

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()
UpperCAmelCase_       :				Tuple		   =      logging.get_logger(__name__)
def        __SCREAMING_SNAKE_CASE    (      )				->							Any:
 __A   : Dict								= DetaConfig(
     num_queries=900 ,encoder_ffn_dim=2048 ,decoder_ffn_dim=2048 ,num_feature_levels=5 ,assign_first_stage=__a ,with_box_refine=__a ,two_stage=__a ,)

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

 return config
def        __SCREAMING_SNAKE_CASE    (      a__						:      Dict					)				->							Union[str, Any]:
 __A   : int								= []

 # 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        __SCREAMING_SNAKE_CASE    (      a__						:      Optional[Any] ,a__						:      int ,a__						:      Dict					)				->							Union[str, Any]:
 __A   : Optional[Any]								= dct.pop(__a					)
 __A   : List[Any]								= val
def        __SCREAMING_SNAKE_CASE    (      a__						:      List[Any] ,a__						:      Tuple					)				->							Dict:
 __A   : Tuple								= config.d_model
 for i in range(config.decoder_layers					):
  # read in weights + bias of input projection layer of self-attention
  __A   : List[str]								= state_dict.pop(f"""transformer.decoder.layers.{i}.self_attn.in_proj_weight"""					)
  __A   : Optional[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
  __A   : Dict								= in_proj_weight[:hidden_size, :]
  __A   : List[Any]								= in_proj_bias[:hidden_size]
  __A   : int								= in_proj_weight[
      hidden_size : hidden_size * 2, :
  ]
  __A   : Any								= in_proj_bias[hidden_size : hidden_size * 2]
  __A   : List[str]								= in_proj_weight[-hidden_size:, :]
  __A   : Dict								= in_proj_bias[-hidden_size:]
def        __SCREAMING_SNAKE_CASE    (      )				->							int:
 __A   : Tuple								= '''http://images.cocodataset.org/val2017/000000039769.jpg'''
 __A   : Union[str, Any]								= Image.open(requests.get(__a ,stream=__a					).raw					)

 return im
@torch.no_grad()
def        __SCREAMING_SNAKE_CASE    (      a__						:      Tuple ,a__						:      List[Any] ,a__						:      Optional[Any]					)				->							Optional[Any]:
 __A   : str								= get_deta_config()

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

 # rename keys
 __A   : int								= create_rename_keys(__a					)
 for src, dest in rename_keys:
  rename_key(__a ,__a ,__a					)
 read_in_decoder_q_k_v(__a ,__a					)

 # 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   : str								= state_dict.pop(__a					)
   __A   : List[str]								= val
  if "input_proj" in key:
   __A   : Tuple								= state_dict.pop(__a					)
   __A   : Tuple								= val
  if "level_embed" in key or "pos_trans" in key or "pix_trans" in key or "enc_output" in key:
   __A   : Dict								= state_dict.pop(__a					)
   __A   : List[str]								= val

    # finally, create HuggingFace model and load state dict
 __A   : str								= DetaForObjectDetection(__a					)
 model.load_state_dict(__a					)
 model.eval()

 __A   : int								= '''cuda''' if torch.cuda.is_available() else '''cpu'''
 model.to(__a					)

 # load image processor
 __A   : Optional[Any]								= DetaImageProcessor(format="""coco_detection"""					)

 # verify our conversion on image
 __A   : Union[str, Any]								= prepare_img()
 __A   : Any								= processor(images=__a ,return_tensors="""pt"""					)
 __A   : List[Any]								= encoding['''pixel_values''']
 __A   : Dict								= model(pixel_values.to(__a					)					)

 # verify logits
 if model_name == "deta-resnet-50":
  __A   : Optional[int]								= torch.tensor(
      [[-7.3_978, -2.5_406, -4.1_668], [-8.2_684, -3.9_933, -3.8_096], [-7.0_515, -3.7_973, -5.8_516]]					)
  __A   : Tuple								= torch.tensor([[0.5_043, 0.4_973, 0.9_998], [0.2_542, 0.5_489, 0.4_748], [0.5_490, 0.2_765, 0.0_570]]					)
 elif model_name == "deta-resnet-50-24-epochs":
  __A   : Any								= torch.tensor(
      [[-7.1_688, -2.4_857, -4.8_669], [-7.8_630, -3.8_154, -4.2_674], [-7.2_730, -4.1_865, -5.5_323]]					)
  __A   : List[Any]								= torch.tensor([[0.5_021, 0.4_971, 0.9_994], [0.2_546, 0.5_486, 0.4_731], [0.1_686, 0.1_986, 0.2_142]]					)

 assert torch.allclose(outputs.logits[0, :3, :3] ,expected_logits.to(__a					) ,atol=1E-4					)
 assert torch.allclose(outputs.pred_boxes[0, :3, :3] ,expected_boxes.to(__a					) ,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(__a					).mkdir(exist_ok=__a					)
  model.save_pretrained(__a					)
  processor.save_pretrained(__a					)

