


import gc
import random
import tempfile
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMInverseScheduler,
    DDIMScheduler,
    DPMSolverMultistepInverseScheduler,
    DPMSolverMultistepScheduler,
    StableDiffusionDiffEditPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import load_image, slow
from diffusers.utils.testing_utils import enable_full_determinism, floats_tensor, require_torch_gpu, torch_device

from ..pipeline_params import TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()

class 		__a				(    __lowerCAmelCase   ,				__lowerCAmelCase   ,				unittest.TestCase      ):
		_a     :  List[Any]           =     StableDiffusionDiffEditPipeline
		_a     :  Tuple           =     TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {'''height''', '''width''', '''image'''} | {'''image_latents'''}
		_a     :  int           =     TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS - {'''image'''} | {'''image_latents'''}
		_a     :  int           =     frozenset(
		    []      )  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
		_a     :  Optional[Any]           =     frozenset([]      )
		def      UpperCAmelCase__    (      self						)			->	Any:




									"""simple docstring"""





									torch.manual_seed(0						)
									_UpperCAmelCase					=      UNetaDConditionModel(
									    block_out_channels=(32, 64)      ,   layers_per_block=2      ,   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=lowerCAmelCase_      ,   )
									_UpperCAmelCase					=      DDIMScheduler(
									    beta_start=0.00085      ,   beta_end=0.012      ,   beta_schedule='scaled_linear'      ,   clip_sample=lowerCAmelCase_      ,   set_alpha_to_one=lowerCAmelCase_      ,   )
									_UpperCAmelCase					=      DDIMInverseScheduler(
									    beta_start=0.00085      ,   beta_end=0.012      ,   beta_schedule='scaled_linear'      ,   clip_sample=lowerCAmelCase_      ,   set_alpha_to_zero=lowerCAmelCase_      ,   )
									torch.manual_seed(0						)
									_UpperCAmelCase					=      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						)
									_UpperCAmelCase					=      CLIPTextConfig(
									    bos_token_id=0      ,   eos_token_id=2      ,   hidden_size=32      ,   intermediate_size=37      ,   layer_norm_eps=1e-0_5      ,   num_attention_heads=4      ,   num_hidden_layers=5      ,   pad_token_id=1      ,   vocab_size=1000      ,   hidden_act='gelu'      ,   projection_dim=512      ,   )
									_UpperCAmelCase					=      CLIPTextModel(lowerCAmelCase_						)
									_UpperCAmelCase					=      CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'						)

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

									return components
		def      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE=0						)			->	List[Any]:




									"""simple docstring"""





									_UpperCAmelCase					=      floats_tensor((1, 16, 16)      ,   rng=random.Random(lowerCAmelCase_						)						).to(lowerCAmelCase_						)
									_UpperCAmelCase					=      floats_tensor((1, 2, 4, 16, 16)      ,   rng=random.Random(lowerCAmelCase_						)						).to(lowerCAmelCase_						)
									if str(lowerCAmelCase_						).startswith('mps'						):
																_UpperCAmelCase					=      torch.manual_seed(lowerCAmelCase_						)
									else:
																_UpperCAmelCase					=      torch.Generator(device=lowerCAmelCase_						).manual_seed(lowerCAmelCase_						)
									_UpperCAmelCase					=      {
									    'prompt': 'a dog and a newt',
									    'mask_image': mask,
									    'image_latents': latents,
									    'generator': generator,
									    'num_inference_steps': 2,
									    'inpaint_strength': 1.0,
									    'guidance_scale': 6.0,
									    'output_type': 'numpy',
									}

									return inputs
		def      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE=0						)			->	List[str]:




									"""simple docstring"""





									_UpperCAmelCase					=      floats_tensor((1, 3, 32, 32)      ,   rng=random.Random(lowerCAmelCase_						)						).to(lowerCAmelCase_						)
									_UpperCAmelCase					=      image.cpu().permute(0      ,   2      ,   3      ,   1						)[0]
									_UpperCAmelCase					=      Image.fromarray(np.uinta(lowerCAmelCase_						)						).convert('RGB'						)
									if str(lowerCAmelCase_						).startswith('mps'						):
																_UpperCAmelCase					=      torch.manual_seed(lowerCAmelCase_						)
									else:
																_UpperCAmelCase					=      torch.Generator(device=lowerCAmelCase_						).manual_seed(lowerCAmelCase_						)
									_UpperCAmelCase					=      {
									    'image': image,
									    'source_prompt': 'a cat and a frog',
									    'target_prompt': 'a dog and a newt',
									    'generator': generator,
									    'num_inference_steps': 2,
									    'num_maps_per_mask': 2,
									    'mask_encode_strength': 1.0,
									    'guidance_scale': 6.0,
									    'output_type': 'numpy',
									}

									return inputs
		def      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE=0						)			->	Union[str, Any]:




