





"""simple docstring"""


import argparse
import json
import re
import string
import sys

import numpy as np


lowerCamelCase__      =      0.5




def     lowercase__			(    lowercase_						,lowercase_	)    ->						List[Any]:




      """simple docstring"""

      _UpperCamelCase					:		List[Any]  =				[".", ",", ";", ":"]

      for token in remove_tokens:
            _UpperCamelCase					:		Dict  =				ground_truth.replace(lowercase_						,""	)
            _UpperCamelCase					:		List[Any]  =				prediction.replace(lowercase_						,""	)

      _UpperCamelCase,     _UpperCamelCase					:		str  =				ground_truth.lower(), prediction.lower()
      _UpperCamelCase,     _UpperCamelCase					:		Any  =				ground_truth.replace("/"						," "	), prediction.replace("/"						," "	)
      _UpperCamelCase,     _UpperCamelCase					:		int  =				set(ground_truth.split(" "	)	), set(prediction.split(" "	)	)

      _UpperCamelCase					:		int  =				ground_truth.intersection(lowercase_	)
      _UpperCamelCase					:		Dict  =				ground_truth.union(lowercase_	)
      _UpperCamelCase					:		str  =				len(lowercase_	) / len(lowercase_	)
      return jaccard




def     lowercase__			(    lowercase_	)    ->						List[str]:




      """simple docstring"""

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

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

      def remove_punc(lowercase_	):
            _UpperCamelCase					:		Optional[Any]  =				set(string.punctuation	)
            return "".join(ch for ch in text if ch not in exclude	)

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

      return white_space_fix(remove_articles(remove_punc(lower(lowercase_	)	)	)	)




def     lowercase__			(    lowercase_						,lowercase_						,lowercase_	)    ->						Tuple:




      """simple docstring"""

      _UpperCamelCase,     _UpperCamelCase,     _UpperCamelCase					:		Tuple  =				0, 0, 0

      _UpperCamelCase					:		Optional[int]  =				"Parties" in qa_id

      # first check if ground truth is empty
      if len(lowercase_	) == 0:
            if len(lowercase_	) > 0:
                  fp += len(lowercase_	)  # false positive for each one
      else:
            for ground_truth in ground_truths:
                  assert len(lowercase_	) > 0
                  # check if there is a match
                  _UpperCamelCase					:		List[Any]  =				False
                  for pred in predictions:
                        if substr_ok:
                              _UpperCamelCase					:		str  =				get_jaccard(lowercase_						,lowercase_	) >= IOU_THRESH or ground_truth in pred
                        else:
                              _UpperCamelCase					:		int  =				get_jaccard(lowercase_						,lowercase_	) >= IOU_THRESH
                        if is_match:
                              _UpperCamelCase					:		Union[str, Any]  =				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.)
                  _UpperCamelCase					:		Dict  =				False
                  for ground_truth in ground_truths:
                        assert len(lowercase_	) > 0
                        if substr_ok:
                              _UpperCamelCase					:		Dict  =				get_jaccard(lowercase_						,lowercase_	) >= IOU_THRESH or ground_truth in pred
                        else:
                              _UpperCamelCase					:		Optional[int]  =				get_jaccard(lowercase_						,lowercase_	) >= IOU_THRESH
                        if is_match:
                              _UpperCamelCase					:		str  =				True

                  if not match_found:
                        fp += 1

      _UpperCamelCase					:		Optional[int]  =				tp / (tp + fp) if tp + fp > 0 else np.nan
      _UpperCamelCase					:		Tuple  =				tp / (tp + fn) if tp + fn > 0 else np.nan

      return precision, recall




def     lowercase__			(    lowercase_	)    ->						List[Any]:




      """simple docstring"""

      _UpperCamelCase					:		List[str]  =				precisions[::-1]
      for i in range(1						,len(lowercase_	)	):
            _UpperCamelCase					:		Any  =				max(precision_best[i - 1]						,precision_best[i]	)
      _UpperCamelCase					:		List[Any]  =				precision_best[::-1]
      return precisions




def     lowercase__			(    lowercase_						,lowercase_	)    ->						Any:




      """simple docstring"""

      _UpperCamelCase					:		Tuple  =				process_precisions(lowercase_	)
      _UpperCamelCase					:		Union[str, Any]  =				np.trapz(lowercase_						,lowercase_	)
      if np.isnan(lowercase_	):
            return 0
      return aupr




def     lowercase__			(    lowercase_						,lowercase_						,lowercase_	)    ->						Dict:




