


'''simple docstring'''

from dataclasses import dataclass
from typing import Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin
@dataclass
class _lowerCamelCase	(						lowercase__							):



		'''simple docstring'''







		A_     :      torch.FloatTensor
		A_     :      torch.FloatTensor
		A_     :      Optional[torch.FloatTensor]				   =    None







class _lowerCamelCase	(						lowercase__		,					lowercase__							):



		'''simple docstring'''







		A_     :      Optional[int]				   =    2







		@register_to_config
		def __init__( self		:						Union[str, Any]  ,					_A		:						float = 0.02  ,					_A		:						float = 100  ,					_A		:						float = 1.007  ,					_A		:						float = 80  ,					_A		:						float = 0.05  ,					_A		:						float = 50  ,					)  ->			str:
			# standard deviation of the initial noise distribution
			__magic_name__	:   Optional[int]					     =					sigma_max

			# setable values
			__magic_name__	:   int					     =					None
			__magic_name__	:   np.IntTensor					     =					None
			__magic_name__	:   torch.FloatTensor					     =					None  # sigma(t_i)







		def        __lowerCAmelCase		( self		:						Any  ,					_A		:						torch.FloatTensor  ,					_A		:						Optional[int] = None      )  ->			torch.FloatTensor:
			return sample







		def        __lowerCAmelCase		( self		:						str  ,					_A		:						int  ,					_A		:						Union[str, torch.device] = None      )  ->			int:
			__magic_name__	:   Tuple					     =					num_inference_steps
			__magic_name__	:   Optional[Any]					     =					np.arange(0  ,					self.num_inference_steps      )[::-1].copy()
			__magic_name__	:   Any					     =					torch.from_numpy(_A      ).to(_A      )
			__magic_name__	:   Optional[int]					     =					[
			    (
			        self.config.sigma_max**2
			        * (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
			    )
			    for i in self.timesteps
			]
			__magic_name__	:   Tuple					     =					torch.tensor(_A  ,					dtype=torch.floataa  ,					device=_A      )







		def        __lowerCAmelCase		( self		:						Dict  ,					_A		:						torch.FloatTensor  ,					_A		:						float  ,					_A		:						Optional[torch.Generator] = None      )  ->			Tuple[torch.FloatTensor, float]:
			if self.config.s_min <= sigma <= self.config.s_max:
				__magic_name__	:   Dict					     =					min(self.config.s_churn / self.num_inference_steps  ,					2**0.5 - 1      )
			else:
				__magic_name__	:   Tuple					     =					0

			# sample eps ~ N(0, S_noise^2 * I)
			__magic_name__	:   Optional[Any]					     =					self.config.s_noise * randn_tensor(sample.shape  ,					generator=_A      ).to(sample.device      )
			__magic_name__	:   Dict					     =					sigma + gamma * sigma
			__magic_name__	:   Union[str, Any]					     =					sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)

			return sample_hat, sigma_hat







		def        __lowerCAmelCase		( self		:						Any  ,					_A		:						torch.FloatTensor  ,					_A		:						float  ,					_A		:						float  ,					_A		:						torch.FloatTensor  ,					_A		:						bool = True  ,					)  ->			Union[KarrasVeOutput, Tuple]:
			__magic_name__	:   List[Any]					     =					sample_hat + sigma_hat * model_output
			__magic_name__	:   int					     =					(sample_hat - pred_original_sample) / sigma_hat
			__magic_name__	:   Any					     =					sample_hat + (sigma_prev - sigma_hat) * derivative

			if not return_dict:
				return (sample_prev, derivative)

			return KarrasVeOutput(
			    prev_sample=_A  ,					derivative=_A  ,					pred_original_sample=_A      )







		def        __lowerCAmelCase		( self		:						str  ,					_A		:						torch.FloatTensor  ,					_A		:						float  ,					_A		:						float  ,					_A		:						torch.FloatTensor  ,					_A		:						torch.FloatTensor  ,					_A		:						torch.FloatTensor  ,					_A		:						bool = True  ,					)  ->			Union[KarrasVeOutput, Tuple]:
			__magic_name__	:   Dict					     =					sample_prev + sigma_prev * model_output
			__magic_name__	:   Any					     =					(sample_prev - pred_original_sample) / sigma_prev
			__magic_name__	:   int					     =					sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)

			if not return_dict:
				return (sample_prev, derivative)

			return KarrasVeOutput(
			    prev_sample=_A  ,					derivative=_A  ,					pred_original_sample=_A      )







		def        __lowerCAmelCase		( self		:						Union[str, Any]  ,					_A		:						str  ,					_A		:						Any  ,					_A		:						str      )  ->			str:
			raise NotImplementedError()



