
import unittest

import numpy as np
import torch
from torch import nn
from transformers import (
    CLIPImageProcessor,
    CLIPTextConfig,
    CLIPTextModelWithProjection,
    CLIPTokenizer,
    CLIPVisionConfig,
    CLIPVisionModelWithProjection,
)

from diffusers import KandinskyPriorPipeline, PriorTransformer, UnCLIPScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import enable_full_determinism, skip_mps

from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()

class        UpperCamelCase__(    lowerCAmelCase__						,						unittest.TestCase	):

	"""simple docstring"""

	_A  =		KandinskyPriorPipeline
	_A  =		["prompt"]
	_A  =		["prompt", "negative_prompt"]
	_A  =		[
	    "num_images_per_prompt",
	    "generator",
	    "num_inference_steps",
	    "latents",
	    "negative_prompt",
	    "guidance_scale",
	    "output_type",
	    "return_dict",
	]
	_A  =		False







	@property
	def        _a					(      self				:			List[Any] ):
			"""simple docstring"""





			return 32







	@property
	def        _a					(      self				:			int ):
			"""simple docstring"""





			return 32







	@property
	def        _a					(      self				:			Optional[int] ):
			"""simple docstring"""





			return self.time_input_dim







	@property
	def        _a					(      self				:			str ):
			"""simple docstring"""





			return self.time_input_dim * 4







	@property
	def        _a					(      self				:			Optional[Any] ):
			"""simple docstring"""





			return 1_00







	@property
	def        _a					(      self				:			Tuple ):
			"""simple docstring"""





			A				=CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip" )
			return tokenizer







	@property
	def        _a					(      self				:			Optional[int] ):
			"""simple docstring"""





			torch.manual_seed(0 )
			A				=CLIPTextConfig(
			    bos_token_id=0							,			eos_token_id=2							,			hidden_size=self.text_embedder_hidden_size							,			projection_dim=self.text_embedder_hidden_size							,			intermediate_size=37							,			layer_norm_eps=1E-05							,			num_attention_heads=4							,			num_hidden_layers=5							,			pad_token_id=1							,			vocab_size=10_00							,			)
			return CLIPTextModelWithProjection(snake_case__ )







	@property
	def        _a					(      self				:			Optional[int] ):
			"""simple docstring"""





			torch.manual_seed(0 )

			A				={
			    "num_attention_heads": 2,
			    "attention_head_dim": 12,
			    "embedding_dim": self.text_embedder_hidden_size,
			    "num_layers": 1,
			}

			A				=PriorTransformer(**snake_case__ )
			# clip_std and clip_mean is initialized to be 0 so PriorTransformer.post_process_latents will always return 0 - set clip_std to be 1 so it won't return 0
			A				=nn.Parameter(torch.ones(model.clip_std.shape ) )
			return model







	@property
	def        _a					(      self				:			str ):
			"""simple docstring"""





			torch.manual_seed(0 )
			A				=CLIPVisionConfig(
			    hidden_size=self.text_embedder_hidden_size							,			image_size=2_24							,			projection_dim=self.text_embedder_hidden_size							,			intermediate_size=37							,			num_attention_heads=4							,			num_channels=3							,			num_hidden_layers=5							,			patch_size=14							,			)

			A				=CLIPVisionModelWithProjection(snake_case__ )
			return model







	@property
	def        _a					(      self				:			Optional[Any] ):
			"""simple docstring"""





			A				=CLIPImageProcessor(
			    crop_size=2_24							,			do_center_crop=snake_case__							,			do_normalize=snake_case__							,			do_resize=snake_case__							,			image_mean=[0.48_145_466, 0.4_578_275, 0.40_821_073]							,			image_std=[0.26_862_954, 0.26_130_258, 0.27_577_711]							,			resample=3							,			size=2_24							,			)

			return image_processor







	def        _a					(      self				:			Dict ):
			"""simple docstring"""





			A				=self.dummy_prior
			A				=self.dummy_image_encoder
			A				=self.dummy_text_encoder
			A				=self.dummy_tokenizer
			A				=self.dummy_image_processor

			A				=UnCLIPScheduler(
			    variance_type="fixed_small_log"							,			prediction_type="sample"							,			num_train_timesteps=10_00							,			clip_sample=snake_case__							,			clip_sample_range=10.0							,			)

			A				={
			    "prior": prior,
			    "image_encoder": image_encoder,
			    "text_encoder": text_encoder,
			    "tokenizer": tokenizer,
			    "scheduler": scheduler,
			    "image_processor": image_processor,
			}

			return components







	def        _a					(      self				:			int							,			snake_case__				:			int							,			snake_case__				:			Optional[Any]=0 ):
			"""simple docstring"""





			if str(snake_case__ ).startswith("mps" ):
					A				=torch.manual_seed(snake_case__ )
			else:
					A				=torch.Generator(device=snake_case__ ).manual_seed(snake_case__ )
			A				={
			    "prompt": "horse",
			    "generator": generator,
			    "guidance_scale": 4.0,
			    "num_inference_steps": 2,
			    "output_type": "np",
			}
			return inputs







	def        _a					(      self				:			int ):
			"""simple docstring"""





			A				="cpu"

			A				=self.get_dummy_components()

			A				=self.pipeline_class(**snake_case__ )
			A				=pipe.to(snake_case__ )

			pipe.set_progress_bar_config(disable=snake_case__ )

