





'''simple docstring'''




import warnings

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


class 					SCREAMING_SNAKE_CASE       (a__				):
     lowerCAmelCase						     =			['''image_processor''', '''tokenizer''']
     lowerCAmelCase						     =			'''CLIPImageProcessor'''
     lowerCAmelCase						     =			('''CLIPTokenizer''', '''CLIPTokenizerFast''')


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

          '''simple docstring'''






          __A		:					Dict						=			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		,      )
               __A		:					Optional[Any]						=			kwargs.pop('feature_extractor')

          __A		:					str						=			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:
               __A		:					int						=			self.tokenizer(_UpperCAmelCase		,      return_tensors=_UpperCAmelCase		,      **_UpperCAmelCase)

          if images is not None:
               __A		:					Optional[int]						=			self.image_processor(_UpperCAmelCase		,      return_tensors=_UpperCAmelCase		,      **_UpperCAmelCase)

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


     def 				SCREAMING_SNAKE_CASE    (		self		,      *_UpperCAmelCase		,      **_UpperCAmelCase):

          '''simple docstring'''






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


     def 				SCREAMING_SNAKE_CASE    (		self		,      *_UpperCAmelCase		,      **_UpperCAmelCase):

          '''simple docstring'''






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


     @property
     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          __A		:					Dict						=			self.tokenizer.model_input_names
          __A		:					Tuple						=			self.image_processor.model_input_names
          return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))


     @property
     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          warnings.warn(
              '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'		,      _UpperCAmelCase		,      )
          return self.image_processor_class


     @property
     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          warnings.warn(
              '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'		,      _UpperCAmelCase		,      )
          return self.image_processor





# 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 		__A   (      UpperCamelCase__       ,    UpperCamelCase__      ):
						UpperCamelCase      =       1

						@register_to_config
						def __init__(		self :Any     ,			__snake_case :Tuple=20_00     ,			__snake_case :Optional[Any]=0.1     ,			__snake_case :Any=20     ,			__snake_case :Optional[int]=1E-3			):
										'''simple docstring'''

										__magic_name__						:						Dict										=None
										__magic_name__						:						List[str]										=None
										__magic_name__						:						str										=None

						def      A__    (		self :Dict     ,			__snake_case :Optional[int]     ,			__snake_case :Union[str, torch.device] = None			):
										'''simple docstring'''

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

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

										# compute
										__magic_name__						:						Tuple										=-1.0 / len(self.timesteps			)

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

										__magic_name__						:						Optional[int]										=torch.sqrt(__snake_case			)
										__magic_name__						:						int										=drift - diffusion**2 * score
										__magic_name__						:						List[str]										=x + drift * dt

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

										return x, x_mean


						def __len__(		self :List[Any]			):
										'''simple docstring'''

										return self.config.num_train_timesteps




