


import argparse
import json

import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel


def 		_lowercase   (SCREAMING_SNAKE_CASE					,				SCREAMING_SNAKE_CASE       ):




     '''simple docstring'''





     __A  :					List[str]						     =       UNetaDModel(**__a       )
     __A  :					Optional[int]						     =       checkpoint['''all_modules.0.W'''].data
     __A  :					Dict						     =       checkpoint['''all_modules.0.W'''].data
     __A  :					Optional[int]						     =       checkpoint['''all_modules.1.weight'''].data
     __A  :					Optional[Any]						     =       checkpoint['''all_modules.1.bias'''].data

     __A  :					Optional[int]						     =       checkpoint['''all_modules.2.weight'''].data
     __A  :					Union[str, Any]						     =       checkpoint['''all_modules.2.bias'''].data

     __A  :					List[Any]						     =       checkpoint['''all_modules.3.weight'''].data
     __A  :					int						     =       checkpoint['''all_modules.3.bias'''].data

     __A  :					int						     =       checkpoint[list(checkpoint.keys()       )[-4]].data
     __A  :					int						     =       checkpoint[list(checkpoint.keys()       )[-3]].data
     __A  :					Tuple						     =       checkpoint[list(checkpoint.keys()       )[-2]].data
     __A  :					int						     =       checkpoint[list(checkpoint.keys()       )[-1]].data

     __A  :					Optional[Any]						     =       4

     def set_attention_weights(SCREAMING_SNAKE_CASE					,				SCREAMING_SNAKE_CASE					,				SCREAMING_SNAKE_CASE       ):
          __A  :					int						     =       old_checkpoint[f"all_modules.{index}.NIN_0.W"].data.T
          __A  :					Any						     =       old_checkpoint[f"all_modules.{index}.NIN_1.W"].data.T
          __A  :					Union[str, Any]						     =       old_checkpoint[f"all_modules.{index}.NIN_2.W"].data.T

          __A  :					Union[str, Any]						     =       old_checkpoint[f"all_modules.{index}.NIN_0.b"].data
          __A  :					int						     =       old_checkpoint[f"all_modules.{index}.NIN_1.b"].data
          __A  :					Dict						     =       old_checkpoint[f"all_modules.{index}.NIN_2.b"].data

          __A  :					Optional[Any]						     =       old_checkpoint[f"all_modules.{index}.NIN_3.W"].data.T
          __A  :					int						     =       old_checkpoint[f"all_modules.{index}.NIN_3.b"].data

          __A  :					Optional[Any]						     =       old_checkpoint[f"all_modules.{index}.GroupNorm_0.weight"].data
          __A  :					Optional[int]						     =       old_checkpoint[f"all_modules.{index}.GroupNorm_0.bias"].data

     def set_resnet_weights(SCREAMING_SNAKE_CASE					,				SCREAMING_SNAKE_CASE					,				SCREAMING_SNAKE_CASE       ):
          __A  :					str						     =       old_checkpoint[f"all_modules.{index}.Conv_0.weight"].data
          __A  :					Any						     =       old_checkpoint[f"all_modules.{index}.Conv_0.bias"].data
          __A  :					Union[str, Any]						     =       old_checkpoint[f"all_modules.{index}.GroupNorm_0.weight"].data
          __A  :					Optional[int]						     =       old_checkpoint[f"all_modules.{index}.GroupNorm_0.bias"].data

          __A  :					Tuple						     =       old_checkpoint[f"all_modules.{index}.Conv_1.weight"].data
          __A  :					Any						     =       old_checkpoint[f"all_modules.{index}.Conv_1.bias"].data
          __A  :					Any						     =       old_checkpoint[f"all_modules.{index}.GroupNorm_1.weight"].data
          __A  :					List[Any]						     =       old_checkpoint[f"all_modules.{index}.GroupNorm_1.bias"].data

          __A  :					Any						     =       old_checkpoint[f"all_modules.{index}.Dense_0.weight"].data
          __A  :					Optional[int]						     =       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  :					List[Any]						     =       old_checkpoint[f"all_modules.{index}.Conv_2.weight"].data
               __A  :					List[Any]						     =       old_checkpoint[f"all_modules.{index}.Conv_2.bias"].data

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

          if hasattr(__a					,				"downsamplers"       ) and block.downsamplers is not None:
               set_resnet_weights(block.resnet_down					,				__a					,				__a       )
               module_index += 1
               __A  :					Union[str, Any]						     =       checkpoint[f"all_modules.{module_index}.Conv_0.weight"].data
               __A  :					Tuple						     =       checkpoint[f"all_modules.{module_index}.Conv_0.bias"].data
               module_index += 1

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

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

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

     __A  :					int						     =       checkpoint[f"all_modules.{module_index}.weight"].data
     __A  :					Optional[Any]						     =       checkpoint[f"all_modules.{module_index}.bias"].data
     module_index += 1
     __A  :					List[str]						     =       checkpoint[f"all_modules.{module_index}.weight"].data
     __A  :					Optional[int]						     =       checkpoint[f"all_modules.{module_index}.bias"].data

     return new_model_architecture.state_dict()


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

      _UpperCamelCase          =     parser.parse_args()

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

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

      _UpperCamelCase          =     convert_ncsnpp_checkpoint(
          checkpoint,
          config,
      )

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

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

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

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





'''simple docstring'''


import os
from string import ascii_letters

_UpperCamelCase          =     ascii_letters + """ \t\n"""


def 		_lowercase   ():




     '''simple docstring'''





     __A  :					Any						     =       os.path.split(os.path.realpath(SCREAMING_SNAKE_CASE       )       )
     __A  :					dict[str, None]						     =       {}
     with open(path[0] + "/dictionary.txt"       ) as dictionary_file:
          for word in dictionary_file.read().split("\n"       ):
               __A  :					int						     =       None
     return english_words


_UpperCamelCase          =     load_dictionary()


def 		_lowercase   (SCREAMING_SNAKE_CASE       ):




     '''simple docstring'''





     __A  :					int						     =       message.upper()
     __A  :					int						     =       remove_non_letters(SCREAMING_SNAKE_CASE       )
     __A  :					str						     =       message.split()
     __A  :					Tuple						     =       len([word for word in possible_words if word in ENGLISH_WORDS]       )
     return float(SCREAMING_SNAKE_CASE       ) / len(SCREAMING_SNAKE_CASE       )


def 		_lowercase   (SCREAMING_SNAKE_CASE       ):




     '''simple docstring'''





     return "".join(symbol for symbol in message if symbol in LETTERS_AND_SPACE       )


def 		_lowercase   (SCREAMING_SNAKE_CASE					,				SCREAMING_SNAKE_CASE = 20					,				SCREAMING_SNAKE_CASE = 85       ):




     '''simple docstring'''





     __A  :					Optional[Any]						     =       get_english_count(SCREAMING_SNAKE_CASE       ) * 100 >= word_percentage
     __A  :					Optional[int]						     =       len(remove_non_letters(SCREAMING_SNAKE_CASE       )       )
     __A  :					Any						     =       (float(SCREAMING_SNAKE_CASE       ) / len(SCREAMING_SNAKE_CASE       )) * 100
     __A  :					int						     =       message_letters_percentage >= letter_percentage
     return words_match and letters_match


if __name__ == "__main__":
      import doctest

      doctest.testmod()

