

from copy import deepcopy
from pathlib import Path
from typing import TYPE_CHECKING, Any, Callable, Dict, List, Optional, Tuple, Union

import torch
from torch import nn
from torch.utils.data import Dataset

from .deepspeed import is_deepspeed_zeroa_enabled
from .generation.configuration_utils import GenerationConfig
from .trainer import Trainer
from .utils import logging


if TYPE_CHECKING:
 from .data.data_collator import DataCollator
 from .modeling_utils import PreTrainedModel
 from .tokenization_utils_base import PreTrainedTokenizerBase
 from .trainer_callback import TrainerCallback
 from .trainer_utils import EvalPrediction, PredictionOutput
 from .training_args import TrainingArguments


SCREAMING_SNAKE_CASE						:Union[str, Any]      =			logging.get_logger(__name__)






class 		__magic_name__ (				__lowerCAmelCase							):






     def __init__(							self						,		_lowercase = None						,		_lowercase = None						,		_lowercase = None						,		_lowercase = None						,		_lowercase = None						,		_lowercase = None						,		_lowercase = None						,		_lowercase = None						,		_lowercase = None						,		_lowercase = (None, None)						,		_lowercase = None						,		)->			Union[str, Any]:
         super().__init__(
             model=_lowerCamelCase						,		args=_lowerCamelCase						,		data_collator=_lowerCamelCase						,		train_dataset=_lowerCamelCase						,		eval_dataset=_lowerCamelCase						,		tokenizer=_lowerCamelCase						,		model_init=_lowerCamelCase						,		compute_metrics=_lowerCamelCase						,		callbacks=_lowerCamelCase						,		optimizers=_lowerCamelCase						,		preprocess_logits_for_metrics=_lowerCamelCase						,		)

         # Override self.model.generation_config if a GenerationConfig is specified in args.
         # Priority: args.generation_config > model.generation_config > default GenerationConfig.
         if self.args.generation_config is not None:
             UpperCamelCase_          =		self.load_generation_config(self.args.generation_config     )
             UpperCamelCase_          =		gen_config






     @staticmethod
     def      UpperCAmelCase_							(							_lowercase     )->			Optional[Any]:

         # GenerationConfig provided, nothing to do
         if isinstance(_lowerCamelCase						,		_lowerCamelCase     ):
             return deepcopy(_lowerCamelCase     )

         # str or Path
         UpperCamelCase_          =		Path(_lowerCamelCase     ) if isinstance(_lowerCamelCase						,		_lowerCamelCase     ) else gen_config_arg
         UpperCamelCase_          =		None

         # Figuring if it is path pointing to a file, pointing to a directory or else a model id or URL
         # This step is required in order to determine config_file_name
         if pretrained_model_name.is_file():
             UpperCamelCase_          =		pretrained_model_name.name
             UpperCamelCase_          =		pretrained_model_name.parent
         # dir path
         elif pretrained_model_name.is_dir():
             pass
         # model id or URL
         else:
             UpperCamelCase_          =		gen_config_arg

         UpperCamelCase_          =		GenerationConfig.from_pretrained(_lowerCamelCase						,		_lowerCamelCase     )
         return gen_config






     def      UpperCAmelCase_							(							self						,		_lowercase = None						,		_lowercase = None						,		_lowercase = "eval"						,		**_lowercase						,		)->			List[Any]:
         UpperCamelCase_          =		gen_kwargs.copy()
         if gen_kwargs.get("max_length"     ) is None and gen_kwargs.get("max_new_tokens"     ) is None:
             UpperCamelCase_          =		self.args.generation_max_length
         UpperCamelCase_          =		(
             gen_kwargs["num_beams"] if gen_kwargs.get("num_beams"     ) is not None else self.args.generation_num_beams
         )
         UpperCamelCase_          =		gen_kwargs

         return super().evaluate(_lowerCamelCase						,		ignore_keys=_lowerCamelCase						,		metric_key_prefix=_lowerCamelCase     )






     def      UpperCAmelCase_							(							self						,		_lowercase						,		_lowercase = None						,		_lowercase = "test"						,		**_lowercase						,		)->			Optional[Any]:
         UpperCamelCase_          =		gen_kwargs.copy()
         if gen_kwargs.get("max_length"     ) is None and gen_kwargs.get("max_new_tokens"     ) is None:
             UpperCamelCase_          =		self.args.generation_max_length
         UpperCamelCase_          =		(
             gen_kwargs["num_beams"] if gen_kwargs.get("num_beams"     ) is not None else self.args.generation_num_beams
         )
         UpperCamelCase_          =		gen_kwargs

