



import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    EulerAncestralDiscreteScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionInstructPixaPixPipeline,
    UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.utils import floats_tensor, load_image, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()

class 	lowerCamelCase_    ( _A						,_A						,_A						,unittest.TestCase						):






						'''simple docstring'''
						a__   =  StableDiffusionInstructPixaPixPipeline
						a__   =  TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"height", "width", "cross_attention_kwargs"}
						a__   =  TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
						a__   =  IMAGE_TO_IMAGE_IMAGE_PARAMS
						a__   =  IMAGE_TO_IMAGE_IMAGE_PARAMS







						def 			SCREAMING_SNAKE_CASE__		(	self      :			int )      ->				List[Any]:
									torch.manual_seed(0 )
									A							:					Union[str, Any]              =						UNetaDConditionModel(
									    block_out_channels=(32, 64)	,	layers_per_block=2	,	sample_size=32	,	in_channels=8	,	out_channels=4	,	down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")	,	up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")	,	cross_attention_dim=32	,	)
									A							:					str              =						PNDMScheduler(skip_prk_steps=__lowerCamelCase )
									torch.manual_seed(0 )
									A							:					List[str]              =						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	,	)
									torch.manual_seed(0 )
									A							:					str              =						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	,	)
									A							:					Union[str, Any]              =						CLIPTextModel(__lowerCamelCase )
									A							:					Optional[int]              =						CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip" )

									A							:					List[str]              =						{
									    "unet": unet,
									    "scheduler": scheduler,
									    "vae": vae,
									    "text_encoder": text_encoder,
									    "tokenizer": tokenizer,
									    "safety_checker": None,
									    "feature_extractor": None,
									}
									return components







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Tuple	,	__lowerCamelCase      :			Union[str, Any]	,	__lowerCamelCase      :			Union[str, Any]=0 )      ->				Optional[Any]:
									A							:					int              =						floats_tensor((1, 3, 32, 32)	,	rng=random.Random(__lowerCamelCase ) ).to(__lowerCamelCase )
									A							:					Optional[Any]              =						image.cpu().permute(0	,	2	,	3	,	1 )[0]
									A							:					Any              =						Image.fromarray(np.uinta(__lowerCamelCase ) ).convert("RGB" )
									if str(__lowerCamelCase ).startswith("mps" ):
												A							:					str              =						torch.manual_seed(__lowerCamelCase )
									else:
												A							:					Optional[int]              =						torch.Generator(device=__lowerCamelCase ).manual_seed(__lowerCamelCase )
									A							:					int              =						{
									    "prompt": "A painting of a squirrel eating a burger",
									    "image": image,
									    "generator": generator,
									    "num_inference_steps": 2,
									    "guidance_scale": 6.0,
									    "image_guidance_scale": 1,
									    "output_type": "numpy",
									}
									return inputs







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Optional[int] )      ->				List[str]:
									A							:					Tuple              =						"cpu"  # ensure determinism for the device-dependent torch.Generator
									A							:					Any              =						self.get_dummy_components()
									A							:					Tuple              =						StableDiffusionInstructPixaPixPipeline(**__lowerCamelCase )
									A							:					Union[str, Any]              =						sd_pipe.to(__lowerCamelCase )
									sd_pipe.set_progress_bar_config(disable=__lowerCamelCase )

									A							:					int              =						self.get_dummy_inputs(__lowerCamelCase )
									A							:					List[str]              =						sd_pipe(**__lowerCamelCase ).images
									A							:					Tuple              =						image[0, -3:, -3:, -1]
									assert image.shape == (1, 32, 32, 3)
									A							:					Optional[int]              =						np.array([0.7526, 0.3750, 0.4547, 0.6117, 0.5866, 0.5016, 0.4327, 0.5642, 0.4815] )

									assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-3







						def 			SCREAMING_SNAKE_CASE__		(	self      :			List[str] )      ->				Optional[Any]:
									A							:					Tuple              =						"cpu"  # ensure determinism for the device-dependent torch.Generator
									A							:					Tuple              =						self.get_dummy_components()
									A							:					int              =						StableDiffusionInstructPixaPixPipeline(**__lowerCamelCase )
									A							:					Union[str, Any]              =						sd_pipe.to(__lowerCamelCase )
									sd_pipe.set_progress_bar_config(disable=__lowerCamelCase )

