






"""simple docstring"""


import argparse
import collections

import jax
import jax.numpy as jnp
import torch
import torch.nn as nn
from clip.model import CLIP
from flax.training import checkpoints
from huggingface_hub import Repository

from transformers import (
    CLIPTokenizer,
    OwlViTConfig,
    OwlViTForObjectDetection,
    OwlViTImageProcessor,
    OwlViTModel,
    OwlViTProcessor,
)


_UpperCamelCase   :						List[str]     						= {
    """vit_b32""": {
        """embed_dim""": 5_12,
        """image_resolution""": 7_68,
        """context_length""": 16,
        """vocab_size""": 4_94_08,
        """vision_layers""": 12,
        """vision_width""": 7_68,
        """vision_patch_size""": 32,
        """transformer_width""": 5_12,
        """transformer_heads""": 8,
        """transformer_layers""": 12,
    },
    """vit_b16""": {
        """embed_dim""": 5_12,
        """image_resolution""": 7_68,
        """context_length""": 16,
        """vocab_size""": 4_94_08,
        """vision_layers""": 12,
        """vision_width""": 7_68,
        """vision_patch_size""": 16,
        """transformer_width""": 5_12,
        """transformer_heads""": 8,
        """transformer_layers""": 12,
    },
    """vit_l14""": {
        """embed_dim""": 7_68,
        """image_resolution""": 8_40,
        """context_length""": 16,
        """vocab_size""": 4_94_08,
        """vision_layers""": 24,
        """vision_width""": 10_24,
        """vision_patch_size""": 14,
        """transformer_width""": 7_68,
        """transformer_heads""": 12,
        """transformer_layers""": 12,
    },
}




def 				a (     UpperCamelCase_							:				Any     ,	UpperCamelCase_							:				List[str]=""     ,	UpperCamelCase_							:				Tuple="/"		)  ->		Optional[int]:
 __A	:       int       =	[]

 for k, v in params.items():
  __A	:       List[Any]       =	parent_key + sep + k if parent_key else k

  if isinstance(UpperCamelCase_     ,	collections.MutableMapping		):
   items.extend(flatten_nested_dict(UpperCamelCase_     ,	UpperCamelCase_     ,	sep=UpperCamelCase_		).items()		)
  else:
   items.append((new_key, v)		)
 return dict(UpperCamelCase_		)




def 				a (     UpperCamelCase_							:				Tuple		)  ->		Optional[Any]:
 return jax.tree_util.tree_map(lambda UpperCamelCase_		: x.astype(jnp.floataa		) if x.dtype == jnp.bfloataa else x     ,	UpperCamelCase_		)




def 				a (     UpperCamelCase_							:				int     ,	UpperCamelCase_							:				List[str]		)  ->		Optional[Any]:
 __A    ,					__A    ,					__A	:       Any       =	pt_attn_layer.in_proj_weight.chunk(3     ,	dim=0		)
 __A    ,					__A    ,					__A	:       Optional[Any]       =	pt_attn_layer.in_proj_bias.chunk(3     ,	dim=0		)

 __A	:       Tuple       =	pt_attn_layer.out_proj.weight
 __A	:       Dict       =	pt_attn_layer.out_proj.bias

 __A	:       str       =	q_proj
 __A	:       Dict       =	q_proj_bias

 __A	:       Optional[Any]       =	k_proj
 __A	:       Union[str, Any]       =	k_proj_bias

 __A	:       Union[str, Any]       =	v_proj
 __A	:       str       =	v_proj_bias

 __A	:       Tuple       =	out_proj_weights
 __A	:       Any       =	out_proj_bias




def 				a (     UpperCamelCase_							:				Tuple     ,	UpperCamelCase_							:				Tuple		)  ->		Dict:
 copy_linear(hf_mlp.fca     ,	pt_mlp.c_fc		)
 copy_linear(hf_mlp.fca     ,	pt_mlp.c_proj		)




def 				a (     UpperCamelCase_							:				Optional[int]     ,	UpperCamelCase_							:				Union[str, Any]		)  ->		str:
 __A	:       Dict       =	pt_linear.weight
 __A	:       Optional[int]       =	pt_linear.bias




def 				a (     UpperCamelCase_							:				str     ,	UpperCamelCase_							:				int		)  ->		int:
 # copy layer norms
 copy_linear(hf_layer.layer_norma     ,	pt_layer.ln_a		)
 copy_linear(hf_layer.layer_norma     ,	pt_layer.ln_a		)

