






# 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       __lowercase      (	lowercase_			,      lowercase_  ):







     '''simple docstring'''
     SCREAMING_SNAKE_CASE   						=				1




     @register_to_config
     def __init__(				self       :			Optional[Any] ,	UpperCamelCase_       :			Dict=2_000 ,	UpperCamelCase_       :			Optional[Any]=0.1 ,	UpperCamelCase_       :			List[Any]=20 ,	UpperCamelCase_       :			List[Any]=1e-3				):


         """simple docstring"""



         __A         =	None
         __A         =	None
         __A         =	None




     def 		lowerCAmelCase_     (				self       :			int ,	UpperCamelCase_       :			Dict ,	UpperCamelCase_       :			Union[str, torch.device] = None				):


         """simple docstring"""



         __A         =	torch.linspace(1 ,	self.config.sampling_eps ,	UpperCamelCase_ ,	device=UpperCamelCase_				)




     def 		lowerCAmelCase_     (				self       :			Optional[Any] ,	UpperCamelCase_       :			int ,	UpperCamelCase_       :			Tuple ,	UpperCamelCase_       :			Union[str, Any] ,	UpperCamelCase_       :			Dict=None				):


         """simple docstring"""



         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
         __A         =	(
             -0.25 * t**2 * (self.config.beta_max - self.config.beta_min) - 0.5 * t * self.config.beta_min
         )
         __A         =	torch.sqrt(1.0 - torch.exp(2.0 * log_mean_coeff				)				)
         __A         =	std.flatten()
         while len(std.shape				) < len(score.shape				):
             __A         =	std.unsqueeze(-1				)
         __A         =	-score / std

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

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

         __A         =	torch.sqrt(UpperCamelCase_				)
         __A         =	drift - diffusion**2 * score
         __A         =	x + drift * dt

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

         return x, x_mean





     def __len__(				self       :			List[str]				):


         """simple docstring"""



         return self.config.num_train_timesteps












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


class       __lowercase      (	lowercase_  ):







     '''simple docstring'''
     SCREAMING_SNAKE_CASE   						=				["image_processor", "tokenizer"]
     SCREAMING_SNAKE_CASE   						=				"AutoImageProcessor"
     SCREAMING_SNAKE_CASE   						=				"AutoTokenizer"




     def __init__(				self       :			str ,	UpperCamelCase_       :			Any ,	UpperCamelCase_       :			str				):


         """simple docstring"""



         super().__init__(UpperCamelCase_ ,	UpperCamelCase_				)
         __A         =	self.image_processor




     def __call__(				self       :			Optional[Any] ,	UpperCamelCase_       :			Optional[Any]=None ,	UpperCamelCase_       :			Any=None ,	UpperCamelCase_       :			List[Any]=None ,	**UpperCamelCase_       :			int				):


         """simple docstring"""



         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:
             __A         =	self.tokenizer(UpperCamelCase_ ,	return_tensors=UpperCamelCase_ ,	**UpperCamelCase_				)

         if images is not None:
             __A         =	self.image_processor(UpperCamelCase_ ,	return_tensors=UpperCamelCase_ ,	**UpperCamelCase_				)

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




     def 		lowerCAmelCase_     (				self       :			List[str] ,	*UpperCamelCase_       :			Tuple ,	**UpperCamelCase_       :			List[str]				):


         """simple docstring"""



         return self.tokenizer.batch_decode(*UpperCamelCase_ ,	**UpperCamelCase_				)




     def 		lowerCAmelCase_     (				self       :			Any ,	*UpperCamelCase_       :			int ,	**UpperCamelCase_       :			Tuple				):


         """simple docstring"""



         return self.tokenizer.decode(*UpperCamelCase_ ,	**UpperCamelCase_				)





     @property
     def 		lowerCAmelCase_     (				self       :			Optional[Any]				):


         """simple docstring"""



         return ["input_ids", "attention_mask", "pixel_values"]




