




import logging
import os
import sys
from pathlib import Path
from unittest.mock import patch

from parameterized import parameterized
from run_eval import run_generate
from run_eval_search import run_search

from transformers.testing_utils import CaptureStdout, TestCasePlus, slow
from utils import ROUGE_KEYS


logging.basicConfig(level=logging.DEBUG)
lowerCamelCase       :	List[str]									=     logging.getLogger()

def     _SCREAMING_SNAKE_CASE    (							lowercase			: Path			,							lowercase			: list  ):


      '''simple docstring'''



      lowerCamelCase_	  =					'\n'.join(lowercase  )
      Path(lowercase  ).open('w'  ).writelines(lowercase  )


lowerCamelCase       :	Optional[int]									=     "patrickvonplaten/t5-tiny-random"
lowerCamelCase       :	Union[str, Any]									=     "sshleifer/bart-tiny-random"
lowerCamelCase       :	Tuple									=     "sshleifer/tiny-mbart"

lowerCamelCase       :	str									=     logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
logging.disable(logging.CRITICAL)  # remove noisy download output from tracebacks







class 						A(		UpperCamelCase     ):



 '''simple docstring'''


 def        a__				(       self      :						List[str]		,							A_      :						Optional[Any]						)     ->			str:

       """simple docstring"""






       lowerCamelCase_	  =					Path(self.get_auto_remove_tmp_dir()						) / 'utest_input.source'
       lowerCamelCase_	  =					input_file_name.parent / 'utest_output.txt'
       assert not output_file_name.exists()
       lowerCamelCase_	  =					[' New York (CNN)When Liana Barrientos was 23 years old, she got married in Westchester County.']
       _dump_articles(A_		,							A_						)

       lowerCamelCase_	  =					str(Path(self.get_auto_remove_tmp_dir()						) / 'scores.json'						)
       lowerCamelCase_	  =					'translation_en_to_de' if model == T5_TINY else 'summarization'
       lowerCamelCase_	  =					f"""
            run_eval_search.py
            {model}
            {input_file_name}
            {output_file_name}
            --score_path {score_path}
            --task {task}
            --num_beams 2
            --length_penalty 2.0
            """.split()

       with patch.object(A_		,							'argv'		,							A_						):
             run_generate()
             assert Path(A_						).exists()
             # os.remove(Path(output_file_name))

 def        a__				(       self      :						Optional[int]						)     ->			Tuple:

       """simple docstring"""






       self.run_eval_tester(A_						)

 @parameterized.expand([BART_TINY, MBART_TINY]						)
 @slow
 def        a__				(       self      :						Optional[int]		,							A_      :						str						)     ->			int:

       """simple docstring"""






       self.run_eval_tester(A_						)





 @parameterized.expand([T5_TINY, MBART_TINY]						)
 @slow
 def        a__				(       self      :						Union[str, Any]		,							A_      :						Optional[Any]						)     ->			Any:

       """simple docstring"""






       lowerCamelCase_	  =					Path(self.get_auto_remove_tmp_dir()						) / 'utest_input.source'
       lowerCamelCase_	  =					input_file_name.parent / 'utest_output.txt'
       assert not output_file_name.exists()

       lowerCamelCase_	  =					{
           'en': ['Machine learning is great, isn\'t it?', 'I like to eat bananas', 'Tomorrow is another great day!'],
           'de': [
               'Maschinelles Lernen ist großartig, oder?',
               'Ich esse gerne Bananen',
               'Morgen ist wieder ein toller Tag!',
           ],
       }

       lowerCamelCase_	  =					Path(self.get_auto_remove_tmp_dir()						)
       lowerCamelCase_	  =					str(tmp_dir / 'scores.json'						)
       lowerCamelCase_	  =					str(tmp_dir / 'val.target'						)
       _dump_articles(A_		,							text['en']						)
       _dump_articles(A_		,							text['de']						)
       lowerCamelCase_	  =					'translation_en_to_de' if model == T5_TINY else 'summarization'
       lowerCamelCase_	  =					f"""
            run_eval_search.py
            {model}
            {str(A_						)}
            {str(A_						)}
            --score_path {score_path}
            --reference_path {reference_path}
            --task {task}
            """.split()
       testargs.extend(['--search', 'num_beams=1:2 length_penalty=0.9:1.0']						)

       with patch.object(A_		,							'argv'		,							A_						):
             with CaptureStdout() as cs:
                   run_search()
             lowerCamelCase_	  =					[' num_beams | length_penalty', model, 'Best score args']
             lowerCamelCase_	  =					['Info']
             if "translation" in task:
                   expected_strings.append('bleu'						)
             else:
                   expected_strings.extend(A_						)
             for w in expected_strings:
                   assert w in cs.out
             for w in un_expected_strings:
                   assert w not in cs.out
             assert Path(A_						).exists()
             os.remove(Path(A_						)						)









