


import json
from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roberta import RobertaTokenizer


UpperCamelCase					:       Any			    =     logging.get_logger(__name__)

UpperCamelCase					:       Any			    =     {"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt""", """tokenizer_file""": """tokenizer.json"""}

UpperCamelCase					:       Union[str, Any]			    =     {
    """vocab_file""": {
        """roberta-base""": """https://huggingface.co/roberta-base/resolve/main/vocab.json""",
        """roberta-large""": """https://huggingface.co/roberta-large/resolve/main/vocab.json""",
        """roberta-large-mnli""": """https://huggingface.co/roberta-large-mnli/resolve/main/vocab.json""",
        """distilroberta-base""": """https://huggingface.co/distilroberta-base/resolve/main/vocab.json""",
        """roberta-base-openai-detector""": """https://huggingface.co/roberta-base-openai-detector/resolve/main/vocab.json""",
        """roberta-large-openai-detector""": (
            """https://huggingface.co/roberta-large-openai-detector/resolve/main/vocab.json"""
        ),
    },
    """merges_file""": {
        """roberta-base""": """https://huggingface.co/roberta-base/resolve/main/merges.txt""",
        """roberta-large""": """https://huggingface.co/roberta-large/resolve/main/merges.txt""",
        """roberta-large-mnli""": """https://huggingface.co/roberta-large-mnli/resolve/main/merges.txt""",
        """distilroberta-base""": """https://huggingface.co/distilroberta-base/resolve/main/merges.txt""",
        """roberta-base-openai-detector""": """https://huggingface.co/roberta-base-openai-detector/resolve/main/merges.txt""",
        """roberta-large-openai-detector""": (
            """https://huggingface.co/roberta-large-openai-detector/resolve/main/merges.txt"""
        ),
    },
    """tokenizer_file""": {
        """roberta-base""": """https://huggingface.co/roberta-base/resolve/main/tokenizer.json""",
        """roberta-large""": """https://huggingface.co/roberta-large/resolve/main/tokenizer.json""",
        """roberta-large-mnli""": """https://huggingface.co/roberta-large-mnli/resolve/main/tokenizer.json""",
        """distilroberta-base""": """https://huggingface.co/distilroberta-base/resolve/main/tokenizer.json""",
        """roberta-base-openai-detector""": (
            """https://huggingface.co/roberta-base-openai-detector/resolve/main/tokenizer.json"""
        ),
        """roberta-large-openai-detector""": (
            """https://huggingface.co/roberta-large-openai-detector/resolve/main/tokenizer.json"""
        ),
    },
}

UpperCamelCase					:       List[str]			    =     {
    """roberta-base""": 512,
    """roberta-large""": 512,
    """roberta-large-mnli""": 512,
    """distilroberta-base""": 512,
    """roberta-base-openai-detector""": 512,
    """roberta-large-openai-detector""": 512,
}



class A__     ( A__  ):



  """simple docstring"""



  _lowercase									=		VOCAB_FILES_NAMES
  _lowercase									=		PRETRAINED_VOCAB_FILES_MAP
  _lowercase									=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  _lowercase									=		['input_ids', 'attention_mask']
  _lowercase									=		RobertaTokenizer

  def __init__( self			:  List[str]				,				lowerCamelCase__			:  Any=None				,				lowerCamelCase__			:  List[Any]=None				,				lowerCamelCase__			:  Dict=None				,				lowerCamelCase__			:  List[str]="replace"				,				lowerCamelCase__			:  List[str]="<s>"				,				lowerCamelCase__			:  Union[str, Any]="</s>"				,				lowerCamelCase__			:  Any="</s>"				,				lowerCamelCase__			:  Any="<s>"				,				lowerCamelCase__			:  int="<unk>"				,				lowerCamelCase__			:  Any="<pad>"				,				lowerCamelCase__			:  Tuple="<mask>"				,				lowerCamelCase__			:  Any=False				,				lowerCamelCase__			:  Dict=True				,				**lowerCamelCase__			:  Optional[Any]				,				):
        super().__init__(
            lowerCamelCase__				,				lowerCamelCase__				,				tokenizer_file=lowerCamelCase__				,				errors=lowerCamelCase__				,				bos_token=lowerCamelCase__				,				eos_token=lowerCamelCase__				,				sep_token=lowerCamelCase__				,				cls_token=lowerCamelCase__				,				unk_token=lowerCamelCase__				,				pad_token=lowerCamelCase__				,				mask_token=lowerCamelCase__				,				add_prefix_space=lowerCamelCase__				,				trim_offsets=lowerCamelCase__				,				**lowerCamelCase__				,				)

