


'''simple docstring'''






import gc
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    EulerAncestralDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionModelEditingPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps

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


enable_full_determinism()






@skip_mps
class      SCREAMING_SNAKE_CASE_	( snake_case					,					snake_case					,					snake_case					,					unittest.TestCase		):
			__a					:      int					=  StableDiffusionModelEditingPipeline
			__a					:      Union[str, Any]					=  TEXT_TO_IMAGE_PARAMS
			__a					:      List[Any]					=  TEXT_TO_IMAGE_BATCH_PARAMS
			__a					:      Tuple					=  TEXT_TO_IMAGE_IMAGE_PARAMS
			__a					:      Any					=  TEXT_TO_IMAGE_IMAGE_PARAMS




			def 			_snake_case   (      self       )			->						int:


								'''simple docstring'''






								torch.manual_seed(0       )
								__SCREAMING_SNAKE_CASE					:							Optional[Any]			   = UNetaDConditionModel(
								    block_out_channels=(3_2, 6_4)					,      layers_per_block=2					,      sample_size=3_2					,      in_channels=4					,      out_channels=4					,      down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')					,      up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')					,      cross_attention_dim=3_2					,      )
								__SCREAMING_SNAKE_CASE					:							Union[str, Any]			   = DDIMScheduler()
								torch.manual_seed(0       )
								__SCREAMING_SNAKE_CASE					:							List[str]			   = AutoencoderKL(
								    block_out_channels=[3_2, 6_4]					,      in_channels=3					,      out_channels=3					,      down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']					,      up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']					,      latent_channels=4					,      )
								torch.manual_seed(0       )
								__SCREAMING_SNAKE_CASE					:							Optional[Any]			   = CLIPTextConfig(
								    bos_token_id=0					,      eos_token_id=2					,      hidden_size=3_2					,      intermediate_size=3_7					,      layer_norm_eps=1e-05					,      num_attention_heads=4					,      num_hidden_layers=5					,      pad_token_id=1					,      vocab_size=1_0_0_0					,      )
								__SCREAMING_SNAKE_CASE					:							Union[str, Any]			   = CLIPTextModel(lowercase       )
								__SCREAMING_SNAKE_CASE					:							Dict			   = CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''       )

								__SCREAMING_SNAKE_CASE					:							List[str]			   = {
								    '''unet''': unet,
								    '''scheduler''': scheduler,
								    '''vae''': vae,
								    '''text_encoder''': text_encoder,
								    '''tokenizer''': tokenizer,
								    '''safety_checker''': None,
								    '''feature_extractor''': None,
								}
								return components




			def 			_snake_case   (      self					,      lowercase					,      lowercase=0       )			->						str:


								'''simple docstring'''






								__SCREAMING_SNAKE_CASE					:							Tuple			   = torch.manual_seed(lowercase       )
								__SCREAMING_SNAKE_CASE					:							str			   = {
								    '''prompt''': '''A field of roses''',
								    '''generator''': generator,
								    # Setting height and width to None to prevent OOMs on CPU.
								    '''height''': None,
								    '''width''': None,
								    '''num_inference_steps''': 2,
								    '''guidance_scale''': 6.0,
								    '''output_type''': '''numpy''',
								}
								return inputs




			def 			_snake_case   (      self       )			->						Dict:


								'''simple docstring'''






								__SCREAMING_SNAKE_CASE					:							List[Any]			   = '''cpu'''  # ensure determinism for the device-dependent torch.Generator
								__SCREAMING_SNAKE_CASE					:							int			   = self.get_dummy_components()
								__SCREAMING_SNAKE_CASE					:							Any			   = StableDiffusionModelEditingPipeline(**lowercase       )
								__SCREAMING_SNAKE_CASE					:							Optional[Any]			   = sd_pipe.to(lowercase       )
								sd_pipe.set_progress_bar_config(disable=lowercase       )

								__SCREAMING_SNAKE_CASE					:							Optional[Any]			   = self.get_dummy_inputs(lowercase       )
								__SCREAMING_SNAKE_CASE					:							Tuple			   = sd_pipe(**lowercase       ).images
								__SCREAMING_SNAKE_CASE					:							List[Any]			   = image[0, -3:, -3:, -1]
								assert image.shape == (1, 6_4, 6_4, 3)

								__SCREAMING_SNAKE_CASE					:							Optional[Any]			   = np.array([0.4_7_5_5, 0.5_1_3_2, 0.4_9_7_6, 0.3_9_0_4, 0.3_5_5_4, 0.4_7_6_5, 0.5_1_3_9, 0.5_1_5_8, 0.4_8_8_9]       )

								assert np.abs(image_slice.flatten() - expected_slice       ).max() < 1e-2




			def 			_snake_case   (      self       )			->						Dict:


								'''simple docstring'''






