


"""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, SwinConfig
from transformers.utils import logging


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







def   __lowerCAmelCase						(						lowercase  :  int			)     ->						List[str]:


				"""simple docstring"""





				snake_case   :				Union[str, Any]				 =   SwinConfig(
				    embed_dim=192						,							depths=(2, 2, 18, 2)						,							num_heads=(6, 12, 24, 48)						,							window_size=12						,							out_features=["stage2", "stage3", "stage4"]						,							)

				snake_case   :				Union[str, Any]				 =   DetaConfig(
				    backbone_config=A_						,							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
				snake_case   :				List[str]				 =   '''huggingface/label-files'''
				if "o365" in model_name:
								snake_case   :				Union[str, Any]				 =   366
								snake_case   :				Optional[int]				 =   '''object365-id2label.json'''
				else:
								snake_case   :				List[str]				 =   91
								snake_case   :				Dict				 =   '''coco-detection-id2label.json'''

				snake_case   :				List[str]				 =   num_labels
				snake_case   :				Union[str, Any]				 =   json.load(open(cached_download(hf_hub_url(A_						,							A_						,							repo_type="dataset"			)			)						,							"r"			)			)
				snake_case   :				Optional[int]				 =   {int(A_			): v for k, v in idalabel.items()}
				snake_case   :				List[Any]				 =   idalabel
				snake_case   :				List[Any]				 =   {v: k for k, v in idalabel.items()}

				return config







def   __lowerCAmelCase						(						lowercase  :  Dict			)     ->						List[Any]:


				"""simple docstring"""





				snake_case   :				str				 =   []

				# stem
				# fmt: off
				rename_keys.append(("backbone.0.body.patch_embed.proj.weight", "model.backbone.model.embeddings.patch_embeddings.projection.weight")			)
				rename_keys.append(("backbone.0.body.patch_embed.proj.bias", "model.backbone.model.embeddings.patch_embeddings.projection.bias")			)
				rename_keys.append(("backbone.0.body.patch_embed.norm.weight", "model.backbone.model.embeddings.norm.weight")			)
				rename_keys.append(("backbone.0.body.patch_embed.norm.bias", "model.backbone.model.embeddings.norm.bias")			)
				# stages
				for i in range(len(config.backbone_config.depths			)			):
								for j in range(config.backbone_config.depths[i]			):
												rename_keys.append((F'backbone.0.body.layers.{i}.blocks.{j}.norm1.weight', F'model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_before.weight')			)
												rename_keys.append((F'backbone.0.body.layers.{i}.blocks.{j}.norm1.bias', F'model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_before.bias')			)
												rename_keys.append((F'backbone.0.body.layers.{i}.blocks.{j}.attn.relative_position_bias_table', F'model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table')			)
												rename_keys.append((F'backbone.0.body.layers.{i}.blocks.{j}.attn.relative_position_index', F'model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index')			)
												rename_keys.append((F'backbone.0.body.layers.{i}.blocks.{j}.attn.proj.weight', F'model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight')			)
												rename_keys.append((F'backbone.0.body.layers.{i}.blocks.{j}.attn.proj.bias', F'model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias')			)
												rename_keys.append((F'backbone.0.body.layers.{i}.blocks.{j}.norm2.weight', F'model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_after.weight')			)
												rename_keys.append((F'backbone.0.body.layers.{i}.blocks.{j}.norm2.bias', F'model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_after.bias')			)
												rename_keys.append((F'backbone.0.body.layers.{i}.blocks.{j}.mlp.fc1.weight', F'model.backbone.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight')			)
												rename_keys.append((F'backbone.0.body.layers.{i}.blocks.{j}.mlp.fc1.bias', F'model.backbone.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias')			)
												rename_keys.append((F'backbone.0.body.layers.{i}.blocks.{j}.mlp.fc2.weight', F'model.backbone.model.encoder.layers.{i}.blocks.{j}.output.dense.weight')			)
												rename_keys.append((F'backbone.0.body.layers.{i}.blocks.{j}.mlp.fc2.bias', F'model.backbone.model.encoder.layers.{i}.blocks.{j}.output.dense.bias')			)