									"""simple docstring"""





									_UpperCAmelCase					=      floats_tensor((1, 3, 32, 32)      ,   rng=random.Random(lowerCAmelCase_						)						).to(lowerCAmelCase_						)
									_UpperCAmelCase					=      image.cpu().permute(0      ,   2      ,   3      ,   1						)[0]
									_UpperCAmelCase					=      Image.fromarray(np.uinta(lowerCAmelCase_						)						).convert('RGB'						)
									if str(lowerCAmelCase_						).startswith('mps'						):
																_UpperCAmelCase					=      torch.manual_seed(lowerCAmelCase_						)
									else:
																_UpperCAmelCase					=      torch.Generator(device=lowerCAmelCase_						).manual_seed(lowerCAmelCase_						)
									_UpperCAmelCase					=      {
									    'image': image,
									    'prompt': 'a cat and a frog',
									    'generator': generator,
									    'num_inference_steps': 2,
									    'inpaint_strength': 1.0,
									    'guidance_scale': 6.0,
									    'decode_latents': True,
									    'output_type': 'numpy',
									}
									return inputs
		def      UpperCAmelCase__    (      self						)			->	Optional[int]:




									"""simple docstring"""





									if not hasattr(self.pipeline_class      ,   '_optional_components'						):
																return

									_UpperCAmelCase					=      self.get_dummy_components()
									_UpperCAmelCase					=      self.pipeline_class(**lowerCAmelCase_						)
									pipe.to(lowerCAmelCase_						)
									pipe.set_progress_bar_config(disable=lowerCAmelCase_						)

									# set all optional components to None and update pipeline config accordingly
									for optional_component in pipe._optional_components:
																setattr(lowerCAmelCase_      ,   lowerCAmelCase_      ,   lowerCAmelCase_						)
									pipe.register_modules(**{optional_component: None for optional_component in pipe._optional_components}						)

									_UpperCAmelCase					=      self.get_dummy_inputs(lowerCAmelCase_						)
									_UpperCAmelCase					=      pipe(**lowerCAmelCase_						)[0]

									with tempfile.TemporaryDirectory() as tmpdir:
																pipe.save_pretrained(lowerCAmelCase_						)
																_UpperCAmelCase					=      self.pipeline_class.from_pretrained(lowerCAmelCase_						)
																pipe_loaded.to(lowerCAmelCase_						)
																pipe_loaded.set_progress_bar_config(disable=lowerCAmelCase_						)

									for optional_component in pipe._optional_components:
																self.assertTrue(
																    getattr(lowerCAmelCase_      ,   lowerCAmelCase_						) is None      ,   f'''`{optional_component}` did not stay set to None after loading.'''      ,   )

									_UpperCAmelCase					=      self.get_dummy_inputs(lowerCAmelCase_						)
									_UpperCAmelCase					=      pipe_loaded(**lowerCAmelCase_						)[0]

									_UpperCAmelCase					=      np.abs(output - output_loaded						).max()
									self.assertLess(lowerCAmelCase_      ,   1e-4						)
		def      UpperCAmelCase__    (      self						)			->	str:




									"""simple docstring"""





									_UpperCAmelCase					=      'cpu'

									_UpperCAmelCase					=      self.get_dummy_components()
									_UpperCAmelCase					=      self.pipeline_class(**lowerCAmelCase_						)
									pipe.to(lowerCAmelCase_						)
									pipe.set_progress_bar_config(disable=lowerCAmelCase_						)

									_UpperCAmelCase					=      self.get_dummy_mask_inputs(lowerCAmelCase_						)
									_UpperCAmelCase					=      pipe.generate_mask(**lowerCAmelCase_						)
									_UpperCAmelCase					=      mask[0, -3:, -3:]

									self.assertEqual(mask.shape      ,   (1, 16, 16)						)
									_UpperCAmelCase					=      np.array([0] * 9						)
									_UpperCAmelCase					=      np.abs(mask_slice.flatten() - expected_slice						).max()
									self.assertLessEqual(lowerCAmelCase_      ,   1e-3						)
									self.assertEqual(mask[0, -3, -4]      ,   0						)
		def      UpperCAmelCase__    (      self						)			->	Union[str, Any]:




