

import argparse
import json
from collections import OrderedDict
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    ConditionalDetrConfig,
    ConditionalDetrForObjectDetection,
    ConditionalDetrForSegmentation,
    ConditionalDetrImageProcessor,
)
from transformers.utils import logging


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

# here we list all keys to be renamed (original name on the left, our name on the right)
lowercase_   							=  []
for i in range(6):
  # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
  rename_keys.append(
      (f"transformer.encoder.layers.{i}.self_attn.out_proj.weight", f"encoder.layers.{i}.self_attn.out_proj.weight")
  )
  rename_keys.append(
      (f"transformer.encoder.layers.{i}.self_attn.out_proj.bias", f"encoder.layers.{i}.self_attn.out_proj.bias")
  )
  rename_keys.append((f"transformer.encoder.layers.{i}.linear1.weight", f"encoder.layers.{i}.fc1.weight"))
  rename_keys.append((f"transformer.encoder.layers.{i}.linear1.bias", f"encoder.layers.{i}.fc1.bias"))
  rename_keys.append((f"transformer.encoder.layers.{i}.linear2.weight", f"encoder.layers.{i}.fc2.weight"))
  rename_keys.append((f"transformer.encoder.layers.{i}.linear2.bias", f"encoder.layers.{i}.fc2.bias"))
  rename_keys.append(
      (f"transformer.encoder.layers.{i}.norm1.weight", f"encoder.layers.{i}.self_attn_layer_norm.weight")
  )
  rename_keys.append((f"transformer.encoder.layers.{i}.norm1.bias", f"encoder.layers.{i}.self_attn_layer_norm.bias"))
  rename_keys.append((f"transformer.encoder.layers.{i}.norm2.weight", f"encoder.layers.{i}.final_layer_norm.weight"))
  rename_keys.append((f"transformer.encoder.layers.{i}.norm2.bias", f"encoder.layers.{i}.final_layer_norm.bias"))
  # decoder layers: 2 times output projection, 2 feedforward neural networks and 3 layernorms
  rename_keys.append(
      (f"transformer.decoder.layers.{i}.self_attn.out_proj.weight", f"decoder.layers.{i}.self_attn.out_proj.weight")
  )
  rename_keys.append(
      (f"transformer.decoder.layers.{i}.self_attn.out_proj.bias", f"decoder.layers.{i}.self_attn.out_proj.bias")
  )
  rename_keys.append(
      (
          f"transformer.decoder.layers.{i}.cross_attn.out_proj.weight",
          f"decoder.layers.{i}.encoder_attn.out_proj.weight",
      )
  )
  rename_keys.append(
      (
          f"transformer.decoder.layers.{i}.cross_attn.out_proj.bias",
          f"decoder.layers.{i}.encoder_attn.out_proj.bias",
      )
  )
  rename_keys.append((f"transformer.decoder.layers.{i}.linear1.weight", f"decoder.layers.{i}.fc1.weight"))
  rename_keys.append((f"transformer.decoder.layers.{i}.linear1.bias", f"decoder.layers.{i}.fc1.bias"))
  rename_keys.append((f"transformer.decoder.layers.{i}.linear2.weight", f"decoder.layers.{i}.fc2.weight"))
  rename_keys.append((f"transformer.decoder.layers.{i}.linear2.bias", f"decoder.layers.{i}.fc2.bias"))
  rename_keys.append(
      (f"transformer.decoder.layers.{i}.norm1.weight", f"decoder.layers.{i}.self_attn_layer_norm.weight")
  )
  rename_keys.append((f"transformer.decoder.layers.{i}.norm1.bias", f"decoder.layers.{i}.self_attn_layer_norm.bias"))
  rename_keys.append(
      (f"transformer.decoder.layers.{i}.norm2.weight", f"decoder.layers.{i}.encoder_attn_layer_norm.weight")
  )
  rename_keys.append(
      (f"transformer.decoder.layers.{i}.norm2.bias", f"decoder.layers.{i}.encoder_attn_layer_norm.bias")
  )
  rename_keys.append((f"transformer.decoder.layers.{i}.norm3.weight", f"decoder.layers.{i}.final_layer_norm.weight"))
  rename_keys.append((f"transformer.decoder.layers.{i}.norm3.bias", f"decoder.layers.{i}.final_layer_norm.bias"))

