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


UpperCAmelCase_           =				logging.get_logger(__name__)

UpperCAmelCase_           =				{
    '''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__	(		__a     ):





   """simple docstring"""




   lowerCAmelCase   :     Any	      = '''longformer'''



   def __init__(						self       :	Tuple		,	_lowercase       :	Union[List[int], int] = 512		,	_lowercase       :	int = 2		,	_lowercase       :	int = 1		,	_lowercase       :	int = 0		,	_lowercase       :	int = 2		,	_lowercase       :	int = 30_522		,	_lowercase       :	int = 768		,	_lowercase       :	int = 12		,	_lowercase       :	int = 12		,	_lowercase       :	int = 3_072		,	_lowercase       :	str = "gelu"		,	_lowercase       :	float = 0.1		,	_lowercase       :	float = 0.1		,	_lowercase       :	int = 512		,	_lowercase       :	int = 2		,	_lowercase       :	float = 0.02		,	_lowercase       :	float = 1E-12		,	_lowercase       :	bool = False		,	**_lowercase       :	Optional[Any]		,	):



    """simple docstring"""



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

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




class 			__magic_name__	(		__a     ):





   """simple docstring"""




   def __init__(						self       :	Optional[Any]		,	_lowercase       :	"PretrainedConfig"		,	_lowercase       :	str = "default"		,	_lowercase       :	"List[PatchingSpec]" = None				):



    """simple docstring"""



    super().__init__(_lowercase		,	_lowercase		,	_lowercase				)
    _UpperCamelCase:  str    						=			True



   @property
   def 	lowerCAmelCase    (						self       :	Any				):



    """simple docstring"""



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



   @property
   def 	lowerCAmelCase    (						self       :	Tuple				):



    """simple docstring"""



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



   @property
   def 	lowerCAmelCase    (						self       :	List[str]				):



    """simple docstring"""



    return 1E-4



   @property
   def 	lowerCAmelCase    (						self       :	List[Any]				):



    """simple docstring"""



    return max(super().default_onnx_opset		,	14				)



   def 	lowerCAmelCase    (						self       :	Union[str, Any]		,	_lowercase       :	"PreTrainedTokenizerBase"		,	_lowercase       :	int = -1		,	_lowercase       :	int = -1		,	_lowercase       :	bool = False		,	_lowercase       :	Optional[TensorType] = None		,	):



    """simple docstring"""



    _UpperCamelCase:  Any    						=			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
    _UpperCamelCase:  Optional[int]    						=			torch.zeros_like(inputs['''input_ids''']				)
    # make every second token global
    _UpperCamelCase:  Tuple    						=			1

    return inputs
import random




def    lowerCAmelCase_  (		lowercase:				int	,       lowercase:				float	,       lowercase:				bool = False		)     ->					dict:







 '''simple docstring'''
 _UpperCamelCase:  dict    						=			{i: [] for i in range(lowercase		)}

 # if probability is greater or equal than 1, then generate a complete graph
 if probability >= 1:
  return complete_graph(lowercase		)
 # if probability is lower or equal than 0, then return a graph without edges
 if probability <= 0:
  return graph

 # for each couple of nodes, add an edge from u to v
 # if the number randomly generated is greater than probability probability
 for i in range(lowercase		):
  for j in range(i + 1	,       lowercase		):
   if random.random() < probability:
    graph[i].append(lowercase		)
    if not directed:
     # if the graph is undirected, add an edge in from j to i, either
     graph[j].append(lowercase		)
 return graph




def    lowerCAmelCase_  (		lowercase:				int		)     ->					dict:







 '''simple docstring'''
 return {
     i: [j for j in range(lowercase		) if i != j] for i in range(lowercase		)
 }


if __name__ == "__main__":
  import doctest

  doctest.testmod()