






import argparse

import torch
from safetensors.torch import load_file

from diffusers import StableDiffusionPipeline





def 		_UpperCAmelCase   (							__A							:   Union[str, Any]						,		__A							:   Any						,		__A							:   Dict						,		__A							:   Tuple						,		__A							:   Tuple      ):
				a_		:  List[Any]											=      StableDiffusionPipeline.from_pretrained(__A						,		torch_dtype=torch.floataa      )

				# load LoRA weight from .safetensors
				a_		:  str											=      load_file(__A      )

				a_		:  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:
												a_		:  Union[str, Any]											=      key.split('''.'''      )[0].split(LORA_PREFIX_TEXT_ENCODER + '''_'''      )[-1].split('''_'''      )
												a_		:  Any											=      pipeline.text_encoder
								else:
												a_		:  Union[str, Any]											=      key.split('''.'''      )[0].split(LORA_PREFIX_UNET + '''_'''      )[-1].split('''_'''      )
												a_		:  Any											=      pipeline.unet

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

								a_		:  Optional[Any]											=      []
								if "lora_down" in key:
												pair_keys.append(key.replace('''lora_down'''						,		'''lora_up'''      )      )
												pair_keys.append(__A      )
								else:
												pair_keys.append(__A      )
												pair_keys.append(key.replace('''lora_up'''						,		'''lora_down'''      )      )

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

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

				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)












'''simple docstring'''







import re
import string

import numpy as np

import datasets


__lowerCAmelCase       		=      '\nReturns the rate at which the input predicted strings exactly match their references, ignoring any strings input as part of the regexes_to_ignore list.\n'

__lowerCAmelCase       		=      '\nArgs:\n    predictions: List of predicted texts.\n    references: List of reference texts.\n    regexes_to_ignore: List, defaults to None. Regex expressions of characters to\n        ignore when calculating the exact matches. Note: these regexes are removed\n        from the input data before the changes based on the options below (e.g. ignore_case,\n        ignore_punctuation, ignore_numbers) are applied.\n    ignore_case: Boolean, defaults to False. If true, turns everything\n        to lowercase so that capitalization differences are ignored.\n    ignore_punctuation: Boolean, defaults to False. If true, removes all punctuation before\n        comparing predictions and references.\n    ignore_numbers: Boolean, defaults to False. If true, removes all punctuation before\n        comparing predictions and references.\nReturns:\n    exact_match: Dictionary containing exact_match rate. Possible values are between 0.0 and 100.0, inclusive.\nExamples:\n    >>> exact_match = datasets.load_metric("exact_match")\n    >>> refs = ["the cat", "theater", "YELLING", "agent007"]\n    >>> preds = ["cat?", "theater", "yelling", "agent"]\n    >>> results = exact_match.compute(references=refs, predictions=preds)\n    >>> print(round(results["exact_match"], 1))\n    25.0\n\n    >>> exact_match = datasets.load_metric("exact_match")\n    >>> refs = ["the cat", "theater", "YELLING", "agent007"]\n    >>> preds = ["cat?", "theater", "yelling", "agent"]\n    >>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=["the ", "yell"], ignore_case=True, ignore_punctuation=True)\n    >>> print(round(results["exact_match"], 1))\n    50.0\n\n\n    >>> exact_match = datasets.load_metric("exact_match")\n    >>> refs = ["the cat", "theater", "YELLING", "agent007"]\n    >>> preds = ["cat?", "theater", "yelling", "agent"]\n    >>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=["the ", "yell", "YELL"], ignore_case=True, ignore_punctuation=True)\n    >>> print(round(results["exact_match"], 1))\n    75.0\n\n    >>> exact_match = datasets.load_metric("exact_match")\n    >>> refs = ["the cat", "theater", "YELLING", "agent007"]\n    >>> preds = ["cat?", "theater", "yelling", "agent"]\n    >>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=["the ", "yell", "YELL"], ignore_case=True, ignore_punctuation=True, ignore_numbers=True)\n    >>> print(round(results["exact_match"], 1))\n    100.0\n\n    >>> exact_match = datasets.load_metric("exact_match")\n    >>> refs = ["The cat sat on the mat.", "Theaters are great.", "It\'s like comparing oranges and apples."]\n    >>> preds = ["The cat sat on the mat?", "Theaters are great.", "It\'s like comparing apples and oranges."]\n    >>> results = exact_match.compute(references=refs, predictions=preds)\n    >>> print(round(results["exact_match"], 1))\n    33.3\n\n'

