



import argparse
import json

import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel



def  lowercase    (				SCREAMING_SNAKE_CASE__    :		int			,    SCREAMING_SNAKE_CASE__    :		str			)					->       str:
 _snake_case      :					Optional[int]					=   UNetaDModel(**SCREAMING_SNAKE_CASE__			)
 _snake_case      :					Optional[Any]					=   checkpoint["""all_modules.0.W"""].data
 _snake_case      :					int					=   checkpoint["""all_modules.0.W"""].data
 _snake_case      :					Tuple					=   checkpoint["""all_modules.1.weight"""].data
 _snake_case      :					str					=   checkpoint["""all_modules.1.bias"""].data

 _snake_case      :					int					=   checkpoint["""all_modules.2.weight"""].data
 _snake_case      :					Tuple					=   checkpoint["""all_modules.2.bias"""].data

 _snake_case      :					Tuple					=   checkpoint["""all_modules.3.weight"""].data
 _snake_case      :					int					=   checkpoint["""all_modules.3.bias"""].data

 _snake_case      :					List[Any]					=   checkpoint[list(checkpoint.keys()			)[-4]].data
 _snake_case      :					Union[str, Any]					=   checkpoint[list(checkpoint.keys()			)[-3]].data
 _snake_case      :					Optional[int]					=   checkpoint[list(checkpoint.keys()			)[-2]].data
 _snake_case      :					List[str]					=   checkpoint[list(checkpoint.keys()			)[-1]].data

 _snake_case      :					str					=   4

 def set_attention_weights(SCREAMING_SNAKE_CASE__    :		List[Any]			,    SCREAMING_SNAKE_CASE__    :		Tuple			,    SCREAMING_SNAKE_CASE__    :		Tuple			):
  _snake_case      :					List[Any]					=   old_checkpoint[F'''all_modules.{index}.NIN_0.W'''].data.T
  _snake_case      :					Tuple					=   old_checkpoint[F'''all_modules.{index}.NIN_1.W'''].data.T
  _snake_case      :					Tuple					=   old_checkpoint[F'''all_modules.{index}.NIN_2.W'''].data.T

  _snake_case      :					Any					=   old_checkpoint[F'''all_modules.{index}.NIN_0.b'''].data
  _snake_case      :					List[str]					=   old_checkpoint[F'''all_modules.{index}.NIN_1.b'''].data
  _snake_case      :					str					=   old_checkpoint[F'''all_modules.{index}.NIN_2.b'''].data

  _snake_case      :					List[Any]					=   old_checkpoint[F'''all_modules.{index}.NIN_3.W'''].data.T
  _snake_case      :					Dict					=   old_checkpoint[F'''all_modules.{index}.NIN_3.b'''].data

  _snake_case      :					List[str]					=   old_checkpoint[F'''all_modules.{index}.GroupNorm_0.weight'''].data
  _snake_case      :					Optional[int]					=   old_checkpoint[F'''all_modules.{index}.GroupNorm_0.bias'''].data

 def set_resnet_weights(SCREAMING_SNAKE_CASE__    :		List[Any]			,    SCREAMING_SNAKE_CASE__    :		Any			,    SCREAMING_SNAKE_CASE__    :		str			):
  _snake_case      :					Dict					=   old_checkpoint[F'''all_modules.{index}.Conv_0.weight'''].data
  _snake_case      :					int					=   old_checkpoint[F'''all_modules.{index}.Conv_0.bias'''].data
  _snake_case      :					Union[str, Any]					=   old_checkpoint[F'''all_modules.{index}.GroupNorm_0.weight'''].data
  _snake_case      :					str					=   old_checkpoint[F'''all_modules.{index}.GroupNorm_0.bias'''].data

  _snake_case      :					Any					=   old_checkpoint[F'''all_modules.{index}.Conv_1.weight'''].data
  _snake_case      :					Union[str, Any]					=   old_checkpoint[F'''all_modules.{index}.Conv_1.bias'''].data
  _snake_case      :					str					=   old_checkpoint[F'''all_modules.{index}.GroupNorm_1.weight'''].data
  _snake_case      :					str					=   old_checkpoint[F'''all_modules.{index}.GroupNorm_1.bias'''].data

