

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	(		__snake_case				,    __snake_case				,    unittest.TestCase				):






			"""simple docstring"""




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


			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				torch.manual_seed(0				)
				snake_case_   		=			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			,					)
				snake_case_   		=			DDIMScheduler(
				    beta_start=0.0_0085			,					beta_end=0.012			,					beta_schedule='scaled_linear'			,					clip_sample=__UpperCamelCase			,					set_alpha_to_one=__UpperCamelCase			,					)
				snake_case_   		=			DDIMInverseScheduler(
				    beta_start=0.0_0085			,					beta_end=0.012			,					beta_schedule='scaled_linear'			,					clip_sample=__UpperCamelCase			,					set_alpha_to_zero=__UpperCamelCase			,					)
				torch.manual_seed(0				)
				snake_case_   		=			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=1_28			,					)
				torch.manual_seed(0				)
				snake_case_   		=			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=10_00			,					hidden_act='gelu'			,					projection_dim=5_12			,					)
				snake_case_   		=			CLIPTextModel(__UpperCamelCase				)
				snake_case_   		=			CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'				)

				snake_case_   		=			{
				    '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			,					__UpperCamelCase			,					__UpperCamelCase=0				):

				"""simple docstring"""


				snake_case_   		=			floats_tensor((1, 16, 16)			,					rng=random.Random(__UpperCamelCase				)				).to(__UpperCamelCase				)
				snake_case_   		=			floats_tensor((1, 2, 4, 16, 16)			,					rng=random.Random(__UpperCamelCase				)				).to(__UpperCamelCase				)
				if str(__UpperCamelCase				).startswith('mps'				):
					snake_case_   		=			torch.manual_seed(__UpperCamelCase				)
				else:
					snake_case_   		=			torch.Generator(device=__UpperCamelCase				).manual_seed(__UpperCamelCase				)
				snake_case_   		=			{
				    '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			,					__UpperCamelCase			,					__UpperCamelCase=0				):

				"""simple docstring"""


				snake_case_   		=			floats_tensor((1, 3, 32, 32)			,					rng=random.Random(__UpperCamelCase				)				).to(__UpperCamelCase				)
				snake_case_   		=			image.cpu().permute(0			,					2			,					3			,					1				)[0]
				snake_case_   		=			Image.fromarray(np.uinta(__UpperCamelCase				)				).convert('RGB'				)
				if str(__UpperCamelCase				).startswith('mps'				):
					snake_case_   		=			torch.manual_seed(__UpperCamelCase				)
				else:
					snake_case_   		=			torch.Generator(device=__UpperCamelCase				).manual_seed(__UpperCamelCase				)
				snake_case_   		=			{
				    '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			,					__UpperCamelCase			,					__UpperCamelCase=0				):

				"""simple docstring"""


				snake_case_   		=			floats_tensor((1, 3, 32, 32)			,					rng=random.Random(__UpperCamelCase				)				).to(__UpperCamelCase				)
				snake_case_   		=			image.cpu().permute(0			,					2			,					3			,					1				)[0]
				snake_case_   		=			Image.fromarray(np.uinta(__UpperCamelCase				)				).convert('RGB'				)
				if str(__UpperCamelCase				).startswith('mps'				):
					snake_case_   		=			torch.manual_seed(__UpperCamelCase				)
				else:
					snake_case_   		=			torch.Generator(device=__UpperCamelCase				).manual_seed(__UpperCamelCase				)
				snake_case_   		=			{
				    '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				):

				"""simple docstring"""


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

				snake_case_   		=			self.get_dummy_components()
				snake_case_   		=			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}				)

				snake_case_   		=			self.get_dummy_inputs(__UpperCamelCase				)
				snake_case_   		=			pipe(**__UpperCamelCase				)[0]

				with tempfile.TemporaryDirectory() as tmpdir:
					pipe.save_pretrained(__UpperCamelCase				)
					snake_case_   		=			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."""			,					)

				snake_case_   		=			self.get_dummy_inputs(__UpperCamelCase				)
				snake_case_   		=			pipe_loaded(**__UpperCamelCase				)[0]

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


			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				snake_case_   		=			'cpu'

				snake_case_   		=			self.get_dummy_components()
				snake_case_   		=			self.pipeline_class(**__UpperCamelCase				)
				pipe.to(__UpperCamelCase				)
				pipe.set_progress_bar_config(disable=__UpperCamelCase				)

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

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


			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				snake_case_   		=			'cpu'

				snake_case_   		=			self.get_dummy_components()
				snake_case_   		=			self.pipeline_class(**__UpperCamelCase				)
				pipe.to(__UpperCamelCase				)
				pipe.set_progress_bar_config(disable=__UpperCamelCase				)

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

				self.assertEqual(image.shape			,					(2, 32, 32, 3)				)
				snake_case_   		=			np.array(
				    [0.5150, 0.5134, 0.5043, 0.5376, 0.4694, 0.5_1050, 0.5015, 0.4407, 0.4799]			,					)
				snake_case_   		=			np.abs(image_slice.flatten() - expected_slice				).max()
				self.assertLessEqual(__UpperCamelCase			,					1E-3				)


