


'''simple docstring'''






# DISCLAIMER: This file is strongly influenced by https://github.com/ermongroup/ddim

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

import flax
import jax
import jax.numpy as jnp

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






@flax.struct.dataclass
class        __snake_case	:







    """simple docstring"""



    lowercase       			= 42

    # setable values
    lowercase       			= 42
    lowercase       			= 42
    lowercase       			= None

    @classmethod
    def __lowercase       (       cls   :  str   ,						lowerCamelCase   :  CommonSchedulerState   ,						lowerCamelCase   :  jnp.ndarray   ,						lowerCamelCase   :  jnp.ndarray		)       ->  Union[str, Any]:
      return cls(common=lowerCamelCase   ,						init_noise_sigma=lowerCamelCase   ,						timesteps=lowerCamelCase		)







@dataclass
class        __snake_case	(       _SCREAMING_SNAKE_CASE):







    """simple docstring"""



    lowercase       			= 42







class        __snake_case	(       _SCREAMING_SNAKE_CASE     ,_SCREAMING_SNAKE_CASE):







    """simple docstring"""



    lowercase       			= [e.name for e in FlaxKarrasDiffusionSchedulers]

    lowercase       			= 42

    @property
    def __lowercase       (       self   :  Optional[int]		)       ->  int:
      return True

    @register_to_config
    def __init__(       self   :  Tuple   ,						lowerCamelCase   :  int = 10_00   ,						lowerCamelCase   :  float = 0.0_001   ,						lowerCamelCase   :  float = 0.02   ,						lowerCamelCase   :  str = "linear"   ,						lowerCamelCase   :  Optional[jnp.ndarray] = None   ,						lowerCamelCase   :  str = "fixed_small"   ,						lowerCamelCase   :  bool = True   ,						lowerCamelCase   :  str = "epsilon"   ,						lowerCamelCase   :  jnp.dtype = jnp.floataa   ,						)       ->  Tuple:
      lowerCAmelCase_      :				Dict      =       dtype

    def __lowercase       (       self   :  Optional[Any]   ,						lowerCamelCase   :  Optional[CommonSchedulerState] = None		)       ->  DDPMSchedulerState:
      if common is None:
        lowerCAmelCase_      :				int      =       CommonSchedulerState.create(self		)

      # standard deviation of the initial noise distribution
      lowerCAmelCase_      :				Optional[Any]      =       jnp.array(1.0   ,						dtype=self.dtype		)

      lowerCAmelCase_      :				int      =       jnp.arange(0   ,						self.config.num_train_timesteps		).round()[::-1]

      return DDPMSchedulerState.create(
          common=lowerCamelCase   ,						init_noise_sigma=lowerCamelCase   ,						timesteps=lowerCamelCase   ,						)

    def __lowercase       (       self   :  Tuple   ,						lowerCamelCase   :  DDPMSchedulerState   ,						lowerCamelCase   :  jnp.ndarray   ,						lowerCamelCase   :  Optional[int] = None		)       ->  jnp.ndarray:
      return sample

    def __lowercase       (       self   :  Union[str, Any]   ,						lowerCamelCase   :  DDPMSchedulerState   ,						lowerCamelCase   :  int   ,						lowerCamelCase   :  Tuple = ()		)       ->  DDPMSchedulerState:
      lowerCAmelCase_      :				int      =       self.config.num_train_timesteps // num_inference_steps
      # creates integer timesteps by multiplying by ratio
      # rounding to avoid issues when num_inference_step is power of 3
      lowerCAmelCase_      :				Optional[int]      =       (jnp.arange(0   ,						lowerCamelCase		) * step_ratio).round()[::-1]

      return state.replace(
          num_inference_steps=lowerCamelCase   ,						timesteps=lowerCamelCase   ,						)

    def __lowercase       (       self   :  Tuple   ,						lowerCamelCase   :  DDPMSchedulerState   ,						lowerCamelCase   :  Any   ,						lowerCamelCase   :  str=None   ,						lowerCamelCase   :  Optional[Any]=None		)       ->  Union[str, Any]:
      lowerCAmelCase_      :				Optional[int]      =       state.common.alphas_cumprod[t]
      lowerCAmelCase_      :				Tuple      =       jnp.where(t > 0   ,						state.common.alphas_cumprod[t - 1]   ,						jnp.array(1.0   ,						dtype=self.dtype		)		)

