






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 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase=0.999						,		_UpperCamelCase="cosine"						,		):


   """simple docstring"""




   if alpha_transform_type == "cosine":

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

   elif alpha_transform_type == "exp":

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

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

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





class        _UpperCAmelCase						(		snake_case				,					snake_case			):
    __lowerCamelCase:   Dict		       =    [e.name for e in KarrasDiffusionSchedulers]
    __lowerCamelCase:   Optional[Any]		       =    2




    @register_to_config
    def __init__(			self  :       Dict  ,     a  :       int = 1_0_0_0  ,     a  :       float = 0.0_0085  ,     a  :       float = 0.012  ,     a  :       str = "linear"  ,     a  :       Optional[Union[np.ndarray, List[float]]] = None  ,     a  :       str = "epsilon"  ,     a  :       str = "linspace"  ,     a  :       int = 0  ,     ):






       '''simple docstring'''






       if trained_betas is not None:
          lowercase_	:    Tuple           =     torch.tensor(a  ,     dtype=torch.floataa			)
       elif beta_schedule == "linear":
          lowercase_	:    Optional[int]           =     torch.linspace(a  ,     a  ,     a  ,     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  ,     a  ,     dtype=torch.floataa			) ** 2
          )
       elif beta_schedule == "squaredcos_cap_v2":
          # Glide cosine schedule
          lowercase_	:    str           =     betas_for_alpha_bar(a			)
       else:
          raise NotImplementedError(f"""{beta_schedule} does is not implemented for {self.__class__}"""			)

       lowercase_	:    int           =     1.0 - self.betas
       lowercase_	:    Tuple           =     torch.cumprod(self.alphas  ,     dim=0			)

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




    def 				lowerCAmelCase__				(			self  :       Dict  ,     a  :       List[Any]  ,     a  :       Optional[Any]=None			):






       '''simple docstring'''






       if schedule_timesteps is None:
          lowercase_	:    Union[str, Any]           =     self.timesteps

       lowercase_	:    Tuple           =     (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_	:    str           =     1 if len(a			) > 1 else 0
       else:
          lowercase_	:    Tuple           =     timestep.cpu().item() if torch.is_tensor(a			) else timestep
          lowercase_	:    Any           =     self._index_counter[timestep_int]

       return indices[pos].item()




    @property
    def 				lowerCAmelCase__				(			self  :       Tuple			):






       '''simple docstring'''






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

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




    def 				lowerCAmelCase__				(			self  :       Tuple  ,     a  :       torch.FloatTensor  ,     a  :       Union[float, torch.FloatTensor]  ,     ):






       '''simple docstring'''






       lowercase_	:    Any           =     self.index_for_timestep(a			)

       if self.state_in_first_order:
          lowercase_	:    List[Any]           =     self.sigmas[step_index]
       else:
          lowercase_	:    Union[str, Any]           =     self.sigmas_interpol[step_index]

       lowercase_	:    List[Any]           =     sample / ((sigma**2 + 1) ** 0.5)
       return sample




    def 				lowerCAmelCase__				(			self  :       List[str]  ,     a  :       int  ,     a  :       Union[str, torch.device] = None  ,     a  :       Optional[int] = None  ,     ):






       '''simple docstring'''






       lowercase_	:    str           =     num_inference_steps

       lowercase_	:    Dict           =     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_	:    List[str]           =     np.linspace(0  ,     num_train_timesteps - 1  ,     a  ,     dtype=a			)[::-1].copy()
       elif self.config.timestep_spacing == "leading":
          lowercase_	:    List[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_	:    List[Any]           =     (np.arange(0  ,     a			) * step_ratio).round()[::-1].copy().astype(a			)
          timesteps += self.config.steps_offset
       elif self.config.timestep_spacing == "trailing":
          lowercase_	:    Union[str, 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_	:    Optional[Any]           =     (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'."""			)

       lowercase_	:    Tuple           =     np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5			)
       lowercase_	:    List[str]           =     torch.from_numpy(np.log(a			)			).to(a			)

       lowercase_	:    Union[str, Any]           =     np.interp(a  ,     np.arange(0  ,     len(a			)			)  ,     a			)
       lowercase_	:    Any           =     np.concatenate([sigmas, [0.0]]			).astype(np.floataa			)
       lowercase_	:    List[Any]           =     torch.from_numpy(a			).to(device=a			)

       # interpolate sigmas
       lowercase_	:    str           =     sigmas.log().lerp(sigmas.roll(1			).log()  ,     0.5			).exp()

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

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

       # interpolate timesteps
       lowercase_	:    List[str]           =     self.sigma_to_t(a			).to(a  ,     dtype=timesteps.dtype			)
       lowercase_	:    Tuple           =     torch.stack((timesteps_interpol[1:-1, None], timesteps[1:, None])  ,     dim=-1			).flatten()

       lowercase_	:    Optional[Any]           =     torch.cat([timesteps[:1], interleaved_timesteps]			)

       lowercase_	:    int           =     None

       # for exp beta schedules, such as the one for `pipeline_shap_e.py`
       # we need an index counter
       lowercase_	:    Optional[int]           =     defaultdict(a			)




    def 				lowerCAmelCase__				(			self  :       Dict  ,     a  :       int			):






       '''simple docstring'''






       lowercase_	:    Any           =     sigma.log()

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

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

       lowercase_	:    Optional[int]           =     self.log_sigmas[low_idx]
       lowercase_	:    List[str]           =     self.log_sigmas[high_idx]

