



import unittest

from transformers import load_tool
from transformers.utils import is_torch_available


if is_torch_available():
	import torch

from transformers.testing_utils import require_torch

from .test_tools_common import ToolTesterMixin







@require_torch
class 							_a							(					unittest.TestCase      ,						lowercase__ ):

			def 			__snake_case    (self )    ->					Any:
						UpperCAmelCase_:						Dict					=			load_tool("""text-to-speech""" )
						self.tool.setup()

			def 			__snake_case    (self )    ->					Tuple:
						torch.manual_seed(0 )
						UpperCAmelCase_:						Dict					=			self.tool("""hey""" )
						UpperCAmelCase_:						Dict					=			result.to_raw()
						self.assertTrue(
						    torch.allclose(
						        resulting_tensor[:3],					torch.tensor([-0.0_0_0_5_9_6_6_6_6_8_8_3_2_1_1_5_8_2_9, -0.0_0_0_3_6_5_7_6_4_0_1_9_0_7_9_5_0_6_4, -0.0_0_0_1_3_4_3_9_5_0_2_7_9_9_8_8_3_4_8_5] ),					) )

			def 			__snake_case    (self )    ->					str:
						torch.manual_seed(0 )
						UpperCAmelCase_:						Any					=			self.tool("""hey""" )
						UpperCAmelCase_:						List[Any]					=			result.to_raw()
						self.assertTrue(
						    torch.allclose(
						        resulting_tensor[:3],					torch.tensor([-0.0_0_0_5_9_6_6_6_6_8_8_3_2_1_1_5_8_2_9, -0.0_0_0_3_6_5_7_6_4_0_1_9_0_7_9_5_0_6_4, -0.0_0_0_1_3_4_3_9_5_0_2_7_9_9_8_8_3_4_8_5] ),					) )










from PIL import Image

def       lowercase_		(		_A			:    Image						):

      """simple docstring"""




      lowerCamelCase__    ,   lowerCamelCase__		:				List[str]    =  image.size
      lowerCamelCase__		:				Dict    =  0
      lowerCamelCase__		:				Dict    =  image.load()
      for i in range(_A						):
            for j in range(_A						):
                  lowerCamelCase__		:				List[str]    =  pixels[j, i]
                  mean += pixel
      mean //= width * height

      for j in range(_A						):
            for i in range(_A						):
                  lowerCamelCase__		:				Union[str, Any]    =  255 if pixels[i, j] > mean else 0
      return image


if __name__ == "__main__":
    A      :    int		=					mean_threshold(Image.open("path_to_image").convert("L"))
    image.save("output_image_path")


