



import gc
import unittest

import torch
from parameterized import parameterized

from diffusers import AutoencoderKL
from diffusers.utils import floats_tensor, load_hf_numpy, require_torch_gpu, slow, torch_all_close, torch_device
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin


enable_full_determinism()

class 				snake_case__(_UpperCamelCase    ,      _UpperCamelCase    ,      unittest.TestCase      ):

					"""simple docstring"""






					lowercase_								=		AutoencoderKL
					lowercase_								=		"""sample"""
					lowercase_								=		1e-2







					@property
					def 			snake_case					(							self :     Optional[Any]					):
									lowercase__					:   str													=					4
									lowercase__					:   Union[str, Any]													=					3
									lowercase__					:   Union[str, Any]													=					(32, 32)

									lowercase__					:   List[str]													=					floats_tensor((batch_size, num_channels) + sizes					).to(SCREAMING_SNAKE_CASE					)

									return {"sample": image}







					@property
					def 			snake_case					(							self :     Optional[Any]					):
									return (3, 32, 32)







					@property
					def 			snake_case					(							self :     Tuple					):
									return (3, 32, 32)







					def 			snake_case					(							self :     Optional[int]					):
									lowercase__					:   List[Any]													=					{
									    "block_out_channels": [32, 64],
									    "in_channels": 3,
									    "out_channels": 3,
									    "down_block_types": ["DownEncoderBlock2D", "DownEncoderBlock2D"],
									    "up_block_types": ["UpDecoderBlock2D", "UpDecoderBlock2D"],
									    "latent_channels": 4,
									}
									lowercase__					:   Tuple													=					self.dummy_input
									return init_dict, inputs_dict







					def 			snake_case					(							self :     List[Any]					):
									pass







					def 			snake_case					(							self :     Any					):
									pass







					@unittest.skipIf(torch_device == "mps"				,       "Gradient checkpointing skipped on MPS"					)
					def 			snake_case					(							self :     Tuple					):
									# enable deterministic behavior for gradient checkpointing
									lowercase__							,							lowercase__					:   Optional[int]													=					self.prepare_init_args_and_inputs_for_common()
									lowercase__					:   List[str]													=					self.model_class(**SCREAMING_SNAKE_CASE					)
									model.to(SCREAMING_SNAKE_CASE					)

									assert not model.is_gradient_checkpointing and model.training

									lowercase__					:   Tuple													=					model(**SCREAMING_SNAKE_CASE					).sample
									# run the backwards pass on the model. For backwards pass, for simplicity purpose,
									# we won't calculate the loss and rather backprop on out.sum()
									model.zero_grad()

									lowercase__					:   Optional[Any]													=					torch.randn_like(SCREAMING_SNAKE_CASE					)
									lowercase__					:   str													=					(out - labels).mean()
									loss.backward()

									# re-instantiate the model now enabling gradient checkpointing
									lowercase__					:   Tuple													=					self.model_class(**SCREAMING_SNAKE_CASE					)
									# clone model
									model_a.load_state_dict(model.state_dict()					)
									model_a.to(SCREAMING_SNAKE_CASE					)
									model_a.enable_gradient_checkpointing()

									assert model_a.is_gradient_checkpointing and model_a.training

									lowercase__					:   Dict													=					model_a(**SCREAMING_SNAKE_CASE					).sample
									# run the backwards pass on the model. For backwards pass, for simplicity purpose,
									# we won't calculate the loss and rather backprop on out.sum()
									model_a.zero_grad()
									lowercase__					:   Tuple													=					(out_a - labels).mean()
									loss_a.backward()

									# compare the output and parameters gradients
									self.assertTrue((loss - loss_a).abs() < 1E-5					)
									lowercase__					:   Union[str, Any]													=					dict(model.named_parameters()					)
									lowercase__					:   Any													=					dict(model_a.named_parameters()					)
									for name, param in named_params.items():
													self.assertTrue(torch_all_close(param.grad.data				,       named_params_a[name].grad.data				,       atol=5E-5					)					)







					def 			snake_case					(							self :     List[Any]					):
									lowercase__							,							lowercase__					:   Tuple													=					AutoencoderKL.from_pretrained("fusing/autoencoder-kl-dummy"				,       output_loading_info=SCREAMING_SNAKE_CASE					)
									self.assertIsNotNone(SCREAMING_SNAKE_CASE					)
									self.assertEqual(len(loading_info["missing_keys"]					)				,       0					)

									model.to(SCREAMING_SNAKE_CASE					)
									lowercase__					:   List[Any]													=					model(**self.dummy_input					)

									assert image is not None, "Make sure output is not None"







					def 			snake_case					(							self :     Optional[int]					):
									lowercase__					:   List[Any]													=					AutoencoderKL.from_pretrained("fusing/autoencoder-kl-dummy"					)
									lowercase__					:   Dict													=					model.to(SCREAMING_SNAKE_CASE					)
									model.eval()

