'''simple docstring'''






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__				(			UpperCamelCase   ,						UpperCamelCase   ,						unittest.TestCase  ):





   """simple docstring"""





   UpperCamelCase_			:		Optional[int]       							=	StableDiffusionDiffEditPipeline
   UpperCamelCase_			:		Optional[int]       							=	TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {'''height''', '''width''', '''image'''} | {'''image_latents'''}
   UpperCamelCase_			:		Any       							=	TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS - {'''image'''} | {'''image_latents'''}
   UpperCamelCase_			:		Dict       							=	frozenset(
       []  )  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
   UpperCamelCase_			:		Any       							=	frozenset([]  )




   def      _lowerCAmelCase						(					self	:						int     )			->       int:







      """simple docstring"""




      torch.manual_seed(0     )
      _UpperCAmelCase				:      List[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		,	attention_head_dim=(2, 4)		,	use_linear_projection=lowerCamelCase__		,	)
      _UpperCAmelCase				:      List[Any]				=  DDIMScheduler(
          beta_start=0.0_0085		,	beta_end=0.012		,	beta_schedule="scaled_linear"		,	clip_sample=lowerCamelCase__		,	set_alpha_to_one=lowerCamelCase__		,	)
      _UpperCAmelCase				:      str				=  DDIMInverseScheduler(
          beta_start=0.0_0085		,	beta_end=0.012		,	beta_schedule="scaled_linear"		,	clip_sample=lowerCamelCase__		,	set_alpha_to_zero=lowerCamelCase__		,	)
      torch.manual_seed(0     )
      _UpperCAmelCase				:      int				=  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		,	sample_size=1_2_8		,	)
      torch.manual_seed(0     )
      _UpperCAmelCase				:      List[str]				=  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		,	hidden_act="gelu"		,	projection_dim=5_1_2		,	)
      _UpperCAmelCase				:      List[str]				=  CLIPTextModel(lowerCamelCase__     )
      _UpperCAmelCase				:      Dict				=  CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"     )

      _UpperCAmelCase				:      Optional[Any]				=  {
          '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      _lowerCAmelCase						(					self	:						Union[str, Any]		,	lowerCAmelCase__	:						Tuple		,	lowerCAmelCase__	:						str=0     )			->       Optional[Any]:







      """simple docstring"""




      _UpperCAmelCase				:      str				=  floats_tensor((1, 1_6, 1_6)		,	rng=random.Random(lowerCamelCase__     )     ).to(lowerCamelCase__     )
      _UpperCAmelCase				:      str				=  floats_tensor((1, 2, 4, 1_6, 1_6)		,	rng=random.Random(lowerCamelCase__     )     ).to(lowerCamelCase__     )
      if str(lowerCamelCase__     ).startswith("mps"     ):
         _UpperCAmelCase				:      Optional[Any]				=  torch.manual_seed(lowerCamelCase__     )
      else:
         _UpperCAmelCase				:      str				=  torch.Generator(device=lowerCamelCase__     ).manual_seed(lowerCamelCase__     )
      _UpperCAmelCase				:      List[str]				=  {
          '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      _lowerCAmelCase						(					self	:						Dict		,	lowerCAmelCase__	:						List[Any]		,	lowerCAmelCase__	:						List[Any]=0     )			->       Union[str, Any]:







      """simple docstring"""




      _UpperCAmelCase				:      Optional[int]				=  floats_tensor((1, 3, 3_2, 3_2)		,	rng=random.Random(lowerCamelCase__     )     ).to(lowerCamelCase__     )
      _UpperCAmelCase				:      Any				=  image.cpu().permute(0		,	2		,	3		,	1     )[0]
      _UpperCAmelCase				:      int				=  Image.fromarray(np.uinta(lowerCamelCase__     )     ).convert("RGB"     )
      if str(lowerCamelCase__     ).startswith("mps"     ):
         _UpperCAmelCase				:      str				=  torch.manual_seed(lowerCamelCase__     )
      else:
         _UpperCAmelCase				:      Any				=  torch.Generator(device=lowerCamelCase__     ).manual_seed(lowerCamelCase__     )
      _UpperCAmelCase				:      Any				=  {
          '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      _lowerCAmelCase						(					self	:						Optional[int]		,	lowerCAmelCase__	:						Any		,	lowerCAmelCase__	:						Dict=0     )			->       Union[str, Any]:







