





"""simple docstring"""
from dataclasses import dataclass
from typing import Optional, Tuple, Union

import flax
import jax.numpy as jnp
from jax import random

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import FlaxSchedulerMixin, FlaxSchedulerOutput, broadcast_to_shape_from_left
@flax.struct.dataclass
class lowerCamelCase__  :
   __UpperCAmelCase        =					None
   __UpperCAmelCase        =					None
   __UpperCAmelCase        =					None



   @classmethod
   def    _UpperCAmelCase	(       cls	) ->    List[str]:







      """simple docstring"""
      return cls()




@dataclass
class lowerCamelCase__  (    A__				):
   __UpperCAmelCase        =					42
   __UpperCAmelCase        =					42
   __UpperCAmelCase        =					None




class lowerCamelCase__  (    A__  ,				A__				):



   @property
   def    _UpperCAmelCase	(       self	) ->    List[Any]:







      """simple docstring"""
      return True



   @register_to_config
   def __init__(       self      ,   snake_case = 2_0_0_0      ,   snake_case = 0.15      ,   snake_case = 0.01      ,   snake_case = 1_3_4_8.0      ,   snake_case = 1E-5      ,   snake_case = 1      ,   ) ->    str:







      """simple docstring"""
      pass



   def    _UpperCAmelCase	(       self	) ->    List[Any]:







      """simple docstring"""
      lowercase    :			Tuple  =       ScoreSdeVeSchedulerState.create()
      return self.set_sigmas(
          _lowerCAmelCase      ,   self.config.num_train_timesteps      ,   self.config.sigma_min      ,   self.config.sigma_max      ,   self.config.sampling_eps      ,   )



   def    _UpperCAmelCase	(       self      ,   snake_case      ,   snake_case      ,   snake_case = ()      ,   snake_case = None	) ->    Tuple:







      """simple docstring"""
      lowercase    :			Optional[int]  =       sampling_eps if sampling_eps is not None else self.config.sampling_eps

      lowercase    :			Any  =       jnp.linspace(1      ,   _lowerCAmelCase      ,   _lowerCAmelCase	)
      return state.replace(timesteps=_lowerCAmelCase	)



   def    _UpperCAmelCase	(       self      ,   snake_case      ,   snake_case      ,   snake_case = None      ,   snake_case = None      ,   snake_case = None      ,   ) ->    List[str]:







      """simple docstring"""
      lowercase    :			Dict  =       sigma_min if sigma_min is not None else self.config.sigma_min
      lowercase    :			Dict  =       sigma_max if sigma_max is not None else self.config.sigma_max
      lowercase    :			Union[str, Any]  =       sampling_eps if sampling_eps is not None else self.config.sampling_eps
      if state.timesteps is None:
         lowercase    :			Dict  =       self.set_timesteps(_lowerCAmelCase      ,   _lowerCAmelCase      ,   _lowerCAmelCase	)

      lowercase    :			Dict  =       jnp.exp(jnp.linspace(jnp.log(_lowerCAmelCase	)      ,   jnp.log(_lowerCAmelCase	)      ,   _lowerCAmelCase	)	)
      lowercase    :			Tuple  =       jnp.array([sigma_min * (sigma_max / sigma_min) ** t for t in state.timesteps]	)

      return state.replace(discrete_sigmas=_lowerCAmelCase      ,   sigmas=_lowerCAmelCase	)



   def    _UpperCAmelCase	(       self      ,   snake_case      ,   snake_case      ,   snake_case	) ->    List[Any]:







      """simple docstring"""
      return jnp.where(timesteps == 0      ,   jnp.zeros_like(_lowerCAmelCase	)      ,   state.discrete_sigmas[timesteps - 1]	)



   def    _UpperCAmelCase	(       self      ,   snake_case      ,   snake_case      ,   snake_case      ,   snake_case      ,   snake_case      ,   snake_case = True      ,   ) ->    Optional[int]:







      """simple docstring"""
      if state.timesteps is None:
         raise ValueError(
             """`state.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"""	)

      lowercase    :			int  =       timestep * jnp.ones(
          sample.shape[0]      ,   )
      lowercase    :			Union[str, Any]  =       (timestep * (len(state.timesteps	) - 1)).long()

      lowercase    :			Union[str, Any]  =       state.discrete_sigmas[timesteps]
      lowercase    :			List[str]  =       self.get_adjacent_sigma(_lowerCAmelCase      ,   _lowerCAmelCase      ,   _lowerCAmelCase	)
      lowercase    :			Any  =       jnp.zeros_like(_lowerCAmelCase	)
      lowercase    :			str  =       (sigma**2 - adjacent_sigma**2) ** 0.5

      # equation 6 in the paper: the model_output modeled by the network is grad_x log pt(x)
      # also equation 47 shows the analog from SDE models to ancestral sampling methods
      lowercase    :			Optional[Any]  =       diffusion.flatten()
      lowercase    :			Tuple  =       broadcast_to_shape_from_left(_lowerCAmelCase      ,   sample.shape	)
      lowercase    :			Dict  =       drift - diffusion**2 * model_output