									if torch_device == "mps":
													lowercase__					:   List[Any]													=					torch.manual_seed(0					)
									else:
													lowercase__					:   Optional[Any]													=					torch.Generator(device=SCREAMING_SNAKE_CASE					).manual_seed(0					)

									lowercase__					:   Tuple													=					torch.randn(
									    1				,       model.config.in_channels				,       model.config.sample_size				,       model.config.sample_size				,       generator=torch.manual_seed(0					)				,       )
									lowercase__					:   Optional[int]													=					image.to(SCREAMING_SNAKE_CASE					)
									with torch.no_grad():
													lowercase__					:   int													=					model(SCREAMING_SNAKE_CASE				,       sample_posterior=SCREAMING_SNAKE_CASE				,       generator=SCREAMING_SNAKE_CASE					).sample

									lowercase__					:   Optional[int]													=					output[0, -1, -3:, -3:].flatten().cpu()

									# Since the VAE Gaussian prior's generator is seeded on the appropriate device,
									# the expected output slices are not the same for CPU and GPU.
									if torch_device == "mps":
													lowercase__					:   Any													=					torch.tensor(
													    [
													        -4.0_0_7_8E-0_1,
													        -3.8_3_2_3E-0_4,
													        -1.2_6_8_1E-0_1,
													        -1.1_4_6_2E-0_1,
													        2.0_0_9_5E-0_1,
													        1.0_8_9_3E-0_1,
													        -8.8_2_4_7E-0_2,
													        -3.0_3_6_1E-0_1,
													        -9.8_6_4_4E-0_3,
													    ]					)
									elif torch_device == "cpu":
													lowercase__					:   Union[str, Any]													=					torch.tensor(
													    [-0.1_352, 0.0_878, 0.0_419, -0.0_818, -0.1_069, 0.0_688, -0.1_458, -0.4_446, -0.0_026]					)
									else:
													lowercase__					:   List[str]													=					torch.tensor(
													    [-0.2_421, 0.4_642, 0.2_507, -0.0_438, 0.0_682, 0.3_160, -0.2_018, -0.0_727, 0.2_485]					)

									self.assertTrue(torch_all_close(SCREAMING_SNAKE_CASE				,       SCREAMING_SNAKE_CASE				,       rtol=1E-2					)					)






@slow
class 				snake_case__(unittest.TestCase      ):

					"""simple docstring"""






					def 			snake_case					(							self :     List[Any]				,       SCREAMING_SNAKE_CASE :     Dict				,       SCREAMING_SNAKE_CASE :     Dict					):
									return f"""gaussian_noise_s={seed}_shape={"_".join([str(SCREAMING_SNAKE_CASE					) for s in shape]					)}.npy"""







					def 			snake_case					(							self :     List[Any]					):
									# clean up the VRAM after each test
									super().tearDown()
									gc.collect()
									torch.cuda.empty_cache()







					def 			snake_case					(							self :     List[Any]				,       SCREAMING_SNAKE_CASE :     Union[str, Any]=0				,       SCREAMING_SNAKE_CASE :     Optional[Any]=(4, 3, 512, 512)				,       SCREAMING_SNAKE_CASE :     Any=False					):
									lowercase__					:   List[Any]													=					torch.floataa if fpaa else torch.floataa
									lowercase__					:   Tuple													=					torch.from_numpy(load_hf_numpy(self.get_file_format(SCREAMING_SNAKE_CASE				,       SCREAMING_SNAKE_CASE					)					)					).to(SCREAMING_SNAKE_CASE					).to(SCREAMING_SNAKE_CASE					)
									return image







					def 			snake_case					(							self :     Any				,       SCREAMING_SNAKE_CASE :     int="CompVis/stable-diffusion-v1-4"				,       SCREAMING_SNAKE_CASE :     str=False					):
									lowercase__					:   Tuple													=					"fp16" if fpaa else None
									lowercase__					:   List[str]													=					torch.floataa if fpaa else torch.floataa

									lowercase__					:   Any													=					AutoencoderKL.from_pretrained(
									    SCREAMING_SNAKE_CASE				,       subfolder="vae"				,       torch_dtype=SCREAMING_SNAKE_CASE				,       revision=SCREAMING_SNAKE_CASE				,       )
									model.to(SCREAMING_SNAKE_CASE					).eval()

									return model







					def 			snake_case					(							self :     List[str]				,       SCREAMING_SNAKE_CASE :     List[Any]=0					):
									if torch_device == "mps":
													return torch.manual_seed(SCREAMING_SNAKE_CASE					)
									return torch.Generator(device=SCREAMING_SNAKE_CASE					).manual_seed(SCREAMING_SNAKE_CASE					)







