



import argparse
import json
from pathlib import Path

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

from transformers import DetaConfig, DetaForObjectDetection, DetaImageProcessor
from transformers.utils import logging


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







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






      '''simple docstring'''







      UpperCAmelCase_	:						Union[str, Any]					   =		DetaConfig(
          num_queries=9_0_0       ,	encoder_ffn_dim=2_0_4_8       ,	decoder_ffn_dim=2_0_4_8       ,	num_feature_levels=5       ,	assign_first_stage=__snake_case       ,	with_box_refine=__snake_case       ,	two_stage=__snake_case       ,	)

      # set labels
      UpperCAmelCase_	:						Optional[int]					   =		9_1
      UpperCAmelCase_	:						Dict					   =		"huggingface/label-files"
      UpperCAmelCase_	:						Optional[int]					   =		"coco-detection-id2label.json"
      UpperCAmelCase_	:						List[Any]					   =		json.load(open(cached_download(hf_hub_url(__snake_case       ,	__snake_case       ,	repo_type="dataset"     )     )       ,	"r"     )     )
      UpperCAmelCase_	:						Any					   =		{int(__snake_case     ): v for k, v in idalabel.items()}
      UpperCAmelCase_	:						str					   =		idalabel
      UpperCAmelCase_	:						int					   =		{v: k for k, v in idalabel.items()}

      return config







def  _snake_case			(							__snake_case     )  ->  int:






      '''simple docstring'''







      UpperCAmelCase_	:						Any					   =		[]

      # stem
      # fmt: off
      rename_keys.append(("backbone.0.body.conv1.weight", "model.backbone.model.embedder.embedder.convolution.weight")     )
      rename_keys.append(("backbone.0.body.bn1.weight", "model.backbone.model.embedder.embedder.normalization.weight")     )
      rename_keys.append(("backbone.0.body.bn1.bias", "model.backbone.model.embedder.embedder.normalization.bias")     )
      rename_keys.append(("backbone.0.body.bn1.running_mean", "model.backbone.model.embedder.embedder.normalization.running_mean")     )
      rename_keys.append(("backbone.0.body.bn1.running_var", "model.backbone.model.embedder.embedder.normalization.running_var")     )
      # stages
      for stage_idx in range(len(config.backbone_config.depths     )     ):
            for layer_idx in range(config.backbone_config.depths[stage_idx]     ):
                  # shortcut
                  if layer_idx == 0:
                        rename_keys.append(
                            (
                                F"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.0.weight""",
                                F"""model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.convolution.weight""",
                            )     )
                        rename_keys.append(
                            (
                                F"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.weight""",
                                F"""model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.weight""",
                            )     )
                        rename_keys.append(
                            (
                                F"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.bias""",
                                F"""model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.bias""",
                            )     )
                        rename_keys.append(
                            (
                                F"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_mean""",
                                F"""model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_mean""",
                            )     )
                        rename_keys.append(
                            (
                                F"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_var""",
                                F"""model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_var""",
                            )     )
                  # 3 convs
                  for i in range(3     ):
                        rename_keys.append(
                            (
                                F"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.conv{i+1}.weight""",
                                F"""model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.convolution.weight""",
                            )     )
                        rename_keys.append(
                            (
                                F"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.weight""",
                                F"""model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.weight""",
                            )     )
                        rename_keys.append(
                            (
                                F"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.bias""",
                                F"""model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.bias""",
                            )     )
                        rename_keys.append(
                            (
                                F"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_mean""",
                                F"""model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_mean""",
                            )     )
                        rename_keys.append(
                            (
                                F"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_var""",
                                F"""model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_var""",
                            )     )
    # transformer encoder
      for i in range(config.encoder_layers     ):
            rename_keys.append((F"""transformer.encoder.layers.{i}.self_attn.sampling_offsets.weight""", F"""model.encoder.layers.{i}.self_attn.sampling_offsets.weight""")     )
            rename_keys.append((F"""transformer.encoder.layers.{i}.self_attn.sampling_offsets.bias""", F"""model.encoder.layers.{i}.self_attn.sampling_offsets.bias""")     )
            rename_keys.append((F"""transformer.encoder.layers.{i}.self_attn.attention_weights.weight""", F"""model.encoder.layers.{i}.self_attn.attention_weights.weight""")     )
            rename_keys.append((F"""transformer.encoder.layers.{i}.self_attn.attention_weights.bias""", F"""model.encoder.layers.{i}.self_attn.attention_weights.bias""")     )
            rename_keys.append((F"""transformer.encoder.layers.{i}.self_attn.value_proj.weight""", F"""model.encoder.layers.{i}.self_attn.value_proj.weight""")     )
            rename_keys.append((F"""transformer.encoder.layers.{i}.self_attn.value_proj.bias""", F"""model.encoder.layers.{i}.self_attn.value_proj.bias""")     )
            rename_keys.append((F"""transformer.encoder.layers.{i}.self_attn.output_proj.weight""", F"""model.encoder.layers.{i}.self_attn.output_proj.weight""")     )
            rename_keys.append((F"""transformer.encoder.layers.{i}.self_attn.output_proj.bias""", F"""model.encoder.layers.{i}.self_attn.output_proj.bias""")     )
            rename_keys.append((F"""transformer.encoder.layers.{i}.norm1.weight""", F"""model.encoder.layers.{i}.self_attn_layer_norm.weight""")     )
            rename_keys.append((F"""transformer.encoder.layers.{i}.norm1.bias""", F"""model.encoder.layers.{i}.self_attn_layer_norm.bias""")     )
            rename_keys.append((F"""transformer.encoder.layers.{i}.linear1.weight""", F"""model.encoder.layers.{i}.fc1.weight""")     )
            rename_keys.append((F"""transformer.encoder.layers.{i}.linear1.bias""", F"""model.encoder.layers.{i}.fc1.bias""")     )
            rename_keys.append((F"""transformer.encoder.layers.{i}.linear2.weight""", F"""model.encoder.layers.{i}.fc2.weight""")     )
            rename_keys.append((F"""transformer.encoder.layers.{i}.linear2.bias""", F"""model.encoder.layers.{i}.fc2.bias""")     )
            rename_keys.append((F"""transformer.encoder.layers.{i}.norm2.weight""", F"""model.encoder.layers.{i}.final_layer_norm.weight""")     )
            rename_keys.append((F"""transformer.encoder.layers.{i}.norm2.bias""", F"""model.encoder.layers.{i}.final_layer_norm.bias""")     )