      """simple docstring"""

      _UpperCamelCase					:		Dict  =				process_precisions(lowercase_	)
      _UpperCamelCase					:		Dict  =				0
      for prec, recall in zip(lowercase_						,lowercase_	):
            if recall >= recall_thresh:
                  _UpperCamelCase					:		Union[str, Any]  =				prec
                  break
      return prec_at_recall




def     lowercase__			(    lowercase_						,lowercase_	)    ->						int:




      """simple docstring"""

      return normalize_answer(lowercase_	) == normalize_answer(lowercase_	)




def     lowercase__			(    lowercase_						,lowercase_						,lowercase_	)    ->						Optional[int]:




      """simple docstring"""

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




def     lowercase__			(    lowercase_						,lowercase_	)    ->						List[Any]:




      """simple docstring"""

      _UpperCamelCase					:		Any  =				0
      _UpperCamelCase					:		List[Any]  =				[]
      _UpperCamelCase					:		List[Any]  =				[]
      for article in dataset:
            for paragraph in article["paragraphs"]:
                  for qa in paragraph["qas"]:
                        total += 1
                        if qa["id"] not in predictions:
                              _UpperCamelCase					:		str  =				"Unanswered question " + qa["id"] + " will receive score 0."
                              print(lowercase_						,file=sys.stderr	)
                              continue
                        _UpperCamelCase					:		Tuple  =				[x["text"] for x in qa["answers"]]
                        _UpperCamelCase					:		Any  =				predictions[qa["id"]]
                        _UpperCamelCase,     _UpperCamelCase					:		int  =				compute_precision_recall(lowercase_						,lowercase_						,qa["id"]	)

                        precisions.append(lowercase_	)
                        recalls.append(lowercase_	)

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

                        exact_match += metric_max_over_ground_truths(lowercase_						,lowercase_						,lowercase_	)

      _UpperCamelCase					:		Tuple  =				[x for _, x in sorted(zip(lowercase_						,lowercase_	)	)]
      recalls.sort()

      _UpperCamelCase					:		Tuple  =				100.0 * fa / total
      _UpperCamelCase					:		List[str]  =				100.0 * exact_match / total
      _UpperCamelCase					:		Union[str, Any]  =				get_aupr(lowercase_						,lowercase_	)

      _UpperCamelCase					:		Any  =				get_prec_at_recall(lowercase_						,lowercase_						,recall_thresh=0.9	)
      _UpperCamelCase					:		List[Any]  =				get_prec_at_recall(lowercase_						,lowercase_						,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__":
  lowerCamelCase__      =      argparse.ArgumentParser(description="Evaluation for CUAD")
  parser.add_argument("dataset_file", help="Dataset file")
  parser.add_argument("prediction_file", help="Prediction File")
  lowerCamelCase__      =      parser.parse_args()
  with open(args.dataset_file) as dataset_file:
    lowerCamelCase__      =      json.load(dataset_file)
    lowerCamelCase__      =      dataset_json["data"]
  with open(args.prediction_file) as prediction_file:
    lowerCamelCase__      =      json.load(prediction_file)
  print(json.dumps(evaluate(dataset, predictions)))







"""simple docstring"""


import os
from shutil import copyfile
from typing import List, Optional, Tuple

from tokenizers import processors

from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
  from .tokenization_mbartaa import MBartaaTokenizer
else:
  lowerCamelCase__      =      None


lowerCamelCase__      =      logging.get_logger(__name__)

lowerCamelCase__      =      {"vocab_file": "sentencepiece.bpe.model", "tokenizer_file": "tokenizer.json"}

lowerCamelCase__      =      {
    "vocab_file": {
        "facebook/mbart-large-50-one-to-many-mmt": (
            "https://huggingface.co/facebook/mbart-large-50-one-to-many-mmt/resolve/main/sentencepiece.bpe.model"
        ),
    },
    "tokenizer_file": {
        "facebook/mbart-large-50-one-to-many-mmt": (
            "https://huggingface.co/facebook/mbart-large-50-one-to-many-mmt/resolve/main/tokenizer.json"
        ),
    },
}

lowerCamelCase__      =      {
    "facebook/mbart-large-50-one-to-many-mmt": 1024,
}