					@parameterized.expand(
					    [
					        # fmt: off
					        [33, [-0.1_603, 0.9_878, -0.0_495, -0.0_790, -0.2_709, 0.8_375, -0.2_060, -0.0_824], [-0.2_395, 0.0_098, 0.0_102, -0.0_709, -0.2_840, -0.0_274, -0.0_718, -0.1_824]],
					        [47, [-0.2_376, 0.1_168, 0.1_332, -0.4_840, -0.2_508, -0.0_791, -0.0_493, -0.4_089], [0.0_350, 0.0_847, 0.0_467, 0.0_344, -0.0_842, -0.0_547, -0.0_633, -0.1_131]],
					        # fmt: on
					    ]					)
					def 			snake_case					(							self :     Optional[int]				,       SCREAMING_SNAKE_CASE :     Dict				,       SCREAMING_SNAKE_CASE :     Optional[int]				,       SCREAMING_SNAKE_CASE :     Dict					):
									lowercase__					:   int													=					self.get_sd_vae_model()
									lowercase__					:   Dict													=					self.get_sd_image(SCREAMING_SNAKE_CASE					)
									lowercase__					:   List[str]													=					self.get_generator(SCREAMING_SNAKE_CASE					)

									with torch.no_grad():
													lowercase__					:   Optional[Any]													=					model(SCREAMING_SNAKE_CASE				,       generator=SCREAMING_SNAKE_CASE				,       sample_posterior=SCREAMING_SNAKE_CASE					).sample

									assert sample.shape == image.shape

									lowercase__					:   Dict													=					sample[-1, -2:, -2:, :2].flatten().float().cpu()
									lowercase__					:   List[Any]													=					torch.tensor(expected_slice_mps if torch_device == "mps" else expected_slice					)

									assert torch_all_close(SCREAMING_SNAKE_CASE				,       SCREAMING_SNAKE_CASE				,       atol=3E-3					)







					@parameterized.expand(
					    [
					        # fmt: off
					        [33, [-0.0_513, 0.0_289, 1.3_799, 0.2_166, -0.2_573, -0.0_871, 0.5_103, -0.0_999]],
					        [47, [-0.4_128, -0.1_320, -0.3_704, 0.1_965, -0.4_116, -0.2_332, -0.3_340, 0.2_247]],
					        # fmt: on
					    ]					)
					@require_torch_gpu
					def 			snake_case					(							self :     Optional[int]				,       SCREAMING_SNAKE_CASE :     List[Any]				,       SCREAMING_SNAKE_CASE :     str					):
									lowercase__					:   Any													=					self.get_sd_vae_model(fpaa=SCREAMING_SNAKE_CASE					)
									lowercase__					:   List[Any]													=					self.get_sd_image(SCREAMING_SNAKE_CASE				,       fpaa=SCREAMING_SNAKE_CASE					)
									lowercase__					:   Dict													=					self.get_generator(SCREAMING_SNAKE_CASE					)

									with torch.no_grad():
													lowercase__					:   str													=					model(SCREAMING_SNAKE_CASE				,       generator=SCREAMING_SNAKE_CASE				,       sample_posterior=SCREAMING_SNAKE_CASE					).sample

									assert sample.shape == image.shape

									lowercase__					:   List[str]													=					sample[-1, -2:, :2, -2:].flatten().float().cpu()
									lowercase__					:   Any													=					torch.tensor(SCREAMING_SNAKE_CASE					)

									assert torch_all_close(SCREAMING_SNAKE_CASE				,       SCREAMING_SNAKE_CASE				,       atol=1E-2					)







					@parameterized.expand(
					    [
					        # fmt: off
					        [33, [-0.1_609, 0.9_866, -0.0_487, -0.0_777, -0.2_716, 0.8_368, -0.2_055, -0.0_814], [-0.2_395, 0.0_098, 0.0_102, -0.0_709, -0.2_840, -0.0_274, -0.0_718, -0.1_824]],
					        [47, [-0.2_377, 0.1_147, 0.1_333, -0.4_841, -0.2_506, -0.0_805, -0.0_491, -0.4_085], [0.0_350, 0.0_847, 0.0_467, 0.0_344, -0.0_842, -0.0_547, -0.0_633, -0.1_131]],
					        # fmt: on
					    ]					)
					def 			snake_case					(							self :     List[str]				,       SCREAMING_SNAKE_CASE :     Dict				,       SCREAMING_SNAKE_CASE :     Optional[Any]				,       SCREAMING_SNAKE_CASE :     Optional[Any]					):
									lowercase__					:   Optional[Any]													=					self.get_sd_vae_model()
									lowercase__					:   Tuple													=					self.get_sd_image(SCREAMING_SNAKE_CASE					)

									with torch.no_grad():
													lowercase__					:   Union[str, Any]													=					model(SCREAMING_SNAKE_CASE					).sample

									assert sample.shape == image.shape

									lowercase__					:   List[str]													=					sample[-1, -2:, -2:, :2].flatten().float().cpu()
									lowercase__					:   Tuple													=					torch.tensor(expected_slice_mps if torch_device == "mps" else expected_slice					)

									assert torch_all_close(SCREAMING_SNAKE_CASE				,       SCREAMING_SNAKE_CASE				,       atol=3E-3					)







