


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__  :
      @property
      def        SCREAMING_SNAKE_CASE							(       self				:       Optional[int]     )		->			Tuple:
             return self.get_dummy_input()
      @property
      def        SCREAMING_SNAKE_CASE							(       self				:       Optional[Any]     )		->			Optional[Any]:
             if self.block_type == "down":
                    return (4, 3_2, 1_6, 1_6)
             elif self.block_type == "mid":
                    return (4, 3_2, 3_2, 3_2)
             elif self.block_type == "up":
                    return (4, 3_2, 6_4, 6_4)

             raise ValueError(F"""'{self.block_type}' is not a supported block_type. Set it to 'up', 'mid', or 'down'."""     )
      def        SCREAMING_SNAKE_CASE							(       self				:       str     ,							SCREAMING_SNAKE_CASE__				:       List[str]=True     ,							SCREAMING_SNAKE_CASE__				:       Optional[Any]=False     ,							SCREAMING_SNAKE_CASE__				:       str=False     ,							SCREAMING_SNAKE_CASE__				:       Any=False     ,							)		->			Tuple:
             a_					:		List[Any]							=  4
             a_					:		Any							=  3_2
             a_					:		List[str]							=  (3_2, 3_2)

             a_					:		Any							=  torch.manual_seed(0     )
             a_					:		Dict							=  torch.device(SCREAMING_SNAKE_CASE__     )
             a_					:		str							=  (batch_size, num_channels) + sizes
             a_					:		Optional[int]							=  randn_tensor(SCREAMING_SNAKE_CASE__     ,							generator=SCREAMING_SNAKE_CASE__     ,							device=SCREAMING_SNAKE_CASE__     )
             a_					:		Union[str, Any]							=  {'hidden_states': hidden_states}

             if include_temb:
                    a_					:		Any							=  1_2_8
                    a_					:		Optional[int]							=  randn_tensor((batch_size, temb_channels)     ,							generator=SCREAMING_SNAKE_CASE__     ,							device=SCREAMING_SNAKE_CASE__     )

             if include_res_hidden_states_tuple:
                    a_					:		int							=  torch.manual_seed(1     )
                    a_					:		List[str]							=  (randn_tensor(SCREAMING_SNAKE_CASE__     ,							generator=SCREAMING_SNAKE_CASE__     ,							device=SCREAMING_SNAKE_CASE__     ),)

             if include_encoder_hidden_states:
                    a_					:		str							=  floats_tensor((batch_size, 3_2, 3_2)     ).to(SCREAMING_SNAKE_CASE__     )

             if include_skip_sample:
                    a_					:		Union[str, Any]							=  randn_tensor(((batch_size, 3) + sizes)     ,							generator=SCREAMING_SNAKE_CASE__     ,							device=SCREAMING_SNAKE_CASE__     )

             return dummy_input
      def        SCREAMING_SNAKE_CASE							(       self				:       Optional[int]     )		->			Optional[int]:
             a_					:		Union[str, Any]							=  {
                 'in_channels': 3_2,
                 'out_channels': 3_2,
                 'temb_channels': 1_2_8,
             }
             if self.block_type == "up":
                    a_					:		Dict							=  3_2

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

             a_					:		Union[str, Any]							=  self.dummy_input
             return init_dict, inputs_dict
      def        SCREAMING_SNAKE_CASE							(       self				:       Dict     ,							SCREAMING_SNAKE_CASE__				:       Tuple     )		->			Optional[int]:
             a_							,		a_					:		Union[str, Any]							=  self.prepare_init_args_and_inputs_for_common()
             a_					:		Tuple							=  self.block_class(**SCREAMING_SNAKE_CASE__     )
             unet_block.to(SCREAMING_SNAKE_CASE__     )
             unet_block.eval()

             with torch.no_grad():
                    a_					:		Tuple							=  unet_block(**SCREAMING_SNAKE_CASE__     )

             if isinstance(SCREAMING_SNAKE_CASE__     ,							SCREAMING_SNAKE_CASE__     ):
                    a_					:		Any							=  output[0]

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

             a_					:		int							=  output[0, -1, -3:, -3:]
             a_					:		str							=  torch.tensor(SCREAMING_SNAKE_CASE__     ).to(SCREAMING_SNAKE_CASE__     )
             assert torch_all_close(output_slice.flatten()     ,							SCREAMING_SNAKE_CASE__     ,							atol=5E-3     )

      @unittest.skipIf(torch_device == 'mps'     ,							'Training is not supported in mps'     )
      def        SCREAMING_SNAKE_CASE							(       self				:       List[str]     )		->			List[str]:
             a_							,		a_					:		Optional[Any]							=  self.prepare_init_args_and_inputs_for_common()
             a_					:		Tuple							=  self.block_class(**SCREAMING_SNAKE_CASE__     )
             model.to(SCREAMING_SNAKE_CASE__     )
             model.train()
             a_					:		str							=  model(**SCREAMING_SNAKE_CASE__     )

             if isinstance(SCREAMING_SNAKE_CASE__     ,							SCREAMING_SNAKE_CASE__     ):
                    a_					:		Dict							=  output[0]

             a_					:		Optional[int]							=  torch.device(SCREAMING_SNAKE_CASE__     )
             a_					:		List[Any]							=  randn_tensor(output.shape     ,							device=SCREAMING_SNAKE_CASE__     )
             a_					:		Union[str, Any]							=  torch.nn.functional.mse_loss(SCREAMING_SNAKE_CASE__     ,							SCREAMING_SNAKE_CASE__     )
             loss.backward()









'''simple docstring'''




import random






def        _lowerCAmelCase			(  __snake_case  : int ,							__snake_case  : float ,							__snake_case  : bool = False			)     ->     dict:
     __A						: dict      =       {i: [] for i in range(__snake_case			)}

     # if probability is greater or equal than 1, then generate a complete graph
     if probability >= 1:
          return complete_graph(__snake_case			)
     # if probability is lower or equal than 0, then return a graph without edges
     if probability <= 0:
          return graph

     # for each couple of nodes, add an edge from u to v
     # if the number randomly generated is greater than probability probability
     for i in range(__snake_case			):
          for j in range(i + 1 ,							__snake_case			):
               if random.random() < probability:
                    graph[i].append(__snake_case			)
                    if not directed:
                         # if the graph is undirected, add an edge in from j to i, either
                         graph[j].append(__snake_case			)
     return graph






def        _lowerCAmelCase			(  __snake_case  : int			)     ->     dict:
     return {
         i: [j for j in range(__snake_case			) if i != j] for i in range(__snake_case			)
     }


if __name__ == "__main__":
  import doctest

  doctest.testmod()