 # copy MLP
 copy_mlp(hf_layer.mlp     ,	pt_layer.mlp		)

 # copy attn
 copy_attn_layer(hf_layer.self_attn     ,	pt_layer.attn		)




def 				a (     UpperCamelCase_							:				Tuple     ,	UpperCamelCase_							:				List[str]		)  ->		Optional[int]:
 for hf_layer, pt_layer in zip(UpperCamelCase_     ,	UpperCamelCase_		):
  copy_layer(UpperCamelCase_     ,	UpperCamelCase_		)




def 				a (     UpperCamelCase_							:				Union[str, Any]     ,	UpperCamelCase_							:				str		)  ->		Any:
 # copy  embeds
 __A	:       Optional[int]       =	pt_model.token_embedding.weight
 __A	:       Tuple       =	pt_model.positional_embedding

 # copy layer norm
 copy_linear(hf_encoder.final_layer_norm     ,	pt_model.ln_final		)

 # copy hidden layers
 copy_layers(hf_encoder.encoder.layers     ,	pt_model.transformer.resblocks		)




def 				a (     UpperCamelCase_							:				int     ,	UpperCamelCase_							:				List[str]		)  ->		Optional[int]:
 # copy projection
 __A	:       Optional[int]       =	pt_model.text_projection.data.T

 # copy text encoder
 copy_encoder(hf_model.text_model     ,	UpperCamelCase_		)




def 				a (     UpperCamelCase_							:				Tuple     ,	UpperCamelCase_							:				Dict		)  ->		Union[str, Any]:
 # copy projection
 __A	:       Tuple       =	pt_model.visual.proj.data.T

 # copy layer norms
 copy_linear(hf_model.vision_model.pre_layernorm     ,	pt_model.visual.ln_pre		)
 copy_linear(hf_model.vision_model.post_layernorm     ,	pt_model.visual.ln_post		)

 # copy embeds
 __A	:       int       =	pt_model.visual.conva.weight.data
 __A	:       str       =	pt_model.visual.class_embedding
 __A	:       List[Any]       =	pt_model.visual.positional_embedding.data

 # copy encoder
 copy_layers(hf_model.vision_model.encoder.layers     ,	pt_model.visual.transformer.resblocks		)




def 				a (     UpperCamelCase_							:				List[Any]     ,	UpperCamelCase_							:				Any		)  ->		Optional[Any]:
 __A	:       Tuple       =	flatten_nested_dict(flax_params['backbone']['merged_class_token']		)

 __A	:       Optional[Any]       =	torch.from_numpy(flax_class_token_params['scale']		)
 __A	:       str       =	torch.from_numpy(flax_class_token_params['bias']		)
 __A	:       Any       =	nn.Parameter(UpperCamelCase_		)
 __A	:       Union[str, Any]       =	nn.Parameter(UpperCamelCase_		)




def 				a (     UpperCamelCase_							:				str     ,	UpperCamelCase_							:				Optional[int]		)  ->		Optional[int]:
 __A	:       Optional[Any]       =	hf_model.state_dict()
 __A	:       List[str]       =	{}

 # Rename class prediction head flax params to pytorch HF
 __A	:       Union[str, Any]       =	flatten_nested_dict(flax_params['class_head']		)

 for flax_key, v in flax_class_params.items():
  __A	:       str       =	flax_key.replace('/'     ,	'.'		)
  __A	:       Tuple       =	torch_key.replace('.kernel'     ,	'.weight'		)
  __A	:       Any       =	torch_key.replace('Dense_0'     ,	'dense0'		)
  __A	:       List[Any]       =	'class_head.' + torch_key

  if "weight" in torch_key and v.ndim == 2:
   __A	:       Optional[Any]       =	v.T

