import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import cached_download, hf_hub_url
from PIL import Image

from transformers import DeformableDetrConfig, DeformableDetrForObjectDetection, DeformableDetrImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
UpperCAmelCase					=				logging.get_logger(__name__)


def SCREAMING_SNAKE_CASE				(     lowercase_:	List[Any]				):
       if "backbone.0.body" in orig_key:
              _UpperCamelCase     :       Dict  						=  orig_key.replace('backbone.0.body'		,						'backbone.conv_encoder.model'				)
       if "transformer" in orig_key:
              _UpperCamelCase     :       Optional[Any]  						=  orig_key.replace('transformer.'		,						''				)
       if "norm1" in orig_key:
              if "encoder" in orig_key:
                     _UpperCamelCase     :       Optional[int]  						=  orig_key.replace('norm1'		,						'self_attn_layer_norm'				)
              else:
                     _UpperCamelCase     :       Tuple  						=  orig_key.replace('norm1'		,						'encoder_attn_layer_norm'				)
       if "norm2" in orig_key:
              if "encoder" in orig_key:
                     _UpperCamelCase     :       Dict  						=  orig_key.replace('norm2'		,						'final_layer_norm'				)
              else:
                     _UpperCamelCase     :       Dict  						=  orig_key.replace('norm2'		,						'self_attn_layer_norm'				)
       if "norm3" in orig_key:
              _UpperCamelCase     :       str  						=  orig_key.replace('norm3'		,						'final_layer_norm'				)
       if "linear1" in orig_key:
              _UpperCamelCase     :       Any  						=  orig_key.replace('linear1'		,						'fc1'				)
       if "linear2" in orig_key:
              _UpperCamelCase     :       List[str]  						=  orig_key.replace('linear2'		,						'fc2'				)
       if "query_embed" in orig_key:
              _UpperCamelCase     :       Dict  						=  orig_key.replace('query_embed'		,						'query_position_embeddings'				)
       if "cross_attn" in orig_key:
              _UpperCamelCase     :       Dict  						=  orig_key.replace('cross_attn'		,						'encoder_attn'				)

       return orig_key


def SCREAMING_SNAKE_CASE				(     lowercase_:	Tuple				):
       # transformer decoder self-attention layers
       for i in range(6				):
              # read in weights + bias of input projection layer of self-attention
              _UpperCamelCase     :       Any  						=  state_dict.pop(F"decoder.layers.{i}.self_attn.in_proj_weight"				)
              _UpperCamelCase     :       Any  						=  state_dict.pop(F"decoder.layers.{i}.self_attn.in_proj_bias"				)
              # next, add query, keys and values (in that order) to the state dict
              _UpperCamelCase     :       List[Any]  						=  in_proj_weight[:256, :]
              _UpperCamelCase     :       List[str]  						=  in_proj_bias[:256]
              _UpperCamelCase     :       int  						=  in_proj_weight[256:512, :]
              _UpperCamelCase     :       List[str]  						=  in_proj_bias[256:512]
              _UpperCamelCase     :       int  						=  in_proj_weight[-256:, :]
              _UpperCamelCase     :       int  						=  in_proj_bias[-256:]


def SCREAMING_SNAKE_CASE				(     ):
       _UpperCamelCase     :       int  						=  'http://images.cocodataset.org/val2017/000000039769.jpg'
       _UpperCamelCase     :       int  						=  Image.open(requests.get(lowercase_		,						stream=lowercase_				).raw				)

       return im


@torch.no_grad()
def SCREAMING_SNAKE_CASE				(     lowercase_:	Tuple		,						lowercase_:	List[Any]		,						lowercase_:	List[str]		,						lowercase_:	str		,						lowercase_:	Optional[int]		,						lowercase_:	List[str]		,						lowercase_:	Tuple		,						):
       _UpperCamelCase     :       List[Any]  						=  DeformableDetrConfig()
       # set config attributes
       if single_scale:
              _UpperCamelCase     :       str  						=  1
       _UpperCamelCase     :       Union[str, Any]  						=  dilation
       _UpperCamelCase     :       Any  						=  with_box_refine
       _UpperCamelCase     :       Any  						=  two_stage
       # set labels
       _UpperCamelCase     :       Union[str, Any]  						=  91
       _UpperCamelCase     :       Tuple  						=  'huggingface/label-files'
       _UpperCamelCase     :       List[str]  						=  'coco-detection-id2label.json'
       _UpperCamelCase     :       List[Any]  						=  json.load(open(cached_download(hf_hub_url(lowercase_		,						lowercase_		,						repo_type='dataset'				)				)		,						'r'				)				)
       _UpperCamelCase     :       Dict  						=  {int(lowercase_				): v for k, v in idalabel.items()}
       _UpperCamelCase     :       int  						=  idalabel
       _UpperCamelCase     :       List[str]  						=  {v: k for k, v in idalabel.items()}

       # load image processor
       _UpperCamelCase     :       Any  						=  DeformableDetrImageProcessor(format='coco_detection'				)

