


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 					__magic_name__		(   UpperCAmelCase__):


     def __init__(					self							:   Dict							,		lowerCamelCase__							:   Distribution							,		lowerCamelCase__							:   List[Any]=None							,		lowerCamelCase__							:   Optional[Any]=None							,		lowerCamelCase__							:   Optional[Any]=0	)     ->					Optional[Any]:





      '''simple docstring'''






      UpperCamelCase__    :			Union[str, Any]      				=      1.0 if scale is None else scale
      UpperCamelCase__    :			Dict      				=      0.0 if loc is None else loc

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


     @property
     def    UpperCAmelCase__		(					self							:   Any	)     ->					Union[str, Any]:





      '''simple docstring'''






      return self.base_dist.mean * self.scale + self.loc


     @property
     def    UpperCAmelCase__		(					self							:   List[Any]	)     ->					Optional[Any]:





      '''simple docstring'''






      return self.base_dist.variance * self.scale**2


     @property
     def    UpperCAmelCase__		(					self							:   List[str]	)     ->					Optional[int]:





      '''simple docstring'''






      return self.variance.sqrt()



class 					__magic_name__		(   nn.Module):


     def __init__(					self							:   Optional[Any]							,		lowerCamelCase__							:   int							,		lowerCamelCase__							:   Dict[str, int]							,		lowerCamelCase__							:   Callable[..., Tuple[torch.Tensor]]							,		**lowerCamelCase__							:   Dict	)     ->					None:





      '''simple docstring'''






      super().__init__(**SCREAMING_SNAKE_CASE__	)
      UpperCamelCase__    :			Optional[int]      				=      args_dim
      UpperCamelCase__    :			List[str]      				=      nn.ModuleList([nn.Linear(SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__	) for dim in args_dim.values()]	)
      UpperCamelCase__    :			int      				=      domain_map


     def    UpperCAmelCase__		(					self							:   Any							,		lowerCamelCase__							:   torch.Tensor	)     ->					Tuple[torch.Tensor]:





      '''simple docstring'''






      UpperCamelCase__    :			int      				=      [proj(SCREAMING_SNAKE_CASE__	) for proj in self.proj]

      return self.domain_map(*SCREAMING_SNAKE_CASE__	)



class 					__magic_name__		(   nn.Module):


     def __init__(					self							:   Optional[Any]							,		lowerCamelCase__							:   int	)     ->					str:





      '''simple docstring'''






      super().__init__()
      UpperCamelCase__    :			str      				=      function


     def    UpperCAmelCase__		(					self							:   str							,		lowerCamelCase__							:   Tuple							,		*lowerCamelCase__							:   Optional[Any]	)     ->					int:





      '''simple docstring'''






      return self.function(SCREAMING_SNAKE_CASE__							,		*SCREAMING_SNAKE_CASE__	)



class 					__magic_name__		:
     A:						type
     A:						int
     A:						Dict[str, int]


     def __init__(					self							:   Optional[Any]							,		lowerCamelCase__							:   int = 1	)     ->					None:





      '''simple docstring'''






      UpperCamelCase__    :			Tuple      				=      dim
      UpperCamelCase__    :			Tuple      				=      {k: dim * self.args_dim[k] for k in self.args_dim}


     def    UpperCAmelCase__		(					self							:   Union[str, Any]							,		lowerCamelCase__							:   List[Any]	)     ->					Union[str, Any]:





      '''simple docstring'''






      if self.dim == 1:
       return self.distribution_class(*SCREAMING_SNAKE_CASE__	)
      else:
       return Independent(self.distribution_class(*SCREAMING_SNAKE_CASE__	)							,		1	)


     def    UpperCAmelCase__		(					self							:   Optional[Any]							,		lowerCamelCase__							:   Any							,		lowerCamelCase__							:   Optional[torch.Tensor] = None							,		lowerCamelCase__							:   Optional[torch.Tensor] = None							,		)     ->					Distribution:





      '''simple docstring'''






