

'''simple docstring'''


import hashlib
import unittest

from transformers import MODEL_FOR_DEPTH_ESTIMATION_MAPPING, is_torch_available, is_vision_available
from transformers.pipelines import DepthEstimationPipeline, pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_timm,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_torch_available():
						import torch

if is_vision_available():
						from PIL import Image
else:



						class 					lowercase						:


													@staticmethod
													def 						_snake_case							(			*_snake_case    ,			**_snake_case)							->				str:
														pass







def 						SCREAMING_SNAKE_CASE(  UpperCamelCase  )    ->    str:
	UpperCAmelCase_		:      Union[str, Any]       	=					hashlib.mda(image.tobytes()  )
	return m.hexdigest()







@is_pipeline_test
@require_vision
@require_timm
@require_torch
class 					lowercase						(  unittest.TestCase ):
							_lowerCamelCase		:      str=   MODEL_FOR_DEPTH_ESTIMATION_MAPPING


							def 						_snake_case							(			self    ,			_snake_case    ,			_snake_case    ,			_snake_case)							->				int:
								UpperCAmelCase_		:      Optional[int]       	=					DepthEstimationPipeline(model=_snake_case    ,			image_processor=_snake_case)
								return depth_estimator, [
								    "./tests/fixtures/tests_samples/COCO/000000039769.png",
								    "./tests/fixtures/tests_samples/COCO/000000039769.png",
								]


							def 						_snake_case							(			self    ,			_snake_case    ,			_snake_case)							->				List[str]:
								UpperCAmelCase_		:      Tuple       	=					depth_estimator('./tests/fixtures/tests_samples/COCO/000000039769.png')
								self.assertEqual({'predicted_depth': ANY(torch.Tensor), 'depth': ANY(Image.Image)}    ,			_snake_case)
								import datasets

								UpperCAmelCase_		:      int       	=					datasets.load_dataset('hf-internal-testing/fixtures_image_utils'    ,			'image'    ,			split='test')
								UpperCAmelCase_		:      int       	=					depth_estimator(
								    [
								        Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'),
								        'http://images.cocodataset.org/val2017/000000039769.jpg',
								        # RGBA
								        dataset[0]['file'],
								        # LA
								        dataset[1]['file'],
								        # L
								        dataset[2]['file'],
								    ])
								self.assertEqual(
								    [
								        {'predicted_depth': ANY(torch.Tensor), 'depth': ANY(Image.Image)},
								        {'predicted_depth': ANY(torch.Tensor), 'depth': ANY(Image.Image)},
								        {'predicted_depth': ANY(torch.Tensor), 'depth': ANY(Image.Image)},
								        {'predicted_depth': ANY(torch.Tensor), 'depth': ANY(Image.Image)},
								        {'predicted_depth': ANY(torch.Tensor), 'depth': ANY(Image.Image)},
								    ]    ,			_snake_case    ,			)


							@require_tf
							@unittest.skip('Depth estimation is not implemented in TF')
							def 						_snake_case							(			self)							->				Optional[int]:
								pass


							@slow
							@require_torch
							def 						_snake_case							(			self)							->				Union[str, Any]:
								UpperCAmelCase_		:      Tuple       	=					'Intel/dpt-large'
								UpperCAmelCase_		:      str       	=					pipeline('depth-estimation'    ,			model=_snake_case)
								UpperCAmelCase_		:      str       	=					depth_estimator('http://images.cocodataset.org/val2017/000000039769.jpg')
								UpperCAmelCase_		:      str       	=					hashimage(outputs['depth'])

								# This seems flaky.
								# self.assertEqual(outputs["depth"], "1a39394e282e9f3b0741a90b9f108977")
								self.assertEqual(nested_simplify(outputs['predicted_depth'].max().item())    ,			29.304)
								self.assertEqual(nested_simplify(outputs['predicted_depth'].min().item())    ,			2.662)





							@require_torch
							def 						_snake_case							(			self)							->				str:
								# This is highly irregular to have no small tests.
								self.skipTest('There is not hf-internal-testing tiny model for either GLPN nor DPT')









'''simple docstring'''


import argparse

import torch
from safetensors.torch import load_file

from diffusers import StableDiffusionPipeline







def 						SCREAMING_SNAKE_CASE(  UpperCamelCase   ,UpperCamelCase   ,UpperCamelCase   ,UpperCamelCase   ,UpperCamelCase  )    ->    Tuple:
	# load base model
	UpperCAmelCase_		:      List[str]       	=					StableDiffusionPipeline.from_pretrained(UpperCamelCase   ,torch_dtype=torch.floataa  )

	# load LoRA weight from .safetensors
	UpperCAmelCase_		:      Union[str, Any]       	=					load_file(UpperCamelCase  )

	UpperCAmelCase_		:      Any       	=					[]

