



import numpy as np
import torch
from torch.utils.data import Dataset, IterableDataset

from ..utils.generic import ModelOutput



class   UpperCAmelCase_    (       a):







							def __init__(   self,			__a,			__a,			__a):

														'''simple docstring'''

														_lowerCAmelCase     :							List[str]    =    dataset
														_lowerCAmelCase     :							str    =    process
														_lowerCAmelCase     :							Optional[Any]    =    params







							def __len__(   self):

														'''simple docstring'''

														return len(self.dataset)







							def __getitem__(   self,			__a):

														'''simple docstring'''

														_lowerCAmelCase     :							Dict    =    self.dataset[i]
														_lowerCAmelCase     :							Tuple    =    self.process(__a,			**self.params)
														return processed



class   UpperCAmelCase_    (       a):







							def __init__(   self,			__a,			__a,			__a,			__a=None):

														'''simple docstring'''

														_lowerCAmelCase     :							str    =    loader
														_lowerCAmelCase     :							List[Any]    =    infer
														_lowerCAmelCase     :							Optional[int]    =    params
														if loader_batch_size == 1:
																					# Let's spare some time by deactivating altogether
																					_lowerCAmelCase     :							Optional[int]    =    None
														_lowerCAmelCase     :							List[Any]    =    loader_batch_size

														# Internal bookkeeping
														_lowerCAmelCase     :							Tuple    =    None
														_lowerCAmelCase     :							List[str]    =    None







							def __len__(   self):

														'''simple docstring'''

														return len(self.loader)







							def __iter__(   self):

														'''simple docstring'''

														_lowerCAmelCase     :							str    =    iter(self.loader)
														return self







							def    snake_case__      (   self):

														'''simple docstring'''

														if isinstance(self._loader_batch_data,			torch.Tensor):
																					# Batch data is simple tensor, just fetch the slice
																					_lowerCAmelCase     :							List[Any]    =    self._loader_batch_data[self._loader_batch_index]
														else:
																					# Batch data is assumed to be BaseModelOutput (or dict)
																					_lowerCAmelCase     :							Union[str, Any]    =    {}
																					for k, element in self._loader_batch_data.items():
																												if isinstance(__a,			__a):
																																			# Convert ModelOutput to tuple first
																																			_lowerCAmelCase     :							Optional[int]    =    element.to_tuple()
																																			if isinstance(element[0],			torch.Tensor):
																																										_lowerCAmelCase     :							Dict    =    tuple(el[self._loader_batch_index].unsqueeze(0) for el in element)
																																			elif isinstance(element[0],			np.ndarray):
																																										_lowerCAmelCase     :							int    =    tuple(np.expand_dims(el[self._loader_batch_index],			0) for el in element)
																																			continue
																												if k in {"hidden_states", "past_key_values", "attentions"} and isinstance(__a,			__a):
																																			# Those are stored as lists of tensors so need specific unbatching.
																																			if isinstance(element[0],			torch.Tensor):
																																										_lowerCAmelCase     :							Optional[Any]    =    tuple(el[self._loader_batch_index].unsqueeze(0) for el in element)
																																			elif isinstance(element[0],			np.ndarray):
																																										_lowerCAmelCase     :							Optional[Any]    =    tuple(np.expand_dims(el[self._loader_batch_index],			0) for el in element)
																																			continue
																												if element is None:
																																			# This can happen for optional data that get passed around
																																			_lowerCAmelCase     :							str    =    None
																												elif isinstance(element[self._loader_batch_index],			torch.Tensor):
																																			# Take correct batch data, but make it looked like batch_size=1
																																			# For compatibility with other methods within transformers

																																			_lowerCAmelCase     :							Tuple    =    element[self._loader_batch_index].unsqueeze(0)
																												elif isinstance(element[self._loader_batch_index],			np.ndarray):
																																			# Take correct batch data, but make it looked like batch_size=1
																																			# For compatibility with other methods within transformers
																																			_lowerCAmelCase     :							Tuple    =    np.expand_dims(element[self._loader_batch_index],			0)
																												else:
																																			# This is typically a list, so no need to `unsqueeze`.
																																			_lowerCAmelCase     :							Optional[int]    =    element[self._loader_batch_index]
            # Recreate the element by reusing the original class to make it look
            # batch_size=1
																					_lowerCAmelCase     :							str    =    self._loader_batch_data.__class__(__a)
														self._loader_batch_index += 1
														return result







							def    snake_case__      (   self):

