

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  UpperCAmelCase__	( _UpperCAmelCase ):






  """simple docstring"""


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

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






  @property
  def    lowercase_	(							self		:	Tuple      )	->							Tuple:
       return self.base_dist.mean * self.scale + self.loc






  @property
  def    lowercase_	(							self		:	Optional[int]      )	->							List[Any]:
       return self.base_dist.variance * self.scale**2







  @property
  def    lowercase_	(							self		:	Dict      )	->							Optional[int]:
       return self.variance.sqrt()






class  UpperCAmelCase__	( nn.Module ):






  """simple docstring"""


  def __init__(							self		:	Union[str, Any]			,     __lowerCamelCase		:	int			,     __lowerCamelCase		:	Dict[str, int]			,     __lowerCamelCase		:	Callable[..., Tuple[torch.Tensor]]			,     **__lowerCamelCase		:	List[Any]      )	->							Any:
       super().__init__(**lowercase_      )
       SCREAMING_SNAKE_CASE__					=					args_dim
       SCREAMING_SNAKE_CASE__					=					nn.ModuleList([nn.Linear(lowercase_			,     lowercase_      ) for dim in args_dim.values()]      )
       SCREAMING_SNAKE_CASE__					=					domain_map







  def    lowercase_	(							self		:	int			,     __lowerCamelCase		:	torch.Tensor      )	->							Tuple:
       SCREAMING_SNAKE_CASE__					=					[proj(lowercase_      ) for proj in self.proj]

       return self.domain_map(*lowercase_      )






class  UpperCAmelCase__	( nn.Module ):






  """simple docstring"""


  def __init__(							self		:	Optional[Any]			,     __lowerCamelCase		:	List[Any]      )	->							Dict:
       super().__init__()
       SCREAMING_SNAKE_CASE__					=					function







  def    lowercase_	(							self		:	Any			,     __lowerCamelCase		:	Tuple			,     *__lowerCamelCase		:	Optional[int]      )	->							Dict:
       return self.function(lowercase_			,     *lowercase_      )






class  UpperCAmelCase__	:






  """simple docstring"""


  a     =      42
  a     =      42
  a     =      42






  def __init__(							self		:	Dict			,     __lowerCamelCase		:	int = 1      )	->							Optional[int]:
       SCREAMING_SNAKE_CASE__					=					dim
       SCREAMING_SNAKE_CASE__					=					{k: dim * self.args_dim[k] for k in self.args_dim}






  def    lowercase_	(							self		:	Optional[Any]			,     __lowerCamelCase		:	Optional[Any]      )	->							Tuple:
       if self.dim == 1:
            return self.distribution_class(*lowercase_      )
       else:
            return Independent(self.distribution_class(*lowercase_      )			,     1      )






  def    lowercase_	(							self		:	Union[str, Any]			,     __lowerCamelCase		:	str			,     __lowerCamelCase		:	Optional[torch.Tensor] = None			,     __lowerCamelCase		:	Optional[torch.Tensor] = None			,     )	->							str:
       SCREAMING_SNAKE_CASE__					=					self._base_distribution(lowercase_      )
       if loc is None and scale is None:
            return distr
       else:
            return AffineTransformed(lowercase_			,     loc=lowercase_			,     scale=lowercase_			,     event_dim=self.event_dim      )






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






  @property
  def    lowercase_	(							self		:	Dict      )	->							List[str]:
       return len(self.event_shape      )






  @property
  def    lowercase_	(							self		:	List[str]      )	->							int:
       return 0.0






  def    lowercase_	(							self		:	str			,     __lowerCamelCase		:	int      )	->							str:
       return ParameterProjection(
           in_features=lowercase_			,     args_dim=self.args_dim			,     domain_map=LambdaLayer(self.domain_map      )			,     )






  def    lowercase_	(							self		:	Union[str, Any]			,     *__lowerCamelCase		:	torch.Tensor      )	->							Optional[Any]:
       raise NotImplementedError()







