



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 (     lowerCamelCase__       ,						lowerCamelCase__       ,						lowerCamelCase__       ,						unittest.TestCase       ):
				__lowerCamelCase =				StableDiffusionInstructPixaPixPipeline
				__lowerCamelCase =				TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'''height''', '''width''', '''cross_attention_kwargs'''}
				__lowerCamelCase =				TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
				__lowerCamelCase =				IMAGE_TO_IMAGE_IMAGE_PARAMS
				__lowerCamelCase =				IMAGE_TO_IMAGE_IMAGE_PARAMS




				def 							snake_case   (							self		):



									"""simple docstring"""





									torch.manual_seed(0		)
									_lowerCAmelCase						=    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						,				)
									_lowerCAmelCase						=    PNDMScheduler(skip_prk_steps=_snake_case		)
									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						,				)
									torch.manual_seed(0		)
									_lowerCAmelCase						=    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						,				)
									_lowerCAmelCase						=    CLIPTextModel(_snake_case		)
									_lowerCAmelCase						=    CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""		)

									_lowerCAmelCase						=    {
									    """unet""": unet,
									    """scheduler""": scheduler,
									    """vae""": vae,
									    """text_encoder""": text_encoder,
									    """tokenizer""": tokenizer,
									    """safety_checker""": None,
									    """feature_extractor""": None,
									}
									return components




				def 							snake_case   (							self						,				_snake_case						,				_snake_case=0		):



									"""simple docstring"""





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



									"""simple docstring"""





									_lowerCAmelCase						=    """cpu"""  # ensure determinism for the device-dependent torch.Generator
									_lowerCAmelCase						=    self.get_dummy_components()
									_lowerCAmelCase						=    StableDiffusionInstructPixaPixPipeline(**_snake_case		)
									_lowerCAmelCase						=    sd_pipe.to(_snake_case		)
									sd_pipe.set_progress_bar_config(disable=_snake_case		)

									_lowerCAmelCase						=    self.get_dummy_inputs(_snake_case		)
									_lowerCAmelCase						=    sd_pipe(**_snake_case		).images
									_lowerCAmelCase						=    image[0, -3:, -3:, -1]
									assert image.shape == (1, 32, 32, 3)
									_lowerCAmelCase						=    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 							snake_case   (							self		):



									"""simple docstring"""





									_lowerCAmelCase						=    """cpu"""  # ensure determinism for the device-dependent torch.Generator
									_lowerCAmelCase						=    self.get_dummy_components()
									_lowerCAmelCase						=    StableDiffusionInstructPixaPixPipeline(**_snake_case		)
									_lowerCAmelCase						=    sd_pipe.to(_snake_case		)
									sd_pipe.set_progress_bar_config(disable=_snake_case		)

									_lowerCAmelCase						=    self.get_dummy_inputs(_snake_case		)
									_lowerCAmelCase						=    """french fries"""
									_lowerCAmelCase						=    sd_pipe(**_snake_case						,				negative_prompt=_snake_case		)
									_lowerCAmelCase						=    output.images
									_lowerCAmelCase						=    image[0, -3:, -3:, -1]

									assert image.shape == (1, 32, 32, 3)
									_lowerCAmelCase						=    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 							snake_case   (							self		):



									"""simple docstring"""





									_lowerCAmelCase						=    """cpu"""  # ensure determinism for the device-dependent torch.Generator
									_lowerCAmelCase						=    self.get_dummy_components()
									_lowerCAmelCase						=    StableDiffusionInstructPixaPixPipeline(**_snake_case		)
									_lowerCAmelCase						=    sd_pipe.to(_snake_case		)
									sd_pipe.set_progress_bar_config(disable=_snake_case		)

									_lowerCAmelCase						=    self.get_dummy_inputs(_snake_case		)
									_lowerCAmelCase						=    [inputs["""prompt"""]] * 2

									_lowerCAmelCase						=    np.array(inputs["""image"""]		).astype(np.floataa		) / 255.0
									_lowerCAmelCase						=    torch.from_numpy(_snake_case		).unsqueeze(0		).to(_snake_case		)
									_lowerCAmelCase						=    image / 2 + 0.5
									_lowerCAmelCase						=    image.permute(0						,				3						,				1						,				2		)
									_lowerCAmelCase						=    image.repeat(2						,				1						,				1						,				1		)