      # transformer decoder
      for i in range(config.decoder_layers     ):
            rename_keys.append((F"""transformer.decoder.layers.{i}.cross_attn.sampling_offsets.weight""", F"""model.decoder.layers.{i}.encoder_attn.sampling_offsets.weight""")     )
            rename_keys.append((F"""transformer.decoder.layers.{i}.cross_attn.sampling_offsets.bias""", F"""model.decoder.layers.{i}.encoder_attn.sampling_offsets.bias""")     )
            rename_keys.append((F"""transformer.decoder.layers.{i}.cross_attn.attention_weights.weight""", F"""model.decoder.layers.{i}.encoder_attn.attention_weights.weight""")     )
            rename_keys.append((F"""transformer.decoder.layers.{i}.cross_attn.attention_weights.bias""", F"""model.decoder.layers.{i}.encoder_attn.attention_weights.bias""")     )
            rename_keys.append((F"""transformer.decoder.layers.{i}.cross_attn.value_proj.weight""", F"""model.decoder.layers.{i}.encoder_attn.value_proj.weight""")     )
            rename_keys.append((F"""transformer.decoder.layers.{i}.cross_attn.value_proj.bias""", F"""model.decoder.layers.{i}.encoder_attn.value_proj.bias""")     )
            rename_keys.append((F"""transformer.decoder.layers.{i}.cross_attn.output_proj.weight""", F"""model.decoder.layers.{i}.encoder_attn.output_proj.weight""")     )
            rename_keys.append((F"""transformer.decoder.layers.{i}.cross_attn.output_proj.bias""", F"""model.decoder.layers.{i}.encoder_attn.output_proj.bias""")     )
            rename_keys.append((F"""transformer.decoder.layers.{i}.norm1.weight""", F"""model.decoder.layers.{i}.encoder_attn_layer_norm.weight""")     )
            rename_keys.append((F"""transformer.decoder.layers.{i}.norm1.bias""", F"""model.decoder.layers.{i}.encoder_attn_layer_norm.bias""")     )
            rename_keys.append((F"""transformer.decoder.layers.{i}.self_attn.out_proj.weight""", F"""model.decoder.layers.{i}.self_attn.out_proj.weight""")     )
            rename_keys.append((F"""transformer.decoder.layers.{i}.self_attn.out_proj.bias""", F"""model.decoder.layers.{i}.self_attn.out_proj.bias""")     )
            rename_keys.append((F"""transformer.decoder.layers.{i}.norm2.weight""", F"""model.decoder.layers.{i}.self_attn_layer_norm.weight""")     )
            rename_keys.append((F"""transformer.decoder.layers.{i}.norm2.bias""", F"""model.decoder.layers.{i}.self_attn_layer_norm.bias""")     )
            rename_keys.append((F"""transformer.decoder.layers.{i}.linear1.weight""", F"""model.decoder.layers.{i}.fc1.weight""")     )
            rename_keys.append((F"""transformer.decoder.layers.{i}.linear1.bias""", F"""model.decoder.layers.{i}.fc1.bias""")     )
            rename_keys.append((F"""transformer.decoder.layers.{i}.linear2.weight""", F"""model.decoder.layers.{i}.fc2.weight""")     )
            rename_keys.append((F"""transformer.decoder.layers.{i}.linear2.bias""", F"""model.decoder.layers.{i}.fc2.bias""")     )
            rename_keys.append((F"""transformer.decoder.layers.{i}.norm3.weight""", F"""model.decoder.layers.{i}.final_layer_norm.weight""")     )
            rename_keys.append((F"""transformer.decoder.layers.{i}.norm3.bias""", F"""model.decoder.layers.{i}.final_layer_norm.bias""")     )

