




'''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       A   :
							lowercase_          =    42

							# setable values
							lowercase_          =    42
							lowercase_          =    42
							lowercase_          =    None







							@classmethod
							def        __lowerCAmelCase      (  cls	:  str							,      lowerCAmelCase_	:  CommonSchedulerState							,      lowerCAmelCase_	:  jnp.ndarray							,      lowerCAmelCase_	:  jnp.ndarray )      ->							str:


														"""simple docstring"""


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



@dataclass
class       A   (		_a   ):
							lowercase_          =    42



class       A   (		_a					,_a   ):
							lowercase_          =    [e.name for e in FlaxKarrasDiffusionSchedulers]

							lowercase_          =    42







							@property
							def        __lowerCAmelCase      (  self	:  str )      ->							List[str]:


														"""simple docstring"""


														return True







							@register_to_config
							def __init__(  self	:  Tuple							,      lowerCAmelCase_	:  int = 10_00							,      lowerCAmelCase_	:  float = 0.0_0_0_1							,      lowerCAmelCase_	:  float = 0.0_2							,      lowerCAmelCase_	:  str = "linear"							,      lowerCAmelCase_	:  Optional[jnp.ndarray] = None							,      lowerCAmelCase_	:  str = "fixed_small"							,      lowerCAmelCase_	:  bool = True							,      lowerCAmelCase_	:  str = "epsilon"							,      lowerCAmelCase_	:  jnp.dtype = jnp.floataa							,      )      ->							Optional[Any]:


														"""simple docstring"""


														_a     					=    dtype







							def        __lowerCAmelCase      (  self	:  str							,      lowerCAmelCase_	:  Optional[CommonSchedulerState] = None )      ->							DDPMSchedulerState:


														"""simple docstring"""


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

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

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

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







							def        __lowerCAmelCase      (  self	:  Optional[int]							,      lowerCAmelCase_	:  DDPMSchedulerState							,      lowerCAmelCase_	:  jnp.ndarray							,      lowerCAmelCase_	:  Optional[int] = None )      ->							jnp.ndarray:


														"""simple docstring"""


														return sample







							def        __lowerCAmelCase      (  self	:  Union[str, Any]							,      lowerCAmelCase_	:  DDPMSchedulerState							,      lowerCAmelCase_	:  int							,      lowerCAmelCase_	:  Tuple = () )      ->							DDPMSchedulerState:


														"""simple docstring"""


														_a     					=    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
														_a     					=    (jnp.arange(0							,      lowerCAmelCase_ ) * step_ratio).round()[::-1]

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







							def        __lowerCAmelCase      (  self	:  Any							,      lowerCAmelCase_	:  DDPMSchedulerState							,      lowerCAmelCase_	:  Dict							,      lowerCAmelCase_	:  int=None							,      lowerCAmelCase_	:  Dict=None )      ->							Optional[Any]:


														"""simple docstring"""


														_a     					=    state.common.alphas_cumprod[t]
														_a     					=    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
														_a     					=    (1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * state.common.betas[t]

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

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

														return variance







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


														"""simple docstring"""


														_a     					=    timestep

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

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

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

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

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

														_a     					=    pred_prev_sample + variance

														if not return_dict:
																					return (pred_prev_sample, state)

														return FlaxDDPMSchedulerOutput(prev_sample=lowerCAmelCase_							,      state=lowerCAmelCase_ )







							def        __lowerCAmelCase      (  self	:  Tuple							,      lowerCAmelCase_	:  DDPMSchedulerState							,      lowerCAmelCase_	:  jnp.ndarray							,      lowerCAmelCase_	:  jnp.ndarray							,      lowerCAmelCase_	:  jnp.ndarray							,      )      ->							jnp.ndarray:


														"""simple docstring"""


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







							def        __lowerCAmelCase      (  self	:  List[str]							,      lowerCAmelCase_	:  DDPMSchedulerState							,      lowerCAmelCase_	:  jnp.ndarray							,      lowerCAmelCase_	:  jnp.ndarray							,      lowerCAmelCase_	:  jnp.ndarray							,      )      ->							jnp.ndarray:


														"""simple docstring"""


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







							def __len__(  self	:  Any )      ->							str:


														"""simple docstring"""


														return self.config.num_train_timesteps







'''simple docstring'''


from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


_snake_case				:				Dict       							=		logging.get_logger(__name__)

_snake_case				:				Optional[Any]       							=		{
    'shi-labs/nat-mini-in1k-224': 'https://huggingface.co/shi-labs/nat-mini-in1k-224/resolve/main/config.json',
    # See all Nat models at https://huggingface.co/models?filter=nat
}

class       A   (		_a					,_a   ):
							lowercase_          =    'nat'

							lowercase_          =    {
							    'num_attention_heads': 'num_heads',
							    'num_hidden_layers': 'num_layers',
							}







							def __init__(  self	:  List[str]							,      lowerCAmelCase_	:  str=4							,      lowerCAmelCase_	:  Any=3							,      lowerCAmelCase_	:  List[Any]=64							,      lowerCAmelCase_	:  Dict=[3, 4, 6, 5]							,      lowerCAmelCase_	:  Dict=[2, 4, 8, 16]							,      lowerCAmelCase_	:  str=7							,      lowerCAmelCase_	:  Dict=3.0							,      lowerCAmelCase_	:  int=True							,      lowerCAmelCase_	:  Tuple=0.0							,      lowerCAmelCase_	:  str=0.0							,      lowerCAmelCase_	:  List[str]=0.1							,      lowerCAmelCase_	:  int="gelu"							,      lowerCAmelCase_	:  List[str]=0.0_2							,      lowerCAmelCase_	:  str=1e-5							,      lowerCAmelCase_	:  Tuple=0.0							,      lowerCAmelCase_	:  str=None							,      lowerCAmelCase_	:  int=None							,      **lowerCAmelCase_	:  List[Any]							,      )      ->							Any:


														"""simple docstring"""


														super().__init__(**lowerCAmelCase_ )

														_a     					=    patch_size
														_a     					=    num_channels
														_a     					=    embed_dim
														_a     					=    depths
														_a     					=    len(lowerCAmelCase_ )
														_a     					=    num_heads
														_a     					=    kernel_size
														_a     					=    mlp_ratio
														_a     					=    qkv_bias
														_a     					=    hidden_dropout_prob
														_a     					=    attention_probs_dropout_prob
														_a     					=    drop_path_rate
														_a     					=    hidden_act
														_a     					=    layer_norm_eps
														_a     					=    initializer_range
														# we set the hidden_size attribute in order to make Nat work with VisionEncoderDecoderModel
														# this indicates the channel dimension after the last stage of the model
														_a     					=    int(embed_dim * 2 ** (len(lowerCAmelCase_ ) - 1) )
														_a     					=    layer_scale_init_value
														_a     					=    ['''stem'''] + [F'stage{idx}' for idx in range(1							,      len(lowerCAmelCase_ ) + 1 )]
														_a					,							_a     					=    get_aligned_output_features_output_indices(
														    out_features=lowerCAmelCase_							,      out_indices=lowerCAmelCase_							,      stage_names=self.stage_names )

