



from dataclasses import dataclass
from typing import Optional, Tuple, Union

import numpy as np
import torch

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







@dataclass
class 		snake_case						(	SCREAMING_SNAKE_CASE_  ):



   '''simple docstring'''






   snake_case_					: torch.FloatTensor
   snake_case_					: torch.FloatTensor
   snake_case_					: Optional[torch.FloatTensor]          =		None







class 		snake_case						(	SCREAMING_SNAKE_CASE_					,SCREAMING_SNAKE_CASE_  ):



   '''simple docstring'''






   snake_case_					: Tuple          =		2

   @register_to_config
   def __init__(					self							:			Optional[Any]				,		lowerCAmelCase							:			float = 0.02				,		lowerCAmelCase							:			float = 100				,		lowerCAmelCase							:			float = 1.007				,		lowerCAmelCase							:			float = 80				,		lowerCAmelCase							:			float = 0.05				,		lowerCAmelCase							:			float = 50				,		)				->				List[Any]:

    """simple docstring"""







    _snake_case    :  Any					=					sigma_max

    # setable values
    _snake_case    :  int					=					None
    _snake_case    :  np.IntTensor					=					None
    _snake_case    :  torch.FloatTensor					=					None  # sigma(t_i)

   def      UpperCamelCase_  (					self							:			Optional[int]				,		lowerCAmelCase							:			torch.FloatTensor				,		lowerCAmelCase							:			Optional[int] = None)				->				torch.FloatTensor:

    """simple docstring"""







    return sample

   def      UpperCamelCase_  (					self							:			Tuple				,		lowerCAmelCase							:			int				,		lowerCAmelCase							:			Union[str, torch.device] = None)				->				List[str]:

    """simple docstring"""







    _snake_case    :  Optional[Any]					=					num_inference_steps
    _snake_case    :  Optional[int]					=					np.arange(0				,		self.num_inference_steps)[::-1].copy()
    _snake_case    :  str					=					torch.from_numpy(lowerCAmelCase).to(lowerCAmelCase)
    _snake_case    :  List[str]					=					[
        (
            self.config.sigma_max**2
            * (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
        )
        for i in self.timesteps
    ]
    _snake_case    :  Optional[Any]					=					torch.tensor(lowerCAmelCase				,		dtype=torch.floataa				,		device=lowerCAmelCase)

   def      UpperCamelCase_  (					self							:			Union[str, Any]				,		lowerCAmelCase							:			torch.FloatTensor				,		lowerCAmelCase							:			float				,		lowerCAmelCase							:			Optional[torch.Generator] = None)				->				Tuple[torch.FloatTensor, float]:

    """simple docstring"""







    if self.config.s_min <= sigma <= self.config.s_max:
     _snake_case    :  str					=					min(self.config.s_churn / self.num_inference_steps				,		2**0.5 - 1)
    else:
     _snake_case    :  Union[str, Any]					=					0

    # sample eps ~ N(0, S_noise^2 * I)
    _snake_case    :  Optional[Any]					=					self.config.s_noise * randn_tensor(sample.shape				,		generator=lowerCAmelCase).to(sample.device)
    _snake_case    :  Dict					=					sigma + gamma * sigma
    _snake_case    :  Tuple					=					sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)

    return sample_hat, sigma_hat

   def      UpperCamelCase_  (					self							:			str				,		lowerCAmelCase							:			torch.FloatTensor				,		lowerCAmelCase							:			float				,		lowerCAmelCase							:			float				,		lowerCAmelCase							:			torch.FloatTensor				,		lowerCAmelCase							:			bool = True				,		)				->				Union[KarrasVeOutput, Tuple]:

    """simple docstring"""







    _snake_case    :  int					=					sample_hat + sigma_hat * model_output
    _snake_case    :  int					=					(sample_hat - pred_original_sample) / sigma_hat
    _snake_case    :  Dict					=					sample_hat + (sigma_prev - sigma_hat) * derivative

    if not return_dict:
     return (sample_prev, derivative)

    return KarrasVeOutput(
        prev_sample=lowerCAmelCase				,		derivative=lowerCAmelCase				,		pred_original_sample=lowerCAmelCase)

   def      UpperCamelCase_  (					self							:			Union[str, Any]				,		lowerCAmelCase							:			torch.FloatTensor				,		lowerCAmelCase							:			float				,		lowerCAmelCase							:			float				,		lowerCAmelCase							:			torch.FloatTensor				,		lowerCAmelCase							:			torch.FloatTensor				,		lowerCAmelCase							:			torch.FloatTensor				,		lowerCAmelCase							:			bool = True				,		)				->				Union[KarrasVeOutput, Tuple]:

    """simple docstring"""







    _snake_case    :  Tuple					=					sample_prev + sigma_prev * model_output
    _snake_case    :  int					=					(sample_prev - pred_original_sample) / sigma_prev
    _snake_case    :  Optional[Any]					=					sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)

    if not return_dict:
     return (sample_prev, derivative)

    return KarrasVeOutput(
        prev_sample=lowerCAmelCase				,		derivative=lowerCAmelCase				,		pred_original_sample=lowerCAmelCase)


   def      UpperCamelCase_  (					self							:			Union[str, Any]				,		lowerCAmelCase							:			List[Any]				,		lowerCAmelCase							:			List[str]				,		lowerCAmelCase							:			List[Any])				->				int:

    """simple docstring"""







    raise NotImplementedError()






import warnings

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







class 		snake_case						(	SCREAMING_SNAKE_CASE_  ):



   '''simple docstring'''






   snake_case_					: List[str]          =		["""image_processor""", """tokenizer"""]
   snake_case_					: str          =		"""ChineseCLIPImageProcessor"""
   snake_case_					: Tuple          =		("""BertTokenizer""", """BertTokenizerFast""")

   def __init__(					self							:			Any				,		lowerCAmelCase							:			Dict=None				,		lowerCAmelCase							:			List[Any]=None				,		**lowerCAmelCase							:			str)				->				Optional[Any]:

    """simple docstring"""







    _snake_case    :  List[str]					=					None
    if "feature_extractor" in kwargs:
     warnings.warn(
         """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
         """ instead."""				,		lowerCAmelCase				,		)
     _snake_case    :  Tuple					=					kwargs.pop("""feature_extractor""")

    _snake_case    :  Dict					=					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__(lowerCAmelCase				,		lowerCAmelCase)
    _snake_case    :  Optional[int]					=					self.image_processor

   def __call__(					self							:			List[Any]				,		lowerCAmelCase							:			str=None				,		lowerCAmelCase							:			Optional[int]=None				,		lowerCAmelCase							:			List[Any]=None				,		**lowerCAmelCase							:			Optional[int])				->				Optional[Any]:

    """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:
     _snake_case    :  Dict					=					self.tokenizer(lowerCAmelCase				,		return_tensors=lowerCAmelCase				,		**lowerCAmelCase)

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

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

   def      UpperCamelCase_  (					self							:			Dict				,		*lowerCAmelCase							:			Tuple				,		**lowerCAmelCase							:			Any)				->				Optional[Any]:

    """simple docstring"""







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

   def      UpperCamelCase_  (					self							:			Tuple				,		*lowerCAmelCase							:			Tuple				,		**lowerCAmelCase							:			int)				->				str:

    """simple docstring"""







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

   @property
   def      UpperCamelCase_  (					self							:			Union[str, Any])				->				List[str]:

    """simple docstring"""







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


   @property
   def      UpperCamelCase_  (					self							:			str)				->				Tuple:

    """simple docstring"""







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