									"""simple docstring"""





									_UpperCAmelCase					=      'cpu'

									_UpperCAmelCase					=      self.get_dummy_components()
									_UpperCAmelCase					=      self.pipeline_class(**lowerCAmelCase_						)
									pipe.to(lowerCAmelCase_						)
									pipe.set_progress_bar_config(disable=lowerCAmelCase_						)

									_UpperCAmelCase					=      self.get_dummy_inversion_inputs(lowerCAmelCase_						)
									_UpperCAmelCase					=      pipe.invert(**lowerCAmelCase_						).images
									_UpperCAmelCase					=      image[0, -1, -3:, -3:]

									self.assertEqual(image.shape      ,   (2, 32, 32, 3)						)
									_UpperCAmelCase					=      np.array(
									    [0.5150, 0.5134, 0.5043, 0.5376, 0.4694, 0.51050, 0.5015, 0.4407, 0.4799]      ,   )
									_UpperCAmelCase					=      np.abs(image_slice.flatten() - expected_slice						).max()
									self.assertLessEqual(lowerCAmelCase_      ,   1e-3						)
		def      UpperCAmelCase__    (      self						)			->	Tuple:




									"""simple docstring"""





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






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




									"""simple docstring"""





									_UpperCAmelCase					=      'cpu'

									_UpperCAmelCase					=      self.get_dummy_components()

									_UpperCAmelCase					=      {'beta_start': 0.00085, 'beta_end': 0.012, 'beta_schedule': 'scaled_linear'}
									_UpperCAmelCase					=      DPMSolverMultistepScheduler(**lowerCAmelCase_						)
									_UpperCAmelCase					=      DPMSolverMultistepInverseScheduler(**lowerCAmelCase_						)

									_UpperCAmelCase					=      self.pipeline_class(**lowerCAmelCase_						)
									pipe.to(lowerCAmelCase_						)
									pipe.set_progress_bar_config(disable=lowerCAmelCase_						)

									_UpperCAmelCase					=      self.get_dummy_inversion_inputs(lowerCAmelCase_						)
									_UpperCAmelCase					=      pipe.invert(**lowerCAmelCase_						).images
									_UpperCAmelCase					=      image[0, -1, -3:, -3:]

									self.assertEqual(image.shape      ,   (2, 32, 32, 3)						)
									_UpperCAmelCase					=      np.array(
									    [0.5150, 0.5134, 0.5043, 0.5376, 0.4694, 0.51050, 0.5015, 0.4407, 0.4799]      ,   )
									_UpperCAmelCase					=      np.abs(image_slice.flatten() - expected_slice						).max()
									self.assertLessEqual(lowerCAmelCase_      ,   1e-3						)



@require_torch_gpu
@slow
class 		__a				(    unittest.TestCase      ):
		def      UpperCAmelCase__    (      self						)			->	Optional[Any]:




									"""simple docstring"""





									super().tearDown()
									gc.collect()
									torch.cuda.empty_cache()
		@classmethod
		def      UpperCAmelCase__    (      cls						)			->	Optional[Any]:




									"""simple docstring"""





									_UpperCAmelCase					=      load_image(
									    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/diffedit/fruit.png'						)

									_UpperCAmelCase					=      raw_image.convert('RGB'						).resize((768, 768)						)

									_UpperCAmelCase					=      raw_image
		def      UpperCAmelCase__    (      self						)			->	Optional[Any]:




									"""simple docstring"""





									_UpperCAmelCase					=      torch.manual_seed(0						)

									_UpperCAmelCase					=      StableDiffusionDiffEditPipeline.from_pretrained(
									    'stabilityai/stable-diffusion-2-1'      ,   safety_checker=lowerCAmelCase_      ,   torch_dtype=torch.floataa						)
									_UpperCAmelCase					=      DDIMScheduler.from_config(pipe.scheduler.config						)
									_UpperCAmelCase					=      DDIMInverseScheduler.from_config(pipe.scheduler.config						)
									pipe.enable_model_cpu_offload()
									pipe.set_progress_bar_config(disable=lowerCAmelCase_						)

