"""simple docstring"""



import math
from collections import defaultdict
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 KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput





def    SCREAMING_SNAKE_CASE       (     snake_case,      snake_case=0.999,      snake_case="cosine",      ):







     if alpha_transform_type == "cosine":

          def alpha_bar_fn(snake_case):
               return math.cos((t + 0.008) / 1.008 * math.pi / 2) ** 2

     elif alpha_transform_type == "exp":

          def alpha_bar_fn(snake_case):
               return math.exp(t * -12.0)

     else:
          raise ValueError(F"Unsupported alpha_tranform_type: {alpha_transform_type}")

     __snake_case				=							[]
     for i in range(__UpperCAmelCase):
          __snake_case				=							i / num_diffusion_timesteps
          __snake_case				=							(i + 1) / num_diffusion_timesteps
          betas.append(min(1 - alpha_bar_fn(__UpperCAmelCase) / alpha_bar_fn(__UpperCAmelCase),      __UpperCAmelCase))
     return torch.tensor(__UpperCAmelCase,      dtype=torch.floataa)




class      _A	(						_snake_case							, _snake_case					):




    """simple docstring"""




    UpperCamelCase_	:					int							=					[e.name for e in KarrasDiffusionSchedulers]
    UpperCamelCase_	:					str							=					2
    @register_to_config
    def __init__(		self							:     int	,      A_							:     int = 1_000	,      A_							:     float = 0.0_00_85	,      A_							:     float = 0.0_12	,      A_							:     str = "linear"	,      A_							:     Optional[Union[np.ndarray, List[float]]] = None	,      A_							:     str = "epsilon"	,      A_							:     Optional[bool] = False	,      A_							:     Optional[bool] = False	,      A_							:     float = 1.0	,      A_							:     str = "linspace"	,      A_							:     int = 0	,      )    ->       Dict:

         if trained_betas is not None:
              __snake_case				=							torch.tensor(A_	,      dtype=torch.floataa    )
         elif beta_schedule == "linear":
              __snake_case				=							torch.linspace(A_	,      A_	,      A_	,      dtype=torch.floataa    )
         elif beta_schedule == "scaled_linear":
              # this schedule is very specific to the latent diffusion model.
              __snake_case				=							(
                  torch.linspace(beta_start**0.5	,      beta_end**0.5	,      A_	,      dtype=torch.floataa    ) ** 2
              )
         elif beta_schedule == "squaredcos_cap_v2":
              # Glide cosine schedule
              __snake_case				=							betas_for_alpha_bar(A_	,      alpha_transform_type='''cosine'''    )
         elif beta_schedule == "exp":
              __snake_case				=							betas_for_alpha_bar(A_	,      alpha_transform_type='''exp'''    )
         else:
              raise NotImplementedError(f"{beta_schedule} does is not implemented for {self.__class__}"    )

         __snake_case				=							1.0 - self.betas
         __snake_case				=							torch.cumprod(self.alphas	,      dim=0    )

         #  set all values
         self.set_timesteps(A_	,      A_	,      A_    )
         __snake_case				=							use_karras_sigmas
    def  lowercase					(		self							:     Optional[int]	,      A_							:     int	,      A_							:     Dict=None    )    ->       Dict:

         if schedule_timesteps is None:
              __snake_case				=							self.timesteps

         __snake_case				=							(schedule_timesteps == timestep).nonzero()

         # The sigma index that is taken for the **very** first `step`
         # is always the second index (or the last index if there is only 1)
         # This way we can ensure we don't accidentally skip a sigma in
         # case we start in the middle of the denoising schedule (e.g. for image-to-image)
         if len(self._index_counter    ) == 0:
              __snake_case				=							1 if len(A_    ) > 1 else 0
         else:
              __snake_case				=							timestep.cpu().item() if torch.is_tensor(A_    ) else timestep
              __snake_case				=							self._index_counter[timestep_int]

         return indices[pos].item()
    @property
    def  lowercase					(		self							:     Optional[int]    )    ->       str:

         if self.config.timestep_spacing in ["linspace", "trailing"]:
              return self.sigmas.max()

         return (self.sigmas.max() ** 2 + 1) ** 0.5
    def  lowercase					(		self							:     Optional[Any]	,      A_							:     torch.FloatTensor	,      A_							:     Union[float, torch.FloatTensor]	,      )    ->       int:
         __snake_case				=							self.index_for_timestep(A_    )

         __snake_case				=							self.sigmas[step_index]
         __snake_case				=							sample / ((sigma**2 + 1) ** 0.5)
         return sample
    def  lowercase					(		self							:     int	,      A_							:     int	,      A_							:     Union[str, torch.device] = None	,      A_							:     Optional[int] = None	,      )    ->       int:
         __snake_case				=							num_inference_steps