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

lowerCamelCase       :	str									=     imread(r"digital_image_processing/image_data/lena_small.jpg")
lowerCamelCase       :	Optional[Any]									=     cvtColor(img, COLOR_BGR2GRAY)

def     _SCREAMING_SNAKE_CASE    (							):


      '''simple docstring'''



      lowerCamelCase_	  =					cn.convert_to_negative(lowercase  )
      # assert negative_img array for at least one True
      assert negative_img.any()

def     _SCREAMING_SNAKE_CASE    (							):


      '''simple docstring'''



      with Image.open('digital_image_processing/image_data/lena_small.jpg'  ) as img:
            # Work around assertion for response
            assert str(cc.change_contrast(lowercase			,							1_10  )  ).startswith(
                '<PIL.Image.Image image mode=RGB size=100x100 at'  )

def     _SCREAMING_SNAKE_CASE    (							):


      '''simple docstring'''



      lowerCamelCase_	  =					canny.gen_gaussian_kernel(9			,							sigma=1.4  )
      # Assert ambiguous array
      assert resp.all()

def     _SCREAMING_SNAKE_CASE    (							):


      '''simple docstring'''



      lowerCamelCase_	  =					imread('digital_image_processing/image_data/lena_small.jpg'			,							0  )
      # assert ambiguous array for all == True
      assert canny_img.all()
      lowerCamelCase_	  =					canny.canny(lowercase  )
      # assert canny array for at least one True
      assert canny_array.any()

def     _SCREAMING_SNAKE_CASE    (							):


      '''simple docstring'''



      assert gg.gaussian_filter(lowercase			,							5			,							sigma=0.9  ).all()

def     _SCREAMING_SNAKE_CASE    (							):


      '''simple docstring'''



      lowerCamelCase_	  =					array([[0.25, 0.5, 0.25], [0.5, -3, 0.5], [0.25, 0.5, 0.25]]  )
      lowerCamelCase_	  =					conv.img_convolve(lowercase			,							lowercase  ).astype(lowercase  )
      assert res.any()

def     _SCREAMING_SNAKE_CASE    (							):


      '''simple docstring'''



      assert med.median_filter(lowercase			,							3  ).any()

def     _SCREAMING_SNAKE_CASE    (							):


      '''simple docstring'''



      lowerCamelCase_  ,       lowerCamelCase_	  =					sob.sobel_filter(lowercase  )
      assert grad.any() and theta.any()

def     _SCREAMING_SNAKE_CASE    (							):


      '''simple docstring'''



      lowerCamelCase_	  =					sp.make_sepia(lowercase			,							20  )
      assert sepia.all()

def     _SCREAMING_SNAKE_CASE    (							lowercase			: str = "digital_image_processing/image_data/lena_small.jpg"  ):


      '''simple docstring'''



      lowerCamelCase_	  =					bs.Burkes(imread(lowercase			,							1  )			,							1_20  )
      burkes.process()
      assert burkes.output_img.any()

def     _SCREAMING_SNAKE_CASE    (							lowercase			: str = "digital_image_processing/image_data/lena_small.jpg"			,							):


      '''simple docstring'''



      lowerCamelCase_	  =					rs.NearestNeighbour(imread(lowercase			,							1  )			,							4_00			,							2_00  )
      nn.process()
      assert nn.output.any()






def     _SCREAMING_SNAKE_CASE    (							):


      '''simple docstring'''



      lowerCamelCase_	  =					'digital_image_processing/image_data/lena.jpg'

      # Reading the image and converting it to grayscale.
      lowerCamelCase_	  =					imread(lowercase			,							0  )

      # Test for get_neighbors_pixel function() return not None
      lowerCamelCase_	  =					0
      lowerCamelCase_	  =					0
      lowerCamelCase_	  =					image[x_coordinate][y_coordinate]

      lowerCamelCase_	  =					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
      lowerCamelCase_	  =					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]  ):
                  lowerCamelCase_	  =					lbp.local_binary_value(lowercase			,							lowercase			,							lowercase  )

      assert lbp_image.any()



