"""simple docstring"""
# as due to their complexity multi-gpu tests could impact other tests, and to aid debug we have those in a separate module.

import os
import sys

from transformers.testing_utils import TestCasePlus, execute_subprocess_async, get_gpu_count, require_torch_gpu, slow

from .utils import load_json



class 	lowerCAmelCase_     (      _lowerCAmelCase       ):



				"""simple docstring"""

				@classmethod
				def 		snake_case			(					cls     ):






										"""simple docstring"""

										return cls

				@slow
				@require_torch_gpu
				def 		snake_case			(					self     ):






										"""simple docstring"""

										snake_case             =      self.get_auto_remove_tmp_dir()
										snake_case             =      F"""
            --model_name Helsinki-NLP/opus-mt-en-ro
            --save_dir {output_dir}
            --data_dir {self.test_file_dir_str}/test_data/wmt_en_ro
            --num_beams 2
            --task translation
        """.split()

										# we want this test to run even if there is only one GPU, but if there are more we use them all
										snake_case             =      get_gpu_count()
										snake_case             =      F"""
            -m torch.distributed.launch
            --nproc_per_node={n_gpu}
            {self.test_file_dir}/run_distributed_eval.py
        """.split()
										snake_case             =      [sys.executable] + distributed_args + args
										execute_subprocess_async(A__		,     env=self.get_env()     )

										snake_case             =      os.path.join(A__		,     'test_bleu.json'     )
										snake_case             =      load_json(A__     )
										# print(metrics)
										self.assertGreaterEqual(metrics['bleu']		,     25     )








import numpy as np
from cva import destroyAllWindows, imread, imshow, waitKey






class       A      :
     """simple docstring"""

     def __init__(     self	: int						,	A__	: List[Any]						,	A__	: int      )					->							List[str]:
           lowerCamelCase__   	=					0
           # max greyscale value for #FFFFFF
           lowerCamelCase__   	=					int(self.get_greyscale(2_5_5						,	2_5_5						,	2_5_5      )      )

           if not self.min_threshold < threshold < self.max_threshold:
                 lowerCamelCase__   	=					F"""Factor value should be from 0 to {self.max_threshold}"""
                 raise ValueError(A__      )

           lowerCamelCase__   	=					input_img
           lowerCamelCase__   	=					threshold
           lowerCamelCase__	,		lowerCamelCase__   	=					self.input_img.shape[1], self.input_img.shape[0]

           # error table size (+4 columns and +1 row) greater than input image because of
           # lack of if statements
           lowerCamelCase__   	=					[
               [0 for _ in range(self.height + 4      )] for __ in range(self.width + 1      )
           ]
           lowerCamelCase__   	=					np.ones((self.width, self.height, 3)						,	np.uinta      ) * 2_5_5




     @classmethod
     def 			__snake_case (     cls	: int						,	A__	: int						,	A__	: int						,	A__	: int      )					->							float:
           return 0.114 * blue + 0.587 * green + 0.2126 * red




     def 			__snake_case (     self	: List[Any]      )					->							None:
           for y in range(self.height      ):
                 for x in range(self.width      ):
                       lowerCamelCase__   	=					int(self.get_greyscale(*self.input_img[y][x]      )      )
                       if self.threshold > greyscale + self.error_table[y][x]:
                             lowerCamelCase__   	=					(0, 0, 0)
                             lowerCamelCase__   	=					greyscale + self.error_table[x][y]
                       else:
                             lowerCamelCase__   	=					(2_5_5, 2_5_5, 2_5_5)
                             lowerCamelCase__   	=					greyscale + self.error_table[x][y] - 2_5_5
                       self.error_table[y][x + 1] += int(8 / 3_2 * current_error      )
                       self.error_table[y][x + 2] += int(4 / 3_2 * current_error      )
                       self.error_table[y + 1][x] += int(8 / 3_2 * current_error      )
                       self.error_table[y + 1][x + 1] += int(4 / 3_2 * current_error      )
                       self.error_table[y + 1][x + 2] += int(2 / 3_2 * current_error      )
                       self.error_table[y + 1][x - 1] += int(4 / 3_2 * current_error      )
                       self.error_table[y + 1][x - 2] += int(2 / 3_2 * current_error      )


if __name__ == "__main__":
      # create Burke's instances with original images in greyscale
      __SCREAMING_SNAKE_CASE     : Optional[Any]      =[
          Burkes(imread("""image_data/lena.jpg""", 1), threshold)
          for threshold in (1, 126, 130, 140)
      ]

      for burkes in burkes_instances:
            burkes.process()

      for burkes in burkes_instances:
            imshow(
                F"""Original image with dithering threshold: {burkes.threshold}""",
                burkes.output_img,
            )

      waitKey(0)
      destroyAllWindows()