




'''simple docstring'''





import importlib
from typing import List, Optional, Union

from seqeval.metrics import accuracy_score, classification_report

import datasets


lowercase__			=	'''\
@inproceedings{ramshaw-marcus-1995-text,
    title = "Text Chunking using Transformation-Based Learning",
    author = "Ramshaw, Lance  and
      Marcus, Mitch",
    booktitle = "Third Workshop on Very Large Corpora",
    year = "1995",
    url = "https://www.aclweb.org/anthology/W95-0107",
}
@misc{seqeval,
  title={{seqeval}: A Python framework for sequence labeling evaluation},
  url={https://github.com/chakki-works/seqeval},
  note={Software available from https://github.com/chakki-works/seqeval},
  author={Hiroki Nakayama},
  year={2018},
}
'''

lowercase__			=	'''\
seqeval is a Python framework for sequence labeling evaluation.
seqeval can evaluate the performance of chunking tasks such as named-entity recognition, part-of-speech tagging, semantic role labeling and so on.

This is well-tested by using the Perl script conlleval, which can be used for
measuring the performance of a system that has processed the CoNLL-2000 shared task data.

seqeval supports following formats:
IOB1
IOB2
IOE1
IOE2
IOBES

See the [README.md] file at https://github.com/chakki-works/seqeval for more information.
'''

lowercase__			=	'''
Produces labelling scores along with its sufficient statistics
from a source against one or more references.

Args:
    predictions: List of List of predicted labels (Estimated targets as returned by a tagger)
    references: List of List of reference labels (Ground truth (correct) target values)
    suffix: True if the IOB prefix is after type, False otherwise. default: False
    scheme: Specify target tagging scheme. Should be one of ["IOB1", "IOB2", "IOE1", "IOE2", "IOBES", "BILOU"].
        default: None
    mode: Whether to count correct entity labels with incorrect I/B tags as true positives or not.
        If you want to only count exact matches, pass mode="strict". default: None.
    sample_weight: Array-like of shape (n_samples,), weights for individual samples. default: None
    zero_division: Which value to substitute as a metric value when encountering zero division. Should be on of 0, 1,
        "warn". "warn" acts as 0, but the warning is raised.

Returns:
    \'scores\': dict. Summary of the scores for overall and per type
        Overall:
            \'accuracy\': accuracy,
            \'precision\': precision,
            \'recall\': recall,
            \'f1\': F1 score, also known as balanced F-score or F-measure,
        Per type:
            \'precision\': precision,
            \'recall\': recall,
            \'f1\': F1 score, also known as balanced F-score or F-measure
Examples:

    >>> predictions = [[\'O\', \'O\', \'B-MISC\', \'I-MISC\', \'I-MISC\', \'I-MISC\', \'O\'], [\'B-PER\', \'I-PER\', \'O\']]
    >>> references = [[\'O\', \'O\', \'O\', \'B-MISC\', \'I-MISC\', \'I-MISC\', \'O\'], [\'B-PER\', \'I-PER\', \'O\']]
    >>> seqeval = datasets.load_metric("seqeval")
    >>> results = seqeval.compute(predictions=predictions, references=references)
    >>> print(list(results.keys()))
    [\'MISC\', \'PER\', \'overall_precision\', \'overall_recall\', \'overall_f1\', \'overall_accuracy\']
    >>> print(results["overall_f1"])
    0.5
    >>> print(results["PER"]["f1"])
    1.0
'''






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






      def    A__(self					)       ->   int:
             return datasets.MetricInfo(
                 description=_DESCRIPTION ,  citation=_CITATION ,  homepage='''https://github.com/chakki-works/seqeval''' ,  inputs_description=_KWARGS_DESCRIPTION ,  features=datasets.Features(
                     {
                         '''predictions''': datasets.Sequence(datasets.Value('''string''' ,  id='''label'''					) ,  id='''sequence'''					),
                         '''references''': datasets.Sequence(datasets.Value('''string''' ,  id='''label'''					) ,  id='''sequence'''					),
                     }					) ,  codebase_urls=['''https://github.com/chakki-works/seqeval'''] ,  reference_urls=['''https://github.com/chakki-works/seqeval'''] ,  )






