





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      _SCREAMING_SNAKE_CASE		:







 '''simple docstring'''

 @property
 def 							SCREAMING_SNAKE_CASE_      (self       :     List[str]) ->Any:



   '''simple docstring'''



   return self.get_dummy_input()

 @property
 def 							SCREAMING_SNAKE_CASE_      (self       :     Dict) ->List[str]:



   '''simple docstring'''



   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 							SCREAMING_SNAKE_CASE_      (self       :     Union[str, Any] ,				UpperCAmelCase_       :     Optional[int]=True ,				UpperCAmelCase_       :     Optional[Any]=False ,				UpperCAmelCase_       :     Union[str, Any]=False ,				UpperCAmelCase_       :     Optional[Any]=False ,				) ->Dict:



   '''simple docstring'''



   lowerCamelCase__:   Optional[Any]    						=4
   lowerCamelCase__:   Optional[int]    						=32
   lowerCamelCase__:   Optional[Any]    						=(32, 32)

   lowerCamelCase__:   Optional[Any]    						=torch.manual_seed(0)
   lowerCamelCase__:   List[Any]    						=torch.device(lowerCAmelCase_)
   lowerCamelCase__:   str    						=(batch_size, num_channels) + sizes
   lowerCamelCase__:   List[Any]    						=randn_tensor(lowerCAmelCase_ ,				generator=lowerCAmelCase_ ,				device=lowerCAmelCase_)
   lowerCamelCase__:   Any    						={"hidden_states": hidden_states}

   if include_temb:
     lowerCamelCase__:   str    						=128
     lowerCamelCase__:   Dict    						=randn_tensor((batch_size, temb_channels) ,				generator=lowerCAmelCase_ ,				device=lowerCAmelCase_)

   if include_res_hidden_states_tuple:
     lowerCamelCase__:   List[str]    						=torch.manual_seed(1)
     lowerCamelCase__:   str    						=(randn_tensor(lowerCAmelCase_ ,				generator=lowerCAmelCase_ ,				device=lowerCAmelCase_),)

   if include_encoder_hidden_states:
     lowerCamelCase__:   Optional[Any]    						=floats_tensor((batch_size, 32, 32)).to(lowerCAmelCase_)

   if include_skip_sample:
     lowerCamelCase__:   List[Any]    						=randn_tensor(((batch_size, 3) + sizes) ,				generator=lowerCAmelCase_ ,				device=lowerCAmelCase_)

   return dummy_input

 def 							SCREAMING_SNAKE_CASE_      (self       :     Any) ->Optional[int]:



   '''simple docstring'''



   lowerCamelCase__:   Optional[Any]    						={
       "in_channels": 32,
       "out_channels": 32,
       "temb_channels": 128,
   }
   if self.block_type == "up":
     lowerCamelCase__:   Union[str, Any]    						=32

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

   lowerCamelCase__:   str    						=self.dummy_input
   return init_dict, inputs_dict

 def 							SCREAMING_SNAKE_CASE_      (self       :     Dict ,				UpperCAmelCase_       :     str) ->Dict:



   '''simple docstring'''



   lowerCamelCase__						,      lowerCamelCase__:   List[str]    						=self.prepare_init_args_and_inputs_for_common()
   lowerCamelCase__:   List[str]    						=self.block_class(**lowerCAmelCase_)
   unet_block.to(lowerCAmelCase_)
   unet_block.eval()

   with torch.no_grad():
     lowerCamelCase__:   Tuple    						=unet_block(**lowerCAmelCase_)

   if isinstance(lowerCAmelCase_ ,				lowerCAmelCase_):
     lowerCamelCase__:   Tuple    						=output[0]

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

   lowerCamelCase__:   Tuple    						=output[0, -1, -3:, -3:]
   lowerCamelCase__:   Any    						=torch.tensor(lowerCAmelCase_).to(lowerCAmelCase_)
   assert torch_all_close(output_slice.flatten() ,				lowerCAmelCase_ ,				atol=5E-3)







 @unittest.skipIf(torch_device == "mps" ,				"Training is not supported in mps")
 def 							SCREAMING_SNAKE_CASE_      (self       :     Tuple) ->Optional[Any]:



   '''simple docstring'''



   lowerCamelCase__						,      lowerCamelCase__:   Tuple    						=self.prepare_init_args_and_inputs_for_common()
   lowerCamelCase__:   Optional[Any]    						=self.block_class(**lowerCAmelCase_)
   model.to(lowerCAmelCase_)
   model.train()
   lowerCamelCase__:   str    						=model(**lowerCAmelCase_)

   if isinstance(lowerCAmelCase_ ,				lowerCAmelCase_):
     lowerCamelCase__:   Any    						=output[0]

   lowerCamelCase__:   int    						=torch.device(lowerCAmelCase_)
   lowerCamelCase__:   List[str]    						=randn_tensor(output.shape ,				device=lowerCAmelCase_)
   lowerCamelCase__:   Tuple    						=torch.nn.functional.mse_loss(lowerCAmelCase_ ,				lowerCAmelCase_)
   loss.backward()








from __future__ import annotations

from collections.abc import Iterable, Iterator
from dataclasses import dataclass

__A             =       (3, 9, -11, 0, 7, 5, 1, -1)
__A             =       (4, 6, 2, 0, 8, 10, 3, -2)



@dataclass
class      _SCREAMING_SNAKE_CASE		:







 '''simple docstring'''
 lowercase_	=    42
 lowercase_	=    42



class      _SCREAMING_SNAKE_CASE		:







 '''simple docstring'''

 def __init__(self       :     Dict ,				UpperCAmelCase_       :     Iterable[int]) ->None:



   '''simple docstring'''



   lowerCamelCase__:   Node | None    						=None
   for i in sorted(UpperCAmelCase_ ,				reverse=UpperCAmelCase_):
     lowerCamelCase__:   Any    						=Node(UpperCAmelCase_ ,				self.head)

 def __iter__(self       :     Optional[int]) ->Iterator[int]:



   '''simple docstring'''



   lowerCamelCase__:   int    						=self.head
   while node:
     yield node.data
     lowerCamelCase__:   List[str]    						=node.next_node

 def __len__(self       :     int) ->int:



   '''simple docstring'''



   return sum(1 for _ in self)







 def __str__(self       :     Union[str, Any]) ->str:



   '''simple docstring'''



   return " -> ".join([str(UpperCAmelCase_) for node in self])





def   lowerCAmelCase_				( __a			,					__a	)  ->	SortedLinkedList:







  """simple docstring"""





  return SortedLinkedList(list(__a	) + list(__a	)	)


if __name__ == "__main__":
     import doctest

     doctest.testmod()
     __A             =       SortedLinkedList
     print(merge_lists(SSL(test_data_odd), SSL(test_data_even)))