       # interpolate sigmas
       lowercase_	:    Union[str, Any]           =     (low - log_sigma) / (low - high)
       lowercase_	:    int           =     w.clamp(0  ,     1			)

       # transform interpolation to time range
       lowercase_	:    Union[str, Any]           =     (1 - w) * low_idx + w * high_idx
       lowercase_	:    int           =     t.view(sigma.shape			)
       return t




    @property
    def 				lowerCAmelCase__				(			self  :       Any			):






       '''simple docstring'''






       return self.sample is None




    def 				lowerCAmelCase__				(			self  :       Tuple  ,     a  :       Union[torch.FloatTensor, np.ndarray]  ,     a  :       Union[float, torch.FloatTensor]  ,     a  :       Union[torch.FloatTensor, np.ndarray]  ,     a  :       bool = True  ,     ):






       '''simple docstring'''






       lowercase_	:    str           =     self.index_for_timestep(a			)

       # advance index counter by 1
       lowercase_	:    List[str]           =     timestep.cpu().item() if torch.is_tensor(a			) else timestep
       self._index_counter[timestep_int] += 1

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

          # store for 2nd order step
          lowercase_	:    str           =     sample
       else:
          # DPM-Solver-2
          # 2. Convert to an ODE derivative for 2nd order
          lowercase_	:    Optional[int]           =     (sample - pred_original_sample) / sigma_interpol

          # 3. delta timestep
          lowercase_	:    Union[str, Any]           =     sigma_next - sigma_hat

          lowercase_	:    int           =     self.sample
          lowercase_	:    Any           =     None

       lowercase_	:    Optional[Any]           =     sample + derivative * dt

       if not return_dict:
          return (prev_sample,)

       return SchedulerOutput(prev_sample=a			)




    def 				lowerCAmelCase__				(			self  :       str  ,     a  :       torch.FloatTensor  ,     a  :       torch.FloatTensor  ,     a  :       torch.FloatTensor  ,     ):






       '''simple docstring'''






       lowercase_	:    str           =     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
          lowercase_	:    Optional[int]           =     self.timesteps.to(original_samples.device  ,     dtype=torch.floataa			)
          lowercase_	:    int           =     timesteps.to(original_samples.device  ,     dtype=torch.floataa			)
       else:
          lowercase_	:    Union[str, Any]           =     self.timesteps.to(original_samples.device			)
          lowercase_	:    Tuple           =     timesteps.to(original_samples.device			)

       lowercase_	:    Union[str, Any]           =     [self.index_for_timestep(a  ,     a			) for t in timesteps]

       lowercase_	:    List[Any]           =     sigmas[step_indices].flatten()
       while len(sigma.shape			) < len(original_samples.shape			):
          lowercase_	:    Tuple           =     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












'''simple docstring'''
import os
from collections import namedtuple

import pytest

from datasets import ClassLabel, Features, Sequence, Value
from datasets.commands.test import TestCommand
from datasets.info import DatasetInfo, DatasetInfosDict


UpperCamelCase__ =			namedtuple(
    '_TestCommandArgs',
    [
        'dataset',
        'name',
        'cache_dir',
        'data_dir',
        'all_configs',
        'save_infos',
        'ignore_verifications',
        'force_redownload',
        'clear_cache',
    ],
    defaults=[None, None, None, False, False, False, False, False],
)




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase							):


   """simple docstring"""




   return (abs(source - target							) / target) < 0.01





@pytest.mark.integration
def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase							):


   """simple docstring"""




   lowercase_	:    Optional[int]           =     _TestCommandArgs(dataset=_UpperCamelCase						,		all_configs=_UpperCamelCase						,		save_infos=_UpperCamelCase							)
   lowercase_	:    int           =     TestCommand(*_UpperCamelCase							)
   test_command.run()
   lowercase_	:    List[str]           =     os.path.join(_UpperCamelCase						,		"README.md"							)
   assert os.path.exists(_UpperCamelCase							)
   lowercase_	:    Any           =     DatasetInfosDict.from_directory(_UpperCamelCase							)
   lowercase_	:    Optional[int]           =     DatasetInfosDict(
       {
           "default": DatasetInfo(
               features=Features(
                   {
                       "tokens": Sequence(Value("string"							)							),
                       "ner_tags": Sequence(
                           ClassLabel(names=["O", "B-PER", "I-PER", "B-ORG", "I-ORG", "B-LOC", "I-LOC"]							)							),
                       "langs": Sequence(Value("string"							)							),
                       "spans": Sequence(Value("string"							)							),
                   }							)						,		splits=[
                   {
                       "name": "train",
                       "num_bytes": 235_1563,
                       "num_examples": 1_0000,
                   },
                   {
                       "name": "validation",
                       "num_bytes": 23_8418,
                       "num_examples": 1000,
                   },
               ]						,		download_size=394_0680						,		dataset_size=258_9981						,		)
       }							)
   assert dataset_infos.keys() == expected_dataset_infos.keys()
   for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
      lowercase_    ,		lowercase_	:    Optional[int]           =     getattr(dataset_infos["default"]						,		_UpperCamelCase							), getattr(expected_dataset_infos["default"]						,		_UpperCamelCase							)
      if key == "num_bytes":
         assert is_apercent_close(_UpperCamelCase						,		_UpperCamelCase							)
      elif key == "splits":
         assert list(_UpperCamelCase							) == list(_UpperCamelCase							)
         for split in result:
            assert result[split].name == expected[split].name
            assert result[split].num_examples == expected[split].num_examples
            assert is_apercent_close(result[split].num_bytes						,		expected[split].num_bytes							)
      else:
         result == expected




