

"""simple docstring"""





import argparse
import json

import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel



def        _lowerCAmelCase      (						UpperCAmelCase     :							List[Any]      ,							UpperCAmelCase     :							str				):


					'''simple docstring'''






					UpperCamelCase__		:    Union[str, Any]         =UNetaDModel(**UpperCAmelCase				)
					UpperCamelCase__		:    Any         =checkpoint['''all_modules.0.W'''].data
					UpperCamelCase__		:    Tuple         =checkpoint['''all_modules.0.W'''].data
					UpperCamelCase__		:    Dict         =checkpoint['''all_modules.1.weight'''].data
					UpperCamelCase__		:    List[str]         =checkpoint['''all_modules.1.bias'''].data

					UpperCamelCase__		:    List[str]         =checkpoint['''all_modules.2.weight'''].data
					UpperCamelCase__		:    Dict         =checkpoint['''all_modules.2.bias'''].data

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

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

					UpperCamelCase__		:    List[Any]         =4

					def set_attention_weights(UpperCAmelCase     :							Optional[Any]      ,							UpperCAmelCase     :							Optional[Any]      ,							UpperCAmelCase     :							Optional[int]				):
										UpperCamelCase__		:    str         =old_checkpoint[F'''all_modules.{index}.NIN_0.W'''].data.T
										UpperCamelCase__		:    Optional[int]         =old_checkpoint[F'''all_modules.{index}.NIN_1.W'''].data.T
										UpperCamelCase__		:    Union[str, Any]         =old_checkpoint[F'''all_modules.{index}.NIN_2.W'''].data.T

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

										UpperCamelCase__		:    Any         =old_checkpoint[F'''all_modules.{index}.NIN_3.W'''].data.T
										UpperCamelCase__		:    str         =old_checkpoint[F'''all_modules.{index}.NIN_3.b'''].data

										UpperCamelCase__		:    Tuple         =old_checkpoint[F'''all_modules.{index}.GroupNorm_0.weight'''].data
										UpperCamelCase__		:    int         =old_checkpoint[F'''all_modules.{index}.GroupNorm_0.bias'''].data

					def set_resnet_weights(UpperCAmelCase     :							List[str]      ,							UpperCAmelCase     :							List[Any]      ,							UpperCAmelCase     :							Any				):
										UpperCamelCase__		:    Optional[int]         =old_checkpoint[F'''all_modules.{index}.Conv_0.weight'''].data
										UpperCamelCase__		:    Optional[Any]         =old_checkpoint[F'''all_modules.{index}.Conv_0.bias'''].data
										UpperCamelCase__		:    List[str]         =old_checkpoint[F'''all_modules.{index}.GroupNorm_0.weight'''].data
										UpperCamelCase__		:    Dict         =old_checkpoint[F'''all_modules.{index}.GroupNorm_0.bias'''].data

										UpperCamelCase__		:    Any         =old_checkpoint[F'''all_modules.{index}.Conv_1.weight'''].data
										UpperCamelCase__		:    List[str]         =old_checkpoint[F'''all_modules.{index}.Conv_1.bias'''].data
										UpperCamelCase__		:    str         =old_checkpoint[F'''all_modules.{index}.GroupNorm_1.weight'''].data
										UpperCamelCase__		:    Tuple         =old_checkpoint[F'''all_modules.{index}.GroupNorm_1.bias'''].data

										UpperCamelCase__		:    Dict         =old_checkpoint[F'''all_modules.{index}.Dense_0.weight'''].data
										UpperCamelCase__		:    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:
															UpperCamelCase__		:    List[str]         =old_checkpoint[F'''all_modules.{index}.Conv_2.weight'''].data
															UpperCamelCase__		:    str         =old_checkpoint[F'''all_modules.{index}.Conv_2.bias'''].data

					for i, block in enumerate(new_model_architecture.downsample_blocks				):
										UpperCamelCase__		:    List[Any]         =hasattr(UpperCAmelCase      ,							'''attentions'''				)
										for j in range(len(block.resnets				)				):
															set_resnet_weights(block.resnets[j]      ,							UpperCAmelCase      ,							UpperCAmelCase				)
															module_index += 1
															if has_attentions:
																				set_attention_weights(block.attentions[j]      ,							UpperCAmelCase      ,							UpperCAmelCase				)
																				module_index += 1

										if hasattr(UpperCAmelCase      ,							'''downsamplers'''				) and block.downsamplers is not None:
															set_resnet_weights(block.resnet_down      ,							UpperCAmelCase      ,							UpperCAmelCase				)
															module_index += 1
															UpperCamelCase__		:    List[Any]         =checkpoint[F'''all_modules.{module_index}.Conv_0.weight'''].data
															UpperCamelCase__		:    str         =checkpoint[F'''all_modules.{module_index}.Conv_0.bias'''].data
															module_index += 1

