



import argparse
import json

import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel




def 			UpperCAmelCase__       (							__snake_case  , __snake_case							)      ->       Optional[Any]:
  _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(__snake_case  , __snake_case  , __snake_case							):
    _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(__snake_case  , __snake_case  , __snake_case							):
    _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__":
    __lowerCamelCase      =    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.""",
    )

    __lowerCamelCase      =    parser.parse_args()

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

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

    __lowerCamelCase      =    convert_ncsnpp_checkpoint(
        checkpoint,
        config,
    )

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

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

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

        __lowerCamelCase      =    ScoreSdeVePipeline(unet=model, scheduler=scheduler)
        pipe.save_pretrained(args.dump_path)
    except:  # noqa: E722
        model.save_pretrained(args.dump_path)
# as due to their complexity multi-gpu tests could impact other tests, and to aid debug we have those in a separate module.

import os
import sys

from transformers.testing_utils import TestCasePlus, execute_subprocess_async, get_gpu_count, require_torch_gpu, slow

from .utils import load_json


class 						a__							(   __snake_case		):



			@classmethod
			def __SCREAMING_SNAKE_CASE							(						cls    ) ->    Optional[int]:
								return cls







			@slow
			@require_torch_gpu
			def __SCREAMING_SNAKE_CASE							(						self    ) ->    Dict:
								__a							=   self.get_auto_remove_tmp_dir()
								__a							=   f'''
            --model_name Helsinki-NLP/opus-mt-en-ro
            --save_dir {output_dir}
            --data_dir {self.test_file_dir_str}/test_data/wmt_en_ro
            --num_beams 2
            --task translation
        '''.split()

								# we want this test to run even if there is only one GPU, but if there are more we use them all
								__a							=   get_gpu_count()
								__a							=   f'''
            -m torch.distributed.launch
            --nproc_per_node={n_gpu}
            {self.test_file_dir}/run_distributed_eval.py
        '''.split()
								__a							=   [sys.executable] + distributed_args + args
								execute_subprocess_async(UpperCAmelCase      ,						env=self.get_env()    )

								__a							=   os.path.join(UpperCAmelCase      ,						'test_bleu.json'    )
								__a							=   load_json(UpperCAmelCase    )
								# print(metrics)
								self.assertGreaterEqual(metrics['bleu']      ,						2_5    )