  _snake_case      :					Union[str, Any]					=   old_checkpoint[F'''all_modules.{index}.Dense_0.weight'''].data
  _snake_case      :					List[str]					=   old_checkpoint[F'''all_modules.{index}.Dense_0.bias'''].data

  if new_layer.in_channels != new_layer.out_channels or new_layer.up or new_layer.down:
   _snake_case      :					List[str]					=   old_checkpoint[F'''all_modules.{index}.Conv_2.weight'''].data
   _snake_case      :					Union[str, Any]					=   old_checkpoint[F'''all_modules.{index}.Conv_2.bias'''].data

 for i, block in enumerate(new_model_architecture.downsample_blocks			):
  _snake_case      :					List[str]					=   hasattr(SCREAMING_SNAKE_CASE__			,    """attentions"""			)
  for j in range(len(block.resnets			)			):
   set_resnet_weights(block.resnets[j]			,    SCREAMING_SNAKE_CASE__			,    SCREAMING_SNAKE_CASE__			)
   module_index += 1
   if has_attentions:
    set_attention_weights(block.attentions[j]			,    SCREAMING_SNAKE_CASE__			,    SCREAMING_SNAKE_CASE__			)
    module_index += 1

  if hasattr(SCREAMING_SNAKE_CASE__			,    """downsamplers"""			) and block.downsamplers is not None:
   set_resnet_weights(block.resnet_down			,    SCREAMING_SNAKE_CASE__			,    SCREAMING_SNAKE_CASE__			)
   module_index += 1
   _snake_case      :					Any					=   checkpoint[F'''all_modules.{module_index}.Conv_0.weight'''].data
   _snake_case      :					Optional[int]					=   checkpoint[F'''all_modules.{module_index}.Conv_0.bias'''].data
   module_index += 1

 set_resnet_weights(new_model_architecture.mid_block.resnets[0]			,    SCREAMING_SNAKE_CASE__			,    SCREAMING_SNAKE_CASE__			)
 module_index += 1
 set_attention_weights(new_model_architecture.mid_block.attentions[0]			,    SCREAMING_SNAKE_CASE__			,    SCREAMING_SNAKE_CASE__			)
 module_index += 1
 set_resnet_weights(new_model_architecture.mid_block.resnets[1]			,    SCREAMING_SNAKE_CASE__			,    SCREAMING_SNAKE_CASE__			)
 module_index += 1

 for i, block in enumerate(new_model_architecture.up_blocks			):
  _snake_case      :					List[Any]					=   hasattr(SCREAMING_SNAKE_CASE__			,    """attentions"""			)
  for j in range(len(block.resnets			)			):
   set_resnet_weights(block.resnets[j]			,    SCREAMING_SNAKE_CASE__			,    SCREAMING_SNAKE_CASE__			)
   module_index += 1
  if has_attentions:
   set_attention_weights(
       block.attentions[0]			,    SCREAMING_SNAKE_CASE__			,    SCREAMING_SNAKE_CASE__			)  # why can there only be a single attention layer for up?
   module_index += 1

  if hasattr(SCREAMING_SNAKE_CASE__			,    """resnet_up"""			) and block.resnet_up is not None:
   _snake_case      :					Optional[Any]					=   checkpoint[F'''all_modules.{module_index}.weight'''].data
   _snake_case      :					Optional[Any]					=   checkpoint[F'''all_modules.{module_index}.bias'''].data
   module_index += 1
   _snake_case      :					Tuple					=   checkpoint[F'''all_modules.{module_index}.weight'''].data
   _snake_case      :					List[Any]					=   checkpoint[F'''all_modules.{module_index}.bias'''].data
   module_index += 1
   set_resnet_weights(block.resnet_up			,    SCREAMING_SNAKE_CASE__			,    SCREAMING_SNAKE_CASE__			)
   module_index += 1

 _snake_case      :					Dict					=   checkpoint[F'''all_modules.{module_index}.weight'''].data
 _snake_case      :					Optional[int]					=   checkpoint[F'''all_modules.{module_index}.bias'''].data
 module_index += 1
 _snake_case      :					Optional[int]					=   checkpoint[F'''all_modules.{module_index}.weight'''].data
 _snake_case      :					Optional[int]					=   checkpoint[F'''all_modules.{module_index}.bias'''].data

 return new_model_architecture.state_dict()


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

  parser.add_argument(
      """--checkpoint_path""",
      default="""/Users/arthurzucker/Work/diffusers/ArthurZ/diffusion_pytorch_model.bin""",
      type=str,
      required=False,
      help="""Path to the checkpoint to convert.""",
  )

  parser.add_argument(
      """--config_file""",
      default="""/Users/arthurzucker/Work/diffusers/ArthurZ/config.json""",
      type=str,
      required=False,
      help="""The config json file corresponding to the architecture.""",
  )

  parser.add_argument(
      """--dump_path""",
      default="""/Users/arthurzucker/Work/diffusers/ArthurZ/diffusion_model_new.pt""",
      type=str,
      required=False,
      help="""Path to the output model.""",
  )

