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 				_lowercase						:






    '''simple docstring'''
    SCREAMING_SNAKE_CASE:							Dict        =						None
    SCREAMING_SNAKE_CASE:							int        =						None
    SCREAMING_SNAKE_CASE:							Any        =						None




    @classmethod
    def     _a						(						cls   ):
       return cls()





@dataclass
class 				_lowercase						(    A__ ):






    '''simple docstring'''
    SCREAMING_SNAKE_CASE:							Optional[Any]        =						42
    SCREAMING_SNAKE_CASE:							Optional[Any]        =						42
    SCREAMING_SNAKE_CASE:							int        =						None





class 				_lowercase						(    A__   ,     A__ ):






    '''simple docstring'''




    @property
    def     _a						(						self   ):
       return True




    @register_to_config
    def __init__(						self					,	lowerCamelCase__ = 2_000					,	lowerCamelCase__ = 0.1_5					,	lowerCamelCase__ = 0.0_1					,	lowerCamelCase__ = 1_348.0					,	lowerCamelCase__ = 1E-5					,	lowerCamelCase__ = 1					,	):
       pass




    def     _a						(						self   ):
       lowerCAmelCase_:       List[str]         =  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     _a						(						self					,	lowerCamelCase__					,	lowerCamelCase__					,	lowerCamelCase__ = ()					,	lowerCamelCase__ = None   ):
       lowerCAmelCase_:       List[str]         =  sampling_eps if sampling_eps is not None else self.config.sampling_eps

       lowerCAmelCase_:       List[Any]         =  jnp.linspace(1					,	_lowerCAmelCase					,	_lowerCAmelCase   )
       return state.replace(timesteps=_lowerCAmelCase   )




    def     _a						(						self					,	lowerCamelCase__					,	lowerCamelCase__					,	lowerCamelCase__ = None					,	lowerCamelCase__ = None					,	lowerCamelCase__ = None					,	):
       lowerCAmelCase_:       List[str]         =  sigma_min if sigma_min is not None else self.config.sigma_min
       lowerCAmelCase_:       int         =  sigma_max if sigma_max is not None else self.config.sigma_max
       lowerCAmelCase_:       Any         =  sampling_eps if sampling_eps is not None else self.config.sampling_eps
       if state.timesteps is None:
          lowerCAmelCase_:       Dict         =  self.set_timesteps(_lowerCAmelCase					,	_lowerCAmelCase					,	_lowerCAmelCase   )

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

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




    def     _a						(						self					,	lowerCamelCase__					,	lowerCamelCase__					,	lowerCamelCase__   ):
       return jnp.where(timesteps == 0					,	jnp.zeros_like(_lowerCAmelCase   )					,	state.discrete_sigmas[timesteps - 1]   )




    def     _a						(						self					,	lowerCamelCase__					,	lowerCamelCase__					,	lowerCamelCase__					,	lowerCamelCase__					,	lowerCamelCase__					,	lowerCamelCase__ = True					,	):
       if state.timesteps is None:
          raise ValueError(
              "`state.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"   )

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

       lowerCAmelCase_:       Optional[int]         =  state.discrete_sigmas[timesteps]
       lowerCAmelCase_:       Optional[Any]         =  self.get_adjacent_sigma(_lowerCAmelCase					,	_lowerCAmelCase					,	_lowerCAmelCase   )
       lowerCAmelCase_:       Dict         =  jnp.zeros_like(_lowerCAmelCase   )
       lowerCAmelCase_:       Union[str, Any]         =  (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
       lowerCAmelCase_:       List[Any]         =  diffusion.flatten()
       lowerCAmelCase_:       Dict         =  broadcast_to_shape_from_left(_lowerCAmelCase					,	sample.shape   )
       lowerCAmelCase_:       Any         =  drift - diffusion**2 * model_output

       #  equation 6: sample noise for the diffusion term of
       lowerCAmelCase_:       List[str]         =  random.split(_lowerCAmelCase					,	num=1   )
       lowerCAmelCase_:       Optional[int]         =  random.normal(key=_lowerCAmelCase					,	shape=sample.shape   )
       lowerCAmelCase_:       List[Any]         =  sample - drift  # subtract because `dt` is a small negative timestep
       # TODO is the variable diffusion the correct scaling term for the noise?
       lowerCAmelCase_:       str         =  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     _a						(						self					,	lowerCamelCase__					,	lowerCamelCase__					,	lowerCamelCase__					,	lowerCamelCase__					,	lowerCamelCase__ = True					,	):
       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
       lowerCAmelCase_:       Optional[int]         =  random.split(_lowerCAmelCase					,	num=1   )
       lowerCAmelCase_:       Optional[int]         =  random.normal(key=_lowerCAmelCase					,	shape=sample.shape   )

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

       # compute corrected sample: model_output term and noise term
       lowerCAmelCase_:       Optional[Any]         =  step_size.flatten()
       lowerCAmelCase_:       Dict         =  broadcast_to_shape_from_left(_lowerCAmelCase					,	sample.shape   )
       lowerCAmelCase_:       Optional[int]         =  sample + step_size * model_output
       lowerCAmelCase_:       Union[str, 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   ):
       return self.config.num_train_timesteps
from ctypes import c_float, sizeof
from enum import Enum
from typing import TYPE_CHECKING, Optional, Union


if TYPE_CHECKING:
 from .. import AutoFeatureExtractor, AutoProcessor, AutoTokenizer  # tests_ignore

class 				_lowercase						(    UpperCAmelCase__ ):






    '''simple docstring'''
    SCREAMING_SNAKE_CASE:							Union[str, Any]        =						c_float




    @property
    def     _a						(						self   ):
       return sizeof(self.value   )





def 		snake_case__  (		lowercase       ,							lowercase       ,							lowercase = 0				):
   # < 0 is possible if using a dynamic axis
   if dimension <= 0:
      lowerCAmelCase_:       int         =  fixed_dimension

   dimension -= num_token_to_add
   return dimension
def 		snake_case__  (		lowercase       ,							lowercase				):
   return num_parameters * dtype.size







def 		snake_case__  (		lowercase				):
   from .. import AutoFeatureExtractor, AutoProcessor, AutoTokenizer  # tests_ignore

   try:
      return AutoProcessor.from_pretrained(lowercase				)
   except (ValueError, OSError, KeyError):
      lowerCAmelCase_     ,  lowerCAmelCase_:       int         =  None, None
      try:
         lowerCAmelCase_:       Dict         =  AutoTokenizer.from_pretrained(lowercase				)
      except (OSError, KeyError):
         pass
      try:
         lowerCAmelCase_:       Optional[int]         =  AutoFeatureExtractor.from_pretrained(lowercase				)
      except (OSError, KeyError):
         pass

      if tokenizer is not None and feature_extractor is not None:
         raise ValueError(
             F'''Couldn\'t auto-detect preprocessor for {model_name}. Found both a tokenizer and a feature extractor.'''				)
      elif tokenizer is None and feature_extractor is None:
         return None
      elif tokenizer is not None:
         return tokenizer
      else:
         return feature_extractor