



from pickle import UnpicklingError

import jax
import jax.numpy as jnp
import numpy as np
from flax.serialization import from_bytes
from flax.traverse_util import flatten_dict

from ..utils import logging


__UpperCAmelCase	       =	logging.get_logger(__name__)
def 		UpperCamelCase     (       snake_case__	:   Optional[int]	,					snake_case__	:   List[Any]     )     ->					Union[str, Any]:
   try:
      with open(snake_case__	,					'rb'     ) as flax_state_f:
         UpperCamelCase   :  Union[str, Any]						     =     from_bytes(snake_case__	,					flax_state_f.read()     )
   except UnpicklingError as e:
      try:
         with open(snake_case__     ) as f:
            if f.read().startswith('version'     ):
               raise OSError(
                   'You seem to have cloned a repository without having git-lfs installed. Please'
                   ' install git-lfs and run `git lfs install` followed by `git lfs pull` in the'
                   ' folder you cloned.'     )
            else:
               raise ValueError from e
      except (UnicodeDecodeError, ValueError):
         raise EnvironmentError(F"""Unable to convert {model_file} to Flax deserializable object. """     )

   return load_flax_weights_in_pytorch_model(snake_case__	,					snake_case__     )


def 		UpperCamelCase     (       snake_case__	:   Any	,					snake_case__	:   Union[str, Any]     )     ->					List[Any]:

   try:
      import torch  # noqa: F401
   except ImportError:
      logger.error(
          'Loading Flax weights in PyTorch requires both PyTorch and Flax to be installed. Please see'
          ' https://pytorch.org/ and https://flax.readthedocs.io/en/latest/installation.html for installation'
          ' instructions.'     )
      raise

   # check if we have bf16 weights
   UpperCamelCase   :  int						     =     flatten_dict(jax.tree_util.tree_map(lambda snake_case__     : x.dtype == jnp.bfloataa	,					snake_case__     )     ).values()
   if any(snake_case__     ):
      # convert all weights to fp32 if they are bf16 since torch.from_numpy can-not handle bf16

      # and bf16 is not fully supported in PT yet.
      logger.warning(
          'Found ``bfloat16`` weights in Flax model. Casting all ``bfloat16`` weights to ``float32`` '
          'before loading those in PyTorch model.'     )
      UpperCamelCase   :  Union[str, Any]						     =     jax.tree_util.tree_map(
          lambda snake_case__     : params.astype(np.floataa     ) if params.dtype == jnp.bfloataa else params	,					snake_case__     )

   UpperCamelCase   :  int						     =     ''

   UpperCamelCase   :  int						     =     flatten_dict(snake_case__	,					sep='.'     )
   UpperCamelCase   :  Dict						     =     pt_model.state_dict()

   # keep track of unexpected & missing keys
   UpperCamelCase   :  Any						     =     []
   UpperCamelCase   :  Tuple						     =     set(pt_model_dict.keys()     )

   for flax_key_tuple, flax_tensor in flax_state_dict.items():
      UpperCamelCase   :  Dict						     =     flax_key_tuple.split('.'     )

      if flax_key_tuple_array[-1] == "kernel" and flax_tensor.ndim == 4:
         UpperCamelCase   :  Dict						     =     flax_key_tuple_array[:-1] + ['weight']
         UpperCamelCase   :  Optional[int]						     =     jnp.transpose(snake_case__	,					(3, 2, 0, 1)     )
      elif flax_key_tuple_array[-1] == "kernel":
         UpperCamelCase   :  Optional[Any]						     =     flax_key_tuple_array[:-1] + ['weight']
         UpperCamelCase   :  Dict						     =     flax_tensor.T
      elif flax_key_tuple_array[-1] == "scale":
         UpperCamelCase   :  Optional[int]						     =     flax_key_tuple_array[:-1] + ['weight']

      if "time_embedding" not in flax_key_tuple_array:
         for i, flax_key_tuple_string in enumerate(snake_case__     ):
            UpperCamelCase   :  Optional[int]						     =     (
                flax_key_tuple_string.replace('_0'	,					'.0'     )
                .replace('_1'	,					'.1'     )
                .replace('_2'	,					'.2'     )
                .replace('_3'	,					'.3'     )
                .replace('_4'	,					'.4'     )
                .replace('_5'	,					'.5'     )
                .replace('_6'	,					'.6'     )
                .replace('_7'	,					'.7'     )
                .replace('_8'	,					'.8'     )
                .replace('_9'	,					'.9'     )
            )