  __A	:       List[Any]       =	nn.Parameter(torch.from_numpy(UpperCamelCase_		)		)

 # Rename box prediction box flax params to pytorch HF
 __A	:       str       =	flatten_nested_dict(flax_params['obj_box_head']		)

 for flax_key, v in flax_box_params.items():
  __A	:       Union[str, Any]       =	flax_key.replace('/'     ,	'.'		)
  __A	:       Tuple       =	torch_key.replace('.kernel'     ,	'.weight'		)
  __A	:       Tuple       =	torch_key.replace('_'     ,	''		).lower()
  __A	:       str       =	'box_head.' + torch_key

  if "weight" in torch_key and v.ndim == 2:
   __A	:       Optional[int]       =	v.T

  __A	:       List[str]       =	nn.Parameter(torch.from_numpy(UpperCamelCase_		)		)

 # Copy flax params to PyTorch params
 for name, param in new_params.items():
  if name in pt_params.keys():
   pt_params[name].copy_(UpperCamelCase_		)




def 				a (     UpperCamelCase_							:				Optional[int]     ,	UpperCamelCase_							:				Tuple		)  ->		Tuple:
 for k, v in flax_attn_params.items():
  if k.startswith('transformer'		):
   __A	:       Optional[Any]       =	k.replace('transformer.resblocks'     ,	'text_model.encoder.layers'		)
  else:
   __A	:       str       =	k.replace('visual.transformer.resblocks'     ,	'vision_model.encoder.layers'		)

  __A	:       Optional[Any]       =	torch_key.replace('attn'     ,	'self_attn'		)
  __A	:       int       =	torch_key.replace('key'     ,	'k_proj'		)
  __A	:       Optional[Any]       =	torch_key.replace('value'     ,	'v_proj'		)
  __A	:       Tuple       =	torch_key.replace('query'     ,	'q_proj'		)
  __A	:       Tuple       =	torch_key.replace('out'     ,	'out_proj'		)

  if "bias" in torch_key and v.ndim == 2:
   __A	:       str       =	v.shape[0] * v.shape[1]
   __A	:       Optional[int]       =	v.reshape(UpperCamelCase_		)

  if "weight" in torch_key and "out" in torch_key:
   __A	:       Optional[Any]       =	(v.shape[0] * v.shape[1], v.shape[2])
   __A	:       Dict       =	v.reshape(UpperCamelCase_		).T

  if "weight" in torch_key and "out" not in torch_key:
   __A	:       Optional[Any]       =	(v.shape[0], v.shape[1] * v.shape[2])
   __A	:       Optional[Any]       =	v.reshape(UpperCamelCase_		).T

  # Copy flax CLIP attn params to HF PyTorch params
  __A	:       Union[str, Any]       =	torch.from_numpy(UpperCamelCase_		)
  hf_backbone.state_dict()[torch_key].copy_(UpperCamelCase_		)




def 				a (     UpperCamelCase_							:				Optional[Any]		)  ->		Tuple:
 __A	:       Tuple       =	{}
 __A	:       Optional[Any]       =	[]

 for k, v in params.items():
  if "attn." in k:
   __A	:       int       =	k[: k.rindex('attn.'		) + 5]
   if base in processed_attn_layers:
    continue

   processed_attn_layers.append(UpperCamelCase_		)
   __A	:       List[Any]       =	params[base + 'out.weight'].shape[-1]
   __A	:       Tuple       =	params[base + 'out.weight'].reshape(UpperCamelCase_     ,	UpperCamelCase_		).T
   __A	:       Any       =	params[base + 'out.bias']
  else:
   __A	:       Optional[int]       =	v
 return new_params




def 				a (     UpperCamelCase_							:				List[str]     ,	UpperCamelCase_							:				Optional[int]		)  ->		List[str]:
 __A	:       Dict       =	CLIP(**UpperCamelCase_		)
 torch_model.eval()
 __A	:       Any       =	torch_model.state_dict()

 __A	:       List[str]       =	flatten_nested_dict(flax_params['backbone']['clip']		)
 __A	:       Optional[int]       =	{}

 for flax_key, v in flax_clip_params.items():
  __A	:       Any       =	flax_key.replace('/'     ,	'.'		)
  __A	:       Any       =	torch_key.replace('text.token_embedding.embedding'     ,	'token_embedding.kernel'		)

  if (
      torch_key.startswith('text.transformer'		)
      or torch_key.startswith('text.text_projection'		)
      or torch_key.startswith('text.ln_final'		)
      or torch_key.startswith('text.positional_embedding'		)
  ):
   __A	:       Optional[int]       =	torch_key[5:]

