

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 ..utils import BaseOutput
from .scheduling_utils_flax import FlaxSchedulerMixin





@flax.struct.dataclass
class        lowerCAmelCase__  :



       '''simple docstring'''







       lowercase_						=    None
       lowercase_						=    None
       lowercase_						=    None  # sigma(t_i)

       @classmethod
       def     __UpperCamelCase     (							cls     ):







             '''simple docstring'''



             return cls()





@dataclass
class        lowerCAmelCase__  (					__magic_name__			):



       '''simple docstring'''







       lowercase_						=    42
       lowercase_						=    42
       lowercase_						=    42





class        lowerCAmelCase__  (					__magic_name__				,							__magic_name__			):



       '''simple docstring'''







       @property
       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             return True

       @register_to_config
       def __init__(							self ,   lowercase__ = 0.02 ,   lowercase__ = 1_0_0 ,   lowercase__ = 1.007 ,   lowercase__ = 8_0 ,   lowercase__ = 0.05 ,   lowercase__ = 5_0 ,   ):







             '''simple docstring'''



             pass

       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             return KarrasVeSchedulerState.create()

       def     __UpperCamelCase     (							self ,   lowercase__ ,   lowercase__ ,   lowercase__ = ()     ):







             '''simple docstring'''



             __A    =jnp.arange(0 ,   lowercase__     )[::-1].copy()
             __A    =[
                 (
                     self.config.sigma_max**2
                     * (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
                 )
                 for i in timesteps
             ]

             return state.replace(
                 num_inference_steps=lowercase__ ,   schedule=jnp.array(lowercase__ ,   dtype=jnp.floataa     ) ,   timesteps=lowercase__ ,   )

       def     __UpperCamelCase     (							self ,   lowercase__ ,   lowercase__ ,   lowercase__ ,   lowercase__ ,   ):







             '''simple docstring'''



             if self.config.s_min <= sigma <= self.config.s_max:
                   __A    =min(self.config.s_churn / state.num_inference_steps ,   2**0.5 - 1     )
             else:
                   __A    =0

             # sample eps ~ N(0, S_noise^2 * I)
             __A    =random.split(lowercase__ ,   num=1     )
             __A    =self.config.s_noise * random.normal(key=lowercase__ ,   shape=sample.shape     )
             __A    =sigma + gamma * sigma
             __A    =sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)

             return sample_hat, sigma_hat

       def     __UpperCamelCase     (							self ,   lowercase__ ,   lowercase__ ,   lowercase__ ,   lowercase__ ,   lowercase__ ,   lowercase__ = True ,   ):







             '''simple docstring'''



             __A    =sample_hat + sigma_hat * model_output
             __A    =(sample_hat - pred_original_sample) / sigma_hat
             __A    =sample_hat + (sigma_prev - sigma_hat) * derivative

             if not return_dict:
                   return (sample_prev, derivative, state)

             return FlaxKarrasVeOutput(prev_sample=lowercase__ ,   derivative=lowercase__ ,   state=lowercase__     )

       def     __UpperCamelCase     (							self ,   lowercase__ ,   lowercase__ ,   lowercase__ ,   lowercase__ ,   lowercase__ ,   lowercase__ ,   lowercase__ ,   lowercase__ = True ,   ):







             '''simple docstring'''



             __A    =sample_prev + sigma_prev * model_output
             __A    =(sample_prev - pred_original_sample) / sigma_prev
             __A    =sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)

             if not return_dict:
                   return (sample_prev, derivative, state)

             return FlaxKarrasVeOutput(prev_sample=lowercase__ ,   derivative=lowercase__ ,   state=lowercase__     )


       def     __UpperCamelCase     (							self ,   lowercase__ ,   lowercase__ ,   lowercase__ ,   lowercase__     ):







             '''simple docstring'''



             raise NotImplementedError()





from typing import TYPE_CHECKING

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


_lowerCamelCase      :					Tuple					    =      {
    '''configuration_squeezebert''': [
        '''SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''SqueezeBertConfig''',
        '''SqueezeBertOnnxConfig''',
    ],
    '''tokenization_squeezebert''': ['''SqueezeBertTokenizer'''],
}

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _lowerCamelCase      :					List[str]					    =      ['''SqueezeBertTokenizerFast''']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _lowerCamelCase      :					Tuple					    =      [
       '''SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''SqueezeBertForMaskedLM''',
       '''SqueezeBertForMultipleChoice''',
       '''SqueezeBertForQuestionAnswering''',
       '''SqueezeBertForSequenceClassification''',
       '''SqueezeBertForTokenClassification''',
       '''SqueezeBertModel''',
       '''SqueezeBertModule''',
       '''SqueezeBertPreTrainedModel''',
   ]


if TYPE_CHECKING:
   from .configuration_squeezebert import (
       SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
       SqueezeBertConfig,
       SqueezeBertOnnxConfig,
   )
   from .tokenization_squeezebert import SqueezeBertTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_squeezebert_fast import SqueezeBertTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_squeezebert import (
          SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
          SqueezeBertForMaskedLM,
          SqueezeBertForMultipleChoice,
          SqueezeBertForQuestionAnswering,
          SqueezeBertForSequenceClassification,
          SqueezeBertForTokenClassification,
          SqueezeBertModel,
          SqueezeBertModule,
          SqueezeBertPreTrainedModel,
      )

else:
   import sys

   _lowerCamelCase      :					List[str]					    =      _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