      def    A__(self ,  a__ ,  a__ ,  a__ = False ,  a__ = None ,  a__ = None ,  a__ = None ,  a__ = "warn" ,  )       ->   Union[str, Any]:
             if scheme is not None:
                    try:
                           SCREAMING_SNAKE_CASE__			:				Tuple       						= importlib.import_module('''seqeval.scheme'''					)
                           SCREAMING_SNAKE_CASE__			:				Tuple       						= getattr(a__ ,  a__					)
                    except AttributeError:
                           raise ValueError(f"""Scheme should be one of [IOB1, IOB2, IOE1, IOE2, IOBES, BILOU], got {scheme}"""					)
             SCREAMING_SNAKE_CASE__			:				Dict       						= classification_report(
                 y_true=a__ ,  y_pred=a__ ,  suffix=a__ ,  output_dict=a__ ,  scheme=a__ ,  mode=a__ ,  sample_weight=a__ ,  zero_division=a__ ,  )
             report.pop('''macro avg'''					)
             report.pop('''weighted avg'''					)
             SCREAMING_SNAKE_CASE__			:				Union[str, Any]       						= report.pop('''micro avg'''					)

             SCREAMING_SNAKE_CASE__			:				Optional[Any]       						= {
                 type_name: {
                     '''precision''': score['''precision'''],
                     '''recall''': score['''recall'''],
                     '''f1''': score['''f1-score'''],
                     '''number''': score['''support'''],
                 }
                 for type_name, score in report.items()
             }
             SCREAMING_SNAKE_CASE__			:				Optional[int]       						= overall_score['''precision''']
             SCREAMING_SNAKE_CASE__			:				List[Any]       						= overall_score['''recall''']
             SCREAMING_SNAKE_CASE__			:				List[Any]       						= overall_score['''f1-score''']
             SCREAMING_SNAKE_CASE__			:				Tuple       						= accuracy_score(y_true=a__ ,  y_pred=a__					)

             return scores







'''simple docstring'''





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()
lowercase__			=	logging.get_logger(__name__)


def 				UpperCamelCase	(  ):




   '''simple docstring'''

   SCREAMING_SNAKE_CASE__			:				Tuple       						= DetaConfig(
       num_queries=9_00	,     encoder_ffn_dim=20_48	,     decoder_ffn_dim=20_48	,     num_feature_levels=5	,     assign_first_stage=snake_case	,     with_box_refine=snake_case	,     two_stage=snake_case	,     )

   # set labels
   SCREAMING_SNAKE_CASE__			:				str       						= 91
   SCREAMING_SNAKE_CASE__			:				int       						= '''huggingface/label-files'''
   SCREAMING_SNAKE_CASE__			:				Optional[int]       						= '''coco-detection-id2label.json'''
   SCREAMING_SNAKE_CASE__			:				Optional[Any]       						= json.load(open(cached_download(hf_hub_url(snake_case	,     snake_case	,     repo_type='''dataset'''   )   )	,     '''r'''   )   )
   SCREAMING_SNAKE_CASE__			:				int       						= {int(snake_case   ): v for k, v in idalabel.items()}
   SCREAMING_SNAKE_CASE__			:				str       						= idalabel
   SCREAMING_SNAKE_CASE__			:				List[str]       						= {v: k for k, v in idalabel.items()}

   return config


def 				UpperCamelCase	(  snake_case   ):




   '''simple docstring'''

   SCREAMING_SNAKE_CASE__			:				Optional[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 				UpperCamelCase	(  snake_case	,     snake_case	,     snake_case   ):




   '''simple docstring'''

   SCREAMING_SNAKE_CASE__			:				Optional[Any]       						= dct.pop(snake_case   )
   SCREAMING_SNAKE_CASE__			:				Union[str, Any]       						= val


def 				UpperCamelCase	(  snake_case	,     snake_case   ):




   '''simple docstring'''

   SCREAMING_SNAKE_CASE__			:				List[Any]       						= config.d_model
   for i in range(config.decoder_layers   ):
      # read in weights + bias of input projection layer of self-attention
      SCREAMING_SNAKE_CASE__			:				Optional[Any]       						= state_dict.pop(f"""transformer.decoder.layers.{i}.self_attn.in_proj_weight"""   )
      SCREAMING_SNAKE_CASE__			:				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
      SCREAMING_SNAKE_CASE__			:				Union[str, Any]       						= in_proj_weight[:hidden_size, :]
      SCREAMING_SNAKE_CASE__			:				Optional[int]       						= in_proj_bias[:hidden_size]
      SCREAMING_SNAKE_CASE__			:				Optional[Any]       						= in_proj_weight[
          hidden_size : hidden_size * 2, :
      ]
      SCREAMING_SNAKE_CASE__			:				List[Any]       						= in_proj_bias[hidden_size : hidden_size * 2]
      SCREAMING_SNAKE_CASE__			:				List[Any]       						= in_proj_weight[-hidden_size:, :]
      SCREAMING_SNAKE_CASE__			:				str       						= in_proj_bias[-hidden_size:]


def 				UpperCamelCase	(  ):




