





'''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 					UpperCAmelCase	(     snake_case_				,						snake_case_  ):
      _lowercase:     Union[str, Any]			=  1




      @register_to_config
      def __init__(     self     :					Optional[int]     ,     __snake_case     :					int = 10_00     ,     __snake_case     :					Optional[Union[np.ndarray, List[float]]] = None    )							-> Optional[Any]:
            # set `betas`, `alphas`, `timesteps`
            self.set_timesteps(__snake_case    )

            # standard deviation of the initial noise distribution
            _lowerCAmelCase					=				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.
            _lowerCAmelCase					=				4

            # running values
            _lowerCAmelCase					=				[]




      def 							lowercase__       (     self     :					int     ,     __snake_case     :					int     ,     __snake_case     :					Union[str, torch.device] = None    )							-> List[Any]:
            _lowerCAmelCase					=				num_inference_steps
            _lowerCAmelCase					=				torch.linspace(1     ,     0     ,     num_inference_steps + 1    )[:-1]
            _lowerCAmelCase					=				torch.cat([steps, torch.tensor([0.0]    )]    )

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

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

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

            _lowerCAmelCase					=				[]




      def 							lowercase__       (     self     :					List[str]     ,     __snake_case     :					torch.FloatTensor     ,     __snake_case     :					int     ,     __snake_case     :					torch.FloatTensor     ,     __snake_case     :					bool = True     ,     )							-> Union[SchedulerOutput, Tuple]:
            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"""    )

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

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

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

            _lowerCAmelCase					=				self._get_prev_sample(__snake_case     ,     __snake_case     ,     __snake_case     ,     __snake_case    )

            if not return_dict:
                  return (prev_sample,)

            return SchedulerOutput(prev_sample=__snake_case    )




      def 							lowercase__       (     self     :					Optional[int]     ,     __snake_case     :					torch.FloatTensor     ,     *__snake_case     :					Union[str, Any]     ,     **__snake_case     :					str    )							-> torch.FloatTensor:
            return sample




      def 							lowercase__       (     self     :					str     ,     __snake_case     :					Tuple     ,     __snake_case     :					Optional[Any]     ,     __snake_case     :					List[str]     ,     __snake_case     :					List[Any]    )							-> Union[str, Any]:
            _lowerCAmelCase					=				self.alphas[timestep_index]
            _lowerCAmelCase					=				self.betas[timestep_index]

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

            _lowerCAmelCase					=				(sample - sigma * ets) / max(__snake_case     ,     1E-8    )
            _lowerCAmelCase					=				next_alpha * pred + ets * next_sigma

            return prev_sample




      def __len__(     self     :					Dict    )							-> int:
            return self.config.num_train_timesteps













'''simple docstring'''






# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


A__							:  int    ={
    '''configuration_xmod''': [
        '''XMOD_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''XmodConfig''',
        '''XmodOnnxConfig''',
    ],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A__							:  List[str]    =[
          '''XMOD_PRETRAINED_MODEL_ARCHIVE_LIST''',
          '''XmodForCausalLM''',
          '''XmodForMaskedLM''',
          '''XmodForMultipleChoice''',
          '''XmodForQuestionAnswering''',
          '''XmodForSequenceClassification''',
          '''XmodForTokenClassification''',
          '''XmodModel''',
          '''XmodPreTrainedModel''',
      ]

if TYPE_CHECKING:
      from .configuration_xmod import XMOD_PRETRAINED_CONFIG_ARCHIVE_MAP, XmodConfig, XmodOnnxConfig

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_xmod import (
                XMOD_PRETRAINED_MODEL_ARCHIVE_LIST,
                XmodForCausalLM,
                XmodForMaskedLM,
                XmodForMultipleChoice,
                XmodForQuestionAnswering,
                XmodForSequenceClassification,
                XmodForTokenClassification,
                XmodModel,
                XmodPreTrainedModel,
            )

else:
      import sys

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






