from ...configuration_utils import PretrainedConfig
from ...utils import logging


SCREAMING_SNAKE_CASE		:     Union[str, Any]         =	logging.get_logger(__name__)

SCREAMING_SNAKE_CASE		:     int         =	{
    "unc-nlp/lxmert-base-uncased": "https://huggingface.co/unc-nlp/lxmert-base-uncased/resolve/main/config.json",
}

class        UpperCamelCase						(				__a				):
						a__		:Dict      =			'''lxmert'''
						a__		:Tuple      =			{}





						def __init__(self			,     __UpperCamelCase=30_522			,     __UpperCamelCase=768			,     __UpperCamelCase=12			,     __UpperCamelCase=9_500			,     __UpperCamelCase=1_600			,     __UpperCamelCase=400			,     __UpperCamelCase=3_072			,     __UpperCamelCase="gelu"			,     __UpperCamelCase=0.1			,     __UpperCamelCase=0.1			,     __UpperCamelCase=512			,     __UpperCamelCase=2			,     __UpperCamelCase=0.02			,     __UpperCamelCase=1E-1_2			,     __UpperCamelCase=9			,     __UpperCamelCase=5			,     __UpperCamelCase=5			,     __UpperCamelCase=2_048			,     __UpperCamelCase=4			,     __UpperCamelCase=6.67			,     __UpperCamelCase=True			,     __UpperCamelCase=True			,     __UpperCamelCase=True			,     __UpperCamelCase=True			,     __UpperCamelCase=True			,     __UpperCamelCase=True			,     __UpperCamelCase=True			,     **__UpperCamelCase			,     )				->				Union[str, Any]:
												UpperCamelCase_		:							List[Any]  =		vocab_size
												UpperCamelCase_		:							Any  =		hidden_size
												UpperCamelCase_		:							List[str]  =		num_attention_heads
												UpperCamelCase_		:							Optional[int]  =		hidden_act
												UpperCamelCase_		:							Optional[Any]  =		intermediate_size
												UpperCamelCase_		:							Union[str, Any]  =		hidden_dropout_prob
												UpperCamelCase_		:							Optional[int]  =		attention_probs_dropout_prob
												UpperCamelCase_		:							Dict  =		max_position_embeddings
												UpperCamelCase_		:							Any  =		type_vocab_size
												UpperCamelCase_		:							Tuple  =		initializer_range
												UpperCamelCase_		:							Optional[int]  =		layer_norm_eps
												UpperCamelCase_		:							Any  =		num_qa_labels
												UpperCamelCase_		:							List[str]  =		num_object_labels
												UpperCamelCase_		:							int  =		num_attr_labels
												UpperCamelCase_		:							List[str]  =		l_layers
												UpperCamelCase_		:							str  =		x_layers
												UpperCamelCase_		:							Dict  =		r_layers
												UpperCamelCase_		:							Dict  =		visual_feat_dim
												UpperCamelCase_		:							List[Any]  =		visual_pos_dim
												UpperCamelCase_		:							int  =		visual_loss_normalizer
												UpperCamelCase_		:							Union[str, Any]  =		task_matched
												UpperCamelCase_		:							Optional[int]  =		task_mask_lm
												UpperCamelCase_		:							Union[str, Any]  =		task_obj_predict
												UpperCamelCase_		:							List[Any]  =		task_qa
												UpperCamelCase_		:							Union[str, Any]  =		visual_obj_loss
												UpperCamelCase_		:							Tuple  =		visual_attr_loss
												UpperCamelCase_		:							Optional[Any]  =		visual_feat_loss
												UpperCamelCase_		:							Optional[Any]  =		{"""vision""": r_layers, """cross_encoder""": x_layers, """language""": l_layers}
												super().__init__(**__UpperCamelCase						)







import unittest

import torch

from diffusers import DDIMScheduler, DDPMScheduler, UNetaDModel
from diffusers.training_utils import set_seed
from diffusers.utils.testing_utils import slow