  # q, k, v projections in self/cross-attention in decoder for conditional DETR
  rename_keys.append(
      (f"transformer.decoder.layers.{i}.sa_qcontent_proj.weight", f"decoder.layers.{i}.sa_qcontent_proj.weight")
  )
  rename_keys.append(
      (f"transformer.decoder.layers.{i}.sa_kcontent_proj.weight", f"decoder.layers.{i}.sa_kcontent_proj.weight")
  )
  rename_keys.append(
      (f"transformer.decoder.layers.{i}.sa_qpos_proj.weight", f"decoder.layers.{i}.sa_qpos_proj.weight")
  )
  rename_keys.append(
      (f"transformer.decoder.layers.{i}.sa_kpos_proj.weight", f"decoder.layers.{i}.sa_kpos_proj.weight")
  )
  rename_keys.append((f"transformer.decoder.layers.{i}.sa_v_proj.weight", f"decoder.layers.{i}.sa_v_proj.weight"))
  rename_keys.append(
      (f"transformer.decoder.layers.{i}.ca_qcontent_proj.weight", f"decoder.layers.{i}.ca_qcontent_proj.weight")
  )
  # rename_keys.append((f"transformer.decoder.layers.{i}.ca_qpos_proj.weight", f"decoder.layers.{i}.ca_qpos_proj.weight"))
  rename_keys.append(
      (f"transformer.decoder.layers.{i}.ca_kcontent_proj.weight", f"decoder.layers.{i}.ca_kcontent_proj.weight")
  )
  rename_keys.append(
      (f"transformer.decoder.layers.{i}.ca_kpos_proj.weight", f"decoder.layers.{i}.ca_kpos_proj.weight")
  )
  rename_keys.append((f"transformer.decoder.layers.{i}.ca_v_proj.weight", f"decoder.layers.{i}.ca_v_proj.weight"))
  rename_keys.append(
      (f"transformer.decoder.layers.{i}.ca_qpos_sine_proj.weight", f"decoder.layers.{i}.ca_qpos_sine_proj.weight")
  )

  rename_keys.append(
      (f"transformer.decoder.layers.{i}.sa_qcontent_proj.bias", f"decoder.layers.{i}.sa_qcontent_proj.bias")
  )
  rename_keys.append(
      (f"transformer.decoder.layers.{i}.sa_kcontent_proj.bias", f"decoder.layers.{i}.sa_kcontent_proj.bias")
  )
  rename_keys.append((f"transformer.decoder.layers.{i}.sa_qpos_proj.bias", f"decoder.layers.{i}.sa_qpos_proj.bias"))
  rename_keys.append((f"transformer.decoder.layers.{i}.sa_kpos_proj.bias", f"decoder.layers.{i}.sa_kpos_proj.bias"))
  rename_keys.append((f"transformer.decoder.layers.{i}.sa_v_proj.bias", f"decoder.layers.{i}.sa_v_proj.bias"))
  rename_keys.append(
      (f"transformer.decoder.layers.{i}.ca_qcontent_proj.bias", f"decoder.layers.{i}.ca_qcontent_proj.bias")
  )
  # rename_keys.append((f"transformer.decoder.layers.{i}.ca_qpos_proj.bias", f"decoder.layers.{i}.ca_qpos_proj.bias"))
  rename_keys.append(
      (f"transformer.decoder.layers.{i}.ca_kcontent_proj.bias", f"decoder.layers.{i}.ca_kcontent_proj.bias")
  )
  rename_keys.append((f"transformer.decoder.layers.{i}.ca_kpos_proj.bias", f"decoder.layers.{i}.ca_kpos_proj.bias"))
  rename_keys.append((f"transformer.decoder.layers.{i}.ca_v_proj.bias", f"decoder.layers.{i}.ca_v_proj.bias"))
  rename_keys.append(
      (f"transformer.decoder.layers.{i}.ca_qpos_sine_proj.bias", f"decoder.layers.{i}.ca_qpos_sine_proj.bias")
  )

