
'''simple docstring'''
# This is the module that test_patching.py uses to test patch_submodule()

import os  # noqa: this is just for tests
import os as renamed_os  # noqa: this is just for tests
from os import path  # noqa: this is just for tests
from os import path as renamed_path  # noqa: this is just for tests
from os.path import join  # noqa: this is just for tests
from os.path import join as renamed_join  # noqa: this is just for tests


a_											=						open  # noqa: we just need to have a builtin inside this module to test it properly






'''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_				(	snake_case__					):
 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=lowercase_ ,       **lowercase_)

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






class    UpperCAmelCase_				(	snake_case__					):





 def __init__(    self ,       lowercase_ ,       lowercase_ = "default" ,       lowercase_ = None):
     super().__init__(lowercase_ ,       lowercase_ ,       lowercase_)
     snake_case_ :    Dict     			=						True





 @property
 def        snake_case__		(    self):
     if self.task == "multiple-choice":
         snake_case_ :    Tuple     			=						{0: "batch", 1: "choice", 2: "sequence"}
     else:
         snake_case_ :    int     			=						{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_ :    Union[str, Any]     			=						super().outputs
     if self.task == "default":
         snake_case_ :    str     			=						{0: "batch"}
     return outputs





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





 @property
 def        snake_case__		(    self):
     # needs to be >= 14 to support tril operator
     return max(super().default_onnx_opset ,       14)





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

     return inputs