					@parameterized.expand(
					    [
					        # fmt: off
					        [13, [-0.2_051, -0.1_803, -0.2_311, -0.2_114, -0.3_292, -0.3_574, -0.2_953, -0.3_323]],
					        [37, [-0.2_632, -0.2_625, -0.2_199, -0.2_741, -0.4_539, -0.4_990, -0.3_720, -0.4_925]],
					        # fmt: on
					    ]					)
					@require_torch_gpu
					def 			snake_case					(							self :     Any				,       SCREAMING_SNAKE_CASE :     Optional[int]				,       SCREAMING_SNAKE_CASE :     Optional[int]					):
									lowercase__					:   List[str]													=					self.get_sd_vae_model()
									lowercase__					:   List[str]													=					self.get_sd_image(SCREAMING_SNAKE_CASE				,       shape=(3, 4, 64, 64)					)

									with torch.no_grad():
													lowercase__					:   Dict													=					model.decode(SCREAMING_SNAKE_CASE					).sample

									assert list(sample.shape					) == [3, 3, 512, 512]

									lowercase__					:   List[Any]													=					sample[-1, -2:, :2, -2:].flatten().cpu()
									lowercase__					:   List[Any]													=					torch.tensor(SCREAMING_SNAKE_CASE					)

									assert torch_all_close(SCREAMING_SNAKE_CASE				,       SCREAMING_SNAKE_CASE				,       atol=1E-3					)







					@parameterized.expand(
					    [
					        # fmt: off
					        [27, [-0.0_369, 0.0_207, -0.0_776, -0.0_682, -0.1_747, -0.1_930, -0.1_465, -0.2_039]],
					        [16, [-0.1_628, -0.2_134, -0.2_747, -0.2_642, -0.3_774, -0.4_404, -0.3_687, -0.4_277]],
					        # fmt: on
					    ]					)
					@require_torch_gpu
					def 			snake_case					(							self :     str				,       SCREAMING_SNAKE_CASE :     Optional[Any]				,       SCREAMING_SNAKE_CASE :     List[Any]					):
									lowercase__					:   str													=					self.get_sd_vae_model(fpaa=SCREAMING_SNAKE_CASE					)
									lowercase__					:   Union[str, Any]													=					self.get_sd_image(SCREAMING_SNAKE_CASE				,       shape=(3, 4, 64, 64)				,       fpaa=SCREAMING_SNAKE_CASE					)

									with torch.no_grad():
													lowercase__					:   int													=					model.decode(SCREAMING_SNAKE_CASE					).sample

									assert list(sample.shape					) == [3, 3, 512, 512]

									lowercase__					:   int													=					sample[-1, -2:, :2, -2:].flatten().float().cpu()
									lowercase__					:   str													=					torch.tensor(SCREAMING_SNAKE_CASE					)

									assert torch_all_close(SCREAMING_SNAKE_CASE				,       SCREAMING_SNAKE_CASE				,       atol=5E-3					)







					@parameterized.expand([(13,), (16,), (27,)]					)
					@require_torch_gpu
					@unittest.skipIf(not is_xformers_available()				,       reason="xformers is not required when using PyTorch 2.0."					)
					def 			snake_case					(							self :     Optional[Any]				,       SCREAMING_SNAKE_CASE :     Tuple					):
									lowercase__					:   int													=					self.get_sd_vae_model(fpaa=SCREAMING_SNAKE_CASE					)
									lowercase__					:   str													=					self.get_sd_image(SCREAMING_SNAKE_CASE				,       shape=(3, 4, 64, 64)				,       fpaa=SCREAMING_SNAKE_CASE					)

									with torch.no_grad():
													lowercase__					:   int													=					model.decode(SCREAMING_SNAKE_CASE					).sample

									model.enable_xformers_memory_efficient_attention()
									with torch.no_grad():
													lowercase__					:   Optional[int]													=					model.decode(SCREAMING_SNAKE_CASE					).sample

									assert list(sample.shape					) == [3, 3, 512, 512]

									assert torch_all_close(SCREAMING_SNAKE_CASE				,       SCREAMING_SNAKE_CASE				,       atol=1E-1					)







