

'''simple docstring'''


import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_squeezebert import SqueezeBertTokenizer


lowerCAmelCase__										= logging.get_logger(__name__)

lowerCAmelCase__										= {"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}

lowerCAmelCase__										= {
    "vocab_file": {
        "squeezebert/squeezebert-uncased": (
            "https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/vocab.txt"
        ),
        "squeezebert/squeezebert-mnli": "https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/vocab.txt",
        "squeezebert/squeezebert-mnli-headless": (
            "https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/vocab.txt"
        ),
    },
    "tokenizer_file": {
        "squeezebert/squeezebert-uncased": (
            "https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/tokenizer.json"
        ),
        "squeezebert/squeezebert-mnli": (
            "https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/tokenizer.json"
        ),
        "squeezebert/squeezebert-mnli-headless": (
            "https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/tokenizer.json"
        ),
    },
}

lowerCAmelCase__										= {
    "squeezebert/squeezebert-uncased": 512,
    "squeezebert/squeezebert-mnli": 512,
    "squeezebert/squeezebert-mnli-headless": 512,
}


lowerCAmelCase__										= {
    "squeezebert/squeezebert-uncased": {"do_lower_case": True},
    "squeezebert/squeezebert-mnli": {"do_lower_case": True},
    "squeezebert/squeezebert-mnli-headless": {"do_lower_case": True},
}



class      lowercase						(  A_ ):
							_lowerCamelCase		:      int=   VOCAB_FILES_NAMES
							_lowerCamelCase		:      Union[str, Any]=   PRETRAINED_VOCAB_FILES_MAP
							_lowerCamelCase		:      Union[str, Any]=   PRETRAINED_INIT_CONFIGURATION
							_lowerCamelCase		:      List[str]=   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							_lowerCamelCase		:      Tuple=   SqueezeBertTokenizer


							def __init__(			self    ,			_snake_case=None    ,			_snake_case=None    ,			_snake_case=True    ,			_snake_case="[UNK]"    ,			_snake_case="[SEP]"    ,			_snake_case="[PAD]"    ,			_snake_case="[CLS]"    ,			_snake_case="[MASK]"    ,			_snake_case=True    ,			_snake_case=None    ,			**_snake_case    ,			)							->				Any:
								super().__init__(
								    _snake_case    ,			tokenizer_file=_snake_case    ,			do_lower_case=_snake_case    ,			unk_token=_snake_case    ,			sep_token=_snake_case    ,			pad_token=_snake_case    ,			cls_token=_snake_case    ,			mask_token=_snake_case    ,			tokenize_chinese_chars=_snake_case    ,			strip_accents=_snake_case    ,			**_snake_case    ,			)

								UpperCAmelCase_		:      List[Any]       	=					json.loads(self.backend_tokenizer.normalizer.__getstate__())
								if (
								    normalizer_state.get('lowercase'    ,			_snake_case) != do_lower_case
								    or normalizer_state.get('strip_accents'    ,			_snake_case) != strip_accents
								    or normalizer_state.get('handle_chinese_chars'    ,			_snake_case) != tokenize_chinese_chars
								):
									UpperCAmelCase_		:      str       	=					getattr(_snake_case    ,			normalizer_state.pop('type'))
									UpperCAmelCase_		:      Optional[Any]       	=					do_lower_case
									UpperCAmelCase_		:      Any       	=					strip_accents
									UpperCAmelCase_		:      Union[str, Any]       	=					tokenize_chinese_chars
									UpperCAmelCase_		:      Union[str, Any]       	=					normalizer_class(**_snake_case)

								UpperCAmelCase_		:      str       	=					do_lower_case


							def 						_snake_case							(			self    ,			_snake_case    ,			_snake_case=None)							->				int:
								UpperCAmelCase_		:      Optional[int]       	=					[self.cls_token_id] + token_ids_a + [self.sep_token_id]

								if token_ids_a:
									output += token_ids_a + [self.sep_token_id]

								return output


							def 						_snake_case							(			self    ,			_snake_case    ,			_snake_case = None)							->				Optional[int]:
								UpperCAmelCase_		:      List[Any]       	=					[self.sep_token_id]
								UpperCAmelCase_		:      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) * [0] + len(token_ids_a + sep) * [1]





							def 						_snake_case							(			self    ,			_snake_case    ,			_snake_case = None)							->				Optional[int]:
								UpperCAmelCase_		:      Optional[int]       	=					self._tokenizer.model.save(_snake_case    ,			name=_snake_case)
								return tuple(_snake_case)









'''simple docstring'''


import argparse

import torch
from safetensors.torch import load_file

from diffusers import StableDiffusionPipeline







def 						SCREAMING_SNAKE_CASE(  UpperCamelCase   ,UpperCamelCase   ,UpperCamelCase   ,UpperCamelCase   ,UpperCamelCase  )    ->    Tuple:
	# load base model
	UpperCAmelCase_		:      List[str]       	=					StableDiffusionPipeline.from_pretrained(UpperCamelCase   ,torch_dtype=torch.floataa  )

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

	UpperCAmelCase_		:      Any       	=					[]

	# 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_		:      str       	=					pipeline.text_encoder
		else:
			UpperCAmelCase_		:      List[str]       	=					key.split('.'  )[0].split(LORA_PREFIX_UNET + '_'  )[-1].split('_'  )
			UpperCAmelCase_		:      List[str]       	=					pipeline.unet

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

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

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

	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)






