


'''simple docstring'''






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

import numpy as np
import torch

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






@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->UnCLIP
class   __snake_case			(  _SCREAMING_SNAKE_CASE):







    """simple docstring"""



    lowercase  					=   42
    lowercase  					=   None







def 							UpperCamelCase_     (       A__   :  int				,       A__   :  List[Any]=0.999				,       A__   :  Union[str, Any]="cosine"				,       ):
  '''simple docstring'''





  if alpha_transform_type == "cosine":

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

  elif alpha_transform_type == "exp":

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

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

  lowerCAmelCase_     : int     =  []
  for i in range(A__   ):
    lowerCAmelCase_     : Union[str, Any]     =  i / num_diffusion_timesteps
    lowerCAmelCase_     : List[Any]     =  (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   __snake_case			(  _SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE):







    """simple docstring"""



    @register_to_config
    def __init__(  self       :    str       ,		lowerCamelCase       :    int = 10_00       ,		lowerCamelCase       :    str = "fixed_small_log"       ,		lowerCamelCase       :    bool = True       ,		lowerCamelCase       :    Optional[float] = 1.0       ,		lowerCamelCase       :    str = "epsilon"       ,		lowerCamelCase       :    str = "squaredcos_cap_v2"       ,		)  ->    Union[str, Any]:
      if beta_schedule != "squaredcos_cap_v2":
        raise ValueError("""UnCLIPScheduler only supports `beta_schedule`: 'squaredcos_cap_v2'"""						)

      lowerCAmelCase_     : Dict     =  betas_for_alpha_bar(lowerCamelCase						)

      lowerCAmelCase_     : Any     =  1.0 - self.betas
      lowerCAmelCase_     : List[Any]     =  torch.cumprod(self.alphas       ,		dim=0						)
      lowerCAmelCase_     : Union[str, Any]     =  torch.tensor(1.0						)

      # standard deviation of the initial noise distribution
      lowerCAmelCase_     : int     =  1.0

      # setable values
      lowerCAmelCase_     : Any     =  None
      lowerCAmelCase_     : Union[str, Any]     =  torch.from_numpy(np.arange(0       ,		lowerCamelCase						)[::-1].copy()						)

      lowerCAmelCase_     : Optional[int]     =  variance_type

    def __lowercase  (  self       :    Dict       ,		lowerCamelCase       :    torch.FloatTensor       ,		lowerCamelCase       :    Optional[int] = None						)  ->    torch.FloatTensor:
      return sample

    def __lowercase  (  self       :    Tuple       ,		lowerCamelCase       :    int       ,		lowerCamelCase       :    Union[str, torch.device] = None						)  ->    int:
      lowerCAmelCase_     : int     =  num_inference_steps
      lowerCAmelCase_     : int     =  (self.config.num_train_timesteps - 1) / (self.num_inference_steps - 1)
      lowerCAmelCase_     : List[str]     =  (np.arange(0       ,		lowerCamelCase						) * step_ratio).round()[::-1].copy().astype(np.intaa						)
      lowerCAmelCase_     : Dict     =  torch.from_numpy(lowerCamelCase						).to(lowerCamelCase						)

    def __lowercase  (  self       :    Optional[Any]       ,		lowerCamelCase       :    List[Any]       ,		lowerCamelCase       :    List[str]=None       ,		lowerCamelCase       :    Union[str, Any]=None       ,		lowerCamelCase       :    Dict=None						)  ->    Tuple:
      if prev_timestep is None:
        lowerCAmelCase_     : List[Any]     =  t - 1

      lowerCAmelCase_     : Union[str, Any]     =  self.alphas_cumprod[t]
      lowerCAmelCase_     : Optional[int]     =  self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
      lowerCAmelCase_     : Optional[Any]     =  1 - alpha_prod_t
      lowerCAmelCase_     : List[Any]     =  1 - alpha_prod_t_prev

      if prev_timestep == t - 1:
        lowerCAmelCase_     : int     =  self.betas[t]
      else:
        lowerCAmelCase_     : Any     =  1 - alpha_prod_t / alpha_prod_t_prev

