





import subprocess
from typing import Union

import numpy as np
import requests

from ..utils import add_end_docstrings, is_torch_available, is_torchaudio_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_torch_available():
       from ..models.auto.modeling_auto import MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING

snake_case__      :							str									=  logging.get_logger(__name__)



def _a       (			lowerCamelCase:     bytes      ,       lowerCamelCase:     int				)			->		np.array:







      '''simple docstring'''






      __A      =       F"""{sampling_rate}"""
      __A      =       '''1'''
      __A      =       '''f32le'''
      __A      =       [
          '''ffmpeg''',
          '''-i''',
          '''pipe:0''',
          '''-ac''',
          ac,
          '''-ar''',
          ar,
          '''-f''',
          format_for_conversion,
          '''-hide_banner''',
          '''-loglevel''',
          '''quiet''',
          '''pipe:1''',
      ]

      try:
            __A      =       subprocess.Popen(lowerCamelCase      ,       stdin=subprocess.PIPE      ,       stdout=subprocess.PIPE				)
      except FileNotFoundError:
            raise ValueError('''ffmpeg was not found but is required to load audio files from filename'''				)
      __A      =       ffmpeg_process.communicate(lowerCamelCase				)
      __A      =       output_stream[0]

      __A      =       np.frombuffer(lowerCamelCase      ,       np.floataa				)
      if audio.shape[0] == 0:
            raise ValueError('''Malformed soundfile'''				)
      return audio







@add_end_docstrings(_lowerCamelCase     )
class A_      (    _lowerCamelCase     ):




   def __init__(self			:Union[str, Any]					,     *_UpperCamelCase			:int					,     **_UpperCamelCase			:Dict				)->      str:
         # Default, might be overriden by the model.config.
         __A      =       5
         super().__init__(*_UpperCamelCase					,     **_UpperCamelCase				)

         if self.framework != "pt":
               raise ValueError(f"""The {self.__class__} is only available in PyTorch."""				)

         self.check_model_type(_UpperCamelCase				)




   def __call__(self			:List[str]					,     _UpperCamelCase			:Union[np.ndarray, bytes, str]					,     **_UpperCamelCase			:Any					,     )->      int:
         return super().__call__(_UpperCamelCase					,     **_UpperCamelCase				)




   def _lowerCAmelCase   (self			:Dict					,     _UpperCamelCase			:List[str]=None					,     **_UpperCamelCase			:List[Any]				)->      List[Any]:
         # No parameters on this pipeline right now
         __A      =       {}
         if top_k is not None:
               if top_k > self.model.config.num_labels:
                     __A      =       self.model.config.num_labels
               __A      =       top_k
         return {}, {}, postprocess_params




   def _lowerCAmelCase   (self			:List[str]					,     _UpperCamelCase			:int				)->      Any:
         if isinstance(_UpperCamelCase					,     _UpperCamelCase				):
               if inputs.startswith('''http://'''				) or inputs.startswith('''https://'''				):
                     # We need to actually check for a real protocol, otherwise it's impossible to use a local file
                     # like http_huggingface_co.png
                     __A      =       requests.get(_UpperCamelCase				).content
               else:
                     with open(_UpperCamelCase					,     '''rb'''				) as f:
                           __A      =       f.read()

         if isinstance(_UpperCamelCase					,     _UpperCamelCase				):
               __A      =       ffmpeg_read(_UpperCamelCase					,     self.feature_extractor.sampling_rate				)

         if isinstance(_UpperCamelCase					,     _UpperCamelCase				):
               # Accepting `"array"` which is the key defined in `datasets` for
               # better integration
               if not ("sampling_rate" in inputs and ("raw" in inputs or "array" in inputs)):
                     raise ValueError(
                         '''When passing a dictionary to AudioClassificationPipeline, the dict needs to contain a '''
                         '''"raw" key containing the numpy array representing the audio and a "sampling_rate" key, '''
                         '''containing the sampling_rate associated with that array'''				)

               __A      =       inputs.pop('''raw'''					,     _UpperCamelCase				)
               if _inputs is None:
                     # Remove path which will not be used from `datasets`.
                     inputs.pop('''path'''					,     _UpperCamelCase				)
                     __A      =       inputs.pop('''array'''					,     _UpperCamelCase				)
               __A      =       inputs.pop('''sampling_rate'''				)
               __A      =       _inputs
               if in_sampling_rate != self.feature_extractor.sampling_rate:
                     import torch

                     if is_torchaudio_available():
                           from torchaudio import functional as F
                     else:
                           raise ImportError(
                               '''torchaudio is required to resample audio samples in AudioClassificationPipeline. '''
                               '''The torchaudio package can be installed through: `pip install torchaudio`.'''				)

