

'''simple docstring'''


import json
import os
from functools import lru_cache
from typing import Dict, List, Optional, Tuple, Union

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_base import BatchEncoding, EncodedInput
from ...utils import PaddingStrategy, logging


lowerCAmelCase__										= logging.get_logger(__name__)


lowerCAmelCase__										= {"vocab_file": "vocab.json", "merges_file": "merges.txt"}

# See all LED models at https://huggingface.co/models?filter=LED
lowerCAmelCase__										= {
    "vocab_file": {
        "allenai/led-base-16384": "https://huggingface.co/allenai/led-base-16384/resolve/main/vocab.json",
    },
    "merges_file": {
        "allenai/led-base-16384": "https://huggingface.co/allenai/led-base-16384/resolve/main/merges.txt",
    },
    "tokenizer_file": {
        "allenai/led-base-16384": "https://huggingface.co/allenai/led-base-16384/resolve/main/tokenizer.json",
    },
}

lowerCAmelCase__										= {
    "allenai/led-base-16384": 16384,
}







@lru_cache()
# Copied from transformers.models.bart.tokenization_bart.bytes_to_unicode
def 						SCREAMING_SNAKE_CASE(  )    ->    str:
	UpperCAmelCase_		:      Optional[Any]       	=					(
	    list(range(ord('!'  )   ,ord('~'  ) + 1  )  ) + list(range(ord('¡'  )   ,ord('¬'  ) + 1  )  ) + list(range(ord('®'  )   ,ord('ÿ'  ) + 1  )  )
	)
	UpperCAmelCase_		:      List[Any]       	=					bs[:]
	UpperCAmelCase_		:      str       	=					0
	for b in range(2**8  ):
		if b not in bs:
			bs.append(UpperCamelCase  )
			cs.append(2**8 + n  )
			n += 1
	UpperCAmelCase_		:      Any       	=					[chr(UpperCamelCase  ) for n in cs]
	return dict(zip(UpperCamelCase   ,UpperCamelCase  )  )







def 						SCREAMING_SNAKE_CASE(  UpperCamelCase  )    ->    Dict:
	UpperCAmelCase_		:      List[Any]       	=					set()
	UpperCAmelCase_		:      List[Any]       	=					word[0]
	for char in word[1:]:
		pairs.add((prev_char, char)  )
		UpperCAmelCase_		:      List[str]       	=					char
	return pairs







class      lowercase						(  a_ ):
							_lowerCamelCase		:      Any=   VOCAB_FILES_NAMES
							_lowerCamelCase		:      Tuple=   PRETRAINED_VOCAB_FILES_MAP
							_lowerCamelCase		:      Optional[int]=   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							_lowerCamelCase		:      Dict=   ["input_ids", "attention_mask"]


							def __init__(			self    ,			_snake_case    ,			_snake_case    ,			_snake_case="replace"    ,			_snake_case="<s>"    ,			_snake_case="</s>"    ,			_snake_case="</s>"    ,			_snake_case="<s>"    ,			_snake_case="<unk>"    ,			_snake_case="<pad>"    ,			_snake_case="<mask>"    ,			_snake_case=False    ,			**_snake_case    ,			)							->				Any:
								UpperCAmelCase_		:      Dict       	=					AddedToken(_snake_case    ,			lstrip=_snake_case    ,			rstrip=_snake_case) if isinstance(_snake_case    ,			_snake_case) else bos_token
								UpperCAmelCase_		:      Union[str, Any]       	=					AddedToken(_snake_case    ,			lstrip=_snake_case    ,			rstrip=_snake_case) if isinstance(_snake_case    ,			_snake_case) else eos_token
								UpperCAmelCase_		:      Any       	=					AddedToken(_snake_case    ,			lstrip=_snake_case    ,			rstrip=_snake_case) if isinstance(_snake_case    ,			_snake_case) else sep_token
								UpperCAmelCase_		:      Dict       	=					AddedToken(_snake_case    ,			lstrip=_snake_case    ,			rstrip=_snake_case) if isinstance(_snake_case    ,			_snake_case) else cls_token
								UpperCAmelCase_		:      Any       	=					AddedToken(_snake_case    ,			lstrip=_snake_case    ,			rstrip=_snake_case) if isinstance(_snake_case    ,			_snake_case) else unk_token
								UpperCAmelCase_		:      Tuple       	=					AddedToken(_snake_case    ,			lstrip=_snake_case    ,			rstrip=_snake_case) if isinstance(_snake_case    ,			_snake_case) else pad_token

