'''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 		__A       (__snake_case   ):






       def __init__(      self				,     UpperCamelCase_				,     UpperCamelCase_=None				,     UpperCamelCase_=None				,     UpperCamelCase_=0      ):
              __UpperCAmelCase  :				Tuple       				=		1.0 if scale is None else scale
              __UpperCAmelCase  :				int       				=		0.0 if loc is None else loc

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






       @property
       def 			_snake_case						(      self      ):
              return self.base_dist.mean * self.scale + self.loc






       @property
       def 			_snake_case						(      self      ):
              return self.base_dist.variance * self.scale**2







       @property
       def 			_snake_case						(      self      ):
              return self.variance.sqrt()






class 		__A       (nn.Module   ):






       def __init__(      self				,     UpperCamelCase_				,     UpperCamelCase_				,     UpperCamelCase_				,     **UpperCamelCase_      ):
              super().__init__(**A_      )
              __UpperCAmelCase  :				Any       				=		args_dim
              __UpperCAmelCase  :				List[Any]       				=		nn.ModuleList([nn.Linear(A_				,     A_      ) for dim in args_dim.values()]      )
              __UpperCAmelCase  :				Tuple       				=		domain_map







       def 			_snake_case						(      self				,     UpperCamelCase_      ):
              __UpperCAmelCase  :				Dict       				=		[proj(A_      ) for proj in self.proj]

              return self.domain_map(*A_      )






class 		__A       (nn.Module   ):






       def __init__(      self				,     UpperCamelCase_      ):
              super().__init__()
              __UpperCAmelCase  :				int       				=		function







       def 			_snake_case						(      self				,     UpperCamelCase_				,     *UpperCamelCase_      ):
              return self.function(A_				,     *A_      )






class 		__A       :
       snake_case       :type
       snake_case       :int
       snake_case       :Dict[str, int]






       def __init__(      self				,     UpperCamelCase_ = 1      ):
              __UpperCAmelCase  :				Tuple       				=		dim
              __UpperCAmelCase  :				Optional[int]       				=		{k: dim * self.args_dim[k] for k in self.args_dim}






       def 			_snake_case						(      self				,     UpperCamelCase_      ):




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






       def 			_snake_case						(      self				,     UpperCamelCase_				,     UpperCamelCase_ = None				,     UpperCamelCase_ = None				,     ):
              __UpperCAmelCase  :				List[Any]       				=		self._base_distribution(A_      )
              if loc is None and scale is None:
                     return distr
              else:
                     return AffineTransformed(A_				,     loc=A_				,     scale=A_				,     event_dim=self.event_dim      )






       @property
       def 			_snake_case						(      self      ):
              return () if self.dim == 1 else (self.dim,)






       @property
       def 			_snake_case						(      self      ):
              return len(self.event_shape      )






       @property
       def 			_snake_case						(      self      ):
              return 0.0






       def 			_snake_case						(      self				,     UpperCamelCase_      ):
              return ParameterProjection(
                  in_features=A_				,     args_dim=self.args_dim				,     domain_map=LambdaLayer(self.domain_map      )				,     )






       def 			_snake_case						(      self				,     *UpperCamelCase_      ):
              raise NotImplementedError()







       @staticmethod
       def 			_snake_case						(      UpperCamelCase_      ):
              return (x + torch.sqrt(torch.square(A_      ) + 4.0      )) / 2.0






class 		__A       (__snake_case   ):
       snake_case       :Dict[str, int]				    =					{"df": 1, "loc": 1, "scale": 1}
       snake_case       :type				    =					StudentT






       @classmethod
       def 			_snake_case						(      cls				,     UpperCamelCase_				,     UpperCamelCase_				,     UpperCamelCase_      ):
              __UpperCAmelCase  :				Any       				=		cls.squareplus(A_      ).clamp_min(torch.finfo(scale.dtype      ).eps      )
              __UpperCAmelCase  :				Dict       				=		2.0 + cls.squareplus(A_      )
              return df.squeeze(-1      ), loc.squeeze(-1      ), scale.squeeze(-1      )






class 		__A       (__snake_case   ):
       snake_case       :Dict[str, int]				    =					{"loc": 1, "scale": 1}
       snake_case       :type				    =					Normal






       @classmethod
       def 			_snake_case						(      cls				,     UpperCamelCase_				,     UpperCamelCase_      ):
              __UpperCAmelCase  :				List[Any]       				=		cls.squareplus(A_      ).clamp_min(torch.finfo(scale.dtype      ).eps      )
              return loc.squeeze(-1      ), scale.squeeze(-1      )






class 		__A       (__snake_case   ):
       snake_case       :Dict[str, int]				    =					{"total_count": 1, "logits": 1}
       snake_case       :type				    =					NegativeBinomial






       @classmethod
       def 			_snake_case						(      cls				,     UpperCamelCase_				,     UpperCamelCase_      ):
              __UpperCAmelCase  :				str       				=		cls.squareplus(A_      )
              return total_count.squeeze(-1      ), logits.squeeze(-1      )






       def 			_snake_case						(      self				,     UpperCamelCase_      ):
              __UpperCAmelCase  :				Any       				=		distr_args
              if self.dim == 1:
                     return self.distribution_class(total_count=A_				,     logits=A_      )
              else:
                     return Independent(self.distribution_class(total_count=A_				,     logits=A_      )				,     1      )







       def 			_snake_case						(      self				,     UpperCamelCase_				,     UpperCamelCase_ = None				,     UpperCamelCase_ = None      ):
              __UpperCAmelCase  :				Optional[int]       				=		distr_args

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

              return self._base_distribution((total_count, logits)      )







'''simple docstring'''




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


_a   :	Dict 							=   {"configuration_reformer": ["REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "ReformerConfig"]}

try:
   if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _a   :	Dict 							=   ["ReformerTokenizer"]

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _a   :	List[Any] 							=   ["ReformerTokenizerFast"]

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _a   :	int 							=   [
       "REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
       "ReformerAttention",
       "ReformerForMaskedLM",
       "ReformerForQuestionAnswering",
       "ReformerForSequenceClassification",
       "ReformerLayer",
       "ReformerModel",
       "ReformerModelWithLMHead",
       "ReformerPreTrainedModel",
   ]


if TYPE_CHECKING:
   from .configuration_reformer import REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, ReformerConfig

   try:
      if not is_sentencepiece_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_reformer import ReformerTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_reformer_fast import ReformerTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_reformer import (
          REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
          ReformerAttention,
          ReformerForMaskedLM,
          ReformerForQuestionAnswering,
          ReformerForSequenceClassification,
          ReformerLayer,
          ReformerModel,
          ReformerModelWithLMHead,
          ReformerPreTrainedModel,
      )

else:
   import sys

   _a   :	Optional[int] 							=   _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)