      """simple docstring"""




      _UpperCAmelCase				:      Union[str, Any]				=  floats_tensor((1, 3, 3_2, 3_2)		,	rng=random.Random(lowerCamelCase__     )     ).to(lowerCamelCase__     )
      _UpperCAmelCase				:      List[str]				=  image.cpu().permute(0		,	2		,	3		,	1     )[0]
      _UpperCAmelCase				:      List[Any]				=  Image.fromarray(np.uinta(lowerCamelCase__     )     ).convert("RGB"     )
      if str(lowerCamelCase__     ).startswith("mps"     ):
         _UpperCAmelCase				:      Optional[Any]				=  torch.manual_seed(lowerCamelCase__     )
      else:
         _UpperCAmelCase				:      List[Any]				=  torch.Generator(device=lowerCamelCase__     ).manual_seed(lowerCamelCase__     )
      _UpperCAmelCase				:      Dict				=  {
          '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      _lowerCAmelCase						(					self	:						Optional[int]     )			->       Tuple:







      """simple docstring"""




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

      _UpperCAmelCase				:      List[Any]				=  self.get_dummy_components()
      _UpperCAmelCase				:      Union[str, Any]				=  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				:      Optional[int]				=  self.get_dummy_inputs(lowerCamelCase__     )
      _UpperCAmelCase				:      int				=  pipe(**lowerCamelCase__     )[0]

      with tempfile.TemporaryDirectory() as tmpdir:
         pipe.save_pretrained(lowerCamelCase__     )
         _UpperCAmelCase				:      List[Any]				=  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				:      List[str]				=  self.get_dummy_inputs(lowerCamelCase__     )
      _UpperCAmelCase				:      str				=  pipe_loaded(**lowerCamelCase__     )[0]

      _UpperCAmelCase				:      Optional[Any]				=  np.abs(output - output_loaded     ).max()
      self.assertLess(lowerCamelCase__		,	1e-4     )




   def      _lowerCAmelCase						(					self	:						Tuple     )			->       Dict:







      """simple docstring"""




      _UpperCAmelCase				:      Optional[Any]				=  'cpu'

      _UpperCAmelCase				:      List[str]				=  self.get_dummy_components()
      _UpperCAmelCase				:      int				=  self.pipeline_class(**lowerCamelCase__     )
      pipe.to(lowerCamelCase__     )
      pipe.set_progress_bar_config(disable=lowerCamelCase__     )

      _UpperCAmelCase				:      List[str]				=  self.get_dummy_mask_inputs(lowerCamelCase__     )
      _UpperCAmelCase				:      Union[str, Any]				=  pipe.generate_mask(**lowerCamelCase__     )
      _UpperCAmelCase				:      List[Any]				=  mask[0, -3:, -3:]

      self.assertEqual(mask.shape		,	(1, 1_6, 1_6)     )
      _UpperCAmelCase				:      Optional[int]				=  np.array([0] * 9     )
      _UpperCAmelCase				:      Any				=  np.abs(mask_slice.flatten() - expected_slice     ).max()
      self.assertLessEqual(lowerCamelCase__		,	1e-3     )
      self.assertEqual(mask[0, -3, -4]		,	0     )




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







      """simple docstring"""




      _UpperCAmelCase				:      str				=  'cpu'

      _UpperCAmelCase				:      Any				=  self.get_dummy_components()
      _UpperCAmelCase				:      Optional[Any]				=  self.pipeline_class(**lowerCamelCase__     )
      pipe.to(lowerCamelCase__     )
      pipe.set_progress_bar_config(disable=lowerCamelCase__     )

      _UpperCAmelCase				:      List[Any]				=  self.get_dummy_inversion_inputs(lowerCamelCase__     )
      _UpperCAmelCase				:      List[str]				=  pipe.invert(**lowerCamelCase__     ).images
      _UpperCAmelCase				:      Tuple				=  image[0, -1, -3:, -3:]

      self.assertEqual(image.shape		,	(2, 3_2, 3_2, 3)     )
      _UpperCAmelCase				:      str				=  np.array(
          [0.5150, 0.5134, 0.5043, 0.5376, 0.4694, 0.5_1050, 0.5015, 0.4407, 0.4799]		,	)
      _UpperCAmelCase				:      List[Any]				=  np.abs(image_slice.flatten() - expected_slice     ).max()
      self.assertLessEqual(lowerCamelCase__		,	1e-3     )




