





"""simple docstring"""


import argparse
import collections

import torch
from flax import traverse_util
from tax import checkpoints

from transformers import TaConfig, TaEncoderModel, TaForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()

def      UpperCamelCase							(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE="attention"     ):
						UpperCamelCase				:		List[str]				   =			params[f"""{prefix}/layers_{i}/{layer_name}/key/kernel"""]
						UpperCamelCase				:		str				   =			params[f"""{prefix}/layers_{i}/{layer_name}/out/kernel"""]
						UpperCamelCase				:		Optional[Any]				   =			params[f"""{prefix}/layers_{i}/{layer_name}/query/kernel"""]
						UpperCamelCase				:		str				   =			params[f"""{prefix}/layers_{i}/{layer_name}/value/kernel"""]
						return k, o, q, v

def      UpperCamelCase							(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE=False     ):
						if split_mlp_wi:
												UpperCamelCase				:		Optional[int]				   =			params[f"""{prefix}/layers_{i}/mlp/wi_0/kernel"""]
												UpperCamelCase				:		Optional[Any]				   =			params[f"""{prefix}/layers_{i}/mlp/wi_1/kernel"""]
												UpperCamelCase				:		int				   =			(wi_a, wi_a)
						else:
												UpperCamelCase				:		Optional[Any]				   =			params[f"""{prefix}/layers_{i}/mlp/wi/kernel"""]

						UpperCamelCase				:		Optional[Any]				   =			params[f"""{prefix}/layers_{i}/mlp/wo/kernel"""]
						return wi, wo

def      UpperCamelCase							(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     ):
						return params[f"""{prefix}/layers_{i}/{layer_name}/scale"""]

def      UpperCamelCase							(SCREAMING_SNAKE_CASE							,     *, SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     ):
						UpperCamelCase				:		Dict				   =			traverse_util.flatten_dict(variables["""target"""]     )
						UpperCamelCase				:		Optional[Any]				   =			{"""/""".join(SCREAMING_SNAKE_CASE     ): v for k, v in old.items()}

						# v1.1 models have a gated GeLU with wi_0 and wi_1 instead of wi
						UpperCamelCase				:		Union[str, Any]				   =			"""encoder/layers_0/mlp/wi_0/kernel""" in old
						print("""Split MLP:"""							,     SCREAMING_SNAKE_CASE     )

						UpperCamelCase				:		Optional[int]				   =			collections.OrderedDict()

						# Shared embeddings.
						UpperCamelCase				:		Dict				   =			old["""token_embedder/embedding"""]

						# Encoder.
						for i in range(SCREAMING_SNAKE_CASE     ):
												# Block i, layer 0 (Self Attention).
												UpperCamelCase				:		int				   =			tax_layer_norm_lookup(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE							,     """encoder"""							,     """pre_attention_layer_norm"""     )
												UpperCamelCase   ,						UpperCamelCase   ,						UpperCamelCase   ,						UpperCamelCase				:		Optional[int]				   =			tax_attention_lookup(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE							,     """encoder"""							,     """attention"""     )
												UpperCamelCase				:		Optional[int]				   =			layer_norm
												UpperCamelCase				:		Tuple				   =			k.T
												UpperCamelCase				:		Any				   =			o.T
												UpperCamelCase				:		Tuple				   =			q.T
												UpperCamelCase				:		str				   =			v.T

												# Block i, layer 1 (MLP).
												UpperCamelCase				:		Optional[int]				   =			tax_layer_norm_lookup(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE							,     """encoder"""							,     """pre_mlp_layer_norm"""     )
												UpperCamelCase   ,						UpperCamelCase				:		Tuple				   =			tax_mlp_lookup(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE							,     """encoder"""							,     SCREAMING_SNAKE_CASE     )
												UpperCamelCase				:		int				   =			layer_norm
												if split_mlp_wi:
																		UpperCamelCase				:		Optional[Any]				   =			wi[0].T
																		UpperCamelCase				:		Optional[int]				   =			wi[1].T
												else:
																		UpperCamelCase				:		str				   =			wi.T
												UpperCamelCase				:		Optional[Any]				   =			wo.T