  __A	:       Dict       =	torch_key.replace('text_projection.kernel'     ,	'text_projection'		)
  __A	:       Dict       =	torch_key.replace('visual.proj.kernel'     ,	'visual.proj'		)
  __A	:       List[str]       =	torch_key.replace('.scale'     ,	'.weight'		)
  __A	:       Tuple       =	torch_key.replace('.kernel'     ,	'.weight'		)

  if "conv" in torch_key or "downsample.0.weight" in torch_key:
   __A	:       Optional[int]       =	v.transpose(3     ,	2     ,	0     ,	1		)

  elif "weight" in torch_key and v.ndim == 2 and "embedding" not in torch_key:
   # Fully connected layers are transposed, embeddings are not
   __A	:       Optional[Any]       =	v.T

  __A	:       Any       =	v

 __A	:       Any       =	_convert_attn_layers(UpperCamelCase_		)
 new_torch_params.update(UpperCamelCase_		)
 __A	:       Optional[Any]       =	{}

 # Copy flax CLIP backbone params to PyTorch params
 for name, param in new_torch_params.items():
  if name in torch_clip_params.keys():
   __A	:       int       =	torch.from_numpy(new_torch_params[name]		)
   torch_clip_params[name].copy_(UpperCamelCase_		)
  else:
   __A	:       Optional[Any]       =	param

 return torch_clip_params, torch_model, attn_params




@torch.no_grad()
def 				a (     UpperCamelCase_							:				Union[str, Any]     ,	UpperCamelCase_							:				Any     ,	UpperCamelCase_							:				Tuple     ,	UpperCamelCase_							:				List[str]     ,	UpperCamelCase_							:				Tuple=None		)  ->		List[Any]:
 __A	:       int       =	Repository(UpperCamelCase_     ,	clone_from=F"google/{pytorch_dump_folder_path}"		)
 repo.git_pull()

 if config_path is not None:
  __A	:       Optional[int]       =	OwlViTConfig.from_pretrained(UpperCamelCase_		)
 else:
  __A	:       Dict       =	OwlViTConfig()

 __A	:       int       =	OwlViTModel(UpperCamelCase_		).eval()
 __A	:       Union[str, Any]       =	OwlViTForObjectDetection(UpperCamelCase_		).eval()

 copy_text_model_and_projection(UpperCamelCase_     ,	UpperCamelCase_		)
 copy_vision_model_and_projection(UpperCamelCase_     ,	UpperCamelCase_		)
 __A	:       Tuple       =	pt_backbone.logit_scale
 copy_flax_attn_params(UpperCamelCase_     ,	UpperCamelCase_		)

 __A	:       str       =	hf_backbone
 copy_class_merge_token(UpperCamelCase_     ,	UpperCamelCase_		)
 copy_class_box_heads(UpperCamelCase_     ,	UpperCamelCase_		)

 # Save HF model
 hf_model.save_pretrained(repo.local_dir		)

 # Initialize image processor
 __A	:       str       =	OwlViTImageProcessor(
     size=config.vision_config.image_size     ,	crop_size=config.vision_config.image_size		)
 # Initialize tokenizer
 __A	:       List[str]       =	CLIPTokenizer.from_pretrained('openai/clip-vit-base-patch32'     ,	pad_token='!'     ,	model_max_length=1_6		)

 # Initialize processor
 __A	:       List[str]       =	OwlViTProcessor(image_processor=UpperCamelCase_     ,	tokenizer=UpperCamelCase_		)
 image_processor.save_pretrained(repo.local_dir		)
 processor.save_pretrained(repo.local_dir		)

 repo.git_add()
 repo.git_commit('Upload model and processor'		)
 repo.git_push()


if __name__ == "__main__":
   _UpperCamelCase   :						Dict     						= argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       """--owlvit_version""",
       default=None,
       type=str,
       required=True,
       help="""OWL-ViT model name [clip_b16, clip_b32, clip_l14].""",
   )
   parser.add_argument(
       """--owlvit_checkpoint""", default=None, type=str, required=True, help="""Path to flax model checkpoint."""
   )
   parser.add_argument("""--hf_config""", default=None, type=str, required=True, help="""Path to HF model config.""")
   parser.add_argument(
       """--pytorch_dump_folder_path""", default="""hf_model""", type=str, help="""Path to the output PyTorch model."""
   )
   _UpperCamelCase   :						int     						= parser.parse_args()

