




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


lowerCamelCase       :	int									=     False







@skip_mps
class 						A(		UpperCamelCase							,   UpperCamelCase							,   UpperCamelCase							,   unittest.TestCase     ):



 '''simple docstring'''


 UpperCamelCase		     =		StableDiffusionAttendAndExcitePipeline
 UpperCamelCase		     =		False
 UpperCamelCase		     =		TEXT_TO_IMAGE_PARAMS
 UpperCamelCase		     =		TEXT_TO_IMAGE_BATCH_PARAMS.union({'''token_indices'''}     )
 UpperCamelCase		     =		TEXT_TO_IMAGE_IMAGE_PARAMS
 UpperCamelCase		     =		TEXT_TO_IMAGE_IMAGE_PARAMS

 @classmethod
 def        a__				(       cls      :						Dict						)     ->			Optional[int]:

       """simple docstring"""






       super().setUpClass()
       torch.use_deterministic_algorithms(A_						)

 @classmethod
 def        a__				(       cls      :						Union[str, Any]						)     ->			Any:

       """simple docstring"""






       super().tearDownClass()
       torch.use_deterministic_algorithms(A_						)

 def        a__				(       self      :						int						)     ->			Dict:

       """simple docstring"""






       torch.manual_seed(0						)
       lowerCamelCase_	  =					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=A_		,							)
       lowerCamelCase_	  =					DDIMScheduler(
           beta_start=0.00085		,							beta_end=0.012		,							beta_schedule='scaled_linear'		,							clip_sample=A_		,							set_alpha_to_one=A_		,							)
       torch.manual_seed(0						)
       lowerCamelCase_	  =					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						)
       lowerCamelCase_	  =					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=1000		,							hidden_act='gelu'		,							projection_dim=512		,							)
       lowerCamelCase_	  =					CLIPTextModel(A_						)
       lowerCamelCase_	  =					CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'						)

       lowerCamelCase_	  =					{
           'unet': unet,
           'scheduler': scheduler,
           'vae': vae,
           'text_encoder': text_encoder,
           'tokenizer': tokenizer,
           'safety_checker': None,
           'feature_extractor': None,
       }

       return components

 def        a__				(       self      :						str		,							A_      :						Any		,							A_      :						Any=0						)     ->			Tuple:

       """simple docstring"""






       if str(A_						).startswith('mps'						):
             lowerCamelCase_	  =					torch.manual_seed(A_						)
       else:
             lowerCamelCase_	  =					torch.Generator(device=A_						).manual_seed(A_						)
       lowerCamelCase_	  =					lowerCamelCase_	  =					{
           '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        a__				(       self      :						Union[str, Any]						)     ->			Union[str, Any]:

       """simple docstring"""






       lowerCamelCase_	  =					'cpu'

       lowerCamelCase_	  =					self.get_dummy_components()
       lowerCamelCase_	  =					self.pipeline_class(**A_						)
       pipe.to(A_						)
       pipe.set_progress_bar_config(disable=A_						)

       lowerCamelCase_	  =					self.get_dummy_inputs(A_						)
       lowerCamelCase_	  =					pipe(**A_						).images
       lowerCamelCase_	  =					image[0, -3:, -3:, -1]

       self.assertEqual(image.shape		,							(1, 64, 64, 3)						)
       lowerCamelCase_	  =					np.array(
           [0.63905364, 0.62897307, 0.48599017, 0.5133624, 0.5550048, 0.45769516, 0.50326973, 0.5023139, 0.45384496]						)
       lowerCamelCase_	  =					np.abs(image_slice.flatten() - expected_slice						).max()
       self.assertLessEqual(A_		,							1E-3						)

 def        a__				(       self      :						Dict						)     ->			Tuple:

       """simple docstring"""






       super().test_cpu_offload_forward_pass(expected_max_diff=5E-4						)

 def        a__				(       self      :						int						)     ->			int:

       """simple docstring"""






       self._test_inference_batch_consistent(batch_sizes=[1, 2]						)

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

       """simple docstring"""






       self._test_inference_batch_single_identical(batch_size=2		,							expected_max_diff=7E-4						)

 def        a__				(       self      :						Optional[int]						)     ->			int:

       """simple docstring"""






       super().test_dict_tuple_outputs_equivalent(expected_max_difference=3E-3						)

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

       """simple docstring"""






       super().test_pt_np_pil_outputs_equivalent(expected_max_diff=5E-4						)

 def        a__				(       self      :						List[Any]						)     ->			List[Any]:

       """simple docstring"""






       super().test_save_load_local(expected_max_difference=5E-4						)





 def        a__				(       self      :						Tuple						)     ->			int:

       """simple docstring"""






       super().test_save_load_optional_components(expected_max_difference=4E-4						)







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



 '''simple docstring'''


 @classmethod
 def        a__				(       cls      :						Tuple						)     ->			Tuple:

       """simple docstring"""






       super().setUpClass()
       torch.use_deterministic_algorithms(A_						)

 @classmethod
 def        a__				(       cls      :						int						)     ->			int:

       """simple docstring"""






       super().tearDownClass()
       torch.use_deterministic_algorithms(A_						)

 def        a__				(       self      :						Dict						)     ->			Dict:

       """simple docstring"""