								__SCREAMING_SNAKE_CASE					:							Optional[int]			   = '''cpu'''  # ensure determinism for the device-dependent torch.Generator
								__SCREAMING_SNAKE_CASE					:							Any			   = self.get_dummy_components()
								__SCREAMING_SNAKE_CASE					:							Tuple			   = StableDiffusionModelEditingPipeline(**lowercase       )
								__SCREAMING_SNAKE_CASE					:							Optional[Any]			   = sd_pipe.to(lowercase       )
								sd_pipe.set_progress_bar_config(disable=lowercase       )

								__SCREAMING_SNAKE_CASE					:							Optional[int]			   = self.get_dummy_inputs(lowercase       )
								__SCREAMING_SNAKE_CASE					:							Optional[Any]			   = '''french fries'''
								__SCREAMING_SNAKE_CASE					:							List[Any]			   = sd_pipe(**lowercase					,      negative_prompt=lowercase       )
								__SCREAMING_SNAKE_CASE					:							Any			   = output.images
								__SCREAMING_SNAKE_CASE					:							str			   = image[0, -3:, -3:, -1]

								assert image.shape == (1, 6_4, 6_4, 3)

								__SCREAMING_SNAKE_CASE					:							List[Any]			   = np.array([0.4_9_9_2, 0.5_1_0_1, 0.5_0_0_4, 0.3_9_4_9, 0.3_6_0_4, 0.4_7_3_5, 0.5_2_1_6, 0.5_2_0_4, 0.4_9_1_3]       )

								assert np.abs(image_slice.flatten() - expected_slice       ).max() < 1e-2




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


								'''simple docstring'''






								__SCREAMING_SNAKE_CASE					:							Optional[Any]			   = '''cpu'''  # ensure determinism for the device-dependent torch.Generator
								__SCREAMING_SNAKE_CASE					:							List[Any]			   = self.get_dummy_components()
								__SCREAMING_SNAKE_CASE					:							Any			   = EulerAncestralDiscreteScheduler(
								    beta_start=0.0_0_0_8_5					,      beta_end=0.0_1_2					,      beta_schedule='''scaled_linear'''       )
								__SCREAMING_SNAKE_CASE					:							str			   = StableDiffusionModelEditingPipeline(**lowercase       )
								__SCREAMING_SNAKE_CASE					:							int			   = sd_pipe.to(lowercase       )
								sd_pipe.set_progress_bar_config(disable=lowercase       )

								__SCREAMING_SNAKE_CASE					:							Dict			   = self.get_dummy_inputs(lowercase       )
								__SCREAMING_SNAKE_CASE					:							Tuple			   = sd_pipe(**lowercase       ).images
								__SCREAMING_SNAKE_CASE					:							Union[str, Any]			   = image[0, -3:, -3:, -1]

								assert image.shape == (1, 6_4, 6_4, 3)

								__SCREAMING_SNAKE_CASE					:							str			   = np.array([0.4_7_4_7, 0.5_3_7_2, 0.4_7_7_9, 0.4_9_8_2, 0.5_5_4_3, 0.4_8_1_6, 0.5_2_3_8, 0.4_9_0_4, 0.5_0_2_7]       )

								assert np.abs(image_slice.flatten() - expected_slice       ).max() < 1e-2




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


								'''simple docstring'''






								__SCREAMING_SNAKE_CASE					:							int			   = '''cpu'''  # ensure determinism for the device-dependent torch.Generator
								__SCREAMING_SNAKE_CASE					:							Dict			   = self.get_dummy_components()
								__SCREAMING_SNAKE_CASE					:							str			   = PNDMScheduler()
								__SCREAMING_SNAKE_CASE					:							List[Any]			   = StableDiffusionModelEditingPipeline(**lowercase       )
								__SCREAMING_SNAKE_CASE					:							List[str]			   = sd_pipe.to(lowercase       )
								sd_pipe.set_progress_bar_config(disable=lowercase       )

								__SCREAMING_SNAKE_CASE					:							Optional[int]			   = self.get_dummy_inputs(lowercase       )
								# the pipeline does not expect pndm so test if it raises error.
								with self.assertRaises(lowercase       ):
													__SCREAMING_SNAKE_CASE					:							Dict			   = sd_pipe(**lowercase       ).images




			def 			_snake_case   (      self       )			->						List[str]:


								'''simple docstring'''






								super().test_inference_batch_single_identical(expected_max_diff=5e-3       )




			def 			_snake_case   (      self       )			->						Optional[Any]:


								'''simple docstring'''






								super().test_attention_slicing_forward_pass(expected_max_diff=5e-3       )






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




			def 			_snake_case   (      self       )			->						Dict:


								'''simple docstring'''






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




			def 			_snake_case   (      self					,      lowercase=0       )			->						str:


								'''simple docstring'''