					@parameterized.expand([(13,), (16,), (37,)]					)
					@require_torch_gpu
					@unittest.skipIf(not is_xformers_available()				,       reason="xformers is not required when using PyTorch 2.0."					)
					def 			snake_case					(							self :     int				,       SCREAMING_SNAKE_CASE :     Optional[Any]					):
									lowercase__					:   List[str]													=					self.get_sd_vae_model()
									lowercase__					:   Optional[int]													=					self.get_sd_image(SCREAMING_SNAKE_CASE				,       shape=(3, 4, 64, 64)					)

									with torch.no_grad():
													lowercase__					:   int													=					model.decode(SCREAMING_SNAKE_CASE					).sample

									model.enable_xformers_memory_efficient_attention()
									with torch.no_grad():
													lowercase__					:   int													=					model.decode(SCREAMING_SNAKE_CASE					).sample

									assert list(sample.shape					) == [3, 3, 512, 512]

									assert torch_all_close(SCREAMING_SNAKE_CASE				,       SCREAMING_SNAKE_CASE				,       atol=1E-2					)







					@parameterized.expand(
					    [
					        # fmt: off
					        [33, [-0.3_001, 0.0_918, -2.6_984, -3.9_720, -3.2_099, -5.0_353, 1.7_338, -0.2_065, 3.4_267]],
					        [47, [-1.5_030, -4.3_871, -6.0_355, -9.1_157, -1.6_661, -2.7_853, 2.1_607, -5.0_823, 2.5_633]],
					        # fmt: on
					    ]					)
					def 			snake_case					(							self :     str				,       SCREAMING_SNAKE_CASE :     Union[str, Any]				,       SCREAMING_SNAKE_CASE :     int					):
									lowercase__					:   Optional[int]													=					self.get_sd_vae_model()
									lowercase__					:   Tuple													=					self.get_sd_image(SCREAMING_SNAKE_CASE					)
									lowercase__					:   Union[str, Any]													=					self.get_generator(SCREAMING_SNAKE_CASE					)

									with torch.no_grad():
													lowercase__					:   Any													=					model.encode(SCREAMING_SNAKE_CASE					).latent_dist
													lowercase__					:   Optional[int]													=					dist.sample(generator=SCREAMING_SNAKE_CASE					)

									assert list(sample.shape					) == [image.shape[0], 4] + [i // 8 for i in image.shape[2:]]

									lowercase__					:   Dict													=					sample[0, -1, -3:, -3:].flatten().cpu()
									lowercase__					:   Optional[Any]													=					torch.tensor(SCREAMING_SNAKE_CASE					)

									lowercase__					:   Any													=					3E-3 if torch_device != "mps" else 1E-2
									assert torch_all_close(SCREAMING_SNAKE_CASE				,       SCREAMING_SNAKE_CASE				,       atol=SCREAMING_SNAKE_CASE					)









import argparse
import json

import torch

from diffusers import DDPMScheduler, LDMPipeline, UNetaDModel, VQModel


def  __lowerCamelCase (       lowerCamelCase__					,			lowerCamelCase__=1						):

				"""simple docstring"""


				if n_shave_prefix_segments >= 0:
								return ".".join(path.split("."						)[n_shave_prefix_segments:]						)
				else:
								return ".".join(path.split("."						)[:n_shave_prefix_segments]						)


def  __lowerCamelCase (       lowerCamelCase__					,			lowerCamelCase__=0						):

				"""simple docstring"""


				lowercase__					:   List[str]													=					[]
				for old_item in old_list:
								lowercase__					:   Optional[Any]													=					old_item.replace("in_layers.0"					,			"norm1"						)
								lowercase__					:   Union[str, Any]													=					new_item.replace("in_layers.2"					,			"conv1"						)

								lowercase__					:   Optional[Any]													=					new_item.replace("out_layers.0"					,			"norm2"						)
								lowercase__					:   Union[str, Any]													=					new_item.replace("out_layers.3"					,			"conv2"						)

								lowercase__					:   Dict													=					new_item.replace("emb_layers.1"					,			"time_emb_proj"						)
								lowercase__					:   int													=					new_item.replace("skip_connection"					,			"conv_shortcut"						)

								lowercase__					:   Tuple													=					shave_segments(lowerCamelCase__					,			n_shave_prefix_segments=lowerCamelCase__						)

								mapping.append({"old": old_item, "new": new_item}						)

				return mapping


def  __lowerCamelCase (       lowerCamelCase__					,			lowerCamelCase__=0						):

				"""simple docstring"""


				lowercase__					:   str													=					[]
				for old_item in old_list:
								lowercase__					:   Optional[int]													=					old_item

								lowercase__					:   Dict													=					new_item.replace("norm.weight"					,			"group_norm.weight"						)
								lowercase__					:   Optional[int]													=					new_item.replace("norm.bias"					,			"group_norm.bias"						)

								lowercase__					:   Tuple													=					new_item.replace("proj_out.weight"					,			"proj_attn.weight"						)
								lowercase__					:   List[Any]													=					new_item.replace("proj_out.bias"					,			"proj_attn.bias"						)

								lowercase__					:   Optional[Any]													=					shave_segments(lowerCamelCase__					,			n_shave_prefix_segments=lowerCamelCase__						)

								mapping.append({"old": old_item, "new": new_item}						)

				return mapping


def  __lowerCamelCase (       lowerCamelCase__					,			lowerCamelCase__					,			lowerCamelCase__					,			lowerCamelCase__=None					,			lowerCamelCase__=None					,			lowerCamelCase__=None						):

				"""simple docstring"""


				assert isinstance(lowerCamelCase__					,			lowerCamelCase__						), "Paths should be a list of dicts containing 'old' and 'new' keys."