       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()





 def        a__				(       self      :						str						)     ->			Dict:

       """simple docstring"""






       lowerCamelCase_	  =					torch.manual_seed(51						)

       lowerCamelCase_	  =					StableDiffusionAttendAndExcitePipeline.from_pretrained(
           'CompVis/stable-diffusion-v1-4'		,							safety_checker=A_		,							torch_dtype=torch.floataa						)
       pipe.to('cuda'						)

       lowerCamelCase_	  =					'a painting of an elephant with glasses'
       lowerCamelCase_	  =					[5, 7]

       lowerCamelCase_	  =					pipe(
           prompt=A_		,							token_indices=A_		,							guidance_scale=7.5		,							generator=A_		,							num_inference_steps=5		,							max_iter_to_alter=5		,							output_type='numpy'		,							).images[0]

       lowerCamelCase_	  =					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









import os
import re
import shutil
import sys
import tempfile
import unittest

import black


lowerCamelCase       :	List[Any]									=     os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, "utils"))

import check_copies  # noqa: E402


# This is the reference code that will be used in the tests.
# If DDPMSchedulerOutput is changed in scheduling_ddpm.py, this code needs to be manually updated.
lowerCamelCase       :	Tuple									=     "    \"\"\"\n    Output class for the scheduler's step function output.\n\n    Args:\n        prev_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):\n            Computed sample (x_{t-1}) of previous timestep. `prev_sample` should be used as next model input in the\n            denoising loop.\n        pred_original_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):\n            The predicted denoised sample (x_{0}) based on the model output from the current timestep.\n            `pred_original_sample` can be used to preview progress or for guidance.\n    \"\"\"\n\n    prev_sample: torch.FloatTensor\n    pred_original_sample: Optional[torch.FloatTensor] = None\n"







class 						A(		unittest.TestCase     ):



 '''simple docstring'''


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

       """simple docstring"""






       lowerCamelCase_	  =					tempfile.mkdtemp()
       os.makedirs(os.path.join(self.diffusers_dir		,							'schedulers/'						)						)
       lowerCamelCase_	  =					self.diffusers_dir
       shutil.copy(
           os.path.join(A_		,							'src/diffusers/schedulers/scheduling_ddpm.py'						)		,							os.path.join(self.diffusers_dir		,							'schedulers/scheduling_ddpm.py'						)		,							)

 def        a__				(       self      :						Tuple						)     ->			List[str]:

       """simple docstring"""






       lowerCamelCase_	  =					'src/diffusers'
       shutil.rmtree(self.diffusers_dir						)

 def        a__				(       self      :						str		,							A_      :						Optional[Any]		,							A_      :						Optional[int]		,							A_      :						str		,							A_      :						Optional[Any]=None						)     ->			int:

       """simple docstring"""






       lowerCamelCase_	  =					comment + f"""\nclass {class_name}(nn.Module):\n""" + class_code
       if overwrite_result is not None:
             lowerCamelCase_	  =					comment + f"""\nclass {class_name}(nn.Module):\n""" + overwrite_result
       lowerCamelCase_	  =					black.Mode(target_versions={black.TargetVersion.PYaa}		,							line_length=119						)
       lowerCamelCase_	  =					black.format_str(A_		,							mode=A_						)
       lowerCamelCase_	  =					os.path.join(self.diffusers_dir		,							'new_code.py'						)
       with open(A_		,							'w'		,							newline='\n'						) as f:
             f.write(A_						)
       if overwrite_result is None:
             self.assertTrue(len(check_copies.is_copy_consistent(A_						)						) == 0						)
       else:
             check_copies.is_copy_consistent(f.name		,							overwrite=A_						)
             with open(A_		,							'r'						) as f:
                   self.assertTrue(f.read()		,							A_						)

 def        a__				(       self      :						Optional[int]						)     ->			Dict:

       """simple docstring"""






       lowerCamelCase_	  =					check_copies.find_code_in_diffusers('schedulers.scheduling_ddpm.DDPMSchedulerOutput'						)
       self.assertEqual(A_		,							A_						)





 def        a__				(       self      :						Any						)     ->			Dict:

       """simple docstring"""






       self.check_copy_consistency(
           '# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput'		,							'DDPMSchedulerOutput'		,							REFERENCE_CODE + '\n'		,							)

       # With no empty line at the end
       self.check_copy_consistency(
           '# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput'		,							'DDPMSchedulerOutput'		,							A_		,							)

       # Copy consistency with rename
       self.check_copy_consistency(
           '# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->Test'		,							'TestSchedulerOutput'		,							re.sub('DDPM'		,							'Test'		,							A_						)		,							)

       # Copy consistency with a really long name
       lowerCamelCase_	  =					'TestClassWithAReallyLongNameBecauseSomePeopleLikeThatForSomeReason'
       self.check_copy_consistency(
           f"""# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->{long_class_name}"""		,							f"""{long_class_name}SchedulerOutput"""		,							re.sub('Bert'		,							A_		,							A_						)		,							)

       # Copy consistency with overwrite
       self.check_copy_consistency(
           '# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->Test'		,							'TestSchedulerOutput'		,							A_		,							overwrite_result=re.sub('DDPM'		,							'Test'		,							A_						)		,							)



