# 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 		_lowercase						:
 lowercase				     =   42

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

 @classmethod
 def 						SCREAMING_SNAKE_CASE__   ( cls	:							Optional[int]   ,      snake_case	:							List[str]   ,      snake_case	:							Tuple   ,      snake_case	:							Any			)   ->						int:

  """simple docstring"""





  return cls(common=__A   ,      init_noise_sigma=__A   ,      timesteps=__A			)





@dataclass
class 		_lowercase						(					snake_case_       ):
 lowercase				     =   42





class 		_lowercase						(					snake_case_						,      snake_case_       ):
 lowercase				     =   [e.name for e in FlaxKarrasDiffusionSchedulers]

 lowercase				     =   42

 @property
 def 						SCREAMING_SNAKE_CASE__   ( self	:							Tuple			)   ->						Dict:

  """simple docstring"""





  return True

 @register_to_config
 def __init__( self	:							str   ,      snake_case	:							Any = 1_0_0_0   ,      snake_case	:							Any = 0.0001   ,      snake_case	:							Optional[Any] = 0.02   ,      snake_case	:							Union[str, Any] = "linear"   ,      snake_case	:							int = None   ,      snake_case	:							Optional[Any] = "fixed_small"   ,      snake_case	:							int = True   ,      snake_case	:							Any = "epsilon"   ,      snake_case	:							Any = jnp.floataa   ,      )   ->						Dict:

  """simple docstring"""





  UpperCamelCase_					:						Any =					dtype

 def 						SCREAMING_SNAKE_CASE__   ( self	:							Any   ,      snake_case	:							Dict = None			)   ->						Optional[int]:

  """simple docstring"""





  if common is None:
   UpperCamelCase_					:						Dict =					CommonSchedulerState.create(self			)

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

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

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

 def 						SCREAMING_SNAKE_CASE__   ( self	:							Dict   ,      snake_case	:							Any   ,      snake_case	:							List[str]   ,      snake_case	:							Optional[Any] = None			)   ->						Union[str, Any]:

  """simple docstring"""





  return sample

 def 						SCREAMING_SNAKE_CASE__   ( self	:							Optional[Any]   ,      snake_case	:							Optional[int]   ,      snake_case	:							Any   ,      snake_case	:							Union[str, Any] = ()			)   ->						List[Any]:

  """simple docstring"""





  UpperCamelCase_					:						Dict =					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
  UpperCamelCase_					:						str =					(jnp.arange(0   ,      __A			) * step_ratio).round()[::-1]

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

 def 						SCREAMING_SNAKE_CASE__   ( self	:							Tuple   ,      snake_case	:							List[Any]   ,      snake_case	:							List[str]   ,      snake_case	:							Optional[Any]=None   ,      snake_case	:							int=None			)   ->						Any:

  """simple docstring"""





  UpperCamelCase_					:						Optional[int] =					state.common.alphas_cumprod[t]
  UpperCamelCase_					:						Union[str, Any] =					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
  UpperCamelCase_					:						List[Any] =					(1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * state.common.betas[t]

  if variance_type is None:
   UpperCamelCase_					:						int =					self.config.variance_type

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

  return variance

 def 						SCREAMING_SNAKE_CASE__   ( self	:							List[str]   ,      snake_case	:							Union[str, Any]   ,      snake_case	:							Tuple   ,      snake_case	:							str   ,      snake_case	:							Any   ,      snake_case	:							List[Any] = None   ,      snake_case	:							int = True   ,      )   ->						Dict:

  """simple docstring"""





  UpperCamelCase_					:						int =					timestep

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

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

  # 1. compute alphas, betas
  UpperCamelCase_					:						List[str] =					state.common.alphas_cumprod[t]
  UpperCamelCase_					:						Tuple =					jnp.where(t > 0   ,      state.common.alphas_cumprod[t - 1]   ,      jnp.array(1.0   ,      dtype=self.dtype			)			)
  UpperCamelCase_					:						Optional[int] =					1 - alpha_prod_t
  UpperCamelCase_					:						Tuple =					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":
   UpperCamelCase_					:						Tuple =					(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
  elif self.config.prediction_type == "sample":
   UpperCamelCase_					:						Dict =					model_output
  elif self.config.prediction_type == "v_prediction":
   UpperCamelCase_					:						Any =					(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:
   UpperCamelCase_					:						Dict =					jnp.clip(__A   ,      -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
  UpperCamelCase_					:						Any =					(alpha_prod_t_prev ** 0.5 * state.common.betas[t]) / beta_prod_t
  UpperCamelCase_					:						Union[str, Any] =					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
  UpperCamelCase_					:						List[str] =					pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

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

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

  UpperCamelCase_					:						Tuple =					pred_prev_sample + variance

  if not return_dict:
   return (pred_prev_sample, state)

  return FlaxDDPMSchedulerOutput(prev_sample=__A   ,      state=__A			)

 def 						SCREAMING_SNAKE_CASE__   ( self	:							Optional[int]   ,      snake_case	:							Dict   ,      snake_case	:							int   ,      snake_case	:							Any   ,      snake_case	:							Any   ,      )   ->						int:

  """simple docstring"""





  return add_noise_common(state.common   ,      __A   ,      __A   ,      __A			)

 def 						SCREAMING_SNAKE_CASE__   ( self	:							Dict   ,      snake_case	:							List[Any]   ,      snake_case	:							Tuple   ,      snake_case	:							List[Any]   ,      snake_case	:							Union[str, Any]   ,      )   ->						Tuple:

  """simple docstring"""





  return get_velocity_common(state.common   ,      __A   ,      __A   ,      __A			)


 def __len__( self	:							str			)   ->						Optional[int]:

  """simple docstring"""





  return self.config.num_train_timesteps







import warnings

from ...utils import logging
from .image_processing_dpt import DPTImageProcessor


_snake_case             =     logging.get_logger(__name__)
class 	UpperCAmelCase_					(					UpperCamelCase    ):

   '''simple docstring'''




   def __init__(		self						,				*__A						,				**__A ):






     """simple docstring"""





     warnings.warn(
         "The class DPTFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"
         " use DPTImageProcessor instead."						,				__A						,				)
     super().__init__(*__A						,				**__A )