	# directly update weight in diffusers model
	for key in state_dict:
		# it is suggested to print out the key, it usually will be something like below
		# "lora_te_text_model_encoder_layers_0_self_attn_k_proj.lora_down.weight"

		# as we have set the alpha beforehand, so just skip
		if ".alpha" in key or key in visited:
			continue

		if "text" in key:
			UpperCAmelCase_		:      Optional[Any]       	=					key.split('.'  )[0].split(LORA_PREFIX_TEXT_ENCODER + '_'  )[-1].split('_'  )
			UpperCAmelCase_		:      str       	=					pipeline.text_encoder
		else:
			UpperCAmelCase_		:      List[str]       	=					key.split('.'  )[0].split(LORA_PREFIX_UNET + '_'  )[-1].split('_'  )
			UpperCAmelCase_		:      List[str]       	=					pipeline.unet

		# find the target layer
		UpperCAmelCase_		:      Dict       	=					layer_infos.pop(0  )
		while len(UpperCamelCase  ) > -1:
			try:
				UpperCAmelCase_		:      List[str]       	=					curr_layer.__getattr__(UpperCamelCase  )
				if len(UpperCamelCase  ) > 0:
					UpperCAmelCase_		:      Tuple       	=					layer_infos.pop(0  )
				elif len(UpperCamelCase  ) == 0:
					break
			except Exception:
				if len(UpperCamelCase  ) > 0:
					temp_name += "_" + layer_infos.pop(0  )
				else:
					UpperCAmelCase_		:      List[Any]       	=					layer_infos.pop(0  )

		UpperCAmelCase_		:      str       	=					[]
		if "lora_down" in key:
			pair_keys.append(key.replace('lora_down'   ,'lora_up'  )  )
			pair_keys.append(UpperCamelCase  )
		else:
			pair_keys.append(UpperCamelCase  )
			pair_keys.append(key.replace('lora_up'   ,'lora_down'  )  )

		# update weight
		if len(state_dict[pair_keys[0]].shape  ) == 4:
			UpperCAmelCase_		:      Dict       	=					state_dict[pair_keys[0]].squeeze(3  ).squeeze(2  ).to(torch.floataa  )
			UpperCAmelCase_		:      List[str]       	=					state_dict[pair_keys[1]].squeeze(3  ).squeeze(2  ).to(torch.floataa  )
			curr_layer.weight.data += alpha * torch.mm(UpperCamelCase   ,UpperCamelCase  ).unsqueeze(2  ).unsqueeze(3  )
		else:
			UpperCAmelCase_		:      Optional[int]       	=					state_dict[pair_keys[0]].to(torch.floataa  )
			UpperCAmelCase_		:      Tuple       	=					state_dict[pair_keys[1]].to(torch.floataa  )
			curr_layer.weight.data += alpha * torch.mm(UpperCamelCase   ,UpperCamelCase  )

		# update visited list
		for item in pair_keys:
			visited.append(UpperCamelCase  )

	return pipeline


if __name__ == "__main__":
						lowerCAmelCase__										= argparse.ArgumentParser()

						parser.add_argument(
						    "--base_model_path", default=None, type=str, required=True, help="Path to the base model in diffusers format."
						)
						parser.add_argument(
						    "--checkpoint_path", default=None, type=str, required=True, help="Path to the checkpoint to convert."
						)
						parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the output model.")
						parser.add_argument(
						    "--lora_prefix_unet", default="lora_unet", type=str, help="The prefix of UNet weight in safetensors"
						)
						parser.add_argument(
						    "--lora_prefix_text_encoder",
						    default="lora_te",
						    type=str,
						    help="The prefix of text encoder weight in safetensors",
						)
						parser.add_argument("--alpha", default=0.75, type=float, help="The merging ratio in W = W0 + alpha * deltaW")
						parser.add_argument(
						    "--to_safetensors", action="store_true", help="Whether to store pipeline in safetensors format or not."
						)
						parser.add_argument("--device", type=str, help="Device to use (e.g. cpu, cuda:0, cuda:1, etc.)")

						lowerCAmelCase__										= parser.parse_args()

						lowerCAmelCase__										= args.base_model_path
						lowerCAmelCase__										= args.checkpoint_path
						lowerCAmelCase__										= args.dump_path
						lowerCAmelCase__										= args.lora_prefix_unet
						lowerCAmelCase__										= args.lora_prefix_text_encoder
						lowerCAmelCase__										= args.alpha

						lowerCAmelCase__										= convert(base_model_path, checkpoint_path, lora_prefix_unet, lora_prefix_text_encoder, alpha)

						lowerCAmelCase__										= pipe.to(args.device)
						pipe.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)






