




'''simple docstring'''


import argparse
import json
import re
import string
import sys

import numpy as np


lowerCAmelCase      :  str      							=0.5
def 							UpperCAmelCase_						(		__lowerCamelCase      :      Any			,__lowerCamelCase      :      Optional[int]				):
   lowercase_    :int					    =				['''.''', ''',''', ''';''', ''':''']

   for token in remove_tokens:
      lowercase_    :int					    =				ground_truth.replace(lowercase_			,""				)
      lowercase_    :Tuple					    =				prediction.replace(lowercase_			,""				)

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

   lowercase_    :Union[str, Any]					    =				ground_truth.intersection(lowercase_				)
   lowercase_    :int					    =				ground_truth.union(lowercase_				)
   lowercase_    :int					    =				len(lowercase_				) / len(lowercase_				)
   return jaccard
def 							UpperCAmelCase_						(		__lowerCamelCase      :      str				):

   def remove_articles(__lowerCamelCase      :      Union[str, Any]				):
      return re.sub(r"\b(a|an|the)\b"			," "			,lowercase_				)

   def white_space_fix(__lowerCamelCase      :      List[Any]				):
      return " ".join(text.split()				)

   def remove_punc(__lowerCamelCase      :      int				):
      lowercase_    :List[str]					    =				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(lowercase_				)				)				)				)
def 							UpperCAmelCase_						(		__lowerCamelCase      :      Any			,__lowerCamelCase      :      Dict			,__lowerCamelCase      :      Union[str, Any]				):
   lowercase_    :Tuple					    =				0, 0, 0

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

         if not match_found:
            fp += 1

   lowercase_    :Dict					    =				tp / (tp + fp) if tp + fp > 0 else np.nan
   lowercase_    :str					    =				tp / (tp + fn) if tp + fn > 0 else np.nan

   return precision, recall
def 							UpperCAmelCase_						(		__lowerCamelCase      :      int				):
   lowercase_    :Optional[Any]					    =				precisions[::-1]
   for i in range(1			,len(lowercase_				)				):
      lowercase_    :Any					    =				max(precision_best[i - 1]			,precision_best[i]				)
   lowercase_    :Tuple					    =				precision_best[::-1]
   return precisions
def 							UpperCAmelCase_						(		__lowerCamelCase      :      Dict			,__lowerCamelCase      :      Optional[int]				):
   lowercase_    :List[str]					    =				process_precisions(lowercase_				)
   lowercase_    :Tuple					    =				np.trapz(lowercase_			,lowercase_				)
   if np.isnan(lowercase_				):
      return 0
   return aupr
def 							UpperCAmelCase_						(		__lowerCamelCase      :      int			,__lowerCamelCase      :      str			,__lowerCamelCase      :      Optional[Any]				):
   lowercase_    :Optional[int]					    =				process_precisions(lowercase_				)
   lowercase_    :Dict					    =				0
   for prec, recall in zip(lowercase_			,lowercase_				):
      if recall >= recall_thresh:
         lowercase_    :Optional[Any]					    =				prec
         break
   return prec_at_recall
def 							UpperCAmelCase_						(		__lowerCamelCase      :      Union[str, Any]			,__lowerCamelCase      :      int				):
   return normalize_answer(lowercase_				) == normalize_answer(lowercase_				)
def 							UpperCAmelCase_						(		__lowerCamelCase      :      Dict			,__lowerCamelCase      :      str			,__lowerCamelCase      :      Any				):
   lowercase_    :Any					    =				0
   for pred in predictions:
      for ground_truth in ground_truths:
         lowercase_    :Optional[int]					    =				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 							UpperCAmelCase_						(		__lowerCamelCase      :      Tuple			,__lowerCamelCase      :      int				):
   lowercase_    :List[Any]					    =				0
   lowercase_    :Any					    =				[]
   lowercase_    :int					    =				[]
   for article in dataset:
      for paragraph in article["paragraphs"]:
         for qa in paragraph["qas"]:
            total += 1
            if qa["id"] not in predictions:
               lowercase_    :int					    =				'''Unanswered question ''' + qa['''id'''] + ''' will receive score 0.'''
               print(lowercase_			,file=sys.stderr				)
               continue
            lowercase_    :List[str]					    =				[x['''text'''] for x in qa['''answers''']]
            lowercase_    :str					    =				predictions[qa['''id''']]
            lowercase_    :List[str]					    =				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_				)

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

   lowercase_    :Dict					    =				1_00.0 * fa / total
   lowercase_    :str					    =				1_00.0 * exact_match / total
   lowercase_    :Tuple					    =				get_aupr(lowercase_			,lowercase_				)