			A				=pipe(**self.get_dummy_inputs(snake_case__ ) )
			A				=output.image_embeds

			A				=pipe(
			    **self.get_dummy_inputs(snake_case__ )							,			return_dict=snake_case__							,			)[0]

			A				=image[0, -10:]
			A				=image_from_tuple[0, -10:]

			assert image.shape == (1, 32)

			A				=np.array(
			    [-0.0_532, 1.7_120, 0.3_656, -1.0_852, -0.8_946, -1.1_756, 0.4_348, 0.2_482, 0.5_146, -0.1_156] )

			assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
			assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1E-2







	@skip_mps
	def        _a					(      self				:			Optional[Any] ):
			"""simple docstring"""





			A				=torch_device == "cpu"
			A				=True
			A				=False

			self._test_inference_batch_single_identical(
			    test_max_difference=snake_case__							,			relax_max_difference=snake_case__							,			test_mean_pixel_difference=snake_case__							,			)







	@skip_mps
	def        _a					(      self				:			List[Any] ):
			"""simple docstring"""





			A				=torch_device == "cpu"
			A				=False

			self._test_attention_slicing_forward_pass(
			    test_max_difference=snake_case__							,			test_mean_pixel_difference=snake_case__							,			)






import argparse
import json

import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel

def    UpperCamelCase_ (	a_  ,				a_		)     ->Optional[int]:
		A				=UNetaDModel(**a_		)
		A				=checkpoint["all_modules.0.W"].data
		A				=checkpoint["all_modules.0.W"].data
		A				=checkpoint["all_modules.1.weight"].data
		A				=checkpoint["all_modules.1.bias"].data

		A				=checkpoint["all_modules.2.weight"].data
		A				=checkpoint["all_modules.2.bias"].data

		A				=checkpoint["all_modules.3.weight"].data
		A				=checkpoint["all_modules.3.bias"].data

		A				=checkpoint[list(checkpoint.keys()		)[-4]].data
		A				=checkpoint[list(checkpoint.keys()		)[-3]].data
		A				=checkpoint[list(checkpoint.keys()		)[-2]].data
		A				=checkpoint[list(checkpoint.keys()		)[-1]].data

		A				=4

		def set_attention_weights(a_  ,				a_  ,				a_		):
				A				=old_checkpoint[f'''all_modules.{index}.NIN_0.W'''].data.T
				A				=old_checkpoint[f'''all_modules.{index}.NIN_1.W'''].data.T
				A				=old_checkpoint[f'''all_modules.{index}.NIN_2.W'''].data.T

				A				=old_checkpoint[f'''all_modules.{index}.NIN_0.b'''].data
				A				=old_checkpoint[f'''all_modules.{index}.NIN_1.b'''].data
				A				=old_checkpoint[f'''all_modules.{index}.NIN_2.b'''].data

				A				=old_checkpoint[f'''all_modules.{index}.NIN_3.W'''].data.T
				A				=old_checkpoint[f'''all_modules.{index}.NIN_3.b'''].data

				A				=old_checkpoint[f'''all_modules.{index}.GroupNorm_0.weight'''].data
				A				=old_checkpoint[f'''all_modules.{index}.GroupNorm_0.bias'''].data

		def set_resnet_weights(a_  ,				a_  ,				a_		):
				A				=old_checkpoint[f'''all_modules.{index}.Conv_0.weight'''].data
				A				=old_checkpoint[f'''all_modules.{index}.Conv_0.bias'''].data
				A				=old_checkpoint[f'''all_modules.{index}.GroupNorm_0.weight'''].data
				A				=old_checkpoint[f'''all_modules.{index}.GroupNorm_0.bias'''].data

				A				=old_checkpoint[f'''all_modules.{index}.Conv_1.weight'''].data
				A				=old_checkpoint[f'''all_modules.{index}.Conv_1.bias'''].data
				A				=old_checkpoint[f'''all_modules.{index}.GroupNorm_1.weight'''].data
				A				=old_checkpoint[f'''all_modules.{index}.GroupNorm_1.bias'''].data

				A				=old_checkpoint[f'''all_modules.{index}.Dense_0.weight'''].data
				A				=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:
						A				=old_checkpoint[f'''all_modules.{index}.Conv_2.weight'''].data
						A				=old_checkpoint[f'''all_modules.{index}.Conv_2.bias'''].data

		for i, block in enumerate(new_model_architecture.downsample_blocks		):
				A				=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
						A				=checkpoint[f'''all_modules.{module_index}.Conv_0.weight'''].data
						A				=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		):
				A				=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:
						A				=checkpoint[f'''all_modules.{module_index}.weight'''].data
						A				=checkpoint[f'''all_modules.{module_index}.bias'''].data
						module_index += 1
						A				=checkpoint[f'''all_modules.{module_index}.weight'''].data
						A				=checkpoint[f'''all_modules.{module_index}.bias'''].data
						module_index += 1
						set_resnet_weights(block.resnet_up  ,				a_  ,				a_		)
						module_index += 1

		A				=checkpoint[f'''all_modules.{module_index}.weight'''].data
		A				=checkpoint[f'''all_modules.{module_index}.bias'''].data
		module_index += 1
		A				=checkpoint[f'''all_modules.{module_index}.weight'''].data
		A				=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)




