



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()
A_		:  Any										=  logging.get_logger(__name__)


def 						UpperCAmelCase__   (   ):






 '''simple docstring'''

 a							=		DetaConfig(
     num_queries=9_00			,       encoder_ffn_dim=20_48			,       decoder_ffn_dim=20_48			,       num_feature_levels=5			,       assign_first_stage=UpperCAmelCase__			,       with_box_refine=UpperCAmelCase__			,       two_stage=UpperCAmelCase__			,       )

 # set labels
 a							=		91
 a							=		"huggingface/label-files"
 a							=		"coco-detection-id2label.json"
 a							=		json.load(open(cached_download(hf_hub_url(UpperCAmelCase__			,       UpperCAmelCase__			,       repo_type="dataset"       )       )			,       "r"       )       )
 a							=		{int(UpperCAmelCase__       ): v for k, v in idalabel.items()}
 a							=		idalabel
 a							=		{v: k for k, v in idalabel.items()}

 return config


def 						UpperCAmelCase__   (   UpperCAmelCase__			:str       ):






 '''simple docstring'''

 a							=		[]

 # 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__   (   UpperCAmelCase__			:Optional[int]			,       UpperCAmelCase__			:Optional[int]			,       UpperCAmelCase__			:Union[str, Any]       ):






 '''simple docstring'''

 a							=		dct.pop(UpperCAmelCase__       )
 a							=		val


def 						UpperCAmelCase__   (   UpperCAmelCase__			:List[Any]			,       UpperCAmelCase__			:Dict       ):






 '''simple docstring'''

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


def 						UpperCAmelCase__   (   ):






 '''simple docstring'''

 a							=		"http://images.cocodataset.org/val2017/000000039769.jpg"
 a							=		Image.open(requests.get(UpperCAmelCase__			,       stream=UpperCAmelCase__       ).raw       )

 return im


@torch.no_grad()
def 						UpperCAmelCase__   (   UpperCAmelCase__			:Optional[int]			,       UpperCAmelCase__			:Any			,       UpperCAmelCase__			:Dict       ):






 '''simple docstring'''

 a							=		get_deta_config()

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

 # rename keys
 a							=		create_rename_keys(UpperCAmelCase__       )
 for src, dest in rename_keys:
  rename_key(UpperCAmelCase__			,       UpperCAmelCase__			,       UpperCAmelCase__       )
 read_in_decoder_q_k_v(UpperCAmelCase__			,       UpperCAmelCase__       )

 # fix some prefixes
 for key in state_dict.copy().keys():
  if "transformer.decoder.class_embed" in key or "transformer.decoder.bbox_embed" in key:
   a							=		state_dict.pop(UpperCAmelCase__       )
   a							=		val
  if "input_proj" in key:
   a							=		state_dict.pop(UpperCAmelCase__       )
   a							=		val
  if "level_embed" in key or "pos_trans" in key or "pix_trans" in key or "enc_output" in key:
   a							=		state_dict.pop(UpperCAmelCase__       )
   a							=		val

    # finally, create HuggingFace model and load state dict
 a							=		DetaForObjectDetection(UpperCAmelCase__       )
 model.load_state_dict(UpperCAmelCase__       )
 model.eval()

 a							=		"cuda" if torch.cuda.is_available() else "cpu"
 model.to(UpperCAmelCase__       )

 # load image processor
 a							=		DetaImageProcessor(format="coco_detection"       )

 # verify our conversion on image
 a							=		prepare_img()
 a							=		processor(images=UpperCAmelCase__			,       return_tensors="pt"       )
 a							=		encoding["pixel_values"]
 a							=		model(pixel_values.to(UpperCAmelCase__       )       )

 # verify logits
 if model_name == "deta-resnet-50":
  a							=		torch.tensor(
      [[-7.3978, -2.5406, -4.1668], [-8.2684, -3.9933, -3.8096], [-7.0515, -3.7973, -5.8516]]       )
  a							=		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":
  a							=		torch.tensor(
      [[-7.1688, -2.4857, -4.8669], [-7.8630, -3.8154, -4.2674], [-7.2730, -4.1865, -5.5323]]       )
  a							=		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(UpperCAmelCase__       )			,       atol=1E-4       )
 assert torch.allclose(outputs.pred_boxes[0, :3, :3]			,       expected_boxes.to(UpperCAmelCase__       )			,       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(UpperCAmelCase__       ).mkdir(exist_ok=UpperCAmelCase__       )
  model.save_pretrained(UpperCAmelCase__       )
  processor.save_pretrained(UpperCAmelCase__       )

 # 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__":
     A_		:  int										=  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.'''
     )
     A_		:  Tuple										=  parser.parse_args()
     convert_deta_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)






import argparse
import os
import re


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_dummies.py
A_		:  int										=  '''src/transformers'''

# Matches is_xxx_available()
A_		:  Union[str, Any]										=  re.compile(r'''is\_([a-z_]*)_available()''')
# Matches from xxx import bla
A_		:  int										=  re.compile(r'''\s+from\s+\S*\s+import\s+([^\(\s].*)\n''')
A_		:  Tuple										=  re.compile(r'''^\s+if\s+not\s+\(?is\_[a-z_]*\_available\(\)''')


A_		:  str										=  '''
{0} = None
'''