      # For t > 0, compute predicted variance βt (see formula (6) and (7) from https://arxiv.org/pdf/2006.11239.pdf)
      # and sample from it to get previous sample
      # x_{t-1} ~ N(pred_prev_sample, variance) == add variance to pred_sample
      lowerCAmelCase_     : List[str]     =  beta_prod_t_prev / beta_prod_t * beta

      if variance_type is None:
        lowerCAmelCase_     : List[str]     =  self.config.variance_type

      # hacks - were probably added for training stability
      if variance_type == "fixed_small_log":
        lowerCAmelCase_     : Union[str, Any]     =  torch.log(torch.clamp(lowerCamelCase       ,		min=1E-20						)						)
        lowerCAmelCase_     : Optional[int]     =  torch.exp(0.5 * variance						)
      elif variance_type == "learned_range":
        # NOTE difference with DDPM scheduler
        lowerCAmelCase_     : str     =  variance.log()
        lowerCAmelCase_     : Tuple     =  beta.log()

        lowerCAmelCase_     : List[Any]     =  (predicted_variance + 1) / 2
        lowerCAmelCase_     : Dict     =  frac * max_log + (1 - frac) * min_log

      return variance

    def __lowercase  (  self       :    Any       ,		lowerCamelCase       :    torch.FloatTensor       ,		lowerCamelCase       :    int       ,		lowerCamelCase       :    torch.FloatTensor       ,		lowerCamelCase       :    Optional[int] = None       ,		lowerCamelCase       :    Any=None       ,		lowerCamelCase       :    bool = True       ,		)  ->    Union[UnCLIPSchedulerOutput, Tuple]:
      lowerCAmelCase_     : Union[str, Any]     =  timestep

      if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type == "learned_range":
        lowerCAmelCase_,				lowerCAmelCase_     : Optional[Any]     =  torch.split(lowerCamelCase       ,		sample.shape[1]       ,		dim=1						)
      else:
        lowerCAmelCase_     : List[Any]     =  None

      # 1. compute alphas, betas
      if prev_timestep is None:
        lowerCAmelCase_     : Tuple     =  t - 1

      lowerCAmelCase_     : Optional[int]     =  self.alphas_cumprod[t]
      lowerCAmelCase_     : Any     =  self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
      lowerCAmelCase_     : Optional[int]     =  1 - alpha_prod_t
      lowerCAmelCase_     : Union[str, Any]     =  1 - alpha_prod_t_prev

      if prev_timestep == t - 1:
        lowerCAmelCase_     : List[str]     =  self.betas[t]
        lowerCAmelCase_     : Optional[int]     =  self.alphas[t]
      else:
        lowerCAmelCase_     : Dict     =  1 - alpha_prod_t / alpha_prod_t_prev
        lowerCAmelCase_     : Dict     =  1 - beta

      # 2. compute predicted original sample from predicted noise also called
      # "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
      if self.config.prediction_type == "epsilon":
        lowerCAmelCase_     : str     =  (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
      elif self.config.prediction_type == "sample":
        lowerCAmelCase_     : Optional[Any]     =  model_output
      else:
        raise ValueError(
            F'prediction_type given as {self.config.prediction_type} must be one of `epsilon` or `sample`'
            """ for the UnCLIPScheduler."""						)

      # 3. Clip "predicted x_0"
      if self.config.clip_sample:
        lowerCAmelCase_     : Tuple     =  torch.clamp(
            lowerCamelCase       ,		-self.config.clip_sample_range       ,		self.config.clip_sample_range						)

      # 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
      # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
      lowerCAmelCase_     : Any     =  (alpha_prod_t_prev ** 0.5 * beta) / beta_prod_t
      lowerCAmelCase_     : Union[str, Any]     =  alpha ** 0.5 * beta_prod_t_prev / beta_prod_t

      # 5. Compute predicted previous sample µ_t
      # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
      lowerCAmelCase_     : int     =  pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

