
'''simple docstring'''
import logging

import torch
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss

from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.bert.modeling_bert import (
    BERT_INPUTS_DOCSTRING,
    BERT_START_DOCSTRING,
    BertEncoder,
    BertModel,
    BertPreTrainedModel,
)


lowercase__						       =      logging.getLogger(__name__)






class 	snake_case__       (      __SCREAMING_SNAKE_CASE					):







		"""simple docstring"""

		def       lowerCAmelCase				(      self				:			Optional[Any]  ,	UpperCamelCase__				:			Union[str, Any]  ,	UpperCamelCase__				:			Union[str, Any]  ,	UpperCamelCase__				:			List[Any]=None  ,	UpperCamelCase__				:			Union[str, Any]=None      )						->			Dict:






				"""simple docstring"""







				snake_case  :       int     			=						self.layer[current_layer](UpperCamelCase__  ,	UpperCamelCase__  ,	head_mask[current_layer]      )

				snake_case  :       List[str]     			=						layer_outputs[0]

				return hidden_states







@add_start_docstrings(
    """The bare Bert Model transformer with PABEE outputting raw hidden-states without any specific head on top."""    ,					__SCREAMING_SNAKE_CASE    ,					)
class 	snake_case__       (      __SCREAMING_SNAKE_CASE					):







		"""simple docstring"""

		def __init__(      self				:			List[Any]  ,	UpperCamelCase__				:			List[str]      )						->			List[Any]:






				"""simple docstring"""







				super().__init__(UpperCamelCase__      )

				snake_case  :       Tuple     			=						BertEncoderWithPabee(UpperCamelCase__      )

				self.init_weights()
				snake_case  :       Dict     			=						0
				snake_case  :       str     			=						0
				snake_case  :       List[Any]     			=						0

				snake_case  :       int     			=						0



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






				"""simple docstring"""







				snake_case  :       Any     			=						threshold



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






				"""simple docstring"""







				snake_case  :       int     			=						patience



		def       lowerCAmelCase				(      self				:			int      )						->			int:






				"""simple docstring"""







				snake_case  :       Optional[int]     			=						0
				snake_case  :       Dict     			=						0



		def       lowerCAmelCase				(      self				:			Dict      )						->			int:






				"""simple docstring"""







				snake_case  :       List[str]     			=						self.inference_layers_num / self.inference_instances_num
				snake_case  :       Union[str, Any]     			=						(
				    f'*** Patience = {self.patience} Avg. Inference Layers = {avg_inf_layers:.2f} Speed Up ='
				    f' {1 - avg_inf_layers / self.config.num_hidden_layers:.2f} ***'
				)
				print(UpperCamelCase__      )



		@add_start_docstrings_to_model_forward(UpperCamelCase__      )
		def       lowerCAmelCase				(      self				:			List[str]  ,	UpperCamelCase__				:			Dict=None  ,	UpperCamelCase__				:			Union[str, Any]=None  ,	UpperCamelCase__				:			Optional[Any]=None  ,	UpperCamelCase__				:			List[str]=None  ,	UpperCamelCase__				:			Optional[int]=None  ,	UpperCamelCase__				:			str=None  ,	UpperCamelCase__				:			List[str]=None  ,	UpperCamelCase__				:			Tuple=None  ,	UpperCamelCase__				:			Dict=None  ,	UpperCamelCase__				:			Tuple=None  ,	UpperCamelCase__				:			Optional[Any]=False  ,	)						->			List[Any]:






				"""simple docstring"""







				if input_ids is not None and inputs_embeds is not None:
						raise ValueError('''You cannot specify both input_ids and inputs_embeds at the same time'''      )
				elif input_ids is not None:
						snake_case  :       List[str]     			=						input_ids.size()
				elif inputs_embeds is not None:
						snake_case  :       List[Any]     			=						inputs_embeds.size()[:-1]
				else:
						raise ValueError('''You have to specify either input_ids or inputs_embeds'''      )

				snake_case  :       Union[str, Any]     			=						input_ids.device if input_ids is not None else inputs_embeds.device

				if attention_mask is None:
						snake_case  :       List[str]     			=						torch.ones(UpperCamelCase__  ,	device=UpperCamelCase__      )
				if token_type_ids is None:
						snake_case  :       Union[str, Any]     			=						torch.zeros(UpperCamelCase__  ,	dtype=torch.long  ,	device=UpperCamelCase__      )

				# We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
				# ourselves in which case we just need to make it broadcastable to all heads.
				snake_case  :       torch.Tensor     			=						self.get_extended_attention_mask(UpperCamelCase__  ,	UpperCamelCase__  ,	UpperCamelCase__      )

				# If a 2D ou 3D attention mask is provided for the cross-attention
				# we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
				if self.config.is_decoder and encoder_hidden_states is not None:
						snake_case			,snake_case			,snake_case  :       Any     			=						encoder_hidden_states.size()
						snake_case  :       List[Any]     			=						(encoder_batch_size, encoder_sequence_length)
						if encoder_attention_mask is None:
								snake_case  :       Any     			=						torch.ones(UpperCamelCase__  ,	device=UpperCamelCase__      )
						snake_case  :       str     			=						self.invert_attention_mask(UpperCamelCase__      )
				else:
						snake_case  :       str     			=						None

				# Prepare head mask if needed
				# 1.0 in head_mask indicate we keep the head
				# attention_probs has shape bsz x n_heads x N x N
				# input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
				# and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
				snake_case  :       Any     			=						self.get_head_mask(UpperCamelCase__  ,	self.config.num_hidden_layers      )

				snake_case  :       List[str]     			=						self.embeddings(
				    input_ids=UpperCamelCase__  ,	position_ids=UpperCamelCase__  ,	token_type_ids=UpperCamelCase__  ,	inputs_embeds=UpperCamelCase__      )
				snake_case  :       Optional[int]     			=						embedding_output

				if self.training:
						snake_case  :       Dict     			=						[]
						for i in range(self.config.num_hidden_layers      ):
								snake_case  :       Union[str, Any]     			=						self.encoder.adaptive_forward(
								    UpperCamelCase__  ,	current_layer=UpperCamelCase__  ,	attention_mask=UpperCamelCase__  ,	head_mask=UpperCamelCase__      )

								snake_case  :       int     			=						self.pooler(UpperCamelCase__      )
								snake_case  :       Optional[int]     			=						output_layers[i](output_dropout(UpperCamelCase__      )      )
								res.append(UpperCamelCase__      )
				elif self.patience == 0:  # Use all layers for inference
						snake_case  :       List[Any]     			=						self.encoder(
						    UpperCamelCase__  ,	attention_mask=UpperCamelCase__  ,	head_mask=UpperCamelCase__  ,	encoder_hidden_states=UpperCamelCase__  ,	encoder_attention_mask=UpperCamelCase__  ,	)
						snake_case  :       str     			=						self.pooler(encoder_outputs[0]      )
						snake_case  :       List[str]     			=						[output_layers[self.config.num_hidden_layers - 1](UpperCamelCase__      )]
				else:
						snake_case  :       Any     			=						0
						snake_case  :       Dict     			=						None
						snake_case  :       Tuple     			=						0
						for i in range(self.config.num_hidden_layers      ):
								calculated_layer_num += 1
								snake_case  :       Tuple     			=						self.encoder.adaptive_forward(
								    UpperCamelCase__  ,	current_layer=UpperCamelCase__  ,	attention_mask=UpperCamelCase__  ,	head_mask=UpperCamelCase__      )

