





import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    StableDiffusionAttendAndExcitePipeline,
    UNetaDConditionModel,
)
from diffusers.utils import load_numpy, skip_mps, slow
from diffusers.utils.testing_utils import require_torch_gpu

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


__UpperCAmelCase       :   Union[str, Any]				       =							False







@skip_mps
class     __snake_case       (							snake_case_   ,			snake_case_   ,			snake_case_   ,			unittest.TestCase			):





       '''simple docstring'''







       lowerCAmelCase__      		=     StableDiffusionAttendAndExcitePipeline
       lowerCAmelCase__      		=     False
       lowerCAmelCase__      		=     TEXT_TO_IMAGE_PARAMS
       lowerCAmelCase__      		=     TEXT_TO_IMAGE_BATCH_PARAMS.union({"""token_indices"""}			)
       lowerCAmelCase__      		=     TEXT_TO_IMAGE_IMAGE_PARAMS
       lowerCAmelCase__      		=     TEXT_TO_IMAGE_IMAGE_PARAMS




       @classmethod
       def 							UpperCAmelCase__ (  cls :    Union[str, Any]		):
         super().setUpClass()
         torch.use_deterministic_algorithms(UpperCAmelCase__		)




       @classmethod
       def 							UpperCAmelCase__ (  cls :    List[Any]		):
         super().tearDownClass()
         torch.use_deterministic_algorithms(UpperCAmelCase__		)




       def 							UpperCAmelCase__ (  self :    List[str]		):
         torch.manual_seed(0		)
         __snake_case:						Optional[Any]   =						UNetaDConditionModel(
             block_out_channels=(32, 64)							,				layers_per_block=1							,				sample_size=32							,				in_channels=4							,				out_channels=4							,				down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")							,				up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""")							,				cross_attention_dim=32							,				attention_head_dim=(2, 4)							,				use_linear_projection=UpperCAmelCase__							,				)
         __snake_case:						List[str]   =						DDIMScheduler(
             beta_start=0.0_0085							,				beta_end=0.012							,				beta_schedule="""scaled_linear"""							,				clip_sample=UpperCAmelCase__							,				set_alpha_to_one=UpperCAmelCase__							,				)
         torch.manual_seed(0		)
         __snake_case:						str   =						AutoencoderKL(
             block_out_channels=[32, 64]							,				in_channels=3							,				out_channels=3							,				down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""]							,				up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""]							,				latent_channels=4							,				sample_size=128							,				)
         torch.manual_seed(0		)
         __snake_case:						Optional[Any]   =						CLIPTextConfig(
             bos_token_id=0							,				eos_token_id=2							,				hidden_size=32							,				intermediate_size=37							,				layer_norm_eps=1E-05							,				num_attention_heads=4							,				num_hidden_layers=5							,				pad_token_id=1							,				vocab_size=1_000							,				hidden_act="""gelu"""							,				projection_dim=512							,				)
         __snake_case:						int   =						CLIPTextModel(UpperCAmelCase__		)
         __snake_case:						Union[str, Any]   =						CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""		)

         __snake_case:						Optional[Any]   =						{
             """unet""": unet,
             """scheduler""": scheduler,
             """vae""": vae,
             """text_encoder""": text_encoder,
             """tokenizer""": tokenizer,
             """safety_checker""": None,
             """feature_extractor""": None,
         }

         return components




       def 							UpperCAmelCase__ (  self :    Union[str, Any]							,				A :    int							,				A :    Optional[int]=0		):
         if str(UpperCAmelCase__		).startswith("""mps"""		):
           __snake_case:						Dict   =						torch.manual_seed(UpperCAmelCase__		)
         else:
           __snake_case:						str   =						torch.Generator(device=UpperCAmelCase__		).manual_seed(UpperCAmelCase__		)
         __snake_case:						Any   =						{
             """prompt""": """a cat and a frog""",
             """token_indices""": [2, 5],
             """generator""": generator,
             """num_inference_steps""": 1,
             """guidance_scale""": 6.0,
             """output_type""": """numpy""",
             """max_iter_to_alter""": 2,
             """thresholds""": {0: 0.7},
         }
         return inputs




       def 							UpperCAmelCase__ (  self :    Dict		):
         __snake_case:						Optional[int]   =						"""cpu"""

         __snake_case:						int   =						self.get_dummy_components()
         __snake_case:						Optional[Any]   =						self.pipeline_class(**UpperCAmelCase__		)
         pipe.to(UpperCAmelCase__		)
         pipe.set_progress_bar_config(disable=UpperCAmelCase__		)

