
'''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


a_											=						logging.get_logger(__name__)

a_											=						{
    "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_				(	__lowerCAmelCase					):
 UpperCAmelCase_		    =						"longformer"





 def __init__(    self ,       lowercase_ = 5_12 ,       lowercase_ = 2 ,       lowercase_ = 1 ,       lowercase_ = 0 ,       lowercase_ = 2 ,       lowercase_ = 3_05_22 ,       lowercase_ = 7_68 ,       lowercase_ = 12 ,       lowercase_ = 12 ,       lowercase_ = 30_72 ,       lowercase_ = "gelu" ,       lowercase_ = 0.1 ,       lowercase_ = 0.1 ,       lowercase_ = 5_12 ,       lowercase_ = 2 ,       lowercase_ = 0.02 ,       lowercase_ = 1E-12 ,       lowercase_ = False ,       **lowercase_ ,       ):
     super().__init__(pad_token_id=lowerCamelCase__ ,       **lowerCamelCase__)

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






class    UpperCAmelCase_				(	__lowerCAmelCase					):





 def __init__(    self ,       lowercase_ ,       lowercase_ = "default" ,       lowercase_ = None):
     super().__init__(lowerCamelCase__ ,       lowerCamelCase__ ,       lowerCamelCase__)
     snake_case_ :    Union[str, Any]     			=						True





 @property
 def        snake_case__		(    self):






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





 @property
 def        snake_case__		(    self):
     snake_case_ :    List[str]     			=						super().outputs
     if self.task == "default":
         snake_case_ :    Tuple     			=						{0: '''batch'''}
     return outputs





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





 @property
 def        snake_case__		(    self):
     return max(super().default_onnx_opset ,       14)





 def        snake_case__		(    self ,       lowercase_ ,       lowercase_ = -1 ,       lowercase_ = -1 ,       lowercase_ = False ,       lowercase_ = None ,       ):
     snake_case_ :    List[str]     			=						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
     snake_case_ :    Optional[Any]     			=						torch.zeros_like(inputs["input_ids"])
     # make every second token global
     snake_case_ :    Tuple     			=						1

     return inputs






'''simple docstring'''
from __future__ import annotations

import math

def 						UpperCamelCase_     (	__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       ):
    """simple docstring"""






    if depth < 0:
        raise ValueError("Depth cannot be less than 0"       )

    if len(__SCREAMING_SNAKE_CASE       ) == 0:
        raise ValueError("Scores cannot be empty"       )

    if depth == height:
        return scores[node_index]

    if is_max:
        return max(
            minimax(depth + 1,		node_index * 2,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       ),		minimax(depth + 1,		node_index * 2 + 1,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       ),		)

    return min(
        minimax(depth + 1,		node_index * 2,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       ),		minimax(depth + 1,		node_index * 2 + 1,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       ),		)

def 						UpperCamelCase_     (	):
    """simple docstring"""






    snake_case_ :    int     			=						[9_0, 2_3, 6, 3_3, 2_1, 6_5, 1_2_3, 3_4_4_2_3]
    snake_case_ :    Union[str, Any]     			=						math.log(len(__SCREAMING_SNAKE_CASE       ),		2       )
    print("Optimal value : ",		end=""       )
    print(minimax(0,		0,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       )       )


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  main()




