




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

import numpy as np
import torch

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


UpperCamelCase								=     logging.get_logger(__name__)  # pylint: disable=invalid-name





@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->EulerDiscrete
class  __lowerCamelCase       (    UpperCamelCase__    ):




    """simple docstring"""

    snake_case__					=      42
    snake_case__					=      None






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

     if alpha_transform_type == "cosine":

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

     elif alpha_transform_type == "exp":

          def alpha_bar_fn(lowerCAmelCase_     :					str			):
               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__					=      1


    @register_to_config
    def __init__(       self     :     Optional[Any]   ,	SCREAMING_SNAKE_CASE__     :     int = 1_000   ,	SCREAMING_SNAKE_CASE__     :     float = 0.0_001   ,	SCREAMING_SNAKE_CASE__     :     float = 0.02   ,	SCREAMING_SNAKE_CASE__     :     str = "linear"   ,	SCREAMING_SNAKE_CASE__     :     Optional[Union[np.ndarray, List[float]]] = None   ,	SCREAMING_SNAKE_CASE__     :     str = "epsilon"   ,	SCREAMING_SNAKE_CASE__     :     str = "linear"   ,	SCREAMING_SNAKE_CASE__     :     Optional[bool] = False   ,	SCREAMING_SNAKE_CASE__     :     str = "linspace"   ,	SCREAMING_SNAKE_CASE__     :     int = 0   ,	)      ->       List[Any]:
         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   )

         lowerCAmelCase__		= np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5   )
         lowerCAmelCase__		= np.concatenate([sigmas[::-1], [0.0]]   ).astype(np.floataa   )
         lowerCAmelCase__		= torch.from_numpy(SCREAMING_SNAKE_CASE__   )

         # setable values
         lowerCAmelCase__		= None
         lowerCAmelCase__		= np.linspace(0   ,	num_train_timesteps - 1   ,	SCREAMING_SNAKE_CASE__   ,	dtype=SCREAMING_SNAKE_CASE__   )[::-1].copy()
         lowerCAmelCase__		= torch.from_numpy(SCREAMING_SNAKE_CASE__   )
         lowerCAmelCase__		= False
         lowerCAmelCase__		= use_karras_sigmas


    @property
    def    a      (       self     :     Optional[Any]   )      ->       List[Any]:
         # 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     :     Dict   ,	SCREAMING_SNAKE_CASE__     :     torch.FloatTensor   ,	SCREAMING_SNAKE_CASE__     :     Union[float, torch.FloatTensor]   )      ->       torch.FloatTensor:
         if isinstance(SCREAMING_SNAKE_CASE__   ,	torch.Tensor   ):
              lowerCAmelCase__		= timestep.to(self.timesteps.device   )
         lowerCAmelCase__		= (self.timesteps == timestep).nonzero().item()
         lowerCAmelCase__		= self.sigmas[step_index]

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

         lowerCAmelCase__		= True
         return sample


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

         # "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   ,	self.config.num_train_timesteps - 1   ,	SCREAMING_SNAKE_CASE__   ,	dtype=SCREAMING_SNAKE_CASE__   )[
                  ::-1
              ].copy()
         elif self.config.timestep_spacing == "leading":
              lowerCAmelCase__		= self.config.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__		= self.config.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(self.config.num_train_timesteps   ,	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__		= np.log(SCREAMING_SNAKE_CASE__   )

         if self.config.interpolation_type == "linear":
              lowerCAmelCase__		= np.interp(SCREAMING_SNAKE_CASE__   ,	np.arange(0   ,	len(SCREAMING_SNAKE_CASE__   )   )   ,	SCREAMING_SNAKE_CASE__   )
         elif self.config.interpolation_type == "log_linear":
              lowerCAmelCase__		= torch.linspace(np.log(sigmas[-1]   )   ,	np.log(sigmas[0]   )   ,	num_inference_steps + 1   ).exp()
         else:
              raise ValueError(
                  f'{self.config.interpolation_type} is not implemented. Please specify interpolation_type to either'
                  " 'linear' or 'log_linear'"   )

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

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


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

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

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

         lowerCAmelCase__		= log_sigmas[low_idx]
         lowerCAmelCase__		= log_sigmas[high_idx]

