


'''simple docstring'''
import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class      lowercase			(							A__      ):

		"""simple docstring"""

		_a									=       ['image_processor', 'tokenizer']
		_a									=       'CLIPImageProcessor'
		_a									=       ('XLMRobertaTokenizer', 'XLMRobertaTokenizerFast')




		def __init__(	self					,     UpperCamelCase_=None					,     UpperCamelCase_=None					,     **UpperCamelCase_							):

							'''simple docstring'''


							UpperCamelCase__     :Tuple							      =				None
							if "feature_extractor" in kwargs:
												warnings.warn(
												    '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
												    ''' instead.'''					,     UpperCamelCase_					,     )
												UpperCamelCase__     :Optional[Any]							      =				kwargs.pop('''feature_extractor'''							)

							UpperCamelCase__     :List[Any]							      =				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__(UpperCamelCase_					,     UpperCamelCase_							)




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

							'''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:
												UpperCamelCase__     :Dict							      =				self.tokenizer(UpperCamelCase_					,     return_tensors=UpperCamelCase_					,     **UpperCamelCase_							)

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

							if text is not None and images is not None:
												UpperCamelCase__     :Dict							      =				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					,     *UpperCamelCase_					,     **UpperCamelCase_							):

							'''simple docstring'''


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




		def 					lowerCAmelCase__       (	self					,     *UpperCamelCase_					,     **UpperCamelCase_							):

							'''simple docstring'''


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




		@property
		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							UpperCamelCase__     :int							      =				self.tokenizer.model_input_names
							UpperCamelCase__     :int							      =				self.image_processor.model_input_names
							return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names							)							)




'''simple docstring'''
# 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			(							A__			,	A__      ):

		"""simple docstring"""

		_a									=       1




		@register_to_config
		def __init__(	self					,     UpperCamelCase_=2000					,     UpperCamelCase_=0.1					,     UpperCamelCase_=20					,     UpperCamelCase_=1e-3							):

							'''simple docstring'''


							UpperCamelCase__     :Dict							      =				None
							UpperCamelCase__     :Any							      =				None
							UpperCamelCase__     :List[str]							      =				None




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_					,     UpperCamelCase_ = None							):

							'''simple docstring'''


							UpperCamelCase__     :Union[str, Any]							      =				torch.linspace(1					,     self.config.sampling_eps					,     UpperCamelCase_					,     device=UpperCamelCase_							)




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_=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
							UpperCamelCase__     :Optional[int]							      =				(
							    -0.25 * t**2 * (self.config.beta_max - self.config.beta_min) - 0.5 * t * self.config.beta_min
							)
							UpperCamelCase__     :Tuple							      =				torch.sqrt(1.0 - torch.exp(2.0 * log_mean_coeff							)							)
							UpperCamelCase__     :Tuple							      =				std.flatten()
							while len(std.shape							) < len(score.shape							):
												UpperCamelCase__     :int							      =				std.unsqueeze(-1							)
							UpperCamelCase__     :List[Any]							      =				-score / std

							# compute
							UpperCamelCase__     :List[str]							      =				-1.0 / len(self.timesteps							)

							UpperCamelCase__     :Optional[int]							      =				self.config.beta_min + t * (self.config.beta_max - self.config.beta_min)
							UpperCamelCase__     :Any							      =				beta_t.flatten()
							while len(beta_t.shape							) < len(x.shape							):
												UpperCamelCase__     :Any							      =				beta_t.unsqueeze(-1							)
							UpperCamelCase__     :Optional[int]							      =				-0.5 * beta_t * x

							UpperCamelCase__     :int							      =				torch.sqrt(UpperCamelCase_							)
							UpperCamelCase__     :List[str]							      =				drift - diffusion**2 * score
							UpperCamelCase__     :Dict							      =				x + drift * dt

							# add noise
							UpperCamelCase__     :List[Any]							      =				randn_tensor(x.shape					,     layout=x.layout					,     generator=UpperCamelCase_					,     device=x.device					,     dtype=x.dtype							)
							UpperCamelCase__     :int							      =				x_mean + diffusion * math.sqrt(-dt							) * noise

							return x, x_mean




		def __len__(	self							):

							'''simple docstring'''


							return self.config.num_train_timesteps