




import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding


class 							__UpperCamelCase			( __snake_case  ):





			'''simple docstring'''



			__a							: Optional[Any]										=["""image_processor""", """tokenizer"""]
			__a							: Optional[Any]										="""ChineseCLIPImageProcessor"""
			__a							: Any										=("""BertTokenizer""", """BertTokenizerFast""")



			def __init__( self       , UpperCAmelCase_=None       , UpperCAmelCase_=None       , **UpperCAmelCase_						):
										lowerCAmelCase   =		None
										if "feature_extractor" in kwargs:
																	warnings.warn(
																	    '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
																	    ''' instead.'''       , _lowercase       , )
																	lowerCAmelCase   =		kwargs.pop('''feature_extractor'''						)

										lowerCAmelCase   =		image_processor if image_processor is not None else feature_extractor
										if image_processor is None:
																	raise ValueError('''You need to specify an `image_processor`.'''						)
										if tokenizer is None:
																	raise ValueError('''You need to specify a `tokenizer`.'''						)

										super().__init__(_lowercase       , _lowercase						)
										lowerCAmelCase   =		self.image_processor



			def __call__( self       , UpperCAmelCase_=None       , UpperCAmelCase_=None       , UpperCAmelCase_=None       , **UpperCAmelCase_						):

										if text is None and images is None:
																	raise ValueError('''You have to specify either text or images. Both cannot be none.'''						)

										if text is not None:
																	lowerCAmelCase   =		self.tokenizer(_lowercase       , return_tensors=_lowercase       , **_lowercase						)

										if images is not None:
																	lowerCAmelCase   =		self.image_processor(_lowercase       , return_tensors=_lowercase       , **_lowercase						)

										if text is not None and images is not None:
																	lowerCAmelCase   =		image_features.pixel_values
																	return encoding
										elif text is not None:
																	return encoding
										else:
																	return BatchEncoding(data=dict(**_lowercase						)       , tensor_type=_lowercase						)



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



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



			@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						)						)






			@property
			def       __snake_case		( self						):
										warnings.warn(
										    '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''       , _lowercase       , )
										return self.image_processor_class









# 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