									_lowerCAmelCase						=    sd_pipe(**_snake_case		).images
									_lowerCAmelCase						=    image[-1, -3:, -3:, -1]

									assert image.shape == (2, 32, 32, 3)
									_lowerCAmelCase						=    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 							snake_case   (							self		):



									"""simple docstring"""





									_lowerCAmelCase						=    """cpu"""  # ensure determinism for the device-dependent torch.Generator
									_lowerCAmelCase						=    self.get_dummy_components()
									_lowerCAmelCase						=    EulerAncestralDiscreteScheduler(
									    beta_start=0.0_0085						,				beta_end=0.012						,				beta_schedule="""scaled_linear"""		)
									_lowerCAmelCase						=    StableDiffusionInstructPixaPixPipeline(**_snake_case		)
									_lowerCAmelCase						=    sd_pipe.to(_snake_case		)
									sd_pipe.set_progress_bar_config(disable=_snake_case		)

									_lowerCAmelCase						=    self.get_dummy_inputs(_snake_case		)
									_lowerCAmelCase						=    sd_pipe(**_snake_case		).images
									_lowerCAmelCase						=    image[0, -3:, -3:, -1]

									_lowerCAmelCase						=    [round(_snake_case						,				4		) for x in image_slice.flatten().tolist()]
									print(""",""".join([str(_snake_case		) for x in slice]		)		)

									assert image.shape == (1, 32, 32, 3)
									_lowerCAmelCase						=    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 							snake_case   (							self		):



									"""simple docstring"""





									super().test_inference_batch_single_identical(expected_max_diff=3e-3		)




				def 							snake_case   (							self		):



									"""simple docstring"""





									_lowerCAmelCase						=    self.get_dummy_components()
									_lowerCAmelCase						=    StableDiffusionInstructPixaPixPipeline(**_snake_case		)
									_lowerCAmelCase						=    VaeImageProcessor(do_resize=_snake_case						,				do_normalize=_snake_case		)
									_lowerCAmelCase						=    pipe.to(_snake_case		)
									pipe.set_progress_bar_config(disable=_snake_case		)

									_lowerCAmelCase						=    pipe(**self.get_dummy_inputs_by_type(_snake_case						,				input_image_type="""pt"""		)		)[0]

									_lowerCAmelCase						=    components["""vae"""]
									_lowerCAmelCase						=    self.get_dummy_inputs_by_type(_snake_case						,				input_image_type="""pt"""		)

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

									_lowerCAmelCase						=    pipe(**_snake_case		)[0]

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





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




				def 							snake_case   (							self		):



									"""simple docstring"""





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




				def 							snake_case   (							self						,				_snake_case=0		):



									"""simple docstring"""





									_lowerCAmelCase						=    torch.manual_seed(_snake_case		)
									_lowerCAmelCase						=    load_image(
									    """https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_pix2pix/example.jpg"""		)
									_lowerCAmelCase						=    {
									    """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 							snake_case   (							self		):



									"""simple docstring"""





									_lowerCAmelCase						=    StableDiffusionInstructPixaPixPipeline.from_pretrained(
									    """timbrooks/instruct-pix2pix"""						,				safety_checker=_snake_case		)
									pipe.to(_snake_case		)
									pipe.set_progress_bar_config(disable=_snake_case		)
									pipe.enable_attention_slicing()

									_lowerCAmelCase						=    self.get_inputs()
									_lowerCAmelCase						=    pipe(**_snake_case		).images
									_lowerCAmelCase						=    image[0, -3:, -3:, -1].flatten()

									assert image.shape == (1, 512, 512, 3)
									_lowerCAmelCase						=    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 							snake_case   (							self		):



									"""simple docstring"""





									_lowerCAmelCase						=    StableDiffusionInstructPixaPixPipeline.from_pretrained(
									    """timbrooks/instruct-pix2pix"""						,				safety_checker=_snake_case		)
									_lowerCAmelCase						=    LMSDiscreteScheduler.from_config(pipe.scheduler.config		)
									pipe.to(_snake_case		)
									pipe.set_progress_bar_config(disable=_snake_case		)
									pipe.enable_attention_slicing()

									_lowerCAmelCase						=    self.get_inputs()
									_lowerCAmelCase						=    pipe(**_snake_case		).images
									_lowerCAmelCase						=    image[0, -3:, -3:, -1].flatten()

									assert image.shape == (1, 512, 512, 3)
									_lowerCAmelCase						=    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 							snake_case   (							self		):