         __snake_case				=							num_train_timesteps or self.config.num_train_timesteps

         # "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
         if self.config.timestep_spacing == "linspace":
              __snake_case				=							np.linspace(0	,      num_train_timesteps - 1	,      A_	,      dtype=A_    )[::-1].copy()
         elif self.config.timestep_spacing == "leading":
              __snake_case				=							num_train_timesteps // self.num_inference_steps
              # creates integer timesteps by multiplying by ratio
              # casting to int to avoid issues when num_inference_step is power of 3
              __snake_case				=							(np.arange(0	,      A_    ) * step_ratio).round()[::-1].copy().astype(A_    )
              timesteps += self.config.steps_offset
         elif self.config.timestep_spacing == "trailing":
              __snake_case				=							num_train_timesteps / self.num_inference_steps
              # creates integer timesteps by multiplying by ratio
              # casting to int to avoid issues when num_inference_step is power of 3
              __snake_case				=							(np.arange(A_	,      0	,      -step_ratio    )).round().copy().astype(A_    )
              timesteps -= 1
         else:
              raise ValueError(
                  f"{self.config.timestep_spacing} is not supported. Please make sure to choose one of 'linspace', 'leading' or 'trailing'."    )

         __snake_case				=							np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5    )
         __snake_case				=							np.log(A_    )
         __snake_case				=							np.interp(A_	,      np.arange(0	,      len(A_    )    )	,      A_    )

         if self.config.use_karras_sigmas:
              __snake_case				=							self._convert_to_karras(in_sigmas=A_	,      num_inference_steps=self.num_inference_steps    )
              __snake_case				=							np.array([self._sigma_to_t(A_	,      A_    ) for sigma in sigmas]    )

         __snake_case				=							np.concatenate([sigmas, [0.0]]    ).astype(np.floataa    )
         __snake_case				=							torch.from_numpy(A_    ).to(device=A_    )
         __snake_case				=							torch.cat([sigmas[:1], sigmas[1:-1].repeat_interleave(2    ), sigmas[-1:]]    )

         __snake_case				=							torch.from_numpy(A_    )
         __snake_case				=							torch.cat([timesteps[:1], timesteps[1:].repeat_interleave(2    )]    )

         if str(A_    ).startswith('''mps'''    ):
              # mps does not support float64
              __snake_case				=							timesteps.to(A_	,      dtype=torch.floataa    )
         else:
              __snake_case				=							timesteps.to(device=A_    )

         # empty dt and derivative
         __snake_case				=							None
         __snake_case				=							None

         # for exp beta schedules, such as the one for `pipeline_shap_e.py`
         # we need an index counter
         __snake_case				=							defaultdict(A_    )
    def  lowercase					(		self							:     List[str]	,      A_							:     Optional[Any]	,      A_							:     Dict    )    ->       Any:
         __snake_case				=							np.log(A_    )

         # get distribution
         __snake_case				=							log_sigma - log_sigmas[:, np.newaxis]

         # get sigmas range
         __snake_case				=							np.cumsum((dists >= 0)	,      axis=0    ).argmax(axis=0    ).clip(max=log_sigmas.shape[0] - 2    )
         __snake_case				=							low_idx + 1

         __snake_case				=							log_sigmas[low_idx]
         __snake_case				=							log_sigmas[high_idx]

         # interpolate sigmas
         __snake_case				=							(low - log_sigma) / (low - high)
         __snake_case				=							np.clip(A_	,      0	,      1    )

         # transform interpolation to time range
         __snake_case				=							(1 - w) * low_idx + w * high_idx
         __snake_case				=							t.reshape(sigma.shape    )
         return t
    def  lowercase					(		self							:     Optional[int]	,      A_							:     torch.FloatTensor	,      A_							:     int    )    ->       Any:
         __snake_case				=							in_sigmas[-1].item()
         __snake_case				=							in_sigmas[0].item()

         __snake_case				=							7.0  # 7.0 is the value used in the paper
         __snake_case				=							np.linspace(0	,      1	,      A_    )
         __snake_case				=							sigma_min ** (1 / rho)
         __snake_case				=							sigma_max ** (1 / rho)
         __snake_case				=							(max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
         return sigmas
    @property
    def  lowercase					(		self							:     Union[str, Any]    )    ->       int:
         return self.dt is None
    def  lowercase					(		self							:     Union[str, Any]	,      A_							:     Union[torch.FloatTensor, np.ndarray]	,      A_							:     Union[float, torch.FloatTensor]	,      A_							:     Union[torch.FloatTensor, np.ndarray]	,      A_							:     bool = True	,      )    ->       Tuple:
         __snake_case				=							self.index_for_timestep(A_    )

