


'''simple docstring'''






from typing import Callable, Dict, Optional, Tuple

import torch
from torch import nn
from torch.distributions import (
    AffineTransform,
    Distribution,
    Independent,
    NegativeBinomial,
    Normal,
    StudentT,
    TransformedDistribution,
)






class   __snake_case			(  _SCREAMING_SNAKE_CASE):







    """simple docstring"""



    def __init__(  self       :    List[str]       ,		lowerCamelCase       :    Distribution       ,		lowerCamelCase       :    List[str]=None       ,		lowerCamelCase       :    Optional[Any]=None       ,		lowerCamelCase       :    List[Any]=0						)  ->    Tuple:
      lowerCAmelCase_     : Optional[int]     =  1.0 if scale is None else scale
      lowerCAmelCase_     : Any     =  0.0 if loc is None else loc

      super().__init__(lowerCamelCase       ,		[AffineTransform(loc=self.loc       ,		scale=self.scale       ,		event_dim=lowerCamelCase						)]						)

    @property
    def __lowercase  (  self       :    int						)  ->    Any:
      return self.base_dist.mean * self.scale + self.loc

    @property
    def __lowercase  (  self       :    Any						)  ->    int:
      return self.base_dist.variance * self.scale**2





    @property
    def __lowercase  (  self       :    List[str]						)  ->    Tuple:
      return self.variance.sqrt()







class   __snake_case			(  nn.Module):







    """simple docstring"""



    def __init__(  self       :    Any       ,		lowerCamelCase       :    int       ,		lowerCamelCase       :    Dict[str, int]       ,		lowerCamelCase       :    Callable[..., Tuple[torch.Tensor]]       ,		**lowerCamelCase       :    Any						)  ->    None:
      super().__init__(**lowerCamelCase						)
      lowerCAmelCase_     : str     =  args_dim
      lowerCAmelCase_     : Tuple     =  nn.ModuleList([nn.Linear(lowerCamelCase       ,		lowerCamelCase						) for dim in args_dim.values()]						)
      lowerCAmelCase_     : str     =  domain_map





    def __lowercase  (  self       :    List[Any]       ,		lowerCamelCase       :    torch.Tensor						)  ->    Tuple[torch.Tensor]:
      lowerCAmelCase_     : Union[str, Any]     =  [proj(lowerCamelCase						) for proj in self.proj]

      return self.domain_map(*lowerCamelCase						)







class   __snake_case			(  nn.Module):







    """simple docstring"""



    def __init__(  self       :    Dict       ,		lowerCamelCase       :    Any						)  ->    int:
      super().__init__()
      lowerCAmelCase_     : Tuple     =  function





    def __lowercase  (  self       :    List[Any]       ,		lowerCamelCase       :    str       ,		*lowerCamelCase       :    Dict						)  ->    List[str]:
      return self.function(lowerCamelCase       ,		*lowerCamelCase						)







class   __snake_case			:







    """simple docstring"""



    lowercase  					=   42
    lowercase  					=   42
    lowercase  					=   42

    def __init__(  self       :    str       ,		lowerCamelCase       :    int = 1						)  ->    None:
      lowerCAmelCase_     : int     =  dim
      lowerCAmelCase_     : List[Any]     =  {k: dim * self.args_dim[k] for k in self.args_dim}

    def __lowercase  (  self       :    int       ,		lowerCamelCase       :    Any						)  ->    Dict:
      if self.dim == 1:
        return self.distribution_class(*lowerCamelCase						)
      else:
        return Independent(self.distribution_class(*lowerCamelCase						)       ,		1						)

    def __lowercase  (  self       :    List[str]       ,		lowerCamelCase       :    str       ,		lowerCamelCase       :    Optional[torch.Tensor] = None       ,		lowerCamelCase       :    Optional[torch.Tensor] = None       ,		)  ->    Distribution:
      lowerCAmelCase_     : Union[str, Any]     =  self._base_distribution(lowerCamelCase						)
      if loc is None and scale is None:
        return distr
      else:
        return AffineTransformed(lowerCamelCase       ,		loc=lowerCamelCase       ,		scale=lowerCamelCase       ,		event_dim=self.event_dim						)

