




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

import flax
import jax.numpy as jnp
from scipy import integrate

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import (
    CommonSchedulerState,
    FlaxKarrasDiffusionSchedulers,
    FlaxSchedulerMixin,
    FlaxSchedulerOutput,
    broadcast_to_shape_from_left,
)



@flax.struct.dataclass
class     a  :

     '''simple docstring'''





     lowerCAmelCase       : CommonSchedulerState

     # setable values
     lowerCAmelCase       : jnp.ndarray
     lowerCAmelCase       : jnp.ndarray
     lowerCAmelCase       : jnp.ndarray
     lowerCAmelCase       : Optional[int]     	=						None

     # running values
     lowerCAmelCase       : Optional[jnp.ndarray]     	=						None
     @classmethod
     def 						lowerCamelCase_     (			cls     :    Optional[Any]      ,					__snake_case     :    CommonSchedulerState      ,					__snake_case     :    jnp.ndarray      ,					__snake_case     :    jnp.ndarray      ,					__snake_case     :    jnp.ndarray       ):
         return cls(common=__snake_case      ,					init_noise_sigma=__snake_case      ,					timesteps=__snake_case      ,					sigmas=__snake_case       )



@dataclass
class     a  (     _A    ):

     '''simple docstring'''





     lowerCAmelCase       : LMSDiscreteSchedulerState



class     a  (     _A			,     _A    ):

     '''simple docstring'''





     lowerCAmelCase       : Tuple     	=						[e.name for e in FlaxKarrasDiffusionSchedulers]

     lowerCAmelCase       : jnp.dtype
     @property
     def 						lowerCamelCase_     (			self     :    Any       ):
         return True
     @register_to_config
     def __init__(			self     :    Tuple      ,					__snake_case     :    int = 10_00      ,					__snake_case     :    float = 0.0_001      ,					__snake_case     :    float = 0.02      ,					__snake_case     :    str = "linear"      ,					__snake_case     :    Optional[jnp.ndarray] = None      ,					__snake_case     :    str = "epsilon"      ,					__snake_case     :    jnp.dtype = jnp.floataa      ,					):
         UpperCAmelCase_											=  dtype
     def 						lowerCamelCase_     (			self     :    Tuple      ,					__snake_case     :    Optional[CommonSchedulerState] = None       ):
         if common is None:
             UpperCAmelCase_											=  CommonSchedulerState.create(self       )

         UpperCAmelCase_											=  jnp.arange(0      ,					self.config.num_train_timesteps       ).round()[::-1]
         UpperCAmelCase_											=  ((1 - common.alphas_cumprod) / common.alphas_cumprod) ** 0.5

         # standard deviation of the initial noise distribution
         UpperCAmelCase_											=  sigmas.max()

         return LMSDiscreteSchedulerState.create(
             common=__snake_case      ,					init_noise_sigma=__snake_case      ,					timesteps=__snake_case      ,					sigmas=__snake_case      ,					)
     def 						lowerCamelCase_     (			self     :    List[Any]      ,					__snake_case     :    LMSDiscreteSchedulerState      ,					__snake_case     :    jnp.ndarray      ,					__snake_case     :    int       ):
         ((UpperCAmelCase_							)				,													)											=  jnp.where(state.timesteps == timestep      ,					size=1       )
         UpperCAmelCase_											=  step_index[0]

         UpperCAmelCase_											=  state.sigmas[step_index]
         UpperCAmelCase_											=  sample / ((sigma**2 + 1) ** 0.5)
         return sample
     def 						lowerCamelCase_     (			self     :    str      ,					__snake_case     :    LMSDiscreteSchedulerState      ,					__snake_case     :    Optional[Any]      ,					__snake_case     :    str      ,					__snake_case     :    Tuple       ):

         def lms_derivative(__snake_case     :    List[str]       ):
             UpperCAmelCase_											=  1.0
             for k in range(__snake_case       ):
                 if current_order == k:
                     continue
                 prod *= (tau - state.sigmas[t - k]) / (state.sigmas[t - current_order] - state.sigmas[t - k])
             return prod

