


"""simple docstring"""







import copy
import json
import os
import tempfile

from transformers import is_torch_available

from .test_configuration_utils import config_common_kwargs




class  UpperCamelCase_	( UpperCamelCase):

	"""simple docstring"""





	def __init__( self			:				Any			,			UpperCAmelCase__			:				List[Any]			,			UpperCAmelCase__			:				Any=None			,			UpperCAmelCase__			:				List[Any]=True			,			UpperCAmelCase__			:				Optional[int]=None			,			**UpperCAmelCase__			:				Tuple   )       ->	List[Any]:
						__SCREAMING_SNAKE_CASE						   =				parent
						__SCREAMING_SNAKE_CASE						   =				config_class
						__SCREAMING_SNAKE_CASE						   =				has_text_modality
						__SCREAMING_SNAKE_CASE						   =				kwargs
						__SCREAMING_SNAKE_CASE						   =				common_properties




	def   UpperCAmelCase_      ( self			:				Union[str, Any]   )       ->	Optional[Any]:
						__SCREAMING_SNAKE_CASE						   =				self.config_class(**self.inputs_dict   )
						__SCREAMING_SNAKE_CASE						   =				(
						    ["hidden_size", "num_attention_heads", "num_hidden_layers"]
						    if self.common_properties is None
						    else self.common_properties
						)

						# Add common fields for text models
						if self.has_text_modality:
											common_properties.extend(["vocab_size"]   )

						# Test that config has the common properties as getters
						for prop in common_properties:
											self.parent.assertTrue(hasattr(UpperCAmelCase__			,			UpperCAmelCase__   )			,			msg=F"""`{prop}` does not exist"""   )

						# Test that config has the common properties as setter
						for idx, name in enumerate(UpperCAmelCase__   ):
											try:
																setattr(UpperCAmelCase__			,			UpperCAmelCase__			,			UpperCAmelCase__   )
																self.parent.assertEqual(
																    getattr(UpperCAmelCase__			,			UpperCAmelCase__   )			,			UpperCAmelCase__			,			msg=F"""`{name} value {idx} expected, but was {getattr(UpperCAmelCase__			,			UpperCAmelCase__   )}"""   )
											except NotImplementedError:
																# Some models might not be able to implement setters for common_properties
																# In that case, a NotImplementedError is raised
																pass

        # Test if config class can be called with Config(prop_name=..)
						for idx, name in enumerate(UpperCAmelCase__   ):
											try:
																__SCREAMING_SNAKE_CASE						   =				self.config_class(**{name: idx}   )
																self.parent.assertEqual(
																    getattr(UpperCAmelCase__			,			UpperCAmelCase__   )			,			UpperCAmelCase__			,			msg=F"""`{name} value {idx} expected, but was {getattr(UpperCAmelCase__			,			UpperCAmelCase__   )}"""   )
											except NotImplementedError:
																# Some models might not be able to implement setters for common_properties
																# In that case, a NotImplementedError is raised
																pass




	def   UpperCAmelCase_      ( self			:				Optional[int]   )       ->	Optional[int]:
						__SCREAMING_SNAKE_CASE						   =				self.config_class(**self.inputs_dict   )
						__SCREAMING_SNAKE_CASE						   =				json.loads(config.to_json_string()   )
						for key, value in self.inputs_dict.items():
											self.parent.assertEqual(obj[key]			,			UpperCAmelCase__   )




	def   UpperCAmelCase_      ( self			:				Dict   )       ->	int:
						__SCREAMING_SNAKE_CASE						   =				self.config_class(**self.inputs_dict   )

						with tempfile.TemporaryDirectory() as tmpdirname:
											__SCREAMING_SNAKE_CASE						   =				os.path.join(UpperCAmelCase__			,			"config.json"   )
											config_first.to_json_file(UpperCAmelCase__   )
											__SCREAMING_SNAKE_CASE						   =				self.config_class.from_json_file(UpperCAmelCase__   )

						self.parent.assertEqual(config_second.to_dict()			,			config_first.to_dict()   )




	def   UpperCAmelCase_      ( self			:				Optional[Any]   )       ->	List[Any]:
						__SCREAMING_SNAKE_CASE						   =				self.config_class(**self.inputs_dict   )

						with tempfile.TemporaryDirectory() as tmpdirname:
											config_first.save_pretrained(UpperCAmelCase__   )
											__SCREAMING_SNAKE_CASE						   =				self.config_class.from_pretrained(UpperCAmelCase__   )

						self.parent.assertEqual(config_second.to_dict()			,			config_first.to_dict()   )




	def   UpperCAmelCase_      ( self			:				Any   )       ->	List[Any]:
						__SCREAMING_SNAKE_CASE						   =				self.config_class(**self.inputs_dict   )

