






import argparse
import json
import re
import string
import sys

import numpy as np


__UpperCAmelCase      =				0.5





def 						A_					(						lowercase_	,  lowercase_			)				->Tuple:







 """simple docstring"""

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

 for token in remove_tokens:
  SCREAMING_SNAKE_CASE				    =		ground_truth.replace(lowercase_	,  ''			)
  SCREAMING_SNAKE_CASE				    =		prediction.replace(lowercase_	,  ''			)

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

 SCREAMING_SNAKE_CASE				    =		ground_truth.intersection(lowercase_			)
 SCREAMING_SNAKE_CASE				    =		ground_truth.union(lowercase_			)
 SCREAMING_SNAKE_CASE				    =		len(lowercase_			) / len(lowercase_			)
 return jaccard





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







 """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_			):
  SCREAMING_SNAKE_CASE				    =		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 						A_					(						lowercase_	,  lowercase_	,  lowercase_			)				->Optional[Any]:







 """simple docstring"""

 SCREAMING_SNAKE_CASE    ,   SCREAMING_SNAKE_CASE    ,   SCREAMING_SNAKE_CASE				    =		0, 0, 0

 SCREAMING_SNAKE_CASE				    =		'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
   SCREAMING_SNAKE_CASE				    =		False
   for pred in predictions:
    if substr_ok:
     SCREAMING_SNAKE_CASE				    =		get_jaccard(lowercase_	,  lowercase_			) >= IOU_THRESH or ground_truth in pred
    else:
     SCREAMING_SNAKE_CASE				    =		get_jaccard(lowercase_	,  lowercase_			) >= IOU_THRESH
    if is_match:
     SCREAMING_SNAKE_CASE				    =		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.)
   SCREAMING_SNAKE_CASE				    =		False
   for ground_truth in ground_truths:
    assert len(lowercase_			) > 0
    if substr_ok:
     SCREAMING_SNAKE_CASE				    =		get_jaccard(lowercase_	,  lowercase_			) >= IOU_THRESH or ground_truth in pred
    else:
     SCREAMING_SNAKE_CASE				    =		get_jaccard(lowercase_	,  lowercase_			) >= IOU_THRESH
    if is_match:
     SCREAMING_SNAKE_CASE				    =		True

   if not match_found:
    fp += 1

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

 return precision, recall





def 						A_					(						lowercase_			)				->Dict:







 """simple docstring"""

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





def 						A_					(						lowercase_	,  lowercase_			)				->int:







 """simple docstring"""

 SCREAMING_SNAKE_CASE				    =		process_precisions(lowercase_			)
 SCREAMING_SNAKE_CASE				    =		np.trapz(lowercase_	,  lowercase_			)
 if np.isnan(lowercase_			):
  return 0
 return aupr





def 						A_					(						lowercase_	,  lowercase_	,  lowercase_			)				->List[str]:







 """simple docstring"""

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





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







 """simple docstring"""

 return normalize_answer(lowercase_			) == normalize_answer(lowercase_			)





def 						A_					(						lowercase_	,  lowercase_	,  lowercase_			)				->List[str]:







 """simple docstring"""

 SCREAMING_SNAKE_CASE				    =		0
 for pred in predictions:
  for ground_truth in ground_truths:
   SCREAMING_SNAKE_CASE				    =		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 						A_					(						lowercase_	,  lowercase_			)				->Dict:







 """simple docstring"""

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

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

 SCREAMING_SNAKE_CASE				    =		1_0_0.0 * fa / total
 SCREAMING_SNAKE_CASE				    =		1_0_0.0 * exact_match / total
 SCREAMING_SNAKE_CASE				    =		get_aupr(lowercase_	,  lowercase_			)

 SCREAMING_SNAKE_CASE				    =		get_prec_at_recall(lowercase_	,  lowercase_	,  recall_thresh=0.9			)
 SCREAMING_SNAKE_CASE				    =		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__":
      __UpperCAmelCase      =				argparse.ArgumentParser(description="Evaluation for CUAD")
      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)
            __UpperCAmelCase      =				dataset_json["data"]
      with open(args.prediction_file) as prediction_file:
            __UpperCAmelCase      =				json.load(prediction_file)
      print(json.dumps(evaluate(dataset, predictions)))









