






"""simple docstring"""





from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging


UpperCamelCase					=     logging.get_logger(__name__)

UpperCamelCase					=     {
    """google/long-t5-local-base""": """https://huggingface.co/google/long-t5-local-base/blob/main/config.json""",
    """google/long-t5-local-large""": """https://huggingface.co/google/long-t5-local-large/blob/main/config.json""",
    """google/long-t5-tglobal-base""": """https://huggingface.co/google/long-t5-tglobal-base/blob/main/config.json""",
    """google/long-t5-tglobal-large""": """https://huggingface.co/google/long-t5-tglobal-large/blob/main/config.json""",
}







class 						_a  (		lowercase_       ):





   '''simple docstring'''






   UpperCamelCase__     	=							"""longt5"""
   UpperCamelCase__     	=							["""past_key_values"""]
   UpperCamelCase__     	=							{"""hidden_size""": """d_model""", """num_attention_heads""": """num_heads""", """num_hidden_layers""": """num_layers"""}





   def __init__(       self					, UpperCAmelCase_=32_128					, UpperCAmelCase_=512					, UpperCAmelCase_=64					, UpperCAmelCase_=2_048					, UpperCAmelCase_=6					, UpperCAmelCase_=None					, UpperCAmelCase_=8					, UpperCAmelCase_=127					, UpperCAmelCase_=16					, UpperCAmelCase_=32					, UpperCAmelCase_=128					, UpperCAmelCase_=0.1					, UpperCAmelCase_=1E-6					, UpperCAmelCase_=1.0					, UpperCAmelCase_="relu"					, UpperCAmelCase_=True					, UpperCAmelCase_="local"					, UpperCAmelCase_=True					, UpperCAmelCase_=0					, UpperCAmelCase_=1					, **UpperCAmelCase_					, )			->   Tuple:




          '''simple docstring'''




          lowercase__:							Dict         =     vocab_size
          lowercase__:							Any         =     d_model
          lowercase__:							str         =     d_kv
          lowercase__:							List[str]         =     d_ff
          lowercase__:							Dict         =     num_layers
          # default = symmetry
          lowercase__:							int         =     num_decoder_layers if num_decoder_layers is not None else self.num_layers
          lowercase__:							int         =     num_heads
          lowercase__:							List[str]         =     local_radius
          lowercase__:							Optional[Any]         =     global_block_size
          lowercase__:							Union[str, Any]         =     relative_attention_num_buckets
          lowercase__:							List[Any]         =     relative_attention_max_distance
          lowercase__:							Tuple         =     dropout_rate
          lowercase__:							Union[str, Any]         =     layer_norm_epsilon
          lowercase__:							Any         =     initializer_factor
          lowercase__:							List[str]         =     feed_forward_proj
          lowercase__:							List[Any]         =     encoder_attention_type
          lowercase__:							Dict         =     use_cache

          lowercase__:							List[Any]         =     self.feed_forward_proj.split("-")
          lowercase__:							int         =     act_info[-1]
          lowercase__:							Optional[Any]         =     act_info[0] == "gated"

          if len(UpperCAmelCase_) > 1 and act_info[0] != "gated" or len(UpperCAmelCase_) > 2:
                 raise ValueError(
                     F"""`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer."""
                     "Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. "
                     "'gated-gelu' or 'relu'")

          # for backwards compatibility
          if feed_forward_proj == "gated-gelu":
                 lowercase__:							str         =     "gelu_new"

          super().__init__(
              pad_token_id=UpperCAmelCase_					, eos_token_id=UpperCAmelCase_					, is_encoder_decoder=UpperCAmelCase_					, **UpperCAmelCase_					, )







class 						_a  (		lowercase_       ):





   '''simple docstring'''






   @property
   def __lowercase							(       self)			->   Mapping[str, Mapping[int, str]]:




          '''simple docstring'''




          lowercase__:							str         =     {
              "input_ids": {0: "batch", 1: "encoder_sequence"},
              "attention_mask": {0: "batch", 1: "encoder_sequence"},
          }
          if self.use_past:
                 lowercase__:							Union[str, Any]         =     "past_encoder_sequence + sequence"
                 lowercase__:							Union[str, Any]         =     {0: "batch"}
                 lowercase__:							List[Any]         =     {0: "batch", 1: "past_decoder_sequence + sequence"}
          else:
                 lowercase__:							Dict         =     {0: "batch", 1: "decoder_sequence"}
                 lowercase__:							Optional[int]         =     {0: "batch", 1: "decoder_sequence"}

          if self.use_past:
                 self.fill_with_past_key_values_(UpperCAmelCase_					, direction="inputs")

          return common_inputs





   @property
   def __lowercase							(       self)			->   int:




          '''simple docstring'''




          return 13














"""simple docstring"""





import argparse
import json
import re
import string
import sys
from collections import Counter






def 							A(    snake_case_						):
       """simple docstring"""





       def remove_articles(snake_case_						):
              return re.sub(r"\b(a|an|the)\b"		,  " "		,  snake_case_						)

       def white_space_fix(snake_case_						):
              return " ".join(text.split()						)

       def remove_punc(snake_case_						):
              lowercase__:							str         =     set(string.punctuation						)
              return "".join(ch for ch in text if ch not in exclude						)

       def lower(snake_case_						):
              return text.lower()

       return white_space_fix(remove_articles(remove_punc(lower(snake_case_						)						)						)						)






def 							A(    snake_case_		,  snake_case_						):
       """simple docstring"""





       lowercase__:							Dict         =     normalize_answer(snake_case_						).split()
       lowercase__:							List[str]         =     normalize_answer(snake_case_						).split()
       lowercase__:							Optional[int]         =     Counter(snake_case_						) & Counter(snake_case_						)
       lowercase__:							Any         =     sum(common.values()						)
       if num_same == 0:
              return 0
       lowercase__:							Any         =     1.0 * num_same / len(snake_case_						)
       lowercase__:							List[Any]         =     1.0 * num_same / len(snake_case_						)
       lowercase__:							int         =     (2 * precision * recall) / (precision + recall)
       return fa






def 							A(    snake_case_		,  snake_case_						):
       """simple docstring"""





       return normalize_answer(snake_case_						) == normalize_answer(snake_case_						)






def 							A(    snake_case_		,  snake_case_		,  snake_case_						):
       """simple docstring"""





       lowercase__:							int         =     []
       for ground_truth in ground_truths:
              lowercase__:							str         =     metric_fn(snake_case_		,  snake_case_						)
              scores_for_ground_truths.append(snake_case_						)
       return max(snake_case_						)






def 							A(    snake_case_		,  snake_case_						):
       """simple docstring"""





       lowercase__:							Optional[int]         =     0
       for article in dataset:
              for paragraph in article["paragraphs"]:
                     for qa in paragraph["qas"]:
                            total += 1
                            if qa["id"] not in predictions:
                                   lowercase__:							Union[str, Any]         =     "Unanswered question " + qa["id"] + " will receive score 0."
                                   print(snake_case_		,  file=sys.stderr						)
                                   continue
                            lowercase__:							List[Any]         =     [x["text"] for x in qa["answers"]]
                            lowercase__:							Optional[Any]         =     predictions[qa["id"]]
                            exact_match += metric_max_over_ground_truths(snake_case_		,  snake_case_		,  snake_case_						)
                            fa += metric_max_over_ground_truths(snake_case_		,  snake_case_		,  snake_case_						)

       lowercase__:							Union[str, Any]         =     1_00.0 * exact_match / total
       lowercase__:							str         =     1_00.0 * fa / total

       return {"exact_match": exact_match, "f1": fa}


if __name__ == "__main__":
  UpperCamelCase					=     """1.1"""
  UpperCamelCase					=     argparse.ArgumentParser(description="""Evaluation for SQuAD """ + expected_version)
  parser.add_argument("""dataset_file""", help="""Dataset file""")
  parser.add_argument("""prediction_file""", help="""Prediction File""")
  UpperCamelCase					=     parser.parse_args()
  with open(args.dataset_file) as dataset_file:
    UpperCamelCase					=     json.load(dataset_file)
    if dataset_json["version"] != expected_version:
      print(
          """Evaluation expects v-""" + expected_version + """, but got dataset with v-""" + dataset_json["""version"""],
          file=sys.stderr,
      )
    UpperCamelCase					=     dataset_json["""data"""]
  with open(args.prediction_file) as prediction_file:
    UpperCamelCase					=     json.load(prediction_file)
  print(json.dumps(evaluate(dataset, predictions)))






