



import argparse
import json
import re
import string
import sys

import numpy as np


UpperCamelCase_									=   0.5







def       lowerCamelCase_  (    _a       :						str   ,		_a       :						str    ):



    '''simple docstring'''




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

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

    UpperCAmelCase_		, UpperCAmelCase_     :       Any  		=					ground_truth.lower(), prediction.lower()
    UpperCAmelCase_		, UpperCAmelCase_     :       Dict  		=					ground_truth.replace("""/"""   ,		""" """    ), prediction.replace("""/"""   ,		""" """    )
    UpperCAmelCase_		, UpperCAmelCase_     :       Tuple  		=					set(ground_truth.split(""" """    )    ), set(prediction.split(""" """    )    )

    UpperCAmelCase_     :       Tuple  		=					ground_truth.intersection(_a    )
    UpperCAmelCase_     :       Optional[Any]  		=					ground_truth.union(_a    )
    UpperCAmelCase_     :       Tuple  		=					len(_a    ) / len(_a    )
    return jaccard







def       lowerCamelCase_  (    _a       :						Optional[int]    ):



    '''simple docstring'''




    def remove_articles(_a       :						Optional[int]    ):
        return re.sub(r"""\b(a|an|the)\b"""   ,		""" """   ,		_a    )

    def white_space_fix(_a       :						Optional[int]    ):
        return " ".join(text.split()    )

    def remove_punc(_a       :						List[str]    ):
        UpperCAmelCase_     :       Tuple  		=					set(string.punctuation    )
        return "".join(ch for ch in text if ch not in exclude    )

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

    return white_space_fix(remove_articles(remove_punc(lower(_a    )    )    )    )







def       lowerCamelCase_  (    _a       :						Union[str, Any]   ,		_a       :						Optional[int]   ,		_a       :						int    ):



    '''simple docstring'''




    UpperCAmelCase_		, UpperCAmelCase_		, UpperCAmelCase_     :       List[str]  		=					0, 0, 0

    UpperCAmelCase_     :       int  		=					"""Parties""" in qa_id

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

            if not match_found:
                fp += 1

    UpperCAmelCase_     :       Tuple  		=					tp / (tp + fp) if tp + fp > 0 else np.nan
    UpperCAmelCase_     :       Union[str, Any]  		=					tp / (tp + fn) if tp + fn > 0 else np.nan

    return precision, recall







def       lowerCamelCase_  (    _a       :						Optional[int]    ):



    '''simple docstring'''




    UpperCAmelCase_     :       str  		=					precisions[::-1]
    for i in range(1   ,		len(_a    )    ):
        UpperCAmelCase_     :       Dict  		=					max(precision_best[i - 1]   ,		precision_best[i]    )
    UpperCAmelCase_     :       str  		=					precision_best[::-1]
    return precisions







def       lowerCamelCase_  (    _a       :						List[str]   ,		_a       :						str    ):



    '''simple docstring'''




    UpperCAmelCase_     :       Optional[int]  		=					process_precisions(_a    )
    UpperCAmelCase_     :       Tuple  		=					np.trapz(_a   ,		_a    )
    if np.isnan(_a    ):
        return 0
    return aupr







def       lowerCamelCase_  (    _a       :						Union[str, Any]   ,		_a       :						Optional[Any]   ,		_a       :						Union[str, Any]    ):



    '''simple docstring'''




    UpperCAmelCase_     :       Optional[Any]  		=					process_precisions(_a    )
    UpperCAmelCase_     :       List[Any]  		=					0
    for prec, recall in zip(_a   ,		_a    ):
        if recall >= recall_thresh:
            UpperCAmelCase_     :       Union[str, Any]  		=					prec
            break
    return prec_at_recall







def       lowerCamelCase_  (    _a       :						List[Any]   ,		_a       :						Tuple    ):



    '''simple docstring'''




    return normalize_answer(_a    ) == normalize_answer(_a    )







def       lowerCamelCase_  (    _a       :						Optional[int]   ,		_a       :						Optional[Any]   ,		_a       :						Optional[int]    ):



    '''simple docstring'''




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







def       lowerCamelCase_  (    _a       :						Optional[int]   ,		_a       :						List[Any]    ):



    '''simple docstring'''




    UpperCAmelCase_     :       str  		=					0
    UpperCAmelCase_     :       int  		=					[]
    UpperCAmelCase_     :       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(_a   ,		file=sys.stderr    )
                    continue
                UpperCAmelCase_     :       Dict  		=					[x["""text"""] for x in qa["""answers"""]]
                UpperCAmelCase_     :       Any  		=					predictions[qa["""id"""]]
                UpperCAmelCase_		, UpperCAmelCase_     :       List[str]  		=					compute_precision_recall(_a   ,		_a   ,		qa["""id"""]    )

                precisions.append(_a    )
                recalls.append(_a    )

