




from ..utils import is_flax_available, is_torch_available


if is_torch_available():
     from .autoencoder_kl import AutoencoderKL
     from .controlnet import ControlNetModel
     from .dual_transformer_ad import DualTransformeraDModel
     from .modeling_utils import ModelMixin
     from .prior_transformer import PriorTransformer
     from .ta_film_transformer import TaFilmDecoder
     from .transformer_ad import TransformeraDModel
     from .unet_ad import UNetaDModel
     from .unet_ad import UNetaDModel
     from .unet_ad_condition import UNetaDConditionModel
     from .unet_ad_condition import UNetaDConditionModel
     from .vq_model import VQModel

if is_flax_available():
     from .controlnet_flax import FlaxControlNetModel
     from .unet_ad_condition_flax import FlaxUNetaDConditionModel
     from .vae_flax import FlaxAutoencoderKL









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


_UpperCAmelCase	:						List[Any]			=     logging.get_logger(__name__)



def       UpperCAmelCase__  (     lowerCamelCase   ):
  lowercase    :int      	=						r"\w+[.]\d+"
  lowercase    :Tuple      	=						re.findall(lowerCamelCase,			lowerCamelCase   )
  for pat in pats:
    lowercase    :List[str]      	=						key.replace(lowerCamelCase,			"_".join(pat.split("."   )   )   )
  return key



def       UpperCAmelCase__  (     lowerCamelCase,			lowerCamelCase,			lowerCamelCase   ):
  lowercase    :List[str]      	=						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    :Optional[int]      	=						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    :int      	=						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    :List[str]      	=						pt_tuple_key[:-1] + ("embedding",)
    return renamed_pt_tuple_key, pt_tensor

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

  # linear layer
  lowercase    :Optional[Any]      	=						pt_tuple_key[:-1] + ("kernel",)
  if pt_tuple_key[-1] == "weight":
    lowercase    :Tuple      	=						pt_tensor.T
    return renamed_pt_tuple_key, pt_tensor

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

  # old PyTorch layer norm bias
  lowercase    :int      	=						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       UpperCAmelCase__  (     lowerCamelCase,			lowerCamelCase,			lowerCamelCase=42   ):
  # Step 1: Convert pytorch tensor to numpy
  lowercase    :Optional[int]      	=						{k: v.numpy() for k, v in pt_state_dict.items()}

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

  lowercase    :Tuple      	=						flatten_dict(lowerCamelCase   )
  lowercase    :Optional[Any]      	=						{}

  # Need to change some parameters name to match Flax names
  for pt_key, pt_tensor in pt_state_dict.items():
    lowercase    :List[Any]      	=						rename_key(lowerCamelCase   )
    lowercase    :List[Any]      	=						tuple(renamed_pt_key.split("."   )   )

    # Correctly rename weight parameters
    lowercase	,    lowercase    :List[str]      	=						rename_key_and_reshape_tensor(lowerCamelCase,			lowerCamelCase,			lowerCamelCase   )

    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    :List[str]      	=						jnp.asarray(lowerCamelCase   )

  return unflatten_dict(lowerCamelCase   )