								if i < 3:
												rename_keys.append((F'backbone.0.body.layers.{i}.downsample.reduction.weight', F'model.backbone.model.encoder.layers.{i}.downsample.reduction.weight')			)
												rename_keys.append((F'backbone.0.body.layers.{i}.downsample.norm.weight', F'model.backbone.model.encoder.layers.{i}.downsample.norm.weight')			)
												rename_keys.append((F'backbone.0.body.layers.{i}.downsample.norm.bias', F'model.backbone.model.encoder.layers.{i}.downsample.norm.bias')			)

				rename_keys.append(("backbone.0.body.norm1.weight", "model.backbone.model.hidden_states_norms.stage2.weight")			)
				rename_keys.append(("backbone.0.body.norm1.bias", "model.backbone.model.hidden_states_norms.stage2.bias")			)
				rename_keys.append(("backbone.0.body.norm2.weight", "model.backbone.model.hidden_states_norms.stage3.weight")			)
				rename_keys.append(("backbone.0.body.norm2.bias", "model.backbone.model.hidden_states_norms.stage3.bias")			)
				rename_keys.append(("backbone.0.body.norm3.weight", "model.backbone.model.hidden_states_norms.stage4.weight")			)
				rename_keys.append(("backbone.0.body.norm3.bias", "model.backbone.model.hidden_states_norms.stage4.bias")			)

				# 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   __lowerCAmelCase						(						lowercase  :  Optional[Any]						,							lowercase  :  Union[str, Any]						,							lowercase  :  str			)     ->						List[Any]:


				"""simple docstring"""





				snake_case   :				Tuple				 =   dct.pop(A_			)
				snake_case   :				Union[str, Any]				 =   val







def   __lowerCAmelCase						(						lowercase  :  Dict						,							lowercase  :  List[str]			)     ->						Optional[int]:


				"""simple docstring"""





				snake_case   :				List[Any]				 =   [int(backbone_config.embed_dim * 2**i			) for i in range(len(backbone_config.depths			)			)]
				for i in range(len(backbone_config.depths			)			):
								snake_case   :				Tuple				 =   num_features[i]
								for j in range(backbone_config.depths[i]			):
												# fmt: off
												# read in weights + bias of input projection layer (in original implementation, this is a single matrix + bias)
												snake_case   :				List[str]				 =   state_dict.pop(F'backbone.0.body.layers.{i}.blocks.{j}.attn.qkv.weight'			)
												snake_case   :				Tuple				 =   state_dict.pop(F'backbone.0.body.layers.{i}.blocks.{j}.attn.qkv.bias'			)
												# next, add query, keys and values (in that order) to the state dict
												snake_case   :				Dict				 =   in_proj_weight[:dim, :]
												snake_case   :				Dict				 =   in_proj_bias[: dim]
												snake_case   :				List[Any]				 =   in_proj_weight[
												    dim : dim * 2, :
												]
												snake_case   :				Optional[Any]				 =   in_proj_bias[
												    dim : dim * 2
												]
												snake_case   :				str				 =   in_proj_weight[
												    -dim :, :
												]
												snake_case   :				int				 =   in_proj_bias[-dim :]
												# fmt: on







def   __lowerCAmelCase						(						lowercase  :  Dict						,							lowercase  :  Any			)     ->						List[Any]:


				"""simple docstring"""





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







def   __lowerCAmelCase						(						)     ->						Optional[Any]:


				"""simple docstring"""





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

				return im







@torch.no_grad()
def   __lowerCAmelCase						(						lowercase  :  int						,							lowercase  :  Optional[int]						,							lowercase  :  Optional[Any]			)     ->						List[Any]:


				"""simple docstring"""





				snake_case   :				str				 =   get_deta_config(A_			)