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

                exact_match += metric_max_over_ground_truths(_a   ,		_a   ,		_a    )

    UpperCAmelCase_     :       Any  		=					[x for _, x in sorted(zip(_a   ,		_a    )    )]
    recalls.sort()

    UpperCAmelCase_     :       List[str]  		=					1_0_0.0 * fa / total
    UpperCAmelCase_     :       Tuple  		=					1_0_0.0 * exact_match / total
    UpperCAmelCase_     :       Union[str, Any]  		=					get_aupr(_a   ,		_a    )

    UpperCAmelCase_     :       Optional[Any]  		=					get_prec_at_recall(_a   ,		_a   ,		recall_thresh=0.9    )
    UpperCAmelCase_     :       List[Any]  		=					get_prec_at_recall(_a   ,		_a   ,		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'''





import unittest

from transformers import MarkupLMConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
 import torch

 from transformers import (
     MarkupLMForQuestionAnswering,
     MarkupLMForSequenceClassification,
     MarkupLMForTokenClassification,
     MarkupLMModel,
 )

# TODO check dependencies
from transformers import MarkupLMFeatureExtractor, MarkupLMProcessor, MarkupLMTokenizer


class 			UpperCamelCase__	:






 def __init__(      self	:str						,					_A	:Any						,					_A	:Union[str, Any]=13						,					_A	:List[Any]=7						,					_A	:int=True						,					_A	:Union[str, Any]=True						,					_A	:List[Any]=True						,					_A	:Dict=True						,					_A	:int=99						,					_A	:Union[str, Any]=32						,					_A	:int=5						,					_A	:List[str]=4						,					_A	:Optional[int]=37						,					_A	:List[str]="gelu"						,					_A	:Any=0.1						,					_A	:int=0.1						,					_A	:List[str]=512						,					_A	:Optional[Any]=16						,					_A	:Optional[Any]=2						,					_A	:List[str]=0.02						,					_A	:Any=3						,					_A	:Tuple=None						,					_A	:str=20						,					_A	:List[Any]=30						,					_A	:Dict=2						,					_A	:Union[str, Any]=2						,					_A	:int=10						,					)					->      Any:







       '''simple docstring'''



       __A													=      parent
       __A													=      batch_size
       __A													=      seq_length
       __A													=      is_training
       __A													=      use_input_mask
       __A													=      use_token_type_ids
       __A													=      use_labels
       __A													=      vocab_size
       __A													=      hidden_size
       __A													=      num_hidden_layers
       __A													=      num_attention_heads
       __A													=      intermediate_size
       __A													=      hidden_act
       __A													=      hidden_dropout_prob
       __A													=      attention_probs_dropout_prob
       __A													=      max_position_embeddings
       __A													=      type_vocab_size
       __A													=      type_sequence_label_size
       __A													=      initializer_range
       __A													=      num_labels
       __A													=      scope
       __A													=      max_xpath_tag_unit_embeddings
       __A													=      max_xpath_subs_unit_embeddings
       __A													=      tag_pad_id
       __A													=      subs_pad_id
       __A													=      max_depth






 def 						lowercase_    (      self	:Tuple      )					->      Optional[int]:







       '''simple docstring'''



       __A													=      ids_tensor([self.batch_size, self.seq_length]						,					self.vocab_size      )

       __A													=      ids_tensor(
           [self.batch_size, self.seq_length, self.max_depth]						,					self.max_xpath_tag_unit_embeddings      )

       __A													=      ids_tensor(
           [self.batch_size, self.seq_length, self.max_depth]						,					self.max_xpath_subs_unit_embeddings      )

       __A													=      None
       if self.use_input_mask:
             __A													=      ids_tensor([self.batch_size, self.seq_length]						,					vocab_size=2      )

       __A													=      None
       if self.use_token_type_ids:
             __A													=      ids_tensor([self.batch_size, self.seq_length]						,					self.type_vocab_size      )

       __A													=      None
       __A													=      None
       if self.use_labels:
             __A													=      ids_tensor([self.batch_size]						,					self.type_sequence_label_size      )
             __A													=      ids_tensor([self.batch_size, self.seq_length]						,					self.num_labels      )