      # For t > 0, compute predicted variance βt (see formula (6) and (7) from https://arxiv.org/pdf/2006.11239.pdf)
      # and sample from it to get previous sample
      # x_{t-1} ~ N(pred_prev_sample, variance) == add variance to pred_sample
      lowerCAmelCase_      :				Optional[Any]      =       (1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * state.common.betas[t]

      if variance_type is None:
        lowerCAmelCase_      :				Optional[int]      =       self.config.variance_type

      # hacks - were probably added for training stability
      if variance_type == "fixed_small":
        lowerCAmelCase_      :				Optional[Any]      =       jnp.clip(lowerCamelCase   ,						a_min=1E-20		)
      # for rl-diffuser https://arxiv.org/abs/2205.09991
      elif variance_type == "fixed_small_log":
        lowerCAmelCase_      :				List[str]      =       jnp.log(jnp.clip(lowerCamelCase   ,						a_min=1E-20		)		)
      elif variance_type == "fixed_large":
        lowerCAmelCase_      :				Union[str, Any]      =       state.common.betas[t]
      elif variance_type == "fixed_large_log":
        # Glide max_log
        lowerCAmelCase_      :				Dict      =       jnp.log(state.common.betas[t]		)
      elif variance_type == "learned":
        return predicted_variance
      elif variance_type == "learned_range":
        lowerCAmelCase_      :				List[Any]      =       variance
        lowerCAmelCase_      :				Dict      =       state.common.betas[t]
        lowerCAmelCase_      :				Any      =       (predicted_variance + 1) / 2
        lowerCAmelCase_      :				Tuple      =       frac * max_log + (1 - frac) * min_log

      return variance

    def __lowercase       (       self   :  Dict   ,						lowerCamelCase   :  DDPMSchedulerState   ,						lowerCamelCase   :  jnp.ndarray   ,						lowerCamelCase   :  int   ,						lowerCamelCase   :  jnp.ndarray   ,						lowerCamelCase   :  Optional[jax.random.KeyArray] = None   ,						lowerCamelCase   :  bool = True   ,						)       ->  Union[FlaxDDPMSchedulerOutput, Tuple]:
      lowerCAmelCase_      :				Optional[Any]      =       timestep

      if key is None:
        lowerCAmelCase_      :				List[Any]      =       jax.random.PRNGKey(0		)

      if model_output.shape[1] == sample.shape[1] * 2 and self.config.variance_type in ["learned", "learned_range"]:
        lowerCAmelCase_,							lowerCAmelCase_      :				Union[str, Any]      =       jnp.split(lowerCamelCase   ,						sample.shape[1]   ,						axis=1		)
      else:
        lowerCAmelCase_      :				int      =       None

      # 1. compute alphas, betas
      lowerCAmelCase_      :				Dict      =       state.common.alphas_cumprod[t]
      lowerCAmelCase_      :				Optional[int]      =       jnp.where(t > 0   ,						state.common.alphas_cumprod[t - 1]   ,						jnp.array(1.0   ,						dtype=self.dtype		)		)
      lowerCAmelCase_      :				Union[str, Any]      =       1 - alpha_prod_t
      lowerCAmelCase_      :				Optional[Any]      =       1 - alpha_prod_t_prev

      # 2. compute predicted original sample from predicted noise also called
      # "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
      if self.config.prediction_type == "epsilon":
        lowerCAmelCase_      :				Optional[int]      =       (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
      elif self.config.prediction_type == "sample":
        lowerCAmelCase_      :				str      =       model_output
      elif self.config.prediction_type == "v_prediction":
        lowerCAmelCase_      :				Dict      =       (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
      else:
        raise ValueError(
            F'prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample` '
            """ for the FlaxDDPMScheduler."""		)

      # 3. Clip "predicted x_0"
      if self.config.clip_sample:
        lowerCAmelCase_      :				Any      =       jnp.clip(lowerCamelCase   ,						-1   ,						1		)

