



import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List

import git
import torch
from torch.utils.data import Dataset

from transformers import BartTokenizer, RagTokenizer, TaTokenizer
def 		UpperCamelCase     (       snake_case__	:   Optional[Any]	,					snake_case__	:   List[str]	,					snake_case__	:   int	,					snake_case__	:   Union[str, Any]	,					snake_case__	:   Dict=True	,					snake_case__	:   Union[str, Any]="pt"     )     ->					int:
   UpperCamelCase   :  str						     =     {'add_prefix_space': True} if isinstance(snake_case__	,					snake_case__     ) and not line.startswith(' '     ) else {}
   UpperCamelCase   :  str						     =     padding_side
   return tokenizer(
       [line]	,					max_length=snake_case__	,					padding='max_length' if pad_to_max_length else None	,					truncation=snake_case__	,					return_tensors=snake_case__	,					add_special_tokens=snake_case__	,					**snake_case__	,					)
def 		UpperCamelCase     (       snake_case__	:   List[str]	,					snake_case__	:   Union[str, Any]	,					snake_case__	:   List[Any]=None	,					)     ->					Optional[int]:
   UpperCamelCase   :  str						     =     input_ids.ne(snake_case__     ).any(dim=0     )
   if attention_mask is None:
      return input_ids[:, keep_column_mask]
   else:
      return (input_ids[:, keep_column_mask], attention_mask[:, keep_column_mask])







class     lowerCAmelCase_		(  a__							):
       def __init__(      self,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_="train",      SCREAMING_SNAKE_CASE_=None,      SCREAMING_SNAKE_CASE_=None,      SCREAMING_SNAKE_CASE_=None,      SCREAMING_SNAKE_CASE_="",      )					->		Optional[int]:
          super().__init__()
          UpperCamelCase   :  Any						     =     Path(SCREAMING_SNAKE_CASE_	).joinpath(type_path + '.source'	)
          UpperCamelCase   :  Tuple						     =     Path(SCREAMING_SNAKE_CASE_	).joinpath(type_path + '.target'	)
          UpperCamelCase   :  str						     =     self.get_char_lens(self.src_file	)
          UpperCamelCase   :  str						     =     max_source_length
          UpperCamelCase   :  Any						     =     max_target_length
          assert min(self.src_lens	) > 0, F"""found empty line in {self.src_file}"""
          UpperCamelCase   :  Union[str, Any]						     =     tokenizer
          UpperCamelCase   :  List[str]						     =     prefix
          if n_obs is not None:
             UpperCamelCase   :  Any						     =     self.src_lens[:n_obs]
          UpperCamelCase   :  Optional[int]						     =     src_lang
          UpperCamelCase   :  Any						     =     tgt_lang
       def __len__(      self	)					->		int:
          return len(self.src_lens	)
       def __getitem__(      self,      SCREAMING_SNAKE_CASE_	)					->		Dict[str, torch.Tensor]:
          UpperCamelCase   :  str						     =     index + 1  # linecache starts at 1
          UpperCamelCase   :  List[str]						     =     self.prefix + linecache.getline(str(self.src_file	),      SCREAMING_SNAKE_CASE_	).rstrip('\n'	)
          UpperCamelCase   :  str						     =     linecache.getline(str(self.tgt_file	),      SCREAMING_SNAKE_CASE_	).rstrip('\n'	)
          assert source_line, F"""empty source line for index {index}"""
          assert tgt_line, F"""empty tgt line for index {index}"""

          # Need to add eos token manually for T5
          if isinstance(self.tokenizer,      SCREAMING_SNAKE_CASE_	):
             source_line += self.tokenizer.eos_token
             tgt_line += self.tokenizer.eos_token

          # Pad source and target to the right
          UpperCamelCase   :  List[str]						     =     (
              self.tokenizer.question_encoder if isinstance(self.tokenizer,      SCREAMING_SNAKE_CASE_	) else self.tokenizer
          )
          UpperCamelCase   :  str						     =     self.tokenizer.generator if isinstance(self.tokenizer,      SCREAMING_SNAKE_CASE_	) else self.tokenizer

          UpperCamelCase   :  Dict						     =     encode_line(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      self.max_source_length,      'right'	)
          UpperCamelCase   :  str						     =     encode_line(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      self.max_target_length,      'right'	)