      # fmt: on

      return rename_keys







def  _snake_case			(							__snake_case       ,	__snake_case       ,	__snake_case     )  ->  List[Any]:






      '''simple docstring'''







      UpperCAmelCase_	:						List[Any]					   =		dct.pop(__snake_case     )
      UpperCAmelCase_	:						str					   =		val







def  _snake_case			(							__snake_case       ,	__snake_case     )  ->  Optional[Any]:






      '''simple docstring'''







      UpperCAmelCase_	:						Optional[int]					   =		config.d_model
      for i in range(config.decoder_layers     ):
            # read in weights + bias of input projection layer of self-attention
            UpperCAmelCase_	:						Optional[Any]					   =		state_dict.pop(F"""transformer.decoder.layers.{i}.self_attn.in_proj_weight"""     )
            UpperCAmelCase_	:						Any					   =		state_dict.pop(F"""transformer.decoder.layers.{i}.self_attn.in_proj_bias"""     )
            # next, add query, keys and values (in that order) to the state dict
            UpperCAmelCase_	:						Optional[int]					   =		in_proj_weight[:hidden_size, :]
            UpperCAmelCase_	:						Any					   =		in_proj_bias[:hidden_size]
            UpperCAmelCase_	:						Optional[int]					   =		in_proj_weight[
                hidden_size : hidden_size * 2, :
            ]
            UpperCAmelCase_	:						List[str]					   =		in_proj_bias[hidden_size : hidden_size * 2]
            UpperCAmelCase_	:						Optional[Any]					   =		in_proj_weight[-hidden_size:, :]
            UpperCAmelCase_	:						Optional[Any]					   =		in_proj_bias[-hidden_size:]







def  _snake_case			(							)  ->  Tuple:






      '''simple docstring'''







      UpperCAmelCase_	:						int					   =		"http://images.cocodataset.org/val2017/000000039769.jpg"
      UpperCAmelCase_	:						List[str]					   =		Image.open(requests.get(__snake_case       ,	stream=__snake_case     ).raw     )

      return im







@torch.no_grad()
def  _snake_case			(							__snake_case       ,	__snake_case       ,	__snake_case     )  ->  Tuple:






      '''simple docstring'''







      UpperCAmelCase_	:						str					   =		get_deta_config()

      # load original state dict
      if model_name == "deta-resnet-50":
            UpperCAmelCase_	:						List[Any]					   =		"adet_checkpoint0011.pth"
      elif model_name == "deta-resnet-50-24-epochs":
            UpperCAmelCase_	:						Tuple					   =		"adet_2x_checkpoint0023.pth"
      else:
            raise ValueError(F"""Model name {model_name} not supported"""     )
      UpperCAmelCase_	:						Tuple					   =		hf_hub_download(repo_id="nielsr/deta-checkpoints"       ,	filename=__snake_case     )
      UpperCAmelCase_	:						str					   =		torch.load(__snake_case       ,	map_location="cpu"     )["model"]