								__SCREAMING_SNAKE_CASE					:							str			   = torch.manual_seed(lowercase       )
								__SCREAMING_SNAKE_CASE					:							int			   = {
								    '''prompt''': '''A field of roses''',
								    '''generator''': generator,
								    '''num_inference_steps''': 3,
								    '''guidance_scale''': 7.5,
								    '''output_type''': '''numpy''',
								}
								return inputs




			def 			_snake_case   (      self       )			->						Any:


								'''simple docstring'''






								__SCREAMING_SNAKE_CASE					:							Dict			   = '''CompVis/stable-diffusion-v1-4'''
								__SCREAMING_SNAKE_CASE					:							Tuple			   = StableDiffusionModelEditingPipeline.from_pretrained(lowercase					,      safety_checker=lowercase       )
								pipe.to(lowercase       )
								pipe.set_progress_bar_config(disable=lowercase       )
								pipe.enable_attention_slicing()

								__SCREAMING_SNAKE_CASE					:							List[Any]			   = self.get_inputs()
								__SCREAMING_SNAKE_CASE					:							Dict			   = pipe(**lowercase       ).images
								__SCREAMING_SNAKE_CASE					:							List[str]			   = image[0, -3:, -3:, -1].flatten()

								assert image.shape == (1, 5_1_2, 5_1_2, 3)

								__SCREAMING_SNAKE_CASE					:							Any			   = np.array(
								    [0.6_7_4_9_4_9_6, 0.6_3_8_6_4_5_3, 0.5_1_4_4_3_2_6_7, 0.6_6_0_9_4_9_0_5, 0.6_1_9_2_1_2_1_5, 0.5_4_9_1_3_3_2, 0.5_7_4_4_4_1_7, 0.5_8_0_7_5_1_0_6, 0.5_1_7_4_6_5_8]       )

								assert np.abs(expected_slice - image_slice       ).max() < 1e-2

								# make sure image changes after editing
								pipe.edit_model('''A pack of roses'''					,      '''A pack of blue roses'''       )

								__SCREAMING_SNAKE_CASE					:							List[Any]			   = pipe(**lowercase       ).images
								__SCREAMING_SNAKE_CASE					:							str			   = image[0, -3:, -3:, -1].flatten()

								assert image.shape == (1, 5_1_2, 5_1_2, 3)

								assert np.abs(expected_slice - image_slice       ).max() > 1e-1




			def 			_snake_case   (      self       )			->						List[str]:


								'''simple docstring'''






								torch.cuda.empty_cache()
								torch.cuda.reset_max_memory_allocated()
								torch.cuda.reset_peak_memory_stats()

								__SCREAMING_SNAKE_CASE					:							Tuple			   = '''CompVis/stable-diffusion-v1-4'''
								__SCREAMING_SNAKE_CASE					:							List[Any]			   = DDIMScheduler.from_pretrained(lowercase					,      subfolder='''scheduler'''       )
								__SCREAMING_SNAKE_CASE					:							int			   = StableDiffusionModelEditingPipeline.from_pretrained(
								    lowercase					,      scheduler=lowercase					,      safety_checker=lowercase       )
								__SCREAMING_SNAKE_CASE					:							str			   = pipe.to(lowercase       )
								pipe.set_progress_bar_config(disable=lowercase       )
								pipe.enable_attention_slicing(1       )
								pipe.enable_sequential_cpu_offload()

								__SCREAMING_SNAKE_CASE					:							List[str]			   = self.get_inputs()
								__SCREAMING_SNAKE_CASE					:							Union[str, Any]			   = pipe(**lowercase       )

								__SCREAMING_SNAKE_CASE					:							str			   = torch.cuda.max_memory_allocated()
								# make sure that less than 4.4 GB is allocated
								assert mem_bytes < 4.4 * 1_0**9






'''simple docstring'''






import abc
import copy
import dataclasses
from dataclasses import dataclass
from typing import ClassVar, Dict, Type, TypeVar

from ..features import Features


_A      =     TypeVar("""T""", bound="""TaskTemplate""")






@dataclass(frozen=snake_case		)
class      SCREAMING_SNAKE_CASE_	( abc.ABC		):
			# `task` is not a ClassVar since we want it to be part of the `asdict` output for JSON serialization
			__a					:      str
			__a					:      ClassVar[Features]
			__a					:      ClassVar[Features]




			def 			_snake_case   (      self					,      lowercase       )			->						T:


								'''simple docstring'''






								return copy.deepcopy(self       )




			@property
			def 			_snake_case   (      self       )			->						Features:


								'''simple docstring'''






								return Features(**self.input_schema					,      **self.label_schema       )




			@property
			@abc.abstractmethod
			def 			_snake_case   (      self       )			->						Dict[str, str]:


								'''simple docstring'''






								raise NotImplementedError




			@classmethod
			def 			_snake_case   (      cls					,      lowercase       )			->						T:


								'''simple docstring'''






								__SCREAMING_SNAKE_CASE					:							Union[str, Any]			   = {f.name for f in dataclasses.fields(cls       )}
								return cls(**{k: v for k, v in template_dict.items() if k in field_names}       )


