
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   :					str					):
					return self.get_dummy_input()






	@property
	def 							UpperCamelCase_    (			self   :					int					):
					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   :					List[str]				,A   :					Optional[Any]=True				,A   :					Optional[Any]=False				,A   :					List[str]=False				,A   :					Optional[Any]=False				,):
					__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       =					1_28
									__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   :					Optional[Any]					):
					__A       =					{
					    "in_channels": 32,
					    "out_channels": 32,
					    "temb_channels": 1_28,
					}
					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   :					Dict				,A   :					Tuple					):
					__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   :					Optional[int]					):
					__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()




'''simple docstring'''






def lowerCamelCase__     (		__lowerCamelCase			:				str = "The quick brown fox jumps over the lazy dog"			,       ):







   '''simple docstring'''




   _UpperCAmelCase  :  Optional[Any]				       =set()

   # Replace all the whitespace in our sentence
   _UpperCAmelCase  :  Dict				       =input_str.replace(' '			,       ''				)
   for alpha in input_str:
      if "a" <= alpha.lower() <= "z":
         frequency.add(alpha.lower()				)
   return len(__lowerCamelCase				) == 2_6





def lowerCamelCase__     (		__lowerCamelCase			:				str = "The quick brown fox jumps over the lazy dog"			,       ):







   '''simple docstring'''




   _UpperCAmelCase  :  Tuple				       =[False] * 2_6
   for char in input_str:
      if char.islower():
         _UpperCAmelCase  :  Dict				       =True
      elif char.isupper():
         _UpperCAmelCase  :  List[str]				       =True
   return all(__lowerCamelCase				)





def lowerCamelCase__     (		__lowerCamelCase			:				str = "The quick brown fox jumps over the lazy dog"			,       ):







   '''simple docstring'''




   return len({char for char in input_str.lower() if char.isalpha()}				) == 2_6





def lowerCamelCase__     (		):







   '''simple docstring'''




   from timeit import timeit

   _UpperCAmelCase  :  List[Any]				       ='from __main__ import is_pangram, is_pangram_faster, is_pangram_fastest'
   print(timeit('is_pangram()'			,       setup=__lowerCamelCase				)				)
   print(timeit('is_pangram_faster()'			,       setup=__lowerCamelCase				)				)
   print(timeit('is_pangram_fastest()'			,       setup=__lowerCamelCase				)				)
   # 5.348480500048026, 2.6477354579837993, 1.8470395830227062
   # 5.036091582966037, 2.644472333951853,  1.8869528750656173


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 benchmark()