# convolutional projection + query embeddings + layernorm of decoder + class and bounding box heads
# for conditional DETR, also convert reference point head and query scale MLP
rename_keys.extend(
    [
        ('input_proj.weight', 'input_projection.weight'),
        ('input_proj.bias', 'input_projection.bias'),
        ('query_embed.weight', 'query_position_embeddings.weight'),
        ('transformer.decoder.norm.weight', 'decoder.layernorm.weight'),
        ('transformer.decoder.norm.bias', 'decoder.layernorm.bias'),
        ('class_embed.weight', 'class_labels_classifier.weight'),
        ('class_embed.bias', 'class_labels_classifier.bias'),
        ('bbox_embed.layers.0.weight', 'bbox_predictor.layers.0.weight'),
        ('bbox_embed.layers.0.bias', 'bbox_predictor.layers.0.bias'),
        ('bbox_embed.layers.1.weight', 'bbox_predictor.layers.1.weight'),
        ('bbox_embed.layers.1.bias', 'bbox_predictor.layers.1.bias'),
        ('bbox_embed.layers.2.weight', 'bbox_predictor.layers.2.weight'),
        ('bbox_embed.layers.2.bias', 'bbox_predictor.layers.2.bias'),
        ('transformer.decoder.ref_point_head.layers.0.weight', 'decoder.ref_point_head.layers.0.weight'),
        ('transformer.decoder.ref_point_head.layers.0.bias', 'decoder.ref_point_head.layers.0.bias'),
        ('transformer.decoder.ref_point_head.layers.1.weight', 'decoder.ref_point_head.layers.1.weight'),
        ('transformer.decoder.ref_point_head.layers.1.bias', 'decoder.ref_point_head.layers.1.bias'),
        ('transformer.decoder.query_scale.layers.0.weight', 'decoder.query_scale.layers.0.weight'),
        ('transformer.decoder.query_scale.layers.0.bias', 'decoder.query_scale.layers.0.bias'),
        ('transformer.decoder.query_scale.layers.1.weight', 'decoder.query_scale.layers.1.weight'),
        ('transformer.decoder.query_scale.layers.1.bias', 'decoder.query_scale.layers.1.bias'),
        ('transformer.decoder.layers.0.ca_qpos_proj.weight', 'decoder.layers.0.ca_qpos_proj.weight'),
        ('transformer.decoder.layers.0.ca_qpos_proj.bias', 'decoder.layers.0.ca_qpos_proj.bias'),
    ]
)
def 	a (     A__      :  List[str] ,  A__      :  str ,  A__      :  List[Any]	)       -> Union[str, Any]:





      """simple docstring"""



      _lowercase      			=state_dict.pop(A__	)
      _lowercase      			=val
def 	a (     A__      :  List[Any]	)       -> Tuple:





      """simple docstring"""



      _lowercase      			=OrderedDict()
      for key, value in state_dict.items():
            if "backbone.0.body" in key:
                  _lowercase      			=key.replace('backbone.0.body' ,  'backbone.conv_encoder.model'	)
                  _lowercase      			=value
            else:
                  _lowercase      			=value

      return new_state_dict
def 	a (     A__      :  Optional[Any] ,  A__      :  List[Any]=False	)       -> List[Any]:





      """simple docstring"""



      _lowercase      			=''
      if is_panoptic:
            _lowercase      			='conditional_detr.'

      # first: transformer encoder
      for i in range(6	):
            # read in weights + bias of input projection layer (in PyTorch's MultiHeadAttention, this is a single matrix + bias)
            _lowercase      			=state_dict.pop(F'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight'''	)
            _lowercase      			=state_dict.pop(F'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_bias'''	)
            # next, add query, keys and values (in that order) to the state dict
            _lowercase      			=in_proj_weight[:256, :]
            _lowercase      			=in_proj_bias[:256]
            _lowercase      			=in_proj_weight[256:512, :]
            _lowercase      			=in_proj_bias[256:512]
            _lowercase      			=in_proj_weight[-256:, :]
            _lowercase      			=in_proj_bias[-256:]
def 	a (     )       -> Tuple:





      """simple docstring"""



      _lowercase      			='http://images.cocodataset.org/val2017/000000039769.jpg'
      _lowercase      			=Image.open(requests.get(A__ ,  stream=A__	).raw	)

      return im
@torch.no_grad()
def 	a (     A__      :  Union[str, Any] ,  A__      :  List[Any]	)       -> Dict:





      """simple docstring"""



