




'''simple docstring'''
import argparse
import json

import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel
def     snake_case_  (       a__		:     List[str]   ,a__		:     Union[str, Any]      ):



    """simple docstring"""




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

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

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

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

    __lowercase          =						4

    def set_attention_weights(a__		:     Optional[Any]   ,a__		:     List[str]   ,a__		:     Optional[int]      ):
        __lowercase          =						old_checkpoint[f'all_modules.{index}.NIN_0.W'].data.T
        __lowercase          =						old_checkpoint[f'all_modules.{index}.NIN_1.W'].data.T
        __lowercase          =						old_checkpoint[f'all_modules.{index}.NIN_2.W'].data.T

        __lowercase          =						old_checkpoint[f'all_modules.{index}.NIN_0.b'].data
        __lowercase          =						old_checkpoint[f'all_modules.{index}.NIN_1.b'].data
        __lowercase          =						old_checkpoint[f'all_modules.{index}.NIN_2.b'].data

        __lowercase          =						old_checkpoint[f'all_modules.{index}.NIN_3.W'].data.T
        __lowercase          =						old_checkpoint[f'all_modules.{index}.NIN_3.b'].data

        __lowercase          =						old_checkpoint[f'all_modules.{index}.GroupNorm_0.weight'].data
        __lowercase          =						old_checkpoint[f'all_modules.{index}.GroupNorm_0.bias'].data

    def set_resnet_weights(a__		:     List[Any]   ,a__		:     Union[str, Any]   ,a__		:     List[Any]      ):
        __lowercase          =						old_checkpoint[f'all_modules.{index}.Conv_0.weight'].data
        __lowercase          =						old_checkpoint[f'all_modules.{index}.Conv_0.bias'].data
        __lowercase          =						old_checkpoint[f'all_modules.{index}.GroupNorm_0.weight'].data
        __lowercase          =						old_checkpoint[f'all_modules.{index}.GroupNorm_0.bias'].data

        __lowercase          =						old_checkpoint[f'all_modules.{index}.Conv_1.weight'].data
        __lowercase          =						old_checkpoint[f'all_modules.{index}.Conv_1.bias'].data
        __lowercase          =						old_checkpoint[f'all_modules.{index}.GroupNorm_1.weight'].data
        __lowercase          =						old_checkpoint[f'all_modules.{index}.GroupNorm_1.bias'].data

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

    for i, block in enumerate(new_model_architecture.downsample_blocks      ):
        __lowercase          =						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
            __lowercase          =						checkpoint[f'all_modules.{module_index}.Conv_0.weight'].data
            __lowercase          =						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      ):
        __lowercase          =						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:
            __lowercase          =						checkpoint[f'all_modules.{module_index}.weight'].data
            __lowercase          =						checkpoint[f'all_modules.{module_index}.bias'].data
            module_index += 1
            __lowercase          =						checkpoint[f'all_modules.{module_index}.weight'].data
            __lowercase          =						checkpoint[f'all_modules.{module_index}.bias'].data
            module_index += 1
            set_resnet_weights(block.resnet_up   ,a__   ,a__      )
            module_index += 1

    __lowercase          =						checkpoint[f'all_modules.{module_index}.weight'].data
    __lowercase          =						checkpoint[f'all_modules.{module_index}.bias'].data
    module_index += 1
    __lowercase          =						checkpoint[f'all_modules.{module_index}.weight'].data
    __lowercase          =						checkpoint[f'all_modules.{module_index}.bias'].data

    return new_model_architecture.state_dict()


if __name__ == "__main__":
  A							:				Tuple			     =	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							:				Dict			     =	parser.parse_args()

  A							:				Optional[int]			     =	torch.load(args.checkpoint_path, map_location="""cpu""")

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

  A							:				Tuple			     =	convert_ncsnpp_checkpoint(
      checkpoint,
      config,
  )

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

  A							:				Optional[int]			     =	UNetaDModel(**config)
  model.load_state_dict(converted_checkpoint)

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

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










'''simple docstring'''
import unittest
import warnings
from dataclasses import dataclass

from transformers.convert_slow_tokenizer import SpmConverter
from transformers.testing_utils import get_tests_dir






@dataclass
class 	SCREAMING_SNAKE_CASE:
       snake_case_			:					str






class 	SCREAMING_SNAKE_CASE(    unittest.TestCase					):

       def    snake_case__      (   self					)   -> Optional[int]:

           """simple docstring"""



           __lowercase          =						get_tests_dir("""fixtures/test_sentencepiece.model"""					)
           __lowercase          =						get_tests_dir("""fixtures/test_sentencepiece_with_bytefallback.model"""					)

           __lowercase          =						FakeOriginalTokenizer(vocab_file=lowerCamelCase__					)

           with warnings.catch_warnings(record=lowerCamelCase__					) as w:
               __lowercase          =						SpmConverter(lowerCamelCase__					)
           self.assertEqual(len(lowerCamelCase__					) ,		0					)

           __lowercase          =						FakeOriginalTokenizer(vocab_file=lowerCamelCase__					)

           with warnings.catch_warnings(record=lowerCamelCase__					) as w:
               __lowercase          =						SpmConverter(lowerCamelCase__					)
           self.assertEqual(len(lowerCamelCase__					) ,		1					)

           self.assertIn(
               """The sentencepiece tokenizer that you are converting to a fast tokenizer uses the byte fallback option"""
               """ which is not implemented in the fast tokenizers.""" ,		str(w[0].message					) ,		)