         return super().predict(_lowerCamelCase						,		ignore_keys=_lowerCamelCase						,		metric_key_prefix=_lowerCamelCase     )






     def      UpperCAmelCase_							(							self						,		_lowercase						,		_lowercase						,		_lowercase						,		_lowercase = None						,		**_lowercase						,		)->			Union[str, Any]:

         if not self.args.predict_with_generate or prediction_loss_only:
             return super().prediction_step(
                 _lowerCamelCase						,		_lowerCamelCase						,		prediction_loss_only=_lowerCamelCase						,		ignore_keys=_lowerCamelCase     )

         UpperCamelCase_          =		"labels" in inputs
         UpperCamelCase_          =		self._prepare_inputs(_lowerCamelCase     )

         # XXX: adapt synced_gpus for fairscale as well
         # Priority (handled in generate):
         # gen_kwargs > model.generation_config > default GenerationConfig()

         if len(_lowerCamelCase     ) == 0 and hasattr(self						,		"_gen_kwargs"     ):
             UpperCamelCase_          =		self._gen_kwargs.copy()

         if gen_kwargs.get("max_length"     ) is None and gen_kwargs.get("max_new_tokens"     ) is None:
             UpperCamelCase_          =		self.model.config.max_length
         UpperCamelCase_          =		(
             gen_kwargs["num_beams"] if gen_kwargs.get("num_beams"     ) is not None else self.model.config.num_beams
         )
         UpperCamelCase_          =		True if is_deepspeed_zeroa_enabled() else False
         UpperCamelCase_          =		(
             gen_kwargs["synced_gpus"] if gen_kwargs.get("synced_gpus"     ) is not None else default_synced_gpus
         )

         # If the `decoder_input_ids` was created from `labels`, evict the former, so that the model can freely generate
         # (otherwise, it would continue generating from the padded `decoder_input_ids`)
         if (
             "labels" in inputs
             and "decoder_input_ids" in inputs
             and inputs["labels"].shape == inputs["decoder_input_ids"].shape
         ):
             UpperCamelCase_          =		{k: v for k, v in inputs.items() if k != "decoder_input_ids"}
         UpperCamelCase_          =		self.model.generate(**_lowerCamelCase						,		**_lowerCamelCase     )

         # Temporary hack to ensure the generation config is not initialized for each iteration of the evaluation loop
         # TODO: remove this hack when the legacy code that initializes generation_config from a model config is
         # removed in https://github.com/huggingface/transformers/blob/98d88b23f54e5a23e741833f1e973fdf600cc2c5/src/transformers/generation/utils.py#L1183
         if self.model.generation_config._from_model_config:
             UpperCamelCase_          =		False

         # Retrieves GenerationConfig from model.generation_config
         UpperCamelCase_          =		self.model.generation_config
         # in case the batch is shorter than max length, the output should be padded
         if generated_tokens.shape[-1] < gen_config.max_length:
             UpperCamelCase_          =		self._pad_tensors_to_max_len(_lowerCamelCase						,		gen_config.max_length     )
         elif gen_config.max_new_tokens is not None and generated_tokens.shape[-1] < gen_config.max_new_tokens + 1:
             UpperCamelCase_          =		self._pad_tensors_to_max_len(_lowerCamelCase						,		gen_config.max_new_tokens + 1     )

         with torch.no_grad():
             if has_labels:
                 with self.compute_loss_context_manager():
                     UpperCamelCase_          =		model(**_lowerCamelCase     )
                 if self.label_smoother is not None:
                     UpperCamelCase_          =		self.label_smoother(_lowerCamelCase						,		inputs["labels"]     ).mean().detach()
                 else:
                     UpperCamelCase_          =		(outputs["loss"] if isinstance(_lowerCamelCase						,		_lowerCamelCase     ) else outputs[0]).mean().detach()
             else:
                 UpperCamelCase_          =		None