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







      """simple docstring"""




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




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







      """simple docstring"""




      _UpperCAmelCase				:      Union[str, Any]				=  'cpu'

      _UpperCAmelCase				:      List[str]				=  self.get_dummy_components()

      _UpperCAmelCase				:      Any				=  {'beta_start': 0.0_0085, 'beta_end': 0.012, 'beta_schedule': 'scaled_linear'}
      _UpperCAmelCase				:      Optional[int]				=  DPMSolverMultistepScheduler(**lowerCamelCase__     )
      _UpperCAmelCase				:      List[str]				=  DPMSolverMultistepInverseScheduler(**lowerCamelCase__     )

      _UpperCAmelCase				:      List[Any]				=  self.pipeline_class(**lowerCamelCase__     )
      pipe.to(lowerCamelCase__     )
      pipe.set_progress_bar_config(disable=lowerCamelCase__     )

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

      self.assertEqual(image.shape		,	(2, 3_2, 3_2, 3)     )
      _UpperCAmelCase				:      Any				=  np.array(
          [0.5150, 0.5134, 0.5043, 0.5376, 0.4694, 0.5_1050, 0.5015, 0.4407, 0.4799]		,	)
      _UpperCAmelCase				:      Union[str, Any]				=  np.abs(image_slice.flatten() - expected_slice     ).max()
      self.assertLessEqual(lowerCamelCase__		,	1e-3     )





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





   """simple docstring"""





   def      _lowerCAmelCase						(					self	:						str     )			->       Optional[int]:







      """simple docstring"""




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




   @classmethod
   def      _lowerCAmelCase						(					cls	:						List[Any]     )			->       str:







      """simple docstring"""




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

      _UpperCAmelCase				:      Dict				=  raw_image.convert("RGB"     ).resize((7_6_8, 7_6_8)     )

      _UpperCAmelCase				:      Optional[Any]				=  raw_image




   def      _lowerCAmelCase						(					self	:						List[Any]     )			->       List[str]:







      """simple docstring"""




      _UpperCAmelCase				:      Dict				=  torch.manual_seed(0     )

      _UpperCAmelCase				:      Optional[int]				=  StableDiffusionDiffEditPipeline.from_pretrained(
          "stabilityai/stable-diffusion-2-1"		,	safety_checker=lowerCamelCase__		,	torch_dtype=torch.floataa     )
      _UpperCAmelCase				:      List[str]				=  DDIMScheduler.from_config(pipe.scheduler.config     )
      _UpperCAmelCase				:      str				=  DDIMInverseScheduler.from_config(pipe.scheduler.config     )
      pipe.enable_model_cpu_offload()
      pipe.set_progress_bar_config(disable=lowerCamelCase__     )

      _UpperCAmelCase				:      str				=  'a bowl of fruit'
      _UpperCAmelCase				:      Tuple				=  'a bowl of pears'

      _UpperCAmelCase				:      int				=  pipe.generate_mask(
          image=self.raw_image		,	source_prompt=lowerCamelCase__		,	target_prompt=lowerCamelCase__		,	generator=lowerCamelCase__		,	)

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

      _UpperCAmelCase				:      Optional[int]				=  pipe(
          prompt=lowerCamelCase__		,	mask_image=lowerCamelCase__		,	image_latents=lowerCamelCase__		,	generator=lowerCamelCase__		,	negative_prompt=lowerCamelCase__		,	inpaint_strength=0.7		,	output_type="numpy"		,	).images[0]

      _UpperCAmelCase				:      List[Any]				=  (
          np.array(
              load_image(
                  "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
                  "/diffedit/pears.png"     ).resize((7_6_8, 7_6_8)     )     )
          / 2_5_5
      )
      assert np.abs((expected_image - image).max()     ) < 5e-1




   def      _lowerCAmelCase						(					self	:						Optional[Any]     )			->       List[str]:







