



'''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 					UpperCAmelCase__	(		_lowerCamelCase							,       _lowerCamelCase							,       unittest.TestCase ):
					a      :     List[str]        =							StableDiffusionDiffEditPipeline
					a      :     Dict        =							TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {"""height""", """width""", """image"""} | {"""image_latents"""}
					a      :     int        =							TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS - {"""image"""} | {"""image_latents"""}
					a      :     Tuple        =							frozenset(
					    [] )  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
					a      :     List[Any]        =							frozenset([] )



					def        UpperCAmelCase_ (    self     )     ->			List[Any]:
						torch.manual_seed(0     )
						__lowerCAmelCase												=  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=A__			,				)
						__lowerCAmelCase												=  DDIMScheduler(
						    beta_start=0.0_00_85			,				beta_end=0.0_12			,				beta_schedule="scaled_linear"			,				clip_sample=A__			,				set_alpha_to_one=A__			,				)
						__lowerCAmelCase												=  DDIMInverseScheduler(
						    beta_start=0.0_00_85			,				beta_end=0.0_12			,				beta_schedule="scaled_linear"			,				clip_sample=A__			,				set_alpha_to_zero=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,
						    "inverse_scheduler": inverse_scheduler,
						    "vae": vae,
						    "text_encoder": text_encoder,
						    "tokenizer": tokenizer,
						    "safety_checker": None,
						    "feature_extractor": None,
						}

						return components



					def        UpperCAmelCase_ (    self			,				UpperCamelCase			,				UpperCamelCase=0     )     ->			Tuple:
						__lowerCAmelCase												=  floats_tensor((1, 16, 16)			,				rng=random.Random(A__     )     ).to(A__     )
						__lowerCAmelCase												=  floats_tensor((1, 2, 4, 16, 16)			,				rng=random.Random(A__     )     ).to(A__     )
						if str(A__     ).startswith("mps"     ):
							__lowerCAmelCase												=  torch.manual_seed(A__     )
						else:
							__lowerCAmelCase												=  torch.Generator(device=A__     ).manual_seed(A__     )
						__lowerCAmelCase												=  {
						    "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			,				UpperCamelCase			,				UpperCamelCase=0     )     ->			int:
						__lowerCAmelCase												=  floats_tensor((1, 3, 32, 32)			,				rng=random.Random(A__     )     ).to(A__     )
						__lowerCAmelCase												=  image.cpu().permute(0			,				2			,				3			,				1     )[0]
						__lowerCAmelCase												=  Image.fromarray(np.uinta(A__     )     ).convert("RGB"     )
						if str(A__     ).startswith("mps"     ):
							__lowerCAmelCase												=  torch.manual_seed(A__     )
						else:
							__lowerCAmelCase												=  torch.Generator(device=A__     ).manual_seed(A__     )
						__lowerCAmelCase												=  {
						    "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			,				UpperCamelCase			,				UpperCamelCase=0     )     ->			str:
						__lowerCAmelCase												=  floats_tensor((1, 3, 32, 32)			,				rng=random.Random(A__     )     ).to(A__     )
						__lowerCAmelCase												=  image.cpu().permute(0			,				2			,				3			,				1     )[0]
						__lowerCAmelCase												=  Image.fromarray(np.uinta(A__     )     ).convert("RGB"     )
						if str(A__     ).startswith("mps"     ):
							__lowerCAmelCase												=  torch.manual_seed(A__     )
						else:
							__lowerCAmelCase												=  torch.Generator(device=A__     ).manual_seed(A__     )
						__lowerCAmelCase												=  {
						    "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     )     ->			str:
						if not hasattr(self.pipeline_class			,				"_optional_components"     ):
							return

						__lowerCAmelCase												=  self.get_dummy_components()
						__lowerCAmelCase												=  self.pipeline_class(**A__     )
						pipe.to(A__     )
						pipe.set_progress_bar_config(disable=A__     )

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

						__lowerCAmelCase												=  self.get_dummy_inputs(A__     )
						__lowerCAmelCase												=  pipe(**A__     )[0]

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

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

						__lowerCAmelCase												=  self.get_dummy_inputs(A__     )
						__lowerCAmelCase												=  pipe_loaded(**A__     )[0]