         UpperCAmelCase_											=  integrate.quad(__snake_case      ,					state.sigmas[t]      ,					state.sigmas[t + 1]      ,					epsrel=1E-4       )[0]

         return integrated_coeff
     def 						lowerCamelCase_     (			self     :    Any      ,					__snake_case     :    LMSDiscreteSchedulerState      ,					__snake_case     :    int      ,					__snake_case     :    Tuple = ()       ):
         UpperCAmelCase_											=  jnp.linspace(self.config.num_train_timesteps - 1      ,					0      ,					__snake_case      ,					dtype=self.dtype       )

         UpperCAmelCase_											=  jnp.floor(__snake_case       ).astype(jnp.intaa       )
         UpperCAmelCase_											=  jnp.ceil(__snake_case       ).astype(jnp.intaa       )

         UpperCAmelCase_											=  jnp.mod(__snake_case      ,					1.0       )

         UpperCAmelCase_											=  ((1 - state.common.alphas_cumprod) / state.common.alphas_cumprod) ** 0.5
         UpperCAmelCase_											=  (1 - frac) * sigmas[low_idx] + frac * sigmas[high_idx]
         UpperCAmelCase_											=  jnp.concatenate([sigmas, jnp.array([0.0]      ,					dtype=self.dtype       )]       )

         UpperCAmelCase_											=  timesteps.astype(jnp.intaa       )

         # initial running values
         UpperCAmelCase_											=  jnp.zeros((0,) + shape      ,					dtype=self.dtype       )

         return state.replace(
             timesteps=__snake_case      ,					sigmas=__snake_case      ,					num_inference_steps=__snake_case      ,					derivatives=__snake_case      ,					)
     def 						lowerCamelCase_     (			self     :    Union[str, Any]      ,					__snake_case     :    LMSDiscreteSchedulerState      ,					__snake_case     :    jnp.ndarray      ,					__snake_case     :    int      ,					__snake_case     :    jnp.ndarray      ,					__snake_case     :    int = 4      ,					__snake_case     :    bool = True      ,					):
         if state.num_inference_steps is None:
             raise ValueError(
                 '''Number of inference steps is \'None\', you need to run \'set_timesteps\' after creating the scheduler'''       )

         UpperCAmelCase_											=  state.sigmas[timestep]

         # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
         if self.config.prediction_type == "epsilon":
             UpperCAmelCase_											=  sample - sigma * model_output
         elif self.config.prediction_type == "v_prediction":
             # * c_out + input * c_skip
             UpperCAmelCase_											=  model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
         else:
             raise ValueError(
                 F'prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`'       )

         # 2. Convert to an ODE derivative
         UpperCAmelCase_											=  (sample - pred_original_sample) / sigma
         UpperCAmelCase_											=  state.replace(derivatives=jnp.append(state.derivatives      ,					__snake_case       )       )
         if len(state.derivatives       ) > order:
             UpperCAmelCase_											=  state.replace(derivatives=jnp.delete(state.derivatives      ,					0       )       )

         # 3. Compute linear multistep coefficients
         UpperCAmelCase_											=  min(timestep + 1      ,					__snake_case       )
         UpperCAmelCase_											=  [self.get_lms_coefficient(__snake_case      ,					__snake_case      ,					__snake_case      ,					__snake_case       ) for curr_order in range(__snake_case       )]

         # 4. Compute previous sample based on the derivatives path
         UpperCAmelCase_											=  sample + sum(
             coeff * derivative for coeff, derivative in zip(__snake_case      ,					reversed(state.derivatives       )       )       )

         if not return_dict:
             return (prev_sample, state)

         return FlaxLMSSchedulerOutput(prev_sample=__snake_case      ,					state=__snake_case       )
     def 						lowerCamelCase_     (			self     :    Dict      ,					__snake_case     :    LMSDiscreteSchedulerState      ,					__snake_case     :    jnp.ndarray      ,					__snake_case     :    jnp.ndarray      ,					__snake_case     :    jnp.ndarray      ,					):
         UpperCAmelCase_											=  state.sigmas[timesteps].flatten()
         UpperCAmelCase_											=  broadcast_to_shape_from_left(__snake_case      ,					noise.shape       )