									_UpperCAmelCase					=      'a bowl of fruit'
									_UpperCAmelCase					=      'a bowl of pears'

									_UpperCAmelCase					=      pipe.generate_mask(
									    image=self.raw_image      ,   source_prompt=lowerCAmelCase_      ,   target_prompt=lowerCAmelCase_      ,   generator=lowerCAmelCase_      ,   )

									_UpperCAmelCase					=      pipe.invert(
									    prompt=lowerCAmelCase_      ,   image=self.raw_image      ,   inpaint_strength=0.7      ,   generator=lowerCAmelCase_						).latents

									_UpperCAmelCase					=      pipe(
									    prompt=lowerCAmelCase_      ,   mask_image=lowerCAmelCase_      ,   image_latents=lowerCAmelCase_      ,   generator=lowerCAmelCase_      ,   negative_prompt=lowerCAmelCase_      ,   inpaint_strength=0.7      ,   output_type='numpy'      ,   ).images[0]

									_UpperCAmelCase					=      (
									    np.array(
									        load_image(
									            'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
									            '/diffedit/pears.png'						).resize((768, 768)						)						)
									    / 255
									)
									assert np.abs((expected_image - image).max()						) < 5e-1






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




									"""simple docstring"""





									_UpperCAmelCase					=      torch.manual_seed(0						)

									_UpperCAmelCase					=      StableDiffusionDiffEditPipeline.from_pretrained(
									    'stabilityai/stable-diffusion-2-1'      ,   safety_checker=lowerCAmelCase_      ,   torch_dtype=torch.floataa						)
									_UpperCAmelCase					=      DPMSolverMultistepScheduler.from_config(pipe.scheduler.config						)
									_UpperCAmelCase					=      DPMSolverMultistepInverseScheduler.from_config(pipe.scheduler.config						)
									pipe.enable_model_cpu_offload()
									pipe.set_progress_bar_config(disable=lowerCAmelCase_						)

									_UpperCAmelCase					=      'a bowl of fruit'
									_UpperCAmelCase					=      'a bowl of pears'

									_UpperCAmelCase					=      pipe.generate_mask(
									    image=self.raw_image      ,   source_prompt=lowerCAmelCase_      ,   target_prompt=lowerCAmelCase_      ,   generator=lowerCAmelCase_      ,   )

									_UpperCAmelCase					=      pipe.invert(
									    prompt=lowerCAmelCase_      ,   image=self.raw_image      ,   inpaint_strength=0.7      ,   generator=lowerCAmelCase_      ,   num_inference_steps=25      ,   ).latents

									_UpperCAmelCase					=      pipe(
									    prompt=lowerCAmelCase_      ,   mask_image=lowerCAmelCase_      ,   image_latents=lowerCAmelCase_      ,   generator=lowerCAmelCase_      ,   negative_prompt=lowerCAmelCase_      ,   inpaint_strength=0.7      ,   num_inference_steps=25      ,   output_type='numpy'      ,   ).images[0]

									_UpperCAmelCase					=      (
									    np.array(
									        load_image(
									            'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
									            '/diffedit/pears.png'						).resize((768, 768)						)						)
									    / 255
									)
									assert np.abs((expected_image - image).max()						) < 5e-1




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCAmelCase__  :List[str]             =							{
    '''configuration_x_clip''': [
        '''XCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''XCLIPConfig''',
        '''XCLIPTextConfig''',
        '''XCLIPVisionConfig''',
    ],
    '''processing_x_clip''': ['''XCLIPProcessor'''],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowerCAmelCase__  :Any             =							[
		    '''XCLIP_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''XCLIPModel''',
		    '''XCLIPPreTrainedModel''',
		    '''XCLIPTextModel''',
		    '''XCLIPVisionModel''',
		]

if TYPE_CHECKING:
		from .configuration_x_clip import (
		    XCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    XCLIPConfig,
		    XCLIPTextConfig,
		    XCLIPVisionConfig,
		)
		from .processing_x_clip import XCLIPProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_x_clip import (
				    XCLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
				    XCLIPModel,
				    XCLIPPreTrainedModel,
				    XCLIPTextModel,
				    XCLIPVisionModel,
				)

else:
		import sys

		lowerCAmelCase__  :str             =							_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
