






import argparse

import torch
from safetensors.torch import load_file

from diffusers import StableDiffusionPipeline



def 					_lowerCamelCase(			lowerCAmelCase__				:		Union[str, Any]    ,    lowerCAmelCase__				:		Dict    ,    lowerCAmelCase__				:		int    ,    lowerCAmelCase__				:		Union[str, Any]    ,    lowerCAmelCase__				:		Union[str, Any]      ):





				'''simple docstring'''







				SCREAMING_SNAKE_CASE_   :	List[str]													=   StableDiffusionPipeline.from_pretrained(lowerCAmelCase__    ,    torch_dtype=torch.floataa      )

				# load LoRA weight from .safetensors
				SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   load_file(lowerCAmelCase__      )

				SCREAMING_SNAKE_CASE_   :	Dict													=   []

				# 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_   :	List[Any]													=   key.split('.'      )[0].split(LORA_PREFIX_TEXT_ENCODER + '_'      )[-1].split('_'      )
												SCREAMING_SNAKE_CASE_   :	int													=   pipeline.text_encoder
								else:
												SCREAMING_SNAKE_CASE_   :	List[Any]													=   key.split('.'      )[0].split(LORA_PREFIX_UNET + '_'      )[-1].split('_'      )
												SCREAMING_SNAKE_CASE_   :	List[Any]													=   pipeline.unet

								# find the target layer
								SCREAMING_SNAKE_CASE_   :	str													=   layer_infos.pop(0      )
								while len(lowerCAmelCase__      ) > -1:
												try:
																SCREAMING_SNAKE_CASE_   :	Optional[int]													=   curr_layer.__getattr__(lowerCAmelCase__      )
																if len(lowerCAmelCase__      ) > 0:
																				SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   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_   :	str													=   layer_infos.pop(0      )

								SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   []
								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_   :	Optional[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_   :	Optional[Any]													=   state_dict[pair_keys[0]].to(torch.floataa      )
												SCREAMING_SNAKE_CASE_   :	Any													=   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__":
				A        =			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.)')

				A        =			parser.parse_args()

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

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

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







import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


A        =			logging.get_logger(__name__)

A        =			{'vocab_file': 'spm_char.model'}

A        =			{
    'vocab_file': {
        'microsoft/speecht5_asr': 'https://huggingface.co/microsoft/speecht5_asr/resolve/main/spm_char.model',
        'microsoft/speecht5_tts': 'https://huggingface.co/microsoft/speecht5_tts/resolve/main/spm_char.model',
        'microsoft/speecht5_vc': 'https://huggingface.co/microsoft/speecht5_vc/resolve/main/spm_char.model',
    }
}

A        =			{
    'microsoft/speecht5_asr': 1024,
    'microsoft/speecht5_tts': 1024,
    'microsoft/speecht5_vc': 1024,
}


class 							__a							(  __A						):





		'''simple docstring'''



		UpperCAmelCase__      :				str				    =						VOCAB_FILES_NAMES
		UpperCAmelCase__      :				List[str]				    =						PRETRAINED_VOCAB_FILES_MAP
		UpperCAmelCase__      :				Optional[int]				    =						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		UpperCAmelCase__      :				Dict				    =						["""input_ids""", """attention_mask"""]


		def __init__(						self ,    UpperCamelCase__ ,    UpperCamelCase__="<s>" ,    UpperCamelCase__="</s>" ,    UpperCamelCase__="<unk>" ,    UpperCamelCase__="<pad>" ,    UpperCamelCase__ = None ,    **UpperCamelCase__ ,    ):
						SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   {} if sp_model_kwargs is None else sp_model_kwargs

						super().__init__(
						    bos_token=UpperCamelCase__ ,    eos_token=UpperCamelCase__ ,    unk_token=UpperCamelCase__ ,    pad_token=UpperCamelCase__ ,    sp_model_kwargs=self.sp_model_kwargs ,    **UpperCamelCase__ ,    )

						SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   vocab_file

						SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   spm.SentencePieceProcessor(**self.sp_model_kwargs       )
						self.sp_model.Load(UpperCamelCase__       )