      UpperCamelCase__    :			Tuple      				=      self._base_distribution(SCREAMING_SNAKE_CASE__	)
      if loc is None and scale is None:
       return distr
      else:
       return AffineTransformed(SCREAMING_SNAKE_CASE__							,		loc=SCREAMING_SNAKE_CASE__							,		scale=SCREAMING_SNAKE_CASE__							,		event_dim=self.event_dim	)


     @property
     def    UpperCAmelCase__		(					self							:   Tuple	)     ->					Tuple:





      '''simple docstring'''






      return () if self.dim == 1 else (self.dim,)


     @property
     def    UpperCAmelCase__		(					self							:   Optional[Any]	)     ->					int:





      '''simple docstring'''






      return len(self.event_shape	)


     @property
     def    UpperCAmelCase__		(					self							:   Union[str, Any]	)     ->					float:





      '''simple docstring'''






      return 0.0


     def    UpperCAmelCase__		(					self							:   Tuple							,		lowerCamelCase__							:   int	)     ->					nn.Module:





      '''simple docstring'''






      return ParameterProjection(
          in_features=SCREAMING_SNAKE_CASE__							,		args_dim=self.args_dim							,		domain_map=LambdaLayer(self.domain_map	)							,		)


     def    UpperCAmelCase__		(					self							:   Union[str, Any]							,		*lowerCamelCase__							:   torch.Tensor	)     ->					Any:





      '''simple docstring'''






      raise NotImplementedError()


     @staticmethod
     def    UpperCAmelCase__		(					lowerCamelCase__							:   torch.Tensor	)     ->					torch.Tensor:





      '''simple docstring'''






      return (x + torch.sqrt(torch.square(SCREAMING_SNAKE_CASE__	) + 4.0	)) / 2.0



class 					__magic_name__		(   UpperCAmelCase__):
     A:						Dict[str, int]								=					{"df": 1, "loc": 1, "scale": 1}
     A:						type								=					StudentT


     @classmethod
     def    UpperCAmelCase__		(					cls							:   Any							,		lowerCamelCase__							:   torch.Tensor							,		lowerCamelCase__							:   torch.Tensor							,		lowerCamelCase__							:   torch.Tensor	)     ->					Optional[Any]:





      '''simple docstring'''






      UpperCamelCase__    :			Tuple      				=      cls.squareplus(SCREAMING_SNAKE_CASE__	).clamp_min(torch.finfo(scale.dtype	).eps	)
      UpperCamelCase__    :			Tuple      				=      2.0 + cls.squareplus(SCREAMING_SNAKE_CASE__	)
      return df.squeeze(-1	), loc.squeeze(-1	), scale.squeeze(-1	)



class 					__magic_name__		(   UpperCAmelCase__):
     A:						Dict[str, int]								=					{"loc": 1, "scale": 1}
     A:						type								=					Normal


     @classmethod
     def    UpperCAmelCase__		(					cls							:   Optional[int]							,		lowerCamelCase__							:   torch.Tensor							,		lowerCamelCase__							:   torch.Tensor	)     ->					int:





      '''simple docstring'''






      UpperCamelCase__    :			Union[str, Any]      				=      cls.squareplus(SCREAMING_SNAKE_CASE__	).clamp_min(torch.finfo(scale.dtype	).eps	)
      return loc.squeeze(-1	), scale.squeeze(-1	)



class 					__magic_name__		(   UpperCAmelCase__):
     A:						Dict[str, int]								=					{"total_count": 1, "logits": 1}
     A:						type								=					NegativeBinomial


     @classmethod
     def    UpperCAmelCase__		(					cls							:   str							,		lowerCamelCase__							:   torch.Tensor							,		lowerCamelCase__							:   torch.Tensor	)     ->					str:





      '''simple docstring'''






      UpperCamelCase__    :			Optional[int]      				=      cls.squareplus(SCREAMING_SNAKE_CASE__	)
      return total_count.squeeze(-1	), logits.squeeze(-1	)


     def    UpperCAmelCase__		(					self							:   Tuple							,		lowerCamelCase__							:   str	)     ->					Distribution:





      '''simple docstring'''






