



import argparse
import json

import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel

def 			a		( _UpperCAmelCase				:   Union[str, Any]					,  _UpperCAmelCase				:   Union[str, Any]		):







		'''simple docstring'''





		__UpperCAmelCase       :	Tuple						=      UNetaDModel(**_UpperCAmelCase		)
		__UpperCAmelCase       :	List[str]						=      checkpoint['''all_modules.0.W'''].data
		__UpperCAmelCase       :	Optional[Any]						=      checkpoint['''all_modules.0.W'''].data
		__UpperCAmelCase       :	List[str]						=      checkpoint['''all_modules.1.weight'''].data
		__UpperCAmelCase       :	str						=      checkpoint['''all_modules.1.bias'''].data

		__UpperCAmelCase       :	int						=      checkpoint['''all_modules.2.weight'''].data
		__UpperCAmelCase       :	Union[str, Any]						=      checkpoint['''all_modules.2.bias'''].data

		__UpperCAmelCase       :	Optional[int]						=      checkpoint['''all_modules.3.weight'''].data
		__UpperCAmelCase       :	List[str]						=      checkpoint['''all_modules.3.bias'''].data

		__UpperCAmelCase       :	str						=      checkpoint[list(checkpoint.keys()		)[-4]].data
		__UpperCAmelCase       :	Any						=      checkpoint[list(checkpoint.keys()		)[-3]].data
		__UpperCAmelCase       :	Union[str, Any]						=      checkpoint[list(checkpoint.keys()		)[-2]].data
		__UpperCAmelCase       :	List[str]						=      checkpoint[list(checkpoint.keys()		)[-1]].data

		__UpperCAmelCase       :	Any						=      4

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

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

				__UpperCAmelCase       :	List[Any]						=      old_checkpoint[f'all_modules.{index}.NIN_3.W'].data.T
				__UpperCAmelCase       :	Optional[Any]						=      old_checkpoint[f'all_modules.{index}.NIN_3.b'].data

				__UpperCAmelCase       :	int						=      old_checkpoint[f'all_modules.{index}.GroupNorm_0.weight'].data
				__UpperCAmelCase       :	str						=      old_checkpoint[f'all_modules.{index}.GroupNorm_0.bias'].data

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

				__UpperCAmelCase       :	Union[str, Any]						=      old_checkpoint[f'all_modules.{index}.Conv_1.weight'].data
				__UpperCAmelCase       :	str						=      old_checkpoint[f'all_modules.{index}.Conv_1.bias'].data
				__UpperCAmelCase       :	Dict						=      old_checkpoint[f'all_modules.{index}.GroupNorm_1.weight'].data
				__UpperCAmelCase       :	Optional[Any]						=      old_checkpoint[f'all_modules.{index}.GroupNorm_1.bias'].data

				__UpperCAmelCase       :	List[str]						=      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       :	Tuple						=      old_checkpoint[f'all_modules.{index}.Conv_2.bias'].data

		for i, block in enumerate(new_model_architecture.downsample_blocks		):
				__UpperCAmelCase       :	Union[str, 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[str]						=      checkpoint[f'all_modules.{module_index}.Conv_0.weight'].data
						__UpperCAmelCase       :	Any						=      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       :	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[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       :	Dict						=      checkpoint[f'all_modules.{module_index}.weight'].data
						__UpperCAmelCase       :	int						=      checkpoint[f'all_modules.{module_index}.bias'].data
						module_index += 1
						__UpperCAmelCase       :	Any						=      checkpoint[f'all_modules.{module_index}.weight'].data
						__UpperCAmelCase       :	Optional[int]						=      checkpoint[f'all_modules.{module_index}.bias'].data
						module_index += 1
						set_resnet_weights(block.resnet_up					,  _UpperCAmelCase					,  _UpperCAmelCase		)
						module_index += 1

		__UpperCAmelCase       :	List[Any]						=      checkpoint[f'all_modules.{module_index}.weight'].data
		__UpperCAmelCase       :	List[Any]						=      checkpoint[f'all_modules.{module_index}.bias'].data
		module_index += 1
		__UpperCAmelCase       :	Tuple						=      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__":
				__A       =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.",
				)

				__A       =parser.parse_args()

				__A       =torch.load(args.checkpoint_path, map_location="cpu")

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

				__A       =convert_ncsnpp_checkpoint(
				    checkpoint,
				    config,
				)

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

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

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

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










import shutil
import tempfile
import unittest

import numpy as np
import pytest

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


if is_torch_available():
				from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_11
else:
				__A       =False

if is_vision_available():
				from PIL import Image

				from transformers import (
				    AutoProcessor,
				    PixaStructImageProcessor,
				    PixaStructProcessor,
				    PreTrainedTokenizerFast,
				    TaTokenizer,
				)