														'''simple docstring'''

														if self._loader_batch_index is not None and self._loader_batch_index < self.loader_batch_size:
																					# We are currently unrolling a batch so we just need to return
																					# the current item within a batch
																					return self.loader_batch_item()

														# We're out of items within a batch
														_lowerCAmelCase     :							Union[str, Any]    =    next(self.iterator)
														_lowerCAmelCase     :							Optional[int]    =    self.infer(__a,			**self.params)
														# We now have a batch of "inferred things".
														if self.loader_batch_size is not None:
																					# Try to infer the size of the batch
																					if isinstance(__a,			torch.Tensor):
																												_lowerCAmelCase     :							Any    =    processed
																					else:
																												_lowerCAmelCase     :							List[Any]    =    list(processed.keys())[0]
																												_lowerCAmelCase     :							List[Any]    =    processed[key]
																					if isinstance(__a,			__a):
																												_lowerCAmelCase     :							Optional[int]    =    len(__a)
																					else:
																												_lowerCAmelCase     :							List[str]    =    first_tensor.shape[0]
																					if 0 < observed_batch_size < self.loader_batch_size:
																												# could be last batch so we can't unroll as many
																												# elements.
																												_lowerCAmelCase     :							Any    =    observed_batch_size
																					# Setting internal index to unwrap the batch
																					_lowerCAmelCase     :							Tuple    =    processed
																					_lowerCAmelCase     :							Tuple    =    0
																					return self.loader_batch_item()
														else:
																					# We're not unrolling batches
																					return processed



class   UpperCAmelCase_    (       a):







							def __init__(   self,			__a,			__a,			__a,			__a=None):

														'''simple docstring'''

														super().__init__(__a,			__a,			__a)







							def __iter__(   self):

														'''simple docstring'''

														_lowerCAmelCase     :							Tuple    =    iter(self.loader)
														_lowerCAmelCase     :							Dict    =    None
														return self







							def    snake_case__      (   self):

														'''simple docstring'''

														if self.subiterator is None:
																					_lowerCAmelCase     :							int    =    self.infer(next(self.iterator),			**self.params)
														try:
																					# Try to return next item
																					_lowerCAmelCase     :							Union[str, Any]    =    next(self.subiterator)
														except StopIteration:
																					# When a preprocess iterator ends, we can start lookig at the next item
																					# ChunkIterator will keep feeding until ALL elements of iterator
																					# all have created their subiterator and have been iterating against.
																					#
																					# Another way to look at it, is we're basically flattening lists of lists
																					# into a single list, but with generators
																					_lowerCAmelCase     :							List[str]    =    self.infer(next(self.iterator),			**self.params)
																					_lowerCAmelCase     :							Optional[Any]    =    next(self.subiterator)
														return processed



class   UpperCAmelCase_    (       a):







							def __iter__(   self):

														'''simple docstring'''

														_lowerCAmelCase     :							Any    =    iter(self.loader)
														return self







							def    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase     :							Dict    =    False
														_lowerCAmelCase     :							Dict    =    []
														if self._loader_batch_index is not None and self._loader_batch_index < self.loader_batch_size:
																					while self._loader_batch_index < self.loader_batch_size:
																												_lowerCAmelCase     :							Union[str, Any]    =    self.loader_batch_item()
																												_lowerCAmelCase     :							int    =    item.pop("is_last")
																												accumulator.append(__a)
																												if is_last:
																																			return accumulator

														while not is_last:
																					_lowerCAmelCase     :							Dict    =    self.infer(next(self.iterator),			**self.params)
																					if self.loader_batch_size is not None:
																												if isinstance(__a,			torch.Tensor):
																																			_lowerCAmelCase     :							List[str]    =    processed
																												else:
																																			_lowerCAmelCase     :							Any    =    list(processed.keys())[0]
																																			_lowerCAmelCase     :							Dict    =    processed[key]
																												if isinstance(__a,			__a):
																																			_lowerCAmelCase     :							Tuple    =    len(__a)
																												else:
																																			_lowerCAmelCase     :							List[str]    =    first_tensor.shape[0]
																												if 0 < observed_batch_size < self.loader_batch_size:
																																			# could be last batch so we can't unroll as many
																																			# elements.
																																			_lowerCAmelCase     :							List[Any]    =    observed_batch_size
																												_lowerCAmelCase     :							Optional[int]    =    processed
																												_lowerCAmelCase     :							List[str]    =    0
																												while self._loader_batch_index < self.loader_batch_size:
																																			_lowerCAmelCase     :							Union[str, Any]    =    self.loader_batch_item()
																																			_lowerCAmelCase     :							Optional[int]    =    item.pop("is_last")
																																			accumulator.append(__a)
																																			if is_last:
																																										return accumulator
																					else:
																												_lowerCAmelCase     :							Optional[int]    =    processed
																												_lowerCAmelCase     :							List[str]    =    item.pop("is_last")
																												accumulator.append(__a)
														return accumulator



class   UpperCAmelCase_    (       a):







							def __init__(   self,			__a,			__a):

