




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 ..utils import randn_tensor
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput



def 							_A   (						lowerCAmelCase_     :					Any   ,				lowerCAmelCase_     :					Dict=0.999   ,				lowerCAmelCase_     :					Union[str, Any]="cosine"   ,				):
     """simple docstring"""

     if alpha_transform_type == "cosine":

          def alpha_bar_fn(lowerCAmelCase_     :					Optional[Any]			):
               return math.cos((t + 0.008) / 1.008 * math.pi / 2			) ** 2

     elif alpha_transform_type == "exp":

          def alpha_bar_fn(lowerCAmelCase_     :					Union[str, Any]			):
               return math.exp(t * -12.0			)

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

     lowerCAmelCase__		= []
     for i in range(lowerCAmelCase_			):
          lowerCAmelCase__		= i / num_diffusion_timesteps
          lowerCAmelCase__		= (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  __lowerCamelCase       (    UpperCamelCase__	,				UpperCamelCase__    ):




    """simple docstring"""

    snake_case__					=      [e.name for e in KarrasDiffusionSchedulers]
    snake_case__					=      2


    @register_to_config
    def __init__(       self     :     Any   ,	SCREAMING_SNAKE_CASE__     :     int = 1_000   ,	SCREAMING_SNAKE_CASE__     :     float = 0.00_085   ,	SCREAMING_SNAKE_CASE__     :     float = 0.012   ,	SCREAMING_SNAKE_CASE__     :     str = "linear"   ,	SCREAMING_SNAKE_CASE__     :     Optional[Union[np.ndarray, List[float]]] = None   ,	SCREAMING_SNAKE_CASE__     :     str = "epsilon"   ,	SCREAMING_SNAKE_CASE__     :     str = "linspace"   ,	SCREAMING_SNAKE_CASE__     :     int = 0   ,	)      ->       Tuple:
         if trained_betas is not None:
              lowerCAmelCase__		= torch.tensor(SCREAMING_SNAKE_CASE__   ,	dtype=torch.floataa   )
         elif beta_schedule == "linear":
              lowerCAmelCase__		= torch.linspace(SCREAMING_SNAKE_CASE__   ,	SCREAMING_SNAKE_CASE__   ,	SCREAMING_SNAKE_CASE__   ,	dtype=torch.floataa   )
         elif beta_schedule == "scaled_linear":
              # this schedule is very specific to the latent diffusion model.
              lowerCAmelCase__		= (
                  torch.linspace(beta_start**0.5   ,	beta_end**0.5   ,	SCREAMING_SNAKE_CASE__   ,	dtype=torch.floataa   ) ** 2
              )
         elif beta_schedule == "squaredcos_cap_v2":
              # Glide cosine schedule
              lowerCAmelCase__		= betas_for_alpha_bar(SCREAMING_SNAKE_CASE__   )
         else:
              raise NotImplementedError(f'{beta_schedule} does is not implemented for {self.__class__}'   )

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

         #  set all values
         self.set_timesteps(SCREAMING_SNAKE_CASE__   ,	SCREAMING_SNAKE_CASE__   ,	SCREAMING_SNAKE_CASE__   )


    def    a      (       self     :     List[str]   ,	SCREAMING_SNAKE_CASE__     :     Any   ,	SCREAMING_SNAKE_CASE__     :     str=None   )      ->       Optional[Any]:
         if schedule_timesteps is None:
              lowerCAmelCase__		= self.timesteps

         lowerCAmelCase__		= (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:
              lowerCAmelCase__		= 1 if len(SCREAMING_SNAKE_CASE__   ) > 1 else 0
         else:
              lowerCAmelCase__		= timestep.cpu().item() if torch.is_tensor(SCREAMING_SNAKE_CASE__   ) else timestep
              lowerCAmelCase__		= self._index_counter[timestep_int]

         return indices[pos].item()


    @property
    def    a      (       self     :     List[str]   )      ->       str:
         # standard deviation of the initial noise distribution
         if self.config.timestep_spacing in ["linspace", "trailing"]:
              return self.sigmas.max()

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


    def    a      (       self     :     List[str]   ,	SCREAMING_SNAKE_CASE__     :     torch.FloatTensor   ,	SCREAMING_SNAKE_CASE__     :     Union[float, torch.FloatTensor]   ,	)      ->       torch.FloatTensor:
         lowerCAmelCase__		= self.index_for_timestep(SCREAMING_SNAKE_CASE__   )

         if self.state_in_first_order:
              lowerCAmelCase__		= self.sigmas[step_index]
         else:
              lowerCAmelCase__		= self.sigmas_interpol[step_index - 1]

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


    def    a      (       self     :     Optional[Any]   ,	SCREAMING_SNAKE_CASE__     :     int   ,	SCREAMING_SNAKE_CASE__     :     Union[str, torch.device] = None   ,	SCREAMING_SNAKE_CASE__     :     Optional[int] = None   ,	)      ->       Union[str, Any]:
         lowerCAmelCase__		= num_inference_steps