						__lowerCAmelCase												=  np.abs(output - output_loaded     ).max()
						self.assertLess(A__			,				1E-4     )



					def        UpperCAmelCase_ (    self     )     ->			int:
						__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_mask_inputs(A__     )
						__lowerCAmelCase												=  pipe.generate_mask(**A__     )
						__lowerCAmelCase												=  mask[0, -3:, -3:]

						self.assertEqual(mask.shape			,				(1, 16, 16)     )
						__lowerCAmelCase												=  np.array([0] * 9     )
						__lowerCAmelCase												=  np.abs(mask_slice.flatten() - expected_slice     ).max()
						self.assertLessEqual(A__			,				1E-3     )
						self.assertEqual(mask[0, -3, -4]			,				0     )



					def        UpperCAmelCase_ (    self     )     ->			Any:
						__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_inversion_inputs(A__     )
						__lowerCAmelCase												=  pipe.invert(**A__     ).images
						__lowerCAmelCase												=  image[0, -1, -3:, -3:]

						self.assertEqual(image.shape			,				(2, 32, 32, 3)     )
						__lowerCAmelCase												=  np.array(
						    [0.51_50, 0.51_34, 0.50_43, 0.53_76, 0.46_94, 0.5_10_50, 0.50_15, 0.44_07, 0.47_99]			,				)
						__lowerCAmelCase												=  np.abs(image_slice.flatten() - expected_slice     ).max()
						self.assertLessEqual(A__			,				1E-3     )



					def        UpperCAmelCase_ (    self     )     ->			int:
						super().test_inference_batch_single_identical(expected_max_diff=5E-3     )



					def        UpperCAmelCase_ (    self     )     ->			int:
						__lowerCAmelCase												=  "cpu"

						__lowerCAmelCase												=  self.get_dummy_components()

						__lowerCAmelCase												=  {"beta_start": 0.0_00_85, "beta_end": 0.0_12, "beta_schedule": "scaled_linear"}
						__lowerCAmelCase												=  DPMSolverMultistepScheduler(**A__     )
						__lowerCAmelCase												=  DPMSolverMultistepInverseScheduler(**A__     )

						__lowerCAmelCase												=  self.pipeline_class(**A__     )
						pipe.to(A__     )
						pipe.set_progress_bar_config(disable=A__     )

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

						self.assertEqual(image.shape			,				(2, 32, 32, 3)     )
						__lowerCAmelCase												=  np.array(
						    [0.51_50, 0.51_34, 0.50_43, 0.53_76, 0.46_94, 0.5_10_50, 0.50_15, 0.44_07, 0.47_99]			,				)
						__lowerCAmelCase												=  np.abs(image_slice.flatten() - expected_slice     ).max()
						self.assertLessEqual(A__			,				1E-3     )






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



					def        UpperCAmelCase_ (    self     )     ->			List[str]:
						super().tearDown()
						gc.collect()
						torch.cuda.empty_cache()



					@classmethod
					def        UpperCAmelCase_ (    cls     )     ->			Any:
						__lowerCAmelCase												=  load_image(
						    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/diffedit/fruit.png"     )

						__lowerCAmelCase												=  raw_image.convert("RGB"     ).resize((768, 768)     )

						__lowerCAmelCase												=  raw_image



					def        UpperCAmelCase_ (    self     )     ->			Optional[Any]:
						__lowerCAmelCase												=  torch.manual_seed(0     )

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

						__lowerCAmelCase												=  "a bowl of fruit"
						__lowerCAmelCase												=  "a bowl of pears"

						__lowerCAmelCase												=  pipe.generate_mask(
						    image=self.raw_image			,				source_prompt=A__			,				target_prompt=A__			,				generator=A__			,				)

						__lowerCAmelCase												=  pipe.invert(
						    prompt=A__			,				image=self.raw_image			,				inpaint_strength=0.7			,				generator=A__     ).latents

