


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 							UpperCamelCase_( lowerCamelCase_				,					lowerCamelCase_=0.9_99				,					lowerCamelCase_="cosine"				,					)       ->		int:
    if alpha_transform_type == "cosine":

        def alpha_bar_fn(lowerCamelCase_					):
            return math.cos((t + 0.0_08) / 1.0_08 * math.pi / 2					) ** 2

    elif alpha_transform_type == "exp":

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

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

    _lowercase			:				str 						=						[]
    for i in range(A__					):
        _lowercase			:				Optional[int] 						=						i / num_diffusion_timesteps
        _lowercase			:				str 						=						(i + 1) / num_diffusion_timesteps
        betas.append(min(1 - alpha_bar_fn(A__					) / alpha_bar_fn(A__					)				,					A__					)					)
    return torch.tensor(A__				,					dtype=torch.floataa					)






class _lowerCamelCase(		__snake_case,					__snake_case  ):
       lowercase_					:      List[str]       					=     [e.name for e in KarrasDiffusionSchedulers]
       lowercase_					:      Dict       					=     2
       @register_to_config
       def __init__(   self, lowerCamelCase = 10_00, lowerCamelCase = 0.0_0_0_8_5, lowerCamelCase = 0.0_1_2, lowerCamelCase = "linear", lowerCamelCase = None, lowerCamelCase = "epsilon", lowerCamelCase = "linspace", lowerCamelCase = 0, )			->  Tuple:




           """simple docstring"""

           if trained_betas is not None:
               _lowercase			:				Dict 						=						torch.tensor(__lowerCamelCase, dtype=torch.floataa)
           elif beta_schedule == "linear":
               _lowercase			:				Union[str, Any] 						=						torch.linspace(__lowerCamelCase, __lowerCamelCase, __lowerCamelCase, dtype=torch.floataa)
           elif beta_schedule == "scaled_linear":
               # this schedule is very specific to the latent diffusion model.
               _lowercase			:				Tuple 						=						(
                   torch.linspace(beta_start**0.5, beta_end**0.5, __lowerCamelCase, dtype=torch.floataa) ** 2
               )
           elif beta_schedule == "squaredcos_cap_v2":
               # Glide cosine schedule
               _lowercase			:				Tuple 						=						betas_for_alpha_bar(__lowerCamelCase)
           else:
               raise NotImplementedError(F'''{beta_schedule} does is not implemented for {self.__class__}''')

           _lowercase			:				List[str] 						=						1.0 - self.betas
           _lowercase			:				Dict 						=						torch.cumprod(self.alphas, dim=0)

           #  set all values
           self.set_timesteps(__lowerCamelCase, __lowerCamelCase, __lowerCamelCase)
       def      UpperCamelCase	(   self, lowerCamelCase, lowerCamelCase=None)			->  List[Any]:




           """simple docstring"""

           if schedule_timesteps is None:
               _lowercase			:				Optional[int] 						=						self.timesteps

           _lowercase			:				Dict 						=						(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:
               _lowercase			:				int 						=						1 if len(__lowerCamelCase) > 1 else 0
           else:
               _lowercase			:				Union[str, Any] 						=						timestep.cpu().item() if torch.is_tensor(__lowerCamelCase) else timestep
               _lowercase			:				Optional[int] 						=						self._index_counter[timestep_int]

           return indices[pos].item()
       @property
       def      UpperCamelCase	(   self)			->  Any:




           """simple docstring"""

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

           return (self.sigmas.max() ** 2 + 1) ** 0.5
       def      UpperCamelCase	(   self, lowerCamelCase, lowerCamelCase, )			->  Dict:




           """simple docstring"""

           _lowercase			:				List[str] 						=						self.index_for_timestep(__lowerCamelCase)

           if self.state_in_first_order:
               _lowercase			:				Dict 						=						self.sigmas[step_index]
           else:
               _lowercase			:				List[Any] 						=						self.sigmas_interpol[step_index]

           _lowercase			:				List[Any] 						=						sample / ((sigma**2 + 1) ** 0.5)
           return sample
       def      UpperCamelCase	(   self, lowerCamelCase, lowerCamelCase = None, lowerCamelCase = None, )			->  Tuple:




           """simple docstring"""