    @property
    def __lowercase  (  self       :    List[Any]						)  ->    Tuple:
      return () if self.dim == 1 else (self.dim,)

    @property
    def __lowercase  (  self       :    Tuple						)  ->    int:
      return len(self.event_shape						)

    @property
    def __lowercase  (  self       :    int						)  ->    float:
      return 0.0

    def __lowercase  (  self       :    Optional[int]       ,		lowerCamelCase       :    int						)  ->    nn.Module:
      return ParameterProjection(
          in_features=lowerCamelCase       ,		args_dim=self.args_dim       ,		domain_map=LambdaLayer(self.domain_map						)       ,		)

    def __lowercase  (  self       :    Dict       ,		*lowerCamelCase       :    torch.Tensor						)  ->    Tuple:
      raise NotImplementedError()





    @staticmethod
    def __lowercase  (  lowerCamelCase       :    torch.Tensor						)  ->    torch.Tensor:
      return (x + torch.sqrt(torch.square(lowerCamelCase						) + 4.0						)) / 2.0







class   __snake_case			(  _SCREAMING_SNAKE_CASE):







    """simple docstring"""



    lowercase  					=   {"df": 1, "loc": 1, "scale": 1}
    lowercase  					=   StudentT

    @classmethod
    def __lowercase  (  cls       :    Union[str, Any]       ,		lowerCamelCase       :    torch.Tensor       ,		lowerCamelCase       :    torch.Tensor       ,		lowerCamelCase       :    torch.Tensor						)  ->    int:
      lowerCAmelCase_     : str     =  cls.squareplus(lowerCamelCase						).clamp_min(torch.finfo(scale.dtype						).eps						)
      lowerCAmelCase_     : Any     =  2.0 + cls.squareplus(lowerCamelCase						)
      return df.squeeze(-1						), loc.squeeze(-1						), scale.squeeze(-1						)







class   __snake_case			(  _SCREAMING_SNAKE_CASE):







    """simple docstring"""



    lowercase  					=   {"loc": 1, "scale": 1}
    lowercase  					=   Normal

    @classmethod
    def __lowercase  (  cls       :    Dict       ,		lowerCamelCase       :    torch.Tensor       ,		lowerCamelCase       :    torch.Tensor						)  ->    Optional[int]:
      lowerCAmelCase_     : List[str]     =  cls.squareplus(lowerCamelCase						).clamp_min(torch.finfo(scale.dtype						).eps						)
      return loc.squeeze(-1						), scale.squeeze(-1						)







class   __snake_case			(  _SCREAMING_SNAKE_CASE):







    """simple docstring"""



    lowercase  					=   {"total_count": 1, "logits": 1}
    lowercase  					=   NegativeBinomial

    @classmethod
    def __lowercase  (  cls       :    int       ,		lowerCamelCase       :    torch.Tensor       ,		lowerCamelCase       :    torch.Tensor						)  ->    int:
      lowerCAmelCase_     : int     =  cls.squareplus(lowerCamelCase						)
      return total_count.squeeze(-1						), logits.squeeze(-1						)

    def __lowercase  (  self       :    Any       ,		lowerCamelCase       :    Optional[int]						)  ->    Distribution:
      lowerCAmelCase_,				lowerCAmelCase_     : Tuple     =  distr_args
      if self.dim == 1:
        return self.distribution_class(total_count=lowerCamelCase       ,		logits=lowerCamelCase						)
      else:
        return Independent(self.distribution_class(total_count=lowerCamelCase       ,		logits=lowerCamelCase						)       ,		1						)





    def __lowercase  (  self       :    Any       ,		lowerCamelCase       :    Optional[Any]       ,		lowerCamelCase       :    Optional[torch.Tensor] = None       ,		lowerCamelCase       :    Optional[torch.Tensor] = None						)  ->    Distribution:
      lowerCAmelCase_,				lowerCAmelCase_     : Optional[Any]     =  distr_args

      if scale is not None:
        # See scaling property of Gamma.
        logits += scale.log()

      return self._base_distribution((total_count, logits)						)







'''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__   )



