



import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path

from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError

from transformers import AutoImageProcessor, ViTImageProcessor
from transformers.testing_utils import TOKEN, USER, get_tests_dir, is_staging_test


sys.path.append(str(Path(__file__).parent.parent / """utils"""))

from test_module.custom_image_processing import CustomImageProcessor  # noqa E402


lowerCamelCase		       =      get_tests_dir("""fixtures""")


class 				_a						(       unittest.TestCase			):





							'''simple docstring'''

							def 		_A	(							self				):







											"""simple docstring"""



											a__	:							str      						= mock.Mock()
											a__	:							Tuple      						= 500
											a__	:							List[str]      						= {}
											a__	:							Optional[Any]      						= HTTPError
											a__	:							Union[str, Any]      						= {}

											# Download this model to make sure it's in the cache.
											a__	:							List[str]      						= ViTImageProcessor.from_pretrained("hf-internal-testing/tiny-random-vit"				)
											# Under the mock environment we get a 500 error when trying to reach the model.
											with mock.patch("requests.Session.request" ,					return_value=__UpperCAmelCase				) as mock_head:
															a__	:							str      						= ViTImageProcessor.from_pretrained("hf-internal-testing/tiny-random-vit"				)
															# This check we did call the fake head request
															mock_head.assert_called()



							def 		_A	(							self				):







											"""simple docstring"""



											a__	:							Optional[Any]      						= ViTImageProcessor.from_pretrained(
											    "https://huggingface.co/hf-internal-testing/tiny-random-vit/resolve/main/preprocessor_config.json"				)




							def 		_A	(							self				):







											"""simple docstring"""



											with self.assertRaises(__UpperCAmelCase				):
															# config is in subfolder, the following should not work without specifying the subfolder
															a__	:							Tuple      						= AutoImageProcessor.from_pretrained("hf-internal-testing/stable-diffusion-all-variants"				)

											a__	:							Optional[int]      						= AutoImageProcessor.from_pretrained(
											    "hf-internal-testing/stable-diffusion-all-variants" ,					subfolder="feature_extractor"				)

											self.assertIsNotNone(__UpperCAmelCase				)





@is_staging_test
class 				_a						(       unittest.TestCase			):





							'''simple docstring'''

							@classmethod
							def 		_A	(							cls				):







											"""simple docstring"""



											a__	:							List[Any]      						= TOKEN
											HfFolder.save_token(__UpperCAmelCase				)



							@classmethod
							def 		_A	(							cls				):







											"""simple docstring"""



											try:
															delete_repo(token=cls._token ,					repo_id="test-image-processor"				)
											except HTTPError:
															pass

											try:
															delete_repo(token=cls._token ,					repo_id="valid_org/test-image-processor-org"				)
											except HTTPError:
															pass

											try:
															delete_repo(token=cls._token ,					repo_id="test-dynamic-image-processor"				)
											except HTTPError:
															pass



							def 		_A	(							self				):







											"""simple docstring"""



											a__	:							Optional[Any]      						= ViTImageProcessor.from_pretrained(__UpperCAmelCase				)
											image_processor.push_to_hub("test-image-processor" ,					use_auth_token=self._token				)

											a__	:							Optional[int]      						= ViTImageProcessor.from_pretrained(f'{USER}/test-image-processor'				)
											for k, v in image_processor.__dict__.items():
															self.assertEqual(__UpperCAmelCase ,					getattr(__UpperCAmelCase ,					__UpperCAmelCase				)				)

											# Reset repo
											delete_repo(token=self._token ,					repo_id="test-image-processor"				)

											# Push to hub via save_pretrained
											with tempfile.TemporaryDirectory() as tmp_dir:
															image_processor.save_pretrained(
															    __UpperCAmelCase ,					repo_id="test-image-processor" ,					push_to_hub=__UpperCAmelCase ,					use_auth_token=self._token				)

											a__	:							Optional[Any]      						= ViTImageProcessor.from_pretrained(f'{USER}/test-image-processor'				)
											for k, v in image_processor.__dict__.items():
															self.assertEqual(__UpperCAmelCase ,					getattr(__UpperCAmelCase ,					__UpperCAmelCase				)				)



							def 		_A	(							self				):







											"""simple docstring"""



											a__	:							List[str]      						= ViTImageProcessor.from_pretrained(__UpperCAmelCase				)
											image_processor.push_to_hub("valid_org/test-image-processor" ,					use_auth_token=self._token				)

											a__	:							List[Any]      						= ViTImageProcessor.from_pretrained("valid_org/test-image-processor"				)
											for k, v in image_processor.__dict__.items():
															self.assertEqual(__UpperCAmelCase ,					getattr(__UpperCAmelCase ,					__UpperCAmelCase				)				)