   '''simple docstring'''

   SCREAMING_SNAKE_CASE__			:				Dict       						= '''http://images.cocodataset.org/val2017/000000039769.jpg'''
   SCREAMING_SNAKE_CASE__			:				List[Any]       						= Image.open(requests.get(snake_case	,     stream=snake_case   ).raw   )

   return im


@torch.no_grad()
def 				UpperCamelCase	(  snake_case	,     snake_case	,     snake_case   ):




   '''simple docstring'''

   SCREAMING_SNAKE_CASE__			:				List[Any]       						= get_deta_config()

   # load original state dict
   if model_name == "deta-resnet-50":
      SCREAMING_SNAKE_CASE__			:				Optional[Any]       						= '''adet_checkpoint0011.pth'''
   elif model_name == "deta-resnet-50-24-epochs":
      SCREAMING_SNAKE_CASE__			:				Any       						= '''adet_2x_checkpoint0023.pth'''
   else:
      raise ValueError(f"""Model name {model_name} not supported"""   )
   SCREAMING_SNAKE_CASE__			:				Optional[int]       						= hf_hub_download(repo_id='''nielsr/deta-checkpoints'''	,     filename=snake_case   )
   SCREAMING_SNAKE_CASE__			:				int       						= torch.load(snake_case	,     map_location='''cpu'''   )['''model''']

   # rename keys
   SCREAMING_SNAKE_CASE__			:				Dict       						= 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:
         SCREAMING_SNAKE_CASE__			:				Optional[int]       						= state_dict.pop(snake_case   )
         SCREAMING_SNAKE_CASE__			:				Dict       						= val
      if "input_proj" in key:
         SCREAMING_SNAKE_CASE__			:				Any       						= state_dict.pop(snake_case   )
         SCREAMING_SNAKE_CASE__			:				Optional[Any]       						= val
      if "level_embed" in key or "pos_trans" in key or "pix_trans" in key or "enc_output" in key:
         SCREAMING_SNAKE_CASE__			:				List[str]       						= state_dict.pop(snake_case   )
         SCREAMING_SNAKE_CASE__			:				Optional[int]       						= val

    # finally, create HuggingFace model and load state dict
   SCREAMING_SNAKE_CASE__			:				List[Any]       						= DetaForObjectDetection(snake_case   )
   model.load_state_dict(snake_case   )
   model.eval()

   SCREAMING_SNAKE_CASE__			:				Tuple       						= '''cuda''' if torch.cuda.is_available() else '''cpu'''
   model.to(snake_case   )

   # load image processor
   SCREAMING_SNAKE_CASE__			:				Union[str, Any]       						= DetaImageProcessor(format='''coco_detection'''   )

   # verify our conversion on image
   SCREAMING_SNAKE_CASE__			:				int       						= prepare_img()
   SCREAMING_SNAKE_CASE__			:				int       						= processor(images=snake_case	,     return_tensors='''pt'''   )
   SCREAMING_SNAKE_CASE__			:				Union[str, Any]       						= encoding['''pixel_values''']
   SCREAMING_SNAKE_CASE__			:				Union[str, Any]       						= model(pixel_values.to(snake_case   )   )

   # verify logits
   if model_name == "deta-resnet-50":
      SCREAMING_SNAKE_CASE__			:				str       						= torch.tensor(
          [[-7.3978, -2.5406, -4.1668], [-8.2684, -3.9933, -3.8096], [-7.0515, -3.7973, -5.8516]]   )
      SCREAMING_SNAKE_CASE__			:				List[Any]       						= 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":
      SCREAMING_SNAKE_CASE__			:				List[str]       						= torch.tensor(
          [[-7.1688, -2.4857, -4.8669], [-7.8630, -3.8154, -4.2674], [-7.2730, -4.1865, -5.5323]]   )
      SCREAMING_SNAKE_CASE__			:				Tuple       						= 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__":
       lowercase__			=	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.'''
       )
       lowercase__			=	parser.parse_args()
       convert_deta_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)