'''simple docstring'''

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


lowerCAmelCase				:Any        =      logging.get_logger(__name__)

lowerCAmelCase				:int        =      {
    '''unc-nlp/lxmert-base-uncased''': '''https://huggingface.co/unc-nlp/lxmert-base-uncased/resolve/main/config.json''',
}
class _lowerCamelCase	(						lowercase__							):



		'''simple docstring'''







		A_     :      List[str]				   =    """lxmert"""
		A_     :      int				   =    {}







		def __init__( self		:						int  ,					_A		:						List[Any]=30522  ,					_A		:						int=768  ,					_A		:						Any=12  ,					_A		:						Optional[Any]=9500  ,					_A		:						Any=1600  ,					_A		:						Tuple=400  ,					_A		:						str=3072  ,					_A		:						Tuple="gelu"  ,					_A		:						int=0.1  ,					_A		:						List[Any]=0.1  ,					_A		:						Dict=512  ,					_A		:						Dict=2  ,					_A		:						Tuple=0.02  ,					_A		:						List[Any]=1E-12  ,					_A		:						Optional[Any]=9  ,					_A		:						List[Any]=5  ,					_A		:						str=5  ,					_A		:						int=2048  ,					_A		:						Tuple=4  ,					_A		:						Tuple=6.67  ,					_A		:						Dict=True  ,					_A		:						str=True  ,					_A		:						str=True  ,					_A		:						Dict=True  ,					_A		:						str=True  ,					_A		:						int=True  ,					_A		:						Tuple=True  ,					**_A		:						int  ,					)  ->			str:
			__magic_name__	:   List[Any]					     =					vocab_size
			__magic_name__	:   List[Any]					     =					hidden_size
			__magic_name__	:   Optional[int]					     =					num_attention_heads
			__magic_name__	:   Dict					     =					hidden_act
			__magic_name__	:   Optional[Any]					     =					intermediate_size
			__magic_name__	:   str					     =					hidden_dropout_prob
			__magic_name__	:   List[str]					     =					attention_probs_dropout_prob
			__magic_name__	:   Tuple					     =					max_position_embeddings
			__magic_name__	:   Dict					     =					type_vocab_size
			__magic_name__	:   str					     =					initializer_range
			__magic_name__	:   str					     =					layer_norm_eps
			__magic_name__	:   Union[str, Any]					     =					num_qa_labels
			__magic_name__	:   str					     =					num_object_labels
			__magic_name__	:   List[str]					     =					num_attr_labels
			__magic_name__	:   Tuple					     =					l_layers
			__magic_name__	:   List[Any]					     =					x_layers
			__magic_name__	:   Optional[int]					     =					r_layers
			__magic_name__	:   Dict					     =					visual_feat_dim
			__magic_name__	:   Optional[int]					     =					visual_pos_dim
			__magic_name__	:   Optional[int]					     =					visual_loss_normalizer
			__magic_name__	:   int					     =					task_matched
			__magic_name__	:   Dict					     =					task_mask_lm
			__magic_name__	:   List[Any]					     =					task_obj_predict
			__magic_name__	:   str					     =					task_qa
			__magic_name__	:   List[Any]					     =					visual_obj_loss
			__magic_name__	:   Dict					     =					visual_attr_loss
			__magic_name__	:   int					     =					visual_feat_loss
			__magic_name__	:   Tuple					     =					{'vision': r_layers, 'cross_encoder': x_layers, 'language': l_layers}
			super().__init__(**_A      )