      UpperCamelCase__				, UpperCamelCase__    :			List[Any]      				=      distr_args
      if self.dim == 1:
       return self.distribution_class(total_count=SCREAMING_SNAKE_CASE__							,		logits=SCREAMING_SNAKE_CASE__	)
      else:
       return Independent(self.distribution_class(total_count=SCREAMING_SNAKE_CASE__							,		logits=SCREAMING_SNAKE_CASE__	)							,		1	)


     def    UpperCAmelCase__		(					self							:   Any							,		lowerCamelCase__							:   Union[str, Any]							,		lowerCamelCase__							:   Optional[torch.Tensor] = None							,		lowerCamelCase__							:   Optional[torch.Tensor] = None	)     ->					Distribution:





      '''simple docstring'''






      UpperCamelCase__				, UpperCamelCase__    :			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 itertools
from dataclasses import dataclass
from typing import Optional

import pandas as pd
import pyarrow as pa

import datasets
from datasets.table import table_cast







@dataclass
class 				_lowercase     (				datasets.BuilderConfig			):


 '''simple docstring'''






 _SCREAMING_SNAKE_CASE     :       Optional[datasets.Features]      	=					None







class 				_lowercase     (				datasets.ArrowBasedBuilder			):


 '''simple docstring'''






 _SCREAMING_SNAKE_CASE     :       Union[str, Any]      	=					PandasConfig
 def a					(      self   :	Union[str, Any]							)  ->    int:
  return datasets.DatasetInfo(features=self.config.features							)
 def a					(      self   :	Any      ,       SCREAMING_SNAKE_CASE__   :	Union[str, Any]							)  ->    str:
  if not self.config.data_files:
   raise ValueError(f"""At least one data file must be specified, but got data_files={self.config.data_files}"""							)
  __lowerCAmelCase											=						dl_manager.download_and_extract(self.config.data_files							)
  if isinstance(SCREAMING_SNAKE_CASE__      ,       (str, list, tuple)							):
   __lowerCAmelCase											=						data_files
   if isinstance(SCREAMING_SNAKE_CASE__      ,       SCREAMING_SNAKE_CASE__							):
    __lowerCAmelCase											=						[files]
   # Use `dl_manager.iter_files` to skip hidden files in an extracted archive
   __lowerCAmelCase											=						[dl_manager.iter_files(SCREAMING_SNAKE_CASE__							) for file in files]
   return [datasets.SplitGenerator(name=datasets.Split.TRAIN      ,       gen_kwargs={"""files""": files}							)]
  __lowerCAmelCase											=						[]
  for split_name, files in data_files.items():
   if isinstance(SCREAMING_SNAKE_CASE__      ,       SCREAMING_SNAKE_CASE__							):
    __lowerCAmelCase											=						[files]
   # Use `dl_manager.iter_files` to skip hidden files in an extracted archive
   __lowerCAmelCase											=						[dl_manager.iter_files(SCREAMING_SNAKE_CASE__							) for file in files]
   splits.append(datasets.SplitGenerator(name=SCREAMING_SNAKE_CASE__      ,       gen_kwargs={"""files""": files}							)							)
  return splits
 def a					(      self   :	Any      ,       SCREAMING_SNAKE_CASE__   :	pa.Table							)  ->    pa.Table:
  if self.config.features is not None:
   # more expensive cast to support nested features with keys in a different order
   # allows str <-> int/float or str to Audio for example
   __lowerCAmelCase											=						table_cast(SCREAMING_SNAKE_CASE__      ,       self.config.features.arrow_schema							)
  return pa_table





 def a					(      self   :	Any      ,       SCREAMING_SNAKE_CASE__   :	Optional[Any]							)  ->    List[Any]:
  for i, file in enumerate(itertools.chain.from_iterable(SCREAMING_SNAKE_CASE__							)							):
   with open(SCREAMING_SNAKE_CASE__      ,       """rb"""							) as f:
    __lowerCAmelCase											=						pa.Table.from_pandas(pd.read_pickle(SCREAMING_SNAKE_CASE__							)							)
    yield i, self._cast_table(SCREAMING_SNAKE_CASE__							)