								# Mask token behave like a normal word, i.e. include the space before it
								UpperCAmelCase_		:      Union[str, Any]       	=					AddedToken(_snake_case    ,			lstrip=_snake_case    ,			rstrip=_snake_case) if isinstance(_snake_case    ,			_snake_case) else mask_token

								super().__init__(
								    errors=_snake_case    ,			bos_token=_snake_case    ,			eos_token=_snake_case    ,			unk_token=_snake_case    ,			sep_token=_snake_case    ,			cls_token=_snake_case    ,			pad_token=_snake_case    ,			mask_token=_snake_case    ,			add_prefix_space=_snake_case    ,			**_snake_case    ,			)

								with open(_snake_case    ,			encoding='utf-8') as vocab_handle:
									UpperCAmelCase_		:      Tuple       	=					json.load(_snake_case)
								UpperCAmelCase_		:      Any       	=					{v: k for k, v in self.encoder.items()}
								UpperCAmelCase_		:      List[str]       	=					errors  # how to handle errors in decoding
								UpperCAmelCase_		:      Union[str, Any]       	=					bytes_to_unicode()
								UpperCAmelCase_		:      int       	=					{v: k for k, v in self.byte_encoder.items()}
								with open(_snake_case    ,			encoding='utf-8') as merges_handle:
									UpperCAmelCase_		:      Tuple       	=					merges_handle.read().split('\n')[1:-1]
								UpperCAmelCase_		:      str       	=					[tuple(merge.split()) for merge in bpe_merges]
								UpperCAmelCase_		:      Optional[Any]       	=					dict(zip(_snake_case    ,			range(len(_snake_case))))
								UpperCAmelCase_		:      str       	=					{}
								UpperCAmelCase_		:      Dict       	=					add_prefix_space

								# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
								UpperCAmelCase_		:      Any       	=					re.compile(r'\'s|\'t|\'re|\'ve|\'m|\'ll|\'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+')


							@property
							# Copied from transformers.models.bart.tokenization_bart.BartTokenizer.vocab_size
							def 						_snake_case							(			self)							->				Optional[int]:
								return len(self.encoder)


							def 						_snake_case							(			self)							->				Optional[Any]:
								return dict(self.encoder    ,			**self.added_tokens_encoder)


							def 						_snake_case							(			self    ,			_snake_case)							->				Optional[int]:
								if token in self.cache:
									return self.cache[token]
								UpperCAmelCase_		:      Union[str, Any]       	=					tuple(_snake_case)
								UpperCAmelCase_		:      Any       	=					get_pairs(_snake_case)

								if not pairs:
									return token

								while True:
									UpperCAmelCase_		:      List[str]       	=					min(_snake_case    ,			key=lambda _snake_case: self.bpe_ranks.get(_snake_case    ,			float('inf')))
									if bigram not in self.bpe_ranks:
										break
									UpperCAmelCase_		:      int       	=					bigram
									UpperCAmelCase_		:      Tuple       	=					[]
									UpperCAmelCase_		:      List[str]       	=					0
									while i < len(_snake_case):
										try:
											UpperCAmelCase_		:      List[str]       	=					word.index(_snake_case    ,			_snake_case)
										except ValueError:
											new_word.extend(word[i:])
											break
										else:
											new_word.extend(word[i:j])
											UpperCAmelCase_		:      Union[str, Any]       	=					j

										if word[i] == first and i < len(_snake_case) - 1 and word[i + 1] == second:
											new_word.append(first + second)
											i += 2
										else:
											new_word.append(word[i])
											i += 1
									UpperCAmelCase_		:      str       	=					tuple(_snake_case)
									UpperCAmelCase_		:      Dict       	=					new_word
									if len(_snake_case) == 1:
										break
									else:
										UpperCAmelCase_		:      int       	=					get_pairs(_snake_case)
								UpperCAmelCase_		:      Tuple       	=					' '.join(_snake_case)
								UpperCAmelCase_		:      Optional[int]       	=					word
								return word


							def 						_snake_case							(			self    ,			_snake_case)							->				int:
								UpperCAmelCase_		:      str       	=					[]
								for token in re.findall(self.pat    ,			_snake_case):
									UpperCAmelCase_		:      Tuple       	=					''.join(
									    self.byte_encoder[b] for b in token.encode('utf-8'))  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
									bpe_tokens.extend(bpe_token for bpe_token in self.bpe(_snake_case).split(' '))
								return bpe_tokens


							def 						_snake_case							(			self    ,			_snake_case)							->				Union[str, Any]:
								return self.encoder.get(_snake_case    ,			self.encoder.get(self.unk_token))


							def 						_snake_case							(			self    ,			_snake_case)							->				Optional[int]:
								return self.decoder.get(_snake_case)


							def 						_snake_case							(			self    ,			_snake_case)							->				Optional[int]:
								UpperCAmelCase_		:      List[str]       	=					''.join(_snake_case)
								UpperCAmelCase_		:      List[str]       	=					bytearray([self.byte_decoder[c] for c in text]).decode('utf-8'    ,			errors=self.errors)
								return text