      _lowercase      			=ConditionalDetrConfig()
      # set backbone and dilation attributes
      if "resnet101" in model_name:
            _lowercase      			='resnet101'
      if "dc5" in model_name:
            _lowercase      			=True
      _lowercase      			='panoptic' in model_name
      if is_panoptic:
            _lowercase      			=250
      else:
            _lowercase      			=91
            _lowercase      			='huggingface/label-files'
            _lowercase      			='coco-detection-id2label.json'
            _lowercase      			=json.load(open(hf_hub_download(A__ ,  A__ ,  repo_type='dataset'	) ,  'r'	)	)
            _lowercase      			={int(A__	): v for k, v in idalabel.items()}
            _lowercase      			=idalabel
            _lowercase      			={v: k for k, v in idalabel.items()}

      # load image processor
      _lowercase      			='coco_panoptic' if is_panoptic else 'coco_detection'
      _lowercase      			=ConditionalDetrImageProcessor(format=A__	)

      # prepare image
      _lowercase      			=prepare_img()
      _lowercase      			=image_processor(images=A__ ,  return_tensors='pt'	)
      _lowercase      			=encoding['pixel_values']

      logger.info(F'''Converting model {model_name}...'''	)

      # load original model from torch hub
      _lowercase      			=torch.hub.load('DeppMeng/ConditionalDETR' ,  A__ ,  pretrained=A__	).eval()
      _lowercase      			=conditional_detr.state_dict()
      # rename keys
      for src, dest in rename_keys:
            if is_panoptic:
                  _lowercase      			='conditional_detr.' + src
            rename_key(A__ ,  A__ ,  A__	)
      _lowercase      			=rename_backbone_keys(A__	)
      # query, key and value matrices need special treatment
      read_in_q_k_v(A__ ,  is_panoptic=A__	)
      # important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
      _lowercase      			='conditional_detr.model.' if is_panoptic else 'model.'
      for key in state_dict.copy().keys():
            if is_panoptic:
                  if (
                      key.startswith('conditional_detr'	)
                      and not key.startswith('class_labels_classifier'	)
                      and not key.startswith('bbox_predictor'	)
                  ):
                        _lowercase      			=state_dict.pop(A__	)
                        _lowercase      			=val
                  elif "class_labels_classifier" in key or "bbox_predictor" in key:
                        _lowercase      			=state_dict.pop(A__	)
                        _lowercase      			=val
                  elif key.startswith('bbox_attention'	) or key.startswith('mask_head'	):
                        continue
                  else:
                        _lowercase      			=state_dict.pop(A__	)
                        _lowercase      			=val
            else:
                  if not key.startswith('class_labels_classifier'	) and not key.startswith('bbox_predictor'	):
                        _lowercase      			=state_dict.pop(A__	)
                        _lowercase      			=val
    # finally, create HuggingFace model and load state dict
      _lowercase      			=ConditionalDetrForSegmentation(A__	) if is_panoptic else ConditionalDetrForObjectDetection(A__	)
      model.load_state_dict(A__	)
      model.eval()
      model.push_to_hub(repo_id=A__ ,  organization='DepuMeng' ,  commit_message='Add model'	)
      # verify our conversion
      _lowercase      			=conditional_detr(A__	)
      _lowercase      			=model(A__	)
      assert torch.allclose(outputs.logits ,  original_outputs['pred_logits'] ,  atol=1e-4	)
      assert torch.allclose(outputs.pred_boxes ,  original_outputs['pred_boxes'] ,  atol=1e-4	)
      if is_panoptic:
            assert torch.allclose(outputs.pred_masks ,  original_outputs['pred_masks'] ,  atol=1e-4	)

      # Save model and image processor
      logger.info(F'''Saving PyTorch model and image processor to {pytorch_dump_folder_path}...'''	)
      Path(A__	).mkdir(exist_ok=A__	)
      model.save_pretrained(A__	)
      image_processor.save_pretrained(A__	)


if __name__ == "__main__":
  lowercase_   							=  argparse.ArgumentParser()

  parser.add_argument(
      '--model_name',
      default='conditional_detr_resnet50',
      type=str,
      help='Name of the CONDITIONAL_DETR 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.'
  )
  lowercase_   							=  parser.parse_args()
  convert_conditional_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)





import flax.linen as nn
import jax
import jax.numpy as jnp





class 					__lowerCAmelCase	(   nn.Module				):
  _a		     =    42
  _a		     =    jnp.floataa




  def     A__	(							self   )    ->					int:
        '''simple docstring'''