  a__      =		parser.parse_args()

  a__      =		torch.load(args.checkpoint_path, map_location="""cpu""")

  with open(args.config_file) as f:
    a__      =		json.loads(f.read())

  a__      =		convert_ncsnpp_checkpoint(
      checkpoint,
      config,
  )

  if "sde" in config:
    del config["sde"]

  a__      =		UNetaDModel(**config)
  model.load_state_dict(converted_checkpoint)

  try:
    a__      =		ScoreSdeVeScheduler.from_config("""/""".join(args.checkpoint_path.split("""/""")[:-1]))

    a__      =		ScoreSdeVePipeline(unet=model, scheduler=scheduler)
    pipe.save_pretrained(args.dump_path)
  except:  # noqa: E722
    model.save_pretrained(args.dump_path)





import math
from collections import OrderedDict

import torch
from packaging import version
from torch import Tensor, nn

from .utils import logging


a__      =		logging.get_logger(__name__)







class 						snake_case  (		nn.Module					):



   '''simple docstring'''






   def __init__(   self							:     Tuple)			->			Optional[int]:

    """simple docstring"""







    super().__init__()
    if version.parse(torch.__version__) < version.parse("""1.12.0"""):
     raise ImportError(
         F'''You are using torch=={torch.__version__}, but torch>=1.12.0 is required to use '''
         """PytorchGELUTanh. Please upgrade torch.""")


   def        UpperCamelCase_					(   self							:     Tuple			,						lowerCAmelCase							:     Tensor)			->			Tensor:

    """simple docstring"""







    return nn.functional.gelu(lowerCAmelCase			,						approximate="""tanh""")







class 						snake_case  (		nn.Module					):



   '''simple docstring'''






   def        UpperCamelCase_					(   self							:     Optional[int]			,						lowerCAmelCase							:     Tensor)			->			Tensor:

    """simple docstring"""







    return 0.5 * input * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi) * (input + 0.044_715 * torch.pow(lowerCAmelCase			,						3.0))))







class 						snake_case  (		nn.Module					):



   '''simple docstring'''






   def __init__(   self							:     str			,						lowerCAmelCase							:     bool = False)			->			Optional[Any]:

    """simple docstring"""







    super().__init__()
    if use_gelu_python:
     _snake_case      :					Optional[int]					=   self._gelu_python
    else:
     _snake_case      :					Any					=   nn.functional.gelu

   def        UpperCamelCase_					(   self							:     Union[str, Any]			,						lowerCAmelCase							:     Tensor)			->			Tensor:

    """simple docstring"""







    return input * 0.5 * (1.0 + torch.erf(input / math.sqrt(2.0)))


   def        UpperCamelCase_					(   self							:     str			,						lowerCAmelCase							:     Tensor)			->			Tensor:

    """simple docstring"""







    return self.act(lowerCAmelCase)







class 						snake_case  (		nn.Module					):



   '''simple docstring'''






   def        UpperCamelCase_					(   self							:     int			,						lowerCAmelCase							:     Tensor)			->			Tensor:

    """simple docstring"""







    return 0.5 * input * (1.0 + torch.tanh(input * 0.7_978_845_608 * (1.0 + 0.044_715 * input * input)))







class 						snake_case  (		nn.Module					):



   '''simple docstring'''






   def        UpperCamelCase_					(   self							:     int			,						lowerCAmelCase							:     Tensor)			->			Tensor:

    """simple docstring"""







    return input * torch.sigmoid(1.702 * input)







class 						snake_case  (		nn.Module					):



   '''simple docstring'''






   def __init__(   self							:     int			,						lowerCAmelCase							:     float			,						lowerCAmelCase							:     float)			->			Optional[int]:

    """simple docstring"""







    if min > max:
     raise ValueError(F'''min should be < max (got min: {min}, max: {max})''')

    super().__init__()
    _snake_case      :					Dict					=   min
    _snake_case      :					Optional[int]					=   max


   def        UpperCamelCase_					(   self							:     Union[str, Any]			,						lowerCAmelCase							:     Tensor)			->			Tensor:

    """simple docstring"""







    return torch.clip(gelu(lowerCAmelCase)			,						self.min			,						self.max)







class 						snake_case  (		nn.Module					):



   '''simple docstring'''






   def __init__(   self							:     Tuple)			->			List[str]:

    """simple docstring"""







    super().__init__()
    _snake_case      :					Union[str, Any]					=   math.sqrt(2 / math.pi)


   def        UpperCamelCase_					(   self							:     Dict			,						lowerCAmelCase							:     Tensor)			->			Tensor:

    """simple docstring"""







    return 0.5 * input * (1 + torch.tanh(self.precomputed_constant * (input + 0.044_715 * torch.pow(lowerCAmelCase			,						3))))







class 						snake_case  (		nn.Module					):



   '''simple docstring'''






   def        UpperCamelCase_					(   self							:     Dict			,						lowerCAmelCase							:     Tensor)			->			Tensor:

    """simple docstring"""







    return nn.functional.silu(lowerCAmelCase)







class 						snake_case  (		nn.Module					):



   '''simple docstring'''






   def __init__(   self							:     List[Any])			->			str:

    """simple docstring"""







    super().__init__()
    if version.parse(torch.__version__) < version.parse("""1.9.0"""):
     _snake_case      :					Optional[int]					=   self._mish_python
    else:
     _snake_case      :					List[str]					=   nn.functional.mish

   def        UpperCamelCase_					(   self							:     str			,						lowerCAmelCase							:     Tensor)			->			Tensor:

    """simple docstring"""







    return input * torch.tanh(nn.functional.softplus(lowerCAmelCase))


   def        UpperCamelCase_					(   self							:     str			,						lowerCAmelCase							:     Tensor)			->			Tensor:

    """simple docstring"""







    return self.act(lowerCAmelCase)







class 						snake_case  (		nn.Module					):



   '''simple docstring'''






   def        UpperCamelCase_					(   self							:     Optional[Any]			,						lowerCAmelCase							:     Tensor)			->			Tensor:

    """simple docstring"""







    return input







class 						snake_case  (		nn.Module					):



   '''simple docstring'''






   def        UpperCamelCase_					(   self							:     List[Any]			,						lowerCAmelCase							:     int			,						lowerCAmelCase							:     List[Any]=0.707_107			,						lowerCAmelCase							:     Optional[Any]=0.282_095)			->			List[Any]:

    """simple docstring"""







    _snake_case      :					List[str]					=   (input - mu).div(sigma * math.sqrt(2.0))
    return 0.5 * (1.0 + torch.erf(lowerCAmelCase))







class 						snake_case  (		nn.Module					):



   '''simple docstring'''






   def        UpperCamelCase_					(   self							:     Any			,						lowerCAmelCase							:     Dict)			->			Union[str, Any]:

    """simple docstring"""







    _snake_case      :					Dict					=   nn.functional.relu(lowerCAmelCase)
    _snake_case      :					Tuple					=   torch.square(lowerCAmelCase)
    return squared







class 						snake_case  (		SCREAMING_SNAKE_CASE_					):



   '''simple docstring'''






   def __getitem__(   self							:     Dict			,						lowerCAmelCase							:     Tuple)			->			str:

    """simple docstring"""







    _snake_case      :					Optional[Any]					=   super().__getitem__(lowerCAmelCase)
    _snake_case		,      _snake_case      :					List[str]					=   content if isinstance(lowerCAmelCase			,						lowerCAmelCase) else (content, {})
    return cls(**lowerCAmelCase)
a__      =		{
    """gelu""": GELUActivation,
    """gelu_10""": (ClippedGELUActivation, {"""min""": -10, """max""": 10}),
    """gelu_fast""": FastGELUActivation,
    """gelu_new""": NewGELUActivation,
    """gelu_python""": (GELUActivation, {"""use_gelu_python""": True}),
    """gelu_pytorch_tanh""": PytorchGELUTanh,
    """gelu_accurate""": AccurateGELUActivation,
    """laplace""": LaplaceActivation,
    """linear""": LinearActivation,
    """mish""": MishActivation,
    """quick_gelu""": QuickGELUActivation,
    """relu""": nn.ReLU,
    """relu2""": ReLUSquaredActivation,
    """relu6""": nn.ReLUa,
    """sigmoid""": nn.Sigmoid,
    """silu""": SiLUActivation,
    """swish""": SiLUActivation,
    """tanh""": nn.Tanh,
}
a__      =		ClassInstantier(ACTaCLS)



def  lowercase    (				SCREAMING_SNAKE_CASE__    :		Any			)					->       int:
 if activation_string in ACTaFN:
  return ACTaFN[activation_string]
 else:
  raise KeyError(F'''function {activation_string} not found in ACT2FN mapping {list(ACTaFN.keys()			)}'''			)


# For backwards compatibility with: from activations import gelu_python
a__      =		get_activation("""gelu_python""")
a__      =		get_activation("""gelu_new""")
a__      =		get_activation("""gelu""")
a__      =		get_activation("""gelu_fast""")
a__      =		get_activation("""quick_gelu""")
a__      =		get_activation("""silu""")
a__      =		get_activation("""mish""")

a__      =		get_activation("""linear""")