         if self.args.prediction_loss_only:
             return loss, None, None

         if has_labels:
             UpperCamelCase_          =		inputs["labels"]
             if labels.shape[-1] < gen_config.max_length:
                 UpperCamelCase_          =		self._pad_tensors_to_max_len(_lowerCamelCase						,		gen_config.max_length     )
             elif gen_config.max_new_tokens is not None and labels.shape[-1] < gen_config.max_new_tokens + 1:
                 UpperCamelCase_          =		self._pad_tensors_to_max_len(_lowerCamelCase						,		gen_config.max_new_tokens + 1     )
         else:
             UpperCamelCase_          =		None

         return loss, generated_tokens, labels






     def      UpperCAmelCase_							(							self						,		_lowercase						,		_lowercase     )->			List[str]:
         if self.tokenizer is not None and hasattr(self.tokenizer						,		"pad_token_id"     ):
             # If PAD token is not defined at least EOS token has to be defined
             UpperCamelCase_          =		(
                 self.tokenizer.pad_token_id if self.tokenizer.pad_token_id is not None else self.tokenizer.eos_token_id
             )
         else:
             if self.model.config.pad_token_id is not None:
                 UpperCamelCase_          =		self.model.config.pad_token_id
             else:
                 raise ValueError("Pad_token_id must be set in the configuration of the model, in order to pad tensors"     )

         UpperCamelCase_          =		pad_token_id * torch.ones(
             (tensor.shape[0], max_length)						,		dtype=tensor.dtype						,		device=tensor.device     )
         UpperCamelCase_          =		tensor
         return padded_tensor

"""simple docstring"""


import argparse

import huggingface_hub
import k_diffusion as K
import torch

from diffusers import UNetaDConditionModel


a							='pcuenq/k-upscaler'





def 			lowerCamelCase_ (					__lowerCAmelCase   ,    __lowerCAmelCase   ,    *, __lowerCAmelCase   ,    __lowerCAmelCase				)							->					Dict:




						'''simple docstring'''



						lowerCamelCase__					={
						    # norm1
						    F'''{diffusers_resnet_prefix}.norm1.linear.weight''': checkpoint[F'''{resnet_prefix}.main.0.mapper.weight'''],
						    F'''{diffusers_resnet_prefix}.norm1.linear.bias''': checkpoint[F'''{resnet_prefix}.main.0.mapper.bias'''],
						    # conv1
						    F'''{diffusers_resnet_prefix}.conv1.weight''': checkpoint[F'''{resnet_prefix}.main.2.weight'''],
						    F'''{diffusers_resnet_prefix}.conv1.bias''': checkpoint[F'''{resnet_prefix}.main.2.bias'''],
						    # norm2
						    F'''{diffusers_resnet_prefix}.norm2.linear.weight''': checkpoint[F'''{resnet_prefix}.main.4.mapper.weight'''],
						    F'''{diffusers_resnet_prefix}.norm2.linear.bias''': checkpoint[F'''{resnet_prefix}.main.4.mapper.bias'''],
						    # conv2
						    F'''{diffusers_resnet_prefix}.conv2.weight''': checkpoint[F'''{resnet_prefix}.main.6.weight'''],
						    F'''{diffusers_resnet_prefix}.conv2.bias''': checkpoint[F'''{resnet_prefix}.main.6.bias'''],
						}

						if resnet.conv_shortcut is not None:
												rv.update(
												    {
												        F'''{diffusers_resnet_prefix}.conv_shortcut.weight''': checkpoint[F'''{resnet_prefix}.skip.weight'''],
												    }				)

						return rv





def 			lowerCamelCase_ (					__lowerCAmelCase   ,    *, __lowerCAmelCase   ,    __lowerCAmelCase				)							->					int:




						'''simple docstring'''