									A							:					Union[str, Any]              =						self.get_dummy_inputs(__lowerCamelCase )
									A							:					Tuple              =						"french fries"
									A							:					List[str]              =						sd_pipe(**__lowerCamelCase	,	negative_prompt=__lowerCamelCase )
									A							:					Dict              =						output.images
									A							:					str              =						image[0, -3:, -3:, -1]

									assert image.shape == (1, 32, 32, 3)
									A							:					Optional[Any]              =						np.array([0.7511, 0.3642, 0.4553, 0.6236, 0.5797, 0.5013, 0.4343, 0.5611, 0.4831] )

									assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-3







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Dict )      ->				int:
									A							:					Optional[Any]              =						"cpu"  # ensure determinism for the device-dependent torch.Generator
									A							:					int              =						self.get_dummy_components()
									A							:					str              =						StableDiffusionInstructPixaPixPipeline(**__lowerCamelCase )
									A							:					List[Any]              =						sd_pipe.to(__lowerCamelCase )
									sd_pipe.set_progress_bar_config(disable=__lowerCamelCase )

									A							:					Optional[Any]              =						self.get_dummy_inputs(__lowerCamelCase )
									A							:					List[Any]              =						[inputs["prompt"]] * 2

									A							:					Any              =						np.array(inputs["image"] ).astype(np.floataa ) / 255.0
									A							:					List[str]              =						torch.from_numpy(__lowerCamelCase ).unsqueeze(0 ).to(__lowerCamelCase )
									A							:					Union[str, Any]              =						image / 2 + 0.5
									A							:					Tuple              =						image.permute(0	,	3	,	1	,	2 )
									A							:					int              =						image.repeat(2	,	1	,	1	,	1 )

									A							:					Union[str, Any]              =						sd_pipe(**__lowerCamelCase ).images
									A							:					Optional[int]              =						image[-1, -3:, -3:, -1]

									assert image.shape == (2, 32, 32, 3)
									A							:					List[Any]              =						np.array([0.5812, 0.5748, 0.5222, 0.5908, 0.5695, 0.7174, 0.6804, 0.5523, 0.5579] )

									assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-3







						def 			SCREAMING_SNAKE_CASE__		(	self      :			str )      ->				List[Any]:
									A							:					int              =						"cpu"  # ensure determinism for the device-dependent torch.Generator
									A							:					Dict              =						self.get_dummy_components()
									A							:					Dict              =						EulerAncestralDiscreteScheduler(
									    beta_start=0.00085	,	beta_end=0.012	,	beta_schedule="scaled_linear" )
									A							:					str              =						StableDiffusionInstructPixaPixPipeline(**__lowerCamelCase )
									A							:					str              =						sd_pipe.to(__lowerCamelCase )
									sd_pipe.set_progress_bar_config(disable=__lowerCamelCase )

									A							:					Optional[int]              =						self.get_dummy_inputs(__lowerCamelCase )
									A							:					Dict              =						sd_pipe(**__lowerCamelCase ).images
									A							:					List[str]              =						image[0, -3:, -3:, -1]

									A							:					List[Any]              =						[round(__lowerCamelCase	,	4 ) for x in image_slice.flatten().tolist()]
									print(",".join([str(__lowerCamelCase ) for x in slice] ) )

									assert image.shape == (1, 32, 32, 3)
									A							:					Tuple              =						np.array([0.7417, 0.3842, 0.4732, 0.5776, 0.5891, 0.5139, 0.4052, 0.5673, 0.4986] )

									assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-3







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Optional[int] )      ->				Tuple:
									super().test_inference_batch_single_identical(expected_max_diff=3e-3 )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			int )      ->				int:
									A							:					Optional[int]              =						self.get_dummy_components()
									A							:					List[Any]              =						StableDiffusionInstructPixaPixPipeline(**__lowerCamelCase )
									A							:					Union[str, Any]              =						VaeImageProcessor(do_resize=__lowerCamelCase	,	do_normalize=__lowerCamelCase )
									A							:					Union[str, Any]              =						pipe.to(__lowerCamelCase )
									pipe.set_progress_bar_config(disable=__lowerCamelCase )

									A							:					Dict              =						pipe(**self.get_dummy_inputs_by_type(__lowerCamelCase	,	input_image_type="pt" ) )[0]

									A							:					Any              =						components["vae"]
									A							:					List[Any]              =						self.get_dummy_inputs_by_type(__lowerCamelCase	,	input_image_type="pt" )

									for image_param in self.image_latents_params:
												if image_param in inputs.keys():
															A							:					Optional[Any]              =						vae.encode(inputs[image_param] ).latent_dist.mode()

									A							:					Optional[int]              =						pipe(**__lowerCamelCase )[0]

									A							:					Dict              =						np.abs(out - out_latents_inputs ).max()
									self.assertLess(__lowerCamelCase	,	1e-4	,	"passing latents as image input generate different result from passing image" )







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






						'''simple docstring'''







