



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





							def 						UpperCamelCase_					(   self		:		Optional[Any]	):

											'''simple docstring'''
											torch.manual_seed(0	)
											__a       =		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=__lowercase      ,				)
											__a       =		DDIMScheduler(
											    beta_start=0.00085      ,				beta_end=0.012      ,				beta_schedule="""scaled_linear"""      ,				clip_sample=__lowercase      ,				set_alpha_to_one=__lowercase      ,				)
											__a       =		DDIMInverseScheduler(
											    beta_start=0.00085      ,				beta_end=0.012      ,				beta_schedule="""scaled_linear"""      ,				clip_sample=__lowercase      ,				set_alpha_to_zero=__lowercase      ,				)
											torch.manual_seed(0	)
											__a       =		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	)
											__a       =		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      ,				)
											__a       =		CLIPTextModel(__lowercase	)
											__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 						UpperCamelCase_					(   self		:		str      ,				__lowercase		:		str      ,				__lowercase		:		Any=0	):

											'''simple docstring'''
											__a       =		floats_tensor((1, 16, 16)      ,				rng=random.Random(__lowercase	)	).to(__lowercase	)
											__a       =		floats_tensor((1, 2, 4, 16, 16)      ,				rng=random.Random(__lowercase	)	).to(__lowercase	)
											if str(__lowercase	).startswith("""mps"""	):
															__a       =		torch.manual_seed(__lowercase	)
											else:
															__a       =		torch.Generator(device=__lowercase	).manual_seed(__lowercase	)
											__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 						UpperCamelCase_					(   self		:		List[str]      ,				__lowercase		:		int      ,				__lowercase		:		Dict=0	):

											'''simple docstring'''
											__a       =		floats_tensor((1, 3, 32, 32)      ,				rng=random.Random(__lowercase	)	).to(__lowercase	)
											__a       =		image.cpu().permute(0      ,				2      ,				3      ,				1	)[0]
											__a       =		Image.fromarray(np.uinta(__lowercase	)	).convert("""RGB"""	)
											if str(__lowercase	).startswith("""mps"""	):
															__a       =		torch.manual_seed(__lowercase	)
											else:
															__a       =		torch.Generator(device=__lowercase	).manual_seed(__lowercase	)
											__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 						UpperCamelCase_					(   self		:		str      ,				__lowercase		:		List[Any]      ,				__lowercase		:		Union[str, Any]=0	):

											'''simple docstring'''
											__a       =		floats_tensor((1, 3, 32, 32)      ,				rng=random.Random(__lowercase	)	).to(__lowercase	)
											__a       =		image.cpu().permute(0      ,				2      ,				3      ,				1	)[0]
											__a       =		Image.fromarray(np.uinta(__lowercase	)	).convert("""RGB"""	)
											if str(__lowercase	).startswith("""mps"""	):
															__a       =		torch.manual_seed(__lowercase	)
											else:
															__a       =		torch.Generator(device=__lowercase	).manual_seed(__lowercase	)
											__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 						UpperCamelCase_					(   self		:		int	):

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

											__a       =		self.get_dummy_components()
											__a       =		self.pipeline_class(**__lowercase	)
											pipe.to(__lowercase	)
											pipe.set_progress_bar_config(disable=__lowercase	)

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

											__a       =		self.get_dummy_inputs(__lowercase	)
											__a       =		pipe(**__lowercase	)[0]

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

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

											__a       =		self.get_dummy_inputs(__lowercase	)
											__a       =		pipe_loaded(**__lowercase	)[0]

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





							def 						UpperCamelCase_					(   self		:		str	):

											'''simple docstring'''
											__a       =		"""cpu"""

											__a       =		self.get_dummy_components()
											__a       =		self.pipeline_class(**__lowercase	)
											pipe.to(__lowercase	)
											pipe.set_progress_bar_config(disable=__lowercase	)

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

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





							def 						UpperCamelCase_					(   self		:		int	):

											'''simple docstring'''
											__a       =		"""cpu"""