											# Reset repo
											delete_repo(token=self._token ,					repo_id="valid_org/test-image-processor"				)

											# Push to hub via save_pretrained
											with tempfile.TemporaryDirectory() as tmp_dir:
															image_processor.save_pretrained(
															    __UpperCAmelCase ,					repo_id="valid_org/test-image-processor-org" ,					push_to_hub=__UpperCAmelCase ,					use_auth_token=self._token				)

											a__	:							Union[str, Any]      						= ViTImageProcessor.from_pretrained("valid_org/test-image-processor-org"				)
											for k, v in image_processor.__dict__.items():
															self.assertEqual(__UpperCAmelCase ,					getattr(__UpperCAmelCase ,					__UpperCAmelCase				)				)




							def 		_A	(							self				):







											"""simple docstring"""



											CustomImageProcessor.register_for_auto_class()
											a__	:							List[str]      						= CustomImageProcessor.from_pretrained(__UpperCAmelCase				)

											image_processor.push_to_hub("test-dynamic-image-processor" ,					use_auth_token=self._token				)

											# This has added the proper auto_map field to the config
											self.assertDictEqual(
											    image_processor.auto_map ,					{"AutoImageProcessor": "custom_image_processing.CustomImageProcessor"} ,					)

											a__	:							int      						= AutoImageProcessor.from_pretrained(
											    f'{USER}/test-dynamic-image-processor' ,					trust_remote_code=__UpperCAmelCase				)
											# Can't make an isinstance check because the new_image_processor is from the CustomImageProcessor class of a dynamic module
											self.assertEqual(new_image_processor.__class__.__name__ ,					"CustomImageProcessor"				)







import argparse

import torch
from safetensors.torch import load_file

from diffusers import StableDiffusionPipeline

def        __a			(   __lowerCAmelCase      ,	__lowerCAmelCase      ,	__lowerCAmelCase      ,	__lowerCAmelCase      ,	__lowerCAmelCase   )     ->     str:
					# load base model
					SCREAMING_SNAKE_CASE							:     Dict						=				StableDiffusionPipeline.from_pretrained(__lowerCAmelCase      ,	torch_dtype=torch.floataa   )

					# load LoRA weight from .safetensors
					SCREAMING_SNAKE_CASE							:     Any						=				load_file(__lowerCAmelCase   )

					SCREAMING_SNAKE_CASE							:     Optional[int]						=				[]

					# 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:
															SCREAMING_SNAKE_CASE							:     Dict						=				key.split('.'   )[0].split(LORA_PREFIX_TEXT_ENCODER + '_'   )[-1].split('_'   )
															SCREAMING_SNAKE_CASE							:     Tuple						=				pipeline.text_encoder
										else:
															SCREAMING_SNAKE_CASE							:     Any						=				key.split('.'   )[0].split(LORA_PREFIX_UNET + '_'   )[-1].split('_'   )
															SCREAMING_SNAKE_CASE							:     Dict						=				pipeline.unet

										# find the target layer
										SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				layer_infos.pop(0   )
										while len(__lowerCAmelCase   ) > -1:
															try:
																				SCREAMING_SNAKE_CASE							:     List[Any]						=				curr_layer.__getattr__(__lowerCAmelCase   )
																				if len(__lowerCAmelCase   ) > 0:
																									SCREAMING_SNAKE_CASE							:     Optional[int]						=				layer_infos.pop(0   )
																				elif len(__lowerCAmelCase   ) == 0:
																									break
															except Exception:
																				if len(__lowerCAmelCase   ) > 0:
																									temp_name += "_" + layer_infos.pop(0   )
																				else:
																									SCREAMING_SNAKE_CASE							:     List[str]						=				layer_infos.pop(0   )

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

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

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

					return pipeline


if __name__ == "__main__":
			_lowerCamelCase    :		Tuple		      =    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    :		List[Any]		      =    parser.parse_args()

			_lowerCamelCase    :		List[str]		      =    args.base_model_path
			_lowerCamelCase    :		str		      =    args.checkpoint_path
			_lowerCamelCase    :		Dict		      =    args.dump_path
			_lowerCamelCase    :		Any		      =    args.lora_prefix_unet
			_lowerCamelCase    :		List[Any]		      =    args.lora_prefix_text_encoder
			_lowerCamelCase    :		List[Any]		      =    args.alpha

			_lowerCamelCase    :		Optional[Any]		      =    convert(base_model_path, checkpoint_path, lora_prefix_unet, lora_prefix_text_encoder, alpha)

			_lowerCamelCase    :		Optional[Any]		      =    pipe.to(args.device)
			pipe.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)