


"""simple docstring"""



import argparse
import json

import torch

from diffusers import DDPMScheduler, LDMPipeline, UNetaDModel, VQModel







def   __lowerCAmelCase						(						lowercase  :  int						,							lowercase  :  Any=1			)     ->						List[str]:


				"""simple docstring"""





				if n_shave_prefix_segments >= 0:
								return ".".join(path.split("."			)[n_shave_prefix_segments:]			)
				else:
								return ".".join(path.split("."			)[:n_shave_prefix_segments]			)







def   __lowerCAmelCase						(						lowercase  :  int						,							lowercase  :  Tuple=0			)     ->						List[str]:


				"""simple docstring"""





				snake_case   :				List[str]				 =   []
				for old_item in old_list:
								snake_case   :				str				 =   old_item.replace("in_layers.0"						,							"norm1"			)
								snake_case   :				int				 =   new_item.replace("in_layers.2"						,							"conv1"			)

								snake_case   :				Union[str, Any]				 =   new_item.replace("out_layers.0"						,							"norm2"			)
								snake_case   :				List[Any]				 =   new_item.replace("out_layers.3"						,							"conv2"			)

								snake_case   :				Dict				 =   new_item.replace("emb_layers.1"						,							"time_emb_proj"			)
								snake_case   :				List[Any]				 =   new_item.replace("skip_connection"						,							"conv_shortcut"			)

								snake_case   :				int				 =   shave_segments(__a						,							n_shave_prefix_segments=__a			)

								mapping.append({"old": old_item, "new": new_item}			)

				return mapping







def   __lowerCAmelCase						(						lowercase  :  int						,							lowercase  :  Union[str, Any]=0			)     ->						Union[str, Any]:


				"""simple docstring"""





				snake_case   :				Optional[int]				 =   []
				for old_item in old_list:
								snake_case   :				Optional[int]				 =   old_item

								snake_case   :				int				 =   new_item.replace("norm.weight"						,							"group_norm.weight"			)
								snake_case   :				Optional[int]				 =   new_item.replace("norm.bias"						,							"group_norm.bias"			)

								snake_case   :				Optional[Any]				 =   new_item.replace("proj_out.weight"						,							"proj_attn.weight"			)
								snake_case   :				List[str]				 =   new_item.replace("proj_out.bias"						,							"proj_attn.bias"			)

								snake_case   :				Any				 =   shave_segments(__a						,							n_shave_prefix_segments=__a			)

								mapping.append({"old": old_item, "new": new_item}			)

				return mapping







def   __lowerCAmelCase						(						lowercase  :  Optional[int]						,							lowercase  :  Any						,							lowercase  :  str						,							lowercase  :  List[Any]=None						,							lowercase  :  List[Any]=None						,							lowercase  :  Union[str, Any]=None			)     ->						Dict:


				"""simple docstring"""





				assert isinstance(__a						,							__a			), "Paths should be a list of dicts containing 'old' and 'new' keys."

				# Splits the attention layers into three variables.
				if attention_paths_to_split is not None:
								for path, path_map in attention_paths_to_split.items():
												snake_case   :				int				 =   old_checkpoint[path]
												snake_case   :				Optional[Any]				 =   old_tensor.shape[0] // 3

												snake_case   :				Dict				 =   (-1, channels) if len(old_tensor.shape			) == 3 else (-1)

												snake_case   :				List[Any]				 =   old_tensor.shape[0] // config["num_head_channels"] // 3

												snake_case   :				Optional[Any]				 =   old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:]			)
												snake_case ,snake_case ,snake_case   :				Any				 =   old_tensor.split(channels // num_heads						,							dim=1			)

												snake_case   :				List[str]				 =   query.reshape(__a			)
												snake_case   :				Optional[int]				 =   key.reshape(__a			)
												snake_case   :				Union[str, Any]				 =   value.reshape(__a			)

				for path in paths:
								snake_case   :				str				 =   path["new"]

								# These have already been assigned
								if attention_paths_to_split is not None and new_path in attention_paths_to_split:
												continue

								# Global renaming happens here
								snake_case   :				List[str]				 =   new_path.replace("middle_block.0"						,							"mid_block.resnets.0"			)
								snake_case   :				List[Any]				 =   new_path.replace("middle_block.1"						,							"mid_block.attentions.0"			)
								snake_case   :				str				 =   new_path.replace("middle_block.2"						,							"mid_block.resnets.1"			)

								if additional_replacements is not None:
												for replacement in additional_replacements:
																snake_case   :				str				 =   new_path.replace(replacement["old"]						,							replacement["new"]			)

