
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        lowerCAmelCase       (				snake_case__   :      List[Any]				,					snake_case__   :      List[str]=0.9_9_9				,					snake_case__   :      str="cosine"				,					)->			Optional[Any]:
    if alpha_transform_type == "cosine":

        def alpha_bar_fn(snake_case__   :      List[Any] ):
            return math.cos((t + 0.0_0_8) / 1.0_0_8 * math.pi / 2 ) ** 2

    elif alpha_transform_type == "exp":

        def alpha_bar_fn(snake_case__   :      Tuple ):
            return math.exp(t * -1_2.0 )

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

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



class 							lowerCamelCase       (		__snake_case			,  __snake_case					):



  """simple docstring"""





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

  @register_to_config
  def __init__(					self						,    __UpperCamelCase = 1000						,    __UpperCamelCase = 0.0_0085						,    __UpperCamelCase = 0.012						,    __UpperCamelCase = "linear"						,    __UpperCamelCase = None						,    __UpperCamelCase = "epsilon"						,    __UpperCamelCase = "linspace"						,    __UpperCamelCase = 0						,    ):
      if trained_betas is not None:
          A_								=					torch.tensor(__UpperCamelCase						,    dtype=torch.floataa					)
      elif beta_schedule == "linear":
          A_								=					torch.linspace(__UpperCamelCase						,    __UpperCamelCase						,    __UpperCamelCase						,    dtype=torch.floataa					)
      elif beta_schedule == "scaled_linear":
          # this schedule is very specific to the latent diffusion model.
          A_								=					(
              torch.linspace(beta_start**0.5						,    beta_end**0.5						,    __UpperCamelCase						,    dtype=torch.floataa					) ** 2
          )
      elif beta_schedule == "squaredcos_cap_v2":
          # Glide cosine schedule
          A_								=					betas_for_alpha_bar(__UpperCamelCase					)
      else:
          raise NotImplementedError(f'{beta_schedule} does is not implemented for {self.__class__}'					)

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

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

  def 					lowercase_						(					self						,    __UpperCamelCase						,    __UpperCamelCase=None					):
      if schedule_timesteps is None:
          A_								=					self.timesteps

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

      return indices[pos].item()

  @property
  def 					lowercase_						(					self					):
      # 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 					lowercase_						(					self						,    __UpperCamelCase						,    __UpperCamelCase						,    ):
      A_								=					self.index_for_timestep(__UpperCamelCase					)

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

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

  def 					lowercase_						(					self						,    __UpperCamelCase						,    __UpperCamelCase = None						,    __UpperCamelCase = None						,    ):
      A_								=					num_inference_steps

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

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

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

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

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

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

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

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

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

      A_								=					None

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

  def 					lowercase_						(					self						,    __UpperCamelCase					):
      # get log sigma
      A_								=					sigma.log()

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

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

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

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

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

  @property
  def 					lowercase_						(					self					):
      return self.sample is None

  def 					lowercase_						(					self						,    __UpperCamelCase						,    __UpperCamelCase						,    __UpperCamelCase						,    __UpperCamelCase = None						,    __UpperCamelCase = True						,    ):
      A_								=					self.index_for_timestep(__UpperCamelCase					)

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

      if self.state_in_first_order:
          A_								=					self.sigmas[step_index]
          A_								=					self.sigmas_interpol[step_index]
          A_								=					self.sigmas_up[step_index]
          A_								=					self.sigmas_down[step_index - 1]
      else:
          # 2nd order / KPDM2's method
          A_								=					self.sigmas[step_index - 1]
          A_								=					self.sigmas_interpol[step_index - 1]
          A_								=					self.sigmas_up[step_index - 1]
          A_								=					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
      A_								=					0
      A_								=					sigma * (gamma + 1)  # Note: sigma_hat == sigma for now

      A_								=					model_output.device
      A_								=					randn_tensor(model_output.shape						,    dtype=model_output.dtype						,    device=__UpperCamelCase						,    generator=__UpperCamelCase					)

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

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

          A_								=					self.sample
          A_								=					None

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

      if not return_dict:
          return (prev_sample,)

      return SchedulerOutput(prev_sample=__UpperCamelCase					)

  def 					lowercase_						(					self						,    __UpperCamelCase						,    __UpperCamelCase						,    __UpperCamelCase						,    ):
      # Make sure sigmas and timesteps have the same device and dtype as original_samples
      A_								=					self.sigmas.to(device=original_samples.device						,    dtype=original_samples.dtype					)
      if original_samples.device.type == "mps" and torch.is_floating_point(__UpperCamelCase					):
          # mps does not support float64
          A_								=					self.timesteps.to(original_samples.device						,    dtype=torch.floataa					)
          A_								=					timesteps.to(original_samples.device						,    dtype=torch.floataa					)
      else:
          A_								=					self.timesteps.to(original_samples.device					)
          A_								=					timesteps.to(original_samples.device					)

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

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

      A_								=					original_samples + noise * sigma
      return noisy_samples

  def __len__(					self					):
      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			,  __snake_case					):



  """simple docstring"""





  def 					lowercase_						(					self					):
      A_								=					load_tool("image-question-answering"					)
      self.tool.setup()
      A_								=					load_tool("image-question-answering"						,    remote=__UpperCamelCase					)

  def 					lowercase_						(					self					):
      A_								=					Image.open(Path(get_tests_dir("fixtures/tests_samples/COCO"					)					) / "000000039769.png"					)
      A_								=					self.tool(__UpperCamelCase						,    "How many cats are sleeping on the couch?"					)
      self.assertEqual(__UpperCamelCase						,    "2"					)

  def 					lowercase_						(					self					):
      A_								=					Image.open(Path(get_tests_dir("fixtures/tests_samples/COCO"					)					) / "000000039769.png"					)
      A_								=					self.remote_tool(__UpperCamelCase						,    "How many cats are sleeping on the couch?"					)
      self.assertEqual(__UpperCamelCase						,    "2"					)

  def 					lowercase_						(					self					):
      A_								=					Image.open(Path(get_tests_dir("fixtures/tests_samples/COCO"					)					) / "000000039769.png"					)
      A_								=					self.tool(image=__UpperCamelCase						,    question="How many cats are sleeping on the couch?"					)
      self.assertEqual(__UpperCamelCase						,    "2"					)

  def 					lowercase_						(					self					):
      A_								=					Image.open(Path(get_tests_dir("fixtures/tests_samples/COCO"					)					) / "000000039769.png"					)
      A_								=					self.remote_tool(image=__UpperCamelCase						,    question="How many cats are sleeping on the couch?"					)
      self.assertEqual(__UpperCamelCase						,    "2"					)






