

"""simple docstring"""





import argparse
import json

import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel

def 						__SCREAMING_SNAKE_CASE						(   A_		,  A_				):
	lowerCAmelCase__			:		Tuple						       =			UNetaDModel(**A_				)
	lowerCAmelCase__			:		str						       =			checkpoint['''all_modules.0.W'''].data
	lowerCAmelCase__			:		Dict						       =			checkpoint['''all_modules.0.W'''].data
	lowerCAmelCase__			:		Tuple						       =			checkpoint['''all_modules.1.weight'''].data
	lowerCAmelCase__			:		int						       =			checkpoint['''all_modules.1.bias'''].data

	lowerCAmelCase__			:		Dict						       =			checkpoint['''all_modules.2.weight'''].data
	lowerCAmelCase__			:		Tuple						       =			checkpoint['''all_modules.2.bias'''].data

	lowerCAmelCase__			:		Optional[int]						       =			checkpoint['''all_modules.3.weight'''].data
	lowerCAmelCase__			:		Optional[Any]						       =			checkpoint['''all_modules.3.bias'''].data

	lowerCAmelCase__			:		str						       =			checkpoint[list(checkpoint.keys()				)[-4]].data
	lowerCAmelCase__			:		Optional[Any]						       =			checkpoint[list(checkpoint.keys()				)[-3]].data
	lowerCAmelCase__			:		List[str]						       =			checkpoint[list(checkpoint.keys()				)[-2]].data
	lowerCAmelCase__			:		str						       =			checkpoint[list(checkpoint.keys()				)[-1]].data

	lowerCAmelCase__			:		List[str]						       =			4

	def set_attention_weights(A_		,  A_		,  A_				):
		lowerCAmelCase__			:		Optional[Any]						       =			old_checkpoint[f'all_modules.{index}.NIN_0.W'].data.T
		lowerCAmelCase__			:		Union[str, Any]						       =			old_checkpoint[f'all_modules.{index}.NIN_1.W'].data.T
		lowerCAmelCase__			:		List[Any]						       =			old_checkpoint[f'all_modules.{index}.NIN_2.W'].data.T

		lowerCAmelCase__			:		Optional[Any]						       =			old_checkpoint[f'all_modules.{index}.NIN_0.b'].data
		lowerCAmelCase__			:		int						       =			old_checkpoint[f'all_modules.{index}.NIN_1.b'].data
		lowerCAmelCase__			:		Any						       =			old_checkpoint[f'all_modules.{index}.NIN_2.b'].data

		lowerCAmelCase__			:		Tuple						       =			old_checkpoint[f'all_modules.{index}.NIN_3.W'].data.T
		lowerCAmelCase__			:		Dict						       =			old_checkpoint[f'all_modules.{index}.NIN_3.b'].data

		lowerCAmelCase__			:		Dict						       =			old_checkpoint[f'all_modules.{index}.GroupNorm_0.weight'].data
		lowerCAmelCase__			:		Optional[Any]						       =			old_checkpoint[f'all_modules.{index}.GroupNorm_0.bias'].data

	def set_resnet_weights(A_		,  A_		,  A_				):
		lowerCAmelCase__			:		List[str]						       =			old_checkpoint[f'all_modules.{index}.Conv_0.weight'].data
		lowerCAmelCase__			:		int						       =			old_checkpoint[f'all_modules.{index}.Conv_0.bias'].data
		lowerCAmelCase__			:		List[Any]						       =			old_checkpoint[f'all_modules.{index}.GroupNorm_0.weight'].data
		lowerCAmelCase__			:		Optional[int]						       =			old_checkpoint[f'all_modules.{index}.GroupNorm_0.bias'].data

		lowerCAmelCase__			:		List[Any]						       =			old_checkpoint[f'all_modules.{index}.Conv_1.weight'].data
		lowerCAmelCase__			:		int						       =			old_checkpoint[f'all_modules.{index}.Conv_1.bias'].data
		lowerCAmelCase__			:		str						       =			old_checkpoint[f'all_modules.{index}.GroupNorm_1.weight'].data
		lowerCAmelCase__			:		List[str]						       =			old_checkpoint[f'all_modules.{index}.GroupNorm_1.bias'].data

		lowerCAmelCase__			:		List[str]						       =			old_checkpoint[f'all_modules.{index}.Dense_0.weight'].data
		lowerCAmelCase__			:		Tuple						       =			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:
			lowerCAmelCase__			:		List[Any]						       =			old_checkpoint[f'all_modules.{index}.Conv_2.weight'].data
			lowerCAmelCase__			:		List[Any]						       =			old_checkpoint[f'all_modules.{index}.Conv_2.bias'].data

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

		if hasattr(A_		,  '''downsamplers'''				) and block.downsamplers is not None:
			set_resnet_weights(block.resnet_down		,  A_		,  A_				)
			module_index += 1
			lowerCAmelCase__			:		Dict						       =			checkpoint[f'all_modules.{module_index}.Conv_0.weight'].data
			lowerCAmelCase__			:		str						       =			checkpoint[f'all_modules.{module_index}.Conv_0.bias'].data
			module_index += 1