SCREAMING_SNAKE_CASE		:     List[str]         =	False

class        UpperCamelCase						(				unittest.TestCase				):





						def        A_							(self			,     __UpperCamelCase=32						)				->				Optional[int]:
												set_seed(0						)
												UpperCamelCase_		:							Optional[Any]  =		UNetaDModel(sample_size=__UpperCamelCase			,     in_channels=3			,     out_channels=3						)
												UpperCamelCase_		:							Union[str, Any]  =		torch.optim.SGD(model.parameters()			,     lr=0.0_001						)
												return model, optimizer





						@slow
						def        A_							(self						)				->				str:
												UpperCamelCase_		:							str  =		"""cpu"""  # ensure full determinism without setting the CUBLAS_WORKSPACE_CONFIG env variable
												UpperCamelCase_		:							Tuple  =		DDPMScheduler(
												    num_train_timesteps=1_000			,     beta_start=0.0_001			,     beta_end=0.02			,     beta_schedule="""linear"""			,     clip_sample=__UpperCamelCase			,     )
												UpperCamelCase_		:							List[Any]  =		DDIMScheduler(
												    num_train_timesteps=1_000			,     beta_start=0.0_001			,     beta_end=0.02			,     beta_schedule="""linear"""			,     clip_sample=__UpperCamelCase			,     )

												assert ddpm_scheduler.config.num_train_timesteps == ddim_scheduler.config.num_train_timesteps

												# shared batches for DDPM and DDIM
												set_seed(0						)
												UpperCamelCase_		:							Any  =		[torch.randn((4, 3, 32, 32)						).clip(-1			,     1						).to(__UpperCamelCase						) for _ in range(4						)]
												UpperCamelCase_		:							str  =		[torch.randn((4, 3, 32, 32)						).to(__UpperCamelCase						) for _ in range(4						)]
												UpperCamelCase_		:							Union[str, Any]  =		[torch.randint(0			,     1_000			,     (4,)						).long().to(__UpperCamelCase						) for _ in range(4						)]

												# train with a DDPM scheduler
												UpperCamelCase_,UpperCamelCase_		:							Optional[int]  =		self.get_model_optimizer(resolution=32						)
												model.train().to(__UpperCamelCase						)
												for i in range(4						):
																		optimizer.zero_grad()
																		UpperCamelCase_		:							Optional[Any]  =		ddpm_scheduler.add_noise(clean_images[i]			,     noise[i]			,     timesteps[i]						)
																		UpperCamelCase_		:							List[Any]  =		model(__UpperCamelCase			,     timesteps[i]						).sample
																		UpperCamelCase_		:							Dict  =		torch.nn.functional.mse_loss(__UpperCamelCase			,     noise[i]						)
																		loss.backward()
																		optimizer.step()
												del model, optimizer

												# recreate the model and optimizer, and retry with DDIM
												UpperCamelCase_,UpperCamelCase_		:							Optional[int]  =		self.get_model_optimizer(resolution=32						)
												model.train().to(__UpperCamelCase						)
												for i in range(4						):
																		optimizer.zero_grad()
																		UpperCamelCase_		:							str  =		ddim_scheduler.add_noise(clean_images[i]			,     noise[i]			,     timesteps[i]						)
																		UpperCamelCase_		:							Optional[int]  =		model(__UpperCamelCase			,     timesteps[i]						).sample
																		UpperCamelCase_		:							Union[str, Any]  =		torch.nn.functional.mse_loss(__UpperCamelCase			,     noise[i]						)
																		loss.backward()
																		optimizer.step()
												del model, optimizer

												self.assertTrue(torch.allclose(__UpperCamelCase			,     __UpperCamelCase			,     atol=1E-5						)						)
												self.assertTrue(torch.allclose(__UpperCamelCase			,     __UpperCamelCase			,     atol=1E-5						)						)