                     __A      =       F.resample(
                         torch.from_numpy(_UpperCamelCase				)					,     _UpperCamelCase					,     self.feature_extractor.sampling_rate				).numpy()

         if not isinstance(_UpperCamelCase					,     np.ndarray				):
               raise ValueError('''We expect a numpy ndarray as input'''				)
         if len(inputs.shape				) != 1:
               raise ValueError('''We expect a single channel audio input for AudioClassificationPipeline'''				)

         __A      =       self.feature_extractor(
             _UpperCamelCase					,     sampling_rate=self.feature_extractor.sampling_rate					,     return_tensors='''pt'''				)
         return processed




   def _lowerCAmelCase   (self			:List[str]					,     _UpperCamelCase			:Dict				)->      int:
         __A      =       self.model(**_UpperCamelCase				)
         return model_outputs






   def _lowerCAmelCase   (self			:List[Any]					,     _UpperCamelCase			:List[Any]					,     _UpperCamelCase			:int=5				)->      Dict:
         __A      =       model_outputs.logits[0].softmax(-1				)
         __A     ,		__A      =       probs.topk(_UpperCamelCase				)

         __A      =       scores.tolist()
         __A      =       ids.tolist()

         __A      =       [{'''score''': score, '''label''': self.model.config.idalabel[_id]} for score, _id in zip(_UpperCamelCase					,     _UpperCamelCase				)]

         return labels












import argparse
import json
import re
import string
import sys

import numpy as np


snake_case__      :							Dict									=  0.5



def _a       (			lowerCamelCase:     List[str]      ,       lowerCamelCase:     List[str]				)			->		List[Any]:







      '''simple docstring'''






      __A      =       ['''.''', ''',''', ''';''', ''':''']

      for token in remove_tokens:
            __A      =       ground_truth.replace(lowerCamelCase      ,       ''''''				)
            __A      =       prediction.replace(lowerCamelCase      ,       ''''''				)

      __A     ,		__A      =       ground_truth.lower(), prediction.lower()
      __A     ,		__A      =       ground_truth.replace('''/'''      ,       ''' '''				), prediction.replace('''/'''      ,       ''' '''				)
      __A     ,		__A      =       set(ground_truth.split(''' '''				)				), set(prediction.split(''' '''				)				)

      __A      =       ground_truth.intersection(lowerCamelCase				)
      __A      =       ground_truth.union(lowerCamelCase				)
      __A      =       len(lowerCamelCase				) / len(lowerCamelCase				)
      return jaccard



def _a       (			lowerCamelCase:     List[str]				)			->		Dict:







      '''simple docstring'''






      def remove_articles(lowerCamelCase:     Tuple				):
            return re.sub(r'''\b(a|an|the)\b'''      ,       ''' '''      ,       lowerCamelCase				)

      def white_space_fix(lowerCamelCase:     Tuple				):
            return " ".join(text.split()				)

      def remove_punc(lowerCamelCase:     List[Any]				):
            __A      =       set(string.punctuation				)
            return "".join(ch for ch in text if ch not in exclude				)

      def lower(lowerCamelCase:     int				):
            return text.lower()

      return white_space_fix(remove_articles(remove_punc(lower(lowerCamelCase				)				)				)				)



def _a       (			lowerCamelCase:     Dict      ,       lowerCamelCase:     int      ,       lowerCamelCase:     List[str]				)			->		str:







      '''simple docstring'''






      __A     ,		__A     ,		__A      =       0, 0, 0

      __A      =       '''Parties''' in qa_id

      # first check if ground truth is empty
      if len(lowerCamelCase				) == 0:
            if len(lowerCamelCase				) > 0:
                  fp += len(lowerCamelCase				)  # false positive for each one
      else:
            for ground_truth in ground_truths:
                  assert len(lowerCamelCase				) > 0
                  # check if there is a match
                  __A      =       False
                  for pred in predictions:
                        if substr_ok:
                              __A      =       get_jaccard(lowerCamelCase      ,       lowerCamelCase				) >= IOU_THRESH or ground_truth in pred
                        else:
                              __A      =       get_jaccard(lowerCamelCase      ,       lowerCamelCase				) >= IOU_THRESH
                        if is_match:
                              __A      =       True

                  if match_found:
                        tp += 1
                  else:
                        fn += 1