				# Splits the attention layers into three variables.
				if attention_paths_to_split is not None:
								for path, path_map in attention_paths_to_split.items():
												lowercase__					:   List[str]													=					old_checkpoint[path]
												lowercase__					:   str													=					old_tensor.shape[0] // 3

												lowercase__					:   List[str]													=					(-1, channels) if len(old_tensor.shape						) == 3 else (-1)

												lowercase__					:   Union[str, Any]													=					old_tensor.shape[0] // config["num_head_channels"] // 3

												lowercase__					:   Union[str, Any]													=					old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:]						)
												lowercase__							,							lowercase__							,							lowercase__					:   Optional[Any]													=					old_tensor.split(channels // num_heads					,			dim=1						)

												lowercase__					:   Dict													=					query.reshape(lowerCamelCase__						)
												lowercase__					:   Dict													=					key.reshape(lowerCamelCase__						)
												lowercase__					:   int													=					value.reshape(lowerCamelCase__						)

				for path in paths:
								lowercase__					:   Union[str, Any]													=					path["new"]

								# These have already been assigned
								if attention_paths_to_split is not None and new_path in attention_paths_to_split:
												continue

								# Global renaming happens here
								lowercase__					:   List[Any]													=					new_path.replace("middle_block.0"					,			"mid_block.resnets.0"						)
								lowercase__					:   Optional[Any]													=					new_path.replace("middle_block.1"					,			"mid_block.attentions.0"						)
								lowercase__					:   List[str]													=					new_path.replace("middle_block.2"					,			"mid_block.resnets.1"						)

								if additional_replacements is not None:
												for replacement in additional_replacements:
																lowercase__					:   Tuple													=					new_path.replace(replacement["old"]					,			replacement["new"]						)

        # proj_attn.weight has to be converted from conv 1D to linear
								if "proj_attn.weight" in new_path:
												lowercase__					:   List[Any]													=					old_checkpoint[path["old"]][:, :, 0]
								else:
												lowercase__					:   List[Any]													=					old_checkpoint[path["old"]]


def  __lowerCamelCase (       lowerCamelCase__					,			lowerCamelCase__						):

				"""simple docstring"""


				lowercase__					:   Union[str, Any]													=					{}

				lowercase__					:   Optional[Any]													=					checkpoint["time_embed.0.weight"]
				lowercase__					:   Tuple													=					checkpoint["time_embed.0.bias"]
				lowercase__					:   Dict													=					checkpoint["time_embed.2.weight"]
				lowercase__					:   Optional[Any]													=					checkpoint["time_embed.2.bias"]

				lowercase__					:   Optional[int]													=					checkpoint["input_blocks.0.0.weight"]
				lowercase__					:   List[Any]													=					checkpoint["input_blocks.0.0.bias"]

				lowercase__					:   Tuple													=					checkpoint["out.0.weight"]
				lowercase__					:   List[Any]													=					checkpoint["out.0.bias"]
				lowercase__					:   Tuple													=					checkpoint["out.2.weight"]
				lowercase__					:   Optional[Any]													=					checkpoint["out.2.bias"]

				# Retrieves the keys for the input blocks only
				lowercase__					:   Dict													=					len({".".join(layer.split("."						)[:2]						) for layer in checkpoint if "input_blocks" in layer}						)
				lowercase__					:   str													=					{
				    layer_id: [key for key in checkpoint if F"""input_blocks.{layer_id}""" in key]
				    for layer_id in range(lowerCamelCase__						)
				}

				# Retrieves the keys for the middle blocks only
				lowercase__					:   Tuple													=					len({".".join(layer.split("."						)[:2]						) for layer in checkpoint if "middle_block" in layer}						)
				lowercase__					:   Union[str, Any]													=					{
				    layer_id: [key for key in checkpoint if F"""middle_block.{layer_id}""" in key]
				    for layer_id in range(lowerCamelCase__						)
				}