        _lowercase      			=nn.Conv(
            self.out_channels				,  kernel_size=(3, 3)				,  strides=(1, 1)				,  padding=((1, 1), (1, 1))				,  dtype=self.dtype				,  )




  def __call__(							self				,  lowerCAmelCase   )    ->					Any:
        '''simple docstring'''






        _lowercase			,			_lowercase			,			_lowercase			,			_lowercase      			=hidden_states.shape
        _lowercase      			=jax.image.resize(
            lowerCAmelCase				,  shape=(batch, height * 2, width * 2, channels)				,  method='nearest'				,  )
        _lowercase      			=self.conv(lowerCAmelCase   )
        return hidden_states





class 					__lowerCAmelCase	(   nn.Module				):
  _a		     =    42
  _a		     =    jnp.floataa




  def     A__	(							self   )    ->					Any:
        '''simple docstring'''






        _lowercase      			=nn.Conv(
            self.out_channels				,  kernel_size=(3, 3)				,  strides=(2, 2)				,  padding=((1, 1), (1, 1))				,  dtype=self.dtype				,  )




  def __call__(							self				,  lowerCAmelCase   )    ->					Optional[int]:
        '''simple docstring'''






        _lowercase      			=self.conv(lowerCAmelCase   )
        return hidden_states





class 					__lowerCAmelCase	(   nn.Module				):
  _a		     =    42
  _a		     =    None
  _a		     =    0.0
  _a		     =    None
  _a		     =    jnp.floataa




  def     A__	(							self   )    ->					Optional[int]:
        '''simple docstring'''






        _lowercase      			=self.in_channels if self.out_channels is None else self.out_channels

        _lowercase      			=nn.GroupNorm(num_groups=32				,  epsilon=1e-5   )
        _lowercase      			=nn.Conv(
            lowerCAmelCase				,  kernel_size=(3, 3)				,  strides=(1, 1)				,  padding=((1, 1), (1, 1))				,  dtype=self.dtype				,  )

        _lowercase      			=nn.Dense(lowerCAmelCase				,  dtype=self.dtype   )

        _lowercase      			=nn.GroupNorm(num_groups=32				,  epsilon=1e-5   )
        _lowercase      			=nn.Dropout(self.dropout_prob   )
        _lowercase      			=nn.Conv(
            lowerCAmelCase				,  kernel_size=(3, 3)				,  strides=(1, 1)				,  padding=((1, 1), (1, 1))				,  dtype=self.dtype				,  )

        _lowercase      			=self.in_channels != out_channels if self.use_nin_shortcut is None else self.use_nin_shortcut

        _lowercase      			=None
        if use_nin_shortcut:
              _lowercase      			=nn.Conv(
                  lowerCAmelCase				,  kernel_size=(1, 1)				,  strides=(1, 1)				,  padding='VALID'				,  dtype=self.dtype				,  )




  def __call__(							self				,  lowerCAmelCase				,  lowerCAmelCase				,  lowerCAmelCase=True   )    ->					Tuple:
        '''simple docstring'''






        _lowercase      			=hidden_states
        _lowercase      			=self.norma(lowerCAmelCase   )
        _lowercase      			=nn.swish(lowerCAmelCase   )
        _lowercase      			=self.conva(lowerCAmelCase   )

        _lowercase      			=self.time_emb_proj(nn.swish(lowerCAmelCase   )   )
        _lowercase      			=jnp.expand_dims(jnp.expand_dims(lowerCAmelCase				,  1   )				,  1   )
        _lowercase      			=hidden_states + temb

        _lowercase      			=self.norma(lowerCAmelCase   )
        _lowercase      			=nn.swish(lowerCAmelCase   )
        _lowercase      			=self.dropout(lowerCAmelCase				,  lowerCAmelCase   )
        _lowercase      			=self.conva(lowerCAmelCase   )

        if self.conv_shortcut is not None:
              _lowercase      			=self.conv_shortcut(lowerCAmelCase   )

        return hidden_states + residual