        # now also get any fps by looping through preds
            for pred in predictions:
                  # Check if there's a match. if so, don't count (don't want to double count based on the above)
                  # but if there's no match, then this is a false positive.
                  # (Note: we get the true positives in the above loop instead of this loop so that we don't double count
                  # multiple predictions that are matched with the same answer.)
                  __A      =       False
                  for ground_truth in ground_truths:
                        assert len(lowerCamelCase				) > 0
                        if substr_ok:
                              __A      =       get_jaccard(lowerCamelCase      ,       lowerCamelCase				) >= IOU_THRESH or ground_truth in pred
                        else:
                              __A      =       get_jaccard(lowerCamelCase      ,       lowerCamelCase				) >= IOU_THRESH
                        if is_match:
                              __A      =       True

                  if not match_found:
                        fp += 1

      __A      =       tp / (tp + fp) if tp + fp > 0 else np.nan
      __A      =       tp / (tp + fn) if tp + fn > 0 else np.nan

      return precision, recall



def _a       (			lowerCamelCase:     Dict				)			->		List[str]:







      '''simple docstring'''






      __A      =       precisions[::-1]
      for i in range(1      ,       len(lowerCamelCase				)				):
            __A      =       max(precision_best[i - 1]      ,       precision_best[i]				)
      __A      =       precision_best[::-1]
      return precisions



def _a       (			lowerCamelCase:     Optional[int]      ,       lowerCamelCase:     Dict				)			->		Tuple:







      '''simple docstring'''






      __A      =       process_precisions(lowerCamelCase				)
      __A      =       np.trapz(lowerCamelCase      ,       lowerCamelCase				)
      if np.isnan(lowerCamelCase				):
            return 0
      return aupr



def _a       (			lowerCamelCase:     Any      ,       lowerCamelCase:     List[str]      ,       lowerCamelCase:     Optional[int]				)			->		str:







      '''simple docstring'''






      __A      =       process_precisions(lowerCamelCase				)
      __A      =       0
      for prec, recall in zip(lowerCamelCase      ,       lowerCamelCase				):
            if recall >= recall_thresh:
                  __A      =       prec
                  break
      return prec_at_recall



def _a       (			lowerCamelCase:     Tuple      ,       lowerCamelCase:     Dict				)			->		Optional[Any]:







      '''simple docstring'''






      return normalize_answer(lowerCamelCase				) == normalize_answer(lowerCamelCase				)



def _a       (			lowerCamelCase:     Any      ,       lowerCamelCase:     List[Any]      ,       lowerCamelCase:     Optional[int]				)			->		List[str]:







      '''simple docstring'''






      __A      =       0
      for pred in predictions:
            for ground_truth in ground_truths:
                  __A      =       metric_fn(lowerCamelCase      ,       lowerCamelCase				)
                  if score == 1:  # break the loop when one prediction matches the ground truth
                        break
            if score == 1:
                  break
      return score



def _a       (			lowerCamelCase:     Union[str, Any]      ,       lowerCamelCase:     int				)			->		int:







      '''simple docstring'''






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

                        precisions.append(lowerCamelCase				)
                        recalls.append(lowerCamelCase				)

                        if precision == 0 and recall == 0:
                              fa += 0
                        else:
                              fa += 2 * (precision * recall) / (precision + recall)

                        exact_match += metric_max_over_ground_truths(lowerCamelCase      ,       lowerCamelCase      ,       lowerCamelCase				)

      __A      =       [x for _, x in sorted(zip(lowerCamelCase      ,       lowerCamelCase				)				)]
      recalls.sort()

      __A      =       100.0 * fa / total
      __A      =       100.0 * exact_match / total
      __A      =       get_aupr(lowerCamelCase      ,       lowerCamelCase				)

      __A      =       get_prec_at_recall(lowerCamelCase      ,       lowerCamelCase      ,       recall_thresh=0.9				)
      __A      =       get_prec_at_recall(lowerCamelCase      ,       lowerCamelCase      ,       recall_thresh=0.8				)

      return {
          "exact_match": exact_match,
          "f1": fa,
          "aupr": aupr,
          "prec_at_80_recall": prec_at_aa_recall,
          "prec_at_90_recall": prec_at_aa_recall,
      }


if __name__ == "__main__":
       snake_case__      :							Dict									=  argparse.ArgumentParser(description='Evaluation for CUAD')
       parser.add_argument('dataset_file', help='Dataset file')
       parser.add_argument('prediction_file', help='Prediction File')
       snake_case__      :							List[str]									=  parser.parse_args()
       with open(args.dataset_file) as dataset_file:
              snake_case__      :							List[Any]									=  json.load(dataset_file)
              snake_case__      :							int									=  dataset_json['data']
       with open(args.prediction_file) as prediction_file:
              snake_case__      :							Union[str, Any]									=  json.load(prediction_file)
       print(json.dumps(evaluate(dataset, predictions)))