						__SCREAMING_SNAKE_CASE						   =				"test"
						with tempfile.TemporaryDirectory() as tmpdirname:
											__SCREAMING_SNAKE_CASE						   =				os.path.join(UpperCAmelCase__			,			UpperCAmelCase__   )
											config_first.save_pretrained(UpperCAmelCase__   )
											__SCREAMING_SNAKE_CASE						   =				self.config_class.from_pretrained(UpperCAmelCase__			,			subfolder=UpperCAmelCase__   )

						self.parent.assertEqual(config_second.to_dict()			,			config_first.to_dict()   )




	def   UpperCAmelCase_      ( self			:				str   )       ->	Union[str, Any]:
						__SCREAMING_SNAKE_CASE						   =				self.config_class(**self.inputs_dict			,			num_labels=5   )
						self.parent.assertEqual(len(config.idalabel   )			,			5   )
						self.parent.assertEqual(len(config.labelaid   )			,			5   )

						__SCREAMING_SNAKE_CASE						   =				3
						self.parent.assertEqual(len(config.idalabel   )			,			3   )
						self.parent.assertEqual(len(config.labelaid   )			,			3   )




	def   UpperCAmelCase_      ( self			:				List[Any]   )       ->	Optional[int]:
						if self.config_class.is_composition:
											return
						__SCREAMING_SNAKE_CASE						   =				self.config_class()
						self.parent.assertIsNotNone(UpperCAmelCase__   )




	def   UpperCAmelCase_      ( self			:				str   )       ->	List[str]:
						__SCREAMING_SNAKE_CASE						   =				copy.deepcopy(UpperCAmelCase__   )
						__SCREAMING_SNAKE_CASE						   =				self.config_class(**UpperCAmelCase__   )
						__SCREAMING_SNAKE_CASE						   =				[]
						for key, value in config_common_kwargs.items():
											if key == "torch_dtype":
																if not is_torch_available():
																					continue
																else:
																					import torch

																					if config.torch_dtype != torch.floataa:
																										wrong_values.append(("torch_dtype", config.torch_dtype, torch.floataa)   )
											elif getattr(UpperCAmelCase__			,			UpperCAmelCase__   ) != value:
																wrong_values.append((key, getattr(UpperCAmelCase__			,			UpperCAmelCase__   ), value)   )

						if len(UpperCAmelCase__   ) > 0:
											__SCREAMING_SNAKE_CASE						   =				"\n".join([F"""- {v[0]}: got {v[1]} instead of {v[2]}""" for v in wrong_values]   )
											raise ValueError(F"""The following keys were not properly set in the config:\n{errors}"""   )




	def   UpperCAmelCase_      ( self			:				Optional[int]   )       ->	int:
						self.create_and_test_config_common_properties()
						self.create_and_test_config_to_json_string()
						self.create_and_test_config_to_json_file()
						self.create_and_test_config_from_and_save_pretrained()
						self.create_and_test_config_from_and_save_pretrained_subfolder()
						self.create_and_test_config_with_num_labels()
						self.check_config_can_be_init_without_params()
						self.check_config_arguments_init()




"""simple docstring"""







from PIL import Image




def    UpperCAmelCase__       (lowerCAmelCase_   ):




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE   ,  __SCREAMING_SNAKE_CASE						   =				image.size
					__SCREAMING_SNAKE_CASE						   =				0
					__SCREAMING_SNAKE_CASE						   =				image.load()
					for i in range(lowerCAmelCase_   ):
										for j in range(lowerCAmelCase_   ):
															__SCREAMING_SNAKE_CASE						   =				pixels[j, i]
															mean += pixel
					mean //= width * height

					for j in range(lowerCAmelCase_   ):
										for i in range(lowerCAmelCase_   ):
															__SCREAMING_SNAKE_CASE						   =				255 if pixels[i, j] > mean else 0
					return image


if __name__ == "__main__":
		a__				:							List[str]						=     mean_threshold(Image.open('''path_to_image''').convert('''L'''))
		image.save('''output_image_path''')
