


"""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 _lowerCAmelCase							( snake_case_      ):







   def __init__(      self		,      UpperCamelCase__		,      UpperCamelCase__=None		,      UpperCamelCase__=None		,      UpperCamelCase__=0		)      ->						Any:

       '''simple docstring'''




       snake_case       : Optional[Any]    =       1.0 if scale is None else scale
       snake_case       : str    =       0.0 if loc is None else loc

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







   @property
   def 					lowerCamelCase						(      self		)      ->						List[str]:

       '''simple docstring'''




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







   @property
   def 					lowerCamelCase						(      self		)      ->						Dict:

       '''simple docstring'''




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







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

       '''simple docstring'''




       return self.variance.sqrt()







class _lowerCAmelCase							( nn.Module      ):







   def __init__(      self		,      UpperCamelCase__		,      UpperCamelCase__		,      UpperCamelCase__		,      **UpperCamelCase__		)      ->						None:

       '''simple docstring'''




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







   def 					lowerCamelCase						(      self		,      UpperCamelCase__		)      ->						Tuple[torch.Tensor]:

       '''simple docstring'''




       snake_case       : List[str]    =       [proj(UpperCamelCase__		) for proj in self.proj]

       return self.domain_map(*UpperCamelCase__		)







class _lowerCAmelCase							( nn.Module      ):







   def __init__(      self		,      UpperCamelCase__		)      ->						Union[str, Any]:

       '''simple docstring'''




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







   def 					lowerCamelCase						(      self		,      UpperCamelCase__		,      *UpperCamelCase__		)      ->						Tuple:

       '''simple docstring'''




       return self.function(UpperCamelCase__		,      *UpperCamelCase__		)







class _lowerCAmelCase							:
   __UpperCAmelCase					:			type
   __UpperCAmelCase					:			int
   __UpperCAmelCase					:			Dict[str, int]







   def __init__(      self		,      UpperCamelCase__ = 1		)      ->						None:

       '''simple docstring'''




       snake_case       : Optional[int]    =       dim
       snake_case       : Optional[int]    =       {k: dim * self.args_dim[k] for k in self.args_dim}







   def 					lowerCamelCase						(      self		,      UpperCamelCase__		)      ->						Union[str, Any]:

       '''simple docstring'''




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







   def 					lowerCamelCase						(      self		,      UpperCamelCase__		,      UpperCamelCase__ = None		,      UpperCamelCase__ = None		,      )      ->						Distribution:

       '''simple docstring'''




       snake_case       : Optional[Any]    =       self._base_distribution(UpperCamelCase__		)
       if loc is None and scale is None:
           return distr
       else:
           return AffineTransformed(UpperCamelCase__		,      loc=UpperCamelCase__		,      scale=UpperCamelCase__		,      event_dim=self.event_dim		)







   @property
   def 					lowerCamelCase						(      self		)      ->						Tuple:

       '''simple docstring'''




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







   @property
   def 					lowerCamelCase						(      self		)      ->						int:

       '''simple docstring'''




       return len(self.event_shape		)







   @property
   def 					lowerCamelCase						(      self		)      ->						float:

       '''simple docstring'''




       return 0.0







   def 					lowerCamelCase						(      self		,      UpperCamelCase__		)      ->						nn.Module:

       '''simple docstring'''




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







   def 					lowerCamelCase						(      self		,      *UpperCamelCase__		)      ->						Dict:

       '''simple docstring'''




       raise NotImplementedError()







   @staticmethod
   def 					lowerCamelCase						(      UpperCamelCase__		)      ->						torch.Tensor:

       '''simple docstring'''




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







class _lowerCAmelCase							( snake_case_      ):
   __UpperCAmelCase					:			Dict[str, int]				    =    {"df": 1, "loc": 1, "scale": 1}
   __UpperCAmelCase					:			type				    =    StudentT







   @classmethod
   def 					lowerCamelCase						(      cls		,      UpperCamelCase__		,      UpperCamelCase__		,      UpperCamelCase__		)      ->						str:

       '''simple docstring'''




       snake_case       : List[Any]    =       cls.squareplus(UpperCamelCase__		).clamp_min(torch.finfo(scale.dtype		).eps		)
       snake_case       : Dict    =       2.0 + cls.squareplus(UpperCamelCase__		)
       return df.squeeze(-1		), loc.squeeze(-1		), scale.squeeze(-1		)







class _lowerCAmelCase							( snake_case_      ):
   __UpperCAmelCase					:			Dict[str, int]				    =    {"loc": 1, "scale": 1}
   __UpperCAmelCase					:			type				    =    Normal







   @classmethod
   def 					lowerCamelCase						(      cls		,      UpperCamelCase__		,      UpperCamelCase__		)      ->						Optional[Any]:

       '''simple docstring'''




       snake_case       : str    =       cls.squareplus(UpperCamelCase__		).clamp_min(torch.finfo(scale.dtype		).eps		)
       return loc.squeeze(-1		), scale.squeeze(-1		)







class _lowerCAmelCase							( snake_case_      ):
   __UpperCAmelCase					:			Dict[str, int]				    =    {"total_count": 1, "logits": 1}
   __UpperCAmelCase					:			type				    =    NegativeBinomial







   @classmethod
   def 					lowerCamelCase						(      cls		,      UpperCamelCase__		,      UpperCamelCase__		)      ->						Dict:

       '''simple docstring'''




       snake_case       : int    =       cls.squareplus(UpperCamelCase__		)
       return total_count.squeeze(-1		), logits.squeeze(-1		)







   def 					lowerCamelCase						(      self		,      UpperCamelCase__		)      ->						Distribution:

       '''simple docstring'''




       snake_case   ,snake_case       : int    =       distr_args
       if self.dim == 1:
           return self.distribution_class(total_count=UpperCamelCase__		,      logits=UpperCamelCase__		)
       else:
           return Independent(self.distribution_class(total_count=UpperCamelCase__		,      logits=UpperCamelCase__		)		,      1		)







   def 					lowerCamelCase						(      self		,      UpperCamelCase__		,      UpperCamelCase__ = None		,      UpperCamelCase__ = None		)      ->						Distribution:

       '''simple docstring'''




       snake_case   ,snake_case       : 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 collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import TensorType, logging


if TYPE_CHECKING:
  from ...onnx.config import PatchingSpec
  from ...tokenization_utils_base import PreTrainedTokenizerBase


__snake_case       =       logging.get_logger(__name__)

__snake_case       =       {
    """allenai/longformer-base-4096""": """https://huggingface.co/allenai/longformer-base-4096/resolve/main/config.json""",
    """allenai/longformer-large-4096""": """https://huggingface.co/allenai/longformer-large-4096/resolve/main/config.json""",
    """allenai/longformer-large-4096-finetuned-triviaqa""": (
        """https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/config.json"""
    ),
    """allenai/longformer-base-4096-extra.pos.embd.only""": (
        """https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/config.json"""
    ),
    """allenai/longformer-large-4096-extra.pos.embd.only""": (
        """https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/config.json"""
    ),
}







class _lowerCAmelCase							( snake_case_      ):
   __UpperCAmelCase					:			Tuple				    =    '''longformer'''







   def __init__(      self		,      UpperCamelCase__ = 512		,      UpperCamelCase__ = 2		,      UpperCamelCase__ = 1		,      UpperCamelCase__ = 0		,      UpperCamelCase__ = 2		,      UpperCamelCase__ = 3_0522		,      UpperCamelCase__ = 768		,      UpperCamelCase__ = 12		,      UpperCamelCase__ = 12		,      UpperCamelCase__ = 3072		,      UpperCamelCase__ = "gelu"		,      UpperCamelCase__ = 0.1		,      UpperCamelCase__ = 0.1		,      UpperCamelCase__ = 512		,      UpperCamelCase__ = 2		,      UpperCamelCase__ = 0.02		,      UpperCamelCase__ = 1e-12		,      UpperCamelCase__ = False		,      **UpperCamelCase__		,      )      ->						str:

       '''simple docstring'''




       super().__init__(pad_token_id=UpperCamelCase__		,      **UpperCamelCase__		)

       snake_case       : int    =       attention_window
       snake_case       : Any    =       sep_token_id
       snake_case       : Dict    =       bos_token_id
       snake_case       : int    =       eos_token_id
       snake_case       : List[str]    =       vocab_size
       snake_case       : Dict    =       hidden_size
       snake_case       : Optional[int]    =       num_hidden_layers
       snake_case       : List[str]    =       num_attention_heads
       snake_case       : Dict    =       hidden_act
       snake_case       : Union[str, Any]    =       intermediate_size
       snake_case       : Optional[int]    =       hidden_dropout_prob
       snake_case       : List[Any]    =       attention_probs_dropout_prob
       snake_case       : Any    =       max_position_embeddings
       snake_case       : Union[str, Any]    =       type_vocab_size
       snake_case       : Union[str, Any]    =       initializer_range
       snake_case       : int    =       layer_norm_eps
       snake_case       : str    =       onnx_export







class _lowerCAmelCase							( snake_case_      ):







   def __init__(      self		,      UpperCamelCase__		,      UpperCamelCase__ = "default"		,      UpperCamelCase__ = None		)      ->						List[str]:

       '''simple docstring'''




       super().__init__(UpperCamelCase__		,      UpperCamelCase__		,      UpperCamelCase__		)
       snake_case       : int    =       True







   @property
   def 					lowerCamelCase						(      self		)      ->						Mapping[str, Mapping[int, str]]:

       '''simple docstring'''




       if self.task == "multiple-choice":
           snake_case       : str    =       {0: "batch", 1: "choice", 2: "sequence"}
       else:
           snake_case       : List[str]    =       {0: "batch", 1: "sequence"}
       return OrderedDict(
           [
               ("input_ids", dynamic_axis),
               ("attention_mask", dynamic_axis),
               ("global_attention_mask", dynamic_axis),
           ]		)







   @property
   def 					lowerCamelCase						(      self		)      ->						Mapping[str, Mapping[int, str]]:

       '''simple docstring'''




       snake_case       : str    =       super().outputs
       if self.task == "default":
           snake_case       : str    =       {0: "batch"}
       return outputs







   @property
   def 					lowerCamelCase						(      self		)      ->						float:

       '''simple docstring'''




       return 1e-4







   @property
   def 					lowerCamelCase						(      self		)      ->						int:

       '''simple docstring'''




       return max(super().default_onnx_opset		,      14		)







   def 					lowerCamelCase						(      self		,      UpperCamelCase__		,      UpperCamelCase__ = -1		,      UpperCamelCase__ = -1		,      UpperCamelCase__ = False		,      UpperCamelCase__ = None		,      )      ->						Mapping[str, Any]:

       '''simple docstring'''




       snake_case       : Optional[int]    =       super().generate_dummy_inputs(
           preprocessor=UpperCamelCase__		,      batch_size=UpperCamelCase__		,      seq_length=UpperCamelCase__		,      is_pair=UpperCamelCase__		,      framework=UpperCamelCase__		)
       import torch

       # for some reason, replacing this code by inputs["global_attention_mask"] = torch.randint(2, inputs["input_ids"].shape, dtype=torch.int64)
       # makes the export fail randomly
       snake_case       : Any    =       torch.zeros_like(inputs["input_ids"]		)
       # make every second token global
       snake_case       : Any    =       1

       return inputs