      # 6. Add noise
      lowerCAmelCase_     : int     =  0
      if t > 0:
        lowerCAmelCase_     : Optional[Any]     =  randn_tensor(
            model_output.shape       ,		dtype=model_output.dtype       ,		generator=lowerCamelCase       ,		device=model_output.device						)

        lowerCAmelCase_     : Optional[Any]     =  self._get_variance(
            lowerCamelCase       ,		predicted_variance=lowerCamelCase       ,		prev_timestep=lowerCamelCase       ,		)

        if self.variance_type == "fixed_small_log":
          lowerCAmelCase_     : Optional[int]     =  variance
        elif self.variance_type == "learned_range":
          lowerCAmelCase_     : Tuple     =  (0.5 * variance).exp()
        else:
          raise ValueError(
              F'variance_type given as {self.variance_type} must be one of `fixed_small_log` or `learned_range`'
              """ for the UnCLIPScheduler."""						)

        lowerCAmelCase_     : str     =  variance * variance_noise

      lowerCAmelCase_     : List[str]     =  pred_prev_sample + variance

      if not return_dict:
        return (pred_prev_sample,)

      return UnCLIPSchedulerOutput(prev_sample=lowerCamelCase       ,		pred_original_sample=lowerCamelCase						)





    def __lowercase  (  self       :    Dict       ,		lowerCamelCase       :    torch.FloatTensor       ,		lowerCamelCase       :    torch.FloatTensor       ,		lowerCamelCase       :    torch.IntTensor       ,		)  ->    torch.FloatTensor:
      # Make sure alphas_cumprod and timestep have same device and dtype as original_samples
      lowerCAmelCase_     : Tuple     =  self.alphas_cumprod.to(device=original_samples.device       ,		dtype=original_samples.dtype						)
      lowerCAmelCase_     : List[str]     =  timesteps.to(original_samples.device						)

      lowerCAmelCase_     : Dict     =  alphas_cumprod[timesteps] ** 0.5
      lowerCAmelCase_     : Dict     =  sqrt_alpha_prod.flatten()
      while len(sqrt_alpha_prod.shape						) < len(original_samples.shape						):
        lowerCAmelCase_     : List[str]     =  sqrt_alpha_prod.unsqueeze(-1						)

      lowerCAmelCase_     : str     =  (1 - alphas_cumprod[timesteps]) ** 0.5
      lowerCAmelCase_     : Tuple     =  sqrt_one_minus_alpha_prod.flatten()
      while len(sqrt_one_minus_alpha_prod.shape						) < len(original_samples.shape						):
        lowerCAmelCase_     : List[str]     =  sqrt_one_minus_alpha_prod.unsqueeze(-1						)

      lowerCAmelCase_     : str     =  sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
      return noisy_samples







'''simple docstring'''






import os

import pytest

from datasets import (
    get_dataset_config_info,
    get_dataset_config_names,
    get_dataset_infos,
    get_dataset_split_names,
    inspect_dataset,
    inspect_metric,
)


__A   :    int											= pytest.mark.integration







@pytest.mark.parametrize("""path"""				,       ["""paws""", """csv"""]   )
def 							UpperCamelCase_     (       A__   :  Union[str, Any]				,       A__   :  Tuple   ):
  '''simple docstring'''





  inspect_dataset(A__				,       A__   )
  lowerCAmelCase_     : Any     =  path + """.py"""
  assert script_name in os.listdir(A__   )
  assert "__pycache__" not in os.listdir(A__   )







@pytest.mark.filterwarnings("""ignore:inspect_metric is deprecated:FutureWarning"""   )
@pytest.mark.filterwarnings("""ignore:metric_module_factory is deprecated:FutureWarning"""   )
@pytest.mark.parametrize("""path"""				,       ["""accuracy"""]   )
def 							UpperCamelCase_     (       A__   :  Tuple				,       A__   :  str   ):
  '''simple docstring'''





  inspect_metric(A__				,       A__   )
  lowerCAmelCase_     : Any     =  path + """.py"""
  assert script_name in os.listdir(A__   )
  assert "__pycache__" not in os.listdir(A__   )