  @staticmethod
  def    lowercase_	(							__lowerCamelCase		:	torch.Tensor      )	->							Any:
       return (x + torch.sqrt(torch.square(lowercase_      ) + 4.0      )) / 2.0






class  UpperCAmelCase__	( _UpperCAmelCase ):






  """simple docstring"""


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






  @classmethod
  def    lowercase_	(							cls		:	List[Any]			,     __lowerCamelCase		:	torch.Tensor			,     __lowerCamelCase		:	torch.Tensor			,     __lowerCamelCase		:	torch.Tensor      )	->							Tuple:
       SCREAMING_SNAKE_CASE__					=					cls.squareplus(lowercase_      ).clamp_min(torch.finfo(scale.dtype      ).eps      )
       SCREAMING_SNAKE_CASE__					=					2.0 + cls.squareplus(lowercase_      )
       return df.squeeze(-1      ), loc.squeeze(-1      ), scale.squeeze(-1      )






class  UpperCAmelCase__	( _UpperCAmelCase ):






  """simple docstring"""


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






  @classmethod
  def    lowercase_	(							cls		:	int			,     __lowerCamelCase		:	torch.Tensor			,     __lowerCamelCase		:	torch.Tensor      )	->							str:
       SCREAMING_SNAKE_CASE__					=					cls.squareplus(lowercase_      ).clamp_min(torch.finfo(scale.dtype      ).eps      )
       return loc.squeeze(-1      ), scale.squeeze(-1      )






class  UpperCAmelCase__	( _UpperCAmelCase ):






  """simple docstring"""


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






  @classmethod
  def    lowercase_	(							cls		:	Optional[int]			,     __lowerCamelCase		:	torch.Tensor			,     __lowerCamelCase		:	torch.Tensor      )	->							str:
       SCREAMING_SNAKE_CASE__					=					cls.squareplus(lowercase_      )
       return total_count.squeeze(-1      ), logits.squeeze(-1      )






  def    lowercase_	(							self		:	int			,     __lowerCamelCase		:	Tuple      )	->							List[str]:
       SCREAMING_SNAKE_CASE__					=					distr_args
       if self.dim == 1:
            return self.distribution_class(total_count=lowercase_			,     logits=lowercase_      )
       else:
            return Independent(self.distribution_class(total_count=lowercase_			,     logits=lowercase_      )			,     1      )







  def    lowercase_	(							self		:	str			,     __lowerCamelCase		:	Optional[int]			,     __lowerCamelCase		:	Optional[torch.Tensor] = None			,     __lowerCamelCase		:	Optional[torch.Tensor] = None      )	->							str:
       SCREAMING_SNAKE_CASE__					=					distr_args

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

       return self._base_distribution((total_count, logits)      )





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


_SCREAMING_SNAKE_CASE  :						Any         =    logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE  :						List[str]         =    {
    '''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  UpperCAmelCase__	( A__ ):






  """simple docstring"""


  a     =      "longformer"






  def __init__(							self		:	int			,     __lowerCamelCase		:	Union[List[int], int] = 512			,     __lowerCamelCase		:	int = 2			,     __lowerCamelCase		:	int = 1			,     __lowerCamelCase		:	int = 0			,     __lowerCamelCase		:	int = 2			,     __lowerCamelCase		:	int = 3_0522			,     __lowerCamelCase		:	int = 768			,     __lowerCamelCase		:	int = 12			,     __lowerCamelCase		:	int = 12			,     __lowerCamelCase		:	int = 3072			,     __lowerCamelCase		:	str = "gelu"			,     __lowerCamelCase		:	float = 0.1			,     __lowerCamelCase		:	float = 0.1			,     __lowerCamelCase		:	int = 512			,     __lowerCamelCase		:	int = 2			,     __lowerCamelCase		:	float = 0.02			,     __lowerCamelCase		:	float = 1e-12			,     __lowerCamelCase		:	bool = False			,     **__lowerCamelCase		:	Tuple			,     )	->							List[Any]:
       super().__init__(pad_token_id=__lowerCamelCase			,     **__lowerCamelCase      )

