





import sys
from typing import Tuple

import numpy as np
import torch
from PIL import Image
from torch import nn

from transformers.image_utils import PILImageResampling
from utils import img_tensorize
class 				SCREAMING_SNAKE_CASE   :


   def __init__(    self			:				Union[str, Any]			,     a			:				Dict			,     a			:				Any=sys.maxsize       )->   int:





     """simple docstring"""







     lowercase__				=  'bilinear'
     lowercase__				=  max_size
     lowercase__				=  short_edge_length


   def __call__(    self			:				Tuple			,     a			:				int       )->   Tuple:





     """simple docstring"""







     lowercase__				=  []
     for img in imgs:
       lowercase__			,    lowercase__				=  img.shape[:2]
       # later: provide list and randomly choose index for resize
       lowercase__				=  np.random.randint(self.short_edge_length[0]			,     self.short_edge_length[1] + 1       )
       if size == 0:
         return img
       lowercase__				=  size * 1.0 / min(_lowerCamelCase			,     _lowerCamelCase       )
       if h < w:
         lowercase__			,    lowercase__				=  size, scale * w
       else:
         lowercase__			,    lowercase__				=  scale * h, size
       if max(_lowerCamelCase			,     _lowerCamelCase       ) > self.max_size:
         lowercase__				=  self.max_size * 1.0 / max(_lowerCamelCase			,     _lowerCamelCase       )
         lowercase__				=  newh * scale
         lowercase__				=  neww * scale
       lowercase__				=  int(neww + 0.5       )
       lowercase__				=  int(newh + 0.5       )

       if img.dtype == np.uinta:
         lowercase__				=  Image.fromarray(_lowerCamelCase       )
         lowercase__				=  pil_image.resize((neww, newh)			,     PILImageResampling.BILINEAR       )
         lowercase__				=  np.asarray(_lowerCamelCase       )
       else:
         lowercase__				=  img.permute(2			,     0			,     1       ).unsqueeze(0       )  # 3, 0, 1)  # hw(c) -> nchw
         lowercase__				=  nn.functional.interpolate(
             _lowerCamelCase			,     (newh, neww)			,     mode=self.interp_method			,     align_corners=_lowerCamelCase       ).squeeze(0       )
       img_augs.append(_lowerCamelCase       )

     return img_augs






class 				SCREAMING_SNAKE_CASE   :


   def __init__(    self			:				Union[str, Any]			,     a			:				List[str]       )->   int:





     """simple docstring"""







     lowercase__				=  ResizeShortestEdge([cfg.INPUT.MIN_SIZE_TEST, cfg.INPUT.MIN_SIZE_TEST]			,     cfg.INPUT.MAX_SIZE_TEST       )
     lowercase__				=  cfg.INPUT.FORMAT
     lowercase__				=  cfg.SIZE_DIVISIBILITY
     lowercase__				=  cfg.PAD_VALUE
     lowercase__				=  cfg.INPUT.MAX_SIZE_TEST
     lowercase__				=  cfg.MODEL.DEVICE
     lowercase__				=  torch.tensor(cfg.MODEL.PIXEL_STD       ).to(self.device       ).view(len(cfg.MODEL.PIXEL_STD       )			,     1			,     1       )
     lowercase__				=  torch.tensor(cfg.MODEL.PIXEL_MEAN       ).to(self.device       ).view(len(cfg.MODEL.PIXEL_STD       )			,     1			,     1       )
     lowercase__				=  lambda a       : (x - self.pixel_mean) / self.pixel_std


   def        SCREAMING_SNAKE_CASE_							(    self			:				int			,     a			:				Dict       )->   Dict:





     """simple docstring"""







     lowercase__				=  tuple(max(_lowerCamelCase       ) for s in zip(*[img.shape for img in images]       )       )
     lowercase__				=  [im.shape[-2:] for im in images]
     lowercase__				=  [
         nn.functional.pad(
             _lowerCamelCase			,     [0, max_size[-1] - size[1], 0, max_size[-2] - size[0]]			,     value=self.pad_value			,     )
         for size, im in zip(_lowerCamelCase			,     _lowerCamelCase       )
     ]

     return torch.stack(_lowerCamelCase       ), torch.tensor(_lowerCamelCase       )


   def __call__(    self			:				List[str]			,     a			:				List[Any]			,     a			:				Optional[Any]=False       )->   Optional[Any]:





     """simple docstring"""







     with torch.no_grad():
       if not isinstance(_lowerCamelCase			,     _lowerCamelCase       ):
         lowercase__				=  [images]
       if single_image:
         assert len(_lowerCamelCase       ) == 1
       for i in range(len(_lowerCamelCase       )       ):
         if isinstance(images[i]			,     torch.Tensor       ):
           images.insert(_lowerCamelCase			,     images.pop(_lowerCamelCase       ).to(self.device       ).float()       )
         elif not isinstance(images[i]			,     torch.Tensor       ):
           images.insert(
               _lowerCamelCase			,     torch.as_tensor(img_tensorize(images.pop(_lowerCamelCase       )			,     input_format=self.input_format       )       )
               .to(self.device       )
               .float()			,     )
            # resize smallest edge
       lowercase__				=  torch.tensor([im.shape[:2] for im in images]       )
       lowercase__				=  self.aug(_lowerCamelCase       )
       # transpose images and convert to torch tensors
       # images = [torch.as_tensor(i.astype("float32")).permute(2, 0, 1).to(self.device) for i in images]
       # now normalize before pad to avoid useless arithmetic
       lowercase__				=  [self.normalizer(_lowerCamelCase       ) for x in images]
       # now pad them to do the following operations
       lowercase__			,    lowercase__				=  self.pad(_lowerCamelCase       )
       # Normalize

       if self.size_divisibility > 0:
         raise NotImplementedError()
       # pad
       lowercase__				=  torch.true_divide(_lowerCamelCase			,     _lowerCamelCase       )
       if single_image:
         return images[0], sizes[0], scales_yx[0]
       else:
         return images, sizes, scales_yx






def 			__UpperCamelCase  (_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		)				->   int:
  boxes[:, 0::2] *= scale_yx[:, 1]
  boxes[:, 1::2] *= scale_yx[:, 0]
  return boxes




def 			__UpperCamelCase  (_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		)				->   Optional[int]:
  assert torch.isfinite(lowerCamelCase_		).all(), "Box tensor contains infinite or NaN!"
  lowercase__			,    lowercase__				=  box_size
  tensor[:, 0].clamp_(min=0		,						max=lowerCamelCase_		)
  tensor[:, 1].clamp_(min=0		,						max=lowerCamelCase_		)
  tensor[:, 2].clamp_(min=0		,						max=lowerCamelCase_		)
  tensor[:, 3].clamp_(min=0		,						max=lowerCamelCase_		)









'''simple docstring'''



import argparse
import json

import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel




def       _lowerCAmelCase  ( lowerCamelCase_	:     Tuple						,				lowerCamelCase_	:     List[Any] ):
			__lowercase   =							UNetaDModel(**lowerCamelCase_ )
			__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(lowerCamelCase_	:     Tuple						,				lowerCamelCase_	:     int						,				lowerCamelCase_	:     str ):
						__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(lowerCamelCase_	:     str						,				lowerCamelCase_	:     Tuple						,				lowerCamelCase_	:     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(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
									__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]						,				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 ):
						__lowercase   =							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:
									__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						,				lowerCamelCase_						,				lowerCamelCase_ )
									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__":
	_SCREAMING_SNAKE_CASE    =			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    =			parser.parse_args()

	_SCREAMING_SNAKE_CASE    =			torch.load(args.checkpoint_path, map_location='''cpu''')

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

	_SCREAMING_SNAKE_CASE    =			convert_ncsnpp_checkpoint(
	    checkpoint,
	    config,
	)

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

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

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

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