      #  equation 6: sample noise for the diffusion term of
      lowercase    :			Tuple  =       random.split(_lowerCAmelCase      ,   num=1	)
      lowercase    :			Any  =       random.normal(key=_lowerCAmelCase      ,   shape=sample.shape	)
      lowercase    :			Union[str, Any]  =       sample - drift  # subtract because `dt` is a small negative timestep
      # TODO is the variable diffusion the correct scaling term for the noise?
      lowercase    :			Any  =       prev_sample_mean + diffusion * noise  # add impact of diffusion field g

      if not return_dict:
         return (prev_sample, prev_sample_mean, state)

      return FlaxSdeVeOutput(prev_sample=_lowerCAmelCase      ,   prev_sample_mean=_lowerCAmelCase      ,   state=_lowerCAmelCase	)



   def    _UpperCAmelCase	(       self      ,   snake_case      ,   snake_case      ,   snake_case      ,   snake_case      ,   snake_case = True      ,   ) ->    Any:







      """simple docstring"""
      if state.timesteps is None:
         raise ValueError(
             """`state.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"""	)

      # For small batch sizes, the paper "suggest replacing norm(z) with sqrt(d), where d is the dim. of z"
      # sample noise for correction
      lowercase    :			Tuple  =       random.split(_lowerCAmelCase      ,   num=1	)
      lowercase    :			int  =       random.normal(key=_lowerCAmelCase      ,   shape=sample.shape	)

      # compute step size from the model_output, the noise, and the snr
      lowercase    :			Any  =       jnp.linalg.norm(_lowerCAmelCase	)
      lowercase    :			List[str]  =       jnp.linalg.norm(_lowerCAmelCase	)
      lowercase    :			Tuple  =       (self.config.snr * noise_norm / grad_norm) ** 2 * 2
      lowercase    :			Optional[Any]  =       step_size * jnp.ones(sample.shape[0]	)

      # compute corrected sample: model_output term and noise term
      lowercase    :			str  =       step_size.flatten()
      lowercase    :			List[str]  =       broadcast_to_shape_from_left(_lowerCAmelCase      ,   sample.shape	)
      lowercase    :			List[str]  =       sample + step_size * model_output
      lowercase    :			Any  =       prev_sample_mean + ((step_size * 2) ** 0.5) * noise

      if not return_dict:
         return (prev_sample, state)

      return FlaxSdeVeOutput(prev_sample=_lowerCAmelCase      ,   state=_lowerCAmelCase	)





   def __len__(       self	) ->    Optional[Any]:







      """simple docstring"""
      return self.config.num_train_timesteps









"""simple docstring"""
import torch.nn as nn

from .imports import is_fpa_available


if is_fpa_available():
   import transformer_engine.pytorch as te




def 			__snake_case						(      __A ,__A=True ,__A=True ,__A=True    )		->				Union[str, Any]:
   if not is_fpa_available():
      raise ImportError("""Using `convert_model` requires transformer_engine to be installed."""    )
   for name, module in model.named_children():
      if isinstance(__A ,nn.Linear    ) and to_transformer_engine and _convert_linear:
         # Return early if the linear layer weights are not multiples of 16
         if any(p % 16 != 0 for p in module.weight.shape    ):
            return
         lowercase    :			Optional[int]  =       module.bias is not None
         lowercase    :			Union[str, Any]  =       te.Linear(
             module.in_features ,module.out_features ,bias=__A ,params_dtype=module.weight.dtype    )
         lowercase    :			Union[str, Any]  =       module.weight.data.clone()
         if has_bias:
            lowercase    :			Dict  =       module.bias.data.clone()

         setattr(__A ,__A ,__A    )
      elif isinstance(__A ,nn.LayerNorm    ) and to_transformer_engine and _convert_ln:
         lowercase    :			List[str]  =       te.LayerNorm(module.normalized_shape[0] ,eps=module.eps ,params_dtype=module.weight.dtype    )
         lowercase    :			Optional[Any]  =       module.weight.data.clone()
         lowercase    :			Any  =       module.bias.data.clone()

         setattr(__A ,__A ,__A    )
      elif isinstance(__A ,te.Linear    ) and not to_transformer_engine and _convert_linear:
         lowercase    :			Any  =       module.bias is not None
         lowercase    :			List[Any]  =       nn.Linear(
             module.in_features ,module.out_features ,bias=__A ,params_dtype=module.weight.dtype    )
         lowercase    :			Union[str, Any]  =       module.weight.data.clone()
         if has_bias:
            lowercase    :			Optional[Any]  =       module.bias.data.clone()

         setattr(__A ,__A ,__A    )
      elif isinstance(__A ,te.LayerNorm    ) and not to_transformer_engine and _convert_ln:
         lowercase    :			Any  =       nn.LayerNorm(module.normalized_shape[0] ,eps=module.eps ,params_dtype=module.weight.dtype    )
         lowercase    :			str  =       module.weight.data.clone()
         lowercase    :			List[str]  =       module.bias.data.clone()

         setattr(__A ,__A ,__A    )
      else:
         convert_model(
             __A ,to_transformer_engine=__A ,_convert_linear=_convert_linear ,_convert_ln=_convert_ln ,)







def 			__snake_case						(      __A    )		->				Union[str, Any]:
   if not is_fpa_available():
      raise ImportError("""Using `has_transformer_engine_layers` requires transformer_engine to be installed."""    )
   for m in model.modules():
      if isinstance(__A ,(te.LayerNorm, te.Linear)    ):
         return True
   return False