														'''simple docstring'''

														_lowerCAmelCase     :							Tuple    =    dataset
														_lowerCAmelCase     :							Optional[int]    =    key







							def __len__(   self):

														'''simple docstring'''

														return len(self.dataset)







							def __getitem__(   self,			__a):

														'''simple docstring'''

														return self.dataset[i][self.key]



class   UpperCAmelCase_    (       a):







							def __init__(   self,			__a,			__a,			__a):

														'''simple docstring'''

														_lowerCAmelCase     :							Optional[int]    =    dataset
														_lowerCAmelCase     :							Any    =    keya
														_lowerCAmelCase     :							List[Any]    =    keya







							def __len__(   self):

														'''simple docstring'''

														return len(self.dataset)







							def __getitem__(   self,			__a):

														'''simple docstring'''

														return {"text": self.dataset[i][self.keya], "text_pair": self.dataset[i][self.keya]}






import argparse

import torch
from safetensors.torch import load_file

from diffusers import StableDiffusionPipeline







def SCREAMING_SNAKE_CASE				(       lowercase_ ,				lowercase_ ,				lowercase_ ,				lowercase_ ,				lowercase_						)   ->       List[Any]:



       """simple docstring"""

       A__       =    StableDiffusionPipeline.from_pretrained(lowercase_ ,				torch_dtype=torch.floataa						)

       # load LoRA weight from .safetensors
       A__       =    load_file(lowercase_						)

       A__       =    []

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

              # find the target layer
              A__       =    layer_infos.pop(0						)
              while len(lowercase_						) > -1:
                     try:
                            A__       =    curr_layer.__getattr__(lowercase_						)
                            if len(lowercase_						) > 0:
                                   A__       =    layer_infos.pop(0						)
                            elif len(lowercase_						) == 0:
                                   break
                     except Exception:
                            if len(lowercase_						) > 0:
                                   temp_name += "_" + layer_infos.pop(0						)
                            else:
                                   A__       =    layer_infos.pop(0						)

              A__       =    []
              if "lora_down" in key:
                     pair_keys.append(key.replace('''lora_down''' ,				'''lora_up'''						)						)
                     pair_keys.append(lowercase_						)
              else:
                     pair_keys.append(lowercase_						)
                     pair_keys.append(key.replace('''lora_up''' ,				'''lora_down'''						)						)

              # update weight
              if len(state_dict[pair_keys[0]].shape						) == 4:
                     A__       =    state_dict[pair_keys[0]].squeeze(3						).squeeze(2						).to(torch.floataa						)
                     A__       =    state_dict[pair_keys[1]].squeeze(3						).squeeze(2						).to(torch.floataa						)
                     curr_layer.weight.data += alpha * torch.mm(lowercase_ ,				lowercase_						).unsqueeze(2						).unsqueeze(3						)
              else:
                     A__       =    state_dict[pair_keys[0]].to(torch.floataa						)
                     A__       =    state_dict[pair_keys[1]].to(torch.floataa						)
                     curr_layer.weight.data += alpha * torch.mm(lowercase_ ,				lowercase_						)

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

       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	:		Tuple							       =   parser.parse_args()

  _lowerCamelCase	:		List[Any]							       =   args.base_model_path
  _lowerCamelCase	:		Optional[int]							       =   args.checkpoint_path
  _lowerCamelCase	:		Dict							       =   args.dump_path
  _lowerCamelCase	:		Optional[Any]							       =   args.lora_prefix_unet
  _lowerCamelCase	:		Optional[int]							       =   args.lora_prefix_text_encoder
  _lowerCamelCase	:		List[Any]							       =   args.alpha

  _lowerCamelCase	:		int							       =   convert(base_model_path, checkpoint_path, lora_prefix_unet, lora_prefix_text_encoder, alpha)

  _lowerCamelCase	:		Tuple							       =   pipe.to(args.device)
  pipe.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)


