




'''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


UpperCamelCase_						:      Dict       =						logging.get_logger(__name__)

UpperCamelCase_						:      List[str]       =						{
    '''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    (						__lowerCAmelCase							):
     SCREAMING_SNAKE_CASE_							:   Optional[Any]       =						"""longformer"""






     def __init__(			self ,_SCREAMING_SNAKE_CASE = 512 ,_SCREAMING_SNAKE_CASE = 2 ,_SCREAMING_SNAKE_CASE = 1 ,_SCREAMING_SNAKE_CASE = 0 ,_SCREAMING_SNAKE_CASE = 2 ,_SCREAMING_SNAKE_CASE = 30_522 ,_SCREAMING_SNAKE_CASE = 768 ,_SCREAMING_SNAKE_CASE = 12 ,_SCREAMING_SNAKE_CASE = 12 ,_SCREAMING_SNAKE_CASE = 3_072 ,_SCREAMING_SNAKE_CASE = "gelu" ,_SCREAMING_SNAKE_CASE = 0.1 ,_SCREAMING_SNAKE_CASE = 0.1 ,_SCREAMING_SNAKE_CASE = 512 ,_SCREAMING_SNAKE_CASE = 2 ,_SCREAMING_SNAKE_CASE = 0.0_2 ,_SCREAMING_SNAKE_CASE = 1e-12 ,_SCREAMING_SNAKE_CASE = False ,**_SCREAMING_SNAKE_CASE ,)							->      Optional[int]:
          super().__init__(pad_token_id=_SCREAMING_SNAKE_CASE ,**_SCREAMING_SNAKE_CASE   )

          _snake_case							=				attention_window
          _snake_case							=				sep_token_id
          _snake_case							=				bos_token_id
          _snake_case							=				eos_token_id
          _snake_case							=				vocab_size
          _snake_case							=				hidden_size
          _snake_case							=				num_hidden_layers
          _snake_case							=				num_attention_heads
          _snake_case							=				hidden_act
          _snake_case							=				intermediate_size
          _snake_case							=				hidden_dropout_prob
          _snake_case							=				attention_probs_dropout_prob
          _snake_case							=				max_position_embeddings
          _snake_case							=				type_vocab_size
          _snake_case							=				initializer_range
          _snake_case							=				layer_norm_eps
          _snake_case							=				onnx_export







class        _a    (						__lowerCAmelCase							):






     def __init__(			self ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE = "default" ,_SCREAMING_SNAKE_CASE = None   )							->      Tuple:
          super().__init__(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   )
          _snake_case							=				True






     @property
     def     _lowercase     (			self   )							->      Mapping[str, Mapping[int, str]]:
          if self.task == "multiple-choice":
               _snake_case							=				{0: "batch", 1: "choice", 2: "sequence"}
          else:
               _snake_case							=				{0: "batch", 1: "sequence"}
          return OrderedDict(
              [
                  ("input_ids", dynamic_axis),
                  ("attention_mask", dynamic_axis),
                  ("global_attention_mask", dynamic_axis),
              ]   )






     @property
     def     _lowercase     (			self   )							->      Mapping[str, Mapping[int, str]]:
          _snake_case							=				super().outputs
          if self.task == "default":
               _snake_case							=				{0: "batch"}
          return outputs






     @property
     def     _lowercase     (			self   )							->      float:
          return 1e-4






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






     def     _lowercase     (			self ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE = -1 ,_SCREAMING_SNAKE_CASE = -1 ,_SCREAMING_SNAKE_CASE = False ,_SCREAMING_SNAKE_CASE = None ,)							->      Mapping[str, Any]:
          _snake_case							=				super().generate_dummy_inputs(
              preprocessor=_SCREAMING_SNAKE_CASE ,batch_size=_SCREAMING_SNAKE_CASE ,seq_length=_SCREAMING_SNAKE_CASE ,is_pair=_SCREAMING_SNAKE_CASE ,framework=_SCREAMING_SNAKE_CASE   )
          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							=				torch.zeros_like(inputs["input_ids"]   )
          # make every second token global
          _snake_case							=				1

          return inputs








'''simple docstring'''





import fire

from utils import calculate_rouge, save_json





def      __a     (       _UpperCamelCase:   Tuple ,						_UpperCamelCase:   Optional[int] ,						_UpperCamelCase:   Optional[int]=None ,						**_UpperCamelCase:   Any   )						->			Optional[Any]:

     """simple docstring"""




     _snake_case							=				[x.strip() for x in open(_UpperCamelCase   ).readlines()]
     _snake_case							=				[x.strip() for x in open(_UpperCamelCase   ).readlines()][: len(_UpperCamelCase   )]
     _snake_case							=				calculate_rouge(_UpperCamelCase ,						_UpperCamelCase ,						**_UpperCamelCase   )
     if save_path is not None:
          save_json(_UpperCamelCase ,						_UpperCamelCase ,						indent=_UpperCamelCase   )
     return metrics  # these print nicely


if __name__ == "__main__":
   fire.Fire(calculate_rouge_path)