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

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


    def    a      (       self     :     Optional[int]   ,	SCREAMING_SNAKE_CASE__     :     torch.FloatTensor   ,	SCREAMING_SNAKE_CASE__     :     Optional[int]   )      ->       torch.FloatTensor:
         lowerCAmelCase__		= in_sigmas[-1].item()
         lowerCAmelCase__		= in_sigmas[0].item()

         lowerCAmelCase__		= 7.0  # 7.0 is the value used in the paper
         lowerCAmelCase__		= np.linspace(0   ,	1   ,	SCREAMING_SNAKE_CASE__   )
         lowerCAmelCase__		= sigma_min ** (1 / rho)
         lowerCAmelCase__		= sigma_max ** (1 / rho)
         lowerCAmelCase__		= (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
         return sigmas


    def    a      (       self     :     List[Any]   ,	SCREAMING_SNAKE_CASE__     :     torch.FloatTensor   ,	SCREAMING_SNAKE_CASE__     :     Union[float, torch.FloatTensor]   ,	SCREAMING_SNAKE_CASE__     :     torch.FloatTensor   ,	SCREAMING_SNAKE_CASE__     :     float = 0.0   ,	SCREAMING_SNAKE_CASE__     :     float = 0.0   ,	SCREAMING_SNAKE_CASE__     :     float = float("inf"   )   ,	SCREAMING_SNAKE_CASE__     :     float = 1.0   ,	SCREAMING_SNAKE_CASE__     :     Optional[torch.Generator] = None   ,	SCREAMING_SNAKE_CASE__     :     bool = True   ,	)      ->       Union[EulerDiscreteSchedulerOutput, Tuple]:

         if (
             isinstance(SCREAMING_SNAKE_CASE__   ,	SCREAMING_SNAKE_CASE__   )
             or isinstance(SCREAMING_SNAKE_CASE__   ,	torch.IntTensor   )
             or isinstance(SCREAMING_SNAKE_CASE__   ,	torch.LongTensor   )
         ):
              raise ValueError(
                  (
                      "Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to"
                      " `EulerDiscreteScheduler.step()` is not supported. Make sure to pass"
                      " one of the `scheduler.timesteps` as a timestep."
                  )   ,	)

         if not self.is_scale_input_called:
              logger.warning(
                  "The `scale_model_input` function should be called before `step` to ensure correct denoising. "
                  "See `StableDiffusionPipeline` for a usage example."   )

         if isinstance(SCREAMING_SNAKE_CASE__   ,	torch.Tensor   ):
              lowerCAmelCase__		= timestep.to(self.timesteps.device   )

         lowerCAmelCase__		= (self.timesteps == timestep).nonzero().item()
         lowerCAmelCase__		= self.sigmas[step_index]

         lowerCAmelCase__		= min(s_churn / (len(self.sigmas   ) - 1)   ,	2**0.5 - 1   ) if s_tmin <= sigma <= s_tmax else 0.0

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

         lowerCAmelCase__		= noise * s_noise
         lowerCAmelCase__		= sigma * (gamma + 1)

         if gamma > 0:
              lowerCAmelCase__		= sample + eps * (sigma_hat**2 - sigma**2) ** 0.5

         # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
         # NOTE: "original_sample" should not be an expected prediction_type but is left in for
         # backwards compatibility
         if self.config.prediction_type == "original_sample" or self.config.prediction_type == "sample":
              lowerCAmelCase__		= model_output
         elif self.config.prediction_type == "epsilon":
              lowerCAmelCase__		= sample - sigma_hat * model_output
         elif self.config.prediction_type == "v_prediction":
              # * c_out + input * c_skip
              lowerCAmelCase__		= model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
         else:
              raise ValueError(
                  f'prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`'   )

         # 2. Convert to an ODE derivative
         lowerCAmelCase__		= (sample - pred_original_sample) / sigma_hat

         lowerCAmelCase__		= self.sigmas[step_index + 1] - sigma_hat

         lowerCAmelCase__		= sample + derivative * dt

         if not return_dict:
              return (prev_sample,)

         return EulerDiscreteSchedulerOutput(prev_sample=SCREAMING_SNAKE_CASE__   ,	pred_original_sample=SCREAMING_SNAKE_CASE__   )


    def    a      (       self     :     str   ,	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__		= [(schedule_timesteps == t).nonzero().item() 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     :     Optional[int]   )      ->       str:
         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"   )