"""simple docstring"""


from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_clip import CLIPTokenizer


snake_case_								=			logging.get_logger(__name__)

snake_case_								=			{"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt""", """tokenizer_file""": """tokenizer.json"""}

snake_case_								=			{
    """vocab_file""": {
        """openai/clip-vit-base-patch32""": """https://huggingface.co/openai/clip-vit-base-patch32/resolve/main/vocab.json""",
    },
    """merges_file""": {
        """openai/clip-vit-base-patch32""": """https://huggingface.co/openai/clip-vit-base-patch32/resolve/main/merges.txt""",
    },
    """tokenizer_file""": {
        """openai/clip-vit-base-patch32""": (
            """https://huggingface.co/openai/clip-vit-base-patch32/resolve/main/tokenizer.json"""
        ),
    },
}

snake_case_								=			{
    """openai/clip-vit-base-patch32""": 77,
}







class  A_     (	SCREAMING_SNAKE_CASE_							):



						"""simple docstring"""
						__UpperCamelCase													=				VOCAB_FILES_NAMES
						__UpperCamelCase													=				PRETRAINED_VOCAB_FILES_MAP
						__UpperCamelCase													=				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						__UpperCamelCase													=				["""input_ids""", """attention_mask"""]
						__UpperCamelCase													=				CLIPTokenizer






						def __init__(   self					:int  ,    lowercase_					:Optional[int]=None  ,    lowercase_					:int=None  ,    lowercase_					:Optional[Any]=None  ,    lowercase_					:Dict="<|endoftext|>"  ,    lowercase_					:List[Any]="<|startoftext|>"  ,    lowercase_					:Any="<|endoftext|>"  ,    lowercase_					:List[str]="<|endoftext|>"  ,    **lowercase_					:Tuple  ,    )							->     List[Any]:
												super().__init__(
												    lowercase_  ,    lowercase_  ,    tokenizer_file=lowercase_  ,    unk_token=lowercase_  ,    bos_token=lowercase_  ,    eos_token=lowercase_  ,    pad_token=lowercase_  ,    **lowercase_  ,    )

												if not isinstance(self.backend_tokenizer.pre_tokenizer  ,    pre_tokenizers.Sequence   ):
																		raise ValueError(
																		    'The `backend_tokenizer` provided does not match the expected format. The CLIP tokenizer has been'
																		    ' heavily modified from transformers version 4.17.0. You need to convert the tokenizer you are using'
																		    ' to be compatible with this version.The easiest way to do so is'
																		    ' `CLIPTokenizerFast.from_pretrained("path_to_local_folder_or_hub_repo, from_slow=True)`. If you want'
																		    ' to use your existing tokenizer, you will have to revert to a version prior to 4.17.0 of'
																		    ' transformers.'   )

												self._wrap_decode_method_backend_tokenizer()






						def       UpperCAmelCase__	(   self					:Dict   )							->     List[Any]:
												UpperCAmelCase			 =					self.backend_tokenizer.decode

												def new_decode_method(*lowercase_					:List[Any]  ,    **lowercase_					:Optional[Any]   ):
																		UpperCAmelCase			 =					orig_decode_method(*lowercase_  ,    **lowercase_   )
																		UpperCAmelCase			 =					text.replace(self.backend_tokenizer.model.end_of_word_suffix  ,    ' '   ).strip()
																		return text

												UpperCAmelCase			 =					new_decode_method






						def       UpperCAmelCase__	(   self					:Optional[int]  ,    lowercase_					:List[int]  ,    lowercase_					:Optional[List[int]] = None   )							->     List[int]:
												UpperCAmelCase			 =					[self.bos_token_id]
												UpperCAmelCase			 =					[self.eos_token_id]

												if token_ids_a is None:
																		return bos_token + token_ids_a + eos_token
												return bos_token + token_ids_a + eos_token + eos_token + token_ids_a + eos_token






						def       UpperCAmelCase__	(   self					:Dict  ,    lowercase_					:List[int]  ,    lowercase_					:Optional[List[int]] = None   )							->     List[int]:
												UpperCAmelCase			 =					[self.bos_token_id]
												UpperCAmelCase			 =					[self.eos_token_id]

												if token_ids_a is None:
																		return len(bos_token + token_ids_a + eos_token   ) * [0]
												return len(bos_token + token_ids_a + eos_token + eos_token + token_ids_a + eos_token   ) * [0]






						def       UpperCAmelCase__	(   self					:Optional[Any]  ,    lowercase_					:str  ,    lowercase_					:Optional[str] = None   )							->     Tuple[str]:
												UpperCAmelCase			 =					self._tokenizer.model.save(lowercase_  ,    name=lowercase_   )
												return tuple(lowercase_   )





