


def   _lowerCAmelCase				(   UpperCamelCase__:     str							,					UpperCamelCase__:     str						) ->   float:





	"""simple docstring"""







	def get_matched_characters(UpperCamelCase__:     str							,					UpperCamelCase__:     str						) -> str:
		A								=				[]
		A								=				min(len(_stra						)							,					len(_stra						)						) // 2
		for i, l in enumerate(_stra						):
			A								=				int(max(0							,					i - limit						)						)
			A								=				int(min(i + limit + 1							,					len(_stra						)						)						)
			if l in _stra[left:right]:
				matched.append(UpperCamelCase__						)
				A								=				f'{_stra[0:_stra.index(UpperCamelCase__						)]} {_stra[_stra.index(UpperCamelCase__						) + 1:]}'

		return "".join(UpperCamelCase__						)

	# matching characters
	A								=				get_matched_characters(UpperCamelCase__							,					UpperCamelCase__						)
	A								=				get_matched_characters(UpperCamelCase__							,					UpperCamelCase__						)
	A								=				len(UpperCamelCase__						)

	# transposition
	A								=				(
	    len([(ca, ca) for ca, ca in zip(UpperCamelCase__							,					UpperCamelCase__						) if ca != ca]						) // 2
	)

	if not match_count:
		A								=				0.0
	else:
		A								=				(
		    1
		    / 3
		    * (
		        match_count / len(UpperCamelCase__						)
		        + match_count / len(UpperCamelCase__						)
		        + (match_count - transpositions) / match_count
		    )
		)

	# common prefix up to 4 characters
	A								=				0
	for ca, ca in zip(stra[:4]							,					stra[:4]						):
		if ca == ca:
			prefix_len += 1
		else:
			break

	return jaro + 0.1 * prefix_len * (1 - jaro)


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	print(jaro_winkler("hello", "world"))







import unittest
from typing import Tuple

import torch

from diffusers.utils import floats_tensor, randn_tensor, torch_all_close, torch_device
from diffusers.utils.testing_utils import require_torch






@require_torch
class       _UpperCamelCase    :

				"""simple docstring"""







				@property
				def      _UpperCAmelCase					(						self   )	->				Any:
					return self.get_dummy_input()

				@property
				def      _UpperCAmelCase					(						self   )	->				Union[str, Any]:
					if self.block_type == "down":
						return (4, 32, 16, 16)
					elif self.block_type == "mid":
						return (4, 32, 32, 32)
					elif self.block_type == "up":
						return (4, 32, 64, 64)

					raise ValueError(f'\'{self.block_type}\' is not a supported block_type. Set it to \'up\', \'mid\', or \'down\'.'   )

				def      _UpperCAmelCase					(						self				,   a__=True				,   a__=False				,   a__=False				,   a__=False				,   )	->				Optional[Any]:
					A								=				4
					A								=				32
					A								=				(32, 32)

					A								=				torch.manual_seed(0   )
					A								=				torch.device(a__   )
					A								=				(batch_size, num_channels) + sizes
					A								=				randn_tensor(a__				,   generator=a__				,   device=a__   )
					A								=				{"""hidden_states""": hidden_states}

					if include_temb:
						A								=				128
						A								=				randn_tensor((batch_size, temb_channels)				,   generator=a__				,   device=a__   )

					if include_res_hidden_states_tuple:
						A								=				torch.manual_seed(1   )
						A								=				(randn_tensor(a__				,   generator=a__				,   device=a__   ),)

					if include_encoder_hidden_states:
						A								=				floats_tensor((batch_size, 32, 32)   ).to(a__   )

					if include_skip_sample:
						A								=				randn_tensor(((batch_size, 3) + sizes)				,   generator=a__				,   device=a__   )

					return dummy_input

				def      _UpperCAmelCase					(						self   )	->				int:
					A								=				{
					    """in_channels""": 32,
					    """out_channels""": 32,
					    """temb_channels""": 128,
					}
					if self.block_type == "up":
						A								=				32

					if self.block_type == "mid":
						init_dict.pop("""out_channels"""   )

					A								=				self.dummy_input
					return init_dict, inputs_dict

				def      _UpperCAmelCase					(						self				,   a__   )	->				Optional[int]:
					A					,			A								=				self.prepare_init_args_and_inputs_for_common()
					A								=				self.block_class(**a__   )
					unet_block.to(a__   )
					unet_block.eval()

					with torch.no_grad():
						A								=				unet_block(**a__   )

					if isinstance(a__				,   a__   ):
						A								=				output[0]

					self.assertEqual(output.shape				,   self.output_shape   )

					A								=				output[0, -1, -3:, -3:]
					A								=				torch.tensor(a__   ).to(a__   )
					assert torch_all_close(output_slice.flatten()				,   a__				,   atol=5e-3   )

				@unittest.skipIf(torch_device == """mps"""				,   """Training is not supported in mps"""   )
				def      _UpperCAmelCase					(						self   )	->				str:
					A					,			A								=				self.prepare_init_args_and_inputs_for_common()
					A								=				self.block_class(**a__   )
					model.to(a__   )
					model.train()
					A								=				model(**a__   )

					if isinstance(a__				,   a__   ):
						A								=				output[0]

					A								=				torch.device(a__   )
					A								=				randn_tensor(output.shape				,   device=a__   )
					A								=				torch.nn.functional.mse_loss(a__				,   a__   )
					loss.backward()