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

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

										if hasattr(UpperCAmelCase      ,							'''resnet_up'''				) and block.resnet_up is not None:
															UpperCamelCase__		:    Tuple         =checkpoint[F'''all_modules.{module_index}.weight'''].data
															UpperCamelCase__		:    List[str]         =checkpoint[F'''all_modules.{module_index}.bias'''].data
															module_index += 1
															UpperCamelCase__		:    Optional[Any]         =checkpoint[F'''all_modules.{module_index}.weight'''].data
															UpperCamelCase__		:    Optional[Any]         =checkpoint[F'''all_modules.{module_index}.bias'''].data
															module_index += 1
															set_resnet_weights(block.resnet_up      ,							UpperCAmelCase      ,							UpperCAmelCase				)
															module_index += 1

					UpperCamelCase__		:    Union[str, Any]         =checkpoint[F'''all_modules.{module_index}.weight'''].data
					UpperCamelCase__		:    Optional[int]         =checkpoint[F'''all_modules.{module_index}.bias'''].data
					module_index += 1
					UpperCamelCase__		:    Optional[Any]         =checkpoint[F'''all_modules.{module_index}.weight'''].data
					UpperCamelCase__		:    Tuple         =checkpoint[F'''all_modules.{module_index}.bias'''].data

					return new_model_architecture.state_dict()


if __name__ == "__main__":
				_SCREAMING_SNAKE_CASE       :  Union[str, Any]        = 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       :  List[Any]        = parser.parse_args()

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

				with open(args.config_file) as f:
								_SCREAMING_SNAKE_CASE       :  Optional[int]        = json.loads(f.read())

				_SCREAMING_SNAKE_CASE       :  Any        = convert_ncsnpp_checkpoint(
				    checkpoint,
				    config,
				)

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

				_SCREAMING_SNAKE_CASE       :  Any        = UNetaDModel(**config)
				model.load_state_dict(converted_checkpoint)

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

								_SCREAMING_SNAKE_CASE       :  List[Any]        = ScoreSdeVePipeline(unet=model, scheduler=scheduler)
								pipe.save_pretrained(args.dump_path)
				except:  # noqa: E722
								model.save_pretrained(args.dump_path)





"""simple docstring"""





import io
import json
import subprocess


_SCREAMING_SNAKE_CASE       :  List[Any]        = [
    ["""en""", """ru"""],
    ["""ru""", """en"""],
    ["""en""", """de"""],
    ["""de""", """en"""],
]

_SCREAMING_SNAKE_CASE       :  str        = 8



def        _lowerCAmelCase      (						UpperCAmelCase     :							Tuple      ,							UpperCAmelCase     :							List[Any]				):


					'''simple docstring'''






					UpperCamelCase__		:    Any         ={}
					for src, tgt in pairs:
										UpperCamelCase__		:    Optional[int]         =F'''{src}-{tgt}'''
										UpperCamelCase__		:    int         =F'''sacrebleu -t wmt19 -l {pair} --echo src'''.split()
										UpperCamelCase__		:    str         =subprocess.run(UpperCAmelCase      ,							stdout=subprocess.PIPE				).stdout.decode('''utf-8'''				).splitlines()
										UpperCamelCase__		:    List[Any]         =F'''sacrebleu -t wmt19 -l {pair} --echo ref'''.split()
										UpperCamelCase__		:    str         =subprocess.run(UpperCAmelCase      ,							stdout=subprocess.PIPE				).stdout.decode('''utf-8'''				).splitlines()
										UpperCamelCase__		:    Any         ={'''src''': src_lines[:n_objs], '''tgt''': tgt_lines[:n_objs]}
					return text


_SCREAMING_SNAKE_CASE       :  List[str]        = get_all_data(pairs, n_objs)
_SCREAMING_SNAKE_CASE       :  Optional[Any]        = """./fsmt_val_data.json"""
with io.open(filename, """w""", encoding="""utf-8""") as f:
				_SCREAMING_SNAKE_CASE       :  Any        = json.dump(text, f, indent=2, ensure_ascii=False)