						lowerCamelCase__					,      lowerCamelCase__					,      lowerCamelCase__					=checkpoint[F'''{attention_prefix}.qkv_proj.weight'''].chunk(3   ,    dim=0				)
						lowerCamelCase__					,      lowerCamelCase__					,      lowerCamelCase__					=checkpoint[F'''{attention_prefix}.qkv_proj.bias'''].chunk(3   ,    dim=0				)
						lowerCamelCase__					={
						    # norm
						    F'''{diffusers_attention_prefix}.norm1.linear.weight''': checkpoint[F'''{attention_prefix}.norm_in.mapper.weight'''],
						    F'''{diffusers_attention_prefix}.norm1.linear.bias''': checkpoint[F'''{attention_prefix}.norm_in.mapper.bias'''],
						    # to_q
						    F'''{diffusers_attention_prefix}.attn1.to_q.weight''': weight_q.squeeze(-1				).squeeze(-1				),
						    F'''{diffusers_attention_prefix}.attn1.to_q.bias''': bias_q,
						    # to_k
						    F'''{diffusers_attention_prefix}.attn1.to_k.weight''': weight_k.squeeze(-1				).squeeze(-1				),
						    F'''{diffusers_attention_prefix}.attn1.to_k.bias''': bias_k,
						    # to_v
						    F'''{diffusers_attention_prefix}.attn1.to_v.weight''': weight_v.squeeze(-1				).squeeze(-1				),
						    F'''{diffusers_attention_prefix}.attn1.to_v.bias''': bias_v,
						    # to_out
						    F'''{diffusers_attention_prefix}.attn1.to_out.0.weight''': checkpoint[F'''{attention_prefix}.out_proj.weight''']
						    .squeeze(-1				)
						    .squeeze(-1				),
						    F'''{diffusers_attention_prefix}.attn1.to_out.0.bias''': checkpoint[F'''{attention_prefix}.out_proj.bias'''],
						}

						return rv





def 			lowerCamelCase_ (					__lowerCAmelCase   ,    *, __lowerCAmelCase   ,    __lowerCAmelCase   ,    __lowerCAmelCase				)							->					str:




						'''simple docstring'''



						lowerCamelCase__					,      lowerCamelCase__					=checkpoint[F'''{attention_prefix}.kv_proj.weight'''].chunk(2   ,    dim=0				)
						lowerCamelCase__					,      lowerCamelCase__					=checkpoint[F'''{attention_prefix}.kv_proj.bias'''].chunk(2   ,    dim=0				)

						lowerCamelCase__					={
						    # norm2 (ada groupnorm)
						    F'''{diffusers_attention_prefix}.norm{diffusers_attention_index}.linear.weight''': checkpoint[
						        F'''{attention_prefix}.norm_dec.mapper.weight'''
						    ],
						    F'''{diffusers_attention_prefix}.norm{diffusers_attention_index}.linear.bias''': checkpoint[
						        F'''{attention_prefix}.norm_dec.mapper.bias'''
						    ],
						    # layernorm on encoder_hidden_state
						    F'''{diffusers_attention_prefix}.attn{diffusers_attention_index}.norm_cross.weight''': checkpoint[
						        F'''{attention_prefix}.norm_enc.weight'''
						    ],
						    F'''{diffusers_attention_prefix}.attn{diffusers_attention_index}.norm_cross.bias''': checkpoint[
						        F'''{attention_prefix}.norm_enc.bias'''
						    ],
						    # to_q
						    F'''{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_q.weight''': checkpoint[
						        F'''{attention_prefix}.q_proj.weight'''
						    ]
						    .squeeze(-1				)
						    .squeeze(-1				),
						    F'''{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_q.bias''': checkpoint[
						        F'''{attention_prefix}.q_proj.bias'''
						    ],
						    # to_k
						    F'''{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_k.weight''': weight_k.squeeze(-1				).squeeze(-1				),
						    F'''{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_k.bias''': bias_k,
						    # to_v
						    F'''{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_v.weight''': weight_v.squeeze(-1				).squeeze(-1				),
						    F'''{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_v.bias''': bias_v,
						    # to_out
						    F'''{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_out.0.weight''': checkpoint[
						        F'''{attention_prefix}.out_proj.weight'''
						    ]
						    .squeeze(-1				)
						    .squeeze(-1				),
						    F'''{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_out.0.bias''': checkpoint[
						        F'''{attention_prefix}.out_proj.bias'''
						    ],
						}

						return rv





def 			lowerCamelCase_ (					__lowerCAmelCase   ,    __lowerCAmelCase   ,    __lowerCAmelCase   ,    __lowerCAmelCase				)							->					Optional[int]:




						'''simple docstring'''



						lowerCamelCase__					="inner_model.u_net.u_blocks" if block_type == "up" else "inner_model.u_net.d_blocks"
						lowerCamelCase__					=F'''{block_prefix}.{block_idx}'''

						lowerCamelCase__					={}

						if not hasattr(__lowerCAmelCase   ,    "attentions"				):
												lowerCamelCase__					=1  # resnet only
						elif not block.attentions[0].add_self_attention:
												lowerCamelCase__					=2  # resnet -> cross-attention
						else:
												lowerCamelCase__					=3  # resnet -> self-attention -> cross-attention)

						for resnet_idx, resnet in enumerate(block.resnets				):
												# diffusers_resnet_prefix = f"{diffusers_up_block_prefix}.resnets.{resnet_idx}"
												lowerCamelCase__					=F'''{block_type}_blocks.{block_idx}.resnets.{resnet_idx}'''
												lowerCamelCase__					=n * resnet_idx if block_type == "up" else n * resnet_idx + 1
												lowerCamelCase__					=F'''{block_prefix}.{idx}''' if block_type == "up" else F'''{block_prefix}.{idx}'''

												diffusers_checkpoint.update(
												    resnet_to_diffusers_checkpoint(
												        __lowerCAmelCase   ,    __lowerCAmelCase   ,    diffusers_resnet_prefix=__lowerCAmelCase   ,    resnet_prefix=__lowerCAmelCase				)				)

						if hasattr(__lowerCAmelCase   ,    "attentions"				):
												for attention_idx, attention in enumerate(block.attentions				):
																		lowerCamelCase__					=F'''{block_type}_blocks.{block_idx}.attentions.{attention_idx}'''
																		lowerCamelCase__					=n * attention_idx + 1 if block_type == "up" else n * attention_idx + 2
																		lowerCamelCase__					=F'''{block_prefix}.{idx}'''
																		lowerCamelCase__					=F'''{block_prefix}.{idx }'''
																		lowerCamelCase__					=1 if not attention.add_self_attention else 2
																		lowerCamelCase__					=(
																		    n * attention_idx + cross_attention_index
																		    if block_type == "up"
																		    else n * attention_idx + cross_attention_index + 1
																		)
																		lowerCamelCase__					=F'''{block_prefix}.{idx }'''

																		diffusers_checkpoint.update(
																		    cross_attn_to_diffusers_checkpoint(
																		        __lowerCAmelCase   ,    diffusers_attention_prefix=__lowerCAmelCase   ,    diffusers_attention_index=2   ,    attention_prefix=__lowerCAmelCase   ,    )				)

																		if attention.add_self_attention is True:
																								diffusers_checkpoint.update(
																								    self_attn_to_diffusers_checkpoint(
																								        __lowerCAmelCase   ,    diffusers_attention_prefix=__lowerCAmelCase   ,    attention_prefix=__lowerCAmelCase   ,    )				)

						return diffusers_checkpoint





def 			lowerCamelCase_ (					__lowerCAmelCase   ,    __lowerCAmelCase				)							->					Tuple:




						'''simple docstring'''



						lowerCamelCase__					={}

						# pre-processing
						diffusers_checkpoint.update(
						    {
						        "conv_in.weight": checkpoint["inner_model.proj_in.weight"],
						        "conv_in.bias": checkpoint["inner_model.proj_in.bias"],
						    }				)

						# timestep and class embedding
						diffusers_checkpoint.update(
						    {
						        "time_proj.weight": checkpoint["inner_model.timestep_embed.weight"].squeeze(-1				),
						        "time_embedding.linear_1.weight": checkpoint["inner_model.mapping.0.weight"],
						        "time_embedding.linear_1.bias": checkpoint["inner_model.mapping.0.bias"],
						        "time_embedding.linear_2.weight": checkpoint["inner_model.mapping.2.weight"],
						        "time_embedding.linear_2.bias": checkpoint["inner_model.mapping.2.bias"],
						        "time_embedding.cond_proj.weight": checkpoint["inner_model.mapping_cond.weight"],
						    }				)

						# down_blocks
						for down_block_idx, down_block in enumerate(model.down_blocks				):
												diffusers_checkpoint.update(block_to_diffusers_checkpoint(__lowerCAmelCase   ,    __lowerCAmelCase   ,    __lowerCAmelCase   ,    "down"				)				)

						# up_blocks
						for up_block_idx, up_block in enumerate(model.up_blocks				):
												diffusers_checkpoint.update(block_to_diffusers_checkpoint(__lowerCAmelCase   ,    __lowerCAmelCase   ,    __lowerCAmelCase   ,    "up"				)				)