						__lowerCAmelCase												=  pipe(
						    prompt=A__			,				mask_image=A__			,				image_latents=A__			,				generator=A__			,				negative_prompt=A__			,				inpaint_strength=0.7			,				output_type="numpy"			,				).images[0]

						__lowerCAmelCase												=  (
						    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     )     ->			List[Any]:
						__lowerCAmelCase												=  torch.manual_seed(0     )

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

						__lowerCAmelCase												=  "a bowl of fruit"
						__lowerCAmelCase												=  "a bowl of pears"

						__lowerCAmelCase												=  pipe.generate_mask(
						    image=self.raw_image			,				source_prompt=A__			,				target_prompt=A__			,				generator=A__			,				)

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

						__lowerCAmelCase												=  pipe(
						    prompt=A__			,				mask_image=A__			,				image_latents=A__			,				generator=A__			,				negative_prompt=A__			,				inpaint_strength=0.7			,				num_inference_steps=25			,				output_type="numpy"			,				).images[0]

						__lowerCAmelCase												=  (
						    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




'''simple docstring'''



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCAmelCase	:		Union[str, Any]				=   {
    '''configuration_distilbert''': [
        '''DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''DistilBertConfig''',
        '''DistilBertOnnxConfig''',
    ],
    '''tokenization_distilbert''': ['''DistilBertTokenizer'''],
}

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowerCAmelCase	:		Tuple				=   ['''DistilBertTokenizerFast''']

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowerCAmelCase	:		Tuple				=   [
	    '''DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''DistilBertForMaskedLM''',
	    '''DistilBertForMultipleChoice''',
	    '''DistilBertForQuestionAnswering''',
	    '''DistilBertForSequenceClassification''',
	    '''DistilBertForTokenClassification''',
	    '''DistilBertModel''',
	    '''DistilBertPreTrainedModel''',
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowerCAmelCase	:		List[str]				=   [
	    '''TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''TFDistilBertForMaskedLM''',
	    '''TFDistilBertForMultipleChoice''',
	    '''TFDistilBertForQuestionAnswering''',
	    '''TFDistilBertForSequenceClassification''',
	    '''TFDistilBertForTokenClassification''',
	    '''TFDistilBertMainLayer''',
	    '''TFDistilBertModel''',
	    '''TFDistilBertPreTrainedModel''',
	]

try:
	if not is_flax_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowerCAmelCase	:		Optional[int]				=   [
	    '''FlaxDistilBertForMaskedLM''',
	    '''FlaxDistilBertForMultipleChoice''',
	    '''FlaxDistilBertForQuestionAnswering''',
	    '''FlaxDistilBertForSequenceClassification''',
	    '''FlaxDistilBertForTokenClassification''',
	    '''FlaxDistilBertModel''',
	    '''FlaxDistilBertPreTrainedModel''',
	]


if TYPE_CHECKING:
	from .configuration_distilbert import (
	    DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    DistilBertConfig,
	    DistilBertOnnxConfig,
	)
	from .tokenization_distilbert import DistilBertTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_distilbert_fast import DistilBertTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_distilbert import (
		    DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    DistilBertForMaskedLM,
		    DistilBertForMultipleChoice,
		    DistilBertForQuestionAnswering,
		    DistilBertForSequenceClassification,
		    DistilBertForTokenClassification,
		    DistilBertModel,
		    DistilBertPreTrainedModel,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_distilbert import (
		    TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFDistilBertForMaskedLM,
		    TFDistilBertForMultipleChoice,
		    TFDistilBertForQuestionAnswering,
		    TFDistilBertForSequenceClassification,
		    TFDistilBertForTokenClassification,
		    TFDistilBertMainLayer,
		    TFDistilBertModel,
		    TFDistilBertPreTrainedModel,
		)

	try:
		if not is_flax_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_flax_distilbert import (
		    FlaxDistilBertForMaskedLM,
		    FlaxDistilBertForMultipleChoice,
		    FlaxDistilBertForQuestionAnswering,
		    FlaxDistilBertForSequenceClassification,
		    FlaxDistilBertForTokenClassification,
		    FlaxDistilBertModel,
		    FlaxDistilBertPreTrainedModel,
		)

else:
	import sys

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