



from ..utils import requires_backends


UpperCamelCase_									=   None







class       _snake_case				:






   '''simple docstring'''






   def __init__(    self:     List[str]  ,*lowerCamelCase_:     Any  ,**lowerCamelCase_:     List[str]							)	->      List[Any]:
       requires_backends(self  ,["""detectron2"""]							)






   @classmethod
   def       A__    (    cls:     Any  ,*lowerCamelCase_:     Any  ,**lowerCamelCase_:     int							)	->      str:
       requires_backends(cls  ,["""detectron2"""]							)







'''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       lowerCamelCase       (    unittest.TestCase	):


					def  SCREAMING_SNAKE_CASE_		(       self						:	List[Any]							,  __snake_case						:	bool = False							):





											'''simple docstring'''





											_snake_case: Any	       =		10

											if only_cross_attention:
																	_snake_case: Optional[int]	       =		12
											else:
																	# when only cross attention is not set, the cross attention dim must be the same as the query dim
																	_snake_case: Optional[int]	       =		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  SCREAMING_SNAKE_CASE_		(       self						:	List[Any]							,  __snake_case						:	List[Any]							,  __snake_case						:	Tuple							):





											'''simple docstring'''





											_snake_case: Optional[int]	       =		2

											_snake_case: Union[str, Any]	       =		torch.rand(__snake_case							,  __snake_case							,  3							,  2							)
											_snake_case: Optional[int]	       =		torch.rand(__snake_case							,  4							,  __snake_case							)
											_snake_case: int	       =		None

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




					def  SCREAMING_SNAKE_CASE_		(       self						:	List[Any]							):





											'''simple docstring'''





											torch.manual_seed(0							)

											_snake_case: int	       =		self.get_constructor_arguments(only_cross_attention=__snake_case							)
											_snake_case: Union[str, Any]	       =		Attention(**__snake_case							)

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

											_snake_case: Optional[int]	       =		self.get_forward_arguments(
											    query_dim=constructor_args['query_dim']							,  added_kv_proj_dim=constructor_args['added_kv_proj_dim']							)

											_snake_case: str	       =		attn(**__snake_case							)

											# only self attention

											torch.manual_seed(0							)

											_snake_case: str	       =		self.get_constructor_arguments(only_cross_attention=__snake_case							)
											_snake_case: Optional[Any]	       =		Attention(**__snake_case							)

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

											_snake_case: Dict	       =		self.get_forward_arguments(
											    query_dim=constructor_args['query_dim']							,  added_kv_proj_dim=constructor_args['added_kv_proj_dim']							)

											_snake_case: str	       =		attn(**__snake_case							)

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







class       lowerCamelCase       (    unittest.TestCase	):


					def  SCREAMING_SNAKE_CASE_		(       self						:	Tuple							):





											'''simple docstring'''





											_snake_case: str	       =		DiffusionPipeline.from_pretrained('hf-internal-testing/tiny-stable-diffusion-pipe'							,  safety_checker=__snake_case							)

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

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

											_snake_case: Any	       =		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(__snake_case							)

																	# can also load the converted weights
																	_snake_case: Union[str, Any]	       =		DiffusionPipeline.from_pretrained(__snake_case							,  device_map='sequential'							,  safety_checker=__snake_case							)

											_snake_case: Union[str, Any]	       =		pipe(
											    'foo'							,  num_inference_steps=2							,  generator=torch.Generator('cpu'							).manual_seed(0							)							,  output_type='np'							,  ).images

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