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









'''simple docstring'''


from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...file_utils import TensorType, is_torch_available
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import logging


lowerCAmelCase      :  str      							=logging.get_logger(__name__)

lowerCAmelCase      :  Union[str, Any]      							={
    '''facebook/blenderbot-3B''': '''https://huggingface.co/facebook/blenderbot-3B/resolve/main/config.json''',
    # See all Blenderbot models at https://huggingface.co/models?filter=blenderbot
}
class    a_     (       _lowerCAmelCase							):
     __A  				=    "blenderbot"
     __A  				=    ["past_key_values"]
     __A  				=    {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}





     def __init__(				self	:      Dict			,   lowercase	:      List[Any]=8_008			,   lowercase	:      List[str]=128			,   lowercase	:      Optional[int]=2			,   lowercase	:      Optional[int]=10_240			,   lowercase	:      Tuple=32			,   lowercase	:      int=24			,   lowercase	:      Dict=10_240			,   lowercase	:      Any=32			,   lowercase	:      Optional[int]=0.0			,   lowercase	:      Dict=0.0			,   lowercase	:      int=True			,   lowercase	:      Tuple=True			,   lowercase	:      List[str]="gelu"			,   lowercase	:      int=2_560			,   lowercase	:      Optional[Any]=0.1			,   lowercase	:      Any=0.0			,   lowercase	:      List[str]=0.0			,   lowercase	:      Optional[Any]=0.02			,   lowercase	:      Tuple=1			,   lowercase	:      Union[str, Any]=False			,   lowercase	:      str=0			,   lowercase	:      List[str]=1			,   lowercase	:      Optional[Any]=2			,   lowercase	:      Union[str, Any]=3			,   lowercase	:      List[str]=2			,   **lowercase	:      Optional[Any]			,   ):





        """simple docstring"""




        lowercase_    :str					    =				vocab_size
        lowercase_    :Any					    =				max_position_embeddings
        lowercase_    :List[Any]					    =				d_model
        lowercase_    :Union[str, Any]					    =				encoder_ffn_dim
        lowercase_    :str					    =				encoder_layers
        lowercase_    :Optional[int]					    =				encoder_attention_heads
        lowercase_    :Dict					    =				decoder_ffn_dim
        lowercase_    :str					    =				decoder_layers
        lowercase_    :int					    =				decoder_attention_heads
        lowercase_    :Union[str, Any]					    =				dropout
        lowercase_    :int					    =				attention_dropout
        lowercase_    :int					    =				activation_dropout
        lowercase_    :str					    =				activation_function
        lowercase_    :Tuple					    =				init_std
        lowercase_    :Any					    =				encoder_layerdrop
        lowercase_    :Dict					    =				decoder_layerdrop
        lowercase_    :int					    =				use_cache
        lowercase_    :Dict					    =				encoder_layers
        lowercase_    :Dict					    =				scale_embedding  # scale factor will be sqrt(d_model) if True

        super().__init__(
            pad_token_id=lowercase			,   bos_token_id=lowercase			,   eos_token_id=lowercase			,   is_encoder_decoder=lowercase			,   decoder_start_token_id=lowercase			,   encoder_no_repeat_ngram_size=lowercase			,   forced_eos_token_id=lowercase			,   **lowercase			,   )







class    a_     (       _lowerCAmelCase							):





     @property
     def 							lowercase__   (				self	:      Union[str, Any]		):





        """simple docstring"""




        if self.task in ["default", "seq2seq-lm"]:
           lowercase_    :Optional[int]					    =				OrderedDict(
               [
                   ("input_ids", {0: "batch", 1: "encoder_sequence"}),
                   ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
               ]		)
           if self.use_past:
              lowercase_    :int					    =				{0: "batch"}
              lowercase_    :List[str]					    =				{0: "batch", 1: "past_decoder_sequence + sequence"}
           else:
              lowercase_    :Union[str, Any]					    =				{0: "batch", 1: "decoder_sequence"}
              lowercase_    :List[Any]					    =				{0: "batch", 1: "decoder_sequence"}
           if self.use_past:
              self.fill_with_past_key_values_(lowercase			,   direction="inputs"		)
        elif self.task == "causal-lm":
           lowercase_    :List[Any]					    =				OrderedDict(
               [
                   ("input_ids", {0: "batch", 1: "encoder_sequence"}),
                   ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
               ]		)
           if self.use_past:
              lowercase_    ,							lowercase_    :Optional[int]					    =				self.num_layers
              for i in range(lowercase		):
                 lowercase_    :List[str]					    =				{0: "batch", 2: "past_sequence + sequence"}
                 lowercase_    :Any					    =				{0: "batch", 2: "past_sequence + sequence"}
        else:
           lowercase_    :str					    =				OrderedDict(
               [
                   ("input_ids", {0: "batch", 1: "encoder_sequence"}),
                   ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
                   ("decoder_input_ids", {0: "batch", 1: "decoder_sequence"}),
                   ("decoder_attention_mask", {0: "batch", 1: "decoder_sequence"}),
               ]		)

