import comet  # From: unbabel-comet
import torch

import datasets


UpperCAmelCase_			    =							datasets.logging.get_logger(__name__)

UpperCAmelCase_			    =							'\\n@inproceedings{rei-EtAl:2020:WMT,\n   author    = {Rei, Ricardo  and  Stewart, Craig  and  Farinha, Ana C  and  Lavie, Alon},\n   title     = {Unbabel\'s Participation in the WMT20 Metrics Shared Task},\n   booktitle      = {Proceedings of the Fifth Conference on Machine Translation},\n   month          = {November},\n   year           = {2020},\n   address        = {Online},\n   publisher      = {Association for Computational Linguistics},\n   pages     = {909--918},\n}\n@inproceedings{rei-etal-2020-comet,\n   title = "{COMET}: A Neural Framework for {MT} Evaluation",\n   author = "Rei, Ricardo  and\n      Stewart, Craig  and\n      Farinha, Ana C  and\n      Lavie, Alon",\n   booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",\n   month = nov,\n   year = "2020",\n   address = "Online",\n   publisher = "Association for Computational Linguistics",\n   url = "https://www.aclweb.org/anthology/2020.emnlp-main.213",\n   pages = "2685--2702",\n}\n'

UpperCAmelCase_			    =							'\\nCrosslingual Optimized Metric for Evaluation of Translation (COMET) is an open-source framework used to train Machine Translation metrics that achieve high levels of correlation with different types of human judgments (HTER, DA\'s or MQM).\nWith the release of the framework the authors also released fully trained models that were used to compete in the WMT20 Metrics Shared Task achieving SOTA in that years competition.\n\nSee the [README.md] file at https://unbabel.github.io/COMET/html/models.html for more information.\n'

UpperCAmelCase_			    =							'\nCOMET score.\n\nArgs:\n\n`sources` (list of str): Source sentences\n`predictions` (list of str): candidate translations\n`references` (list of str): reference translations\n`cuda` (bool): If set to True, runs COMET using GPU\n`show_progress` (bool): Shows progress\n`model`: COMET model to be used. Will default to `wmt-large-da-estimator-1719` if None.\n\nReturns:\n    `samples`: List of dictionaries with `src`, `mt`, `ref` and `score`.\n    `scores`: List of scores.\n\nExamples:\n\n    >>> comet_metric = datasets.load_metric(\'comet\')\n    >>> # comet_metric = load_metric(\'comet\', \'wmt20-comet-da\')  # you can also choose which model to use\n    >>> source = ["Dem Feuer konnte Einhalt geboten werden", "Schulen und Kindergärten wurden eröffnet."]\n    >>> hypothesis = ["The fire could be stopped", "Schools and kindergartens were open"]\n    >>> reference = ["They were able to control the fire.", "Schools and kindergartens opened"]\n    >>> results = comet_metric.compute(predictions=hypothesis, references=reference, sources=source)\n    >>> print([round(v, 2) for v in results["scores"]])\n    [0.19, 0.92]\n'




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





   """simple docstring"""




   def   lowerCAmelCase						(	self					:  str							):



    """simple docstring"""



    return datasets.MetricInfo(
        description=_DESCRIPTION		,  citation=_CITATION		,  homepage='''https://unbabel.github.io/COMET/html/index.html'''		,  inputs_description=_KWARGS_DESCRIPTION		,  features=datasets.Features(
            {
                '''sources''': datasets.Value('''string'''		,  id='''sequence'''							),
                '''predictions''': datasets.Value('''string'''		,  id='''sequence'''							),
                '''references''': datasets.Value('''string'''		,  id='''sequence'''							),
            }							)		,  codebase_urls=['''https://github.com/Unbabel/COMET''']		,  reference_urls=[
            '''https://github.com/Unbabel/COMET''',
            '''https://www.aclweb.org/anthology/2020.emnlp-main.213/''',
            '''http://www.statmt.org/wmt20/pdf/2020.wmt-1.101.pdf6''',
        ]		,  )



   def   lowerCAmelCase						(	self					:  Union[str, Any]		,  _lowercase					:  Any							):



    """simple docstring"""



    if self.config_name == "default":
     _UpperCamelCase:     Optional[Any]							= comet.load_from_checkpoint(comet.download_model('''wmt20-comet-da'''							)							)
    else:
     _UpperCamelCase:     List[str]							= comet.load_from_checkpoint(comet.download_model(self.config_name							)							)



   def   lowerCAmelCase						(	self					:  List[Any]		,  _lowercase					:  Any		,  _lowercase					:  Union[str, Any]		,  _lowercase					:  Any		,  _lowercase					:  int=None		,  _lowercase					:  str=False							):



    """simple docstring"""



    if gpus is None:
     _UpperCamelCase:     List[str]							= 1 if torch.cuda.is_available() else 0
    _UpperCamelCase:     List[Any]							= {'''src''': sources, '''mt''': predictions, '''ref''': references}
    _UpperCamelCase:     Tuple							= [dict(zip(UpperCamelCase__		,  UpperCamelCase__							)							) for t in zip(*data.values()							)]
    _UpperCamelCase:     Optional[Any]							= self.scorer.predict(UpperCamelCase__		,  gpus=UpperCamelCase__		,  progress_bar=UpperCamelCase__							)
    return {"mean_score": mean_score, "scores": scores}
import argparse

import torch
from safetensors.torch import load_file

from diffusers import StableDiffusionPipeline




def       lowerCAmelCase_     (		lowercase:							Optional[int]  ,					lowercase:							Any  ,					lowercase:							str  ,					lowercase:							List[str]  ,					lowercase:							Optional[int]		)    ->				int:







 '''simple docstring'''
 # load base model
 _UpperCamelCase:     Optional[int]							= StableDiffusionPipeline.from_pretrained(lowercase  ,					torch_dtype=torch.floataa		)

 # load LoRA weight from .safetensors
 _UpperCamelCase:     List[str]							= load_file(lowercase		)

 _UpperCamelCase:     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:
   _UpperCamelCase:     int							= key.split('''.'''		)[0].split(LORA_PREFIX_TEXT_ENCODER + '''_'''		)[-1].split('''_'''		)
   _UpperCamelCase:     str							= pipeline.text_encoder
  else:
   _UpperCamelCase:     Optional[Any]							= key.split('''.'''		)[0].split(LORA_PREFIX_UNET + '''_'''		)[-1].split('''_'''		)
   _UpperCamelCase:     Any							= pipeline.unet

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

  _UpperCamelCase:     Any							= []
  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:     str							= state_dict[pair_keys[0]].squeeze(3		).squeeze(2		).to(torch.floataa		)
   _UpperCamelCase:     Optional[int]							= 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:     str							= state_dict[pair_keys[0]].to(torch.floataa		)
   _UpperCamelCase:     Dict							= 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__":
  UpperCAmelCase_			    =							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=0.7_5, 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.)''')

  UpperCAmelCase_			    =							parser.parse_args()

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

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

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