





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,
)


UpperCAmelCase	:	List[str]		     =       pytest.mark.integration
@pytest.mark.parametrize("path"       ,       ["paws", "csv"]						)
def      _A       (		SCREAMING_SNAKE_CASE     :   str       ,       SCREAMING_SNAKE_CASE     :   List[str]						):





  """simple docstring"""



  inspect_dataset(__snake_case       ,       __snake_case						)
  a__	:					Optional[int]										=path + ".py"
  assert script_name in os.listdir(__snake_case						)
  assert "__pycache__" not in os.listdir(__snake_case						)
@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      _A       (		SCREAMING_SNAKE_CASE     :   Any       ,       SCREAMING_SNAKE_CASE     :   List[Any]						):





  """simple docstring"""



  inspect_metric(__snake_case       ,       __snake_case						)
  a__	:					Dict										=path + ".py"
  assert script_name in os.listdir(__snake_case						)
  assert "__pycache__" not in os.listdir(__snake_case						)
@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      _A       (		SCREAMING_SNAKE_CASE     :   Dict       ,       SCREAMING_SNAKE_CASE     :   Any       ,       SCREAMING_SNAKE_CASE     :   Dict						):





  """simple docstring"""



  a__	:					int										=get_dataset_config_info(__snake_case       ,       config_name=__snake_case						)
  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      _A       (		SCREAMING_SNAKE_CASE     :   List[str]       ,       SCREAMING_SNAKE_CASE     :   Optional[Any]       ,       SCREAMING_SNAKE_CASE     :   Optional[int]						):





  """simple docstring"""



  with pytest.raises(__snake_case						):
    get_dataset_config_info(__snake_case       ,       config_name=__snake_case						)
@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      _A       (		SCREAMING_SNAKE_CASE     :   int       ,       SCREAMING_SNAKE_CASE     :   Any						):





  """simple docstring"""



  a__	:					Optional[Any]										=get_dataset_config_names(__snake_case						)
  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      _A       (		SCREAMING_SNAKE_CASE     :   Dict       ,       SCREAMING_SNAKE_CASE     :   List[Any]       ,       SCREAMING_SNAKE_CASE     :   Dict						):





  """simple docstring"""



  a__	:					Optional[Any]										=get_dataset_infos(__snake_case						)
  assert list(infos.keys()						) == expected_configs
  a__	:					Union[str, Any]										=expected_configs[0]
  assert expected_config in infos
  a__	:					str										=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      _A       (		SCREAMING_SNAKE_CASE     :   Optional[Any]       ,       SCREAMING_SNAKE_CASE     :   int       ,       SCREAMING_SNAKE_CASE     :   Dict						):





  """simple docstring"""



  a__	:					Optional[int]										=get_dataset_infos(__snake_case						)
  assert expected_config in infos
  a__	:					Union[str, 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      _A       (		SCREAMING_SNAKE_CASE     :   Dict       ,       SCREAMING_SNAKE_CASE     :   str       ,       SCREAMING_SNAKE_CASE     :   Tuple						):





  """simple docstring"""



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










# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch

import math
from typing import Union

import torch

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







class       lowerCamelCase__   (       lowerCAmelCase   ,							lowerCAmelCase):
     SCREAMING_SNAKE_CASE__      			=			1





     @register_to_config
     def __init__(self		,     UpperCAmelCase=2_0_0_0		,     UpperCAmelCase=0.1		,     UpperCAmelCase=2_0		,     UpperCAmelCase=1e-3       )     ->   List[str]:
      _lowercase							      =None
      _lowercase							      =None
      _lowercase							      =None





     def      __A					(self		,     UpperCAmelCase		,     UpperCAmelCase = None       )     ->   str:
      _lowercase							      =torch.linspace(1		,     self.config.sampling_eps		,     UpperCAmelCase		,     device=UpperCAmelCase       )





     def      __A					(self		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase=None       )     ->   Optional[int]:
      if self.timesteps is None:
       raise ValueError(
           '''`self.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler'''       )

      # TODO(Patrick) better comments + non-PyTorch
      # postprocess model score
      _lowercase							      =(
          -0.25 * t**2 * (self.config.beta_max - self.config.beta_min) - 0.5 * t * self.config.beta_min
      )
      _lowercase							      =torch.sqrt(1.0 - torch.exp(2.0 * log_mean_coeff       )       )
      _lowercase							      =std.flatten()
      while len(std.shape       ) < len(score.shape       ):
       _lowercase							      =std.unsqueeze(-1       )
      _lowercase							      =-score / std

      # compute
      _lowercase							      =-1.0 / len(self.timesteps       )

      _lowercase							      =self.config.beta_min + t * (self.config.beta_max - self.config.beta_min)
      _lowercase							      =beta_t.flatten()
      while len(beta_t.shape       ) < len(x.shape       ):
       _lowercase							      =beta_t.unsqueeze(-1       )
      _lowercase							      =-0.5 * beta_t * x

      _lowercase							      =torch.sqrt(UpperCAmelCase       )
      _lowercase							      =drift - diffusion**2 * score
      _lowercase							      =x + drift * dt

      # add noise
      _lowercase							      =randn_tensor(x.shape		,     layout=x.layout		,     generator=UpperCAmelCase		,     device=x.device		,     dtype=x.dtype       )
      _lowercase							      =x_mean + diffusion * math.sqrt(-dt       ) * noise

      return x, x_mean





     def __len__(self       )     ->   str:
      return self.config.num_train_timesteps