       # prepare image
       _UpperCamelCase     :       str  						=  prepare_img()
       _UpperCamelCase     :       Optional[Any]  						=  image_processor(images=lowercase_		,						return_tensors='pt'				)
       _UpperCamelCase     :       Any  						=  encoding['pixel_values']

       logger.info('Converting model...'				)

       # load original state dict
       _UpperCamelCase     :       Union[str, Any]  						=  torch.load(lowercase_		,						map_location='cpu'				)['model']
       # rename keys
       for key in state_dict.copy().keys():
              _UpperCamelCase     :       Tuple  						=  state_dict.pop(lowercase_				)
              _UpperCamelCase     :       Union[str, Any]  						=  val
       # query, key and value matrices need special treatment
       read_in_q_k_v(lowercase_				)
       # important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
       _UpperCamelCase     :       int  						=  'model.'
       for key in state_dict.copy().keys():
              if not key.startswith('class_embed'				) and not key.startswith('bbox_embed'				):
                     _UpperCamelCase     :       Dict  						=  state_dict.pop(lowercase_				)
                     _UpperCamelCase     :       Dict  						=  val
    # finally, create HuggingFace model and load state dict
       _UpperCamelCase     :       int  						=  DeformableDetrForObjectDetection(lowercase_				)
       model.load_state_dict(lowercase_				)
       model.eval()

       _UpperCamelCase     :       Any  						=  'cuda' if torch.cuda.is_available() else 'cpu'
       model.to(lowercase_				)
       # verify our conversion
       _UpperCamelCase     :       Dict  						=  model(pixel_values.to(lowercase_				)				)

       _UpperCamelCase     :       Optional[Any]  						=  torch.tensor(
           [[-9.6645, -4.3449, -5.8705], [-9.7035, -3.8504, -5.0724], [-10.5634, -5.3379, -7.5116]]				)
       _UpperCamelCase     :       Dict  						=  torch.tensor([[0.8693, 0.2289, 0.2492], [0.3150, 0.5489, 0.5845], [0.5563, 0.7580, 0.8518]]				)

       if single_scale:
              _UpperCamelCase     :       Optional[int]  						=  torch.tensor(
                  [[-9.9051, -4.2541, -6.4852], [-9.6947, -4.0854, -6.8033], [-10.0665, -5.8470, -7.7003]]				)
              _UpperCamelCase     :       Any  						=  torch.tensor([[0.7292, 0.4991, 0.5532], [0.7959, 0.2426, 0.4236], [0.7582, 0.3518, 0.4451]]				)

       if single_scale and dilation:
              _UpperCamelCase     :       Optional[Any]  						=  torch.tensor(
                  [[-8.9652, -4.1074, -5.6635], [-9.0596, -4.9447, -6.6075], [-10.1178, -4.5275, -6.2671]]				)
              _UpperCamelCase     :       int  						=  torch.tensor([[0.7665, 0.4130, 0.4769], [0.8364, 0.1841, 0.3391], [0.6261, 0.3895, 0.7978]]				)

       if with_box_refine:
              _UpperCamelCase     :       Dict  						=  torch.tensor(
                  [[-8.8895, -5.4187, -6.8153], [-8.4706, -6.1668, -7.6184], [-9.0042, -5.5359, -6.9141]]				)
              _UpperCamelCase     :       List[str]  						=  torch.tensor([[0.7828, 0.2208, 0.4323], [0.0892, 0.5996, 0.1319], [0.5524, 0.6389, 0.8914]]				)

       if with_box_refine and two_stage:
              _UpperCamelCase     :       int  						=  torch.tensor(
                  [[-6.7108, -4.3213, -6.3777], [-8.9014, -6.1799, -6.7240], [-6.9315, -4.4735, -6.2298]]				)
              _UpperCamelCase     :       Optional[Any]  						=  torch.tensor([[0.2583, 0.5499, 0.4683], [0.7652, 0.9068, 0.4882], [0.5490, 0.2763, 0.0564]]				)

       print('Logits:'		,						outputs.logits[0, :3, :3]				)

       assert torch.allclose(outputs.logits[0, :3, :3]		,						expected_logits.to(lowercase_				)		,						atol=1e-4				)
       assert torch.allclose(outputs.pred_boxes[0, :3, :3]		,						expected_boxes.to(lowercase_				)		,						atol=1e-4				)

       print('Everything ok!'				)

       # Save model and image processor
       logger.info(F"Saving PyTorch model and image processor to {pytorch_dump_folder_path}..."				)
       Path(lowercase_				).mkdir(exist_ok=lowercase_				)
       model.save_pretrained(lowercase_				)
       image_processor.save_pretrained(lowercase_				)

