
import torch

from diffusers import CMStochasticIterativeScheduler

from .test_schedulers import SchedulerCommonTest

class     _UpperCamelCase			(   _A					):






			'''simple docstring'''

			__UpperCamelCase :  str    =	(CMStochasticIterativeScheduler,)
			__UpperCamelCase :  Optional[int]    =	10

			def  lowerCAmelCase__		(    self	:    str  ,		**snake_case_	:    int ):
							UpperCamelCase_:							Dict		=  {
							    """num_train_timesteps""": 201,
							    """sigma_min""": 0.002,
							    """sigma_max""": 80.0,
							}

							config.update(**snake_case_ )
							return config

			def  lowerCAmelCase__		(    self	:    Dict ):
							UpperCamelCase_:							Any		=  10

							UpperCamelCase_:							str		=  self.get_scheduler_config()
							UpperCamelCase_:							Optional[Any]		=  self.scheduler_classes[0](**snake_case_ )

							scheduler.set_timesteps(snake_case_ )

							UpperCamelCase_:							Dict		=  scheduler.timesteps[0]
							UpperCamelCase_:							List[str]		=  scheduler.timesteps[1]

							UpperCamelCase_:							List[str]		=  self.dummy_sample
							UpperCamelCase_:							str		=  0.1 * sample

							UpperCamelCase_:							Optional[int]		=  scheduler.step(snake_case_  ,		snake_case_  ,		snake_case_ ).prev_sample
							UpperCamelCase_:							List[str]		=  scheduler.step(snake_case_  ,		snake_case_  ,		snake_case_ ).prev_sample

							self.assertEqual(output_a.shape  ,		sample.shape )
							self.assertEqual(output_a.shape  ,		output_a.shape )

			def  lowerCAmelCase__		(    self	:    Tuple ):
							for timesteps in [10, 50, 100, 1000]:
											self.check_over_configs(num_train_timesteps=snake_case_ )

			def  lowerCAmelCase__		(    self	:    str ):
							for clip_denoised in [True, False]:
											self.check_over_configs(clip_denoised=snake_case_ )

			def  lowerCAmelCase__		(    self	:    Dict ):
							UpperCamelCase_:							Optional[Any]		=  self.scheduler_classes[0]
							UpperCamelCase_:							List[Any]		=  self.get_scheduler_config()
							UpperCamelCase_:							int		=  scheduler_class(**snake_case_ )

							UpperCamelCase_:							Tuple		=  1
							scheduler.set_timesteps(snake_case_ )
							UpperCamelCase_:							Optional[Any]		=  scheduler.timesteps

							UpperCamelCase_:							Optional[int]		=  torch.manual_seed(0 )

							UpperCamelCase_:							Any		=  self.dummy_model()
							UpperCamelCase_:							Dict		=  self.dummy_sample_deter * scheduler.init_noise_sigma

							for i, t in enumerate(snake_case_ ):
											# 1. scale model input
											UpperCamelCase_:							Optional[Any]		=  scheduler.scale_model_input(snake_case_  ,		snake_case_ )

											# 2. predict noise residual
											UpperCamelCase_:							List[str]		=  model(snake_case_  ,		snake_case_ )

											# 3. predict previous sample x_t-1
											UpperCamelCase_:							str		=  scheduler.step(snake_case_  ,		snake_case_  ,		snake_case_  ,		generator=snake_case_ ).prev_sample

											UpperCamelCase_:							Union[str, Any]		=  pred_prev_sample

							UpperCamelCase_:							Optional[int]		=  torch.sum(torch.abs(snake_case_ ) )
							UpperCamelCase_:							int		=  torch.mean(torch.abs(snake_case_ ) )

							assert abs(result_sum.item() - 192.7614 ) < 1e-2
							assert abs(result_mean.item() - 0.2510 ) < 1e-3

			def  lowerCAmelCase__		(    self	:    Optional[int] ):
							UpperCamelCase_:							int		=  self.scheduler_classes[0]
							UpperCamelCase_:							List[Any]		=  self.get_scheduler_config()
							UpperCamelCase_:							str		=  scheduler_class(**snake_case_ )

							UpperCamelCase_:							Any		=  [106, 0]
							scheduler.set_timesteps(timesteps=snake_case_ )
							UpperCamelCase_:							Any		=  scheduler.timesteps

							UpperCamelCase_:							Union[str, Any]		=  torch.manual_seed(0 )

							UpperCamelCase_:							Dict		=  self.dummy_model()
							UpperCamelCase_:							Union[str, Any]		=  self.dummy_sample_deter * scheduler.init_noise_sigma

							for t in timesteps:
											# 1. scale model input
											UpperCamelCase_:							Optional[int]		=  scheduler.scale_model_input(snake_case_  ,		snake_case_ )

											# 2. predict noise residual
											UpperCamelCase_:							str		=  model(snake_case_  ,		snake_case_ )

											# 3. predict previous sample x_t-1
											UpperCamelCase_:							Any		=  scheduler.step(snake_case_  ,		snake_case_  ,		snake_case_  ,		generator=snake_case_ ).prev_sample

											UpperCamelCase_:							str		=  pred_prev_sample

							UpperCamelCase_:							Optional[Any]		=  torch.sum(torch.abs(snake_case_ ) )
							UpperCamelCase_:							str		=  torch.mean(torch.abs(snake_case_ ) )

							assert abs(result_sum.item() - 347.6357 ) < 1e-2
							assert abs(result_mean.item() - 0.4527 ) < 1e-3

			def  lowerCAmelCase__		(    self	:    Optional[Any] ):
							UpperCamelCase_:							Any		=  self.scheduler_classes[0]
							UpperCamelCase_:							str		=  self.get_scheduler_config()
							UpperCamelCase_:							Optional[int]		=  scheduler_class(**snake_case_ )