 # 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__":
   UpperCAmelCase_       :				Tuple		   =      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.'''
   )
   UpperCAmelCase_       :				List[Any]		   =      parser.parse_args()
   convert_deta_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)








from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast
from ...utils import logging


UpperCAmelCase_       :				int		   =      logging.get_logger(__name__)

UpperCAmelCase_       :				Any		   =      {
    '''uclanlp/plbart-base''': '''https://huggingface.co/uclanlp/plbart-base/resolve/main/config.json''',
    # See all PLBART models at https://huggingface.co/models?filter=plbart
}






class     lowerCamelCase_			(  _lowercase  ):
	_lowercase		:      str    				=							'''plbart'''
	_lowercase		:      str    				=							['''past_key_values''']
	_lowercase		:      Any    				=							{'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}




	def __init__(    self	:    List[Any]				,			__A	:    str=5_0005				,			__A	:    Union[str, Any]=1024				,			__A	:    int=6				,			__A	:    Dict=3072				,			__A	:    Union[str, Any]=12				,			__A	:    Tuple=6				,			__A	:    Tuple=3072				,			__A	:    Optional[int]=12				,			__A	:    int=0.0				,			__A	:    List[Any]=0.0				,			__A	:    Union[str, Any]=True				,			__A	:    Any=True				,			__A	:    Optional[int]="gelu"				,			__A	:    Optional[int]=768				,			__A	:    Any=0.1				,			__A	:    Union[str, Any]=0.1				,			__A	:    Union[str, Any]=0.0				,			__A	:    int=0.0_2				,			__A	:    Union[str, Any]=0.0				,			__A	:    Tuple=True				,			__A	:    List[str]=1				,			__A	:    str=0				,			__A	:    List[Any]=2				,			__A	:    Any=2				,			**__A	:    Union[str, Any]				,			):
		__A   : List[Any]								= vocab_size
		__A   : str								= max_position_embeddings
		__A   : Union[str, Any]								= d_model
		__A   : List[str]								= encoder_ffn_dim
		__A   : Optional[Any]								= encoder_layers
		__A   : Tuple								= encoder_attention_heads
		__A   : List[Any]								= decoder_ffn_dim
		__A   : Optional[int]								= decoder_layers
		__A   : Any								= decoder_attention_heads
		__A   : Optional[int]								= dropout
		__A   : Any								= attention_dropout
		__A   : Tuple								= activation_dropout
		__A   : List[Any]								= activation_function
		__A   : Tuple								= init_std
		__A   : List[Any]								= encoder_layerdrop
		__A   : str								= decoder_layerdrop
		__A   : Union[str, Any]								= classifier_dropout
		__A   : Optional[Any]								= use_cache
		__A   : List[str]								= encoder_layers
		__A   : Optional[int]								= scale_embedding  # scale factor will be sqrt(d_model) if True
		super().__init__(
		    pad_token_id=__A				,			bos_token_id=__A				,			eos_token_id=__A				,			is_encoder_decoder=__A				,			forced_eos_token_id=__A				,			**__A				,			)






class     lowerCamelCase_			(  _lowercase  ):




	@property
	def 					lowerCAmelCase_					(    self	:    Any       ):
		return OrderedDict(
		    [
		        ("""input_ids""", {0: """batch""", 1: """sequence"""}),
		        ("""attention_mask""", {0: """batch""", 1: """sequence"""}),
		    ]       )




	@property
	def 					lowerCAmelCase_					(    self	:    Tuple       ):
		if self.use_past:
			return OrderedDict(
			    [
			        ("""last_hidden_state""", {0: """batch""", 1: """sequence"""}),
			        ("""past_keys""", {0: """batch""", 2: """sequence"""}),
			        ("""encoder_last_hidden_state""", {0: """batch""", 1: """sequence"""}),
			    ]       )
		else:
			return OrderedDict(
			    [
			        ("""last_hidden_state""", {0: """batch""", 1: """sequence"""}),
			        ("""encoder_last_hidden_state""", {0: """batch""", 1: """sequence"""}),
			    ]       )