          UpperCamelCase   :  List[Any]						     =     source_inputs['input_ids'].squeeze()
          UpperCamelCase   :  List[str]						     =     target_inputs['input_ids'].squeeze()
          UpperCamelCase   :  str						     =     source_inputs['attention_mask'].squeeze()
          return {
              "input_ids": source_ids,
              "attention_mask": src_mask,
              "decoder_input_ids": target_ids,
          }
       @staticmethod
       def  snake_case_	(      SCREAMING_SNAKE_CASE_	)					->		str:
          return [len(SCREAMING_SNAKE_CASE_	) for x in Path(SCREAMING_SNAKE_CASE_	).open().readlines()]






       def  snake_case_	(      self,      SCREAMING_SNAKE_CASE_	)					->		Dict[str, torch.Tensor]:
          UpperCamelCase   :  List[str]						     =     torch.stack([x['input_ids'] for x in batch]	)
          UpperCamelCase   :  Any						     =     torch.stack([x['attention_mask'] for x in batch]	)
          UpperCamelCase   :  List[Any]						     =     torch.stack([x['decoder_input_ids'] for x in batch]	)
          UpperCamelCase   :  List[Any]						     =     (
              self.tokenizer.generator.pad_token_id
              if isinstance(self.tokenizer,      SCREAMING_SNAKE_CASE_	)
              else self.tokenizer.pad_token_id
          )
          UpperCamelCase   :  str						     =     (
              self.tokenizer.question_encoder.pad_token_id
              if isinstance(self.tokenizer,      SCREAMING_SNAKE_CASE_	)
              else self.tokenizer.pad_token_id
          )
          UpperCamelCase   :  str						     =     trim_batch(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_	)
          UpperCamelCase   :  str						     =     trim_batch(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      attention_mask=SCREAMING_SNAKE_CASE_	)
          UpperCamelCase   :  Optional[Any]						     =     {
              'input_ids': source_ids,
              'attention_mask': source_mask,
              'decoder_input_ids': y,
          }
          return batch






__UpperCAmelCase	       =	getLogger(__name__)
def 		UpperCamelCase     (       snake_case__	:   List[List]     )     ->					Any:
   return list(itertools.chain.from_iterable(snake_case__     )     )
def 		UpperCamelCase     (       snake_case__	:   str     )     ->					None:
   UpperCamelCase   :  Optional[int]						     =     get_git_info()
   save_json(snake_case__	,					os.path.join(snake_case__	,					'git_log.json'     )     )
def 		UpperCamelCase     (       snake_case__	:   int	,					snake_case__	:   int	,					snake_case__	:   Tuple=4	,					**snake_case__	:   List[str]     )     ->					int:
   with open(snake_case__	,					'w'     ) as f:
      json.dump(snake_case__	,					snake_case__	,					indent=snake_case__	,					**snake_case__     )
def 		UpperCamelCase     (       snake_case__	:   Optional[Any]     )     ->					int:
   with open(snake_case__     ) as f:
      return json.load(snake_case__     )
def 		UpperCamelCase     (       )     ->					List[str]:
   UpperCamelCase   :  List[Any]						     =     git.Repo(search_parent_directories=snake_case__     )
   UpperCamelCase   :  Dict						     =     {
       'repo_id': str(snake_case__     ),
       'repo_sha': str(repo.head.object.hexsha     ),
       'repo_branch': str(repo.active_branch     ),
       'hostname': str(socket.gethostname()     ),
   }
   return repo_infos
def 		UpperCamelCase     (       snake_case__	:   Callable	,					snake_case__	:   Iterable     )     ->					List:
   return list(map(snake_case__	,					snake_case__     )     )
def 		UpperCamelCase     (       snake_case__	:   List[str]	,					snake_case__	:   List[str]     )     ->					List[Any]:
   with open(snake_case__	,					'wb'     ) as f:
      return pickle.dump(snake_case__	,					snake_case__     )
def 		UpperCamelCase     (       snake_case__	:   int     )     ->					Optional[Any]:

   def remove_articles(snake_case__	:   Dict     ):
      return re.sub(R'\b(a|an|the)\b'	,					' '	,					snake_case__     )

   def white_space_fix(snake_case__	:   int     ):
      return " ".join(text.split()     )

   def remove_punc(snake_case__	:   Any     ):
      UpperCamelCase   :  Optional[int]						     =     set(string.punctuation     )
      return "".join(ch for ch in text if ch not in exclude     )

   def lower(snake_case__	:   Union[str, Any]     ):
      return text.lower()