@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11		,reason="""`Pix2StructImageProcessor` requires `torch>=1.11.0`."""		,)
@require_vision
@require_torch
class 						UpperCAmelCase__     (     unittest.TestCase  ):




			'''simple docstring'''






			def 					snake_case__	(  self				:					List[str]					):
					'''simple docstring'''





					__UpperCAmelCase       :	Optional[Any]						=      tempfile.mkdtemp()

					__UpperCAmelCase       :	Any						=      PixaStructImageProcessor()
					__UpperCAmelCase       :	Any						=      TaTokenizer.from_pretrained('''t5-small'''					)

					__UpperCAmelCase       :	Tuple						=      PixaStructProcessor(a_			,			a_					)

					processor.save_pretrained(self.tmpdirname					)




			def 					snake_case__	(  self				:					Union[str, Any]			,			**a_				:					Any					):
					'''simple docstring'''





					return AutoProcessor.from_pretrained(self.tmpdirname			,			**a_					).tokenizer




			def 					snake_case__	(  self				:					int			,			**a_				:					Any					):
					'''simple docstring'''





					return AutoProcessor.from_pretrained(self.tmpdirname			,			**a_					).image_processor




			def 					snake_case__	(  self				:					List[Any]					):
					'''simple docstring'''





					shutil.rmtree(self.tmpdirname					)




			def 					snake_case__	(  self				:					List[str]					):
					'''simple docstring'''





					__UpperCAmelCase       :	Optional[Any]						=      [np.random.randint(2_55			,			size=(3, 30, 4_00)			,			dtype=np.uinta					)]

					__UpperCAmelCase       :	Any						=      [Image.fromarray(np.moveaxis(a_			,			0			,			-1					)					) for x in image_inputs]

					return image_inputs




			def 					snake_case__	(  self				:					Optional[int]					):
					'''simple docstring'''





					__UpperCAmelCase       :	str						=      PixaStructProcessor(tokenizer=self.get_tokenizer()			,			image_processor=self.get_image_processor()					)
					processor.save_pretrained(self.tmpdirname					)

					__UpperCAmelCase       :	Tuple						=      self.get_tokenizer(bos_token='''(BOS)'''			,			eos_token='''(EOS)'''					)
					__UpperCAmelCase       :	Any						=      self.get_image_processor(do_normalize=a_			,			padding_value=1.0					)

					__UpperCAmelCase       :	Dict						=      PixaStructProcessor.from_pretrained(
					    self.tmpdirname			,			bos_token='''(BOS)'''			,			eos_token='''(EOS)'''			,			do_normalize=a_			,			padding_value=1.0					)

					self.assertEqual(processor.tokenizer.get_vocab()			,			tokenizer_add_kwargs.get_vocab()					)
					self.assertIsInstance(processor.tokenizer			,			a_					)

					self.assertEqual(processor.image_processor.to_json_string()			,			image_processor_add_kwargs.to_json_string()					)
					self.assertIsInstance(processor.image_processor			,			a_					)




			def 					snake_case__	(  self				:					List[Any]					):
					'''simple docstring'''





					__UpperCAmelCase       :	Tuple						=      self.get_image_processor()
					__UpperCAmelCase       :	Tuple						=      self.get_tokenizer()

					__UpperCAmelCase       :	Any						=      PixaStructProcessor(tokenizer=a_			,			image_processor=a_					)

					__UpperCAmelCase       :	Tuple						=      self.prepare_image_inputs()

					__UpperCAmelCase       :	List[Any]						=      image_processor(a_			,			return_tensors='''np'''					)
					__UpperCAmelCase       :	Union[str, Any]						=      processor(images=a_			,			return_tensors='''np'''					)

					for key in input_feat_extract.keys():
							self.assertAlmostEqual(input_feat_extract[key].sum()			,			input_processor[key].sum()			,			delta=1e-2					)




			def 					snake_case__	(  self				:					Optional[int]					):
					'''simple docstring'''





					__UpperCAmelCase       :	Any						=      self.get_image_processor()
					__UpperCAmelCase       :	str						=      self.get_tokenizer()

					__UpperCAmelCase       :	str						=      PixaStructProcessor(tokenizer=a_			,			image_processor=a_					)

					__UpperCAmelCase       :	List[str]						=      '''lower newer'''

					__UpperCAmelCase       :	Any						=      processor(text=a_					)

					__UpperCAmelCase       :	Dict						=      tokenizer(a_			,			return_token_type_ids=a_			,			add_special_tokens=a_					)

					for key in encoded_tok.keys():
							self.assertListEqual(encoded_tok[key]			,			encoded_processor[key]					)




			def 					snake_case__	(  self				:					str					):
					'''simple docstring'''





