



'''simple docstring'''
import argparse
import json
import re
import string
import sys

import numpy as np


_UpperCamelCase									=      0.5


def 	lowercase_						( lowerCAmelCase__						:	Union[str, Any]      ,				lowerCAmelCase__						:	List[str]			):

			"""simple docstring"""





			__UpperCAmelCase :   int    					=  [""".""", """,""", """;""", """:"""]

			for token in remove_tokens:
						__UpperCAmelCase :   List[str]    					=  ground_truth.replace(lowerCAmelCase__      ,				""""""			)
						__UpperCAmelCase :   Tuple    					=  prediction.replace(lowerCAmelCase__      ,				""""""			)

			__UpperCAmelCase					,	__UpperCAmelCase :   List[Any]    					=  ground_truth.lower(), prediction.lower()
			__UpperCAmelCase					,	__UpperCAmelCase :   List[str]    					=  ground_truth.replace("""/"""      ,				""" """			), prediction.replace("""/"""      ,				""" """			)
			__UpperCAmelCase					,	__UpperCAmelCase :   Any    					=  set(ground_truth.split(""" """			)			), set(prediction.split(""" """			)			)

			__UpperCAmelCase :   List[Any]    					=  ground_truth.intersection(lowerCAmelCase__			)
			__UpperCAmelCase :   List[str]    					=  ground_truth.union(lowerCAmelCase__			)
			__UpperCAmelCase :   str    					=  len(lowerCAmelCase__			) / len(lowerCAmelCase__			)
			return jaccard


def 	lowercase_						( lowerCAmelCase__						:	Any			):

			"""simple docstring"""





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

			def white_space_fix(lowerCAmelCase__						:	List[str]			):
						return " ".join(text.split()			)

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

			def lower(lowerCAmelCase__						:	Tuple			):
						return text.lower()

			return white_space_fix(remove_articles(remove_punc(lower(lowerCAmelCase__			)			)			)			)


def 	lowercase_						( lowerCAmelCase__						:	List[str]      ,				lowerCAmelCase__						:	str      ,				lowerCAmelCase__						:	Union[str, Any]			):

			"""simple docstring"""





			__UpperCAmelCase					,	__UpperCAmelCase					,	__UpperCAmelCase :   Union[str, Any]    					=  0, 0, 0

			__UpperCAmelCase :   Tuple    					=  """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
									__UpperCAmelCase :   Any    					=  False
									for pred in predictions:
												if substr_ok:
															__UpperCAmelCase :   List[Any]    					=  get_jaccard(lowerCAmelCase__      ,				lowerCAmelCase__			) >= IOU_THRESH or ground_truth in pred
												else:
															__UpperCAmelCase :   Tuple    					=  get_jaccard(lowerCAmelCase__      ,				lowerCAmelCase__			) >= IOU_THRESH
												if is_match:
															__UpperCAmelCase :   List[str]    					=  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 :   Optional[Any]    					=  False
									for ground_truth in ground_truths:
												assert len(lowerCAmelCase__			) > 0
												if substr_ok:
															__UpperCAmelCase :   str    					=  get_jaccard(lowerCAmelCase__      ,				lowerCAmelCase__			) >= IOU_THRESH or ground_truth in pred
												else:
															__UpperCAmelCase :   Optional[Any]    					=  get_jaccard(lowerCAmelCase__      ,				lowerCAmelCase__			) >= IOU_THRESH
												if is_match:
															__UpperCAmelCase :   str    					=  True

									if not match_found:
												fp += 1

			__UpperCAmelCase :   List[str]    					=  tp / (tp + fp) if tp + fp > 0 else np.nan
			__UpperCAmelCase :   str    					=  tp / (tp + fn) if tp + fn > 0 else np.nan

			return precision, recall


def 	lowercase_						( lowerCAmelCase__						:	List[str]			):

			"""simple docstring"""





			__UpperCAmelCase :   List[str]    					=  precisions[::-1]
			for i in range(1      ,				len(lowerCAmelCase__			)			):
						__UpperCAmelCase :   Optional[Any]    					=  max(precision_best[i - 1]      ,				precision_best[i]			)
			__UpperCAmelCase :   Dict    					=  precision_best[::-1]
			return precisions


def 	lowercase_						( lowerCAmelCase__						:	List[str]      ,				lowerCAmelCase__						:	Tuple			):

