






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






						def 			lowerCamelCase__    (    self	)	->int:







								'''simple docstring'''


								torch.manual_seed(0	)
								_a							=	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=a_     ,     )
								_a							=	DDIMScheduler(
								    beta_start=0.00_085     ,     beta_end=0.012     ,     beta_schedule="scaled_linear"     ,     clip_sample=a_     ,     set_alpha_to_one=a_     ,     )
								_a							=	DDIMInverseScheduler(
								    beta_start=0.00_085     ,     beta_end=0.012     ,     beta_schedule="scaled_linear"     ,     clip_sample=a_     ,     set_alpha_to_zero=a_     ,     )
								torch.manual_seed(0	)
								_a							=	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	)
								_a							=	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     ,     )
								_a							=	CLIPTextModel(a_	)
								_a							=	CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"	)

								_a							=	{
								    "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     ,     a_     ,     a_=0	)	->Optional[Any]:







								'''simple docstring'''


								_a							=	floats_tensor((1, 1_6, 1_6)     ,     rng=random.Random(a_	)	).to(a_	)
								_a							=	floats_tensor((1, 2, 4, 1_6, 1_6)     ,     rng=random.Random(a_	)	).to(a_	)
								if str(a_	).startswith("mps"	):
										_a							=	torch.manual_seed(a_	)
								else:
										_a							=	torch.Generator(device=a_	).manual_seed(a_	)
								_a							=	{
								    "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     ,     a_     ,     a_=0	)	->Any:







								'''simple docstring'''


								_a							=	floats_tensor((1, 3, 3_2, 3_2)     ,     rng=random.Random(a_	)	).to(a_	)
								_a							=	image.cpu().permute(0     ,     2     ,     3     ,     1	)[0]
								_a							=	Image.fromarray(np.uinta(a_	)	).convert("RGB"	)
								if str(a_	).startswith("mps"	):
										_a							=	torch.manual_seed(a_	)
								else:
										_a							=	torch.Generator(device=a_	).manual_seed(a_	)
								_a							=	{
								    "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     ,     a_     ,     a_=0	)	->Dict:







								'''simple docstring'''


								_a							=	floats_tensor((1, 3, 3_2, 3_2)     ,     rng=random.Random(a_	)	).to(a_	)
								_a							=	image.cpu().permute(0     ,     2     ,     3     ,     1	)[0]
								_a							=	Image.fromarray(np.uinta(a_	)	).convert("RGB"	)
								if str(a_	).startswith("mps"	):
										_a							=	torch.manual_seed(a_	)
								else:
										_a							=	torch.Generator(device=a_	).manual_seed(a_	)
								_a							=	{
								    "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	)	->str:







								'''simple docstring'''


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

								_a							=	self.get_dummy_components()
								_a							=	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}	)

								_a							=	self.get_dummy_inputs(a_	)
								_a							=	pipe(**a_	)[0]

								with tempfile.TemporaryDirectory() as tmpdir:
										pipe.save_pretrained(a_	)
										_a							=	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.'''     ,     )

								_a							=	self.get_dummy_inputs(a_	)
								_a							=	pipe_loaded(**a_	)[0]

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






						def 			lowerCamelCase__    (    self	)	->Dict:







								'''simple docstring'''


								_a							=	"cpu"

								_a							=	self.get_dummy_components()
								_a							=	self.pipeline_class(**a_	)
								pipe.to(a_	)
								pipe.set_progress_bar_config(disable=a_	)

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

								self.assertEqual(mask.shape     ,     (1, 1_6, 1_6)	)
								_a							=	np.array([0] * 9	)
								_a							=	np.abs(mask_slice.flatten() - expected_slice	).max()
								self.assertLessEqual(a_     ,     1E-3	)
								self.assertEqual(mask[0, -3, -4]     ,     0	)






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







								'''simple docstring'''


								_a							=	"cpu"

								_a							=	self.get_dummy_components()
								_a							=	self.pipeline_class(**a_	)
								pipe.to(a_	)
								pipe.set_progress_bar_config(disable=a_	)

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

								self.assertEqual(image.shape     ,     (2, 3_2, 3_2, 3)	)
								_a							=	np.array(
								    [0.5_150, 0.5_134, 0.5_043, 0.5_376, 0.4_694, 0.51_050, 0.5_015, 0.4_407, 0.4_799]     ,     )
								_a							=	np.abs(image_slice.flatten() - expected_slice	).max()
								self.assertLessEqual(a_     ,     1E-3	)






						def 			lowerCamelCase__    (    self	)	->Dict:







								'''simple docstring'''


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






						def 			lowerCamelCase__    (    self	)	->Dict:







								'''simple docstring'''


								_a							=	"cpu"

								_a							=	self.get_dummy_components()

								_a							=	{"beta_start": 0.00_085, "beta_end": 0.012, "beta_schedule": "scaled_linear"}
								_a							=	DPMSolverMultistepScheduler(**a_	)
								_a							=	DPMSolverMultistepInverseScheduler(**a_	)

								_a							=	self.pipeline_class(**a_	)
								pipe.to(a_	)
								pipe.set_progress_bar_config(disable=a_	)

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

								self.assertEqual(image.shape     ,     (2, 3_2, 3_2, 3)	)
								_a							=	np.array(
								    [0.5_150, 0.5_134, 0.5_043, 0.5_376, 0.4_694, 0.51_050, 0.5_015, 0.4_407, 0.4_799]     ,     )
								_a							=	np.abs(image_slice.flatten() - expected_slice	).max()
								self.assertLessEqual(a_     ,     1E-3	)







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






						def 			lowerCamelCase__    (    self	)	->Tuple:







								'''simple docstring'''


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






						@classmethod
						def 			lowerCamelCase__    (    cls	)	->Any:







								'''simple docstring'''


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

								_a							=	raw_image.convert("RGB"	).resize((7_6_8, 7_6_8)	)

								_a							=	raw_image






						def 			lowerCamelCase__    (    self	)	->Dict:







								'''simple docstring'''


								_a							=	torch.manual_seed(0	)

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

								_a							=	"a bowl of fruit"
								_a							=	"a bowl of pears"

								_a							=	pipe.generate_mask(
								    image=self.raw_image     ,     source_prompt=a_     ,     target_prompt=a_     ,     generator=a_     ,     )

								_a							=	pipe.invert(
								    prompt=a_     ,     image=self.raw_image     ,     inpaint_strength=0.7     ,     generator=a_	).latents

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

								_a							=	(
								    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[int]:







								'''simple docstring'''


								_a							=	torch.manual_seed(0	)

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

								_a							=	"a bowl of fruit"
								_a							=	"a bowl of pears"

								_a							=	pipe.generate_mask(
								    image=self.raw_image     ,     source_prompt=a_     ,     target_prompt=a_     ,     generator=a_     ,     )

								_a							=	pipe.invert(
								    prompt=a_     ,     image=self.raw_image     ,     inpaint_strength=0.7     ,     generator=a_     ,     num_inference_steps=2_5     ,     ).latents

								_a							=	pipe(
								    prompt=a_     ,     mask_image=a_     ,     image_latents=a_     ,     generator=a_     ,     negative_prompt=a_     ,     inpaint_strength=0.7     ,     num_inference_steps=2_5     ,     output_type="numpy"     ,     ).images[0]

								_a							=	(
								    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_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCamelCase							       =    {"""configuration_fnet""": ["""FNET_PRETRAINED_CONFIG_ARCHIVE_MAP""", """FNetConfig"""]}

try:
     if not is_sentencepiece_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCamelCase							       =    ["""FNetTokenizer"""]

try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCamelCase							       =    ["""FNetTokenizerFast"""]

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCamelCase							       =    [
         """FNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """FNetForMaskedLM""",
         """FNetForMultipleChoice""",
         """FNetForNextSentencePrediction""",
         """FNetForPreTraining""",
         """FNetForQuestionAnswering""",
         """FNetForSequenceClassification""",
         """FNetForTokenClassification""",
         """FNetLayer""",
         """FNetModel""",
         """FNetPreTrainedModel""",
     ]


if TYPE_CHECKING:
     from .configuration_fnet import FNET_PRETRAINED_CONFIG_ARCHIVE_MAP, FNetConfig

     try:
          if not is_sentencepiece_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_fnet import FNetTokenizer

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_fnet_fast import FNetTokenizerFast

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_fnet import (
              FNET_PRETRAINED_MODEL_ARCHIVE_LIST,
              FNetForMaskedLM,
              FNetForMultipleChoice,
              FNetForNextSentencePrediction,
              FNetForPreTraining,
              FNetForQuestionAnswering,
              FNetForSequenceClassification,
              FNetForTokenClassification,
              FNetLayer,
              FNetModel,
              FNetPreTrainedModel,
          )


else:
     import sys

     UpperCamelCase							       =    _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


