





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 lowerCamelCase__   (							snake_case_     :  Optional[Any]	,				snake_case_     :  Union[str, Any]=0.999	,				snake_case_     :  List[str]="cosine"	,				)				->    Optional[int]:
   if alpha_transform_type == "cosine":

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

   elif alpha_transform_type == "exp":

      def alpha_bar_fn(snake_case_     :  Optional[Any]	):
         return math.exp(t * -12.0	)

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

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







class 		SCREAMING_SNAKE_CASE__    (	__SCREAMING_SNAKE_CASE						,				__SCREAMING_SNAKE_CASE  ):
      A_						:    Tuple							=   [e.name for e in KarrasDiffusionSchedulers]
      A_						:    str							=   2


      @register_to_config
      def __init__(self	:				Tuple    ,      a__	:				Tuple = 1000    ,      a__	:				List[str] = 0.0_0_0_8_5    ,      a__	:				Optional[Any] = 0.0_1_2    ,      a__	:				Union[str, Any] = "linear"    ,      a__	:				Dict = None    ,      a__	:				Optional[int] = "epsilon"    ,      a__	:				Union[str, Any] = "linspace"    ,      a__	:				Optional[int] = 0    ,      ):





         """simple docstring"""



         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				)
         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				)


      def        a			(self	:				Tuple    ,      a__	:				Optional[int]    ,      a__	:				str=None				):





         """simple docstring"""



         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        a			(self	:				Optional[Any]				):





         """simple docstring"""



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

         return (self.sigmas.max() ** 2 + 1) ** 0.5


      def        a			(self	:				Any    ,      a__	:				Dict    ,      a__	:				Dict    ,      ):





         """simple docstring"""



         __snake_case								=       self.index_for_timestep(_a				)

         if self.state_in_first_order:
            __snake_case								=       self.sigmas[step_index]
         else:
            __snake_case								=       self.sigmas_interpol[step_index]

         __snake_case								=       sample / ((sigma**2 + 1) ** 0.5)
         return sample


      def        a			(self	:				Tuple    ,      a__	:				List[Any]    ,      a__	:				str = None    ,      a__	:				Tuple = None    ,      ):





         """simple docstring"""



         __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								=       torch.from_numpy(np.log(_a				)				).to(_a				)

         __snake_case								=       np.interp(_a    ,      np.arange(0    ,      len(_a				)				)    ,      _a				)
         __snake_case								=       np.concatenate([sigmas, [0.0]]				).astype(np.floataa				)
         __snake_case								=       torch.from_numpy(_a				).to(device=_a				)

         # interpolate sigmas
         __snake_case								=       sigmas.log().lerp(sigmas.roll(1				).log()    ,      0.5				).exp()

         __snake_case								=       torch.cat([sigmas[:1], sigmas[1:].repeat_interleave(2				), sigmas[-1:]]				)
         __snake_case								=       torch.cat(
             [sigmas_interpol[:1], sigmas_interpol[1:].repeat_interleave(2				), sigmas_interpol[-1:]]				)

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

         # interpolate timesteps
         __snake_case								=       self.sigma_to_t(_a				).to(_a    ,      dtype=timesteps.dtype				)
         __snake_case								=       torch.stack((timesteps_interpol[1:-1, None], timesteps[1:, None])    ,      dim=-1				).flatten()

         __snake_case								=       torch.cat([timesteps[:1], interleaved_timesteps]				)

         __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        a			(self	:				int    ,      a__	:				Optional[int]				):





         """simple docstring"""



         __snake_case								=       sigma.log()

         # get distribution
         __snake_case								=       log_sigma - self.log_sigmas[:, None]

         # get sigmas range
         __snake_case								=       dists.ge(0				).cumsum(dim=0				).argmax(dim=0				).clamp(max=self.log_sigmas.shape[0] - 2				)
         __snake_case								=       low_idx + 1

         __snake_case								=       self.log_sigmas[low_idx]
         __snake_case								=       self.log_sigmas[high_idx]

         # interpolate sigmas
         __snake_case								=       (low - log_sigma) / (low - high)
         __snake_case								=       w.clamp(0    ,      1				)

         # transform interpolation to time range
         __snake_case								=       (1 - w) * low_idx + w * high_idx
         __snake_case								=       t.view(sigma.shape				)
         return t


      @property
      def        a			(self	:				int				):





         """simple docstring"""



         return self.sample is None


      def        a			(self	:				List[Any]    ,      a__	:				List[Any]    ,      a__	:				str    ,      a__	:				Tuple    ,      a__	:				Dict = True    ,      ):





         """simple docstring"""



         __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_interpol[step_index + 1]
            __snake_case								=       self.sigmas[step_index + 1]
         else:
            # 2nd order / KDPM2's method
            __snake_case								=       self.sigmas[step_index - 1]
            __snake_case								=       self.sigmas_interpol[step_index]
            __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_interpol
            __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_interpol
            __snake_case								=       model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
                sample / (sigma_input**2 + 1)
            )
         elif self.config.prediction_type == "sample":
            raise NotImplementedError('''prediction_type not implemented yet: sample'''				)
         else:
            raise ValueError(
                f"""prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`"""				)

         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_interpol - sigma_hat

            # store for 2nd order step
            __snake_case								=       sample
         else:
            # DPM-Solver-2
            # 2. Convert to an ODE derivative for 2nd order
            __snake_case								=       (sample - pred_original_sample) / sigma_interpol

            # 3. delta timestep
            __snake_case								=       sigma_next - sigma_hat

            __snake_case								=       self.sample
            __snake_case								=       None

         __snake_case								=       sample + derivative * dt

         if not return_dict:
            return (prev_sample,)

         return SchedulerOutput(prev_sample=_a				)


      def        a			(self	:				List[Any]    ,      a__	:				Optional[Any]    ,      a__	:				int    ,      a__	:				str    ,      ):





         """simple docstring"""



         __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	:				int				):





         """simple docstring"""



         return self.config.num_train_timesteps








# 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

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


snake_case_           =      {
    'configuration_vivit': ['VIVIT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'VivitConfig'],
}
try:
    if not is_vision_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    snake_case_           =      ['VivitImageProcessor']


try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    snake_case_           =      [
        'VIVIT_PRETRAINED_MODEL_ARCHIVE_LIST',
        'VivitModel',
        'VivitPreTrainedModel',
        'VivitForVideoClassification',
    ]


if TYPE_CHECKING:
    from .configuration_vivit import VIVIT_PRETRAINED_CONFIG_ARCHIVE_MAP, VivitConfig

    try:
        if not is_vision_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .image_processing_vivit import VivitImageProcessor

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_vivit import (
            VIVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
            VivitForVideoClassification,
            VivitModel,
            VivitPreTrainedModel,
        )


else:
    import sys

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