			"""simple docstring"""





			__UpperCAmelCase :   Optional[Any]    					=  process_precisions(lowerCAmelCase__			)
			__UpperCAmelCase :   Tuple    					=  np.trapz(lowerCAmelCase__      ,				lowerCAmelCase__			)
			if np.isnan(lowerCAmelCase__			):
						return 0
			return aupr


def 	lowercase_						( lowerCAmelCase__						:	Dict      ,				lowerCAmelCase__						:	int      ,				lowerCAmelCase__						:	List[Any]			):

			"""simple docstring"""





			__UpperCAmelCase :   int    					=  process_precisions(lowerCAmelCase__			)
			__UpperCAmelCase :   Union[str, Any]    					=  0
			for prec, recall in zip(lowerCAmelCase__      ,				lowerCAmelCase__			):
						if recall >= recall_thresh:
									__UpperCAmelCase :   str    					=  prec
									break
			return prec_at_recall


def 	lowercase_						( lowerCAmelCase__						:	Dict      ,				lowerCAmelCase__						:	Union[str, Any]			):

			"""simple docstring"""





			return normalize_answer(lowerCAmelCase__			) == normalize_answer(lowerCAmelCase__			)


def 	lowercase_						( lowerCAmelCase__						:	Union[str, Any]      ,				lowerCAmelCase__						:	Optional[int]      ,				lowerCAmelCase__						:	str			):

			"""simple docstring"""





			__UpperCAmelCase :   List[str]    					=  0
			for pred in predictions:
						for ground_truth in ground_truths:
									__UpperCAmelCase :   Optional[int]    					=  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 	lowercase_						( lowerCAmelCase__						:	List[Any]      ,				lowerCAmelCase__						:	List[str]			):

			"""simple docstring"""





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

			__UpperCAmelCase :   Union[str, Any]    					=  [x for _, x in sorted(zip(lowerCAmelCase__      ,				lowerCAmelCase__			)			)]
			recalls.sort()

			__UpperCAmelCase :   Optional[Any]    					=  100.0 * fa / total
			__UpperCAmelCase :   List[str]    					=  100.0 * exact_match / total
			__UpperCAmelCase :   Optional[Any]    					=  get_aupr(lowerCAmelCase__      ,				lowerCAmelCase__			)

			__UpperCAmelCase :   List[Any]    					=  get_prec_at_recall(lowerCAmelCase__      ,				lowerCAmelCase__      ,				recall_thresh=0.9			)
			__UpperCAmelCase :   List[str]    					=  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__":
					_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 os
import warnings
from pathlib import Path
from typing import Optional, Union

from .dynamic_module_utils import custom_object_save
from .tokenization_utils_base import PreTrainedTokenizerBase
from .utils import PushToHubMixin, copy_func, direct_transformers_import, logging


_UpperCamelCase									=      logging.get_logger(__name__)

# Dynamically import the Transformers module to grab the attribute classes of the processor form their names.
_UpperCamelCase									=      direct_transformers_import(Path(__file__).parent)


_UpperCamelCase									=      {
    '''AutoTokenizer''': '''PreTrainedTokenizerBase''',
    '''AutoFeatureExtractor''': '''FeatureExtractionMixin''',
    '''AutoImageProcessor''': '''ImageProcessingMixin''',
}



class _A	(     __SCREAMING_SNAKE_CASE     ):
		_SCREAMING_SNAKE_CASE       :     str       =      ["feature_extractor", "tokenizer"]
		# Names need to be attr_class for attr in attributes
		_SCREAMING_SNAKE_CASE       :     Optional[int]       =      None
		_SCREAMING_SNAKE_CASE       :     str       =      None
		_SCREAMING_SNAKE_CASE       :     List[str]       =      None





		def __init__(  self			,							*__UpperCAmelCase			,							**__UpperCAmelCase					)							->  Optional[int]:

					'''simple docstring'''
					# Sanitize args and kwargs
					for key in kwargs:
								if key not in self.attributes:
											raise TypeError(f'Unexpected keyword argument {key}.'					)
					for arg, attribute_name in zip(__UpperCAmelCase			,							self.attributes					):
								if attribute_name in kwargs:
											raise TypeError(f'Got multiple values for argument {attribute_name}.'					)
								else:
											__UpperCAmelCase :   int    					=  arg

					if len(__UpperCAmelCase					) != len(self.attributes					):
								raise ValueError(
								    f'This processor requires {len(self.attributes					)} arguments: {", ".join(self.attributes					)}. Got '
								    f'{len(__UpperCAmelCase					)} arguments instead.'					)

					# Check each arg is of the proper class (this will also catch a user initializing in the wrong order)
					for attribute_name, arg in kwargs.items():
								__UpperCAmelCase :   int    					=  getattr(self			,							f'{attribute_name}_class'					)
								# Nothing is ever going to be an instance of "AutoXxx", in that case we check the base class.
								__UpperCAmelCase :   str    					=  AUTO_TO_BASE_CLASS_MAPPING.get(__UpperCAmelCase			,							__UpperCAmelCase					)
								if isinstance(__UpperCAmelCase			,							__UpperCAmelCase					):
											__UpperCAmelCase :   str    					=  tuple(getattr(__UpperCAmelCase			,							__UpperCAmelCase					) for n in class_name if n is not None					)
								else:
											__UpperCAmelCase :   Optional[Any]    					=  getattr(__UpperCAmelCase			,							__UpperCAmelCase					)

								if not isinstance(__UpperCAmelCase			,							__UpperCAmelCase					):
											raise ValueError(
											    f'Received a {type(__UpperCAmelCase					).__name__} for argument {attribute_name}, but a {class_name} was expected.'					)

								setattr(self			,							__UpperCAmelCase			,							__UpperCAmelCase					)





		def __repr__(  self					)							->  List[str]:

					'''simple docstring'''
					__UpperCAmelCase :   Any    					=  [f'- {name}: {repr(getattr(self			,							__UpperCAmelCase					)					)}' for name in self.attributes]
					__UpperCAmelCase :   List[Any]    					=  """\n""".join(__UpperCAmelCase					)
					return f'{self.__class__.__name__}:\n{attributes_repr}'





		def 				__A							(  self			,							__UpperCAmelCase			,							__UpperCAmelCase = False			,							**__UpperCAmelCase					)							->  Any:

					'''simple docstring'''
					os.makedirs(__UpperCAmelCase			,							exist_ok=__UpperCAmelCase					)

					if push_to_hub:
								__UpperCAmelCase :   Optional[int]    					=  kwargs.pop("""commit_message"""			,							__UpperCAmelCase					)
								__UpperCAmelCase :   List[Any]    					=  kwargs.pop("""repo_id"""			,							save_directory.split(os.path.sep					)[-1]					)
								__UpperCAmelCase :   Any    					=  self._create_repo(__UpperCAmelCase			,							**__UpperCAmelCase					)
								__UpperCAmelCase :   List[str]    					=  self._get_files_timestamps(__UpperCAmelCase					)
					# If we have a custom config, we copy the file defining it in the folder and set the attributes so it can be
					# loaded from the Hub.
					if self._auto_class is not None:
								__UpperCAmelCase :   Union[str, Any]    					=  [getattr(self			,							__UpperCAmelCase					) for attribute_name in self.attributes]
								__UpperCAmelCase :   Dict    					=  [(a.init_kwargs if isinstance(__UpperCAmelCase			,							__UpperCAmelCase					) else a) for a in attrs]
								custom_object_save(self			,							__UpperCAmelCase			,							config=__UpperCAmelCase					)

					for attribute_name in self.attributes:
								__UpperCAmelCase :   Optional[int]    					=  getattr(self			,							__UpperCAmelCase					)
								# Include the processor class in the attribute config so this processor can then be reloaded with the
								# `AutoProcessor` API.
								if hasattr(__UpperCAmelCase			,							"""_set_processor_class"""					):
											attribute._set_processor_class(self.__class__.__name__					)
								attribute.save_pretrained(__UpperCAmelCase					)

					if self._auto_class is not None:
								# We added an attribute to the init_kwargs of the tokenizers, which needs to be cleaned up.
								for attribute_name in self.attributes:
											__UpperCAmelCase :   str    					=  getattr(self			,							__UpperCAmelCase					)
											if isinstance(__UpperCAmelCase			,							__UpperCAmelCase					):
														del attribute.init_kwargs["auto_map"]

					if push_to_hub:
								self._upload_modified_files(
								    __UpperCAmelCase			,							__UpperCAmelCase			,							__UpperCAmelCase			,							commit_message=__UpperCAmelCase			,							token=kwargs.get("""use_auth_token"""					)			,							)