         __snake_case:						List[str]   =						self.get_dummy_inputs(UpperCAmelCase__		)
         __snake_case:						Optional[int]   =						pipe(**UpperCAmelCase__		).images
         __snake_case:						List[Any]   =						image[0, -3:, -3:, -1]

         self.assertEqual(image.shape							,				(1, 64, 64, 3)		)
         __snake_case:						str   =						np.array(
             [0.6390_5364, 0.6289_7307, 0.4859_9017, 0.513_3624, 0.555_0048, 0.4576_9516, 0.5032_6973, 0.502_3139, 0.4538_4496]		)
         __snake_case:						Any   =						np.abs(image_slice.flatten() - expected_slice		).max()
         self.assertLessEqual(UpperCAmelCase__							,				1E-3		)




       def 							UpperCAmelCase__ (  self :    int		):
         super().test_cpu_offload_forward_pass(expected_max_diff=5E-4		)




       def 							UpperCAmelCase__ (  self :    List[str]		):
         # NOTE: Larger batch sizes cause this test to timeout, only test on smaller batches
         self._test_inference_batch_consistent(batch_sizes=[1, 2]		)




       def 							UpperCAmelCase__ (  self :    List[str]		):
         self._test_inference_batch_single_identical(batch_size=2							,				expected_max_diff=7E-4		)




       def 							UpperCAmelCase__ (  self :    str		):
         super().test_dict_tuple_outputs_equivalent(expected_max_difference=3E-3		)




       def 							UpperCAmelCase__ (  self :    Optional[int]		):
         super().test_pt_np_pil_outputs_equivalent(expected_max_diff=5E-4		)




       def 							UpperCAmelCase__ (  self :    Optional[Any]		):
         super().test_save_load_local(expected_max_difference=5E-4		)






       def 							UpperCAmelCase__ (  self :    Any		):
         super().test_save_load_optional_components(expected_max_difference=4E-4		)







@require_torch_gpu
@slow
class     __snake_case       (							unittest.TestCase			):





       '''simple docstring'''







       @classmethod
       def 							UpperCAmelCase__ (  cls :    int		):
         super().setUpClass()
         torch.use_deterministic_algorithms(UpperCAmelCase__		)




       @classmethod
       def 							UpperCAmelCase__ (  cls :    str		):
         super().tearDownClass()
         torch.use_deterministic_algorithms(UpperCAmelCase__		)




       def 							UpperCAmelCase__ (  self :    Union[str, Any]		):
         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()






       def 							UpperCAmelCase__ (  self :    int		):
         __snake_case:						Dict   =						torch.manual_seed(51		)

         __snake_case:						Union[str, Any]   =						StableDiffusionAttendAndExcitePipeline.from_pretrained(
             """CompVis/stable-diffusion-v1-4"""							,				safety_checker=UpperCAmelCase__							,				torch_dtype=torch.floataa		)
         pipe.to("""cuda"""		)

         __snake_case:						List[Any]   =						"""a painting of an elephant with glasses"""
         __snake_case:						int   =						[5, 7]

         __snake_case:						Optional[Any]   =						pipe(
             prompt=UpperCAmelCase__							,				token_indices=UpperCAmelCase__							,				guidance_scale=7.5							,				generator=UpperCAmelCase__							,				num_inference_steps=5							,				max_iter_to_alter=5							,				output_type="""numpy"""							,				).images[0]

         __snake_case:						int   =						load_numpy(
             """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/attend-and-excite/elephant_glasses.npy"""		)
         assert np.abs((expected_image - image).max()		) < 5E-1














"""simple docstring"""




from collections.abc import Iterable
from typing import Generic, TypeVar

_snake_case          =   TypeVar('_T')






class        UpperCamelCase			(	Generic[_T]      ):


 def __init__(     self		:						Optional[int]							,						UpperCAmelCase__		:						Iterable[_T] | None = None							)					->       None:
   _a  :       list[_T]             =	list(iterable or []							)
   _a  :       list[_T]             =	[]


 def __len__(     self		:						str							)					->       int:
   return len(self._stacka							) + len(self._stacka							)


 def __repr__(     self		:						List[str]							)					->       str:
   return f"""Queue({tuple(self._stacka[::-1] + self._stacka							)})"""


 def _lowercase	(     self		:						Union[str, Any]							,						UpperCAmelCase__		:						_T							)					->       None:
   self._stacka.append(UpperCAmelCase__							)







 def _lowercase	(     self		:						Optional[Any]							)					->       _T:
   _a  :       Any             =	self._stacka.pop
   _a  :       Union[str, Any]             =	self._stacka.append

   if not self._stacka:
     while self._stacka:
       stacka_append(stacka_pop()							)

   if not self._stacka:
     raise IndexError("""Queue is empty"""							)
   return self._stacka.pop()


if __name__ == "__main__":
 from doctest import testmod

 testmod()