        # proj_attn.weight has to be converted from conv 1D to linear
								if "proj_attn.weight" in new_path:
												snake_case   :				int				 =   old_checkpoint[path["old"]][:, :, 0]
								else:
												snake_case   :				Dict				 =   old_checkpoint[path["old"]]







def   __lowerCAmelCase						(						lowercase  :  Tuple						,							lowercase  :  str			)     ->						Tuple:


				"""simple docstring"""





				snake_case   :				Optional[int]				 =   {}

				snake_case   :				List[str]				 =   checkpoint["time_embed.0.weight"]
				snake_case   :				str				 =   checkpoint["time_embed.0.bias"]
				snake_case   :				Optional[int]				 =   checkpoint["time_embed.2.weight"]
				snake_case   :				List[str]				 =   checkpoint["time_embed.2.bias"]

				snake_case   :				List[str]				 =   checkpoint["input_blocks.0.0.weight"]
				snake_case   :				str				 =   checkpoint["input_blocks.0.0.bias"]

				snake_case   :				Optional[int]				 =   checkpoint["out.0.weight"]
				snake_case   :				Optional[int]				 =   checkpoint["out.0.bias"]
				snake_case   :				Any				 =   checkpoint["out.2.weight"]
				snake_case   :				Optional[Any]				 =   checkpoint["out.2.bias"]

				# Retrieves the keys for the input blocks only
				snake_case   :				str				 =   len({".".join(layer.split("."			)[:2]			) for layer in checkpoint if "input_blocks" in layer}			)
				snake_case   :				int				 =   {
				    layer_id: [key for key in checkpoint if F'input_blocks.{layer_id}' in key]
				    for layer_id in range(__a			)
				}

				# Retrieves the keys for the middle blocks only
				snake_case   :				Tuple				 =   len({".".join(layer.split("."			)[:2]			) for layer in checkpoint if "middle_block" in layer}			)
				snake_case   :				Optional[int]				 =   {
				    layer_id: [key for key in checkpoint if F'middle_block.{layer_id}' in key]
				    for layer_id in range(__a			)
				}

				# Retrieves the keys for the output blocks only
				snake_case   :				Dict				 =   len({".".join(layer.split("."			)[:2]			) for layer in checkpoint if "output_blocks" in layer}			)
				snake_case   :				Dict				 =   {
				    layer_id: [key for key in checkpoint if F'output_blocks.{layer_id}' in key]
				    for layer_id in range(__a			)
				}

				for i in range(1						,							__a			):
								snake_case   :				Tuple				 =   (i - 1) // (config["num_res_blocks"] + 1)
								snake_case   :				int				 =   (i - 1) % (config["num_res_blocks"] + 1)

								snake_case   :				Optional[Any]				 =   [key for key in input_blocks[i] if F'input_blocks.{i}.0' in key]
								snake_case   :				Dict				 =   [key for key in input_blocks[i] if F'input_blocks.{i}.1' in key]

								if F'input_blocks.{i}.0.op.weight' in checkpoint:
												snake_case   :				List[str]				 =   checkpoint[
												    F'input_blocks.{i}.0.op.weight'
												]
												snake_case   :				Dict				 =   checkpoint[
												    F'input_blocks.{i}.0.op.bias'
												]
												continue

								snake_case   :				Optional[Any]				 =   renew_resnet_paths(__a			)
								snake_case   :				Union[str, Any]				 =   {"old": F'input_blocks.{i}.0', "new": F'down_blocks.{block_id}.resnets.{layer_in_block_id}'}
								snake_case   :				List[str]				 =   {"old": "resnets.2.op", "new": "downsamplers.0.op"}
								assign_to_checkpoint(
								    __a						,							__a						,							__a						,							additional_replacements=[meta_path, resnet_op]						,							config=__a			)