						UpperCamelCase				:		List[str]				   =			old[
						    """encoder/relpos_bias/rel_embedding"""
						].T
						UpperCamelCase				:		Optional[Any]				   =			old["""encoder/encoder_norm/scale"""]

						if not is_encoder_only:
												# Decoder.
												for i in range(SCREAMING_SNAKE_CASE     ):
																		# Block i, layer 0 (Self Attention).
																		UpperCamelCase				:		Union[str, Any]				   =			tax_layer_norm_lookup(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE							,     """decoder"""							,     """pre_self_attention_layer_norm"""     )
																		UpperCamelCase   ,						UpperCamelCase   ,						UpperCamelCase   ,						UpperCamelCase				:		Dict				   =			tax_attention_lookup(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE							,     """decoder"""							,     """self_attention"""     )
																		UpperCamelCase				:		Tuple				   =			layer_norm
																		UpperCamelCase				:		int				   =			k.T
																		UpperCamelCase				:		Any				   =			o.T
																		UpperCamelCase				:		List[str]				   =			q.T
																		UpperCamelCase				:		List[str]				   =			v.T

																		# Block i, layer 1 (Cross Attention).
																		UpperCamelCase				:		List[Any]				   =			tax_layer_norm_lookup(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE							,     """decoder"""							,     """pre_cross_attention_layer_norm"""     )
																		UpperCamelCase   ,						UpperCamelCase   ,						UpperCamelCase   ,						UpperCamelCase				:		str				   =			tax_attention_lookup(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE							,     """decoder"""							,     """encoder_decoder_attention"""     )
																		UpperCamelCase				:		Any				   =			layer_norm
																		UpperCamelCase				:		List[Any]				   =			k.T
																		UpperCamelCase				:		int				   =			o.T
																		UpperCamelCase				:		str				   =			q.T
																		UpperCamelCase				:		Tuple				   =			v.T

																		# Block i, layer 2 (MLP).
																		UpperCamelCase				:		Union[str, Any]				   =			tax_layer_norm_lookup(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE							,     """decoder"""							,     """pre_mlp_layer_norm"""     )
																		UpperCamelCase   ,						UpperCamelCase				:		Optional[int]				   =			tax_mlp_lookup(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE							,     """decoder"""							,     SCREAMING_SNAKE_CASE     )
																		UpperCamelCase				:		List[str]				   =			layer_norm
																		if split_mlp_wi:
																								UpperCamelCase				:		List[Any]				   =			wi[0].T
																								UpperCamelCase				:		Dict				   =			wi[1].T
																		else:
																								UpperCamelCase				:		Tuple				   =			wi.T
																		UpperCamelCase				:		int				   =			wo.T

												UpperCamelCase				:		List[str]				   =			old["""decoder/decoder_norm/scale"""]
												UpperCamelCase				:		Union[str, Any]				   =			old[
												    """decoder/relpos_bias/rel_embedding"""
												].T

												# LM Head (only in v1.1 checkpoints, in v1.0 embeddings are used instead)
												if "decoder/logits_dense/kernel" in old:
																		UpperCamelCase				:		Dict				   =			old["""decoder/logits_dense/kernel"""].T

						return new

def      UpperCamelCase							(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     ):
						UpperCamelCase				:		Optional[Any]				   =			collections.OrderedDict([(k, torch.from_numpy(v.copy()     )) for (k, v) in converted_params.items()]     )

						# Add what is missing.
						if "encoder.embed_tokens.weight" not in state_dict:
												UpperCamelCase				:		Optional[int]				   =			state_dict["""shared.weight"""]

						if not is_encoder_only:
												if "decoder.embed_tokens.weight" not in state_dict:
																		UpperCamelCase				:		Any				   =			state_dict["""shared.weight"""]

												if "lm_head.weight" not in state_dict:  # For old 1.0 models.
																		print("""Using shared word embeddings as lm_head."""     )
																		UpperCamelCase				:		Optional[Any]				   =			state_dict["""shared.weight"""]

