

import argparse
import json
import re
import string
import sys
from collections import Counter



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



  """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  )							->			str:



  """simple docstring"""






  _snake_case		=							normalize_answer(_UpperCamelCase  ).split()
  _snake_case		=							normalize_answer(_UpperCamelCase  ).split()
  _snake_case		=							Counter(_UpperCamelCase  ) & Counter(_UpperCamelCase  )
  _snake_case		=							sum(common.values()  )
  if num_same == 0:
    return 0
  _snake_case		=							1.0 * num_same / len(_UpperCamelCase  )
  _snake_case		=							1.0 * num_same / len(_UpperCamelCase  )
  _snake_case		=							(2 * precision * recall) / (precision + recall)
  return fa



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



  """simple docstring"""






  return normalize_answer(_UpperCamelCase  ) == normalize_answer(_UpperCamelCase  )



def    snake_case_(_UpperCamelCase				, _UpperCamelCase				, _UpperCamelCase  )							->			str:



  """simple docstring"""






  _snake_case		=							[]
  for ground_truth in ground_truths:
    _snake_case		=							metric_fn(_UpperCamelCase				, _UpperCamelCase  )
    scores_for_ground_truths.append(_UpperCamelCase  )
  return max(_UpperCamelCase  )



def    snake_case_(_UpperCamelCase				, _UpperCamelCase  )							->			int:



  """simple docstring"""






  _snake_case		=							_snake_case		=							_snake_case		=							0
  _snake_case		=							[]
  for passage in dataset:
    for qa in passage["qas"]:
      total += 1
      if qa["id"] not in predictions:
        _snake_case		=							F"""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		=							metric_max_over_ground_truths(_UpperCamelCase				, _UpperCamelCase				, _UpperCamelCase  )
      if int(_exact_match  ) == 1:
        correct_ids.append(qa['''id''']  )
      exact_match += _exact_match

      fa += metric_max_over_ground_truths(_UpperCamelCase				, _UpperCamelCase				, _UpperCamelCase  )

  _snake_case		=							exact_match / total
  _snake_case		=							fa / total

  return {"exact_match": exact_match, "f1": fa}, correct_ids


if __name__ == "__main__":
 __A     :   Dict    	=			'''1.0'''
 __A     :   Dict    	=			argparse.ArgumentParser('''Official evaluation script for ReCoRD v1.0.''')
 parser.add_argument('''data_file''', help='''The dataset file in JSON format.''')
 parser.add_argument('''pred_file''', help='''The model prediction file in JSON format.''')
 parser.add_argument('''--output_correct_ids''', action='''store_true''', help='''Output the correctly answered query IDs.''')
 __A     :   Optional[Any]    	=			parser.parse_args()

 with open(args.data_file) as data_file:
  __A     :   List[Any]    	=			json.load(data_file)
  if dataset_json["version"] != expected_version:
   print(
       f'''Evaluation expects v-{expected_version}, but got dataset with v-{dataset_json["version"]}''',
       file=sys.stderr,
   )
  __A     :   List[str]    	=			dataset_json['''data''']

 with open(args.pred_file) as pred_file:
  __A     :   Optional[int]    	=			json.load(pred_file)

 __A	,  __A     :   int    	=			evaluate(dataset, predictions)

 if args.output_correct_ids:
  print(f'''Output {len(correct_ids)} correctly answered question IDs.''')
  with open('''correct_ids.json''', '''w''') as f:
   json.dump(correct_ids, f)




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)