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

         lowerCAmelCase__		= np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5   )
         lowerCAmelCase__		= torch.from_numpy(np.log(SCREAMING_SNAKE_CASE__   )   ).to(SCREAMING_SNAKE_CASE__   )

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

         # compute up and down sigmas
         lowerCAmelCase__		= sigmas.roll(-1   )
         lowerCAmelCase__		= 0.0
         lowerCAmelCase__		= (sigmas_next**2 * (sigmas**2 - sigmas_next**2) / sigmas**2) ** 0.5
         lowerCAmelCase__		= (sigmas_next**2 - sigmas_up**2) ** 0.5
         lowerCAmelCase__		= 0.0

         # compute interpolated sigmas
         lowerCAmelCase__		= sigmas.log().lerp(sigmas_down.log()   ,	0.5   ).exp()
         lowerCAmelCase__		= 0.0

         # set sigmas
         lowerCAmelCase__		= torch.cat([sigmas[:1], sigmas[1:].repeat_interleave(2   ), sigmas[-1:]]   )
         lowerCAmelCase__		= torch.cat(
             [sigmas_interpol[:1], sigmas_interpol[1:].repeat_interleave(2   ), sigmas_interpol[-1:]]   )
         lowerCAmelCase__		= torch.cat([sigmas_up[:1], sigmas_up[1:].repeat_interleave(2   ), sigmas_up[-1:]]   )
         lowerCAmelCase__		= torch.cat([sigmas_down[:1], sigmas_down[1:].repeat_interleave(2   ), sigmas_down[-1:]]   )

         if str(SCREAMING_SNAKE_CASE__   ).startswith("mps"   ):
              # mps does not support float64
              lowerCAmelCase__		= torch.from_numpy(SCREAMING_SNAKE_CASE__   ).to(SCREAMING_SNAKE_CASE__   ,	dtype=torch.floataa   )
         else:
              lowerCAmelCase__		= torch.from_numpy(SCREAMING_SNAKE_CASE__   ).to(SCREAMING_SNAKE_CASE__   )

         lowerCAmelCase__		= self.sigma_to_t(SCREAMING_SNAKE_CASE__   ).to(SCREAMING_SNAKE_CASE__   ,	dtype=timesteps.dtype   )
         lowerCAmelCase__		= torch.stack((timesteps_interpol[:-2, None], timesteps[1:, None])   ,	dim=-1   ).flatten()

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

         lowerCAmelCase__		= None

         # for exp beta schedules, such as the one for `pipeline_shap_e.py`
         # we need an index counter
         lowerCAmelCase__		= defaultdict(SCREAMING_SNAKE_CASE__   )


    def    a      (       self     :     Optional[Any]   ,	SCREAMING_SNAKE_CASE__     :     int   )      ->       Any:
         # get log sigma
         lowerCAmelCase__		= sigma.log()

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

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

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

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

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


    @property
    def    a      (       self     :     Dict   )      ->       int:
         return self.sample is None


    def    a      (       self     :     str   ,	SCREAMING_SNAKE_CASE__     :     Union[torch.FloatTensor, np.ndarray]   ,	SCREAMING_SNAKE_CASE__     :     Union[float, torch.FloatTensor]   ,	SCREAMING_SNAKE_CASE__     :     Union[torch.FloatTensor, np.ndarray]   ,	SCREAMING_SNAKE_CASE__     :     Optional[torch.Generator] = None   ,	SCREAMING_SNAKE_CASE__     :     bool = True   ,	)      ->       Union[SchedulerOutput, Tuple]:
         lowerCAmelCase__		= self.index_for_timestep(SCREAMING_SNAKE_CASE__   )

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

         if self.state_in_first_order:
              lowerCAmelCase__		= self.sigmas[step_index]
              lowerCAmelCase__		= self.sigmas_interpol[step_index]
              lowerCAmelCase__		= self.sigmas_up[step_index]
              lowerCAmelCase__		= self.sigmas_down[step_index - 1]
         else:
              # 2nd order / KPDM2's method
              lowerCAmelCase__		= self.sigmas[step_index - 1]
              lowerCAmelCase__		= self.sigmas_interpol[step_index - 1]
              lowerCAmelCase__		= self.sigmas_up[step_index - 1]
              lowerCAmelCase__		= self.sigmas_down[step_index - 1]

         # 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
         lowerCAmelCase__		= 0
         lowerCAmelCase__		= sigma * (gamma + 1)  # Note: sigma_hat == sigma for now

         lowerCAmelCase__		= model_output.device
         lowerCAmelCase__		= randn_tensor(model_output.shape   ,	dtype=model_output.dtype   ,	device=SCREAMING_SNAKE_CASE__   ,	generator=SCREAMING_SNAKE_CASE__   )

         # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
         if self.config.prediction_type == "epsilon":
              lowerCAmelCase__		= sigma_hat if self.state_in_first_order else sigma_interpol
              lowerCAmelCase__		= sample - sigma_input * model_output
         elif self.config.prediction_type == "v_prediction":
              lowerCAmelCase__		= sigma_hat if self.state_in_first_order else sigma_interpol
              lowerCAmelCase__		= 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
              lowerCAmelCase__		= (sample - pred_original_sample) / sigma_hat
              # 3. delta timestep
              lowerCAmelCase__		= sigma_interpol - sigma_hat

              # store for 2nd order step
              lowerCAmelCase__		= sample
              lowerCAmelCase__		= dt
              lowerCAmelCase__		= sample + derivative * dt
         else:
              # DPM-Solver-2
              # 2. Convert to an ODE derivative for 2nd order
              lowerCAmelCase__		= (sample - pred_original_sample) / sigma_interpol
              # 3. delta timestep
              lowerCAmelCase__		= sigma_down - sigma_hat

              lowerCAmelCase__		= self.sample
              lowerCAmelCase__		= None

              lowerCAmelCase__		= sample + derivative * dt
              lowerCAmelCase__		= prev_sample + noise * sigma_up

         if not return_dict:
              return (prev_sample,)

         return SchedulerOutput(prev_sample=SCREAMING_SNAKE_CASE__   )


    def    a      (       self     :     Optional[Any]   ,	SCREAMING_SNAKE_CASE__     :     torch.FloatTensor   ,	SCREAMING_SNAKE_CASE__     :     torch.FloatTensor   ,	SCREAMING_SNAKE_CASE__     :     torch.FloatTensor   ,	)      ->       torch.FloatTensor:
         # Make sure sigmas and timesteps have the same device and dtype as original_samples
         lowerCAmelCase__		= self.sigmas.to(device=original_samples.device   ,	dtype=original_samples.dtype   )
         if original_samples.device.type == "mps" and torch.is_floating_point(SCREAMING_SNAKE_CASE__   ):
              # mps does not support float64
              lowerCAmelCase__		= self.timesteps.to(original_samples.device   ,	dtype=torch.floataa   )
              lowerCAmelCase__		= timesteps.to(original_samples.device   ,	dtype=torch.floataa   )
         else:
              lowerCAmelCase__		= self.timesteps.to(original_samples.device   )
              lowerCAmelCase__		= timesteps.to(original_samples.device   )

         lowerCAmelCase__		= [self.index_for_timestep(SCREAMING_SNAKE_CASE__   ,	SCREAMING_SNAKE_CASE__   ) for t in timesteps]

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

         lowerCAmelCase__		= original_samples + noise * sigma
         return noisy_samples


    def __len__(       self     :     Tuple   )      ->       Optional[int]:
         return self.config.num_train_timesteps






import unittest
from pathlib import Path

from transformers import is_vision_available, load_tool
from transformers.testing_utils import get_tests_dir

from .test_tools_common import ToolTesterMixin


if is_vision_available():
     from PIL import Image





class  __lowerCamelCase       (    unittest.TestCase	,				UpperCamelCase__    ):




    """simple docstring"""

    def    a      (       self     :     Any   )      ->       Tuple:
         lowerCAmelCase__		= load_tool("image-captioning"   )
         self.tool.setup()
         lowerCAmelCase__		= load_tool("image-captioning"   ,	remote=SCREAMING_SNAKE_CASE__   )


    def    a      (       self     :     int   )      ->       List[str]:
         lowerCAmelCase__		= Image.open(Path(get_tests_dir("fixtures/tests_samples/COCO"   )   ) / "000000039769.png"   )
         lowerCAmelCase__		= self.tool(SCREAMING_SNAKE_CASE__   )
         self.assertEqual(SCREAMING_SNAKE_CASE__   ,	"two cats sleeping on a couch"   )


    def    a      (       self     :     List[str]   )      ->       int:
         lowerCAmelCase__		= Image.open(Path(get_tests_dir("fixtures/tests_samples/COCO"   )   ) / "000000039769.png"   )
         lowerCAmelCase__		= self.remote_tool(SCREAMING_SNAKE_CASE__   )
         self.assertEqual(SCREAMING_SNAKE_CASE__   ,	"two cats sleeping on a couch"   )


    def    a      (       self     :     Any   )      ->       Any:
         lowerCAmelCase__		= Image.open(Path(get_tests_dir("fixtures/tests_samples/COCO"   )   ) / "000000039769.png"   )
         lowerCAmelCase__		= self.tool(image=SCREAMING_SNAKE_CASE__   )
         self.assertEqual(SCREAMING_SNAKE_CASE__   ,	"two cats sleeping on a couch"   )


    def    a      (       self     :     Dict   )      ->       Any:
         lowerCAmelCase__		= Image.open(Path(get_tests_dir("fixtures/tests_samples/COCO"   )   ) / "000000039769.png"   )
         lowerCAmelCase__		= self.remote_tool(image=SCREAMING_SNAKE_CASE__   )
         self.assertEqual(SCREAMING_SNAKE_CASE__   ,	"two cats sleeping on a couch"   )
