




from __future__ import annotations







def lowerCamelCase__  (     _lowercase ):

     '''simple docstring'''


     if len(__UpperCAmelCase ) == 0:
          return []
     UpperCAmelCase_,       UpperCAmelCase_     :				int  =     min(__UpperCAmelCase ), max(__UpperCAmelCase )
     UpperCAmelCase_     :				int  =     int(max_value - min_value ) + 1
     UpperCAmelCase_     :				List[Any]  =     [[] for _ in range(__UpperCAmelCase )]

     for i in my_list:
          buckets[int(i - min_value )].append(__UpperCAmelCase )

     return [v for bucket in buckets for v in sorted(__UpperCAmelCase )]


if __name__ == "__main__":
     from doctest import testmod

     testmod()
     assert bucket_sort([4, 5, 3, 2, 1]) == [1, 2, 3, 4, 5]
     assert bucket_sort([0, 1, -10, 15, 2, -2]) == [-10, -2, 0, 1, 2, 15]





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__					:  Optional[int]      							=				logging.get_logger(__name__)

lowerCamelCase__					:  Dict      							=				{
    '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_				( _SCREAMING_SNAKE_CASE   ):


 '''simple docstring'''







 lowercase_							       =       "longformer"



 def __init__(				self    :						Union[str, Any]							,							_lowerCAmelCase    :						Union[List[int], int] = 512							,							_lowerCAmelCase    :						int = 2							,							_lowerCAmelCase    :						int = 1							,							_lowerCAmelCase    :						int = 0							,							_lowerCAmelCase    :						int = 2							,							_lowerCAmelCase    :						int = 30_522							,							_lowerCAmelCase    :						int = 768							,							_lowerCAmelCase    :						int = 12							,							_lowerCAmelCase    :						int = 12							,							_lowerCAmelCase    :						int = 3_072							,							_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    :						Union[str, 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 			lowerCamelCase_				( _SCREAMING_SNAKE_CASE   ):


 '''simple docstring'''







 def __init__(				self    :						Optional[Any]							,							_lowerCAmelCase    :						"PretrainedConfig"							,							_lowerCAmelCase    :						str = "default"							,							_lowerCAmelCase    :						"List[PatchingSpec]" = None		):
       super().__init__(_lowerCAmelCase							,							_lowerCAmelCase							,							_lowerCAmelCase		)
       SCREAMING_SNAKE_CASE_      		=     True



 @property
 def  lowerCAmelCase_    (				self    :						Any		):
       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  lowerCAmelCase_    (				self    :						Optional[int]		):
       SCREAMING_SNAKE_CASE_      		=     super().outputs
       if self.task == "default":
             SCREAMING_SNAKE_CASE_      		=     {0: 'batch'}
       return outputs



 @property
 def  lowerCAmelCase_    (				self    :						str		):
       return 1E-4



 @property
 def  lowerCAmelCase_    (				self    :						Optional[Any]		):
       # needs to be >= 14 to support tril operator
       return max(super().default_onnx_opset							,							14		)




 def  lowerCAmelCase_    (				self    :						str							,							_lowerCAmelCase    :						"PreTrainedTokenizerBase"							,							_lowerCAmelCase    :						int = -1							,							_lowerCAmelCase    :						int = -1							,							_lowerCAmelCase    :						bool = False							,							_lowerCAmelCase    :						Optional[TensorType] = None							,							):
       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