





"""simple docstring"""





import argparse
import json

import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel


def       UpperCAmelCase__							(  SCREAMING_SNAKE_CASE					:						Tuple							,						SCREAMING_SNAKE_CASE					:						Any      ):







      '''simple docstring'''




      lowerCAmelCase  						=	UNetaDModel(**SCREAMING_SNAKE_CASE      )
      lowerCAmelCase  						=	checkpoint["""all_modules.0.W"""].data
      lowerCAmelCase  						=	checkpoint["""all_modules.0.W"""].data
      lowerCAmelCase  						=	checkpoint["""all_modules.1.weight"""].data
      lowerCAmelCase  						=	checkpoint["""all_modules.1.bias"""].data

      lowerCAmelCase  						=	checkpoint["""all_modules.2.weight"""].data
      lowerCAmelCase  						=	checkpoint["""all_modules.2.bias"""].data

      lowerCAmelCase  						=	checkpoint["""all_modules.3.weight"""].data
      lowerCAmelCase  						=	checkpoint["""all_modules.3.bias"""].data

      lowerCAmelCase  						=	checkpoint[list(checkpoint.keys()      )[-4]].data
      lowerCAmelCase  						=	checkpoint[list(checkpoint.keys()      )[-3]].data
      lowerCAmelCase  						=	checkpoint[list(checkpoint.keys()      )[-2]].data
      lowerCAmelCase  						=	checkpoint[list(checkpoint.keys()      )[-1]].data

      lowerCAmelCase  						=	4

      def set_attention_weights(SCREAMING_SNAKE_CASE					:						List[str]							,						SCREAMING_SNAKE_CASE					:						Any							,						SCREAMING_SNAKE_CASE					:						Dict      ):
            lowerCAmelCase  						=	old_checkpoint[F'all_modules.{index}.NIN_0.W'].data.T
            lowerCAmelCase  						=	old_checkpoint[F'all_modules.{index}.NIN_1.W'].data.T
            lowerCAmelCase  						=	old_checkpoint[F'all_modules.{index}.NIN_2.W'].data.T

            lowerCAmelCase  						=	old_checkpoint[F'all_modules.{index}.NIN_0.b'].data
            lowerCAmelCase  						=	old_checkpoint[F'all_modules.{index}.NIN_1.b'].data
            lowerCAmelCase  						=	old_checkpoint[F'all_modules.{index}.NIN_2.b'].data

            lowerCAmelCase  						=	old_checkpoint[F'all_modules.{index}.NIN_3.W'].data.T
            lowerCAmelCase  						=	old_checkpoint[F'all_modules.{index}.NIN_3.b'].data

            lowerCAmelCase  						=	old_checkpoint[F'all_modules.{index}.GroupNorm_0.weight'].data
            lowerCAmelCase  						=	old_checkpoint[F'all_modules.{index}.GroupNorm_0.bias'].data

      def set_resnet_weights(SCREAMING_SNAKE_CASE					:						Any							,						SCREAMING_SNAKE_CASE					:						Tuple							,						SCREAMING_SNAKE_CASE					:						Union[str, Any]      ):
            lowerCAmelCase  						=	old_checkpoint[F'all_modules.{index}.Conv_0.weight'].data
            lowerCAmelCase  						=	old_checkpoint[F'all_modules.{index}.Conv_0.bias'].data
            lowerCAmelCase  						=	old_checkpoint[F'all_modules.{index}.GroupNorm_0.weight'].data
            lowerCAmelCase  						=	old_checkpoint[F'all_modules.{index}.GroupNorm_0.bias'].data

            lowerCAmelCase  						=	old_checkpoint[F'all_modules.{index}.Conv_1.weight'].data
            lowerCAmelCase  						=	old_checkpoint[F'all_modules.{index}.Conv_1.bias'].data
            lowerCAmelCase  						=	old_checkpoint[F'all_modules.{index}.GroupNorm_1.weight'].data
            lowerCAmelCase  						=	old_checkpoint[F'all_modules.{index}.GroupNorm_1.bias'].data

            lowerCAmelCase  						=	old_checkpoint[F'all_modules.{index}.Dense_0.weight'].data
            lowerCAmelCase  						=	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:
                  lowerCAmelCase  						=	old_checkpoint[F'all_modules.{index}.Conv_2.weight'].data
                  lowerCAmelCase  						=	old_checkpoint[F'all_modules.{index}.Conv_2.bias'].data

      for i, block in enumerate(new_model_architecture.downsample_blocks      ):
            lowerCAmelCase  						=	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
                  lowerCAmelCase  						=	checkpoint[F'all_modules.{module_index}.Conv_0.weight'].data
                  lowerCAmelCase  						=	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      ):
            lowerCAmelCase  						=	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:
                  lowerCAmelCase  						=	checkpoint[F'all_modules.{module_index}.weight'].data
                  lowerCAmelCase  						=	checkpoint[F'all_modules.{module_index}.bias'].data
                  module_index += 1
                  lowerCAmelCase  						=	checkpoint[F'all_modules.{module_index}.weight'].data
                  lowerCAmelCase  						=	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