      """simple docstring"""




      _UpperCAmelCase				:      List[str]				=  torch.manual_seed(0     )

      _UpperCAmelCase				:      Dict				=  StableDiffusionDiffEditPipeline.from_pretrained(
          "stabilityai/stable-diffusion-2-1"		,	safety_checker=lowerCamelCase__		,	torch_dtype=torch.floataa     )
      _UpperCAmelCase				:      Tuple				=  DPMSolverMultistepScheduler.from_config(pipe.scheduler.config     )
      _UpperCAmelCase				:      Optional[int]				=  DPMSolverMultistepInverseScheduler.from_config(pipe.scheduler.config     )
      pipe.enable_model_cpu_offload()
      pipe.set_progress_bar_config(disable=lowerCamelCase__     )

      _UpperCAmelCase				:      str				=  'a bowl of fruit'
      _UpperCAmelCase				:      Optional[Any]				=  'a bowl of pears'

      _UpperCAmelCase				:      List[Any]				=  pipe.generate_mask(
          image=self.raw_image		,	source_prompt=lowerCamelCase__		,	target_prompt=lowerCamelCase__		,	generator=lowerCamelCase__		,	)

      _UpperCAmelCase				:      str				=  pipe.invert(
          prompt=lowerCamelCase__		,	image=self.raw_image		,	inpaint_strength=0.7		,	generator=lowerCamelCase__		,	num_inference_steps=2_5		,	).latents

      _UpperCAmelCase				:      int				=  pipe(
          prompt=lowerCamelCase__		,	mask_image=lowerCamelCase__		,	image_latents=lowerCamelCase__		,	generator=lowerCamelCase__		,	negative_prompt=lowerCamelCase__		,	inpaint_strength=0.7		,	num_inference_steps=2_5		,	output_type="numpy"		,	).images[0]

      _UpperCAmelCase				:      Tuple				=  (
          np.array(
              load_image(
                  "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
                  "/diffedit/pears.png"     ).resize((7_6_8, 7_6_8)     )     )
          / 2_5_5
      )
      assert np.abs((expected_image - image).max()     ) < 5e-1




'''simple docstring'''
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


snake_case_					:      Tuple    			=    {
    'configuration_layoutlmv2': ['LAYOUTLMV2_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LayoutLMv2Config'],
    'processing_layoutlmv2': ['LayoutLMv2Processor'],
    'tokenization_layoutlmv2': ['LayoutLMv2Tokenizer'],
}

try:
					if not is_tokenizers_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					snake_case_					:      int    			=    ['LayoutLMv2TokenizerFast']

try:
					if not is_vision_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					snake_case_					:      Union[str, Any]    			=    ['LayoutLMv2FeatureExtractor']
					snake_case_					:      str    			=    ['LayoutLMv2ImageProcessor']

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					snake_case_					:      Optional[int]    			=    [
					    'LAYOUTLMV2_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'LayoutLMv2ForQuestionAnswering',
					    'LayoutLMv2ForSequenceClassification',
					    'LayoutLMv2ForTokenClassification',
					    'LayoutLMv2Layer',
					    'LayoutLMv2Model',
					    'LayoutLMv2PreTrainedModel',
					]

if TYPE_CHECKING:
					from .configuration_layoutlmva import LAYOUTLMV2_PRETRAINED_CONFIG_ARCHIVE_MAP, LayoutLMvaConfig
					from .processing_layoutlmva import LayoutLMvaProcessor
					from .tokenization_layoutlmva import LayoutLMvaTokenizer

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_layoutlmva_fast import LayoutLMvaTokenizerFast

					try:
										if not is_vision_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .feature_extraction_layoutlmva import LayoutLMvaFeatureExtractor, LayoutLMvaImageProcessor

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_layoutlmva import (
										    LAYOUTLMV2_PRETRAINED_MODEL_ARCHIVE_LIST,
										    LayoutLMvaForQuestionAnswering,
										    LayoutLMvaForSequenceClassification,
										    LayoutLMvaForTokenClassification,
										    LayoutLMvaLayer,
										    LayoutLMvaModel,
										    LayoutLMvaPreTrainedModel,
										)
else:
					import sys

					snake_case_					:      int    			=    _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