        a__	:  List[Any]     =   json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()       )
        if pre_tok_state.get("add_prefix_space"				,				lowerCamelCase__       ) != add_prefix_space:
              a__	:  Any     =   getattr(lowerCamelCase__				,				pre_tok_state.pop("type"       )       )
              a__	:  int     =   add_prefix_space
              a__	:  Tuple     =   pre_tok_class(**lowerCamelCase__       )

        a__	:  str     =   add_prefix_space

        a__	:  Tuple     =   "post_processor"
        a__	:  Dict     =   getattr(self.backend_tokenizer				,				lowerCamelCase__				,				lowerCamelCase__       )
        if tokenizer_component_instance:
              a__	:  Tuple     =   json.loads(tokenizer_component_instance.__getstate__()       )

              # The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
              if "sep" in state:
                    a__	:  Tuple     =   tuple(state["sep"]       )
              if "cls" in state:
                    a__	:  str     =   tuple(state["cls"]       )

              a__	:  str     =   False

              if state.get("add_prefix_space"				,				lowerCamelCase__       ) != add_prefix_space:
                    a__	:  str     =   add_prefix_space
                    a__	:  Any     =   True

              if state.get("trim_offsets"				,				lowerCamelCase__       ) != trim_offsets:
                    a__	:  int     =   trim_offsets
                    a__	:  Dict     =   True

              if changes_to_apply:
                    a__	:  Union[str, Any]     =   getattr(lowerCamelCase__				,				state.pop("type"       )       )
                    a__	:  str     =   component_class(**lowerCamelCase__       )
                    setattr(self.backend_tokenizer				,				lowerCamelCase__				,				lowerCamelCase__       )

  @property
  def    _UpperCamelCase( self			:  Union[str, Any]       ):
        if self._mask_token is None:
              if self.verbose:
                    logger.error("Using mask_token, but it is not set yet."       )
              return None
        return str(self._mask_token       )

  @mask_token.setter
  def    _UpperCamelCase( self			:  List[Any]				,				lowerCamelCase__			:  Tuple       ):
        a__	:  List[Any]     =   AddedToken(lowerCamelCase__				,				lstrip=lowerCamelCase__				,				rstrip=lowerCamelCase__       ) if isinstance(lowerCamelCase__				,				lowerCamelCase__       ) else value
        a__	:  List[str]     =   value

  def    _UpperCamelCase( self			:  Union[str, Any]				,				*lowerCamelCase__			:  int				,				**lowerCamelCase__			:  int       ):
        a__	:  Optional[int]     =   kwargs.get("is_split_into_words"				,				lowerCamelCase__       )
        assert self.add_prefix_space or not is_split_into_words, (
            f'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
            "to use it with pretokenized inputs."
        )

        return super()._batch_encode_plus(*lowerCamelCase__				,				**lowerCamelCase__       )

  def    _UpperCamelCase( self			:  Tuple				,				*lowerCamelCase__			:  Dict				,				**lowerCamelCase__			:  List[str]       ):
        a__	:  Dict     =   kwargs.get("is_split_into_words"				,				lowerCamelCase__       )

        assert self.add_prefix_space or not is_split_into_words, (
            f'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
            "to use it with pretokenized inputs."
        )

        return super()._encode_plus(*lowerCamelCase__				,				**lowerCamelCase__       )

  def    _UpperCamelCase( self			:  str				,				lowerCamelCase__			:  str				,				lowerCamelCase__			:  Optional[str] = None       ):
        a__	:  int     =   self._tokenizer.model.save(lowerCamelCase__				,				name=lowerCamelCase__       )
        return tuple(lowerCamelCase__       )

  def    _UpperCamelCase( self			:  Optional[int]				,				lowerCamelCase__			:  Dict				,				lowerCamelCase__			:  Optional[int]=None       ):
        a__	:  Union[str, Any]     =   [self.bos_token_id] + token_ids_a + [self.eos_token_id]
        if token_ids_a is None:
              return output

        return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]

  def    _UpperCamelCase( self			:  Dict				,				lowerCamelCase__			:  List[int]				,				lowerCamelCase__			:  Optional[List[int]] = None       ):
        a__	:  Tuple     =   [self.sep_token_id]
        a__	:  int     =   [self.cls_token_id]

        if token_ids_a is None:
              return len(cls + token_ids_a + sep       ) * [0]
        return len(cls + token_ids_a + sep + sep + token_ids_a + sep       ) * [0]






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)