


import argparse
import json
import re
import string
import sys

import numpy as np


_A							     =       0.5

def 			__SCREAMING_SNAKE_CASE		(		UpperCamelCase		:      Optional[Any]     ,					UpperCamelCase		:      Union[str, Any]	)     ->	Optional[int]:



	"""simple docstring"""






	a_     			=   [""".""", """,""", """;""", """:"""]

	for token in remove_tokens:
		a_     			=   ground_truth.replace(A__     ,					""""""	)
		a_     			=   prediction.replace(A__     ,					""""""	)

	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(A__	)
	a_     			=   ground_truth.union(A__	)
	a_     			=   len(A__	) / len(A__	)
	return jaccard

def 			__SCREAMING_SNAKE_CASE		(		UpperCamelCase		:      Optional[Any]	)     ->	Any:



	"""simple docstring"""






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

	def white_space_fix(UpperCamelCase		:      Optional[Any]	):
		return " ".join(text.split()	)

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

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

	return white_space_fix(remove_articles(remove_punc(lower(A__	)	)	)	)

def 			__SCREAMING_SNAKE_CASE		(		UpperCamelCase		:      Any     ,					UpperCamelCase		:      Optional[Any]     ,					UpperCamelCase		:      Optional[Any]	)     ->	Tuple:



	"""simple docstring"""






	a_			, a_			, a_     			=   0, 0, 0

	a_     			=   """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
			a_     			=   False
			for pred in predictions:
				if substr_ok:
					a_     			=   get_jaccard(A__     ,					A__	) >= IOU_THRESH or ground_truth in pred
				else:
					a_     			=   get_jaccard(A__     ,					A__	) >= 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(A__	) > 0
				if substr_ok:
					a_     			=   get_jaccard(A__     ,					A__	) >= IOU_THRESH or ground_truth in pred
				else:
					a_     			=   get_jaccard(A__     ,					A__	) >= 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 			__SCREAMING_SNAKE_CASE		(		UpperCamelCase		:      int	)     ->	List[Any]:



	"""simple docstring"""






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

def 			__SCREAMING_SNAKE_CASE		(		UpperCamelCase		:      str     ,					UpperCamelCase		:      Union[str, Any]	)     ->	Optional[int]:



	"""simple docstring"""






	a_     			=   process_precisions(A__	)
	a_     			=   np.trapz(A__     ,					A__	)
	if np.isnan(A__	):
		return 0
	return aupr

def 			__SCREAMING_SNAKE_CASE		(		UpperCamelCase		:      Optional[Any]     ,					UpperCamelCase		:      List[str]     ,					UpperCamelCase		:      int	)     ->	Optional[int]:



	"""simple docstring"""






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

def 			__SCREAMING_SNAKE_CASE		(		UpperCamelCase		:      Optional[Any]     ,					UpperCamelCase		:      List[Any]	)     ->	Optional[int]:



	"""simple docstring"""






	return normalize_answer(A__	) == normalize_answer(A__	)

def 			__SCREAMING_SNAKE_CASE		(		UpperCamelCase		:      Tuple     ,					UpperCamelCase		:      int     ,					UpperCamelCase		:      Dict	)     ->	int:



	"""simple docstring"""






	a_     			=   0
	for pred in predictions:
		for ground_truth in ground_truths:
			a_     			=   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 			__SCREAMING_SNAKE_CASE		(		UpperCamelCase		:      Union[str, Any]     ,					UpperCamelCase		:      Optional[Any]	)     ->	List[str]:



	"""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(A__     ,					file=sys.stderr	)
					continue
				a_     			=   [x["""text"""] for x in qa["""answers"""]]
				a_     			=   predictions[qa["""id"""]]
				a_			, a_     			=   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__	)

	a_     			=   [x for _, x in sorted(zip(A__     ,					A__	)	)]
	recalls.sort()

	a_     			=   100.0 * fa / total
	a_     			=   100.0 * exact_match / total
	a_     			=   get_aupr(A__     ,					A__	)

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





import argparse

import torch
from flax.training.checkpoints import restore_checkpoint

from transformers import FNetConfig, FNetForPreTraining
from transformers.utils import logging


logging.set_verbosity_info()




def    lowerCamelCase__			(		A__			:   int    ,     A__			:   Tuple    ,     A__			:   str      ):





       '''simple docstring'''



       __lowerCamelCase  =		FNetConfig.from_json_file(A__      )
       print(f'Building PyTorch model from configuration: {config}'      )
       __lowerCamelCase  =		FNetForPreTraining(A__      )

       __lowerCamelCase  =		restore_checkpoint(A__    ,     A__      )
       __lowerCamelCase  =		checkpoint_dict["""target"""]