       __A													=      self.get_config()

       return (
           config,
           input_ids,
           xpath_tags_seq,
           xpath_subs_seq,
           token_type_ids,
           input_mask,
           sequence_labels,
           token_labels,
       )






 def 						lowercase_    (      self	:List[Any]      )					->      int:







       '''simple docstring'''



       return MarkupLMConfig(
           vocab_size=self.vocab_size						,					hidden_size=self.hidden_size						,					num_hidden_layers=self.num_hidden_layers						,					num_attention_heads=self.num_attention_heads						,					intermediate_size=self.intermediate_size						,					hidden_act=self.hidden_act						,					hidden_dropout_prob=self.hidden_dropout_prob						,					attention_probs_dropout_prob=self.attention_probs_dropout_prob						,					max_position_embeddings=self.max_position_embeddings						,					type_vocab_size=self.type_vocab_size						,					initializer_range=self.initializer_range						,					max_xpath_tag_unit_embeddings=self.max_xpath_tag_unit_embeddings						,					max_xpath_subs_unit_embeddings=self.max_xpath_subs_unit_embeddings						,					tag_pad_id=self.tag_pad_id						,					subs_pad_id=self.subs_pad_id						,					max_depth=self.max_depth						,					)






 def 						lowercase_    (      self	:Union[str, Any]						,					_A	:List[str]						,					_A	:str						,					_A	:int						,					_A	:str						,					_A	:Dict						,					_A	:str						,					_A	:Optional[int]						,					_A	:Optional[Any]						,					)					->      Tuple:







       '''simple docstring'''



       __A													=      MarkupLMModel(config=_A      )
       model.to(_A      )
       model.eval()
       print('Configs:'						,					model.config.tag_pad_id						,					model.config.subs_pad_id      )
       __A													=      model(_A						,					attention_mask=_A						,					token_type_ids=_A      )
       __A													=      model(_A						,					token_type_ids=_A      )
       __A													=      model(_A      )
       self.parent.assertEqual(result.last_hidden_state.shape						,					(self.batch_size, self.seq_length, self.hidden_size)      )
       self.parent.assertEqual(result.pooler_output.shape						,					(self.batch_size, self.hidden_size)      )






 def 						lowercase_    (      self	:Any						,					_A	:Optional[Any]						,					_A	:Any						,					_A	:Optional[int]						,					_A	:Any						,					_A	:int						,					_A	:List[Any]						,					_A	:Dict						,					_A	:List[str]						,					)					->      Any:







       '''simple docstring'''



       __A													=      self.num_labels
       __A													=      MarkupLMForSequenceClassification(_A      )
       model.to(_A      )
       model.eval()
       __A													=      model(
           _A						,					xpath_tags_seq=_A						,					xpath_subs_seq=_A						,					attention_mask=_A						,					token_type_ids=_A						,					labels=_A						,					)
       self.parent.assertEqual(result.logits.shape						,					(self.batch_size, self.num_labels)      )






 def 						lowercase_    (      self	:Any						,					_A	:List[str]						,					_A	:str						,					_A	:str						,					_A	:List[Any]						,					_A	:Optional[Any]						,					_A	:Union[str, Any]						,					_A	:Dict						,					_A	:Union[str, Any]						,					)					->      Dict:







       '''simple docstring'''



       __A													=      self.num_labels
       __A													=      MarkupLMForTokenClassification(config=_A      )
       model.to(_A      )
       model.eval()
       __A													=      model(
           _A						,					xpath_tags_seq=_A						,					xpath_subs_seq=_A						,					attention_mask=_A						,					token_type_ids=_A						,					labels=_A						,					)
       self.parent.assertEqual(result.logits.shape						,					(self.batch_size, self.seq_length, self.num_labels)      )






 def 						lowercase_    (      self	:int						,					_A	:str						,					_A	:Tuple						,					_A	:Any						,					_A	:int						,					_A	:Any						,					_A	:Any						,					_A	:Union[str, Any]						,					_A	:List[Any]						,					)					->      List[str]:







       '''simple docstring'''



       __A													=      MarkupLMForQuestionAnswering(config=_A      )
       model.to(_A      )
       model.eval()
       __A													=      model(
           _A						,					xpath_tags_seq=_A						,					xpath_subs_seq=_A						,					attention_mask=_A						,					token_type_ids=_A						,					start_positions=_A						,					end_positions=_A						,					)
       self.parent.assertEqual(result.start_logits.shape						,					(self.batch_size, self.seq_length)      )
       self.parent.assertEqual(result.end_logits.shape						,					(self.batch_size, self.seq_length)      )






 def 						lowercase_    (      self	:Dict      )					->      List[str]:







       '''simple docstring'''



       __A													=      self.prepare_config_and_inputs()
       (
           (
           __A
       )						,   (
           __A
       )						,   (
           __A
       )						,   (
           __A
       )						,   (
           __A
       )						,   (
           __A
       )						,   (
           __A
       )						,   (
           __A
       )						,   
       )													=      config_and_inputs
       __A													=      {
           'input_ids': input_ids,
           'xpath_tags_seq': xpath_tags_seq,
           'xpath_subs_seq': xpath_subs_seq,
           'token_type_ids': token_type_ids,
           'attention_mask': input_mask,
       }
       return config, inputs_dict


@require_torch
class 			UpperCamelCase__	(     SCREAMING_SNAKE_CASE   ,  SCREAMING_SNAKE_CASE   ,  unittest.TestCase):
 UpperCAmelCase__     :     List[Any]    					=       (
     (
         MarkupLMModel,
         MarkupLMForSequenceClassification,
         MarkupLMForTokenClassification,
         MarkupLMForQuestionAnswering,
     )
     if is_torch_available()
     else None
 )
 UpperCAmelCase__     :     Tuple    					=       (
     {
         'feature-extraction': MarkupLMModel,
         'question-answering': MarkupLMForQuestionAnswering,
         'text-classification': MarkupLMForSequenceClassification,
         'token-classification': MarkupLMForTokenClassification,
         'zero-shot': MarkupLMForSequenceClassification,
     }
     if is_torch_available()
     else {}
 )






 def 						lowercase_    (      self	:Union[str, Any]						,					_A	:List[str]						,					_A	:Optional[Any]						,					_A	:Dict						,					_A	:Optional[Any]						,					_A	:Optional[int]      )					->      Tuple:







       '''simple docstring'''



       return True






 def 						lowercase_    (      self	:List[str]      )					->      Union[str, Any]:







       '''simple docstring'''



       __A													=      MarkupLMModelTester(self      )
       __A													=      ConfigTester(self						,					config_class=_A						,					hidden_size=37      )






 def 						lowercase_    (      self	:Optional[Any]      )					->      int:







       '''simple docstring'''



       self.config_tester.run_common_tests()






 def 						lowercase_    (      self	:Union[str, Any]      )					->      Optional[Any]:







       '''simple docstring'''



       __A													=      self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*_A      )






 def 						lowercase_    (      self	:Optional[Any]      )					->      Any:







       '''simple docstring'''



       __A													=      self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_sequence_classification(*_A      )






 def 						lowercase_    (      self	:Tuple      )					->      Union[str, Any]:







       '''simple docstring'''



       __A													=      self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_token_classification(*_A      )






 def 						lowercase_    (      self	:List[Any]      )					->      Tuple:







       '''simple docstring'''



       __A													=      self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_question_answering(*_A      )





def 							snake_case				(		)->      str:







      """simple docstring"""
      __A													=      '\n    <!DOCTYPE html>\n    <html>\n    <head>\n    <title>Page Title</title>\n    </head>\n    <body>\n\n    <h1>This is a Heading</h1>\n    <p>This is a paragraph.</p>\n\n    </body>\n    </html>\n    '

      return html_string


@require_torch
class 			UpperCamelCase__	(     unittest.TestCase):






 @cached_property
 def 						lowercase_    (      self	:Optional[int]      )					->      Dict:







       '''simple docstring'''



       __A													=      MarkupLMFeatureExtractor()
       __A													=      MarkupLMTokenizer.from_pretrained('microsoft/markuplm-base'      )

       return MarkupLMProcessor(_A						,					_A      )






 @slow
 def 						lowercase_    (      self	:Any      )					->      str:







       '''simple docstring'''



       __A													=      MarkupLMModel.from_pretrained('microsoft/markuplm-base'      ).to(_A      )

       __A													=      self.default_processor

       __A													=      processor(prepare_html_string()						,					return_tensors='pt'      )
       __A													=      inputs.to(_A      )

       # forward pass
       with torch.no_grad():
             __A													=      model(**_A      )

       # verify the last hidden states
       __A													=      torch.Size([1, 14, 768]      )
       self.assertEqual(outputs.last_hidden_state.shape						,					_A      )

       __A													=      torch.tensor(
           [[0.0_675, -0.0_052, 0.5_001], [-0.2_281, 0.0_802, 0.2_192], [-0.0_583, -0.3_311, 0.1_185]]      ).to(_A      )

       self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :3, :3]						,					_A						,					atol=1E-4      )      )