									"""simple docstring"""





									_lowerCAmelCase						=    StableDiffusionInstructPixaPixPipeline.from_pretrained(
									    """timbrooks/instruct-pix2pix"""						,				safety_checker=_snake_case		)
									_lowerCAmelCase						=    DDIMScheduler.from_config(pipe.scheduler.config		)
									pipe.to(_snake_case		)
									pipe.set_progress_bar_config(disable=_snake_case		)
									pipe.enable_attention_slicing()

									_lowerCAmelCase						=    self.get_inputs()
									_lowerCAmelCase						=    pipe(**_snake_case		).images
									_lowerCAmelCase						=    image[0, -3:, -3:, -1].flatten()

									assert image.shape == (1, 512, 512, 3)
									_lowerCAmelCase						=    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 							snake_case   (							self		):



									"""simple docstring"""





									_lowerCAmelCase						=    0

									def callback_fn(_snake_case						,				_snake_case						,				_snake_case		) -> None:
														_lowerCAmelCase						=    True
														nonlocal number_of_steps
														number_of_steps += 1
														if step == 1:
																			_lowerCAmelCase						=    latents.detach().cpu().numpy()
																			assert latents.shape == (1, 4, 64, 64)
																			_lowerCAmelCase						=    latents[0, -3:, -3:, -1]
																			_lowerCAmelCase						=    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:
																			_lowerCAmelCase						=    latents.detach().cpu().numpy()
																			assert latents.shape == (1, 4, 64, 64)
																			_lowerCAmelCase						=    latents[0, -3:, -3:, -1]
																			_lowerCAmelCase						=    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

									_lowerCAmelCase						=    False

									_lowerCAmelCase						=    StableDiffusionInstructPixaPixPipeline.from_pretrained(
									    """timbrooks/instruct-pix2pix"""						,				safety_checker=_snake_case						,				torch_dtype=torch.floataa		)
									_lowerCAmelCase						=    pipe.to(_snake_case		)
									pipe.set_progress_bar_config(disable=_snake_case		)
									pipe.enable_attention_slicing()

									_lowerCAmelCase						=    self.get_inputs()
									pipe(**_snake_case						,				callback=_snake_case						,				callback_steps=1		)
									assert callback_fn.has_been_called
									assert number_of_steps == 3




				def 							snake_case   (							self		):



									"""simple docstring"""





									torch.cuda.empty_cache()
									torch.cuda.reset_max_memory_allocated()
									torch.cuda.reset_peak_memory_stats()

									_lowerCAmelCase						=    StableDiffusionInstructPixaPixPipeline.from_pretrained(
									    """timbrooks/instruct-pix2pix"""						,				safety_checker=_snake_case						,				torch_dtype=torch.floataa		)
									_lowerCAmelCase						=    pipe.to(_snake_case		)
									pipe.set_progress_bar_config(disable=_snake_case		)
									pipe.enable_attention_slicing(1		)
									pipe.enable_sequential_cpu_offload()

									_lowerCAmelCase						=    self.get_inputs()
									_lowerCAmelCase						=    pipe(**_snake_case		)

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




				def 							snake_case   (							self		):



									"""simple docstring"""





									_lowerCAmelCase						=    self.get_inputs()
									# resize to resolution that is divisible by 8 but not 16 or 32
									_lowerCAmelCase						=    inputs["""image"""].resize((504, 504)		)

									_lowerCAmelCase						=    """timbrooks/instruct-pix2pix"""
									_lowerCAmelCase						=    StableDiffusionInstructPixaPixPipeline.from_pretrained(
									    _snake_case						,				safety_checker=_snake_case						,				)
									pipe.to(_snake_case		)
									pipe.set_progress_bar_config(disable=_snake_case		)
									pipe.enable_attention_slicing()

									_lowerCAmelCase						=    pipe(**_snake_case		)
									_lowerCAmelCase						=    output.images[0]

									_lowerCAmelCase						=    image[255:258, 383:386, -1]

									assert image.shape == (504, 504, 3)
									_lowerCAmelCase						=    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