								if len(__a			):
												snake_case   :				Optional[Any]				 =   renew_attention_paths(__a			)
												snake_case   :				List[str]				 =   {
												    "old": F'input_blocks.{i}.1',
												    "new": F'down_blocks.{block_id}.attentions.{layer_in_block_id}',
												}
												snake_case   :				Union[str, Any]				 =   {
												    F'input_blocks.{i}.1.qkv.bias': {
												        "key": F'down_blocks.{block_id}.attentions.{layer_in_block_id}.key.bias',
												        "query": F'down_blocks.{block_id}.attentions.{layer_in_block_id}.query.bias',
												        "value": F'down_blocks.{block_id}.attentions.{layer_in_block_id}.value.bias',
												    },
												    F'input_blocks.{i}.1.qkv.weight': {
												        "key": F'down_blocks.{block_id}.attentions.{layer_in_block_id}.key.weight',
												        "query": F'down_blocks.{block_id}.attentions.{layer_in_block_id}.query.weight',
												        "value": F'down_blocks.{block_id}.attentions.{layer_in_block_id}.value.weight',
												    },
												}
												assign_to_checkpoint(
												    __a						,							__a						,							__a						,							additional_replacements=[meta_path]						,							attention_paths_to_split=__a						,							config=__a						,							)

				snake_case   :				Any				 =   middle_blocks[0]
				snake_case   :				Optional[Any]				 =   middle_blocks[1]
				snake_case   :				Any				 =   middle_blocks[2]

				snake_case   :				Tuple				 =   renew_resnet_paths(__a			)
				assign_to_checkpoint(__a						,							__a						,							__a						,							config=__a			)

				snake_case   :				List[str]				 =   renew_resnet_paths(__a			)
				assign_to_checkpoint(__a						,							__a						,							__a						,							config=__a			)

				snake_case   :				int				 =   renew_attention_paths(__a			)
				snake_case   :				Tuple				 =   {
				    "middle_block.1.qkv.bias": {
				        "key": "mid_block.attentions.0.key.bias",
				        "query": "mid_block.attentions.0.query.bias",
				        "value": "mid_block.attentions.0.value.bias",
				    },
				    "middle_block.1.qkv.weight": {
				        "key": "mid_block.attentions.0.key.weight",
				        "query": "mid_block.attentions.0.query.weight",
				        "value": "mid_block.attentions.0.value.weight",
				    },
				}
				assign_to_checkpoint(
				    __a						,							__a						,							__a						,							attention_paths_to_split=__a						,							config=__a			)

				for i in range(__a			):
								snake_case   :				Optional[int]				 =   i // (config["num_res_blocks"] + 1)
								snake_case   :				Dict				 =   i % (config["num_res_blocks"] + 1)
								snake_case   :				Tuple				 =   [shave_segments(__a						,							2			) for name in output_blocks[i]]
								snake_case   :				Any				 =   {}

								for layer in output_block_layers:
												snake_case ,snake_case   :				Tuple				 =   layer.split("."			)[0], shave_segments(__a						,							1			)
												if layer_id in output_block_list:
																output_block_list[layer_id].append(__a			)
												else:
																snake_case   :				Tuple				 =   [layer_name]

								if len(__a			) > 1:
												snake_case   :				Optional[int]				 =   [key for key in output_blocks[i] if F'output_blocks.{i}.0' in key]
												snake_case   :				str				 =   [key for key in output_blocks[i] if F'output_blocks.{i}.1' in key]

												snake_case   :				Optional[int]				 =   renew_resnet_paths(__a			)
												snake_case   :				str				 =   renew_resnet_paths(__a			)

												snake_case   :				int				 =   {"old": F'output_blocks.{i}.0', "new": F'up_blocks.{block_id}.resnets.{layer_in_block_id}'}
												assign_to_checkpoint(__a						,							__a						,							__a						,							additional_replacements=[meta_path]						,							config=__a			)

												if ["conv.weight", "conv.bias"] in output_block_list.values():
																snake_case   :				List[Any]				 =   list(output_block_list.values()			).index(["conv.weight", "conv.bias"]			)
																snake_case   :				List[str]				 =   checkpoint[
																    F'output_blocks.{i}.{index}.conv.weight'
																]
																snake_case   :				List[str]				 =   checkpoint[
																    F'output_blocks.{i}.{index}.conv.bias'
																]

