
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 	_lowerCAmelCase					(	__a					,      __a					,      unittest.TestCase       ):
				_lowercase					=StableDiffusionDiffEditPipeline
				_lowercase					=TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {'''height''', '''width''', '''image'''} | {'''image_latents'''}
				_lowercase					=TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS - {'''image'''} | {'''image_latents'''}
				_lowercase					=frozenset(
				    []       )  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
				_lowercase					=frozenset([]       )




				def      __a						(					self     )				->      Tuple:
							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=_UpperCamelCase				,				)
							lowerCAmelCase_						=	DDIMScheduler(
							    beta_start=0.00085				,				beta_end=0.012				,				beta_schedule="scaled_linear"				,				clip_sample=_UpperCamelCase				,				set_alpha_to_one=_UpperCamelCase				,				)
							lowerCAmelCase_						=	DDIMInverseScheduler(
							    beta_start=0.00085				,				beta_end=0.012				,				beta_schedule="scaled_linear"				,				clip_sample=_UpperCamelCase				,				set_alpha_to_zero=_UpperCamelCase				,				)
							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-0_5				,				num_attention_heads=4				,				num_hidden_layers=5				,				pad_token_id=1				,				vocab_size=1_000				,				hidden_act="gelu"				,				projection_dim=512				,				)
							lowerCAmelCase_						=	CLIPTextModel(_UpperCamelCase     )
							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      __a						(					self				,				_UpperCamelCase				,				_UpperCamelCase=0     )				->      Dict:
							lowerCAmelCase_						=	floats_tensor((1, 16, 16)				,				rng=random.Random(_UpperCamelCase     )     ).to(_UpperCamelCase     )
							lowerCAmelCase_						=	floats_tensor((1, 2, 4, 16, 16)				,				rng=random.Random(_UpperCamelCase     )     ).to(_UpperCamelCase     )
							if str(_UpperCamelCase     ).startswith("mps"     ):
										lowerCAmelCase_						=	torch.manual_seed(_UpperCamelCase     )
							else:
										lowerCAmelCase_						=	torch.Generator(device=_UpperCamelCase     ).manual_seed(_UpperCamelCase     )
							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      __a						(					self				,				_UpperCamelCase				,				_UpperCamelCase=0     )				->      List[str]:
							lowerCAmelCase_						=	floats_tensor((1, 3, 32, 32)				,				rng=random.Random(_UpperCamelCase     )     ).to(_UpperCamelCase     )
							lowerCAmelCase_						=	image.cpu().permute(0				,				2				,				3				,				1     )[0]
							lowerCAmelCase_						=	Image.fromarray(np.uinta(_UpperCamelCase     )     ).convert("RGB"     )
							if str(_UpperCamelCase     ).startswith("mps"     ):
										lowerCAmelCase_						=	torch.manual_seed(_UpperCamelCase     )
							else:
										lowerCAmelCase_						=	torch.Generator(device=_UpperCamelCase     ).manual_seed(_UpperCamelCase     )
							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      __a						(					self				,				_UpperCamelCase				,				_UpperCamelCase=0     )				->      Optional[int]:
							lowerCAmelCase_						=	floats_tensor((1, 3, 32, 32)				,				rng=random.Random(_UpperCamelCase     )     ).to(_UpperCamelCase     )
							lowerCAmelCase_						=	image.cpu().permute(0				,				2				,				3				,				1     )[0]
							lowerCAmelCase_						=	Image.fromarray(np.uinta(_UpperCamelCase     )     ).convert("RGB"     )
							if str(_UpperCamelCase     ).startswith("mps"     ):
										lowerCAmelCase_						=	torch.manual_seed(_UpperCamelCase     )
							else:
										lowerCAmelCase_						=	torch.Generator(device=_UpperCamelCase     ).manual_seed(_UpperCamelCase     )
							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      __a						(					self     )				->      Tuple:
							if not hasattr(self.pipeline_class				,				"_optional_components"     ):
										return

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

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

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

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

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

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

							lowerCAmelCase_						=	np.abs(output - output_loaded     ).max()
							self.assertLess(_UpperCamelCase				,				1e-4     )




				def      __a						(					self     )				->      List[str]:
							lowerCAmelCase_						=	"cpu"

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

							lowerCAmelCase_						=	self.get_dummy_mask_inputs(_UpperCamelCase     )
							lowerCAmelCase_						=	pipe.generate_mask(**_UpperCamelCase     )
							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(_UpperCamelCase				,				1e-3     )
							self.assertEqual(mask[0, -3, -4]				,				0     )




				def      __a						(					self     )				->      List[Any]:
							lowerCAmelCase_						=	"cpu"

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

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

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




				def      __a						(					self     )				->      Tuple:
							super().test_inference_batch_single_identical(expected_max_diff=5e-3     )




				def      __a						(					self     )				->      Optional[Any]:
							lowerCAmelCase_						=	"cpu"

							lowerCAmelCase_						=	self.get_dummy_components()

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

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

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

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





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




				def      __a						(					self     )				->      Optional[Any]:
							super().tearDown()
							gc.collect()
							torch.cuda.empty_cache()




				@classmethod
				def      __a						(					cls     )				->      str:
							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      __a						(					self     )				->      List[Any]:
							lowerCAmelCase_						=	torch.manual_seed(0     )

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

							lowerCAmelCase_						=	"a bowl of fruit"
							lowerCAmelCase_						=	"a bowl of pears"

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

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

							lowerCAmelCase_						=	pipe(
							    prompt=_UpperCamelCase				,				mask_image=_UpperCamelCase				,				image_latents=_UpperCamelCase				,				generator=_UpperCamelCase				,				negative_prompt=_UpperCamelCase				,				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      __a						(					self     )				->      Optional[int]:
							lowerCAmelCase_						=	torch.manual_seed(0     )