				# Retrieves the keys for the output blocks only
				lowercase__					:   Tuple													=					len({".".join(layer.split("."						)[:2]						) for layer in checkpoint if "output_blocks" in layer}						)
				lowercase__					:   Tuple													=					{
				    layer_id: [key for key in checkpoint if F"""output_blocks.{layer_id}""" in key]
				    for layer_id in range(lowerCamelCase__						)
				}

				for i in range(1					,			lowerCamelCase__						):
								lowercase__					:   Tuple													=					(i - 1) // (config["num_res_blocks"] + 1)
								lowercase__					:   Optional[int]													=					(i - 1) % (config["num_res_blocks"] + 1)

								lowercase__					:   List[Any]													=					[key for key in input_blocks[i] if F"""input_blocks.{i}.0""" in key]
								lowercase__					:   Dict													=					[key for key in input_blocks[i] if F"""input_blocks.{i}.1""" in key]

								if F"""input_blocks.{i}.0.op.weight""" in checkpoint:
												lowercase__					:   int													=					checkpoint[
												    F"""input_blocks.{i}.0.op.weight"""
												]
												lowercase__					:   List[str]													=					checkpoint[
												    F"""input_blocks.{i}.0.op.bias"""
												]
												continue

								lowercase__					:   Union[str, Any]													=					renew_resnet_paths(lowerCamelCase__						)
								lowercase__					:   Optional[int]													=					{"old": F"""input_blocks.{i}.0""", "new": F"""down_blocks.{block_id}.resnets.{layer_in_block_id}"""}
								lowercase__					:   Optional[int]													=					{"old": "resnets.2.op", "new": "downsamplers.0.op"}
								assign_to_checkpoint(
								    lowerCamelCase__					,			lowerCamelCase__					,			lowerCamelCase__					,			additional_replacements=[meta_path, resnet_op]					,			config=lowerCamelCase__						)

								if len(lowerCamelCase__						):
												lowercase__					:   Tuple													=					renew_attention_paths(lowerCamelCase__						)
												lowercase__					:   str													=					{
												    "old": F"""input_blocks.{i}.1""",
												    "new": F"""down_blocks.{block_id}.attentions.{layer_in_block_id}""",
												}
												lowercase__					:   List[str]													=					{
												    F"""input_blocks.{i}.1.qkv.bias""": {
												        "key": F"""down_blocks.{block_id}.attentions.{layer_in_block_id}.key.bias""",
												        "query": F"""down_blocks.{block_id}.attentions.{layer_in_block_id}.query.bias""",
												        "value": F"""down_blocks.{block_id}.attentions.{layer_in_block_id}.value.bias""",
												    },
												    F"""input_blocks.{i}.1.qkv.weight""": {
												        "key": F"""down_blocks.{block_id}.attentions.{layer_in_block_id}.key.weight""",
												        "query": F"""down_blocks.{block_id}.attentions.{layer_in_block_id}.query.weight""",
												        "value": F"""down_blocks.{block_id}.attentions.{layer_in_block_id}.value.weight""",
												    },
												}
												assign_to_checkpoint(
												    lowerCamelCase__					,			lowerCamelCase__					,			lowerCamelCase__					,			additional_replacements=[meta_path]					,			attention_paths_to_split=lowerCamelCase__					,			config=lowerCamelCase__					,			)

				lowercase__					:   int													=					middle_blocks[0]
				lowercase__					:   Dict													=					middle_blocks[1]
				lowercase__					:   Dict													=					middle_blocks[2]

				lowercase__					:   Any													=					renew_resnet_paths(lowerCamelCase__						)
				assign_to_checkpoint(lowerCamelCase__					,			lowerCamelCase__					,			lowerCamelCase__					,			config=lowerCamelCase__						)

				lowercase__					:   List[Any]													=					renew_resnet_paths(lowerCamelCase__						)
				assign_to_checkpoint(lowerCamelCase__					,			lowerCamelCase__					,			lowerCamelCase__					,			config=lowerCamelCase__						)

				lowercase__					:   Optional[int]													=					renew_attention_paths(lowerCamelCase__						)
				lowercase__					:   Optional[int]													=					{
				    "middle_block.1.qkv.bias": {
				        "key": "mid_block.attentions.0.key.bias",
				        "query": "mid_block.attentions.0.query.bias",
				        "value": "mid_block.attentions.0.value.bias",
				    },
				    "middle_block.1.qkv.weight": {
				        "key": "mid_block.attentions.0.key.weight",
				        "query": "mid_block.attentions.0.query.weight",
				        "value": "mid_block.attentions.0.value.weight",
				    },
				}
				assign_to_checkpoint(
				    lowerCamelCase__					,			lowerCamelCase__					,			lowerCamelCase__					,			attention_paths_to_split=lowerCamelCase__					,			config=lowerCamelCase__						)

				for i in range(lowerCamelCase__						):
								lowercase__					:   List[Any]													=					i // (config["num_res_blocks"] + 1)
								lowercase__					:   Optional[int]													=					i % (config["num_res_blocks"] + 1)
								lowercase__					:   List[Any]													=					[shave_segments(lowerCamelCase__					,			2						) for name in output_blocks[i]]
								lowercase__					:   Optional[Any]													=					{}

