




# 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     __lowerCAmelCase :

			"""simple docstring"""







			snake_case_   		=       42

			# setable values
			snake_case_   		=       42
			snake_case_   		=       42
			snake_case_   		=       None


			@classmethod
			def       lowercase_    (      cls			,						lowerCamelCase__			,						lowerCamelCase__			,						lowerCamelCase__							)    ->      str:



							'''simple docstring'''
							return cls(common=lowerCamelCase__			,						init_noise_sigma=lowerCamelCase__			,						timesteps=lowerCamelCase__							)







@dataclass
class     __lowerCAmelCase (       __magic_name__ ):

			"""simple docstring"""







			snake_case_   		=       42







class     __lowerCAmelCase (       __magic_name__			,				__magic_name__ ):

			"""simple docstring"""







			snake_case_   		=       [e.name for e in FlaxKarrasDiffusionSchedulers]

			snake_case_   		=       42


			@property
			def       lowercase_    (      self							)    ->      Tuple:



							'''simple docstring'''
							return True


			@register_to_config
			def __init__(      self			,						lowerCamelCase__ = 1_000			,						lowerCamelCase__ = 0.00_01			,						lowerCamelCase__ = 0.02			,						lowerCamelCase__ = "linear"			,						lowerCamelCase__ = None			,						lowerCamelCase__ = "fixed_small"			,						lowerCamelCase__ = True			,						lowerCamelCase__ = "epsilon"			,						lowerCamelCase__ = jnp.floataa			,						)    ->      List[Any]:



							'''simple docstring'''
							__lowerCamelCase		       =    dtype


			def       lowercase_    (      self			,						lowerCamelCase__ = None							)    ->      DDPMSchedulerState:



							'''simple docstring'''
							if common is None:
											__lowerCamelCase		       =    CommonSchedulerState.create(self							)

							# standard deviation of the initial noise distribution
							__lowerCamelCase		       =    jnp.array(1.0			,						dtype=self.dtype							)

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

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


			def       lowercase_    (      self			,						lowerCamelCase__			,						lowerCamelCase__			,						lowerCamelCase__ = None							)    ->      jnp.ndarray:



							'''simple docstring'''
							return sample


			def       lowercase_    (      self			,						lowerCamelCase__			,						lowerCamelCase__			,						lowerCamelCase__ = ()							)    ->      DDPMSchedulerState:



							'''simple docstring'''
							__lowerCamelCase		       =    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		       =    (jnp.arange(0			,						lowerCamelCase__							) * step_ratio).round()[::-1]

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


			def       lowercase_    (      self			,						lowerCamelCase__			,						lowerCamelCase__			,						lowerCamelCase__=None			,						lowerCamelCase__=None							)    ->      Optional[int]:



							'''simple docstring'''
							__lowerCamelCase		       =    state.common.alphas_cumprod[t]
							__lowerCamelCase		       =    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		       =    (1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * state.common.betas[t]

							if variance_type is None:
											__lowerCamelCase		       =    self.config.variance_type

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

							return variance


			def       lowercase_    (      self			,						lowerCamelCase__			,						lowerCamelCase__			,						lowerCamelCase__			,						lowerCamelCase__			,						lowerCamelCase__ = None			,						lowerCamelCase__ = True			,						)    ->      Union[FlaxDDPMSchedulerOutput, Tuple]:



							'''simple docstring'''
							__lowerCamelCase		       =    timestep

							if key is None:
											__lowerCamelCase		       =    jax.random.PRNGKey(0							)

							if model_output.shape[1] == sample.shape[1] * 2 and self.config.variance_type in ["learned", "learned_range"]:
											__lowerCamelCase       ,						__lowerCamelCase		       =    jnp.split(lowerCamelCase__			,						sample.shape[1]			,						axis=1							)
							else:
											__lowerCamelCase		       =    None

							# 1. compute alphas, betas
							__lowerCamelCase		       =    state.common.alphas_cumprod[t]
							__lowerCamelCase		       =    jnp.where(t > 0			,						state.common.alphas_cumprod[t - 1]			,						jnp.array(1.0			,						dtype=self.dtype							)							)
							__lowerCamelCase		       =    1 - alpha_prod_t
							__lowerCamelCase		       =    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		       =    (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
							elif self.config.prediction_type == "sample":
											__lowerCamelCase		       =    model_output
							elif self.config.prediction_type == "v_prediction":
											__lowerCamelCase		       =    (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		       =    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		       =    (alpha_prod_t_prev ** 0.5 * state.common.betas[t]) / beta_prod_t
							__lowerCamelCase		       =    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		       =    pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

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

							__lowerCamelCase		       =    jnp.where(t > 0			,						random_variance()			,						jnp.zeros(model_output.shape			,						dtype=self.dtype							)							)

							__lowerCamelCase		       =    pred_prev_sample + variance

							if not return_dict:
											return (pred_prev_sample, state)

							return FlaxDDPMSchedulerOutput(prev_sample=lowerCamelCase__			,						state=lowerCamelCase__							)


			def       lowercase_    (      self			,						lowerCamelCase__			,						lowerCamelCase__			,						lowerCamelCase__			,						lowerCamelCase__			,						)    ->      jnp.ndarray:



							'''simple docstring'''
							return add_noise_common(state.common			,						lowerCamelCase__			,						lowerCamelCase__			,						lowerCamelCase__							)


			def       lowercase_    (      self			,						lowerCamelCase__			,						lowerCamelCase__			,						lowerCamelCase__			,						lowerCamelCase__			,						)    ->      jnp.ndarray:



							'''simple docstring'''
							return get_velocity_common(state.common			,						lowerCamelCase__			,						lowerCamelCase__			,						lowerCamelCase__							)






			def __len__(      self							)    ->      List[str]:



							'''simple docstring'''
							return self.config.num_train_timesteps






from typing import TYPE_CHECKING

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


_A		       =						{'configuration_vit_mae': ['VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ViTMAEConfig']}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _A		       =						[
       'VIT_MAE_PRETRAINED_MODEL_ARCHIVE_LIST',
       'ViTMAEForPreTraining',
       'ViTMAELayer',
       'ViTMAEModel',
       'ViTMAEPreTrainedModel',
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _A		       =						[
       'TFViTMAEForPreTraining',
       'TFViTMAEModel',
       'TFViTMAEPreTrainedModel',
   ]

if TYPE_CHECKING:
   from .configuration_vit_mae import VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTMAEConfig

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_vit_mae import (
          VIT_MAE_PRETRAINED_MODEL_ARCHIVE_LIST,
          ViTMAEForPreTraining,
          ViTMAELayer,
          ViTMAEModel,
          ViTMAEPreTrainedModel,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_vit_mae import TFViTMAEForPreTraining, TFViTMAEModel, TFViTMAEPreTrainedModel


else:
   import sys

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