A_		:  Tuple										=  '''
class {0}(metaclass=DummyObject):
    _backends = {1}

    def __init__(self, *args, **kwargs):
        requires_backends(self, {1})
'''


A_		:  Any										=  '''
def {0}(*args, **kwargs):
    requires_backends({0}, {1})
'''


def 						UpperCAmelCase__   (   UpperCAmelCase__			:Union[str, Any]       ):






 '''simple docstring'''

 if _re_test_backend.search(UpperCAmelCase__       ) is None:
  return None
 a							=		[b[0] for b in _re_backend.findall(UpperCAmelCase__       )]
 backends.sort()
 return "_and_".join(UpperCAmelCase__       )


def 						UpperCAmelCase__   (   ):






 '''simple docstring'''

 with open(os.path.join(UpperCAmelCase__			,       "__init__.py"       )			,       "r"			,       encoding="utf-8"			,       newline="\n"       ) as f:
  a							=		f.readlines()

 # Get to the point we do the actual imports for type checking
 a							=		0
 while not lines[line_index].startswith("if TYPE_CHECKING"       ):
  line_index += 1

 a							=		{}
 # Go through the end of the file
 while line_index < len(UpperCAmelCase__       ):
  # If the line is an if is_backend_available, we grab all objects associated.
  a							=		find_backend(lines[line_index]       )
  if backend is not None:
   while not lines[line_index].startswith("    else:"       ):
    line_index += 1
   line_index += 1

   a							=		[]
   # Until we unindent, add backend objects to the list
   while len(lines[line_index]       ) <= 1 or lines[line_index].startswith(" " * 8       ):
    a							=		lines[line_index]
    a							=		_re_single_line_import.search(UpperCAmelCase__       )
    if single_line_import_search is not None:
     objects.extend(single_line_import_search.groups()[0].split(", "       )       )
    elif line.startswith(" " * 12       ):
     objects.append(line[12:-2]       )
    line_index += 1

   a							=		objects
  else:
   line_index += 1

 return backend_specific_objects


def 						UpperCAmelCase__   (   UpperCAmelCase__			:Union[str, Any]			,       UpperCAmelCase__			:Any       ):






 '''simple docstring'''

 if name.isupper():
  return DUMMY_CONSTANT.format(UpperCAmelCase__       )
 elif name.islower():
  return DUMMY_FUNCTION.format(UpperCAmelCase__			,       UpperCAmelCase__       )
 else:
  return DUMMY_CLASS.format(UpperCAmelCase__			,       UpperCAmelCase__       )


def 						UpperCAmelCase__   (   UpperCAmelCase__			:List[str]=None       ):






 '''simple docstring'''

 if backend_specific_objects is None:
  a							=		read_init()
 # For special correspondence backend to module name as used in the function requires_modulename
 a							=		{}

 for backend, objects in backend_specific_objects.items():
  a							=		"[" + ", ".join(F"""\"{b}\"""" for b in backend.split("_and_"       )       ) + "]"
  a							=		"# This file is autogenerated by the command `make fix-copies`, do not edit.\n"
  dummy_file += "from ..utils import DummyObject, requires_backends\n\n"
  dummy_file += "\n".join([create_dummy_object(UpperCAmelCase__			,       UpperCAmelCase__       ) for o in objects]       )
  a							=		dummy_file

 return dummy_files


def 						UpperCAmelCase__   (   UpperCAmelCase__			:int=False       ):






 '''simple docstring'''

 a							=		create_dummy_files()
 # For special correspondence backend to shortcut as used in utils/dummy_xxx_objects.py
 a							=		{"torch": "pt"}

 # Locate actual dummy modules and read their content.
 a							=		os.path.join(UpperCAmelCase__			,       "utils"       )
 a							=		{
     backend: os.path.join(UpperCAmelCase__			,       F"""dummy_{short_names.get(UpperCAmelCase__			,       UpperCAmelCase__       )}_objects.py"""       )
     for backend in dummy_files.keys()
 }

 a							=		{}
 for backend, file_path in dummy_file_paths.items():
  if os.path.isfile(UpperCAmelCase__       ):
   with open(UpperCAmelCase__			,       "r"			,       encoding="utf-8"			,       newline="\n"       ) as f:
    a							=		f.read()
  else:
   a							=		""

 for backend in dummy_files.keys():
  if dummy_files[backend] != actual_dummies[backend]:
   if overwrite:
    print(
        F"""Updating transformers.utils.dummy_{short_names.get(UpperCAmelCase__			,       UpperCAmelCase__       )}_objects.py as the main """
        "__init__ has new objects."       )
    with open(dummy_file_paths[backend]			,       "w"			,       encoding="utf-8"			,       newline="\n"       ) as f:
     f.write(dummy_files[backend]       )
   else:
    raise ValueError(
        "The main __init__ has objects that are not present in "
        F"""transformers.utils.dummy_{short_names.get(UpperCAmelCase__			,       UpperCAmelCase__       )}_objects.py. Run `make fix-copies` """
        "to fix this."       )


if __name__ == "__main__":
     A_		:  Dict										=  argparse.ArgumentParser()
     parser.add_argument('''--fix_and_overwrite''', action='''store_true''', help='''Whether to fix inconsistencies.''')
     A_		:  List[str]										=  parser.parse_args()

     check_dummies(args.fix_and_overwrite)

