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



def 						lowercase_			(   A__		)							->							Tuple:
    """simple docstring"""

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

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

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

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

    return white_space_fix(remove_articles(remove_punc(lower(A__		)		)		)		)



def 						lowercase_			(   A__ ,		A__		)							->							Union[str, Any]:
    """simple docstring"""

    snake_case      =					normalize_answer(A__		).split()
    snake_case      =					normalize_answer(A__		).split()
    snake_case      =					Counter(A__		) & Counter(A__		)
    snake_case      =					sum(common.values()		)
    if num_same == 0:
        return 0
    snake_case      =					1.0 * num_same / len(A__		)
    snake_case      =					1.0 * num_same / len(A__		)
    snake_case      =					(2 * precision * recall) / (precision + recall)
    return fa



def 						lowercase_			(   A__ ,		A__		)							->							Optional[int]:
    """simple docstring"""

    return normalize_answer(A__		) == normalize_answer(A__		)



def 						lowercase_			(   A__ ,		A__ ,		A__		)							->							Dict:
    """simple docstring"""

    snake_case      =					[]
    for ground_truth in ground_truths:
        snake_case      =					metric_fn(A__ ,		A__		)
        scores_for_ground_truths.append(A__		)
    return max(A__		)



def 						lowercase_			(   A__ ,		A__		)							->							Dict:
    """simple docstring"""

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

    snake_case      =					100.0 * exact_match / total
    snake_case      =					100.0 * fa / total

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


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



from typing import List, Optional, Union

import numpy as np

from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging


_A  					= logging.get_logger(__name__)







class      lowerCamelCase		(  A_  ):
     UpperCAmelCase__				:							int           =		["input_values", "attention_mask"]





     def __init__(self      :    Tuple				,    _A      :    Optional[Any]=1				,    _A      :    Dict=1_6_0_0_0				,    _A      :    int=0.0				,    _A      :    List[str]=False				,    _A      :    Optional[int]=True				,    **_A      :    List[Any]				,    )						->						str:
         super().__init__(feature_size=_A				,    sampling_rate=_A				,    padding_value=_A				,    **_A   )
         snake_case      =					return_attention_mask
         snake_case      =					do_normalize





     @staticmethod
     def 		UpperCAmelCase(_A      :    List[np.ndarray]				,    _A      :    List[np.ndarray]				,    _A      :    float = 0.0   )						->						List[np.ndarray]:
         if attention_mask is not None:
             snake_case      =					np.array(_A				,    np.intaa   )
             snake_case      =					[]

             for vector, length in zip(_A				,    attention_mask.sum(-1   )   ):
                 snake_case      =					(vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1E-7   )
                 if length < normed_slice.shape[0]:
                     snake_case      =					padding_value

                 normed_input_values.append(_A   )
         else:
             snake_case      =					[(x - x.mean()) / np.sqrt(x.var() + 1E-7   ) for x in input_values]

         return normed_input_values





     def __call__(self      :    Optional[Any]				,    _A      :    Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]				,    _A      :    Union[bool, str, PaddingStrategy] = False				,    _A      :    Optional[int] = None				,    _A      :    bool = False				,    _A      :    Optional[int] = None				,    _A      :    Optional[bool] = None				,    _A      :    Optional[Union[str, TensorType]] = None				,    _A      :    Optional[int] = None				,    **_A      :    List[Any]				,    )						->						BatchFeature:

         if sampling_rate is not None:
             if sampling_rate != self.sampling_rate:
                 raise ValueError(
                     f'The model corresponding to this feature extractor: {self} was trained using a sampling rate of'
                     f' {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled with'
                     f' {self.sampling_rate} and not {sampling_rate}.'   )
         else:
             logger.warning(
                 "It is strongly recommended to pass the ``sampling_rate`` argument to this function. "
                 "Failing to do so can result in silent errors that might be hard to debug."   )

         snake_case      =					isinstance(_A				,    np.ndarray   ) and len(raw_speech.shape   ) > 1
         if is_batched_numpy and len(raw_speech.shape   ) > 2:
             raise ValueError(f'Only mono-channel audio is supported for input to {self}'   )
         snake_case      =					is_batched_numpy or (
             isinstance(_A				,    (list, tuple)   ) and (isinstance(raw_speech[0]				,    (np.ndarray, tuple, list)   ))
         )

         # always return batch
         if not is_batched:
             snake_case      =					[raw_speech]

         # convert into correct format for padding
         snake_case      =					BatchFeature({"input_values": raw_speech}   )

         snake_case      =					self.pad(
             _A				,    padding=_A				,    max_length=_A				,    truncation=_A				,    pad_to_multiple_of=_A				,    return_attention_mask=_A				,    )

         # convert input values to correct format
         snake_case      =					padded_inputs["input_values"]
         if not isinstance(input_values[0]				,    np.ndarray   ):
             snake_case      =					[np.asarray(_A				,    dtype=np.floataa   ) for array in input_values]
         elif (
             not isinstance(_A				,    np.ndarray   )
             and isinstance(input_values[0]				,    np.ndarray   )
             and input_values[0].dtype is np.dtype(np.floataa   )
         ):
             snake_case      =					[array.astype(np.floataa   ) for array in input_values]
         elif isinstance(_A				,    np.ndarray   ) and input_values.dtype is np.dtype(np.floataa   ):
             snake_case      =					input_values.astype(np.floataa   )

         # convert attention_mask to correct format
         snake_case      =					padded_inputs.get("attention_mask"   )
         if attention_mask is not None:
             snake_case      =					[np.asarray(_A				,    dtype=np.intaa   ) for array in attention_mask]

         # zero-mean and unit-variance normalization
         if self.do_normalize:
             snake_case      =					(
                 attention_mask
                 if self._get_padding_strategies(_A				,    max_length=_A   ) is not PaddingStrategy.DO_NOT_PAD
                 else None
             )
             snake_case      =					self.zero_mean_unit_var_norm(
                 padded_inputs["input_values"]				,    attention_mask=_A				,    padding_value=self.padding_value   )

         if return_tensors is not None:
             snake_case      =					padded_inputs.convert_to_tensors(_A   )

         return padded_inputs