      # 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
      # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
      lowerCAmelCase_      :				Optional[int]      =       (alpha_prod_t_prev ** 0.5 * state.common.betas[t]) / beta_prod_t
      lowerCAmelCase_      :				List[str]      =       state.common.alphas[t] ** 0.5 * beta_prod_t_prev / beta_prod_t

      # 5. Compute predicted previous sample µ_t
      # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
      lowerCAmelCase_      :				int      =       pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

      # 6. Add noise
      def random_variance():
        lowerCAmelCase_      :				Any      =       jax.random.split(lowerCamelCase   ,						num=1		)
        lowerCAmelCase_      :				Tuple      =       jax.random.normal(lowerCamelCase   ,						shape=model_output.shape   ,						dtype=self.dtype		)
        return (self._get_variance(lowerCamelCase   ,						lowerCamelCase   ,						predicted_variance=lowerCamelCase		) ** 0.5) * noise

      lowerCAmelCase_      :				Union[str, Any]      =       jnp.where(t > 0   ,						random_variance()   ,						jnp.zeros(model_output.shape   ,						dtype=self.dtype		)		)

      lowerCAmelCase_      :				Tuple      =       pred_prev_sample + variance

      if not return_dict:
        return (pred_prev_sample, state)

      return FlaxDDPMSchedulerOutput(prev_sample=lowerCamelCase   ,						state=lowerCamelCase		)

    def __lowercase       (       self   :  Optional[int]   ,						lowerCamelCase   :  DDPMSchedulerState   ,						lowerCamelCase   :  jnp.ndarray   ,						lowerCamelCase   :  jnp.ndarray   ,						lowerCamelCase   :  jnp.ndarray   ,						)       ->  jnp.ndarray:
      return add_noise_common(state.common   ,						lowerCamelCase   ,						lowerCamelCase   ,						lowerCamelCase		)

    def __lowercase       (       self   :  Tuple   ,						lowerCamelCase   :  DDPMSchedulerState   ,						lowerCamelCase   :  jnp.ndarray   ,						lowerCamelCase   :  jnp.ndarray   ,						lowerCamelCase   :  jnp.ndarray   ,						)       ->  jnp.ndarray:
      return get_velocity_common(state.common   ,						lowerCamelCase   ,						lowerCamelCase   ,						lowerCamelCase		)





    def __len__(       self   :  Optional[Any]		)       ->  str:
      return self.config.num_train_timesteps







'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


__A :  List[str]    							=				{
    "configuration_owlvit": [
        "OWLVIT_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "OwlViTConfig",
        "OwlViTOnnxConfig",
        "OwlViTTextConfig",
        "OwlViTVisionConfig",
    ],
    "processing_owlvit": ["OwlViTProcessor"],
}


try:
  if not is_vision_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __A :  Optional[int]    							=				["OwlViTFeatureExtractor"]
  __A :  str    							=				["OwlViTImageProcessor"]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __A :  Tuple    							=				[
      "OWLVIT_PRETRAINED_MODEL_ARCHIVE_LIST",
      "OwlViTModel",
      "OwlViTPreTrainedModel",
      "OwlViTTextModel",
      "OwlViTVisionModel",
      "OwlViTForObjectDetection",
  ]

if TYPE_CHECKING:
  from .configuration_owlvit import (
      OWLVIT_PRETRAINED_CONFIG_ARCHIVE_MAP,
      OwlViTConfig,
      OwlViTOnnxConfig,
      OwlViTTextConfig,
      OwlViTVisionConfig,
  )
  from .processing_owlvit import OwlViTProcessor

  try:
    if not is_vision_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .feature_extraction_owlvit import OwlViTFeatureExtractor
    from .image_processing_owlvit import OwlViTImageProcessor

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_owlvit import (
        OWLVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
        OwlViTForObjectDetection,
        OwlViTModel,
        OwlViTPreTrainedModel,
        OwlViTTextModel,
        OwlViTVisionModel,
    )

else:
  import sys

  __A :  List[Any]    							=				_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



