


'''simple docstring'''
import tempfile
import unittest

import numpy as np
import torch

from diffusers import DiffusionPipeline
from diffusers.models.attention_processor import Attention, AttnAddedKVProcessor







class 							A__					( unittest.TestCase						):





  def 		A						(	self   :			Optional[int]							,      _a   :			bool = False				)			->					Any:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE												=10

       if only_cross_attention:
            _SCREAMING_SNAKE_CASE												=12
       else:
            # when only cross attention is not set, the cross attention dim must be the same as the query dim
            _SCREAMING_SNAKE_CASE												=query_dim

       return {
           "query_dim": query_dim,
           "cross_attention_dim": cross_attention_dim,
           "heads": 2,
           "dim_head": 4,
           "added_kv_proj_dim": 6,
           "norm_num_groups": 1,
           "only_cross_attention": only_cross_attention,
           "processor": AttnAddedKVProcessor(),
       }





  def 		A						(	self   :			Optional[int]							,      _a   :			Dict							,      _a   :			Any				)			->					str:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE												=2

       _SCREAMING_SNAKE_CASE												=torch.rand(_lowerCAmelCase							,      _lowerCAmelCase							,      3							,      2				)
       _SCREAMING_SNAKE_CASE												=torch.rand(_lowerCAmelCase							,      4							,      _lowerCAmelCase				)
       _SCREAMING_SNAKE_CASE												=None

       return {
           "hidden_states": hidden_states,
           "encoder_hidden_states": encoder_hidden_states,
           "attention_mask": attention_mask,
       }





  def 		A						(	self   :			Optional[Any]				)			->					Tuple:





       '''simple docstring'''






       torch.manual_seed(0				)

       _SCREAMING_SNAKE_CASE												=self.get_constructor_arguments(only_cross_attention=_lowerCAmelCase				)
       _SCREAMING_SNAKE_CASE												=Attention(**_lowerCAmelCase				)

       self.assertTrue(attn.to_k is not None				)
       self.assertTrue(attn.to_v is not None				)

       _SCREAMING_SNAKE_CASE												=self.get_forward_arguments(
           query_dim=constructor_args['query_dim']							,      added_kv_proj_dim=constructor_args['added_kv_proj_dim']				)

       _SCREAMING_SNAKE_CASE												=attn(**_lowerCAmelCase				)

       # only self attention

       torch.manual_seed(0				)

       _SCREAMING_SNAKE_CASE												=self.get_constructor_arguments(only_cross_attention=_lowerCAmelCase				)
       _SCREAMING_SNAKE_CASE												=Attention(**_lowerCAmelCase				)

       self.assertTrue(attn.to_k is None				)
       self.assertTrue(attn.to_v is None				)

       _SCREAMING_SNAKE_CASE												=self.get_forward_arguments(
           query_dim=constructor_args['query_dim']							,      added_kv_proj_dim=constructor_args['added_kv_proj_dim']				)

       _SCREAMING_SNAKE_CASE												=attn(**_lowerCAmelCase				)

       self.assertTrue((only_cross_attn_out != self_and_cross_attn_out).all()				)







class 							A__					( unittest.TestCase						):





  def 		A						(	self   :			Any				)			->					Any:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE												=DiffusionPipeline.from_pretrained('hf-internal-testing/tiny-stable-diffusion-pipe'							,      safety_checker=_lowerCAmelCase				)

       _SCREAMING_SNAKE_CASE												=pipe(
           'foo'							,      num_inference_steps=2							,      generator=torch.Generator('cpu'				).manual_seed(0				)							,      output_type='np'							,      ).images

       # the initial conversion succeeds
       _SCREAMING_SNAKE_CASE												=DiffusionPipeline.from_pretrained(
           'hf-internal-testing/tiny-stable-diffusion-pipe'							,      device_map='sequential'							,      safety_checker=_lowerCAmelCase				)

       _SCREAMING_SNAKE_CASE												=pipe(
           'foo'							,      num_inference_steps=2							,      generator=torch.Generator('cpu'				).manual_seed(0				)							,      output_type='np'							,      ).images

       with tempfile.TemporaryDirectory() as tmpdir:
            # save the converted model
            pipe.save_pretrained(_lowerCAmelCase				)

            # can also load the converted weights
            _SCREAMING_SNAKE_CASE												=DiffusionPipeline.from_pretrained(_lowerCAmelCase							,      device_map='sequential'							,      safety_checker=_lowerCAmelCase				)

       _SCREAMING_SNAKE_CASE												=pipe(
           'foo'							,      num_inference_steps=2							,      generator=torch.Generator('cpu'				).manual_seed(0				)							,      output_type='np'							,      ).images

       self.assertTrue(np.allclose(_lowerCAmelCase							,      _lowerCAmelCase							,      atol=1e-5				)				)
       self.assertTrue(np.allclose(_lowerCAmelCase							,      _lowerCAmelCase							,      atol=1e-5				)				)



'''simple docstring'''
lowerCamelCase				:   int      				=  {
    0: "underwear",
    1: "pants",
    2: "belt",
    3: "suit",
    4: "shoe",
    5: "socks",
    6: "shirt",
    7: "tie",
    8: "watch",
}

lowerCamelCase				:   Union[str, Any]      				=  [[1, 4], [2, 4], [3], [], [], [4], [2, 7], [3], []]

lowerCamelCase				:   int      				=  [0 for x in range(len(graph))]
lowerCamelCase				:   Optional[Any]      				=  []


def  _lowerCAmelCase      (		_UpperCamelCase		:  List[str]							,				_UpperCamelCase		:  Union[str, Any]       )   ->    Union[str, Any]:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE												=1
     while stack:
          _SCREAMING_SNAKE_CASE												=stack.pop()
          print(_UpperCamelCase							,				clothes[current_clothing]       )
          order += 1


def  _lowerCAmelCase      (		_UpperCamelCase		:  str							,				_UpperCamelCase		:  Optional[Any]							,				_UpperCamelCase		:  Dict       )   ->    Optional[Any]:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE												=1
     for v in graph[u]:
          if not visited[v]:
               depth_first_search(_UpperCamelCase							,				_UpperCamelCase							,				_UpperCamelCase       )

     stack.append(_UpperCamelCase       )


def  _lowerCAmelCase      (		_UpperCamelCase		:  Dict							,				_UpperCamelCase		:  List[str]       )   ->    Optional[int]:
     """simple docstring"""




     for v in range(len(_UpperCamelCase       )       ):
          if not visited[v]:
               depth_first_search(_UpperCamelCase							,				_UpperCamelCase							,				_UpperCamelCase       )


if __name__ == "__main__":
     topological_sort(graph, visited)
     print(stack)
     print_stack(stack, clothes)