						return state_dict

def      UpperCamelCase							(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     ):
						UpperCamelCase				:		Tuple				   =			checkpoints.load_tax_checkpoint(SCREAMING_SNAKE_CASE     )
						UpperCamelCase				:		int				   =			convert_tax_to_pytorch(SCREAMING_SNAKE_CASE							,     num_layers=config.num_layers							,     is_encoder_only=SCREAMING_SNAKE_CASE     )
						UpperCamelCase				:		Optional[int]				   =			make_state_dict(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     )
						model.load_state_dict(SCREAMING_SNAKE_CASE							,     strict=SCREAMING_SNAKE_CASE     )

def      UpperCamelCase							(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE = False     ):
						UpperCamelCase				:		Optional[int]				   =			TaConfig.from_json_file(SCREAMING_SNAKE_CASE     )
						print(f"""Building PyTorch model from configuration: {config}"""     )
						# Non-v1.1 checkpoints could also use T5Model, but this works for all.
						# The v1.0 checkpoints will simply have an LM head that is the word embeddings.
						if is_encoder_only:
												UpperCamelCase				:		int				   =			TaEncoderModel(SCREAMING_SNAKE_CASE     )
						else:
												UpperCamelCase				:		Union[str, Any]				   =			TaForConditionalGeneration(SCREAMING_SNAKE_CASE     )

						# Load weights from tf checkpoint
						load_tax_weights_in_ta(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     )

						# Save pytorch-model
						print(f"""Save PyTorch model to {pytorch_dump_path}"""     )
						model.save_pretrained(SCREAMING_SNAKE_CASE     )

						# Verify that we can load the checkpoint.
						model.from_pretrained(SCREAMING_SNAKE_CASE     )
						print("""Done"""     )


if __name__ == "__main__":
		__magic_name__       :					Any						=			argparse.ArgumentParser(description="""Converts a native T5X checkpoint into a PyTorch checkpoint.""")
		# Required parameters
		parser.add_argument(
		    """--t5x_checkpoint_path""", default=None, type=str, required=True, help="""Path to the T5X checkpoint."""
		)
		parser.add_argument(
		    """--config_file""",
		    default=None,
		    type=str,
		    required=True,
		    help="""The config json file corresponding to the pre-trained T5 model.\nThis specifies the model architecture.""",
		)
		parser.add_argument(
		    """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
		)
		parser.add_argument(
		    """--is_encoder_only""", action="""store_true""", help="""Check if the model is encoder-decoder model""", default=False
		)
		__magic_name__       :					Tuple						=			parser.parse_args()
		convert_tax_checkpoint_to_pytorch(
		    args.tax_checkpoint_path, args.config_file, args.pytorch_dump_path, args.is_encoder_only
		)





'''simple docstring'''
import gc
import math
import unittest

import torch

from diffusers import UNetaDModel
from diffusers.utils import floats_tensor, logging, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin


lowercase__						       =      logging.get_logger(__name__)

enable_full_determinism()






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







		"""simple docstring"""

		lowerCamelCase     							=						UNetaDModel
		lowerCamelCase     							=						"""sample"""



		@property
		def       lowerCAmelCase				(      self				:			Optional[Any]      )						->			Optional[int]:






				"""simple docstring"""







				snake_case  :       Any     			=						4
				snake_case  :       List[Any]     			=						3
				snake_case  :       str     			=						(32, 32)

				snake_case  :       Optional[Any]     			=						floats_tensor((batch_size, num_channels) + sizes      ).to(UpperCamelCase__      )
				snake_case  :       Tuple     			=						torch.tensor([10]      ).to(UpperCamelCase__      )

				return {"sample": noise, "timestep": time_step}



		@property
		def       lowerCAmelCase				(      self				:			Any      )						->			Union[str, Any]:






				"""simple docstring"""







				return (3, 32, 32)



		@property
		def       lowerCAmelCase				(      self				:			str      )						->			Dict:






				"""simple docstring"""







				return (3, 32, 32)



		def       lowerCAmelCase				(      self				:			Union[str, Any]      )						->			int:






				"""simple docstring"""







				snake_case  :       Any     			=						{
				    '''block_out_channels''': (32, 64),
				    '''down_block_types''': ('''DownBlock2D''', '''AttnDownBlock2D'''),
				    '''up_block_types''': ('''AttnUpBlock2D''', '''UpBlock2D'''),
				    '''attention_head_dim''': 3,
				    '''out_channels''': 3,
				    '''in_channels''': 3,
				    '''layers_per_block''': 2,
				    '''sample_size''': 32,
				}
				snake_case  :       Dict     			=						self.dummy_input
				return init_dict, inputs_dict







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







		"""simple docstring"""

		lowerCamelCase     							=						UNetaDModel
		lowerCamelCase     							=						"""sample"""



		@property
		def       lowerCAmelCase				(      self				:			Any      )						->			List[str]:






				"""simple docstring"""







				snake_case  :       str     			=						4
				snake_case  :       Tuple     			=						4
				snake_case  :       str     			=						(32, 32)

				snake_case  :       List[Any]     			=						floats_tensor((batch_size, num_channels) + sizes      ).to(UpperCamelCase__      )
				snake_case  :       str     			=						torch.tensor([10]      ).to(UpperCamelCase__      )

				return {"sample": noise, "timestep": time_step}



		@property
		def       lowerCAmelCase				(      self				:			List[Any]      )						->			Union[str, Any]:






				"""simple docstring"""







				return (4, 32, 32)



		@property
		def       lowerCAmelCase				(      self				:			Optional[Any]      )						->			int:






				"""simple docstring"""







				return (4, 32, 32)



		def       lowerCAmelCase				(      self				:			Union[str, Any]      )						->			Union[str, Any]:






				"""simple docstring"""







				snake_case  :       List[Any]     			=						{
				    '''sample_size''': 32,
				    '''in_channels''': 4,
				    '''out_channels''': 4,
				    '''layers_per_block''': 2,
				    '''block_out_channels''': (32, 64),
				    '''attention_head_dim''': 32,
				    '''down_block_types''': ('''DownBlock2D''', '''DownBlock2D'''),
				    '''up_block_types''': ('''UpBlock2D''', '''UpBlock2D'''),
				}
				snake_case  :       str     			=						self.dummy_input
				return init_dict, inputs_dict



		def       lowerCAmelCase				(      self				:			int      )						->			str:






				"""simple docstring"""







				snake_case			,snake_case  :       int     			=						UNetaDModel.from_pretrained('''fusing/unet-ldm-dummy-update'''  ,	output_loading_info=UpperCamelCase__      )

				self.assertIsNotNone(UpperCamelCase__      )
				self.assertEqual(len(loading_info['''missing_keys''']      )  ,	0      )

				model.to(UpperCamelCase__      )
				snake_case  :       List[Any]     			=						model(**self.dummy_input      ).sample

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



		@unittest.skipIf(torch_device != '''cuda'''  ,	'''This test is supposed to run on GPU'''      )
		def       lowerCAmelCase				(      self				:			int      )						->			Dict:






				"""simple docstring"""







				snake_case			,snake_case  :       str     			=						UNetaDModel.from_pretrained('''fusing/unet-ldm-dummy-update'''  ,	output_loading_info=UpperCamelCase__      )
				model.to(UpperCamelCase__      )
				snake_case  :       Any     			=						model(**self.dummy_input      ).sample

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



		@unittest.skipIf(torch_device != '''cuda'''  ,	'''This test is supposed to run on GPU'''      )
		def       lowerCAmelCase				(      self				:			Any      )						->			str:






				"""simple docstring"""







				snake_case			,snake_case  :       Any     			=						UNetaDModel.from_pretrained('''fusing/unet-ldm-dummy-update'''  ,	output_loading_info=UpperCamelCase__      )
				model_accelerate.to(UpperCamelCase__      )
				model_accelerate.eval()

				snake_case  :       Optional[Any]     			=						torch.randn(
				    1  ,	model_accelerate.config.in_channels  ,	model_accelerate.config.sample_size  ,	model_accelerate.config.sample_size  ,	generator=torch.manual_seed(0      )  ,	)
				snake_case  :       int     			=						noise.to(UpperCamelCase__      )
				snake_case  :       List[str]     			=						torch.tensor([10] * noise.shape[0]      ).to(UpperCamelCase__      )

				snake_case  :       Union[str, Any]     			=						model_accelerate(UpperCamelCase__  ,	UpperCamelCase__      )['''sample''']