       # Push to hub
       if push_to_hub:
              _UpperCamelCase     :       List[str]  						=  'deformable-detr'
              model_name += "-single-scale" if single_scale else ""
              model_name += "-dc5" if dilation else ""
              model_name += "-with-box-refine" if with_box_refine else ""
              model_name += "-two-stage" if two_stage else ""
              print('Pushing model to hub...'				)
              model.push_to_hub(repo_path_or_name=lowercase_		,						organization='nielsr'		,						commit_message='Add model'				)


if __name__ == "__main__":
 UpperCAmelCase					=				argparse.ArgumentParser()

 parser.add_argument(
     '''--checkpoint_path''',
     type=str,
     default='''/home/niels/checkpoints/deformable_detr/r50_deformable_detr-checkpoint.pth''',
     help='''Path to Pytorch checkpoint (.pth file) you\'d like to convert.''',
 )
 parser.add_argument('''--single_scale''', action='''store_true''', help='''Whether to set config.num_features_levels = 1.''')
 parser.add_argument('''--dilation''', action='''store_true''', help='''Whether to set config.dilation=True.''')
 parser.add_argument('''--with_box_refine''', action='''store_true''', help='''Whether to set config.with_box_refine=True.''')
 parser.add_argument('''--two_stage''', action='''store_true''', help='''Whether to set config.two_stage=True.''')
 parser.add_argument(
     '''--pytorch_dump_folder_path''',
     default=None,
     type=str,
     required=True,
     help='''Path to the folder to output PyTorch model.''',
 )
 parser.add_argument(
     '''--push_to_hub''', action='''store_true''', help='''Whether or not to push the converted model to the 🤗 hub.'''
 )
 UpperCAmelCase					=				parser.parse_args()
 convert_deformable_detr_checkpoint(
     args.checkpoint_path,
     args.single_scale,
     args.dilation,
     args.with_box_refine,
     args.two_stage,
     args.pytorch_dump_folder_path,
     args.push_to_hub,
 )




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


def SCREAMING_SNAKE_CASE				(     lowercase_:	List[Any]				):

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

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

       def remove_punc(lowercase_:	List[str]				):
              _UpperCamelCase     :       str  						=  set(string.punctuation				)
              return "".join(ch for ch in text if ch not in exclude				)

       def lower(lowercase_:	List[str]				):
              return text.lower()

       return white_space_fix(remove_articles(remove_punc(lower(lowercase_				)				)				)				)


def SCREAMING_SNAKE_CASE				(     lowercase_:	List[Any]		,						lowercase_:	Tuple				):
       _UpperCamelCase     :       int  						=  normalize_answer(lowercase_				).split()
       _UpperCamelCase     :       int  						=  normalize_answer(lowercase_				).split()
       _UpperCamelCase     :       Union[str, Any]  						=  Counter(lowercase_				) & Counter(lowercase_				)
       _UpperCamelCase     :       Optional[int]  						=  sum(common.values()				)
       if num_same == 0:
              return 0
       _UpperCamelCase     :       Optional[Any]  						=  1.0 * num_same / len(lowercase_				)
       _UpperCamelCase     :       Optional[int]  						=  1.0 * num_same / len(lowercase_				)
       _UpperCamelCase     :       List[str]  						=  (2 * precision * recall) / (precision + recall)
       return fa


def SCREAMING_SNAKE_CASE				(     lowercase_:	str		,						lowercase_:	Union[str, Any]				):
       return normalize_answer(lowercase_				) == normalize_answer(lowercase_				)


def SCREAMING_SNAKE_CASE				(     lowercase_:	Optional[int]		,						lowercase_:	int		,						lowercase_:	Union[str, Any]				):
       _UpperCamelCase     :       Any  						=  []
       for ground_truth in ground_truths:
              _UpperCamelCase     :       Optional[Any]  						=  metric_fn(lowercase_		,						lowercase_				)
              scores_for_ground_truths.append(lowercase_				)
       return max(lowercase_				)


def SCREAMING_SNAKE_CASE				(     lowercase_:	Union[str, Any]		,						lowercase_:	List[Any]				):
       _UpperCamelCase     :       List[Any]  						=  0
       for article in dataset:
              for paragraph in article["paragraphs"]:
                     for qa in paragraph["qas"]:
                            total += 1
                            if qa["id"] not in predictions:
                                   _UpperCamelCase     :       Union[str, Any]  						=  'Unanswered question ' + qa['id'] + ' will receive score 0.'
                                   print(lowercase_		,						file=sys.stderr				)
                                   continue
                            _UpperCamelCase     :       Dict  						=  [x['text'] for x in qa['answers']]
                            _UpperCamelCase     :       List[str]  						=  predictions[qa['id']]
                            exact_match += metric_max_over_ground_truths(lowercase_		,						lowercase_		,						lowercase_				)
                            fa += metric_max_over_ground_truths(lowercase_		,						lowercase_		,						lowercase_				)

       _UpperCamelCase     :       Dict  						=  100.0 * exact_match / total
       _UpperCamelCase     :       Optional[Any]  						=  100.0 * fa / total

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


if __name__ == "__main__":
 UpperCAmelCase					=				'''1.1'''
 UpperCAmelCase					=				argparse.ArgumentParser(description='''Evaluation for SQuAD ''' + expected_version)
 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)
  if dataset_json["version"] != expected_version:
   print(
       '''Evaluation expects v-''' + expected_version + ''', but got dataset with v-''' + dataset_json['''version'''],
       file=sys.stderr,
   )
  UpperCAmelCase					=				dataset_json['''data''']
 with open(args.prediction_file) as prediction_file:
  UpperCAmelCase					=				json.load(prediction_file)
 print(json.dumps(evaluate(dataset, predictions)))