							UpperCamelCase_:							Optional[int]		=  [39, 30, 12, 15, 0]

							with self.assertRaises(snake_case_  ,		msg="""`timesteps` must be in descending order.""" ):
											scheduler.set_timesteps(timesteps=snake_case_ )

			def  lowerCAmelCase__		(    self	:    Dict ):
							UpperCamelCase_:							List[Any]		=  self.scheduler_classes[0]
							UpperCamelCase_:							Any		=  self.get_scheduler_config()
							UpperCamelCase_:							Optional[Any]		=  scheduler_class(**snake_case_ )

							UpperCamelCase_:							Optional[Any]		=  [39, 30, 12, 1, 0]
							UpperCamelCase_:							Optional[Any]		=  len(snake_case_ )

							with self.assertRaises(snake_case_  ,		msg="""Can only pass one of `num_inference_steps` or `timesteps`.""" ):
											scheduler.set_timesteps(num_inference_steps=snake_case_  ,		timesteps=snake_case_ )




			def  lowerCAmelCase__		(    self	:    Dict ):
							UpperCamelCase_:							str		=  self.scheduler_classes[0]
							UpperCamelCase_:							str		=  self.get_scheduler_config()
							UpperCamelCase_:							List[str]		=  scheduler_class(**snake_case_ )

							UpperCamelCase_:							Optional[Any]		=  [scheduler.config.num_train_timesteps]

							with self.assertRaises(
							    snake_case_  ,		msg="""`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}"""  ,		):
											scheduler.set_timesteps(timesteps=snake_case_ )








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


lowerCamelCase_  :	Tuple 				=					logging.get_logger(__name__)

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

class     _UpperCamelCase			(   _A					):






			'''simple docstring'''

			__UpperCamelCase :  Any    =	"""lxmert"""
			__UpperCamelCase :  Optional[Any]    =	{}

			def __init__(    self	:    Dict  ,		snake_case_	:    List[str]=3_0522  ,		snake_case_	:    int=768  ,		snake_case_	:    str=12  ,		snake_case_	:    Tuple=9500  ,		snake_case_	:    Optional[Any]=1600  ,		snake_case_	:    str=400  ,		snake_case_	:    List[str]=3072  ,		snake_case_	:    Dict="gelu"  ,		snake_case_	:    Optional[Any]=0.1  ,		snake_case_	:    int=0.1  ,		snake_case_	:    Optional[int]=512  ,		snake_case_	:    Tuple=2  ,		snake_case_	:    Optional[int]=0.02  ,		snake_case_	:    Union[str, Any]=1e-12  ,		snake_case_	:    Tuple=9  ,		snake_case_	:    Tuple=5  ,		snake_case_	:    Union[str, Any]=5  ,		snake_case_	:    List[str]=2048  ,		snake_case_	:    Any=4  ,		snake_case_	:    int=6.67  ,		snake_case_	:    Dict=True  ,		snake_case_	:    Tuple=True  ,		snake_case_	:    int=True  ,		snake_case_	:    str=True  ,		snake_case_	:    List[Any]=True  ,		snake_case_	:    List[Any]=True  ,		snake_case_	:    List[str]=True  ,		**snake_case_	:    Optional[int]  ,		):
							UpperCamelCase_:							int		=  vocab_size
							UpperCamelCase_:							Tuple		=  hidden_size
							UpperCamelCase_:							Optional[Any]		=  num_attention_heads
							UpperCamelCase_:							List[str]		=  hidden_act
							UpperCamelCase_:							int		=  intermediate_size
							UpperCamelCase_:							Any		=  hidden_dropout_prob
							UpperCamelCase_:							List[Any]		=  attention_probs_dropout_prob
							UpperCamelCase_:							List[str]		=  max_position_embeddings
							UpperCamelCase_:							Optional[Any]		=  type_vocab_size
							UpperCamelCase_:							Optional[Any]		=  initializer_range
							UpperCamelCase_:							str		=  layer_norm_eps
							UpperCamelCase_:							List[str]		=  num_qa_labels
							UpperCamelCase_:							List[str]		=  num_object_labels
							UpperCamelCase_:							Tuple		=  num_attr_labels
							UpperCamelCase_:							List[Any]		=  l_layers
							UpperCamelCase_:							List[Any]		=  x_layers
							UpperCamelCase_:							Any		=  r_layers
							UpperCamelCase_:							str		=  visual_feat_dim
							UpperCamelCase_:							Union[str, Any]		=  visual_pos_dim
							UpperCamelCase_:							str		=  visual_loss_normalizer
							UpperCamelCase_:							Dict		=  task_matched
							UpperCamelCase_:							Tuple		=  task_mask_lm
							UpperCamelCase_:							List[str]		=  task_obj_predict
							UpperCamelCase_:							Any		=  task_qa
							UpperCamelCase_:							Dict		=  visual_obj_loss
							UpperCamelCase_:							Optional[Any]		=  visual_attr_loss
							UpperCamelCase_:							List[Any]		=  visual_feat_loss
							UpperCamelCase_:							Tuple		=  {"""vision""": r_layers, """cross_encoder""": x_layers, """language""": l_layers}
							super().__init__(**snake_case_ )