				# two models don't need to stay in the device at the same time
				del model_accelerate
				torch.cuda.empty_cache()
				gc.collect()

				snake_case			,snake_case  :       List[Any]     			=						UNetaDModel.from_pretrained(
				    '''fusing/unet-ldm-dummy-update'''  ,	output_loading_info=UpperCamelCase__  ,	low_cpu_mem_usage=UpperCamelCase__      )
				model_normal_load.to(UpperCamelCase__      )
				model_normal_load.eval()
				snake_case  :       Union[str, Any]     			=						model_normal_load(UpperCamelCase__  ,	UpperCamelCase__      )['''sample''']

				assert torch_all_close(UpperCamelCase__  ,	UpperCamelCase__  ,	rtol=1e-3      )



		def       lowerCAmelCase				(      self				:			Optional[Any]      )						->			Optional[int]:






				"""simple docstring"""







				snake_case  :       int     			=						UNetaDModel.from_pretrained('''fusing/unet-ldm-dummy-update'''      )
				model.eval()
				model.to(UpperCamelCase__      )

				snake_case  :       Any     			=						torch.randn(
				    1  ,	model.config.in_channels  ,	model.config.sample_size  ,	model.config.sample_size  ,	generator=torch.manual_seed(0      )  ,	)
				snake_case  :       Dict     			=						noise.to(UpperCamelCase__      )
				snake_case  :       Any     			=						torch.tensor([10] * noise.shape[0]      ).to(UpperCamelCase__      )

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

				snake_case  :       int     			=						output[0, -1, -3:, -3:].flatten().cpu()
				# fmt: off
				snake_case  :       Tuple     			=						torch.tensor([-13.3_258, -20.1_100, -15.9_873, -17.6_617, -23.0_596, -17.9_419, -13.3_675, -16.1_889, -12.3_800]      )
				# fmt: on

				self.assertTrue(torch_all_close(UpperCamelCase__  ,	UpperCamelCase__  ,	rtol=1e-3      )      )







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







		"""simple docstring"""

		lowerCamelCase     							=						UNetaDModel
		lowerCamelCase     							=						"""sample"""



		@property
		def       lowerCAmelCase				(      self				:			List[str]  ,	UpperCamelCase__				:			List[Any]=(32, 32)      )						->			Any:






				"""simple docstring"""







				snake_case  :       Optional[Any]     			=						4
				snake_case  :       Tuple     			=						3

				snake_case  :       List[Any]     			=						floats_tensor((batch_size, num_channels) + sizes      ).to(UpperCamelCase__      )
				snake_case  :       str     			=						torch.tensor(batch_size * [10]      ).to(dtype=torch.intaa  ,	device=UpperCamelCase__      )

				return {"sample": noise, "timestep": time_step}



		@property
		def       lowerCAmelCase				(      self				:			List[str]      )						->			List[Any]:






				"""simple docstring"""







				return (3, 32, 32)



		@property
		def       lowerCAmelCase				(      self				:			Optional[Any]      )						->			Dict:






				"""simple docstring"""







				return (3, 32, 32)



		def       lowerCAmelCase				(      self				:			int      )						->			Optional[int]:






				"""simple docstring"""







				snake_case  :       Dict     			=						{
				    '''block_out_channels''': [32, 64, 64, 64],
				    '''in_channels''': 3,
				    '''layers_per_block''': 1,
				    '''out_channels''': 3,
				    '''time_embedding_type''': '''fourier''',
				    '''norm_eps''': 1e-6,
				    '''mid_block_scale_factor''': math.sqrt(2.0      ),
				    '''norm_num_groups''': None,
				    '''down_block_types''': [
				        '''SkipDownBlock2D''',
				        '''AttnSkipDownBlock2D''',
				        '''SkipDownBlock2D''',
				        '''SkipDownBlock2D''',
				    ],
				    '''up_block_types''': [
				        '''SkipUpBlock2D''',
				        '''SkipUpBlock2D''',
				        '''AttnSkipUpBlock2D''',
				        '''SkipUpBlock2D''',
				    ],
				}
				snake_case  :       Dict     			=						self.dummy_input
				return init_dict, inputs_dict



