

import argparse
import json
import re
import string
import sys

import numpy as np


__A    	=			0.5



def    snake_case_(_UpperCamelCase				, _UpperCamelCase  )							->			str:



  """simple docstring"""






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

  for token in remove_tokens:
    _snake_case		=							ground_truth.replace(_UpperCamelCase				, ''''''  )
    _snake_case		=							prediction.replace(_UpperCamelCase				, ''''''  )

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

  _snake_case		=							ground_truth.intersection(_UpperCamelCase  )
  _snake_case		=							ground_truth.union(_UpperCamelCase  )
  _snake_case		=							len(_UpperCamelCase  ) / len(_UpperCamelCase  )
  return jaccard



def    snake_case_(_UpperCamelCase  )							->			int:



  """simple docstring"""






  def remove_articles(_UpperCamelCase  ):
    return re.sub(R'''\b(a|an|the)\b'''				, ''' '''				, _UpperCamelCase  )

  def white_space_fix(_UpperCamelCase  ):
    return " ".join(text.split()  )

  def remove_punc(_UpperCamelCase  ):
    _snake_case		=							set(string.punctuation  )
    return "".join(ch for ch in text if ch not in exclude  )

  def lower(_UpperCamelCase  ):
    return text.lower()

  return white_space_fix(remove_articles(remove_punc(lower(_UpperCamelCase  )  )  )  )



def    snake_case_(_UpperCamelCase				, _UpperCamelCase				, _UpperCamelCase  )							->			Union[str, Any]:



  """simple docstring"""






  _snake_case,  _snake_case,  _snake_case		=							0, 0, 0

  _snake_case		=							'''Parties''' in qa_id

  # first check if ground truth is empty
  if len(_UpperCamelCase  ) == 0:
    if len(_UpperCamelCase  ) > 0:
      fp += len(_UpperCamelCase  )  # false positive for each one
  else:
    for ground_truth in ground_truths:
      assert len(_UpperCamelCase  ) > 0
      # check if there is a match
      _snake_case		=							False
      for pred in predictions:
        if substr_ok:
          _snake_case		=							get_jaccard(_UpperCamelCase				, _UpperCamelCase  ) >= IOU_THRESH or ground_truth in pred
        else:
          _snake_case		=							get_jaccard(_UpperCamelCase				, _UpperCamelCase  ) >= IOU_THRESH
        if is_match:
          _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.)
      _snake_case		=							False
      for ground_truth in ground_truths:
        assert len(_UpperCamelCase  ) > 0
        if substr_ok:
          _snake_case		=							get_jaccard(_UpperCamelCase				, _UpperCamelCase  ) >= IOU_THRESH or ground_truth in pred
        else:
          _snake_case		=							get_jaccard(_UpperCamelCase				, _UpperCamelCase  ) >= IOU_THRESH
        if is_match:
          _snake_case		=							True

      if not match_found:
        fp += 1

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

  return precision, recall



def    snake_case_(_UpperCamelCase  )							->			int:



  """simple docstring"""






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



def    snake_case_(_UpperCamelCase				, _UpperCamelCase  )							->			Dict:



  """simple docstring"""






  _snake_case		=							process_precisions(_UpperCamelCase  )
  _snake_case		=							np.trapz(_UpperCamelCase				, _UpperCamelCase  )
  if np.isnan(_UpperCamelCase  ):
    return 0
  return aupr



def    snake_case_(_UpperCamelCase				, _UpperCamelCase				, _UpperCamelCase  )							->			List[Any]:



  """simple docstring"""






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



def    snake_case_(_UpperCamelCase				, _UpperCamelCase  )							->			List[Any]:



  """simple docstring"""






  return normalize_answer(_UpperCamelCase  ) == normalize_answer(_UpperCamelCase  )



def    snake_case_(_UpperCamelCase				, _UpperCamelCase				, _UpperCamelCase  )							->			Optional[Any]:



  """simple docstring"""






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



def    snake_case_(_UpperCamelCase				, _UpperCamelCase  )							->			Tuple:



  """simple docstring"""






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

        precisions.append(_UpperCamelCase  )
        recalls.append(_UpperCamelCase  )

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

        exact_match += metric_max_over_ground_truths(_UpperCamelCase				, _UpperCamelCase				, _UpperCamelCase  )

  _snake_case		=							[x for _, x in sorted(zip(_UpperCamelCase				, _UpperCamelCase  )  )]
  recalls.sort()

  _snake_case		=							100.0 * fa / total
  _snake_case		=							100.0 * exact_match / total
  _snake_case		=							get_aupr(_UpperCamelCase				, _UpperCamelCase  )

  _snake_case		=							get_prec_at_recall(_UpperCamelCase				, _UpperCamelCase				, recall_thresh=0.9  )
  _snake_case		=							get_prec_at_recall(_UpperCamelCase				, _UpperCamelCase				, 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 clip import load

from transformers import CLIPConfig, CLIPModel



def    snake_case_(_UpperCamelCase				, _UpperCamelCase  )							->			Optional[Any]:



  """simple docstring"""






  _snake_case,  _snake_case,  _snake_case		=							pt_attn_layer.in_proj_weight.chunk(3				, dim=0  )
  _snake_case,  _snake_case,  _snake_case		=							pt_attn_layer.in_proj_bias.chunk(3				, dim=0  )