      # rename keys
      UpperCAmelCase_	:						Tuple					   =		create_rename_keys(__snake_case     )
      for src, dest in rename_keys:
            rename_key(__snake_case       ,	__snake_case       ,	__snake_case     )
      read_in_decoder_q_k_v(__snake_case       ,	__snake_case     )

      # fix some prefixes
      for key in state_dict.copy().keys():
            if "transformer.decoder.class_embed" in key or "transformer.decoder.bbox_embed" in key:
                  UpperCAmelCase_	:						Tuple					   =		state_dict.pop(__snake_case     )
                  UpperCAmelCase_	:						Tuple					   =		val
            if "input_proj" in key:
                  UpperCAmelCase_	:						Tuple					   =		state_dict.pop(__snake_case     )
                  UpperCAmelCase_	:						Tuple					   =		val
            if "level_embed" in key or "pos_trans" in key or "pix_trans" in key or "enc_output" in key:
                  UpperCAmelCase_	:						List[str]					   =		state_dict.pop(__snake_case     )
                  UpperCAmelCase_	:						Optional[int]					   =		val

    # finally, create HuggingFace model and load state dict
      UpperCAmelCase_	:						int					   =		DetaForObjectDetection(__snake_case     )
      model.load_state_dict(__snake_case     )
      model.eval()

      UpperCAmelCase_	:						int					   =		"cuda" if torch.cuda.is_available() else "cpu"
      model.to(__snake_case     )

      # load image processor
      UpperCAmelCase_	:						Any					   =		DetaImageProcessor(format="coco_detection"     )

      # verify our conversion on image
      UpperCAmelCase_	:						Optional[Any]					   =		prepare_img()
      UpperCAmelCase_	:						Any					   =		processor(images=__snake_case       ,	return_tensors="pt"     )
      UpperCAmelCase_	:						Optional[Any]					   =		encoding["pixel_values"]
      UpperCAmelCase_	:						Tuple					   =		model(pixel_values.to(__snake_case     )     )

      # verify logits
      if model_name == "deta-resnet-50":
            UpperCAmelCase_	:						Tuple					   =		torch.tensor(
                [[-7.3978, -2.5406, -4.1668], [-8.2684, -3.9933, -3.8096], [-7.0515, -3.7973, -5.8516]]     )
            UpperCAmelCase_	:						int					   =		torch.tensor([[0.5043, 0.4973, 0.9998], [0.2542, 0.5489, 0.4748], [0.5490, 0.2765, 0.0570]]     )
      elif model_name == "deta-resnet-50-24-epochs":
            UpperCAmelCase_	:						str					   =		torch.tensor(
                [[-7.1688, -2.4857, -4.8669], [-7.8630, -3.8154, -4.2674], [-7.2730, -4.1865, -5.5323]]     )
            UpperCAmelCase_	:						Optional[int]					   =		torch.tensor([[0.5021, 0.4971, 0.9994], [0.2546, 0.5486, 0.4731], [0.1686, 0.1986, 0.2142]]     )

      assert torch.allclose(outputs.logits[0, :3, :3]       ,	expected_logits.to(__snake_case     )       ,	atol=1E-4     )
      assert torch.allclose(outputs.pred_boxes[0, :3, :3]       ,	expected_boxes.to(__snake_case     )       ,	atol=1E-4     )
      print("Everything ok!"     )

      if pytorch_dump_folder_path:
            # Save model and processor
            logger.info(F"""Saving PyTorch model and processor to {pytorch_dump_folder_path}..."""     )
            Path(__snake_case     ).mkdir(exist_ok=__snake_case     )
            model.save_pretrained(__snake_case     )
            processor.save_pretrained(__snake_case     )

      # Push to hub
      if push_to_hub:
            print("Pushing model and processor to hub..."     )
            model.push_to_hub(F"""jozhang97/{model_name}"""     )
            processor.push_to_hub(F"""jozhang97/{model_name}"""     )