				# load original state dict
				if model_name == "deta-swin-large":
								snake_case   :				Dict				 =   hf_hub_download(repo_id="nielsr/deta-checkpoints"						,							filename="adet_swin_ft.pth"			)
				elif model_name == "deta-swin-large-o365":
								snake_case   :				Dict				 =   hf_hub_download(repo_id="jozhang97/deta-swin-l-o365"						,							filename="deta_swin_pt_o365.pth"			)
				else:
								raise ValueError(F'Model name {model_name} not supported'			)

				snake_case   :				Dict				 =   torch.load(A_						,							map_location="cpu"			)['''model''']

				# original state dict
				for name, param in state_dict.items():
								print(A_						,							param.shape			)

				# rename keys
				snake_case   :				Tuple				 =   create_rename_keys(A_			)
				for src, dest in rename_keys:
								rename_key(A_						,							A_						,							A_			)
				read_in_swin_q_k_v(A_						,							config.backbone_config			)
				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:
												snake_case   :				List[Any]				 =   state_dict.pop(A_			)
												snake_case   :				Any				 =   val
								if "input_proj" in key:
												snake_case   :				Optional[int]				 =   state_dict.pop(A_			)
												snake_case   :				Any				 =   val
								if "level_embed" in key or "pos_trans" in key or "pix_trans" in key or "enc_output" in key:
												snake_case   :				Union[str, Any]				 =   state_dict.pop(A_			)
												snake_case   :				Tuple				 =   val

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

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

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

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

				# verify logits
				print("Logits:"						,							outputs.logits[0, :3, :3]			)
				print("Boxes:"						,							outputs.pred_boxes[0, :3, :3]			)
				if model_name == "deta-swin-large":
								snake_case   :				str				 =   torch.tensor(
								    [[-7.6308, -2.8485, -5.3737], [-7.2037, -4.5505, -4.8027], [-7.2943, -4.2611, -4.6617]]			)
								snake_case   :				Optional[Any]				 =   torch.tensor([[0.4987, 0.4969, 0.9999], [0.2549, 0.5498, 0.4805], [0.5498, 0.2757, 0.0569]]			)
				elif model_name == "deta-swin-large-o365":
								snake_case   :				Optional[Any]				 =   torch.tensor(
								    [[-8.0122, -3.5720, -4.9717], [-8.1547, -3.6886, -4.6389], [-7.6610, -3.6194, -5.0134]]			)
								snake_case   :				List[Any]				 =   torch.tensor([[0.2523, 0.5549, 0.4881], [0.7715, 0.4149, 0.4601], [0.5503, 0.2753, 0.0575]]			)
				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__":
		__snake_case             =   argparse.ArgumentParser()

		parser.add_argument(
		    """--model_name""",
		    type=str,
		    default="""deta-swin-large""",
		    choices=["""deta-swin-large""", """deta-swin-large-o365"""],
		    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."""
		)
		__snake_case             =   parser.parse_args()
		convert_deta_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)









"""simple docstring"""



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


__snake_case             =   {
    """configuration_conditional_detr""": [
        """CONDITIONAL_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """ConditionalDetrConfig""",
        """ConditionalDetrOnnxConfig""",
    ]
}

try:
  if not is_vision_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __snake_case             =   ["""ConditionalDetrFeatureExtractor"""]
  __snake_case             =   ["""ConditionalDetrImageProcessor"""]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __snake_case             =   [
      """CONDITIONAL_DETR_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """ConditionalDetrForObjectDetection""",
      """ConditionalDetrForSegmentation""",
      """ConditionalDetrModel""",
      """ConditionalDetrPreTrainedModel""",
  ]


if TYPE_CHECKING:
  from .configuration_conditional_detr import (
      CONDITIONAL_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP,
      ConditionalDetrConfig,
      ConditionalDetrOnnxConfig,
  )

  try:
    if not is_vision_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .feature_extraction_conditional_detr import ConditionalDetrFeatureExtractor
    from .image_processing_conditional_detr import ConditionalDetrImageProcessor

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_conditional_detr import (
        CONDITIONAL_DETR_PRETRAINED_MODEL_ARCHIVE_LIST,
        ConditionalDetrForObjectDetection,
        ConditionalDetrForSegmentation,
        ConditionalDetrModel,
        ConditionalDetrPreTrainedModel,
    )

else:
  import sys

  __snake_case             =   _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)





