


from dataclasses import dataclass
from typing import Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin


@dataclass
class     __UpperCAmelCase							(       __A							):


     """simple docstring"""
     _lowerCamelCase   =      42
     _lowerCamelCase   =      42
     _lowerCamelCase   =      None







class     __UpperCAmelCase							(       __A ,					__A							):


     """simple docstring"""
     _lowerCamelCase   =      2


     @register_to_config
     def __init__(	self  ,       __A = 0.02  ,       __A = 100  ,       __A = 1.007  ,       __A = 80  ,       __A = 0.05  ,       __A = 50  ,       ):
        # standard deviation of the initial noise distribution
        __a					=  sigma_max

        # setable values
        __a					=  None
        __a					=  None
        __a					=  None  # sigma(t_i)


     def 	snake_case_  (	self  ,       __A  ,       __A = None    ):
        return sample


     def 	snake_case_  (	self  ,       __A  ,       __A = None    ):
        __a					=  num_inference_steps
        __a					=  np.arange(0  ,       self.num_inference_steps    )[::-1].copy()
        __a					=  torch.from_numpy(__A    ).to(__A    )
        __a					=  [
            (
                self.config.sigma_max**2
                * (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
            )
            for i in self.timesteps
        ]
        __a					=  torch.tensor(__A  ,       dtype=torch.floataa  ,       device=__A    )


     def 	snake_case_  (	self  ,       __A  ,       __A  ,       __A = None    ):
        if self.config.s_min <= sigma <= self.config.s_max:
           __a					=  min(self.config.s_churn / self.num_inference_steps  ,       2**0.5 - 1    )
        else:
           __a					=  0

        # sample eps ~ N(0, S_noise^2 * I)
        __a					=  self.config.s_noise * randn_tensor(sample.shape  ,       generator=__A    ).to(sample.device    )
        __a					=  sigma + gamma * sigma
        __a					=  sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)

        return sample_hat, sigma_hat


     def 	snake_case_  (	self  ,       __A  ,       __A  ,       __A  ,       __A  ,       __A = True  ,       ):
        __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)

        return KarrasVeOutput(
            prev_sample=__A  ,       derivative=__A  ,       pred_original_sample=__A    )


     def 	snake_case_  (	self  ,       __A  ,       __A  ,       __A  ,       __A  ,       __A  ,       __A  ,       __A = True  ,       ):
        __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)

        return KarrasVeOutput(
            prev_sample=__A  ,       derivative=__A  ,       pred_original_sample=__A    )


     def 	snake_case_  (	self  ,       __A  ,       __A  ,       __A    ):
        raise NotImplementedError()







from typing import TYPE_CHECKING

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


SCREAMING_SNAKE_CASE							    =							{
    'configuration_tapas': ['TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP', 'TapasConfig'],
    'tokenization_tapas': ['TapasTokenizer'],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  SCREAMING_SNAKE_CASE							    =							[
      'TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST',
      'TapasForMaskedLM',
      'TapasForQuestionAnswering',
      'TapasForSequenceClassification',
      'TapasModel',
      'TapasPreTrainedModel',
      'load_tf_weights_in_tapas',
  ]
try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  SCREAMING_SNAKE_CASE							    =							[
      'TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST',
      'TFTapasForMaskedLM',
      'TFTapasForQuestionAnswering',
      'TFTapasForSequenceClassification',
      'TFTapasModel',
      'TFTapasPreTrainedModel',
  ]


if TYPE_CHECKING:
  from .configuration_tapas import TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP, TapasConfig
  from .tokenization_tapas import TapasTokenizer

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tapas import (
        TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
        TapasForMaskedLM,
        TapasForQuestionAnswering,
        TapasForSequenceClassification,
        TapasModel,
        TapasPreTrainedModel,
        load_tf_weights_in_tapas,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_tapas import (
        TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFTapasForMaskedLM,
        TFTapasForQuestionAnswering,
        TFTapasForSequenceClassification,
        TFTapasModel,
        TFTapasPreTrainedModel,
    )


else:
  import sys

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