		@property
		def   __snake_case			(						self       ):
						return self.sp_model.get_piece_size()


		def   __snake_case			(						self       ):
						SCREAMING_SNAKE_CASE_   :	List[str]													=   {self.convert_ids_to_tokens(UpperCamelCase__       ): i for i in range(self.vocab_size       )}
						vocab.update(self.added_tokens_encoder       )
						return vocab


		def __getstate__(						self       ):
						SCREAMING_SNAKE_CASE_   :	str													=   self.__dict__.copy()
						SCREAMING_SNAKE_CASE_   :	List[str]													=   None
						return state


		def __setstate__(						self ,    UpperCamelCase__       ):
						SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   d

						# for backward compatibility
						if not hasattr(self ,    'sp_model_kwargs'       ):
										SCREAMING_SNAKE_CASE_   :	Any													=   {}

						SCREAMING_SNAKE_CASE_   :	List[Any]													=   spm.SentencePieceProcessor(**self.sp_model_kwargs       )
						self.sp_model.Load(self.vocab_file       )


		def   __snake_case			(						self ,    UpperCamelCase__       ):
						return self.sp_model.encode(UpperCamelCase__ ,    out_type=UpperCamelCase__       )


		def   __snake_case			(						self ,    UpperCamelCase__       ):
						return self.sp_model.piece_to_id(UpperCamelCase__       )


		def   __snake_case			(						self ,    UpperCamelCase__       ):
						SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   self.sp_model.IdToPiece(UpperCamelCase__       )
						return token


		def   __snake_case			(						self ,    UpperCamelCase__       ):
						SCREAMING_SNAKE_CASE_   :	int													=   []
						SCREAMING_SNAKE_CASE_   :	int													=   ''
						for token in tokens:
										# make sure that special tokens are not decoded using sentencepiece model
										if token in self.all_special_tokens:
														out_string += self.sp_model.decode(UpperCamelCase__       ) + token
														SCREAMING_SNAKE_CASE_   :	Any													=   []
										else:
														current_sub_tokens.append(UpperCamelCase__       )
						out_string += self.sp_model.decode(UpperCamelCase__       )
						return out_string.strip()


		def   __snake_case			(						self ,    UpperCamelCase__ ,    UpperCamelCase__=None       ):
						if token_ids_a is None:
										return token_ids_a + [self.eos_token_id]
						# We don't expect to process pairs, but leave the pair logic for API consistency
						return token_ids_a + token_ids_a + [self.eos_token_id]


		def   __snake_case			(						self ,    UpperCamelCase__ ,    UpperCamelCase__ = None ,    UpperCamelCase__ = False       ):
						if already_has_special_tokens:
										return super().get_special_tokens_mask(
										    token_ids_a=UpperCamelCase__ ,    token_ids_a=UpperCamelCase__ ,    already_has_special_tokens=UpperCamelCase__       )

						SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   [1]
						if token_ids_a is None:
										return ([0] * len(UpperCamelCase__       )) + suffix_ones
						return ([0] * len(UpperCamelCase__       )) + ([0] * len(UpperCamelCase__       )) + suffix_ones






		def   __snake_case			(						self ,    UpperCamelCase__ ,    UpperCamelCase__ = None       ):
						if not os.path.isdir(UpperCamelCase__       ):
										logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''       )
										return
						SCREAMING_SNAKE_CASE_   :	Optional[int]													=   os.path.join(
						    UpperCamelCase__ ,    (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']       )

						if os.path.abspath(self.vocab_file       ) != os.path.abspath(UpperCamelCase__       ) and os.path.isfile(self.vocab_file       ):
										copyfile(self.vocab_file ,    UpperCamelCase__       )
						elif not os.path.isfile(self.vocab_file       ):
										with open(UpperCamelCase__ ,    'wb'       ) as fi:
														SCREAMING_SNAKE_CASE_   :	Tuple													=   self.sp_model.serialized_model_proto()
														fi.write(UpperCamelCase__       )

						return (out_vocab_file,)