		@slow
		def       lowerCAmelCase				(      self				:			List[Any]      )						->			int:






				"""simple docstring"""







				snake_case			,snake_case  :       str     			=						UNetaDModel.from_pretrained('''google/ncsnpp-celebahq-256'''  ,	output_loading_info=UpperCamelCase__      )
				self.assertIsNotNone(UpperCamelCase__      )
				self.assertEqual(len(loading_info['''missing_keys''']      )  ,	0      )

				model.to(UpperCamelCase__      )
				snake_case  :       str     			=						self.dummy_input
				snake_case  :       Dict     			=						floats_tensor((4, 3) + (256, 256)      ).to(UpperCamelCase__      )
				snake_case  :       Union[str, Any]     			=						noise
				snake_case  :       List[Any]     			=						model(**UpperCamelCase__      )

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



		@slow
		def       lowerCAmelCase				(      self				:			Union[str, Any]      )						->			str:






				"""simple docstring"""







				snake_case  :       int     			=						UNetaDModel.from_pretrained('''google/ncsnpp-celebahq-256'''      )
				model.to(UpperCamelCase__      )

				snake_case  :       Dict     			=						4
				snake_case  :       Optional[int]     			=						3
				snake_case  :       Tuple     			=						(256, 256)

				snake_case  :       Optional[int]     			=						torch.ones((batch_size, num_channels) + sizes      ).to(UpperCamelCase__      )
				snake_case  :       List[str]     			=						torch.tensor(batch_size * [1e-4]      ).to(UpperCamelCase__      )

				with torch.no_grad():
						snake_case  :       List[str]     			=						model(UpperCamelCase__  ,	UpperCamelCase__      ).sample

				snake_case  :       Dict     			=						output[0, -3:, -3:, -1].flatten().cpu()
				# fmt: off
				snake_case  :       Optional[int]     			=						torch.tensor([-4_842.8_691, -6_499.6_631, -3_800.1_953, -7_978.2_686, -10_980.7_129, -20_028.8_535, 8_148.2_822, 2_342.2_905, 567.7_608]      )
				# fmt: on

				self.assertTrue(torch_all_close(UpperCamelCase__  ,	UpperCamelCase__  ,	rtol=1e-2      )      )



		def       lowerCAmelCase				(      self				:			List[Any]      )						->			Any:






				"""simple docstring"""







				snake_case  :       Union[str, Any]     			=						UNetaDModel.from_pretrained('''fusing/ncsnpp-ffhq-ve-dummy-update'''      )
				model.to(UpperCamelCase__      )

				snake_case  :       Optional[Any]     			=						4
				snake_case  :       List[Any]     			=						3
				snake_case  :       Union[str, Any]     			=						(32, 32)

				snake_case  :       Any     			=						torch.ones((batch_size, num_channels) + sizes      ).to(UpperCamelCase__      )
				snake_case  :       Any     			=						torch.tensor(batch_size * [1e-4]      ).to(UpperCamelCase__      )

				with torch.no_grad():
						snake_case  :       Dict     			=						model(UpperCamelCase__  ,	UpperCamelCase__      ).sample

				snake_case  :       Optional[Any]     			=						output[0, -3:, -3:, -1].flatten().cpu()
				# fmt: off
				snake_case  :       int     			=						torch.tensor([-0.0_325, -0.0_900, -0.0_869, -0.0_332, -0.0_725, -0.0_270, -0.0_101, 0.0_227, 0.0_256]      )
				# fmt: on

				self.assertTrue(torch_all_close(UpperCamelCase__  ,	UpperCamelCase__  ,	rtol=1e-2      )      )



		def       lowerCAmelCase				(      self				:			List[str]      )						->			List[Any]:






				"""simple docstring"""







				pass