      lowerCAmelCase  						=	checkpoint[F'all_modules.{module_index}.weight'].data
      lowerCAmelCase  						=	checkpoint[F'all_modules.{module_index}.bias'].data
      module_index += 1
      lowerCAmelCase  						=	checkpoint[F'all_modules.{module_index}.weight'].data
      lowerCAmelCase  						=	checkpoint[F'all_modules.{module_index}.bias'].data

      return new_model_architecture.state_dict()


if __name__ == "__main__":
       SCREAMING_SNAKE_CASE__ =       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.",
       )

       SCREAMING_SNAKE_CASE__ =       parser.parse_args()

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

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

       SCREAMING_SNAKE_CASE__ =       convert_ncsnpp_checkpoint(
           checkpoint,
           config,
       )

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

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

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

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













"""simple docstring"""





SCREAMING_SNAKE_CASE__ =       [0.33, 0.44, 0.55, 0.44, 0.33]
SCREAMING_SNAKE_CASE__ =       3
SCREAMING_SNAKE_CASE__ =       5

# Buffer Space (with Parameters Space)
SCREAMING_SNAKE_CASE__ =       []
SCREAMING_SNAKE_CASE__ =       []

# Machine Time
SCREAMING_SNAKE_CASE__ =       0


def       UpperCAmelCase__							(  SCREAMING_SNAKE_CASE					:						Tuple      ):







      '''simple docstring'''




      global buffer_space, params_space, machine_time, K, m, t

      # Choosing Dynamical Systems (All)
      for key, value in enumerate(SCREAMING_SNAKE_CASE      ):
            # Evolution Parameter
            lowerCAmelCase  						=	float(seed / value      )

            # Control Theory: Orbit Change
            lowerCAmelCase  						=	(buffer_space[(key + 1) % m] + e) % 1

            # Control Theory: Trajectory Change
            lowerCAmelCase  						=	(params_space[key] + e) % 1 + 3

            # Modification (Transition Function) - Jumps
            lowerCAmelCase  						=	round(float(r * value * (1 - value)      )							,						10      )
            lowerCAmelCase  						=	r  # Saving to Parameters Space

      # Logistic Map
      assert max(SCREAMING_SNAKE_CASE      ) < 1
      assert max(SCREAMING_SNAKE_CASE      ) < 4

      # Machine Time
      machine_time += 1


def       UpperCAmelCase__							(  ):







      '''simple docstring'''




      global buffer_space, params_space, machine_time, K, m, t

      # PRNG (Xorshift by George Marsaglia)
      def xorshift(SCREAMING_SNAKE_CASE					:						List[str]							,						SCREAMING_SNAKE_CASE					:						Tuple      ):
            x ^= y >> 13
            y ^= x << 17
            x ^= y >> 5
            return x

      # Choosing Dynamical Systems (Increment)
      lowerCAmelCase  						=	machine_time % m

      # Evolution (Time Length)
      for _ in range(0							,						SCREAMING_SNAKE_CASE      ):
            # Variables (Position + Parameters)
            lowerCAmelCase  						=	params_space[key]
            lowerCAmelCase  						=	buffer_space[key]

            # Modification (Transition Function) - Flow
            lowerCAmelCase  						=	round(float(r * value * (1 - value)      )							,						10      )
            lowerCAmelCase  						=	(machine_time * 0.01 + r * 1.01) % 1 + 3

      # Choosing Chaotic Data
      lowerCAmelCase  						=	int(buffer_space[(key + 2) % m] * (10**10)      )
      lowerCAmelCase  						=	int(buffer_space[(key - 2) % m] * (10**10)      )

      # Machine Time
      machine_time += 1

      return xorshift(SCREAMING_SNAKE_CASE							,						SCREAMING_SNAKE_CASE      ) % 0xff_fff_fff


def       UpperCAmelCase__							(  ):







      '''simple docstring'''




      global buffer_space, params_space, machine_time, K, m, t

      lowerCAmelCase  						=	K
      lowerCAmelCase  						=	[0] * m
      lowerCAmelCase  						=	0


if __name__ == "__main__":
       # Initialization
       reset()

       # Pushing Data (Input)
       import random

       SCREAMING_SNAKE_CASE__ =       random.sample(range(0Xf_f_f_f_f_f_f_f), 100)
       for chunk in message:
              push(chunk)

       # for controlling
       SCREAMING_SNAKE_CASE__ =       ""

       # Pulling Data (Output)
       while inp in ("e", "E"):
              print(f'{format(pull(), "#04x")}')
              print(buffer_space)
              print(params_space)
              SCREAMING_SNAKE_CASE__ =       input("(e)exit? ").strip()