         # advance index counter by 1
         __snake_case				=							timestep.cpu().item() if torch.is_tensor(A_    ) else timestep
         self._index_counter[timestep_int] += 1

         if self.state_in_first_order:
              __snake_case				=							self.sigmas[step_index]
              __snake_case				=							self.sigmas[step_index + 1]
         else:
              # 2nd order / Heun's method
              __snake_case				=							self.sigmas[step_index - 1]
              __snake_case				=							self.sigmas[step_index]

         # currently only gamma=0 is supported. This usually works best anyways.
         # We can support gamma in the future but then need to scale the timestep before
         # passing it to the model which requires a change in API
         __snake_case				=							0
         __snake_case				=							sigma * (gamma + 1)  # Note: sigma_hat == sigma for now

         # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
         if self.config.prediction_type == "epsilon":
              __snake_case				=							sigma_hat if self.state_in_first_order else sigma_next
              __snake_case				=							sample - sigma_input * model_output
         elif self.config.prediction_type == "v_prediction":
              __snake_case				=							sigma_hat if self.state_in_first_order else sigma_next
              __snake_case				=							model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
                  sample / (sigma_input**2 + 1)
              )
         elif self.config.prediction_type == "sample":
              __snake_case				=							model_output
         else:
              raise ValueError(
                  f"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`"    )

         if self.config.clip_sample:
              __snake_case				=							pred_original_sample.clamp(
                  -self.config.clip_sample_range	,      self.config.clip_sample_range    )

         if self.state_in_first_order:
              # 2. Convert to an ODE derivative for 1st order
              __snake_case				=							(sample - pred_original_sample) / sigma_hat
              # 3. delta timestep
              __snake_case				=							sigma_next - sigma_hat

              # store for 2nd order step
              __snake_case				=							derivative
              __snake_case				=							dt
              __snake_case				=							sample
         else:
              # 2. 2nd order / Heun's method
              __snake_case				=							(sample - pred_original_sample) / sigma_next
              __snake_case				=							(self.prev_derivative + derivative) / 2

              # 3. take prev timestep & sample
              __snake_case				=							self.dt
              __snake_case				=							self.sample

              # free dt and derivative
              # Note, this puts the scheduler in "first order mode"
              __snake_case				=							None
              __snake_case				=							None
              __snake_case				=							None

         __snake_case				=							sample + derivative * dt

         if not return_dict:
              return (prev_sample,)

         return SchedulerOutput(prev_sample=A_    )
    def  lowercase					(		self							:     Dict	,      A_							:     torch.FloatTensor	,      A_							:     torch.FloatTensor	,      A_							:     torch.FloatTensor	,      )    ->       List[str]:
         __snake_case				=							self.sigmas.to(device=original_samples.device	,      dtype=original_samples.dtype    )
         if original_samples.device.type == "mps" and torch.is_floating_point(A_    ):
              # mps does not support float64
              __snake_case				=							self.timesteps.to(original_samples.device	,      dtype=torch.floataa    )
              __snake_case				=							timesteps.to(original_samples.device	,      dtype=torch.floataa    )
         else:
              __snake_case				=							self.timesteps.to(original_samples.device    )
              __snake_case				=							timesteps.to(original_samples.device    )

         __snake_case				=							[self.index_for_timestep(A_	,      A_    ) for t in timesteps]

         __snake_case				=							sigmas[step_indices].flatten()
         while len(sigma.shape    ) < len(original_samples.shape    ):
              __snake_case				=							sigma.unsqueeze(-1    )

         __snake_case				=							original_samples + noise * sigma
         return noisy_samples







    def __len__(		self							:     Optional[int]    )    ->       str:
         return self.config.num_train_timesteps
"""simple docstring"""



from typing import TYPE_CHECKING

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


__lowercase		:		Optional[Any]														=							{
    "configuration_falcon": ["FALCON_PRETRAINED_CONFIG_ARCHIVE_MAP", "FalconConfig"],
}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __lowercase		:		str														=							[
           "FALCON_PRETRAINED_MODEL_ARCHIVE_LIST",
           "FalconForCausalLM",
           "FalconModel",
           "FalconPreTrainedModel",
           "FalconForSequenceClassification",
           "FalconForTokenClassification",
           "FalconForQuestionAnswering",
       ]


if TYPE_CHECKING:
       from .configuration_falcon import FALCON_PRETRAINED_CONFIG_ARCHIVE_MAP, FalconConfig

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_falcon import (
                  FALCON_PRETRAINED_MODEL_ARCHIVE_LIST,
                  FalconForCausalLM,
                  FalconForQuestionAnswering,
                  FalconForSequenceClassification,
                  FalconForTokenClassification,
                  FalconModel,
                  FalconPreTrainedModel,
              )


else:
       import sys

       __lowercase		:		List[Any]														=							_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)