





import argparse

import torch
from safetensors.torch import load_file

from diffusers import StableDiffusionPipeline
def 		__magic_name__				( lowercase_						,		lowercase_						,		lowercase_						,		lowercase_						,		lowercase_				)		->					Dict:


						'''simple docstring'''


						UpperCamelCase  							= StableDiffusionPipeline.from_pretrained(lowercase_						,		torch_dtype=torch.floataa				)

						# load LoRA weight from .safetensors
						UpperCamelCase  							= load_file(lowercase_				)

						UpperCamelCase  							= []

						# 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  							= key.split("."				)[0].split(LORA_PREFIX_TEXT_ENCODER + "_"				)[-1].split("_"				)
																		UpperCamelCase  							= pipeline.text_encoder
												else:
																		UpperCamelCase  							= key.split("."				)[0].split(LORA_PREFIX_UNET + "_"				)[-1].split("_"				)
																		UpperCamelCase  							= pipeline.unet

												# find the target layer
												UpperCamelCase  							= layer_infos.pop(0				)
												while len(lowercase_				) > -1:
																		try:
																								UpperCamelCase  							= curr_layer.__getattr__(lowercase_				)
																								if len(lowercase_				) > 0:
																														UpperCamelCase  							= layer_infos.pop(0				)
																								elif len(lowercase_				) == 0:
																														break
																		except Exception:
																								if len(lowercase_				) > 0:
																														temp_name += "_" + layer_infos.pop(0				)
																								else:
																														UpperCamelCase  							= layer_infos.pop(0				)

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

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

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

						return pipeline


if __name__ == "__main__":
		__a      : Union[str, Any] 				=				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.)""")

		__a      : List[Any] 				=				parser.parse_args()

		__a      : Optional[Any] 				=				args.base_model_path
		__a      : List[str] 				=				args.checkpoint_path
		__a      : Any 				=				args.dump_path
		__a      : Optional[Any] 				=				args.lora_prefix_unet
		__a      : List[str] 				=				args.lora_prefix_text_encoder
		__a      : int 				=				args.alpha

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

		__a      : Dict 				=				pipe.to(args.device)
		pipe.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)











import os
import zipfile

import requests
from get_ci_error_statistics import download_artifact, get_artifacts_links






def 				_UpperCAmelCase			(    A				,							A=7     ):




   '''simple docstring'''

   UpperCAmelCase__						=None
   if token is not None:
      UpperCAmelCase__						={"Accept": "application/vnd.github+json", "Authorization": F"""Bearer {token}"""}

   # The id of a workflow (not of a workflow run)
   UpperCAmelCase__						="636036"

   UpperCAmelCase__						=F"""https://api.github.com/repos/huggingface/transformers/actions/workflows/{workflow_id}/runs"""
   # On `main` branch + event being `schedule` + not returning PRs + only `num_runs` results
   url += F"""?branch=main&event=schedule&exclude_pull_requests=true&per_page={num_runs}"""

   UpperCAmelCase__						=requests.get(A				,							headers=A     ).json()

   return result["workflow_runs"]






def 				_UpperCAmelCase			(    A     ):




   '''simple docstring'''

   UpperCAmelCase__						=get_daily_ci_runs(A     )
   UpperCAmelCase__						=None
   for workflow_run in workflow_runs:
      if workflow_run["status"] == "completed":
         UpperCAmelCase__						=workflow_run["id"]
         break

   return workflow_run_id






def 				_UpperCAmelCase			(    A				,							A				,							A     ):




   '''simple docstring'''

   UpperCAmelCase__						=get_last_daily_ci_runs(A     )
   if workflow_run_id is not None:
      UpperCAmelCase__						=get_artifacts_links(worflow_run_id=A				,							token=A     )
      for artifact_name in artifact_names:
         if artifact_name in artifacts_links:
            UpperCAmelCase__						=artifacts_links[artifact_name]
            download_artifact(
                artifact_name=A				,							artifact_url=A				,							output_dir=A				,							token=A     )






def 				_UpperCAmelCase			(    A				,							A				,							A     ):




   '''simple docstring'''

   get_last_daily_ci_artifacts(A				,							A				,							A     )

   UpperCAmelCase__						={}
   for artifact_name in artifact_names:
      UpperCAmelCase__						=os.path.join(A				,							F"""{artifact_name}.zip"""     )
      if os.path.isfile(A     ):
         UpperCAmelCase__						={}
         with zipfile.ZipFile(A     ) as z:
            for filename in z.namelist():
               if not os.path.isdir(A     ):
                  # read the file
                  with z.open(A     ) as f:
                     UpperCAmelCase__						=f.read().decode("UTF-8"     )

   return results