        return common_inputs





     @property
     # Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.outputs
     def 							lowercase__   (				self	:      Dict		):





        """simple docstring"""




        if self.task in ["default", "seq2seq-lm"]:
           lowercase_    :Union[str, Any]					    =				super().outputs
        else:
           lowercase_    :Optional[Any]					    =				super(lowercase			,   self		).outputs
           if self.use_past:
              lowercase_    ,							lowercase_    :Any					    =				self.num_layers
              for i in range(lowercase		):
                 lowercase_    :str					    =				{0: "batch", 2: "past_sequence + sequence"}
                 lowercase_    :Any					    =				{0: "batch", 2: "past_sequence + sequence"}
        return common_outputs





     def 							lowercase__   (				self	:      Tuple			,   lowercase	:      PreTrainedTokenizer			,   lowercase	:      int = -1			,   lowercase	:      int = -1			,   lowercase	:      bool = False			,   lowercase	:      Optional[TensorType] = None			,   ):





        """simple docstring"""




        lowercase_    :int					    =				self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
            lowercase			,   lowercase			,   lowercase			,   lowercase			,   lowercase		)
        # Generate decoder inputs
        lowercase_    :List[Any]					    =				seq_length if not self.use_past else 1
        lowercase_    :Optional[Any]					    =				self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
            lowercase			,   lowercase			,   lowercase			,   lowercase			,   lowercase		)
        lowercase_    :Tuple					    =				{F'decoder_{name}': tensor for name, tensor in decoder_inputs.items()}
        lowercase_    :Any					    =				dict(**lowercase			,   **lowercase		)

        if self.use_past:
           if not is_torch_available():
              raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed."		)
           else:
              import torch
           lowercase_    ,							lowercase_    :int					    =				common_inputs["input_ids"].shape
           lowercase_    :Tuple					    =				common_inputs["decoder_input_ids"].shape[1]
           lowercase_    ,							lowercase_    :Tuple					    =				self.num_attention_heads
           lowercase_    :List[Any]					    =				(
               batch,
               num_encoder_attention_heads,
               encoder_seq_length,
               self._config.hidden_size // num_encoder_attention_heads,
           )
           lowercase_    :List[Any]					    =				decoder_seq_length
           lowercase_    :str					    =				(
               batch,
               num_decoder_attention_heads,
               decoder_past_length,
               self._config.hidden_size // num_decoder_attention_heads,
           )
           lowercase_    :Optional[int]					    =				torch.cat(
               [common_inputs["decoder_attention_mask"], torch.ones(lowercase			,   lowercase		)]			,   dim=1		)
           lowercase_    :int					    =				[]
           lowercase_    ,							lowercase_    :Tuple					    =				self.num_layers

           for _ in range(lowercase		):
              common_inputs["past_key_values"].append(
                  (
                      torch.zeros(lowercase		),
                      torch.zeros(lowercase		),
                      torch.zeros(lowercase		),
                      torch.zeros(lowercase		),
                  )		)
        return common_inputs





     def 							lowercase__   (				self	:      Optional[Any]			,   lowercase	:      PreTrainedTokenizer			,   lowercase	:      int = -1			,   lowercase	:      int = -1			,   lowercase	:      bool = False			,   lowercase	:      Optional[TensorType] = None			,   ):





