

'''simple docstring'''






import re

import jax.numpy as jnp
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.random import PRNGKey

from ..utils import logging


lowerCAmelCase__											=	logging.get_logger(__name__)


def 		_A     (    A__      ):






 """simple docstring"""



 __lowercase							  = R'''\w+[.]\d+'''
 __lowercase							  = re.findall(A__     ,				A__      )
 for pat in pats:
  __lowercase							  = key.replace(A__     ,				'''_'''.join(pat.split('''.'''      )      )      )
 return key


def 		_A     (    A__     ,				A__     ,				A__      ):






 """simple docstring"""



 __lowercase							  = pt_tuple_key[:-1] + ('''scale''',)
 if (
     any('''norm''' in str_ for str_ in pt_tuple_key      )
     and (pt_tuple_key[-1] == "bias")
     and (pt_tuple_key[:-1] + ("bias",) not in random_flax_state_dict)
     and (pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict)
 ):
  __lowercase							  = pt_tuple_key[:-1] + ('''scale''',)
  return renamed_pt_tuple_key, pt_tensor
 elif pt_tuple_key[-1] in ["weight", "gamma"] and pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict:
  __lowercase							  = pt_tuple_key[:-1] + ('''scale''',)
  return renamed_pt_tuple_key, pt_tensor

 # embedding
 if pt_tuple_key[-1] == "weight" and pt_tuple_key[:-1] + ("embedding",) in random_flax_state_dict:
  __lowercase							  = pt_tuple_key[:-1] + ('''embedding''',)
  return renamed_pt_tuple_key, pt_tensor

 # conv layer
 __lowercase							  = pt_tuple_key[:-1] + ('''kernel''',)
 if pt_tuple_key[-1] == "weight" and pt_tensor.ndim == 4:
  __lowercase							  = pt_tensor.transpose(2     ,				3     ,				1     ,				0      )
  return renamed_pt_tuple_key, pt_tensor

 # linear layer
 __lowercase							  = pt_tuple_key[:-1] + ('''kernel''',)
 if pt_tuple_key[-1] == "weight":
  __lowercase							  = pt_tensor.T
  return renamed_pt_tuple_key, pt_tensor

 # old PyTorch layer norm weight
 __lowercase							  = pt_tuple_key[:-1] + ('''weight''',)
 if pt_tuple_key[-1] == "gamma":
  return renamed_pt_tuple_key, pt_tensor

 # old PyTorch layer norm bias
 __lowercase							  = pt_tuple_key[:-1] + ('''bias''',)
 if pt_tuple_key[-1] == "beta":
  return renamed_pt_tuple_key, pt_tensor

 return pt_tuple_key, pt_tensor






def 		_A     (    A__     ,				A__     ,				A__=42      ):






 """simple docstring"""



 __lowercase							  = {k: v.numpy() for k, v in pt_state_dict.items()}

 # Step 2: Since the model is stateless, get random Flax params
 __lowercase							  = flax_model.init_weights(PRNGKey(A__      )      )

 __lowercase							  = flatten_dict(A__      )
 __lowercase							  = {}

 # Need to change some parameters name to match Flax names
 for pt_key, pt_tensor in pt_state_dict.items():
  __lowercase							  = rename_key(A__      )
  __lowercase							  = tuple(renamed_pt_key.split('''.'''      )      )

  # Correctly rename weight parameters
  __lowercase  ,	__lowercase							  = rename_key_and_reshape_tensor(A__     ,				A__     ,				A__      )

  if flax_key in random_flax_state_dict:
   if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
    raise ValueError(
        F"PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape "
        F"{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}."      )

        # also add unexpected weight so that warning is thrown
  __lowercase							  = jnp.asarray(A__      )

 return unflatten_dict(A__      )




'''simple docstring'''






import argparse

from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection

from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline


if __name__ == "__main__":
 lowerCAmelCase__											=	argparse.ArgumentParser()

 parser.add_argument('''--dump_path''', default=None, type=str, required=True, help='''Path to the output model.''')

 parser.add_argument(
     '''--txt2img_unclip''',
     default='''kakaobrain/karlo-v1-alpha''',
     type=str,
     required=False,
     help='''The pretrained txt2img unclip.''',
 )

 lowerCAmelCase__											=	parser.parse_args()

 lowerCAmelCase__											=	UnCLIPPipeline.from_pretrained(args.txtaimg_unclip)

 lowerCAmelCase__											=	CLIPImageProcessor()
 lowerCAmelCase__											=	CLIPVisionModelWithProjection.from_pretrained('''openai/clip-vit-large-patch14''')

 lowerCAmelCase__											=	UnCLIPImageVariationPipeline(
     decoder=txtaimg.decoder,
     text_encoder=txtaimg.text_encoder,
     tokenizer=txtaimg.tokenizer,
     text_proj=txtaimg.text_proj,
     feature_extractor=feature_extractor,
     image_encoder=image_encoder,
     super_res_first=txtaimg.super_res_first,
     super_res_last=txtaimg.super_res_last,
     decoder_scheduler=txtaimg.decoder_scheduler,
     super_res_scheduler=txtaimg.super_res_scheduler,
 )

 imgaimg.save_pretrained(args.dump_path)