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

   parser.add_argument(
       '''--model_name''',
       type=str,
       default='''deta-resnet-50''',
       choices=['''deta-resnet-50''', '''deta-resnet-50-24-epochs'''],
       help='''Name of the model you\'d like to convert.''',
   )
   parser.add_argument(
       '''--pytorch_dump_folder_path''',
       default=None,
       type=str,
       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.'''
   )
   __lowerCamelCase								=      parser.parse_args()
   convert_deta_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)





import torch
from transformers import AutoModelForSequenceClassification, AutoTokenizer, Trainer, TrainingArguments

import datasets


__lowerCamelCase								=      '''\
@article{eddine2021frugalscore,
  title={FrugalScore: Learning Cheaper, Lighter and Faster Evaluation Metrics for Automatic Text Generation},
  author={Eddine, Moussa Kamal and Shang, Guokan and Tixier, Antoine J-P and Vazirgiannis, Michalis},
  journal={arXiv preprint arXiv:2110.08559},
  year={2021}
}
'''

__lowerCamelCase								=      '''\
FrugalScore is a reference-based metric for NLG models evaluation. It is based on a distillation approach that allows to learn a fixed, low cost version of any expensive NLG metric, while retaining most of its original performance.
'''


__lowerCamelCase								=      '''
Calculates how good are predictions given some references, using certain scores.
Args:
    predictions (list of str): list of predictions to score. Each predictions
        should be a string.
    references (list of str): list of reference for each prediction. Each
        reference should be a string.
    batch_size (int): the batch size for predictions.
    max_length (int): maximum sequence length.
    device (str): either gpu or cpu
Returns:
    scores (list of int): list of scores.
Examples:
    >>> frugalscore = datasets.load_metric("frugalscore")
    >>> results = frugalscore.compute(predictions=[\'hello there\', \'huggingface\'], references=[\'hello world\', \'hugging face\'])
    >>> print([round(s, 3) for s in results["scores"]])
    [0.631, 0.645]
'''


@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION			,			_KWARGS_DESCRIPTION	)
class 	snake_case_			(datasets.Metric	):




       """simple docstring"""






       def 			A_ (  self):

             """simple docstring"""





             return datasets.MetricInfo(
                 description=_DESCRIPTION  ,citation=_CITATION  ,inputs_description=_KWARGS_DESCRIPTION  ,features=datasets.Features(
                     {
                         "predictions": datasets.Value("string"),
                         "references": datasets.Value("string"),
                     })  ,homepage="https://github.com/moussaKam/FrugalScore"  ,)



       def 			A_ (  self  ,lowercase):

             """simple docstring"""





             if self.config_name == "default":
                   UpperCAmelCase_	:						str					   =		"moussaKam/frugalscore_tiny_bert-base_bert-score"
             else:
                   UpperCAmelCase_	:						Optional[int]					   =		self.config_name
             UpperCAmelCase_	:						Dict					   =		AutoModelForSequenceClassification.from_pretrained(lowercase)
             UpperCAmelCase_	:						str					   =		AutoTokenizer.from_pretrained(lowercase)



       def 			A_ (  self  ,lowercase  ,lowercase  ,lowercase=32  ,lowercase=128  ,lowercase=None  ,):

             """simple docstring"""





             assert len(lowercase) == len(
                 lowercase), "predictions and references should have the same number of sentences."
             if device is not None:
                   assert device in ["gpu", "cpu"], "device should be either gpu or cpu."
             else:
                   UpperCAmelCase_	:						Union[str, Any]					   =		"gpu" if torch.cuda.is_available() else "cpu"
             UpperCAmelCase_	:						Optional[Any]					   =		TrainingArguments(
                 "trainer"  ,fpaa=(device == "gpu")  ,per_device_eval_batch_size=lowercase  ,report_to="all"  ,no_cuda=(device == "cpu")  ,log_level="warning"  ,)
             UpperCAmelCase_	:						List[str]					   =		{"sentence1": predictions, "sentence2": references}
             UpperCAmelCase_	:						List[Any]					   =		datasets.Dataset.from_dict(lowercase)

             def tokenize_function(lowercase):
                   return self.tokenizer(
                       data["sentence1"]  ,data["sentence2"]  ,max_length=lowercase  ,truncation=lowercase  ,padding=lowercase)

             UpperCAmelCase_	:						int					   =		raw_datasets.map(lowercase  ,batched=lowercase)
             tokenized_datasets.remove_columns(["sentence1", "sentence2"])
             UpperCAmelCase_	:						Union[str, Any]					   =		Trainer(self.model  ,lowercase  ,tokenizer=self.tokenizer)
             UpperCAmelCase_	:						Tuple					   =		trainer.predict(lowercase)
             return {"scores": list(predictions.predictions.squeeze(-1))}
