




'''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				):



					'''simple docstring'''





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



					def     __snake_case						(	self     :						Optional[int])    ->							List[str]:
								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=0.0_00_85      ,       beta_end=0.0_12      ,       beta_schedule='scaled_linear'      ,       clip_sample=_lowercase      ,       set_alpha_to_one=_lowercase      ,       )
								A_									= DDIMInverseScheduler(
								    beta_start=0.0_00_85      ,       beta_end=0.0_12      ,       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=1_000      ,       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     __snake_case						(	self     :						Tuple      ,       _lowercase     :						int      ,       _lowercase     :						List[str]=0)    ->							Any:
								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     __snake_case						(	self     :						List[Any]      ,       _lowercase     :						List[Any]      ,       _lowercase     :						Tuple=0)    ->							Any:
								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     __snake_case						(	self     :						Optional[int]      ,       _lowercase     :						Optional[Any]      ,       _lowercase     :						Any=0)    ->							str:
								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     __snake_case						(	self     :						Optional[int])    ->							Tuple:







								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     __snake_case						(	self     :						Any)    ->							Dict:
								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     __snake_case						(	self     :						str)    ->							List[str]:
								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(
								    [0.51_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]      ,       )
								A_									= np.abs(image_slice.flatten() - expected_slice).max()
								self.assertLessEqual(_lowercase      ,       1E-3)



					def     __snake_case						(	self     :						List[Any])    ->							Dict:
								super().test_inference_batch_single_identical(expected_max_diff=5E-3)



					def     __snake_case						(	self     :						int)    ->							Optional[Any]:
								A_									= 'cpu'

								A_									= self.get_dummy_components()

								A_									= {'beta_start': 0.0_00_85, 'beta_end': 0.0_12, '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(
								    [0.51_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]      ,       )
								A_									= np.abs(image_slice.flatten() - expected_slice).max()
								self.assertLessEqual(_lowercase      ,       1E-3)





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



					'''simple docstring'''





					def     __snake_case						(	self     :						Union[str, Any])    ->							Union[str, Any]:
								super().tearDown()
								gc.collect()
								torch.cuda.empty_cache()



					@classmethod
					def     __snake_case						(	cls     :						Any)    ->							List[Any]:
								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     __snake_case						(	self     :						str)    ->							Optional[int]:
								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     __snake_case						(	self     :						Optional[int])    ->							Tuple:
								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










import argparse

import torch
from huggingface_hub import hf_hub_download

from transformers import AutoTokenizer, RobertaPreLayerNormConfig, RobertaPreLayerNormForMaskedLM
from transformers.utils import logging


logging.set_verbosity_info()
__UpperCamelCase   :			Optional[Any]          = logging.get_logger(__name__)





def 						__A						(      __lowerCamelCase				,     __lowerCamelCase       ) ->			Dict:
	a									=				RobertaPreLayerNormConfig.from_pretrained(
	    __lowerCamelCase				,     architectures=["""RobertaPreLayerNormForMaskedLM"""]       )

	# convert state_dict
	a									=				torch.load(hf_hub_download(repo_id=__lowerCamelCase				,     filename="""pytorch_model.bin"""       )       )
	a									=				{}
	for tensor_key, tensor_value in original_state_dict.items():
		# The transformer implementation gives the model a unique name, rather than overwiriting 'roberta'
		if tensor_key.startswith("""roberta."""       ):
			a									=				"""roberta_prelayernorm.""" + tensor_key[len("""roberta."""       ) :]

		# The original implementation contains weights which are not used, remove them from the state_dict
		if tensor_key.endswith(""".self.LayerNorm.weight"""       ) or tensor_key.endswith(""".self.LayerNorm.bias"""       ):
			continue

		a									=				tensor_value

	a									=				RobertaPreLayerNormForMaskedLM.from_pretrained(
	    pretrained_model_name_or_path=__lowerCamelCase				,     config=__lowerCamelCase				,     state_dict=__lowerCamelCase       )
	model.save_pretrained(__lowerCamelCase       )

	# convert tokenizer
	a									=				AutoTokenizer.from_pretrained(__lowerCamelCase       )
	tokenizer.save_pretrained(__lowerCamelCase       )


if __name__ == "__main__":
		__UpperCamelCase   :			Union[str, Any]          = argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    "--checkpoint-repo",
		    default=None,
		    type=str,
		    required=True,
		    help="Path the official PyTorch dump, e.g. 'andreasmadsen/efficient_mlm_m0.40'.",
		)
		parser.add_argument(
		    "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
		)
		__UpperCamelCase   :			Any          = parser.parse_args()
		convert_roberta_prelayernorm_checkpoint_to_pytorch(args.checkpoint_repo, args.pytorch_dump_folder_path)

