

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


lowerCamelCase           =					logging.get_logger(__name__)

lowerCamelCase           =					{
    '''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 						__magic_name__			(							lowerCamelCase__							):

   '''simple docstring'''




   lowerCamelCase__ :	Optional[Any]    =   'longformer'



   def __init__(				self,    lowercase_ = 512,    lowercase_ = 2,    lowercase_ = 1,    lowercase_ = 0,    lowercase_ = 2,    lowercase_ = 30522,    lowercase_ = 768,    lowercase_ = 12,    lowercase_ = 12,    lowercase_ = 3072,    lowercase_ = "gelu",    lowercase_ = 0.1,    lowercase_ = 0.1,    lowercase_ = 512,    lowercase_ = 2,    lowercase_ = 0.02,    lowercase_ = 1E-12,    lowercase_ = False,    **lowercase_,    )							->    Dict:






     """simple docstring"""






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

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







class 						__magic_name__			(							lowerCamelCase__							):

   '''simple docstring'''




   def __init__(				self,    lowercase_,    lowercase_ = "default",    lowercase_ = None       )							->    Optional[int]:






     """simple docstring"""






     super().__init__(lowercase_,    lowercase_,    lowercase_       )
     a__  =True



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






     """simple docstring"""






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



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






     """simple docstring"""






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



   @property
   def  _UpperCAmelCase (				self       )							->    float:






     """simple docstring"""






     return 1E-4



   @property
   def  _UpperCAmelCase (				self       )							->    int:






     """simple docstring"""






     return max(super().default_onnx_opset,    14       )



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






     """simple docstring"""






     a__  =super().generate_dummy_inputs(
         preprocessor=lowercase_,    batch_size=lowercase_,    seq_length=lowercase_,    is_pair=lowercase_,    framework=lowercase_       )
     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
     a__  =torch.zeros_like(inputs['''input_ids''']       )
     # make every second token global
     a__  =1

     return inputs



from .glue import GlueDataset, GlueDataTrainingArguments
from .language_modeling import (
    LineByLineTextDataset,
    LineByLineWithRefDataset,
    LineByLineWithSOPTextDataset,
    TextDataset,
    TextDatasetForNextSentencePrediction,
)
from .squad import SquadDataset, SquadDataTrainingArguments