        """simple docstring"""




        lowercase_    :str					    =				self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
            lowercase			,   lowercase			,   lowercase			,   lowercase			,   lowercase		)

        if self.use_past:
           if not is_torch_available():
              raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed."		)
           else:
              import torch
           lowercase_    ,							lowercase_    :List[str]					    =				common_inputs["input_ids"].shape
           lowercase_    :int					    =				seqlen
           lowercase_    ,							lowercase_    :int					    =				self.num_layers
           lowercase_    ,							lowercase_    :Union[str, Any]					    =				self.num_attention_heads
           lowercase_    :Any					    =				(
               batch,
               num_encoder_attention_heads,
               past_key_values_length,
               self._config.hidden_size // num_encoder_attention_heads,
           )
           lowercase_    :Optional[Any]					    =				common_inputs["attention_mask"].dtype
           lowercase_    :List[Any]					    =				torch.cat(
               [common_inputs["attention_mask"], torch.ones(lowercase			,   lowercase			,   dtype=lowercase		)]			,   dim=1		)
           lowercase_    :Any					    =				[
               (torch.zeros(lowercase		), torch.zeros(lowercase		)) for _ in range(lowercase		)
           ]
        return common_inputs





     def 							lowercase__   (				self	:      Dict			,   lowercase	:      PreTrainedTokenizer			,   lowercase	:      int = -1			,   lowercase	:      int = -1			,   lowercase	:      bool = False			,   lowercase	:      Optional[TensorType] = None			,   ):





        """simple docstring"""




        lowercase_    :Optional[int]					    =				compute_effective_axis_dimension(
            lowercase			,   fixed_dimension=OnnxConfig.default_fixed_batch			,   num_token_to_add=0		)

        # If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
        lowercase_    :Optional[Any]					    =				tokenizer.num_special_tokens_to_add(lowercase		)
        lowercase_    :int					    =				compute_effective_axis_dimension(
            lowercase			,   fixed_dimension=OnnxConfig.default_fixed_sequence			,   num_token_to_add=lowercase		)

        # Generate dummy inputs according to compute batch and sequence
        lowercase_    :int					    =				[" ".join([tokenizer.unk_token]		) * seq_length] * batch_size
        lowercase_    :Dict					    =				dict(tokenizer(lowercase			,   return_tensors=lowercase		)		)
        return common_inputs





     def 							lowercase__   (				self	:      Optional[Any]			,   lowercase	:      PreTrainedTokenizer			,   lowercase	:      int = -1			,   lowercase	:      int = -1			,   lowercase	:      bool = False			,   lowercase	:      Optional[TensorType] = None			,   ):





        """simple docstring"""




        if self.task in ["default", "seq2seq-lm"]:
           lowercase_    :str					    =				self._generate_dummy_inputs_for_default_and_seqaseq_lm(
               lowercase			,   batch_size=lowercase			,   seq_length=lowercase			,   is_pair=lowercase			,   framework=lowercase		)

        elif self.task == "causal-lm":
           lowercase_    :Optional[Any]					    =				self._generate_dummy_inputs_for_causal_lm(
               lowercase			,   batch_size=lowercase			,   seq_length=lowercase			,   is_pair=lowercase			,   framework=lowercase		)
        else:
           lowercase_    :List[str]					    =				self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
               lowercase			,   batch_size=lowercase			,   seq_length=lowercase			,   is_pair=lowercase			,   framework=lowercase		)

        return common_inputs





     def 							lowercase__   (				self	:      Optional[int]			,   lowercase	:      str			,   lowercase	:      Union[str, Any]			,   lowercase	:      int			,   lowercase	:      List[Any]		):





        """simple docstring"""




        if self.task in ["default", "seq2seq-lm"]:
           lowercase_    :Optional[int]					    =				super()._flatten_past_key_values_(lowercase			,   lowercase			,   lowercase			,   lowercase		)
        else:
           lowercase_    :Tuple					    =				super(lowercase			,   self		)._flatten_past_key_values_(
               lowercase			,   lowercase			,   lowercase			,   lowercase		)







     def 							lowercase__   (				self	:      List[Any]			,   lowercase	:      Mapping[str, Mapping[int, str]]			,   lowercase	:      str		):





        """simple docstring"""




        if direction not in ["inputs", "outputs"]:
           raise ValueError(F'direction must either be "inputs" or "outputs", but {direction} was given'		)

        lowercase_    :Any					    =				"past_key_values" if direction == "inputs" else "present"
        lowercase_    ,							lowercase_    :Optional[int]					    =				self.num_layers

        lowercase_    :str					    =				"past_encoder_sequence"
        lowercase_    :Tuple					    =				"past_decoder_sequence" if direction == "inputs" else "past_decoder_sequence + sequence"

        for i in range(lowercase		):
           lowercase_    :Dict					    =				{0: "batch", 2: decoder_sequence}
           lowercase_    :Optional[int]					    =				{0: "batch", 2: decoder_sequence}
           lowercase_    :Optional[int]					    =				{0: "batch", 2: encoder_sequence}
           lowercase_    :int					    =				{0: "batch", 2: encoder_sequence}