						# post-processing
						diffusers_checkpoint.update(
						    {
						        "conv_out.weight": checkpoint["inner_model.proj_out.weight"],
						        "conv_out.bias": checkpoint["inner_model.proj_out.bias"],
						    }				)

						return diffusers_checkpoint





def 			lowerCamelCase_ (					__lowerCAmelCase				)							->					Optional[int]:




						'''simple docstring'''



						lowerCamelCase__					=original_config["input_channels"] + original_config["unet_cond_dim"]
						lowerCamelCase__					=original_config["input_channels"] + (1 if original_config["has_variance"] else 0)

						lowerCamelCase__					=original_config["channels"]

						assert (
						    len(set(original_config["depths"]				)				) == 1
						), "UNet2DConditionModel currently do not support blocks with different number of layers"
						lowerCamelCase__					=original_config["depths"][0]

						lowerCamelCase__					=original_config["mapping_cond_dim"]
						lowerCamelCase__					=original_config["cross_cond_dim"]

						lowerCamelCase__					=[]
						lowerCamelCase__					=[]
						for s, c in zip(original_config["self_attn_depths"]   ,    original_config["cross_attn_depths"]				):
												if s:
																		lowerCamelCase__					="self"
																		lowerCamelCase__					="cross" if c else None
												elif c:
																		lowerCamelCase__					="cross"
																		lowerCamelCase__					=None
												else:
																		lowerCamelCase__					=None
																		lowerCamelCase__					=None
												attna_types.append(__lowerCAmelCase				)
												attna_types.append(__lowerCAmelCase				)

						lowerCamelCase__					=UNetaDConditionModel(
						    in_channels=__lowerCAmelCase   ,    out_channels=__lowerCAmelCase   ,    down_block_types=("KDownBlock2D", "KCrossAttnDownBlock2D", "KCrossAttnDownBlock2D", "KCrossAttnDownBlock2D")   ,    mid_block_type=__lowerCAmelCase   ,    up_block_types=("KCrossAttnUpBlock2D", "KCrossAttnUpBlock2D", "KCrossAttnUpBlock2D", "KUpBlock2D")   ,    block_out_channels=__lowerCAmelCase   ,    layers_per_block=__lowerCAmelCase   ,    act_fn="gelu"   ,    norm_num_groups=__lowerCAmelCase   ,    cross_attention_dim=__lowerCAmelCase   ,    attention_head_dim=64   ,    time_cond_proj_dim=__lowerCAmelCase   ,    resnet_time_scale_shift="scale_shift"   ,    time_embedding_type="fourier"   ,    timestep_post_act="gelu"   ,    conv_in_kernel=1   ,    conv_out_kernel=1   ,    )

						return unet





def 			lowerCamelCase_ (					__lowerCAmelCase				)							->					int:




						'''simple docstring'''



						lowerCamelCase__					=torch.device("cuda" if torch.cuda.is_available() else "cpu"				)

						lowerCamelCase__					=huggingface_hub.hf_hub_download(__lowerCAmelCase   ,    "config_laion_text_cond_latent_upscaler_2.json"				)
						lowerCamelCase__					=huggingface_hub.hf_hub_download(
						    __lowerCAmelCase   ,    "laion_text_cond_latent_upscaler_2_1_00470000_slim.pth"				)
						print(F'''loading original model configuration from {orig_config_path}'''				)
						print(F'''loading original model checkpoint from {orig_weights_path}'''				)

						print("converting to diffusers unet"				)
						lowerCamelCase__					=K.config.load_config(open(__lowerCAmelCase				)				)["model"]
						lowerCamelCase__					=unet_model_from_original_config(__lowerCAmelCase				)

						lowerCamelCase__					=torch.load(__lowerCAmelCase   ,    map_location=__lowerCAmelCase				)["model_ema"]
						lowerCamelCase__					=unet_to_diffusers_checkpoint(__lowerCAmelCase   ,    __lowerCAmelCase				)

						model.load_state_dict(__lowerCAmelCase   ,    strict=__lowerCAmelCase				)
						model.save_pretrained(args.dump_path				)
						print(F'''saving converted unet model in {args.dump_path}'''				)


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

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

	main(args)