@pytest.mark.parametrize(
    """path, config_name, expected_splits"""				,       [
        ("""squad""", """plain_text""", ["""train""", """validation"""]),
        ("""dalle-mini/wit""", """dalle-mini--wit""", ["""train"""]),
        ("""paws""", """labeled_final""", ["""train""", """test""", """validation"""]),
    ]				,       )
def 							UpperCamelCase_     (       A__   :  List[str]				,       A__   :  List[str]				,       A__   :  Optional[Any]   ):
  '''simple docstring'''





  lowerCAmelCase_     : List[Any]     =  get_dataset_config_info(A__				,       config_name=A__   )
  assert info.config_name == config_name
  assert list(info.splits.keys()   ) == expected_splits







@pytest.mark.parametrize(
    """path, config_name, expected_exception"""				,       [
        ("""paws""", None, ValueError),
    ]				,       )
def 							UpperCamelCase_     (       A__   :  List[str]				,       A__   :  List[str]				,       A__   :  int   ):
  '''simple docstring'''





  with pytest.raises(A__   ):
    get_dataset_config_info(A__				,       config_name=A__   )







@pytest.mark.parametrize(
    """path, expected"""				,       [
        ("""squad""", """plain_text"""),
        ("""acronym_identification""", """default"""),
        ("""lhoestq/squad""", """plain_text"""),
        ("""lhoestq/test""", """default"""),
        ("""lhoestq/demo1""", """lhoestq--demo1"""),
        ("""dalle-mini/wit""", """dalle-mini--wit"""),
    ]				,       )
def 							UpperCamelCase_     (       A__   :  str				,       A__   :  List[str]   ):
  '''simple docstring'''





  lowerCAmelCase_     : Dict     =  get_dataset_config_names(A__   )
  assert expected in config_names







@pytest.mark.parametrize(
    """path, expected_configs, expected_splits_in_first_config"""				,       [
        ("""squad""", ["""plain_text"""], ["""train""", """validation"""]),
        ("""dalle-mini/wit""", ["""dalle-mini--wit"""], ["""train"""]),
        ("""paws""", ["""labeled_final""", """labeled_swap""", """unlabeled_final"""], ["""train""", """test""", """validation"""]),
    ]				,       )
def 							UpperCamelCase_     (       A__   :  List[Any]				,       A__   :  Dict				,       A__   :  Optional[int]   ):
  '''simple docstring'''





  lowerCAmelCase_     : Tuple     =  get_dataset_infos(A__   )
  assert list(infos.keys()   ) == expected_configs
  lowerCAmelCase_     : Optional[int]     =  expected_configs[0]
  assert expected_config in infos
  lowerCAmelCase_     : int     =  infos[expected_config]
  assert info.config_name == expected_config
  assert list(info.splits.keys()   ) == expected_splits_in_first_config







@pytest.mark.parametrize(
    """path, expected_config, expected_splits"""				,       [
        ("""squad""", """plain_text""", ["""train""", """validation"""]),
        ("""dalle-mini/wit""", """dalle-mini--wit""", ["""train"""]),
        ("""paws""", """labeled_final""", ["""train""", """test""", """validation"""]),
    ]				,       )
def 							UpperCamelCase_     (       A__   :  List[str]				,       A__   :  Optional[Any]				,       A__   :  List[Any]   ):
  '''simple docstring'''





  lowerCAmelCase_     : Dict     =  get_dataset_infos(A__   )
  assert expected_config in infos
  lowerCAmelCase_     : List[Any]     =  infos[expected_config]
  assert info.config_name == expected_config
  assert list(info.splits.keys()   ) == expected_splits







@pytest.mark.parametrize(
    """path, config_name, expected_exception"""				,       [
        ("""paws""", None, ValueError),
    ]				,       )
def 							UpperCamelCase_     (       A__   :  List[str]				,       A__   :  Any				,       A__   :  Dict   ):
  '''simple docstring'''





  with pytest.raises(A__   ):
    get_dataset_split_names(A__				,       config_name=A__   )