  _snake_case		=							pt_attn_layer.out_proj.weight
  _snake_case		=							pt_attn_layer.out_proj.bias

  _snake_case		=							q_proj
  _snake_case		=							q_proj_bias

  _snake_case		=							k_proj
  _snake_case		=							k_proj_bias

  _snake_case		=							v_proj
  _snake_case		=							v_proj_bias

  _snake_case		=							out_proj_weights
  _snake_case		=							out_proj_bias



def    snake_case_(_UpperCamelCase				, _UpperCamelCase  )							->			List[str]:



  """simple docstring"""






  copy_linear(hf_mlp.fca				, pt_mlp.c_fc  )
  copy_linear(hf_mlp.fca				, pt_mlp.c_proj  )



def    snake_case_(_UpperCamelCase				, _UpperCamelCase  )							->			str:



  """simple docstring"""






  _snake_case		=							pt_linear.weight
  _snake_case		=							pt_linear.bias



def    snake_case_(_UpperCamelCase				, _UpperCamelCase  )							->			Tuple:



  """simple docstring"""






  copy_linear(hf_layer.layer_norma				, pt_layer.ln_a  )
  copy_linear(hf_layer.layer_norma				, pt_layer.ln_a  )

  # copy MLP
  copy_mlp(hf_layer.mlp				, pt_layer.mlp  )

  # copy attn
  copy_attn_layer(hf_layer.self_attn				, pt_layer.attn  )



def    snake_case_(_UpperCamelCase				, _UpperCamelCase  )							->			List[Any]:



  """simple docstring"""






  for hf_layer, pt_layer in zip(_UpperCamelCase				, _UpperCamelCase  ):
    copy_layer(_UpperCamelCase				, _UpperCamelCase  )



def    snake_case_(_UpperCamelCase				, _UpperCamelCase  )							->			List[Any]:



  """simple docstring"""






  _snake_case		=							pt_model.token_embedding.weight
  _snake_case		=							pt_model.positional_embedding

  # copy layer norm
  copy_linear(hf_encoder.final_layer_norm				, pt_model.ln_final  )

  # copy hidden layers
  copy_layers(hf_encoder.encoder.layers				, pt_model.transformer.resblocks  )



def    snake_case_(_UpperCamelCase				, _UpperCamelCase  )							->			Tuple:



  """simple docstring"""






  _snake_case		=							pt_model.text_projection.data.T

  # copy text encoder
  copy_encoder(hf_model.text_model				, _UpperCamelCase  )



def    snake_case_(_UpperCamelCase				, _UpperCamelCase  )							->			int:



  """simple docstring"""






  _snake_case		=							pt_model.visual.proj.data.T

  # copy layer norms
  copy_linear(hf_model.vision_model.pre_layrnorm				, pt_model.visual.ln_pre  )
  copy_linear(hf_model.vision_model.post_layernorm				, pt_model.visual.ln_post  )

  # copy embeds
  _snake_case		=							pt_model.visual.conva.weight.data
  _snake_case		=							pt_model.visual.class_embedding
  _snake_case		=							pt_model.visual.positional_embedding.data

  # copy encoder
  copy_layers(hf_model.vision_model.encoder.layers				, pt_model.visual.transformer.resblocks  )



@torch.no_grad()
def    snake_case_(_UpperCamelCase				, _UpperCamelCase				, _UpperCamelCase=None  )							->			Any:



  """simple docstring"""






  if config_path is not None:
    _snake_case		=							CLIPConfig.from_pretrained(_UpperCamelCase  )
  else:
    _snake_case		=							CLIPConfig(projection_dim=512				, text_config={}				, vision_config={}  )

  _snake_case		=							CLIPModel(_UpperCamelCase  ).eval()

  _snake_case,  _snake_case		=							load(_UpperCamelCase				, device='''cpu'''				, jit=_UpperCamelCase  )
  _snake_case		=							pt_model.eval()

  copy_text_model_and_projection(_UpperCamelCase				, _UpperCamelCase  )
  copy_vison_model_and_projection(_UpperCamelCase				, _UpperCamelCase  )
  _snake_case		=							pt_model.logit_scale

  _snake_case		=							torch.arange(0				, 77  ).unsqueeze(0  )
  _snake_case		=							torch.randn(1				, 3				, 224				, 224  )

  _snake_case		=							hf_model(input_ids=_UpperCamelCase				, pixel_values=_UpperCamelCase				, return_dict=_UpperCamelCase  )
  _snake_case		=							hf_outputs.logits_per_image
  _snake_case		=							hf_outputs.logits_per_text
  _snake_case,  _snake_case		=							pt_model(_UpperCamelCase				, _UpperCamelCase  )

  assert torch.allclose(_UpperCamelCase				, _UpperCamelCase				, atol=1E-3  )
  assert torch.allclose(_UpperCamelCase				, _UpperCamelCase				, atol=1E-3  )

  hf_model.save_pretrained(_UpperCamelCase  )


if __name__ == "__main__":
 __A    	=			argparse.ArgumentParser()
 parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
 parser.add_argument('''--checkpoint_path''', default=None, type=str, help='''Path to fairseq checkpoint''')
 parser.add_argument('''--config_path''', default=None, type=str, help='''Path to hf config.json of model to convert''')
 __A    	=			parser.parse_args()

 convert_clip_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)