           _lowercase			:				Any 						=						num_inference_steps

           _lowercase			:				Any 						=						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":
               _lowercase			:				Optional[Any] 						=						np.linspace(0, num_train_timesteps - 1, __lowerCamelCase, dtype=__lowerCamelCase)[::-1].copy()
           elif self.config.timestep_spacing == "leading":
               _lowercase			:				Any 						=						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
               _lowercase			:				int 						=						(np.arange(0, __lowerCamelCase) * step_ratio).round()[::-1].copy().astype(__lowerCamelCase)
               timesteps += self.config.steps_offset
           elif self.config.timestep_spacing == "trailing":
               _lowercase			:				str 						=						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
               _lowercase			:				Optional[int] 						=						(np.arange(__lowerCamelCase, 0, -step_ratio)).round().copy().astype(__lowerCamelCase)
               timesteps -= 1
           else:
               raise ValueError(
                   F'''{self.config.timestep_spacing} is not supported. Please make sure to choose one of \'linspace\', \'leading\' or \'trailing\'.''')

           _lowercase			:				str 						=						np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5)
           _lowercase			:				Optional[Any] 						=						torch.from_numpy(np.log(__lowerCamelCase)).to(__lowerCamelCase)

           _lowercase			:				Union[str, Any] 						=						np.interp(__lowerCamelCase, np.arange(0, len(__lowerCamelCase)), __lowerCamelCase)
           _lowercase			:				Tuple 						=						np.concatenate([sigmas, [0.0]]).astype(np.floataa)
           _lowercase			:				int 						=						torch.from_numpy(__lowerCamelCase).to(device=__lowerCamelCase)

           # interpolate sigmas
           _lowercase			:				Dict 						=						sigmas.log().lerp(sigmas.roll(1).log(), 0.5).exp()

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

           if str(__lowerCamelCase).startswith('mps'):
               # mps does not support float64
               _lowercase			:				str 						=						torch.from_numpy(__lowerCamelCase).to(__lowerCamelCase, dtype=torch.floataa)
           else:
               _lowercase			:				List[Any] 						=						torch.from_numpy(__lowerCamelCase).to(__lowerCamelCase)

           # interpolate timesteps
           _lowercase			:				Dict 						=						self.sigma_to_t(__lowerCamelCase).to(__lowerCamelCase, dtype=timesteps.dtype)
           _lowercase			:				str 						=						torch.stack((timesteps_interpol[1:-1, None], timesteps[1:, None]), dim=-1).flatten()

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

           _lowercase			:				Dict 						=						None

           # for exp beta schedules, such as the one for `pipeline_shap_e.py`
           # we need an index counter
           _lowercase			:				Any 						=						defaultdict(__lowerCamelCase)
       def      UpperCamelCase	(   self, lowerCamelCase)			->  Dict:




           """simple docstring"""

           _lowercase			:				int 						=						sigma.log()

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

           # get sigmas range
           _lowercase			:				Optional[Any] 						=						dists.ge(0).cumsum(dim=0).argmax(dim=0).clamp(max=self.log_sigmas.shape[0] - 2)
           _lowercase			:				Union[str, Any] 						=						low_idx + 1

           _lowercase			:				Any 						=						self.log_sigmas[low_idx]
           _lowercase			:				Optional[Any] 						=						self.log_sigmas[high_idx]

           # interpolate sigmas
           _lowercase			:				List[Any] 						=						(low - log_sigma) / (low - high)
           _lowercase			:				List[Any] 						=						w.clamp(0, 1)

           # transform interpolation to time range
           _lowercase			:				Optional[Any] 						=						(1 - w) * low_idx + w * high_idx
           _lowercase			:				Tuple 						=						t.view(sigma.shape)
           return t
       @property
       def      UpperCamelCase	(   self)			->  List[str]:




           """simple docstring"""

           return self.sample is None
       def      UpperCamelCase	(   self, lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase = True, )			->  Union[str, Any]:




           """simple docstring"""

           _lowercase			:				int 						=						self.index_for_timestep(__lowerCamelCase)