# fmt: off
lowerCamelCase__      =      ["ar_AR", "cs_CZ", "de_DE", "en_XX", "es_XX", "et_EE", "fi_FI", "fr_XX", "gu_IN", "hi_IN", "it_IT", "ja_XX", "kk_KZ", "ko_KR", "lt_LT", "lv_LV", "my_MM", "ne_NP", "nl_XX", "ro_RO", "ru_RU", "si_LK", "tr_TR", "vi_VN", "zh_CN", "af_ZA", "az_AZ", "bn_IN", "fa_IR", "he_IL", "hr_HR", "id_ID", "ka_GE", "km_KH", "mk_MK", "ml_IN", "mn_MN", "mr_IN", "pl_PL", "ps_AF", "pt_XX", "sv_SE", "sw_KE", "ta_IN", "te_IN", "th_TH", "tl_XX", "uk_UA", "ur_PK", "xh_ZA", "gl_ES", "sl_SI"]



class    __SCREAMING_SNAKE_CASE			(  _UpperCamelCase      ):







  '''simple docstring'''


  SCREAMING_SNAKE_CASE__							:Tuple		   =    VOCAB_FILES_NAMES
  SCREAMING_SNAKE_CASE__							:List[Any]		   =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  SCREAMING_SNAKE_CASE__							:Union[str, Any]		   =    PRETRAINED_VOCAB_FILES_MAP
  SCREAMING_SNAKE_CASE__							:int		   =    ["input_ids", "attention_mask"]
  SCREAMING_SNAKE_CASE__							:Dict		   =    MBartaaTokenizer

  SCREAMING_SNAKE_CASE__							:List[int]		   =    []
  SCREAMING_SNAKE_CASE__							:List[int]		   =    []

  def __init__(						self      :				str    ,      __a      :				int=None    ,      __a      :				List[str]=None    ,      __a      :				Optional[Any]=None    ,      __a      :				List[str]=None    ,      __a      :				Tuple="</s>"    ,      __a      :				Union[str, Any]="</s>"    ,      __a      :				str="<s>"    ,      __a      :				Any="<unk>"    ,      __a      :				List[str]="<pad>"    ,      __a      :				Optional[Any]="<mask>"    ,      **__a      :				Any    ,      )						-> Union[str, Any]:
        # Mask token behave like a normal word, i.e. include the space before it
        _UpperCamelCase					:		Dict  =				AddedToken(__a    ,      lstrip=__a    ,      rstrip=__a		) if isinstance(__a    ,      __a		) else mask_token

        _UpperCamelCase					:		str  =				kwargs.get("additional_special_tokens"    ,      []		)
        kwargs["additional_special_tokens"] += [
            code for code in FAIRSEQ_LANGUAGE_CODES if code not in kwargs["additional_special_tokens"]
        ]

        super().__init__(
            __a    ,      src_lang=__a    ,      tgt_lang=__a    ,      tokenizer_file=__a    ,      eos_token=__a    ,      sep_token=__a    ,      cls_token=__a    ,      unk_token=__a    ,      pad_token=__a    ,      mask_token=__a    ,      **__a    ,      )

        _UpperCamelCase					:		Optional[int]  =				vocab_file
        _UpperCamelCase					:		int  =				False if not self.vocab_file else True

        _UpperCamelCase					:		int  =				{
            lang_code: self.convert_tokens_to_ids(__a		) for lang_code in FAIRSEQ_LANGUAGE_CODES
        }

        _UpperCamelCase					:		List[str]  =				src_lang if src_lang is not None else "en_XX"
        _UpperCamelCase					:		Dict  =				tgt_lang
        _UpperCamelCase					:		int  =				self.lang_code_to_id[self._src_lang]
        self.set_src_lang_special_tokens(self._src_lang		)

  @property
  def       __SCREAMING_SNAKE_CASE (						self      :				Optional[int]		)						-> str:
        return self._src_lang

