


"""simple docstring"""






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

import flax
import jax.numpy as jnp
from jax import random

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .scheduling_utils_flax import FlaxSchedulerMixin


@flax.struct.dataclass
class     __lowerCamelCase  :

     '''simple docstring'''
     # setable values
     A_       :	Optional[int]    =		None
     A_       :	Optional[jnp.ndarray]    =		None
     A_       :	Optional[jnp.ndarray]    =		None  # sigma(t_i)


     @classmethod
     def 							_UpperCAmelCase      (      cls							)       ->							Any:
          return cls()







@dataclass
class     __lowerCamelCase  (					a__				):

     '''simple docstring'''
     A_       :	jnp.ndarray
     A_       :	jnp.ndarray
     A_       :	KarrasVeSchedulerState







class     __lowerCamelCase  (					a__  ,					a__				):

     '''simple docstring'''


     @property
     def 							_UpperCAmelCase      (      self							)       ->							List[str]:
          return True


     @register_to_config
     def __init__(      self    ,				__UpperCAmelCase = 0.02    ,				__UpperCAmelCase = 100    ,				__UpperCAmelCase = 1.007    ,				__UpperCAmelCase = 80    ,				__UpperCAmelCase = 0.05    ,				__UpperCAmelCase = 50    ,				)       ->							Tuple:
          pass


     def 							_UpperCAmelCase      (      self							)       ->							int:
          return KarrasVeSchedulerState.create()


     def 							_UpperCAmelCase      (      self    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase = ()							)       ->							KarrasVeSchedulerState:
          _a    	=				jnp.arange(0    ,				__UpperCAmelCase							)[::-1].copy()
          _a    	=				[
              (
                  self.config.sigma_max**2
                  * (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
              )
              for i in timesteps
          ]

          return state.replace(
              num_inference_steps=__UpperCAmelCase    ,				schedule=jnp.array(__UpperCAmelCase    ,				dtype=jnp.floataa							)    ,				timesteps=__UpperCAmelCase    ,				)


     def 							_UpperCAmelCase      (      self    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				)       ->							Tuple[jnp.ndarray, float]:
          if self.config.s_min <= sigma <= self.config.s_max:
               _a    	=				min(self.config.s_churn / state.num_inference_steps    ,				2**0.5 - 1							)
          else:
               _a    	=				0

          # sample eps ~ N(0, S_noise^2 * I)
          _a    	=				random.split(__UpperCAmelCase    ,				num=1							)
          _a    	=				self.config.s_noise * random.normal(key=__UpperCAmelCase    ,				shape=sample.shape							)
          _a    	=				sigma + gamma * sigma
          _a    	=				sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)

          return sample_hat, sigma_hat


     def 							_UpperCAmelCase      (      self    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase = True    ,				)       ->							Union[FlaxKarrasVeOutput, Tuple]:
          _a    	=				sample_hat + sigma_hat * model_output
          _a    	=				(sample_hat - pred_original_sample) / sigma_hat
          _a    	=				sample_hat + (sigma_prev - sigma_hat) * derivative

          if not return_dict:
               return (sample_prev, derivative, state)

          return FlaxKarrasVeOutput(prev_sample=__UpperCAmelCase    ,				derivative=__UpperCAmelCase    ,				state=__UpperCAmelCase							)


     def 							_UpperCAmelCase      (      self    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase = True    ,				)       ->							Union[FlaxKarrasVeOutput, Tuple]:
          _a    	=				sample_prev + sigma_prev * model_output
          _a    	=				(sample_prev - pred_original_sample) / sigma_prev
          _a    	=				sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)

          if not return_dict:
               return (sample_prev, derivative, state)

          return FlaxKarrasVeOutput(prev_sample=__UpperCAmelCase    ,				derivative=__UpperCAmelCase    ,				state=__UpperCAmelCase							)





     def 							_UpperCAmelCase      (      self    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase							)       ->							List[str]:
          raise NotImplementedError()



"""simple docstring"""






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


__snake_case		=       {
    '''configuration_pix2struct''': [
        '''PIX2STRUCT_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''Pix2StructConfig''',
        '''Pix2StructTextConfig''',
        '''Pix2StructVisionConfig''',
    ],
    '''processing_pix2struct''': ['''Pix2StructProcessor'''],
}

try:
   if not is_vision_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   __snake_case		=       ['''Pix2StructImageProcessor''']


try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   __snake_case		=       [
       '''PIX2STRUCT_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''Pix2StructPreTrainedModel''',
       '''Pix2StructForConditionalGeneration''',
       '''Pix2StructVisionModel''',
       '''Pix2StructTextModel''',
   ]

if TYPE_CHECKING:
   from .configuration_pixastruct import (
       PIX2STRUCT_PRETRAINED_CONFIG_ARCHIVE_MAP,
       PixaStructConfig,
       PixaStructTextConfig,
       PixaStructVisionConfig,
   )
   from .processing_pixastruct import PixaStructProcessor

   try:
      if not is_vision_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .image_processing_pixastruct import PixaStructImageProcessor

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_pixastruct import (
          PIX2STRUCT_PRETRAINED_MODEL_ARCHIVE_LIST,
          PixaStructForConditionalGeneration,
          PixaStructPreTrainedModel,
          PixaStructTextModel,
          PixaStructVisionModel,
      )

else:
   import sys

   __snake_case		=       _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)