from datetime import date

import requests

_SCREAMING_SNAKE_CASE   						=    """https://api.carbonintensity.org.uk/intensity"""





def 				__lowercase	(						)				->str:

  '''simple docstring'''




  a__     		=     requests.get(lowerCamelCase__					).json()["data"][0]
  return last_half_hour["intensity"]["actual"]





def 				__lowercase	(						lowerCamelCase__ ,				lowerCamelCase__					)				->list:

  '''simple docstring'''




  return requests.get(f"{BASE_URL}/{start}/{end}"					).json()["data"]


if __name__ == "__main__":
     for entry in fetch_from_to(start=date(2020, 10, 1), end=date(2020, 10, 3)):
          print("""from {from} to {to}: {intensity[actual]}""".format(**entry))
     print(F"""{fetch_last_half_hour() = }""")


import argparse
import json

import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel
def 				__lowercase	(						lowerCamelCase__ ,				lowerCamelCase__					)				->List[str]:

  '''simple docstring'''




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

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

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

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

  a__     		=     4

  def set_attention_weights(lowerCamelCase__ ,				lowerCamelCase__ ,				lowerCamelCase__					):
    a__     		=     old_checkpoint[f"all_modules.{index}.NIN_0.W"].data.T
    a__     		=     old_checkpoint[f"all_modules.{index}.NIN_1.W"].data.T
    a__     		=     old_checkpoint[f"all_modules.{index}.NIN_2.W"].data.T

    a__     		=     old_checkpoint[f"all_modules.{index}.NIN_0.b"].data
    a__     		=     old_checkpoint[f"all_modules.{index}.NIN_1.b"].data
    a__     		=     old_checkpoint[f"all_modules.{index}.NIN_2.b"].data

    a__     		=     old_checkpoint[f"all_modules.{index}.NIN_3.W"].data.T
    a__     		=     old_checkpoint[f"all_modules.{index}.NIN_3.b"].data

    a__     		=     old_checkpoint[f"all_modules.{index}.GroupNorm_0.weight"].data
    a__     		=     old_checkpoint[f"all_modules.{index}.GroupNorm_0.bias"].data

  def set_resnet_weights(lowerCamelCase__ ,				lowerCamelCase__ ,				lowerCamelCase__					):
    a__     		=     old_checkpoint[f"all_modules.{index}.Conv_0.weight"].data
    a__     		=     old_checkpoint[f"all_modules.{index}.Conv_0.bias"].data
    a__     		=     old_checkpoint[f"all_modules.{index}.GroupNorm_0.weight"].data
    a__     		=     old_checkpoint[f"all_modules.{index}.GroupNorm_0.bias"].data

    a__     		=     old_checkpoint[f"all_modules.{index}.Conv_1.weight"].data
    a__     		=     old_checkpoint[f"all_modules.{index}.Conv_1.bias"].data
    a__     		=     old_checkpoint[f"all_modules.{index}.GroupNorm_1.weight"].data
    a__     		=     old_checkpoint[f"all_modules.{index}.GroupNorm_1.bias"].data

    a__     		=     old_checkpoint[f"all_modules.{index}.Dense_0.weight"].data
    a__     		=     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:
      a__     		=     old_checkpoint[f"all_modules.{index}.Conv_2.weight"].data
      a__     		=     old_checkpoint[f"all_modules.{index}.Conv_2.bias"].data

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

    if hasattr(lowerCamelCase__ ,				"downsamplers"					) and block.downsamplers is not None:
      set_resnet_weights(block.resnet_down ,				lowerCamelCase__ ,				lowerCamelCase__					)
      module_index += 1
      a__     		=     checkpoint[f"all_modules.{module_index}.Conv_0.weight"].data
      a__     		=     checkpoint[f"all_modules.{module_index}.Conv_0.bias"].data
      module_index += 1

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

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

    if hasattr(lowerCamelCase__ ,				"resnet_up"					) and block.resnet_up is not None:
      a__     		=     checkpoint[f"all_modules.{module_index}.weight"].data
      a__     		=     checkpoint[f"all_modules.{module_index}.bias"].data
      module_index += 1
      a__     		=     checkpoint[f"all_modules.{module_index}.weight"].data
      a__     		=     checkpoint[f"all_modules.{module_index}.bias"].data
      module_index += 1
      set_resnet_weights(block.resnet_up ,				lowerCamelCase__ ,				lowerCamelCase__					)
      module_index += 1

  a__     		=     checkpoint[f"all_modules.{module_index}.weight"].data
  a__     		=     checkpoint[f"all_modules.{module_index}.bias"].data
  module_index += 1
  a__     		=     checkpoint[f"all_modules.{module_index}.weight"].data
  a__     		=     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)