"""simple docstring"""







import argparse

import torch

from transformers import YosoConfig, YosoForMaskedLM



def      __UpperCAmelCase      (    __lowerCamelCase	)							->      Optional[int]:
		if "model" in orig_key:
				lowercase__   :    Tuple				=			orig_key.replace('''model.'''						,     ''''''	)
		if "norm1" in orig_key:
				lowercase__   :    List[str]				=			orig_key.replace('''norm1'''						,     '''attention.output.LayerNorm'''	)
		if "norm2" in orig_key:
				lowercase__   :    List[str]				=			orig_key.replace('''norm2'''						,     '''output.LayerNorm'''	)
		if "norm" in orig_key:
				lowercase__   :    List[str]				=			orig_key.replace('''norm'''						,     '''LayerNorm'''	)
		if "transformer" in orig_key:
				lowercase__   :    Union[str, Any]				=			orig_key.split('''.'''	)[0].split('''_'''	)[-1]
				lowercase__   :    List[str]				=			orig_key.replace(f"""transformer_{layer_num}"""						,     f"""encoder.layer.{layer_num}"""	)
		if "mha.attn" in orig_key:
				lowercase__   :    Union[str, Any]				=			orig_key.replace('''mha.attn'''						,     '''attention.self'''	)
		if "mha" in orig_key:
				lowercase__   :    str				=			orig_key.replace('''mha'''						,     '''attention'''	)
		if "W_q" in orig_key:
				lowercase__   :    Any				=			orig_key.replace('''W_q'''						,     '''self.query'''	)
		if "W_k" in orig_key:
				lowercase__   :    List[Any]				=			orig_key.replace('''W_k'''						,     '''self.key'''	)
		if "W_v" in orig_key:
				lowercase__   :    Any				=			orig_key.replace('''W_v'''						,     '''self.value'''	)
		if "ff1" in orig_key:
				lowercase__   :    Optional[int]				=			orig_key.replace('''ff1'''						,     '''intermediate.dense'''	)
		if "ff2" in orig_key:
				lowercase__   :    Optional[Any]				=			orig_key.replace('''ff2'''						,     '''output.dense'''	)
		if "ff" in orig_key:
				lowercase__   :    List[str]				=			orig_key.replace('''ff'''						,     '''output.dense'''	)
		if "mlm_class" in orig_key:
				lowercase__   :    int				=			orig_key.replace('''mlm.mlm_class'''						,     '''cls.predictions.decoder'''	)
		if "mlm" in orig_key:
				lowercase__   :    Optional[Any]				=			orig_key.replace('''mlm'''						,     '''cls.predictions.transform'''	)
		if "cls" not in orig_key:
				lowercase__   :    Optional[Any]				=			'''yoso.''' + orig_key

		return orig_key



def      __UpperCAmelCase      (    __lowerCamelCase						,     __lowerCamelCase	)							->      Optional[int]:
		for key in orig_state_dict.copy().keys():
				lowercase__   :    Optional[Any]				=			orig_state_dict.pop(__lowerCamelCase	)

				if ("pooler" in key) or ("sen_class" in key):
						continue
				else:
						lowercase__   :    Tuple				=			val

		lowercase__   :    Union[str, Any]				=			orig_state_dict['''cls.predictions.decoder.bias''']
		lowercase__   :    List[str]				=			torch.arange(__lowerCamelCase	).expand((1, -1)	) + 2

		return orig_state_dict



def      __UpperCAmelCase      (    __lowerCamelCase						,     __lowerCamelCase						,     __lowerCamelCase	)							->      Optional[Any]:
		lowercase__   :    Tuple				=			torch.load(__lowerCamelCase						,     map_location='''cpu'''	)['''model_state_dict''']
		lowercase__   :    List[Any]				=			YosoConfig.from_json_file(__lowerCamelCase	)
		lowercase__   :    List[Any]				=			YosoForMaskedLM(__lowerCamelCase	)

		lowercase__   :    Optional[Any]				=			convert_checkpoint_helper(config.max_position_embeddings						,     __lowerCamelCase	)

		print(model.load_state_dict(__lowerCamelCase	)	)
		model.eval()
		model.save_pretrained(__lowerCamelCase	)

		print(f"""Checkpoint successfuly converted. Model saved at {pytorch_dump_path}"""	)


if __name__ == "__main__":
							lowerCAmelCase_   						=   argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    '--pytorch_model_path', default=None, type=str, required=True, help='Path to YOSO pytorch checkpoint.'
							)
							parser.add_argument(
							    '--config_file',
							    default=None,
							    type=str,
							    required=True,
							    help='The json file for YOSO model config.',
							)
							parser.add_argument(
							    '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
							)
							lowerCAmelCase_   						=   parser.parse_args()
							convert_yoso_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)