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Optional[int] )      ->				Dict:
									super().tearDown()
									gc.collect()
									torch.cuda.empty_cache()







						def 			SCREAMING_SNAKE_CASE__		(	self      :			List[Any]	,	__lowerCamelCase      :			str=0 )      ->				List[str]:
									A							:					str              =						torch.manual_seed(__lowerCamelCase )
									A							:					Tuple              =						load_image(
									    "https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_pix2pix/example.jpg" )
									A							:					List[str]              =						{
									    "prompt": "turn him into a cyborg",
									    "image": image,
									    "generator": generator,
									    "num_inference_steps": 3,
									    "guidance_scale": 7.5,
									    "image_guidance_scale": 1.0,
									    "output_type": "numpy",
									}
									return inputs







						def 			SCREAMING_SNAKE_CASE__		(	self      :			str )      ->				Any:
									A							:					List[str]              =						StableDiffusionInstructPixaPixPipeline.from_pretrained(
									    "timbrooks/instruct-pix2pix"	,	safety_checker=__lowerCamelCase )
									pipe.to(__lowerCamelCase )
									pipe.set_progress_bar_config(disable=__lowerCamelCase )
									pipe.enable_attention_slicing()

									A							:					Union[str, Any]              =						self.get_inputs()
									A							:					Dict              =						pipe(**__lowerCamelCase ).images
									A							:					List[Any]              =						image[0, -3:, -3:, -1].flatten()

									assert image.shape == (1, 5_12, 5_12, 3)
									A							:					Any              =						np.array([0.5902, 0.6015, 0.6027, 0.5983, 0.6092, 0.6061, 0.5765, 0.5785, 0.5555] )

									assert np.abs(expected_slice - image_slice ).max() < 1e-3







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Dict )      ->				Tuple:
									A							:					Optional[int]              =						StableDiffusionInstructPixaPixPipeline.from_pretrained(
									    "timbrooks/instruct-pix2pix"	,	safety_checker=__lowerCamelCase )
									A							:					Union[str, Any]              =						LMSDiscreteScheduler.from_config(pipe.scheduler.config )
									pipe.to(__lowerCamelCase )
									pipe.set_progress_bar_config(disable=__lowerCamelCase )
									pipe.enable_attention_slicing()

									A							:					Union[str, Any]              =						self.get_inputs()
									A							:					List[Any]              =						pipe(**__lowerCamelCase ).images
									A							:					List[str]              =						image[0, -3:, -3:, -1].flatten()

									assert image.shape == (1, 5_12, 5_12, 3)
									A							:					Any              =						np.array([0.6578, 0.6817, 0.6972, 0.6761, 0.6856, 0.6916, 0.6428, 0.6516, 0.6301] )

									assert np.abs(expected_slice - image_slice ).max() < 1e-3







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Tuple )      ->				List[str]:
									A							:					Union[str, Any]              =						StableDiffusionInstructPixaPixPipeline.from_pretrained(
									    "timbrooks/instruct-pix2pix"	,	safety_checker=__lowerCamelCase )
									A							:					Union[str, Any]              =						DDIMScheduler.from_config(pipe.scheduler.config )
									pipe.to(__lowerCamelCase )
									pipe.set_progress_bar_config(disable=__lowerCamelCase )
									pipe.enable_attention_slicing()

									A							:					int              =						self.get_inputs()
									A							:					List[str]              =						pipe(**__lowerCamelCase ).images
									A							:					Tuple              =						image[0, -3:, -3:, -1].flatten()

									assert image.shape == (1, 5_12, 5_12, 3)
									A							:					int              =						np.array([0.3828, 0.3834, 0.3818, 0.3792, 0.3865, 0.3752, 0.3792, 0.3847, 0.3753] )

									assert np.abs(expected_slice - image_slice ).max() < 1e-3







						def 			SCREAMING_SNAKE_CASE__		(	self      :			List[Any] )      ->				Optional[int]:
									A							:					List[Any]              =						0

									def callback_fn(__lowerCamelCase      :			int	,	__lowerCamelCase      :			int	,	__lowerCamelCase      :			torch.FloatTensor ) -> None:
												A							:					str              =						True
												nonlocal number_of_steps
												number_of_steps += 1
												if step == 1:
															A							:					str              =						latents.detach().cpu().numpy()
															assert latents.shape == (1, 4, 64, 64)
															A							:					Optional[Any]              =						latents[0, -3:, -3:, -1]
															A							:					int              =						np.array([-0.2463, -0.4644, -0.9756, 1.5176, 1.4414, 0.7866, 0.9897, 0.8521, 0.7983] )

															assert np.abs(latents_slice.flatten() - expected_slice ).max() < 5e-2
												elif step == 2:
															A							:					Optional[int]              =						latents.detach().cpu().numpy()
															assert latents.shape == (1, 4, 64, 64)
															A							:					Optional[int]              =						latents[0, -3:, -3:, -1]
															A							:					Union[str, Any]              =						np.array([-0.2644, -0.4626, -0.9653, 1.5176, 1.4551, 0.7686, 0.9805, 0.8452, 0.8115] )