								for layer in output_block_layers:
												lowercase__							,							lowercase__					:   str													=					layer.split("."						)[0], shave_segments(lowerCamelCase__					,			1						)
												if layer_id in output_block_list:
																output_block_list[layer_id].append(lowerCamelCase__						)
												else:
																lowercase__					:   Tuple													=					[layer_name]

								if len(lowerCamelCase__						) > 1:
												lowercase__					:   Dict													=					[key for key in output_blocks[i] if F"""output_blocks.{i}.0""" in key]
												lowercase__					:   Dict													=					[key for key in output_blocks[i] if F"""output_blocks.{i}.1""" in key]

												lowercase__					:   Optional[Any]													=					renew_resnet_paths(lowerCamelCase__						)
												lowercase__					:   Optional[Any]													=					renew_resnet_paths(lowerCamelCase__						)

												lowercase__					:   Tuple													=					{"old": F"""output_blocks.{i}.0""", "new": F"""up_blocks.{block_id}.resnets.{layer_in_block_id}"""}
												assign_to_checkpoint(lowerCamelCase__					,			lowerCamelCase__					,			lowerCamelCase__					,			additional_replacements=[meta_path]					,			config=lowerCamelCase__						)

												if ["conv.weight", "conv.bias"] in output_block_list.values():
																lowercase__					:   List[str]													=					list(output_block_list.values()						).index(["conv.weight", "conv.bias"]						)
																lowercase__					:   Tuple													=					checkpoint[
																    F"""output_blocks.{i}.{index}.conv.weight"""
																]
																lowercase__					:   Optional[Any]													=					checkpoint[
																    F"""output_blocks.{i}.{index}.conv.bias"""
																]

																# Clear attentions as they have been attributed above.
																if len(lowerCamelCase__						) == 2:
																				lowercase__					:   int													=					[]

												if len(lowerCamelCase__						):
																lowercase__					:   Tuple													=					renew_attention_paths(lowerCamelCase__						)
																lowercase__					:   str													=					{
																    "old": F"""output_blocks.{i}.1""",
																    "new": F"""up_blocks.{block_id}.attentions.{layer_in_block_id}""",
																}
																lowercase__					:   Union[str, Any]													=					{
																    F"""output_blocks.{i}.1.qkv.bias""": {
																        "key": F"""up_blocks.{block_id}.attentions.{layer_in_block_id}.key.bias""",
																        "query": F"""up_blocks.{block_id}.attentions.{layer_in_block_id}.query.bias""",
																        "value": F"""up_blocks.{block_id}.attentions.{layer_in_block_id}.value.bias""",
																    },
																    F"""output_blocks.{i}.1.qkv.weight""": {
																        "key": F"""up_blocks.{block_id}.attentions.{layer_in_block_id}.key.weight""",
																        "query": F"""up_blocks.{block_id}.attentions.{layer_in_block_id}.query.weight""",
																        "value": F"""up_blocks.{block_id}.attentions.{layer_in_block_id}.value.weight""",
																    },
																}
																assign_to_checkpoint(
																    lowerCamelCase__					,			lowerCamelCase__					,			lowerCamelCase__					,			additional_replacements=[meta_path]					,			attention_paths_to_split=to_split if any("qkv" in key for key in attentions						) else None					,			config=lowerCamelCase__					,			)
								else:
												lowercase__					:   int													=					renew_resnet_paths(lowerCamelCase__					,			n_shave_prefix_segments=1						)
												for path in resnet_0_paths:
																lowercase__					:   List[Any]													=					".".join(["output_blocks", str(lowerCamelCase__						), path["old"]]						)
																lowercase__					:   Any													=					".".join(["up_blocks", str(lowerCamelCase__						), "resnets", str(lowerCamelCase__						), path["new"]]						)

																lowercase__					:   List[Any]													=					checkpoint[old_path]

				return new_checkpoint


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

				parser.add_argument(
				    '''--checkpoint_path''', default=None, type=str, required=True, help='''Path to the checkpoint to convert.'''
				)

				parser.add_argument(
				    '''--config_file''',
				    default=None,
				    type=str,
				    required=True,
				    help='''The config json file corresponding to the architecture.''',
				)

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

				lowerCAmelCase__					  =  parser.parse_args()

				lowerCAmelCase__					  =  torch.load(args.checkpoint_path)

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

				lowerCAmelCase__					  =  convert_ldm_checkpoint(checkpoint, config)

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

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

				try:
								lowerCAmelCase__					  =  DDPMScheduler.from_config('''/'''.join(args.checkpoint_path.split('''/''')[:-1]))
								lowerCAmelCase__					  =  VQModel.from_pretrained('''/'''.join(args.checkpoint_path.split('''/''')[:-1]))

								lowerCAmelCase__					  =  LDMPipeline(unet=model, scheduler=scheduler, vae=vqvae)
								pipe.save_pretrained(args.dump_path)
				except:  # noqa: E722
								model.save_pretrained(args.dump_path)