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

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

		if hasattr(A_		,  '''resnet_up'''				) and block.resnet_up is not None:
			lowerCAmelCase__			:		Tuple						       =			checkpoint[f'all_modules.{module_index}.weight'].data
			lowerCAmelCase__			:		int						       =			checkpoint[f'all_modules.{module_index}.bias'].data
			module_index += 1
			lowerCAmelCase__			:		Tuple						       =			checkpoint[f'all_modules.{module_index}.weight'].data
			lowerCAmelCase__			:		Tuple						       =			checkpoint[f'all_modules.{module_index}.bias'].data
			module_index += 1
			set_resnet_weights(block.resnet_up		,  A_		,  A_				)
			module_index += 1

	lowerCAmelCase__			:		int						       =			checkpoint[f'all_modules.{module_index}.weight'].data
	lowerCAmelCase__			:		Dict						       =			checkpoint[f'all_modules.{module_index}.bias'].data
	module_index += 1
	lowerCAmelCase__			:		Any						       =			checkpoint[f'all_modules.{module_index}.weight'].data
	lowerCAmelCase__			:		Optional[int]						       =			checkpoint[f'all_modules.{module_index}.bias'].data

	return new_model_architecture.state_dict()


if __name__ == "__main__":
							__UpperCamelCase						:   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.''',
							)

							__UpperCamelCase						:   str							   =	parser.parse_args()

							__UpperCamelCase						:   int							   =	torch.load(args.checkpoint_path, map_location='''cpu''')

							with open(args.config_file) as f:
														__UpperCamelCase						:   List[Any]							   =	json.loads(f.read())

							__UpperCamelCase						:   Tuple							   =	convert_ncsnpp_checkpoint(
							    checkpoint,
							    config,
							)

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

							__UpperCamelCase						:   Optional[Any]							   =	UNetaDModel(**config)
							model.load_state_dict(converted_checkpoint)

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

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







"""simple docstring"""





import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin


if is_torch_available():
							import torch

if is_vision_available():
							from PIL import Image

							from transformers import SwinaSRImageProcessor
							from transformers.image_transforms import get_image_size







class        SCREAMING_SNAKE_CASE      (	unittest.TestCase ):






					"""simple docstring"""



					def __init__( self    :       Dict     ,lowercase_    :       List[str]     ,lowercase_    :       List[str]=7     ,lowercase_    :       List[Any]=3     ,lowercase_    :       str=1_8     ,lowercase_    :       Any=3_0     ,lowercase_    :       int=4_0_0     ,lowercase_    :       Tuple=True     ,lowercase_    :       Optional[int]=1 / 2_5_5     ,lowercase_    :       int=True     ,lowercase_    :       Union[str, Any]=8     ,):
						lowerCAmelCase__			:		Tuple						       =			parent
						lowerCAmelCase__			:		Optional[Any]						       =			batch_size
						lowerCAmelCase__			:		int						       =			num_channels
						lowerCAmelCase__			:		Union[str, Any]						       =			image_size
						lowerCAmelCase__			:		Optional[Any]						       =			min_resolution
						lowerCAmelCase__			:		Union[str, Any]						       =			max_resolution
						lowerCAmelCase__			:		int						       =			do_rescale
						lowerCAmelCase__			:		Tuple						       =			rescale_factor
						lowerCAmelCase__			:		Dict						       =			do_pad
						lowerCAmelCase__			:		str						       =			pad_size







					def  __lowerCAmelCase				( self    :       int	):
						return {
						    "do_rescale": self.do_rescale,
						    "rescale_factor": self.rescale_factor,
						    "do_pad": self.do_pad,
						    "pad_size": self.pad_size,
						}







					def  __lowerCAmelCase				( self    :       Union[str, Any]     ,lowercase_    :       List[str]=False     ,lowercase_    :       Any=False     ,lowercase_    :       Dict=False	):
						assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

						if equal_resolution:
							lowerCAmelCase__			:		int						       =			[]
							for i in range(self.batch_size	):
								image_inputs.append(
								    np.random.randint(
								        2_5_5     ,size=(self.num_channels, self.max_resolution, self.max_resolution)     ,dtype=np.uinta	)	)
						else:
							lowerCAmelCase__			:		str						       =			[]
							for i in range(self.batch_size	):
								lowerCAmelCase__       ,lowerCAmelCase__			:		Any						       =			np.random.choice(np.arange(self.min_resolution     ,self.max_resolution	)     ,2	)
								image_inputs.append(np.random.randint(2_5_5     ,size=(self.num_channels, width, height)     ,dtype=np.uinta	)	)

						if not numpify and not torchify:
							# PIL expects the channel dimension as last dimension
							lowerCAmelCase__			:		Optional[int]						       =			[Image.fromarray(np.moveaxis(lowercase_     ,0     ,-1	)	) for x in image_inputs]

						if torchify:
							lowerCAmelCase__			:		int						       =			[torch.from_numpy(lowercase_	) for x in image_inputs]

						return image_inputs







@require_torch
@require_vision
class        SCREAMING_SNAKE_CASE      (	a_					,							unittest.TestCase ):