																# Clear attentions as they have been attributed above.
																if len(__a			) == 2:
																				snake_case   :				List[Any]				 =   []

												if len(__a			):
																snake_case   :				Any				 =   renew_attention_paths(__a			)
																snake_case   :				Union[str, Any]				 =   {
																    "old": F'output_blocks.{i}.1',
																    "new": F'up_blocks.{block_id}.attentions.{layer_in_block_id}',
																}
																snake_case   :				List[str]				 =   {
																    F'output_blocks.{i}.1.qkv.bias': {
																        "key": F'up_blocks.{block_id}.attentions.{layer_in_block_id}.key.bias',
																        "query": F'up_blocks.{block_id}.attentions.{layer_in_block_id}.query.bias',
																        "value": F'up_blocks.{block_id}.attentions.{layer_in_block_id}.value.bias',
																    },
																    F'output_blocks.{i}.1.qkv.weight': {
																        "key": F'up_blocks.{block_id}.attentions.{layer_in_block_id}.key.weight',
																        "query": F'up_blocks.{block_id}.attentions.{layer_in_block_id}.query.weight',
																        "value": F'up_blocks.{block_id}.attentions.{layer_in_block_id}.value.weight',
																    },
																}
																assign_to_checkpoint(
																    __a						,							__a						,							__a						,							additional_replacements=[meta_path]						,							attention_paths_to_split=to_split if any("qkv" in key for key in attentions			) else None						,							config=__a						,							)
								else:
												snake_case   :				Optional[int]				 =   renew_resnet_paths(__a						,							n_shave_prefix_segments=1			)
												for path in resnet_0_paths:
																snake_case   :				List[Any]				 =   ".".join(["output_blocks", str(__a			), path["old"]]			)
																snake_case   :				List[str]				 =   ".".join(["up_blocks", str(__a			), "resnets", str(__a			), path["new"]]			)

																snake_case   :				str				 =   checkpoint[old_path]

				return new_checkpoint


if __name__ == "__main__":
		__snake_case             =   argparse.ArgumentParser()

		parser.add_argument(
		    """--checkpoint_path""", default=None, type=str, required=True, help="""Path to the checkpoint to convert."""
		)

		parser.add_argument(
		    """--config_file""",
		    default=None,
		    type=str,
		    required=True,
		    help="""The config json file corresponding to the architecture.""",
		)

		parser.add_argument("""--dump_path""", default=None, type=str, required=True, help="""Path to the output model.""")

		__snake_case             =   parser.parse_args()

		__snake_case             =   torch.load(args.checkpoint_path)

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

		__snake_case             =   convert_ldm_checkpoint(checkpoint, config)

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

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

		try:
				__snake_case             =   DDPMScheduler.from_config("""/""".join(args.checkpoint_path.split("""/""")[:-1]))
				__snake_case             =   VQModel.from_pretrained("""/""".join(args.checkpoint_path.split("""/""")[:-1]))

				__snake_case             =   LDMPipeline(unet=model, scheduler=scheduler, vae=vqvae)
				pipe.save_pretrained(args.dump_path)
		except:  # noqa: E722
				model.save_pretrained(args.dump_path)









"""simple docstring"""



from pathlib import Path

import fire
from tqdm import tqdm







def   __lowerCAmelCase						(						lowercase  :  Optional[Any]="ro"						,							lowercase  :  Union[str, Any]="en"						,							lowercase  :  str="wmt16"						,							lowercase  :  Any=None			)     ->						None:


    """simple docstring"""





    try:
        import datasets
    except (ModuleNotFoundError, ImportError):
        raise ImportError("run pip install datasets"			)
    snake_case   :				Any				 =   F'{src_lang}-{tgt_lang}'
    print(F'Converting {dataset}-{pair}'			)
    snake_case   :				Union[str, Any]				 =   datasets.load_dataset(lowercase						,							lowercase			)
    if save_dir is None:
        snake_case   :				int				 =   F'{dataset}-{pair}'
    snake_case   :				Optional[Any]				 =   Path(lowercase			)
    save_dir.mkdir(exist_ok=lowercase			)

    for split in ds.keys():
        print(F'Splitting {split} with {ds[split].num_rows} records'			)

        # to save to val.source, val.target like summary datasets
        snake_case   :				Any				 =   "val" if split == "validation" else split
        snake_case   :				List[str]				 =   save_dir.joinpath(F'{fn}.source'			)
        snake_case   :				int				 =   save_dir.joinpath(F'{fn}.target'			)
        snake_case   :				str				 =   src_path.open("w+"			)
        snake_case   :				Optional[int]				 =   tgt_path.open("w+"			)

        # reader is the bottleneck so writing one record at a time doesn't slow things down
        for x in tqdm(ds[split]			):
            snake_case   :				int				 =   x["translation"]
            src_fp.write(ex[src_lang] + "\n"			)
            tgt_fp.write(ex[tgt_lang] + "\n"			)

    print(F'Saved {dataset} dataset to {save_dir}'			)


if __name__ == "__main__":
  fire.Fire(download_wmt_dataset)





