




'''simple docstring'''

import math
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import SchedulerMixin, SchedulerOutput






class 						a				(   lowerCamelCase__   ,     lowerCamelCase__     ):
				snake_case_      =		1


				@register_to_config
				def __init__(		self    :					int       ,				lowercase_    :					Optional[Any] = 1000       ,				lowercase_    :					Tuple = None					):
									self.set_timesteps(lowercase_					)

									# standard deviation of the initial noise distribution
									snake_case_									=				1.0

									# For now we only support F-PNDM, i.e. the runge-kutta method
									# For more information on the algorithm please take a look at the paper: https://arxiv.org/pdf/2202.09778.pdf
									# mainly at formula (9), (12), (13) and the Algorithm 2.
									snake_case_									=				4

									# running values
									snake_case_									=				[]


				def   A_ (		self    :					str       ,				lowercase_    :					List[str]       ,				lowercase_    :					Dict = None					):
									snake_case_									=				num_inference_steps
									snake_case_									=				torch.linspace(1       ,				0       ,				num_inference_steps + 1					)[:-1]
									snake_case_									=				torch.cat([steps, torch.tensor([0.0]					)]					)

									if self.config.trained_betas is not None:
														snake_case_									=				torch.tensor(self.config.trained_betas       ,				dtype=torch.floataa					)
									else:
														snake_case_									=				torch.sin(steps * math.pi / 2					) ** 2

									snake_case_									=				(1.0 - self.betas**2) ** 0.5

									snake_case_									=				(torch.atana(self.betas       ,				self.alphas					) / math.pi * 2)[:-1]
									snake_case_									=				timesteps.to(lowercase_					)

									snake_case_									=				[]


				def   A_ (		self    :					Tuple       ,				lowercase_    :					Optional[int]       ,				lowercase_    :					Union[str, Any]       ,				lowercase_    :					int       ,				lowercase_    :					List[str] = True       ,				):






									if self.num_inference_steps is None:
														raise ValueError(
														    '''Number of inference steps is \'None\', you need to run \'set_timesteps\' after creating the scheduler'''					)

									snake_case_									=				(self.timesteps == timestep).nonzero().item()
									snake_case_									=				timestep_index + 1

									snake_case_									=				sample * self.betas[timestep_index] + model_output * self.alphas[timestep_index]
									self.ets.append(lowercase_					)

									if len(self.ets					) == 1:
														snake_case_									=				self.ets[-1]
									elif len(self.ets					) == 2:
														snake_case_									=				(3 * self.ets[-1] - self.ets[-2]) / 2
									elif len(self.ets					) == 3:
														snake_case_									=				(23 * self.ets[-1] - 16 * self.ets[-2] + 5 * self.ets[-3]) / 12
									else:
														snake_case_									=				(1 / 24) * (55 * self.ets[-1] - 59 * self.ets[-2] + 37 * self.ets[-3] - 9 * self.ets[-4])

									snake_case_									=				self._get_prev_sample(lowercase_       ,				lowercase_       ,				lowercase_       ,				lowercase_					)

									if not return_dict:
														return (prev_sample,)

									return SchedulerOutput(prev_sample=lowercase_					)


				def   A_ (		self    :					Optional[int]       ,				lowercase_    :					List[Any]       ,				*lowercase_    :					List[Any]       ,				**lowercase_    :					Optional[int]					):
									return sample


				def   A_ (		self    :					str       ,				lowercase_    :					Union[str, Any]       ,				lowercase_    :					Tuple       ,				lowercase_    :					Union[str, Any]       ,				lowercase_    :					str					):
									snake_case_									=				self.alphas[timestep_index]
									snake_case_									=				self.betas[timestep_index]

									snake_case_									=				self.alphas[prev_timestep_index]
									snake_case_									=				self.betas[prev_timestep_index]

									snake_case_									=				(sample - sigma * ets) / max(lowercase_       ,				1e-8					)
									snake_case_									=				next_alpha * pred + ets * next_sigma

									return prev_sample


				def __len__(		self    :					List[str]					):
									return self.config.num_train_timesteps




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCamelCase           =					{
    '''configuration_deberta''': ['''DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''DebertaConfig''', '''DebertaOnnxConfig'''],
    '''tokenization_deberta''': ['''DebertaTokenizer'''],
}

try:
       if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCamelCase           =					['''DebertaTokenizerFast''']

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCamelCase           =					[
           '''DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST''',
           '''DebertaForMaskedLM''',
           '''DebertaForQuestionAnswering''',
           '''DebertaForSequenceClassification''',
           '''DebertaForTokenClassification''',
           '''DebertaModel''',
           '''DebertaPreTrainedModel''',
       ]

try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCamelCase           =					[
           '''TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST''',
           '''TFDebertaForMaskedLM''',
           '''TFDebertaForQuestionAnswering''',
           '''TFDebertaForSequenceClassification''',
           '''TFDebertaForTokenClassification''',
           '''TFDebertaModel''',
           '''TFDebertaPreTrainedModel''',
       ]


if TYPE_CHECKING:
       from .configuration_deberta import DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, DebertaConfig, DebertaOnnxConfig
       from .tokenization_deberta import DebertaTokenizer

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_deberta_fast import DebertaTokenizerFast

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_deberta import (
                  DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
                  DebertaForMaskedLM,
                  DebertaForQuestionAnswering,
                  DebertaForSequenceClassification,
                  DebertaForTokenClassification,
                  DebertaModel,
                  DebertaPreTrainedModel,
              )

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_deberta import (
                  TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TFDebertaForMaskedLM,
                  TFDebertaForQuestionAnswering,
                  TFDebertaForSequenceClassification,
                  TFDebertaForTokenClassification,
                  TFDebertaModel,
                  TFDebertaPreTrainedModel,
              )


else:
       import sys

       lowerCamelCase           =					_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