							def 						_snake_case							(			self    ,			_snake_case    ,			_snake_case = None)							->				Tuple[str]:
								if not os.path.isdir(_snake_case):
									logger.error(F"""Vocabulary path ({save_directory}) should be a directory""")
									return
								UpperCAmelCase_		:      Optional[Any]       	=					os.path.join(
								    _snake_case    ,			(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'])
								UpperCAmelCase_		:      Dict       	=					os.path.join(
								    _snake_case    ,			(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['merges_file'])

								with open(_snake_case    ,			'w'    ,			encoding='utf-8') as f:
									f.write(json.dumps(self.encoder    ,			indent=2    ,			sort_keys=_snake_case    ,			ensure_ascii=_snake_case) + '\n')

								UpperCAmelCase_		:      Any       	=					0
								with open(_snake_case    ,			'w'    ,			encoding='utf-8') as writer:
									writer.write('#version: 0.2\n')
									for bpe_tokens, token_index in sorted(self.bpe_ranks.items()    ,			key=lambda _snake_case: kv[1]):
										if index != token_index:
											logger.warning(
											    F"""Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."""
											    ' Please check that the tokenizer is not corrupted!')
											UpperCAmelCase_		:      Union[str, Any]       	=					token_index
										writer.write(' '.join(_snake_case) + '\n')
										index += 1

								return vocab_file, merge_file


							def 						_snake_case							(			self    ,			_snake_case    ,			_snake_case = None)							->				List[int]:
								if token_ids_a is None:
									return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
								UpperCAmelCase_		:      str       	=					[self.cls_token_id]
								UpperCAmelCase_		:      str       	=					[self.sep_token_id]
								return cls + token_ids_a + sep + sep + token_ids_a + sep


							def 						_snake_case							(			self    ,			_snake_case    ,			_snake_case = None    ,			_snake_case = False)							->				List[int]:
								if already_has_special_tokens:
									return super().get_special_tokens_mask(
									    token_ids_a=_snake_case    ,			token_ids_a=_snake_case    ,			already_has_special_tokens=_snake_case)

								if token_ids_a is None:
									return [1] + ([0] * len(_snake_case)) + [1]
								return [1] + ([0] * len(_snake_case)) + [1, 1] + ([0] * len(_snake_case)) + [1]


							def 						_snake_case							(			self    ,			_snake_case    ,			_snake_case = None)							->				List[int]:
								UpperCAmelCase_		:      List[str]       	=					[self.sep_token_id]
								UpperCAmelCase_		:      Dict       	=					[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]


							def 						_snake_case							(			self    ,			_snake_case    ,			_snake_case=False    ,			**_snake_case)							->				int:
								UpperCAmelCase_		:      List[Any]       	=					kwargs.pop('add_prefix_space'    ,			self.add_prefix_space)
								if (is_split_into_words or add_prefix_space) and (len(_snake_case) > 0 and not text[0].isspace()):
									UpperCAmelCase_		:      Dict       	=					' ' + text
								return (text, kwargs)





							def 						_snake_case							(			self    ,			_snake_case    ,			_snake_case = None    ,			_snake_case = PaddingStrategy.DO_NOT_PAD    ,			_snake_case = None    ,			_snake_case = None    ,			)							->				dict:
								UpperCAmelCase_		:      int       	=					super()._pad(
								    encoded_inputs=_snake_case    ,			max_length=_snake_case    ,			padding_strategy=_snake_case    ,			pad_to_multiple_of=_snake_case    ,			return_attention_mask=_snake_case    ,			)

								# Load from model defaults
								if return_attention_mask is None:
									UpperCAmelCase_		:      Union[str, Any]       	=					'attention_mask' in self.model_input_names

								if return_attention_mask and "global_attention_mask" in encoded_inputs:
									UpperCAmelCase_		:      int       	=					encoded_inputs[self.model_input_names[0]]
									# `global_attention_mask` need to have the same length as other (sequential) inputs.
									UpperCAmelCase_		:      List[Any]       	=					len(encoded_inputs['global_attention_mask']) != len(_snake_case)

									if needs_to_be_padded:
										UpperCAmelCase_		:      Union[str, Any]       	=					len(_snake_case) - len(encoded_inputs['global_attention_mask'])

										if self.padding_side == "right":
											# Use `-1` since `0` in `global_attention_mask` means `local attention` instead of `not to attend`
											UpperCAmelCase_		:      Optional[int]       	=					(
											    encoded_inputs['global_attention_mask'] + [-1] * difference
											)
										elif self.padding_side == "left":
											UpperCAmelCase_		:      List[Any]       	=					[-1] * difference + encoded_inputs[
											    'global_attention_mask'
											]
										else:
											raise ValueError('Invalid padding strategy:' + str(self.padding_side))

								return encoded_inputs









'''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)