           # advance index counter by 1
           _lowercase			:				Optional[Any] 						=						timestep.cpu().item() if torch.is_tensor(__lowerCamelCase) else timestep
           self._index_counter[timestep_int] += 1

           if self.state_in_first_order:
               _lowercase			:				List[Any] 						=						self.sigmas[step_index]
               _lowercase			:				str 						=						self.sigmas_interpol[step_index + 1]
               _lowercase			:				Tuple 						=						self.sigmas[step_index + 1]
           else:
               # 2nd order / KDPM2's method
               _lowercase			:				List[str] 						=						self.sigmas[step_index - 1]
               _lowercase			:				str 						=						self.sigmas_interpol[step_index]
               _lowercase			:				str 						=						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
           _lowercase			:				Union[str, Any] 						=						0
           _lowercase			:				int 						=						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":
               _lowercase			:				str 						=						sigma_hat if self.state_in_first_order else sigma_interpol
               _lowercase			:				Dict 						=						sample - sigma_input * model_output
           elif self.config.prediction_type == "v_prediction":
               _lowercase			:				List[str] 						=						sigma_hat if self.state_in_first_order else sigma_interpol
               _lowercase			:				Union[str, Any] 						=						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
               _lowercase			:				Union[str, Any] 						=						(sample - pred_original_sample) / sigma_hat
               # 3. delta timestep
               _lowercase			:				Union[str, Any] 						=						sigma_interpol - sigma_hat

               # store for 2nd order step
               _lowercase			:				Tuple 						=						sample
           else:
               # DPM-Solver-2
               # 2. Convert to an ODE derivative for 2nd order
               _lowercase			:				List[Any] 						=						(sample - pred_original_sample) / sigma_interpol

               # 3. delta timestep
               _lowercase			:				Dict 						=						sigma_next - sigma_hat

               _lowercase			:				Tuple 						=						self.sample
               _lowercase			:				Union[str, Any] 						=						None

           _lowercase			:				Union[str, Any] 						=						sample + derivative * dt

           if not return_dict:
               return (prev_sample,)

           return SchedulerOutput(prev_sample=__lowerCamelCase)
       def      UpperCamelCase	(   self, lowerCamelCase, lowerCamelCase, lowerCamelCase, )			->  List[str]:




           """simple docstring"""

           _lowercase			:				int 						=						self.sigmas.to(device=original_samples.device, dtype=original_samples.dtype)
           if original_samples.device.type == "mps" and torch.is_floating_point(__lowerCamelCase):
               # mps does not support float64
               _lowercase			:				str 						=						self.timesteps.to(original_samples.device, dtype=torch.floataa)
               _lowercase			:				str 						=						timesteps.to(original_samples.device, dtype=torch.floataa)
           else:
               _lowercase			:				Dict 						=						self.timesteps.to(original_samples.device)
               _lowercase			:				Optional[Any] 						=						timesteps.to(original_samples.device)

           _lowercase			:				Tuple 						=						[self.index_for_timestep(__lowerCamelCase, __lowerCamelCase) for t in timesteps]

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

           _lowercase			:				Any 						=						original_samples + noise * sigma
           return noisy_samples




       def __len__(   self)			->  List[str]:




           """simple docstring"""

           return self.config.num_train_timesteps





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__A			:					Dict						=			{
    'configuration_bigbird_pegasus': [
        'BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'BigBirdPegasusConfig',
        'BigBirdPegasusOnnxConfig',
    ],
}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __A			:					int						=			[
        'BIGBIRD_PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST',
        'BigBirdPegasusForCausalLM',
        'BigBirdPegasusForConditionalGeneration',
        'BigBirdPegasusForQuestionAnswering',
        'BigBirdPegasusForSequenceClassification',
        'BigBirdPegasusModel',
        'BigBirdPegasusPreTrainedModel',
    ]


if TYPE_CHECKING:
    from .configuration_bigbird_pegasus import (
        BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP,
        BigBirdPegasusConfig,
        BigBirdPegasusOnnxConfig,
    )

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_bigbird_pegasus import (
            BIGBIRD_PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST,
            BigBirdPegasusForCausalLM,
            BigBirdPegasusForConditionalGeneration,
            BigBirdPegasusForQuestionAnswering,
            BigBirdPegasusForSequenceClassification,
            BigBirdPegasusModel,
            BigBirdPegasusPreTrainedModel,
        )


else:
    import sys

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