

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


lowercase_			=  logging.get_logger(__name__)

lowercase_			=  {
    "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 			A						(							_UpperCAmelCase			):







    """simple docstring"""





    lowerCamelCase		     =							'longformer'


    def __init__(				self			:	Optional[int],lowercase_			:	Union[List[int], int] = 5_1_2,lowercase_			:	int = 2,lowercase_			:	int = 1,lowercase_			:	int = 0,lowercase_			:	int = 2,lowercase_			:	int = 3_0_5_2_2,lowercase_			:	int = 7_6_8,lowercase_			:	int = 1_2,lowercase_			:	int = 1_2,lowercase_			:	int = 3_0_7_2,lowercase_			:	str = "gelu",lowercase_			:	float = 0.1,lowercase_			:	float = 0.1,lowercase_			:	int = 5_1_2,lowercase_			:	int = 2,lowercase_			:	float = 0.02,lowercase_			:	float = 1E-12,lowercase_			:	bool = False,**lowercase_			:	Dict,)->   str:





        '''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 			A						(							_UpperCAmelCase			):







    """simple docstring"""





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





        '''simple docstring'''







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


    @property
    def 						snake_case__					(				self			:	List[Any]		)->   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 						snake_case__					(				self			:	str		)->   Mapping[str, Mapping[int, str]]:





        '''simple docstring'''







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


    @property
    def 						snake_case__					(				self			:	Union[str, Any]		)->   float:





        '''simple docstring'''







        return 1E-4


    @property
    def 						snake_case__					(				self			:	Optional[Any]		)->   int:





        '''simple docstring'''







        return max(super().default_onnx_opset,1_4		)


    def 						snake_case__					(				self			:	Optional[int],lowercase_			:	"PreTrainedTokenizerBase",lowercase_			:	int = -1,lowercase_			:	int = -1,lowercase_			:	bool = False,lowercase_			:	Optional[TensorType] = 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




import numpy as np

from transformers import Pipeline
def 						_snake_case(   SCREAMING_SNAKE_CASE__      :     int				)							->				int:





    '''simple docstring'''






    A__   				=       np.max(SCREAMING_SNAKE_CASE__   ,      axis=-1   ,      keepdims=SCREAMING_SNAKE_CASE__				)
    A__   				=       np.exp(outputs - maxes				)
    return shifted_exp / shifted_exp.sum(axis=-1   ,      keepdims=SCREAMING_SNAKE_CASE__				)


class 			A						(							_UpperCAmelCase			):







    """simple docstring"""





    def 						snake_case__					(				self			:	Dict,**lowercase_			:	Tuple		)->   Tuple:





        '''simple docstring'''







        A__   				=       {}
        if "second_text" in kwargs:
            A__   				=       kwargs['second_text']
        return preprocess_kwargs, {}, {}


    def 						snake_case__					(				self			:	List[Any],lowercase_			:	int,lowercase_			:	Optional[int]=None		)->   List[str]:





        '''simple docstring'''







        return self.tokenizer(lowercase_,text_pair=lowercase_,return_tensors=self.framework		)


    def 						snake_case__					(				self			:	str,lowercase_			:	Dict		)->   List[str]:





        '''simple docstring'''







        return self.model(**lowercase_		)


    def 						snake_case__					(				self			:	Dict,lowercase_			:	Optional[int]		)->   Dict:





        '''simple docstring'''







        A__   				=       model_outputs.logits[0].numpy()
        A__   				=       softmax(lowercase_		)

        A__   				=       np.argmax(lowercase_		)
        A__   				=       self.model.config.idalabel[best_class]
        A__   				=       probabilities[best_class].item()
        A__   				=       logits.tolist()
        return {"label": label, "score": score, "logits": logits}