   # Initialize PyToch clip model
   _UpperCamelCase   :						Union[str, Any]     						= args.owlvit_version
   if model_name == "clip_b16":
      _UpperCamelCase   :						List[Any]     						= CONFIGS["""vit_b16"""]
   elif model_name == "clip_b32":
      _UpperCamelCase   :						Union[str, Any]     						= CONFIGS["""vit_b32"""]
   elif model_name == "clip_l14":
      _UpperCamelCase   :						Optional[int]     						= CONFIGS["""vit_l14"""]

   # Load from checkpoint and convert params to float-32
   _UpperCamelCase   :						Tuple     						= checkpoints.restore_checkpoint(args.owlvit_checkpoint, target=None)["""optimizer"""]["""target"""]
   _UpperCamelCase   :						Optional[int]     						= jax.tree_util.tree_map(lambda x: x.astype(jnp.floataa) if x.dtype == jnp.bfloataa else x, variables)
   del variables

   # Convert CLIP backbone
   _UpperCamelCase						,					_UpperCamelCase						,					_UpperCamelCase   :						int     						= convert_clip_backbone(flax_params, torch_config)

   convert_owlvit_checkpoint(clip_pt, flax_params, attn_params, args.pytorch_dump_folder_path, args.hf_config)













"""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()
_UpperCamelCase   :						str     						= logging.get_logger(__name__)




def 				a (     )  ->		int:
 __A	:       Dict       =	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=UpperCamelCase_     ,	with_box_refine=UpperCamelCase_     ,	two_stage=UpperCamelCase_     ,	)

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

 return config




def 				a (     UpperCamelCase_							:				Dict		)  ->		List[str]:
 __A	:       Optional[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 				a (     UpperCamelCase_							:				Optional[Any]     ,	UpperCamelCase_							:				List[str]     ,	UpperCamelCase_							:				List[Any]		)  ->		Dict:
 __A	:       List[str]       =	dct.pop(UpperCamelCase_		)
 __A	:       Optional[Any]       =	val




def 				a (     UpperCamelCase_							:				str     ,	UpperCamelCase_							:				List[Any]		)  ->		Union[str, Any]:
 # transformer decoder self-attention layers
 __A	:       Dict       =	config.d_model
 for i in range(config.decoder_layers		):
  # read in weights + bias of input projection layer of self-attention
  __A	:       Any       =	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	:       Optional[int]       =	in_proj_weight[:hidden_size, :]
  __A	:       Any       =	in_proj_bias[:hidden_size]
  __A	:       Any       =	in_proj_weight[
      hidden_size : hidden_size * 2, :
  ]
  __A	:       List[Any]       =	in_proj_bias[hidden_size : hidden_size * 2]
  __A	:       Optional[Any]       =	in_proj_weight[-hidden_size:, :]
  __A	:       Optional[Any]       =	in_proj_bias[-hidden_size:]




def 				a (     )  ->		Dict:
 __A	:       str       =	'http://images.cocodataset.org/val2017/000000039769.jpg'
 __A	:       Union[str, Any]       =	Image.open(requests.get(UpperCamelCase_     ,	stream=UpperCamelCase_		).raw		)

 return im




@torch.no_grad()
def 				a (     UpperCamelCase_							:				Union[str, Any]     ,	UpperCamelCase_							:				Tuple     ,	UpperCamelCase_							:				Optional[int]		)  ->		Optional[int]:
 __A	:       Dict       =	get_deta_config()

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

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

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

 __A	:       Any       =	'cuda' if torch.cuda.is_available() else 'cpu'
 model.to(UpperCamelCase_		)

 # load image processor
 __A	:       Any       =	DetaImageProcessor(format='coco_detection'		)

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

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