   return white_space_fix(remove_articles(remove_punc(lower(snake_case__     )     )     )     )
def 		UpperCamelCase     (       snake_case__	:   Any	,					snake_case__	:   Dict     )     ->					Tuple:
   UpperCamelCase   :  Tuple						     =     normalize_answer(snake_case__     ).split()
   UpperCamelCase   :  int						     =     normalize_answer(snake_case__     ).split()
   UpperCamelCase   :  List[Any]						     =     Counter(snake_case__     ) & Counter(snake_case__     )
   UpperCamelCase   :  List[str]						     =     sum(common.values()     )
   if num_same == 0:
      return 0
   UpperCamelCase   :  int						     =     1.0 * num_same / len(snake_case__     )
   UpperCamelCase   :  Optional[Any]						     =     1.0 * num_same / len(snake_case__     )
   UpperCamelCase   :  str						     =     (2 * precision * recall) / (precision + recall)
   return fa
def 		UpperCamelCase     (       snake_case__	:   Optional[Any]	,					snake_case__	:   str     )     ->					List[str]:
   return normalize_answer(snake_case__     ) == normalize_answer(snake_case__     )
def 		UpperCamelCase     (       snake_case__	:   List[str]	,					snake_case__	:   List[str]     )     ->					Dict:
   assert len(snake_case__     ) == len(snake_case__     )
   UpperCamelCase   :  Union[str, Any]						     =     0
   for hypo, pred in zip(snake_case__	,					snake_case__     ):
      em += exact_match_score(snake_case__	,					snake_case__     )
   if len(snake_case__     ) > 0:
      em /= len(snake_case__     )
   return {"em": em}
def 		UpperCamelCase     (       snake_case__	:   Optional[int]     )     ->					List[str]:
   return model_prefix.startswith('rag'     )


def 		UpperCamelCase     (       snake_case__	:   Tuple	,					snake_case__	:   Union[str, Any]	,					snake_case__	:   Tuple     )     ->					str:
   UpperCamelCase   :  List[Any]						     =     {p: p for p in extra_params}
   # T5 models don't have `dropout` param, they have `dropout_rate` instead
   UpperCamelCase   :  Optional[Any]						     =     'dropout_rate'
   for p in extra_params:
      if getattr(snake_case__	,					snake_case__	,					snake_case__     ):
         if not hasattr(snake_case__	,					snake_case__     ) and not hasattr(snake_case__	,					equivalent_param[p]     ):
            logger.info('config doesn\'t have a `{}` attribute'.format(snake_case__     )     )
            delattr(snake_case__	,					snake_case__     )
            continue
         UpperCamelCase   :  List[str]						     =     p if hasattr(snake_case__	,					snake_case__     ) else equivalent_param[p]
         setattr(snake_case__	,					snake_case__	,					getattr(snake_case__	,					snake_case__     )     )
         delattr(snake_case__	,					snake_case__     )
   return hparams, config








import argparse

import torch
from safetensors.torch import load_file

from diffusers import StableDiffusionPipeline
def 		UpperCamelCase     (       snake_case__	:   List[Any]	,					snake_case__	:   Union[str, Any]	,					snake_case__	:   Tuple	,					snake_case__	:   Union[str, Any]	,					snake_case__	:   List[Any]     )     ->					Tuple:
   # load base model
   UpperCamelCase   :  Tuple						     =     StableDiffusionPipeline.from_pretrained(snake_case__	,					torch_dtype=torch.floataa     )

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

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

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

      UpperCamelCase   :  List[Any]						     =     []
      if "lora_down" in key:
         pair_keys.append(key.replace('lora_down'	,					'lora_up'     )     )
         pair_keys.append(snake_case__     )
      else:
         pair_keys.append(snake_case__     )
         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   :  Union[str, Any]						     =     state_dict[pair_keys[1]].squeeze(3     ).squeeze(2     ).to(torch.floataa     )
         curr_layer.weight.data += alpha * torch.mm(snake_case__	,					snake_case__     ).unsqueeze(2     ).unsqueeze(3     )
      else:
         UpperCamelCase   :  Dict						     =     state_dict[pair_keys[0]].to(torch.floataa     )
         UpperCamelCase   :  List[Any]						     =     state_dict[pair_keys[1]].to(torch.floataa     )
         curr_layer.weight.data += alpha * torch.mm(snake_case__	,					snake_case__     )

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

   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=0.75, 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.)''')

    __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)



