


"""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   :
							__a					 =       42

							# setable values
							__a					 =       42
							__a					 =       42
							__a					 =       None




							@classmethod
							def   __a   (     cls:    Union[str, Any]     ,							A_:    CommonSchedulerState     ,							A_:    jnp.ndarray     ,							A_:    jnp.ndarray   ):
										return cls(common=A_     ,							init_noise_sigma=A_     ,							timesteps=A_   )




@dataclass
class 			__snake_case   (lowerCamelCase  ):
							__a					 =       42




class 			__snake_case   (lowerCamelCase			,		lowerCamelCase  ):
							__a					 =       [e.name for e in FlaxKarrasDiffusionSchedulers]

							__a					 =       42




							@property
							def   __a   (     self:    str   ):
										return True




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




							def   __a   (     self:    int     ,							A_:    Optional[CommonSchedulerState] = None   ):
										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=A_     ,							init_noise_sigma=A_     ,							timesteps=A_     ,							)




							def   __a   (     self:    List[Any]     ,							A_:    DDPMSchedulerState     ,							A_:    jnp.ndarray     ,							A_:    Optional[int] = None   ):
										return sample




							def   __a   (     self:    str     ,							A_:    DDPMSchedulerState     ,							A_:    int     ,							A_:    Tuple = ()   ):
										__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     ,							A_   ) * step_ratio).round()[::-1]

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




							def   __a   (     self:    int     ,							A_:    DDPMSchedulerState     ,							A_:    List[str]     ,							A_:    List[Any]=None     ,							A_:    Optional[int]=None   ):
										__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(A_     ,							a_min=1E-20   )
										# for rl-diffuser https://arxiv.org/abs/2205.09991
										elif variance_type == "fixed_small_log":
													__lowerCamelCase          =      jnp.log(jnp.clip(A_     ,							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   __a   (     self:    List[Any]     ,							A_:    DDPMSchedulerState     ,							A_:    jnp.ndarray     ,							A_:    int     ,							A_:    jnp.ndarray     ,							A_:    Optional[jax.random.KeyArray] = None     ,							A_:    bool = True     ,							):
										__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(A_     ,							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(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
										__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(A_     ,							num=1   )
													__lowerCamelCase          =      jax.random.normal(A_     ,							shape=model_output.shape     ,							dtype=self.dtype   )
													return (self._get_variance(A_     ,							A_     ,							predicted_variance=A_   ) ** 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=A_     ,							state=A_   )




							def   __a   (     self:    Union[str, Any]     ,							A_:    DDPMSchedulerState     ,							A_:    jnp.ndarray     ,							A_:    jnp.ndarray     ,							A_:    jnp.ndarray     ,							):
										return add_noise_common(state.common     ,							A_     ,							A_     ,							A_   )




							def   __a   (     self:    Dict     ,							A_:    DDPMSchedulerState     ,							A_:    jnp.ndarray     ,							A_:    jnp.ndarray     ,							A_:    jnp.ndarray     ,							):
										return get_velocity_common(state.common     ,							A_     ,							A_     ,							A_   )




							def __len__(     self:    str   ):
										return self.config.num_train_timesteps








"""simple docstring"""




from typing import TYPE_CHECKING

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


__magic_name__						: Any    =				{'configuration_glpn': ['GLPN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GLPNConfig']}

try:
					if not is_vision_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__magic_name__						: Dict    =				['GLPNFeatureExtractor']
					__magic_name__						: Union[str, Any]    =				['GLPNImageProcessor']

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__magic_name__						: int    =				[
					    'GLPN_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'GLPNForDepthEstimation',
					    'GLPNLayer',
					    'GLPNModel',
					    'GLPNPreTrainedModel',
					]


if TYPE_CHECKING:
					from .configuration_glpn import GLPN_PRETRAINED_CONFIG_ARCHIVE_MAP, GLPNConfig

					try:
										if not is_vision_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .feature_extraction_glpn import GLPNFeatureExtractor
										from .image_processing_glpn import GLPNImageProcessor

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_glpn import (
										    GLPN_PRETRAINED_MODEL_ARCHIVE_LIST,
										    GLPNForDepthEstimation,
										    GLPNLayer,
										    GLPNModel,
										    GLPNPreTrainedModel,
										)


else:
					import sys

					__magic_name__						: Optional[int]    =				_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