       SCREAMING_SNAKE_CASE__					=					attention_window
       SCREAMING_SNAKE_CASE__					=					sep_token_id
       SCREAMING_SNAKE_CASE__					=					bos_token_id
       SCREAMING_SNAKE_CASE__					=					eos_token_id
       SCREAMING_SNAKE_CASE__					=					vocab_size
       SCREAMING_SNAKE_CASE__					=					hidden_size
       SCREAMING_SNAKE_CASE__					=					num_hidden_layers
       SCREAMING_SNAKE_CASE__					=					num_attention_heads
       SCREAMING_SNAKE_CASE__					=					hidden_act
       SCREAMING_SNAKE_CASE__					=					intermediate_size
       SCREAMING_SNAKE_CASE__					=					hidden_dropout_prob
       SCREAMING_SNAKE_CASE__					=					attention_probs_dropout_prob
       SCREAMING_SNAKE_CASE__					=					max_position_embeddings
       SCREAMING_SNAKE_CASE__					=					type_vocab_size
       SCREAMING_SNAKE_CASE__					=					initializer_range
       SCREAMING_SNAKE_CASE__					=					layer_norm_eps
       SCREAMING_SNAKE_CASE__					=					onnx_export






class  UpperCAmelCase__	( A__ ):






  """simple docstring"""


  def __init__(							self		:	Dict			,     __lowerCamelCase		:	"PretrainedConfig"			,     __lowerCamelCase		:	str = "default"			,     __lowerCamelCase		:	"List[PatchingSpec]" = None      )	->							List[str]:
       super().__init__(__lowerCamelCase			,     __lowerCamelCase			,     __lowerCamelCase      )
       SCREAMING_SNAKE_CASE__					=					True






  @property
  def    lowercase_	(							self		:	Optional[Any]      )	->							Mapping[str, Mapping[int, str]]:
       if self.task == "multiple-choice":
            SCREAMING_SNAKE_CASE__					=					{0: '''batch''', 1: '''choice''', 2: '''sequence'''}
       else:
            SCREAMING_SNAKE_CASE__					=					{0: '''batch''', 1: '''sequence'''}
       return OrderedDict(
           [
               ('''input_ids''', dynamic_axis),
               ('''attention_mask''', dynamic_axis),
               ('''global_attention_mask''', dynamic_axis),
           ]      )






  @property
  def    lowercase_	(							self		:	Optional[int]      )	->							Mapping[str, Mapping[int, str]]:
       SCREAMING_SNAKE_CASE__					=					super().outputs
       if self.task == "default":
            SCREAMING_SNAKE_CASE__					=					{0: '''batch'''}
       return outputs






  @property
  def    lowercase_	(							self		:	Optional[int]      )	->							float:
       return 1e-4






  @property
  def    lowercase_	(							self		:	Union[str, Any]      )	->							int:
       # needs to be >= 14 to support tril operator
       return max(super().default_onnx_opset			,     14      )







  def    lowercase_	(							self		:	Dict			,     __lowerCamelCase		:	"PreTrainedTokenizerBase"			,     __lowerCamelCase		:	int = -1			,     __lowerCamelCase		:	int = -1			,     __lowerCamelCase		:	bool = False			,     __lowerCamelCase		:	Optional[TensorType] = None			,     )	->							Mapping[str, Any]:
       SCREAMING_SNAKE_CASE__					=					super().generate_dummy_inputs(
           preprocessor=__lowerCamelCase			,     batch_size=__lowerCamelCase			,     seq_length=__lowerCamelCase			,     is_pair=__lowerCamelCase			,     framework=__lowerCamelCase      )
       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
       SCREAMING_SNAKE_CASE__					=					torch.zeros_like(inputs['''input_ids''']      )
       # make every second token global
       SCREAMING_SNAKE_CASE__					=					1

       return inputs