       # Embeddings
       # Position IDs
       __lowerCamelCase  =		fnet_pretraining_model.state_dict()

       __lowerCamelCase  =		state_dict["""fnet.embeddings.position_ids"""]
       __lowerCamelCase  =		{"""fnet.embeddings.position_ids""": position_ids}
       # Embedding Layers
       __lowerCamelCase  =		torch.tensor(
           pretrained_model_params["""encoder"""]["""embedder"""]["""word"""]["""embedding"""]      )
       __lowerCamelCase  =		torch.tensor(
           pretrained_model_params["""encoder"""]["""embedder"""]["""position"""]["""embedding"""][0]      )
       __lowerCamelCase  =		torch.tensor(
           pretrained_model_params["""encoder"""]["""embedder"""]["""type"""]["""embedding"""]      )
       __lowerCamelCase  =		torch.tensor(
           pretrained_model_params["""encoder"""]["""embedder"""]["""hidden_mapping_in"""]["""kernel"""]      ).T
       __lowerCamelCase  =		torch.tensor(
           pretrained_model_params["""encoder"""]["""embedder"""]["""hidden_mapping_in"""]["""bias"""]      )
       __lowerCamelCase  =		torch.tensor(
           pretrained_model_params["""encoder"""]["""embedder"""]["""layer_norm"""]["""scale"""]      )
       __lowerCamelCase  =		torch.tensor(
           pretrained_model_params["""encoder"""]["""embedder"""]["""layer_norm"""]["""bias"""]      )

       # Encoder Layers
       for layer in range(config.num_hidden_layers      ):
              __lowerCamelCase  =		torch.tensor(
                  pretrained_model_params["""encoder"""][f'encoder_{layer}']["""mixing_layer_norm"""]["""scale"""]      )
              __lowerCamelCase  =		torch.tensor(
                  pretrained_model_params["""encoder"""][f'encoder_{layer}']["""mixing_layer_norm"""]["""bias"""]      )

              __lowerCamelCase  =		torch.tensor(
                  pretrained_model_params["""encoder"""][f'feed_forward_{layer}']["""intermediate"""]["""kernel"""]      ).T
              __lowerCamelCase  =		torch.tensor(
                  pretrained_model_params["""encoder"""][f'feed_forward_{layer}']["""intermediate"""]["""bias"""]      )

              __lowerCamelCase  =		torch.tensor(
                  pretrained_model_params["""encoder"""][f'feed_forward_{layer}']["""output"""]["""kernel"""]      ).T
              __lowerCamelCase  =		torch.tensor(
                  pretrained_model_params["""encoder"""][f'feed_forward_{layer}']["""output"""]["""bias"""]      )

              __lowerCamelCase  =		torch.tensor(
                  pretrained_model_params["""encoder"""][f'encoder_{layer}']["""output_layer_norm"""]["""scale"""]      )
              __lowerCamelCase  =		torch.tensor(
                  pretrained_model_params["""encoder"""][f'encoder_{layer}']["""output_layer_norm"""]["""bias"""]      )

       # Pooler Layers
       __lowerCamelCase  =		torch.tensor(pretrained_model_params["""encoder"""]["""pooler"""]["""kernel"""]      ).T
       __lowerCamelCase  =		torch.tensor(pretrained_model_params["""encoder"""]["""pooler"""]["""bias"""]      )

       # Masked LM Layers
       __lowerCamelCase  =		torch.tensor(
           pretrained_model_params["""predictions_dense"""]["""kernel"""]      ).T
       __lowerCamelCase  =		torch.tensor(
           pretrained_model_params["""predictions_dense"""]["""bias"""]      )
       __lowerCamelCase  =		torch.tensor(
           pretrained_model_params["""predictions_layer_norm"""]["""scale"""]      )
       __lowerCamelCase  =		torch.tensor(
           pretrained_model_params["""predictions_layer_norm"""]["""bias"""]      )
       __lowerCamelCase  =		torch.tensor(
           pretrained_model_params["""encoder"""]["""embedder"""]["""word"""]["""embedding"""]      )
       __lowerCamelCase  =		torch.tensor(
           pretrained_model_params["""predictions_output"""]["""output_bias"""]      )
       __lowerCamelCase  =		torch.tensor(pretrained_model_params["""predictions_output"""]["""output_bias"""]      )

       # Seq Relationship Layers
       __lowerCamelCase  =		torch.tensor(
           pretrained_model_params["""classification"""]["""output_kernel"""]      )
       __lowerCamelCase  =		torch.tensor(
           pretrained_model_params["""classification"""]["""output_bias"""]      )

       # Load State Dict
       fnet_pretraining_model.load_state_dict(A__      )

       # Save PreTrained
       print(f'Saving pretrained model to {save_path}'      )
       fnet_pretraining_model.save_pretrained(A__      )


if __name__ == "__main__":
       UpperCAmelCase_   							=      argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           '--flax_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
       )
       parser.add_argument(
           '--fnet_config_file',
           default=None,
           type=str,
           required=True,
           help=(
               'The config json file corresponding to the pre-trained FNet model. \n'
               'This specifies the model architecture.'
           ),
       )
       parser.add_argument('--save_path', default=None, type=str, required=True, help='Path to the output model.')
       UpperCAmelCase_   							=      parser.parse_args()
       convert_flax_checkpoint_to_pytorch(args.flax_checkpoint_path, args.fnet_config_file, args.save_path)

