pipeline,
)
from transformers.testing_utils import is_pipeline_test, require_tf, require_torch
from transformers.utils import is_torch_available

from .test_pipelines_common import ANY


if is_torch_available():
 import torch



@is_pipeline_test
class 					a						(       unittest.TestCase				):



    """simple docstring"""



    UpperCAmelCase						 =					MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
    UpperCAmelCase						 =					TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING

    def 				UpperCamelCase	(     self: Dict    ,						UpperCamelCase: str    ,						UpperCamelCase: Optional[Any]    ,						UpperCamelCase: Any       ):






         """simple docstring"""


         A__										=  TextaTextGenerationPipeline(model=UpperCamelCase    ,						tokenizer=UpperCamelCase       )
         return generator, ["Something to write", "Something else"]

    def 				UpperCamelCase	(     self: List[Any]    ,						UpperCamelCase: Any    ,						UpperCamelCase: Dict       ):






         """simple docstring"""


         A__										=  generator("""Something there"""       )
         self.assertEqual(UpperCamelCase    ,						[{"""generated_text""": ANY(UpperCamelCase       )}]       )
         # These are encoder decoder, they don't just append to incoming string
         self.assertFalse(outputs[0]["""generated_text"""].startswith("""Something there"""       )       )

         A__										=  generator(["""This is great !""", """Something else"""]    ,						num_return_sequences=2    ,						do_sample=UpperCamelCase       )
         self.assertEqual(
             UpperCamelCase    ,						[
                 [{"""generated_text""": ANY(UpperCamelCase       )}, {"""generated_text""": ANY(UpperCamelCase       )}],
                 [{"""generated_text""": ANY(UpperCamelCase       )}, {"""generated_text""": ANY(UpperCamelCase       )}],
             ]    ,						)

         A__										=  generator(
             ["""This is great !""", """Something else"""]    ,						num_return_sequences=2    ,						batch_size=2    ,						do_sample=UpperCamelCase       )
         self.assertEqual(
             UpperCamelCase    ,						[
                 [{"""generated_text""": ANY(UpperCamelCase       )}, {"""generated_text""": ANY(UpperCamelCase       )}],
                 [{"""generated_text""": ANY(UpperCamelCase       )}, {"""generated_text""": ANY(UpperCamelCase       )}],
             ]    ,						)

         with self.assertRaises(UpperCamelCase       ):
              generator(4       )

    @require_torch
    def 				UpperCamelCase	(     self: Optional[Any]       ):






         """simple docstring"""


         A__										=  pipeline("""text2text-generation"""    ,						model="""patrickvonplaten/t5-tiny-random"""    ,						framework="""pt"""       )
         # do_sample=False necessary for reproducibility
         A__										=  generator("""Something there"""    ,						do_sample=UpperCamelCase       )
         self.assertEqual(UpperCamelCase    ,						[{"""generated_text""": """"""}]       )

         A__										=  3
         A__										=  generator(
             """Something there"""    ,						num_return_sequences=UpperCamelCase    ,						num_beams=UpperCamelCase    ,						)
         A__										=  [
             {"""generated_text""": """Beide Beide Beide Beide Beide Beide Beide Beide Beide"""},
             {"""generated_text""": """Beide Beide Beide Beide Beide Beide Beide Beide"""},
             {"""generated_text""": """"""},
         ]
         self.assertEqual(UpperCamelCase    ,						UpperCamelCase       )

         A__										=  generator("""This is a test"""    ,						do_sample=UpperCamelCase    ,						num_return_sequences=2    ,						return_tensors=UpperCamelCase       )
         self.assertEqual(
             UpperCamelCase    ,						[
                 {"""generated_token_ids""": ANY(torch.Tensor       )},
                 {"""generated_token_ids""": ANY(torch.Tensor       )},
             ]    ,						)
         A__										=  generator.model.config.eos_token_id
         A__										=  """<pad>"""
         A__										=  generator(
             ["""This is a test""", """This is a second test"""]    ,						do_sample=UpperCamelCase    ,						num_return_sequences=2    ,						batch_size=2    ,						return_tensors=UpperCamelCase    ,						)
         self.assertEqual(
             UpperCamelCase    ,						[
                 [
                     {"""generated_token_ids""": ANY(torch.Tensor       )},
                     {"""generated_token_ids""": ANY(torch.Tensor       )},
                 ],
                 [
                     {"""generated_token_ids""": ANY(torch.Tensor       )},
                     {"""generated_token_ids""": ANY(torch.Tensor       )},
                 ],
             ]    ,						)





    @require_tf
    def 				UpperCamelCase	(     self: Any       ):






         """simple docstring"""


         A__										=  pipeline("""text2text-generation"""    ,						model="""patrickvonplaten/t5-tiny-random"""    ,						framework="""tf"""       )
         # do_sample=False necessary for reproducibility
         A__										=  generator("""Something there"""    ,						do_sample=UpperCamelCase       )
         self.assertEqual(UpperCamelCase    ,						[{"""generated_text""": """"""}]       )