								snake_case  :       List[Any]     			=						self.pooler(UpperCamelCase__      )
								snake_case  :       List[Any]     			=						output_layers[i](UpperCamelCase__      )
								if regression:
										snake_case  :       List[str]     			=						logits.detach()
										if patient_result is not None:
												snake_case  :       Any     			=						patient_result.detach()
										if (patient_result is not None) and torch.abs(patient_result - labels      ) < self.regression_threshold:
												patient_counter += 1
										else:
												snake_case  :       Tuple     			=						0
								else:
										snake_case  :       Optional[int]     			=						logits.detach().argmax(dim=1      )
										if patient_result is not None:
												snake_case  :       List[Any]     			=						patient_result.detach().argmax(dim=1      )
										if (patient_result is not None) and torch.all(labels.eq(UpperCamelCase__      )      ):
												patient_counter += 1
										else:
												snake_case  :       Tuple     			=						0

								snake_case  :       Tuple     			=						logits
								if patient_counter == self.patience:
										break
						snake_case  :       List[str]     			=						[patient_result]
						self.inference_layers_num += calculated_layer_num
						self.inference_instances_num += 1

				return res







@add_start_docstrings(
    """Bert Model transformer with PABEE and a sequence classification/regression head on top (a linear layer on top of
    the pooled output) e.g. for GLUE tasks. """    ,					__SCREAMING_SNAKE_CASE    ,					)
class 	snake_case__       (      __SCREAMING_SNAKE_CASE					):







		"""simple docstring"""

		def __init__(      self				:			Union[str, Any]  ,	UpperCamelCase__				:			List[str]      )						->			List[Any]:






				"""simple docstring"""







				super().__init__(UpperCamelCase__      )
				snake_case  :       Dict     			=						config.num_labels

				snake_case  :       Dict     			=						BertModelWithPabee(UpperCamelCase__      )
				snake_case  :       List[Any]     			=						nn.Dropout(config.hidden_dropout_prob      )
				snake_case  :       List[Any]     			=						nn.ModuleList(
				    [nn.Linear(config.hidden_size  ,	self.config.num_labels      ) for _ in range(config.num_hidden_layers      )]      )

				self.init_weights()



		@add_start_docstrings_to_model_forward(UpperCamelCase__      )
		def       lowerCAmelCase				(      self				:			Optional[Any]  ,	UpperCamelCase__				:			int=None  ,	UpperCamelCase__				:			List[str]=None  ,	UpperCamelCase__				:			int=None  ,	UpperCamelCase__				:			Optional[int]=None  ,	UpperCamelCase__				:			Union[str, Any]=None  ,	UpperCamelCase__				:			str=None  ,	UpperCamelCase__				:			Union[str, Any]=None  ,	)						->			Any:






				"""simple docstring"""







				snake_case  :       Optional[Any]     			=						self.bert(
				    input_ids=UpperCamelCase__  ,	attention_mask=UpperCamelCase__  ,	token_type_ids=UpperCamelCase__  ,	position_ids=UpperCamelCase__  ,	head_mask=UpperCamelCase__  ,	inputs_embeds=UpperCamelCase__  ,	output_dropout=self.dropout  ,	output_layers=self.classifiers  ,	regression=self.num_labels == 1  ,	)

				snake_case  :       str     			=						(logits[-1],)

				if labels is not None:
						snake_case  :       Any     			=						None
						snake_case  :       int     			=						0
						for ix, logits_item in enumerate(UpperCamelCase__      ):
								if self.num_labels == 1:
										#  We are doing regression
										snake_case  :       Optional[Any]     			=						MSELoss()
										snake_case  :       Optional[Any]     			=						loss_fct(logits_item.view(-1      )  ,	labels.view(-1      )      )
								else:
										snake_case  :       str     			=						CrossEntropyLoss()
										snake_case  :       str     			=						loss_fct(logits_item.view(-1  ,	self.num_labels      )  ,	labels.view(-1      )      )
								if total_loss is None:
										snake_case  :       Dict     			=						loss
								else:
										total_loss += loss * (ix + 1)
								total_weights += ix + 1
						snake_case  :       Any     			=						(total_loss / total_weights,) + outputs

				return outputs




'''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