  @src_lang.setter
  def       __SCREAMING_SNAKE_CASE (						self      :				Union[str, Any]    ,      __a      :				str		)						-> None:
        _UpperCamelCase					:		int  =				new_src_lang
        self.set_src_lang_special_tokens(self._src_lang		)

  def       __SCREAMING_SNAKE_CASE (						self      :				Optional[int]    ,      __a      :				List[int]    ,      __a      :				Optional[List[int]] = None		)						-> List[int]:
        if token_ids_a is None:
              return self.prefix_tokens + token_ids_a + self.suffix_tokens
        # We don't expect to process pairs, but leave the pair logic for API consistency
        return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens

  def       __SCREAMING_SNAKE_CASE (						self      :				Any    ,      __a      :				List[str]    ,      __a      :				str = "en_XX"    ,      __a      :				Optional[List[str]] = None    ,      __a      :				str = "ro_RO"    ,      **__a      :				Tuple    ,      )						-> BatchEncoding:
        _UpperCamelCase					:		List[Any]  =				src_lang
        _UpperCamelCase					:		Union[str, Any]  =				tgt_lang
        return super().prepare_seqaseq_batch(__a    ,      __a    ,      **__a		)

  def       __SCREAMING_SNAKE_CASE (						self      :				List[str]		)						-> int:
        return self.set_src_lang_special_tokens(self.src_lang		)

  def       __SCREAMING_SNAKE_CASE (						self      :				Tuple		)						-> Tuple:
        return self.set_tgt_lang_special_tokens(self.tgt_lang		)

  def       __SCREAMING_SNAKE_CASE (						self      :				List[str]    ,      __a      :				str		)						-> None:
        _UpperCamelCase					:		List[Any]  =				self.convert_tokens_to_ids(__a		)
        _UpperCamelCase					:		Union[str, Any]  =				[self.cur_lang_code_id]
        _UpperCamelCase					:		List[Any]  =				[self.eos_token_id]

        _UpperCamelCase					:		Union[str, Any]  =				self.convert_ids_to_tokens(self.prefix_tokens		)
        _UpperCamelCase					:		Union[str, Any]  =				self.convert_ids_to_tokens(self.suffix_tokens		)

        _UpperCamelCase					:		Optional[Any]  =				processors.TemplateProcessing(
            single=prefix_tokens_str + ["$A"] + suffix_tokens_str    ,      pair=prefix_tokens_str + ["$A", "$B"] + suffix_tokens_str    ,      special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str    ,      self.prefix_tokens + self.suffix_tokens		)		)    ,      )

  def       __SCREAMING_SNAKE_CASE (						self      :				Dict    ,      __a      :				str		)						-> None:
        _UpperCamelCase					:		Tuple  =				self.convert_tokens_to_ids(__a		)
        _UpperCamelCase					:		Union[str, Any]  =				[self.cur_lang_code_id]
        _UpperCamelCase					:		Optional[Any]  =				[self.eos_token_id]

        _UpperCamelCase					:		int  =				self.convert_ids_to_tokens(self.prefix_tokens		)
        _UpperCamelCase					:		Any  =				self.convert_ids_to_tokens(self.suffix_tokens		)

        _UpperCamelCase					:		Tuple  =				processors.TemplateProcessing(
            single=prefix_tokens_str + ["$A"] + suffix_tokens_str    ,      pair=prefix_tokens_str + ["$A", "$B"] + suffix_tokens_str    ,      special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str    ,      self.prefix_tokens + self.suffix_tokens		)		)    ,      )

  def       __SCREAMING_SNAKE_CASE (						self      :				Dict    ,      __a      :				Optional[int]    ,      __a      :				str    ,      __a      :				Optional[str]    ,      __a      :				Optional[str]    ,      **__a      :				int		)						-> List[str]:
        if src_lang is None or tgt_lang is None:
              raise ValueError("Translation requires a `src_lang` and a `tgt_lang` for this model"		)
        _UpperCamelCase					:		List[str]  =				src_lang
        _UpperCamelCase					:		List[str]  =				self(__a    ,      add_special_tokens=__a    ,      return_tensors=__a    ,      **__a		)
        _UpperCamelCase					:		str  =				self.convert_tokens_to_ids(__a		)
        _UpperCamelCase					:		str  =				tgt_lang_id
        return inputs


  def       __SCREAMING_SNAKE_CASE (						self      :				Union[str, Any]    ,      __a      :				str    ,      __a      :				Optional[str] = None		)						-> Tuple[str]:
        if not self.can_save_slow_tokenizer:
              raise ValueError(
                  "Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
                  "tokenizer."		)

        if not os.path.isdir(__a		):
              logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''		)
              return
        _UpperCamelCase					:		int  =				os.path.join(
            __a    ,      (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]		)

        if os.path.abspath(self.vocab_file		) != os.path.abspath(__a		):
              copyfile(self.vocab_file    ,      __a		)

        return (out_vocab_file,)