      UpperCamelCase   :  List[str]						     =     '.'.join(snake_case__     )

      if flax_key in pt_model_dict:
         if flax_tensor.shape != pt_model_dict[flax_key].shape:
            raise ValueError(
                F"""Flax checkpoint seems to be incorrect. Weight {flax_key_tuple} was expected """
                F"""to be of shape {pt_model_dict[flax_key].shape}, but is {flax_tensor.shape}."""     )
         else:
            # add weight to pytorch dict
            UpperCamelCase   :  Optional[int]						     =     np.asarray(snake_case__     ) if not isinstance(snake_case__	,					np.ndarray     ) else flax_tensor
            UpperCamelCase   :  Optional[Any]						     =     torch.from_numpy(snake_case__     )
            # remove from missing keys
            missing_keys.remove(snake_case__     )
      else:
         # weight is not expected by PyTorch model
         unexpected_keys.append(snake_case__     )

   pt_model.load_state_dict(snake_case__     )

   # re-transform missing_keys to list
   UpperCamelCase   :  Tuple						     =     list(snake_case__     )

   if len(snake_case__     ) > 0:
      logger.warning(
          'Some weights of the Flax model were not used when initializing the PyTorch model'
          F""" {pt_model.__class__.__name__}: {unexpected_keys}\n- This IS expected if you are initializing"""
          F""" {pt_model.__class__.__name__} from a Flax model trained on another task or with another architecture"""
          ' (e.g. initializing a BertForSequenceClassification model from a FlaxBertForPreTraining model).\n- This'
          F""" IS NOT expected if you are initializing {pt_model.__class__.__name__} from a Flax model that you expect"""
          ' to be exactly identical (e.g. initializing a BertForSequenceClassification model from a'
          ' FlaxBertForSequenceClassification model).'     )
   if len(snake_case__     ) > 0:
      logger.warning(
          F"""Some weights of {pt_model.__class__.__name__} were not initialized from the Flax model and are newly"""
          F""" initialized: {missing_keys}\nYou should probably TRAIN this model on a down-stream task to be able to"""
          ' use it for predictions and inference.'     )

   return pt_model








import argparse

import torch
from safetensors.torch import load_file

from diffusers import StableDiffusionPipeline
def 		UpperCamelCase     (       snake_case__	:   List[Any]	,					snake_case__	:   Union[str, Any]	,					snake_case__	:   Tuple	,					snake_case__	:   Union[str, Any]	,					snake_case__	:   List[Any]     )     ->					Tuple:
   # load base model
   UpperCamelCase   :  Tuple						     =     StableDiffusionPipeline.from_pretrained(snake_case__	,					torch_dtype=torch.floataa     )

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

   UpperCamelCase   :  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:
         UpperCamelCase   :  Optional[Any]						     =     key.split('.'     )[0].split(LORA_PREFIX_TEXT_ENCODER + '_'     )[-1].split('_'     )
         UpperCamelCase   :  Optional[Any]						     =     pipeline.text_encoder
      else:
         UpperCamelCase   :  Tuple						     =     key.split('.'     )[0].split(LORA_PREFIX_UNET + '_'     )[-1].split('_'     )
         UpperCamelCase   :  List[str]						     =     pipeline.unet

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

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

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

   return pipeline


if __name__ == "__main__":
    __UpperCAmelCase	       =	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.)''')

    __UpperCAmelCase	       =	parser.parse_args()

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

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

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