		@classmethod
		def 				__A							(  cls			,							__UpperCAmelCase			,							__UpperCAmelCase = None			,							__UpperCAmelCase = False			,							__UpperCAmelCase = False			,							__UpperCAmelCase = None			,							__UpperCAmelCase = "main"			,							**__UpperCAmelCase			,							)							->  Optional[int]:

					'''simple docstring'''
					__UpperCAmelCase :   List[str]    					=  cache_dir
					__UpperCAmelCase :   Tuple    					=  force_download
					__UpperCAmelCase :   Optional[int]    					=  local_files_only
					__UpperCAmelCase :   str    					=  revision

					__UpperCAmelCase :   int    					=  kwargs.pop("""use_auth_token"""			,							__UpperCAmelCase					)
					if use_auth_token is not None:
								warnings.warn(
								    """The `use_auth_token` argument is deprecated and will be removed in v5 of Transformers."""			,							__UpperCAmelCase					)
								if token is not None:
											raise ValueError(
											    """`token` and `use_auth_token` are both specified. Please set only the argument `token`."""					)
								__UpperCAmelCase :   Any    					=  use_auth_token

					if token is not None:
								# change to `token` in a follow-up PR
								__UpperCAmelCase :   Dict    					=  token

					__UpperCAmelCase :   Optional[int]    					=  cls._get_arguments_from_pretrained(__UpperCAmelCase			,							**__UpperCAmelCase					)
					return cls(*__UpperCAmelCase					)





		@classmethod
		def 				__A							(  cls			,							__UpperCAmelCase="AutoProcessor"					)							->  Dict:

					'''simple docstring'''
					if not isinstance(__UpperCAmelCase			,							__UpperCAmelCase					):
								__UpperCAmelCase :   List[Any]    					=  auto_class.__name__

					import transformers.models.auto as auto_module

					if not hasattr(__UpperCAmelCase			,							__UpperCAmelCase					):
								raise ValueError(f'{auto_class} is not a valid auto class.'					)

					__UpperCAmelCase :   Optional[Any]    					=  auto_class





		@classmethod
		def 				__A							(  cls			,							__UpperCAmelCase			,							**__UpperCAmelCase					)							->  List[str]:

					'''simple docstring'''
					__UpperCAmelCase :   Union[str, Any]    					=  []
					for attribute_name in cls.attributes:
								__UpperCAmelCase :   List[Any]    					=  getattr(cls			,							f'{attribute_name}_class'					)
								if isinstance(__UpperCAmelCase			,							__UpperCAmelCase					):
											__UpperCAmelCase :   str    					=  tuple(getattr(__UpperCAmelCase			,							__UpperCAmelCase					) if n is not None else None for n in class_name					)
											__UpperCAmelCase :   int    					=  kwargs.get("""use_fast"""			,							__UpperCAmelCase					)
											if use_fast and classes[1] is not None:
														__UpperCAmelCase :   int    					=  classes[1]
											else:
														__UpperCAmelCase :   Optional[Any]    					=  classes[0]
								else:
											__UpperCAmelCase :   str    					=  getattr(__UpperCAmelCase			,							__UpperCAmelCase					)

								args.append(attribute_class.from_pretrained(__UpperCAmelCase			,							**__UpperCAmelCase					)					)
					return args





		@property
		def 				__A							(  self					)							->  Tuple:

					'''simple docstring'''
					__UpperCAmelCase :   Optional[Any]    					=  getattr(self			,							self.attributes[0]					)
					return getattr(__UpperCAmelCase			,							"""model_input_names"""			,							__UpperCAmelCase					)


_UpperCamelCase									=      copy_func(ProcessorMixin.push_to_hub)
if ProcessorMixin.push_to_hub.__doc__ is not None:
					_UpperCamelCase									=      ProcessorMixin.push_to_hub.__doc__.format(
					    object='''processor''', object_class='''AutoProcessor''', object_files='''processor files'''
					)