			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


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







			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				snake_case_   		=			'cpu'

				snake_case_   		=			self.get_dummy_components()

				snake_case_   		=			{'beta_start': 0.0_0085, 'beta_end': 0.012, 'beta_schedule': 'scaled_linear'}
				snake_case_   		=			DPMSolverMultistepScheduler(**__UpperCamelCase				)
				snake_case_   		=			DPMSolverMultistepInverseScheduler(**__UpperCamelCase				)

				snake_case_   		=			self.pipeline_class(**__UpperCamelCase				)
				pipe.to(__UpperCamelCase				)
				pipe.set_progress_bar_config(disable=__UpperCamelCase				)

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

				self.assertEqual(image.shape			,					(2, 32, 32, 3)				)
				snake_case_   		=			np.array(
				    [0.5150, 0.5134, 0.5043, 0.5376, 0.4694, 0.5_1050, 0.5015, 0.4407, 0.4799]			,					)
				snake_case_   		=			np.abs(image_slice.flatten() - expected_slice				).max()
				self.assertLessEqual(__UpperCamelCase			,					1E-3				)







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






			"""simple docstring"""




			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


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


			@classmethod
			def       __lowerCAmelCase			(     cls				):

				"""simple docstring"""


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

				snake_case_   		=			raw_image.convert('RGB'				).resize((7_68, 7_68)				)

				snake_case_   		=			raw_image


			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				snake_case_   		=			torch.manual_seed(0				)

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

				snake_case_   		=			'a bowl of fruit'
				snake_case_   		=			'a bowl of pears'

				snake_case_   		=			pipe.generate_mask(
				    image=self.raw_image			,					source_prompt=__UpperCamelCase			,					target_prompt=__UpperCamelCase			,					generator=__UpperCamelCase			,					)

				snake_case_   		=			pipe.invert(
				    prompt=__UpperCamelCase			,					image=self.raw_image			,					inpaint_strength=0.7			,					generator=__UpperCamelCase				).latents

				snake_case_   		=			pipe(
				    prompt=__UpperCamelCase			,					mask_image=__UpperCamelCase			,					image_latents=__UpperCamelCase			,					generator=__UpperCamelCase			,					negative_prompt=__UpperCamelCase			,					inpaint_strength=0.7			,					output_type='numpy'			,					).images[0]

				snake_case_   		=			(
				    np.array(
				        load_image(
				            'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
				            '/diffedit/pears.png'				).resize((7_68, 7_68)				)				)
				    / 2_55
				)
				assert np.abs((expected_image - image).max()				) < 5E-1







			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				snake_case_   		=			torch.manual_seed(0				)

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

				snake_case_   		=			'a bowl of fruit'
				snake_case_   		=			'a bowl of pears'

				snake_case_   		=			pipe.generate_mask(
				    image=self.raw_image			,					source_prompt=__UpperCamelCase			,					target_prompt=__UpperCamelCase			,					generator=__UpperCamelCase			,					)

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

				snake_case_   		=			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]

				snake_case_   		=			(
				    np.array(
				        load_image(
				            'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
				            '/diffedit/pears.png'				).resize((7_68, 7_68)				)				)
				    / 2_55
				)
				assert np.abs((expected_image - image).max()				) < 5E-1



from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


A									=  logging.get_logger(__name__)

A									=  {
    'google/bit-50': 'https://huggingface.co/google/bit-50/resolve/main/config.json',
}


class SCREAMING_SNAKE_CASE	(		__snake_case				,    __snake_case				):






			"""simple docstring"""




			__A    	=	"""bit"""
			__A    	=	["""preactivation""", """bottleneck"""]
			__A    	=	["""SAME""", """VALID"""]


			def __init__(     self			,					__UpperCamelCase=3			,					__UpperCamelCase=64			,					__UpperCamelCase=[2_56, 5_12, 10_24, 20_48]			,					__UpperCamelCase=[3, 4, 6, 3]			,					__UpperCamelCase="preactivation"			,					__UpperCamelCase="relu"			,					__UpperCamelCase=None			,					__UpperCamelCase=32			,					__UpperCamelCase=0.0			,					__UpperCamelCase=False			,					__UpperCamelCase=32			,					__UpperCamelCase=1			,					__UpperCamelCase=None			,					__UpperCamelCase=None			,					**__UpperCamelCase			,					):

				"""simple docstring"""


				super().__init__(**__UpperCamelCase				)
				if layer_type not in self.layer_types:
					raise ValueError(f"""layer_type={layer_type} is not one of {','.join(self.layer_types				)}"""				)
				if global_padding is not None:
					if global_padding.upper() in self.supported_padding:
						snake_case_   		=			global_padding.upper()
					else:
						raise ValueError(f"""Padding strategy {global_padding} not supported"""				)
				snake_case_   		=			num_channels
				snake_case_   		=			embedding_size
				snake_case_   		=			hidden_sizes
				snake_case_   		=			depths
				snake_case_   		=			layer_type
				snake_case_   		=			hidden_act
				snake_case_   		=			global_padding
				snake_case_   		=			num_groups
				snake_case_   		=			drop_path_rate
				snake_case_   		=			embedding_dynamic_padding
				snake_case_   		=			output_stride
				snake_case_   		=			width_factor

				snake_case_   		=			['stem'] + [f"""stage{idx}""" for idx in range(1			,					len(__UpperCamelCase				) + 1				)]
				snake_case_		,							snake_case_   		=			get_aligned_output_features_output_indices(
				    out_features=__UpperCamelCase			,					out_indices=__UpperCamelCase			,					stage_names=self.stage_names				)