															assert np.abs(latents_slice.flatten() - expected_slice ).max() < 5e-2

									A							:					Optional[int]              =						False

									A							:					Tuple              =						StableDiffusionInstructPixaPixPipeline.from_pretrained(
									    "timbrooks/instruct-pix2pix"	,	safety_checker=__lowerCamelCase	,	torch_dtype=torch.floataa )
									A							:					List[str]              =						pipe.to(__lowerCamelCase )
									pipe.set_progress_bar_config(disable=__lowerCamelCase )
									pipe.enable_attention_slicing()

									A							:					Optional[Any]              =						self.get_inputs()
									pipe(**__lowerCamelCase	,	callback=__lowerCamelCase	,	callback_steps=1 )
									assert callback_fn.has_been_called
									assert number_of_steps == 3







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Any )      ->				Tuple:
									torch.cuda.empty_cache()
									torch.cuda.reset_max_memory_allocated()
									torch.cuda.reset_peak_memory_stats()

									A							:					List[Any]              =						StableDiffusionInstructPixaPixPipeline.from_pretrained(
									    "timbrooks/instruct-pix2pix"	,	safety_checker=__lowerCamelCase	,	torch_dtype=torch.floataa )
									A							:					Optional[int]              =						pipe.to(__lowerCamelCase )
									pipe.set_progress_bar_config(disable=__lowerCamelCase )
									pipe.enable_attention_slicing(1 )
									pipe.enable_sequential_cpu_offload()

									A							:					str              =						self.get_inputs()
									A							:					int              =						pipe(**__lowerCamelCase )

									A							:					Any              =						torch.cuda.max_memory_allocated()
									# make sure that less than 2.2 GB is allocated
									assert mem_bytes < 2.2 * 10**9







						def 			SCREAMING_SNAKE_CASE__		(	self      :			int )      ->				Optional[Any]:
									A							:					Optional[Any]              =						self.get_inputs()
									# resize to resolution that is divisible by 8 but not 16 or 32
									A							:					int              =						inputs["image"].resize((5_04, 5_04) )

									A							:					Optional[Any]              =						"timbrooks/instruct-pix2pix"
									A							:					Tuple              =						StableDiffusionInstructPixaPixPipeline.from_pretrained(
									    __lowerCamelCase	,	safety_checker=__lowerCamelCase	,	)
									pipe.to(__lowerCamelCase )
									pipe.set_progress_bar_config(disable=__lowerCamelCase )
									pipe.enable_attention_slicing()

									A							:					int              =						pipe(**__lowerCamelCase )
									A							:					Optional[Any]              =						output.images[0]

									A							:					Tuple              =						image[2_55:2_58, 3_83:3_86, -1]

									assert image.shape == (5_04, 5_04, 3)
									A							:					Dict              =						np.array([0.2726, 0.2529, 0.2664, 0.2655, 0.2641, 0.2642, 0.2591, 0.2649, 0.2590] )

									assert np.abs(image_slice.flatten() - expected_slice ).max() < 5e-3




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()
__SCREAMING_SNAKE_CASE           =  logging.get_logger(__name__)




def 	UpperCAmelCase   (						_lowerCamelCase ,    _lowerCamelCase    ):
			A							:					Optional[Any]              =						RobertaPreLayerNormConfig.from_pretrained(
			    _lowerCamelCase ,    architectures=["RobertaPreLayerNormForMaskedLM"]    )

			# convert state_dict
			A							:					List[Any]              =						torch.load(hf_hub_download(repo_id=_lowerCamelCase ,    filename="pytorch_model.bin"    )    )
			A							:					Union[str, Any]              =						{}
			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							:					int              =						"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							:					Any              =						tensor_value

			A							:					Optional[int]              =						RobertaPreLayerNormForMaskedLM.from_pretrained(
			    pretrained_model_name_or_path=_lowerCamelCase ,    config=_lowerCamelCase ,    state_dict=_lowerCamelCase    )
			model.save_pretrained(_lowerCamelCase    )

			# convert tokenizer
			A							:					Optional[Any]              =						AutoTokenizer.from_pretrained(_lowerCamelCase    )
			tokenizer.save_pretrained(_lowerCamelCase    )


if __name__ == "__main__":
							__SCREAMING_SNAKE_CASE           =  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."""
							)
							__SCREAMING_SNAKE_CASE           =  parser.parse_args()
							convert_roberta_prelayernorm_checkpoint_to_pytorch(args.checkpoint_repo, args.pytorch_dump_folder_path)