




from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType


class 							__UpperCamelCase			( __UpperCAmelCase  ):





				'''simple docstring'''



				__a							: List[str]										=["""image_processor""", """tokenizer"""]
				__a							: Optional[Any]										="""BridgeTowerImageProcessor"""
				__a							: Optional[Any]										=("""RobertaTokenizer""", """RobertaTokenizerFast""")



				def __init__( self       , UpperCAmelCase_       , UpperCAmelCase_						):
											super().__init__(UpperCAmelCase_       , UpperCAmelCase_						)



				def __call__( self       , UpperCAmelCase_       , UpperCAmelCase_ = None       , UpperCAmelCase_ = True       , UpperCAmelCase_ = False       , UpperCAmelCase_ = None       , UpperCAmelCase_ = None       , UpperCAmelCase_ = 0       , UpperCAmelCase_ = None       , UpperCAmelCase_ = None       , UpperCAmelCase_ = None       , UpperCAmelCase_ = False       , UpperCAmelCase_ = False       , UpperCAmelCase_ = False       , UpperCAmelCase_ = False       , UpperCAmelCase_ = True       , UpperCAmelCase_ = None       , **UpperCAmelCase_       , ):
											lowerCAmelCase   =		self.tokenizer(
											    text=UpperCAmelCase_       , add_special_tokens=UpperCAmelCase_       , padding=UpperCAmelCase_       , truncation=UpperCAmelCase_       , max_length=UpperCAmelCase_       , stride=UpperCAmelCase_       , pad_to_multiple_of=UpperCAmelCase_       , return_token_type_ids=UpperCAmelCase_       , return_attention_mask=UpperCAmelCase_       , return_overflowing_tokens=UpperCAmelCase_       , return_special_tokens_mask=UpperCAmelCase_       , return_offsets_mapping=UpperCAmelCase_       , return_length=UpperCAmelCase_       , verbose=UpperCAmelCase_       , return_tensors=UpperCAmelCase_       , **UpperCAmelCase_       , )
											# add pixel_values + pixel_mask
											lowerCAmelCase   =		self.image_processor(
											    UpperCAmelCase_       , return_tensors=UpperCAmelCase_       , do_normalize=UpperCAmelCase_       , do_center_crop=UpperCAmelCase_       , **UpperCAmelCase_						)
											encoding.update(UpperCAmelCase_						)

											return encoding



				def       __snake_case		( self       , *UpperCAmelCase_       , **UpperCAmelCase_						):
											return self.tokenizer.batch_decode(*UpperCAmelCase_       , **UpperCAmelCase_						)



				def       __snake_case		( self       , *UpperCAmelCase_       , **UpperCAmelCase_						):
											return self.tokenizer.decode(*UpperCAmelCase_       , **UpperCAmelCase_						)






				@property
				def       __snake_case		( self						):
											lowerCAmelCase   =		self.tokenizer.model_input_names
											lowerCAmelCase   =		self.image_processor.model_input_names
											return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names						)						)









# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch

import math
from typing import Union

import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import randn_tensor
from .scheduling_utils import SchedulerMixin


class 							__UpperCamelCase			( __UpperCAmelCase     ,					__UpperCAmelCase  ):





				'''simple docstring'''



				__a							: Any										=1



				@register_to_config
				def __init__( self       , UpperCAmelCase_=20_00       , UpperCAmelCase_=0.1       , UpperCAmelCase_=20       , UpperCAmelCase_=1E-3						):
											lowerCAmelCase   =		None
											lowerCAmelCase   =		None
											lowerCAmelCase   =		None



				def       __snake_case		( self       , UpperCAmelCase_       , UpperCAmelCase_ = None						):
											lowerCAmelCase   =		torch.linspace(1       , self.config.sampling_eps       , UpperCAmelCase_       , device=UpperCAmelCase_						)



				def       __snake_case		( self       , UpperCAmelCase_       , UpperCAmelCase_       , UpperCAmelCase_       , UpperCAmelCase_=None						):
											if self.timesteps is None:
																		raise ValueError(
																		    '''`self.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler'''						)

											# TODO(Patrick) better comments + non-PyTorch
											# postprocess model score
											lowerCAmelCase   =		(
											    -0.25 * t**2 * (self.config.beta_max - self.config.beta_min) - 0.5 * t * self.config.beta_min
											)
											lowerCAmelCase   =		torch.sqrt(1.0 - torch.exp(2.0 * log_mean_coeff						)						)
											lowerCAmelCase   =		std.flatten()
											while len(std.shape						) < len(score.shape						):
																		lowerCAmelCase   =		std.unsqueeze(-1						)
											lowerCAmelCase   =		-score / std

											# compute
											lowerCAmelCase   =		-1.0 / len(self.timesteps						)

											lowerCAmelCase   =		self.config.beta_min + t * (self.config.beta_max - self.config.beta_min)
											lowerCAmelCase   =		beta_t.flatten()
											while len(beta_t.shape						) < len(x.shape						):
																		lowerCAmelCase   =		beta_t.unsqueeze(-1						)
											lowerCAmelCase   =		-0.5 * beta_t * x

											lowerCAmelCase   =		torch.sqrt(UpperCAmelCase_						)
											lowerCAmelCase   =		drift - diffusion**2 * score
											lowerCAmelCase   =		x + drift * dt

											# add noise
											lowerCAmelCase   =		randn_tensor(x.shape       , layout=x.layout       , generator=UpperCAmelCase_       , device=x.device       , dtype=x.dtype						)
											lowerCAmelCase   =		x_mean + diffusion * math.sqrt(-dt						) * noise

											return x, x_mean






				def __len__( self						):
											return self.config.num_train_timesteps