					"""simple docstring"""



					lowercase__       				= SwinaSRImageProcessor if is_vision_available() else None







					def  __lowerCAmelCase				( self    :       Dict	):
						lowerCAmelCase__			:		Dict						       =			SwinaSRImageProcessingTester(self	)







					@property
					def  __lowerCAmelCase				( self    :       str	):
						return self.image_processor_tester.prepare_image_processor_dict()







					def  __lowerCAmelCase				( self    :       List[str]	):
						lowerCAmelCase__			:		Any						       =			self.image_processing_class(**self.image_processor_dict	)
						self.assertTrue(hasattr(lowercase_     ,'''do_rescale'''	)	)
						self.assertTrue(hasattr(lowercase_     ,'''rescale_factor'''	)	)
						self.assertTrue(hasattr(lowercase_     ,'''do_pad'''	)	)
						self.assertTrue(hasattr(lowercase_     ,'''pad_size'''	)	)







					def  __lowerCAmelCase				( self    :       Dict	):
						pass







					def  __lowerCAmelCase				( self    :       List[str]     ,lowercase_    :       Optional[Any]	):
						lowerCAmelCase__       ,lowerCAmelCase__			:		Tuple						       =			get_image_size(lowercase_	)
						lowerCAmelCase__			:		Dict						       =			self.image_processor_tester.pad_size

						lowerCAmelCase__			:		List[Any]						       =			(old_height // size + 1) * size - old_height
						lowerCAmelCase__			:		Union[str, Any]						       =			(old_width // size + 1) * size - old_width
						return old_height + pad_height, old_width + pad_width







					def  __lowerCAmelCase				( self    :       List[Any]	):
						# Initialize image_processor
						lowerCAmelCase__			:		str						       =			self.image_processing_class(**self.image_processor_dict	)
						# create random PIL images
						lowerCAmelCase__			:		int						       =			self.image_processor_tester.prepare_inputs(equal_resolution=lowercase_	)
						for image in image_inputs:
							self.assertIsInstance(lowercase_     ,Image.Image	)

						# Test not batched input
						lowerCAmelCase__			:		List[str]						       =			image_processor(image_inputs[0]     ,return_tensors='''pt'''	).pixel_values
						lowerCAmelCase__       ,lowerCAmelCase__			:		List[Any]						       =			self.calculate_expected_size(np.array(image_inputs[0]	)	)
						self.assertEqual(
						    encoded_images.shape     ,(
						        1,
						        self.image_processor_tester.num_channels,
						        expected_height,
						        expected_width,
						    )     ,)







					def  __lowerCAmelCase				( self    :       int	):
						# Initialize image_processor
						lowerCAmelCase__			:		str						       =			self.image_processing_class(**self.image_processor_dict	)
						# create random numpy tensors
						lowerCAmelCase__			:		Tuple						       =			self.image_processor_tester.prepare_inputs(equal_resolution=lowercase_     ,numpify=lowercase_	)
						for image in image_inputs:
							self.assertIsInstance(lowercase_     ,np.ndarray	)

						# Test not batched input
						lowerCAmelCase__			:		Optional[Any]						       =			image_processor(image_inputs[0]     ,return_tensors='''pt'''	).pixel_values
						lowerCAmelCase__       ,lowerCAmelCase__			:		List[Any]						       =			self.calculate_expected_size(image_inputs[0]	)
						self.assertEqual(
						    encoded_images.shape     ,(
						        1,
						        self.image_processor_tester.num_channels,
						        expected_height,
						        expected_width,
						    )     ,)







					def  __lowerCAmelCase				( self    :       Dict	):
						# Initialize image_processor
						lowerCAmelCase__			:		Tuple						       =			self.image_processing_class(**self.image_processor_dict	)
						# create random PyTorch tensors
						lowerCAmelCase__			:		Union[str, Any]						       =			self.image_processor_tester.prepare_inputs(equal_resolution=lowercase_     ,torchify=lowercase_	)
						for image in image_inputs:
							self.assertIsInstance(lowercase_     ,torch.Tensor	)

						# Test not batched input
						lowerCAmelCase__			:		Tuple						       =			image_processor(image_inputs[0]     ,return_tensors='''pt'''	).pixel_values
						lowerCAmelCase__       ,lowerCAmelCase__			:		int						       =			self.calculate_expected_size(image_inputs[0]	)
						self.assertEqual(
						    encoded_images.shape     ,(
						        1,
						        self.image_processor_tester.num_channels,
						        expected_height,
						        expected_width,
						    )     ,)