											__a       =		self.get_dummy_components()
											__a       =		self.pipeline_class(**__lowercase	)
											pipe.to(__lowercase	)
											pipe.set_progress_bar_config(disable=__lowercase	)

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

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





							def 						UpperCamelCase_					(   self		:		Dict	):

											'''simple docstring'''
											super().test_inference_batch_single_identical(expected_max_diff=5E-3	)







							def 						UpperCamelCase_					(   self		:		Optional[Any]	):

											'''simple docstring'''
											__a       =		"""cpu"""

											__a       =		self.get_dummy_components()

											__a       =		{"""beta_start""": 0.00085, """beta_end""": 0.012, """beta_schedule""": """scaled_linear"""}
											__a       =		DPMSolverMultistepScheduler(**__lowercase	)
											__a       =		DPMSolverMultistepInverseScheduler(**__lowercase	)

											__a       =		self.pipeline_class(**__lowercase	)
											pipe.to(__lowercase	)
											pipe.set_progress_bar_config(disable=__lowercase	)

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

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






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





							def 						UpperCamelCase_					(   self		:		Optional[int]	):

											'''simple docstring'''
											super().tearDown()
											gc.collect()
											torch.cuda.empty_cache()





							@classmethod
							def 						UpperCamelCase_					(   cls		:		List[str]	):

											'''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((768, 768)	)

											__a       =		raw_image





							def 						UpperCamelCase_					(   self		:		str	):

											'''simple docstring'''
											__a       =		torch.manual_seed(0	)

											__a       =		StableDiffusionDiffEditPipeline.from_pretrained(
											    """stabilityai/stable-diffusion-2-1"""      ,				safety_checker=__lowercase      ,				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=__lowercase	)

											__a       =		"""a bowl of fruit"""
											__a       =		"""a bowl of pears"""

											__a       =		pipe.generate_mask(
											    image=self.raw_image      ,				source_prompt=__lowercase      ,				target_prompt=__lowercase      ,				generator=__lowercase      ,				)

											__a       =		pipe.invert(
											    prompt=__lowercase      ,				image=self.raw_image      ,				inpaint_strength=0.7      ,				generator=__lowercase	).latents

											__a       =		pipe(
											    prompt=__lowercase      ,				mask_image=__lowercase      ,				image_latents=__lowercase      ,				generator=__lowercase      ,				negative_prompt=__lowercase      ,				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((768, 768)	)	)
											    / 255
											)
											assert np.abs((expected_image - image).max()	) < 5E-1







							def 						UpperCamelCase_					(   self		:		str	):

											'''simple docstring'''
											__a       =		torch.manual_seed(0	)

											__a       =		StableDiffusionDiffEditPipeline.from_pretrained(
											    """stabilityai/stable-diffusion-2-1"""      ,				safety_checker=__lowercase      ,				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=__lowercase	)

											__a       =		"""a bowl of fruit"""
											__a       =		"""a bowl of pears"""

											__a       =		pipe.generate_mask(
											    image=self.raw_image      ,				source_prompt=__lowercase      ,				target_prompt=__lowercase      ,				generator=__lowercase      ,				)

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

											__a       =		pipe(
											    prompt=__lowercase      ,				mask_image=__lowercase      ,				image_latents=__lowercase      ,				generator=__lowercase      ,				negative_prompt=__lowercase      ,				inpaint_strength=0.7      ,				num_inference_steps=25      ,				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((768, 768)	)	)
											    / 255
											)
											assert np.abs((expected_image - image).max()	) < 5E-1





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available


lowerCamelCase__			= {"""configuration_speech_encoder_decoder""": ["""SpeechEncoderDecoderConfig"""]}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCamelCase__			= ["""SpeechEncoderDecoderModel"""]

try:
						if not is_flax_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCamelCase__			= ["""FlaxSpeechEncoderDecoderModel"""]

if TYPE_CHECKING:
						from .configuration_speech_encoder_decoder import SpeechEncoderDecoderConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_speech_encoder_decoder import SpeechEncoderDecoderModel

						try:
												if not is_flax_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_flax_speech_encoder_decoder import FlaxSpeechEncoderDecoderModel

else:
						import sys

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