					__UpperCAmelCase       :	str						=      self.get_image_processor()
					__UpperCAmelCase       :	List[Any]						=      self.get_tokenizer()

					__UpperCAmelCase       :	Tuple						=      PixaStructProcessor(tokenizer=a_			,			image_processor=a_					)

					__UpperCAmelCase       :	List[str]						=      '''lower newer'''
					__UpperCAmelCase       :	Tuple						=      self.prepare_image_inputs()

					__UpperCAmelCase       :	Dict						=      processor(text=a_			,			images=a_					)

					self.assertListEqual(
					    list(inputs.keys()					)			,			['''flattened_patches''', '''attention_mask''', '''decoder_attention_mask''', '''decoder_input_ids''']					)

					# test if it raises when no input is passed
					with pytest.raises(a_					):
							processor()




			def 					snake_case__	(  self				:					Any					):
					'''simple docstring'''





					__UpperCAmelCase       :	str						=      self.get_image_processor()
					__UpperCAmelCase       :	Tuple						=      self.get_tokenizer()

					__UpperCAmelCase       :	List[Any]						=      PixaStructProcessor(tokenizer=a_			,			image_processor=a_					)

					__UpperCAmelCase       :	int						=      '''lower newer'''
					__UpperCAmelCase       :	List[Any]						=      self.prepare_image_inputs()

					__UpperCAmelCase       :	int						=      processor(text=a_			,			images=a_					)

					__UpperCAmelCase       :	Tuple						=      [5_12, 10_24, 20_48, 40_96]
					__UpperCAmelCase       :	Tuple						=      [7_70, 7_70, 7_70, 7_70]
					# with text
					for i, max_patch in enumerate(a_					):
							__UpperCAmelCase       :	Dict						=      processor(text=a_			,			images=a_			,			max_patches=a_					)
							self.assertEqual(inputs['''flattened_patches'''][0].shape[0]			,			a_					)
							self.assertEqual(inputs['''flattened_patches'''][0].shape[1]			,			expected_hidden_size[i]					)

					# without text input
					for i, max_patch in enumerate(a_					):
							__UpperCAmelCase       :	Any						=      processor(images=a_			,			max_patches=a_					)
							self.assertEqual(inputs['''flattened_patches'''][0].shape[0]			,			a_					)
							self.assertEqual(inputs['''flattened_patches'''][0].shape[1]			,			expected_hidden_size[i]					)




			def 					snake_case__	(  self				:					Optional[Any]					):
					'''simple docstring'''





					__UpperCAmelCase       :	List[Any]						=      self.get_image_processor()
					__UpperCAmelCase       :	str						=      self.get_tokenizer()

					__UpperCAmelCase       :	List[Any]						=      PixaStructProcessor(tokenizer=a_			,			image_processor=a_					)

					__UpperCAmelCase       :	Union[str, Any]						=      [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

					__UpperCAmelCase       :	List[Any]						=      processor.batch_decode(a_					)
					__UpperCAmelCase       :	Any						=      tokenizer.batch_decode(a_					)

					self.assertListEqual(a_			,			a_					)




			def 					snake_case__	(  self				:					Union[str, Any]					):
					'''simple docstring'''





					__UpperCAmelCase       :	Optional[int]						=      self.get_image_processor()
					__UpperCAmelCase       :	Optional[Any]						=      self.get_tokenizer()

					__UpperCAmelCase       :	Any						=      PixaStructProcessor(tokenizer=a_			,			image_processor=a_					)

					__UpperCAmelCase       :	Dict						=      '''lower newer'''
					__UpperCAmelCase       :	List[str]						=      self.prepare_image_inputs()

					__UpperCAmelCase       :	Optional[Any]						=      processor(text=a_			,			images=a_					)

					# For now the processor supports only ["flattened_patches", "input_ids", "attention_mask", "decoder_attention_mask"]
					self.assertListEqual(
					    list(inputs.keys()					)			,			['''flattened_patches''', '''attention_mask''', '''decoder_attention_mask''', '''decoder_input_ids''']					)

					__UpperCAmelCase       :	Optional[int]						=      processor(text=a_					)

					# For now the processor supports only ["flattened_patches", "input_ids", "attention_mask", "decoder_attention_mask"]
					self.assertListEqual(list(inputs.keys()					)			,			['''input_ids''', '''attention_mask''']					)