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

							lowerCAmelCase_						=	"a bowl of fruit"
							lowerCAmelCase_						=	"a bowl of pears"

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

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

							lowerCAmelCase_						=	pipe(
							    prompt=_UpperCamelCase				,				mask_image=_UpperCamelCase				,				image_latents=_UpperCamelCase				,				generator=_UpperCamelCase				,				negative_prompt=_UpperCamelCase				,				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





import functools
import operator

from ...configuration_utils import PretrainedConfig
from ...utils import logging


_A   =     logging.get_logger(__name__)

_A   =     {
    "asapp/sew-tiny-100k": "https://huggingface.co/asapp/sew-tiny-100k/resolve/main/config.json",
    # See all SEW models at https://huggingface.co/models?filter=sew
}





class 	_lowerCAmelCase					(	__a       ):
				_lowercase					='''sew'''




				def __init__(					self				,				_UpperCamelCase=32				,				_UpperCamelCase=768				,				_UpperCamelCase=12				,				_UpperCamelCase=12				,				_UpperCamelCase=3_072				,				_UpperCamelCase=2				,				_UpperCamelCase="gelu"				,				_UpperCamelCase=0.1				,				_UpperCamelCase=0.1				,				_UpperCamelCase=0.1				,				_UpperCamelCase=0.0				,				_UpperCamelCase=0.1				,				_UpperCamelCase=0.1				,				_UpperCamelCase=0.02				,				_UpperCamelCase=1e-5				,				_UpperCamelCase="group"				,				_UpperCamelCase="gelu"				,				_UpperCamelCase=(64, 128, 128, 128, 128, 256, 256, 256, 256, 512, 512, 512, 512)				,				_UpperCamelCase=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1)				,				_UpperCamelCase=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1)				,				_UpperCamelCase=False				,				_UpperCamelCase=128				,				_UpperCamelCase=16				,				_UpperCamelCase=True				,				_UpperCamelCase=0.05				,				_UpperCamelCase=10				,				_UpperCamelCase=2				,				_UpperCamelCase=0.0				,				_UpperCamelCase=10				,				_UpperCamelCase=0				,				_UpperCamelCase="mean"				,				_UpperCamelCase=False				,				_UpperCamelCase=False				,				_UpperCamelCase=256				,				_UpperCamelCase=0				,				_UpperCamelCase=1				,				_UpperCamelCase=2				,				**_UpperCamelCase				,				)				->      Union[str, Any]:
							super().__init__(**_UpperCamelCase				,				pad_token_id=_UpperCamelCase				,				bos_token_id=_UpperCamelCase				,				eos_token_id=_UpperCamelCase     )
							lowerCAmelCase_						=	hidden_size
							lowerCAmelCase_						=	feat_extract_norm
							lowerCAmelCase_						=	feat_extract_activation
							lowerCAmelCase_						=	list(_UpperCamelCase     )
							lowerCAmelCase_						=	list(_UpperCamelCase     )
							lowerCAmelCase_						=	list(_UpperCamelCase     )
							lowerCAmelCase_						=	conv_bias
							lowerCAmelCase_						=	num_conv_pos_embeddings
							lowerCAmelCase_						=	num_conv_pos_embedding_groups
							lowerCAmelCase_						=	len(self.conv_dim     )
							lowerCAmelCase_						=	num_hidden_layers
							lowerCAmelCase_						=	intermediate_size
							lowerCAmelCase_						=	squeeze_factor
							lowerCAmelCase_						=	hidden_act
							lowerCAmelCase_						=	num_attention_heads
							lowerCAmelCase_						=	hidden_dropout
							lowerCAmelCase_						=	attention_dropout
							lowerCAmelCase_						=	activation_dropout
							lowerCAmelCase_						=	feat_proj_dropout
							lowerCAmelCase_						=	final_dropout
							lowerCAmelCase_						=	layerdrop
							lowerCAmelCase_						=	layer_norm_eps
							lowerCAmelCase_						=	initializer_range
							lowerCAmelCase_						=	vocab_size

							if (
							    (len(self.conv_stride     ) != self.num_feat_extract_layers)
							    or (len(self.conv_kernel     ) != self.num_feat_extract_layers)
							    or (len(self.conv_dim     ) != self.num_feat_extract_layers)
							):
										raise ValueError(
										    "Configuration for convolutional layers is incorrect."
										    "It is required that `len(config.conv_dim)` == `len(config.conv_stride)` == `len(config.conv_kernel)`,"
										    f"""but is `len(config.conv_dim) = {len(self.conv_dim     )}`, `len(config.conv_stride)"""
										    f"""= {len(self.conv_stride     )}`, `len(config.conv_kernel) = {len(self.conv_kernel     )}`."""     )

							# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
							lowerCAmelCase_						=	apply_spec_augment
							lowerCAmelCase_						=	mask_time_prob
							lowerCAmelCase_						=	mask_time_length
							lowerCAmelCase_						=	mask_time_min_masks
							lowerCAmelCase_						=	mask_feature_prob
							lowerCAmelCase_						=	mask_feature_length
							lowerCAmelCase_						=	mask_feature_min_masks

							# ctc loss
							lowerCAmelCase_						=	ctc_loss_reduction
							lowerCAmelCase_						=	ctc_zero_infinity

							# sequence classification
							lowerCAmelCase_						=	use_weighted_layer_sum
							lowerCAmelCase_						=	classifier_proj_size




				@property
				def      __a						(					self     )				->      Optional[Any]:
							return functools.reduce(operator.mul				,				self.conv_stride				,				1     )