         UpperCAmelCase_											=  original_samples + noise * sigma

         return noisy_samples




     def __len__(			self     :    Optional[int]       ):
         return self.config.num_train_timesteps











import copy
from typing import Dict

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import CONFIG_MAPPING
from ..bit import BitConfig


_lowerCamelCase    				=		logging.get_logger(__name__)

_lowerCamelCase    				=		{
    'google/vit-hybrid-base-bit-384': 'https://huggingface.co/vit-hybrid-base-bit-384/resolve/main/config.json',
    # See all ViT hybrid models at https://huggingface.co/models?filter=vit
}



class     a  (     _A    ):

     '''simple docstring'''





     lowerCAmelCase       : Any     	=						'vit-hybrid'
     def __init__(			self     :    str      ,					__snake_case     :    str=None      ,					__snake_case     :    List[Any]=7_68      ,					__snake_case     :    List[Any]=12      ,					__snake_case     :    Dict=12      ,					__snake_case     :    Optional[int]=30_72      ,					__snake_case     :    Optional[int]="gelu"      ,					__snake_case     :    Union[str, Any]=0.0      ,					__snake_case     :    Any=0.0      ,					__snake_case     :    List[Any]=0.02      ,					__snake_case     :    Union[str, Any]=1E-12      ,					__snake_case     :    Optional[int]=2_24      ,					__snake_case     :    Optional[int]=1      ,					__snake_case     :    Tuple=3      ,					__snake_case     :    Union[str, Any]=[1, 10_24, 24, 24]      ,					__snake_case     :    List[Any]=True      ,					**__snake_case     :    Tuple      ,					):
         super().__init__(**__snake_case       )

         if backbone_config is None:
             logger.info('''`backbone_config` is `None`. Initializing the config with a `BiT` backbone.'''       )
             UpperCAmelCase_											=  {
                 '''global_padding''': '''same''',
                 '''layer_type''': '''bottleneck''',
                 '''depths''': [3, 4, 9],
                 '''out_features''': ['''stage3'''],
                 '''embedding_dynamic_padding''': True,
             }

         if isinstance(__snake_case      ,					__snake_case       ):
             if "model_type" in backbone_config:
                 UpperCAmelCase_											=  CONFIG_MAPPING[backbone_config['''model_type''']]
             else:
                 logger.info(
                     '''`model_type` is not found in `backbone_config`. Use `Bit` as the backbone configuration class.'''       )
                 UpperCAmelCase_											=  BitConfig
             UpperCAmelCase_											=  backbone_config_class(**__snake_case       )

         UpperCAmelCase_											=  backbone_featmap_shape
         UpperCAmelCase_											=  backbone_config
         UpperCAmelCase_											=  hidden_size
         UpperCAmelCase_											=  num_hidden_layers
         UpperCAmelCase_											=  num_attention_heads
         UpperCAmelCase_											=  intermediate_size
         UpperCAmelCase_											=  hidden_act
         UpperCAmelCase_											=  hidden_dropout_prob
         UpperCAmelCase_											=  attention_probs_dropout_prob
         UpperCAmelCase_											=  initializer_range
         UpperCAmelCase_											=  layer_norm_eps
         UpperCAmelCase_											=  image_size
         UpperCAmelCase_											=  patch_size
         UpperCAmelCase_											=  num_channels
         UpperCAmelCase_											=  qkv_bias




     def 						lowerCamelCase_     (			self     :    Any       ):
         UpperCAmelCase_											=  copy.deepcopy(self.__dict__       )
         UpperCAmelCase_											=  self.backbone_config.to_dict()
         UpperCAmelCase_											=  self.__class__.model_type
         return output