__lowerCAmelCase       		=      '\n'





@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION	,   _KWARGS_DESCRIPTION       )
class 	SCREAMING_SNAKE_CASE					(					datasets.Metric       ):






			def 			SCREAMING_SNAKE_CASE      ( self      :			Optional[Any]					)			->			int:
							return datasets.MetricInfo(
							    description=_DESCRIPTION  ,						citation=_CITATION  ,						inputs_description=_KWARGS_DESCRIPTION  ,						features=datasets.Features(
							        {
							            '''predictions''': datasets.Value('''string'''  ,						id='''sequence'''					),
							            '''references''': datasets.Value('''string'''  ,						id='''sequence'''					),
							        }					)  ,						reference_urls=[]  ,						)







			def 			SCREAMING_SNAKE_CASE      ( self      :			str  ,						__SCREAMING_SNAKE_CASE      :			List[Any]  ,						__SCREAMING_SNAKE_CASE      :			Optional[int]  ,						__SCREAMING_SNAKE_CASE      :			Dict=None  ,						__SCREAMING_SNAKE_CASE      :			int=False  ,						__SCREAMING_SNAKE_CASE      :			Optional[int]=False  ,						__SCREAMING_SNAKE_CASE      :			Dict=False  ,						)			->			str:
							if regexes_to_ignore is not None:
											for s in regexes_to_ignore:
															a_		:  Optional[Any]											=      np.array([re.sub(__SCREAMING_SNAKE_CASE  ,						''''''  ,						__SCREAMING_SNAKE_CASE					) for x in predictions]					)
															a_		:  int											=      np.array([re.sub(__SCREAMING_SNAKE_CASE  ,						''''''  ,						__SCREAMING_SNAKE_CASE					) for x in references]					)
							else:
											a_		:  List[str]											=      np.asarray(__SCREAMING_SNAKE_CASE					)
											a_		:  Any											=      np.asarray(__SCREAMING_SNAKE_CASE					)

							if ignore_case:
											a_		:  List[str]											=      np.char.lower(__SCREAMING_SNAKE_CASE					)
											a_		:  List[Any]											=      np.char.lower(__SCREAMING_SNAKE_CASE					)

							if ignore_punctuation:
											a_		:  Any											=      string.punctuation.maketrans(''''''  ,						''''''  ,						string.punctuation					)
											a_		:  Union[str, Any]											=      np.char.translate(__SCREAMING_SNAKE_CASE  ,						table=__SCREAMING_SNAKE_CASE					)
											a_		:  int											=      np.char.translate(__SCREAMING_SNAKE_CASE  ,						table=__SCREAMING_SNAKE_CASE					)

							if ignore_numbers:
											a_		:  int											=      string.digits.maketrans(''''''  ,						''''''  ,						string.digits					)
											a_		:  Optional[int]											=      np.char.translate(__SCREAMING_SNAKE_CASE  ,						table=__SCREAMING_SNAKE_CASE					)
											a_		:  Dict											=      np.char.translate(__SCREAMING_SNAKE_CASE  ,						table=__SCREAMING_SNAKE_CASE					)

							a_		:  Optional[Any]											=      predictions == references

							return {"exact_match": np.mean(__SCREAMING_SNAKE_CASE					) * 100}




