'''simple docstring'''

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()
A_     :						List[str]    							=logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
A_     :						Union[str, Any]    							=[]
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 snake_case_      (					__snake_case  :							Optional[Any]	,   __snake_case  :							Optional[int]	,   __snake_case  :							List[Any])			->					str:
   lowerCAmelCase_			=      state_dict.pop(__snake_case)
   lowerCAmelCase_			=      val



def snake_case_      (					__snake_case  :							Dict)			->					Any:
   lowerCAmelCase_			=      OrderedDict()
   for key, value in state_dict.items():
      if "backbone.0.body" in key:
         lowerCAmelCase_			=      key.replace('''backbone.0.body'''	,   '''backbone.conv_encoder.model''')
         lowerCAmelCase_			=      value
      else:
         lowerCAmelCase_			=      value

   return new_state_dict



def snake_case_      (					__snake_case  :							Optional[int]	,   __snake_case  :							List[Any]=False)			->					List[str]:
   lowerCAmelCase_			=      ''''''
   if is_panoptic:
      lowerCAmelCase_			=      '''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)
      lowerCAmelCase_			=      state_dict.pop(F'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight''')
      lowerCAmelCase_			=      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
      lowerCAmelCase_			=      in_proj_weight[:256, :]
      lowerCAmelCase_			=      in_proj_bias[:256]
      lowerCAmelCase_			=      in_proj_weight[256:512, :]
      lowerCAmelCase_			=      in_proj_bias[256:512]
      lowerCAmelCase_			=      in_proj_weight[-256:, :]
      lowerCAmelCase_			=      in_proj_bias[-256:]



def snake_case_      (					)			->					int:
   lowerCAmelCase_			=      '''http://images.cocodataset.org/val2017/000000039769.jpg'''
   lowerCAmelCase_			=      Image.open(requests.get(__snake_case	,   stream=__snake_case).raw)

   return im



@torch.no_grad()
def snake_case_      (					__snake_case  :							List[Any]	,   __snake_case  :							Union[str, Any])			->					Optional[Any]:
   lowerCAmelCase_			=      ConditionalDetrConfig()
   # set backbone and dilation attributes
   if "resnet101" in model_name:
      lowerCAmelCase_			=      '''resnet101'''
   if "dc5" in model_name:
      lowerCAmelCase_			=      True
   lowerCAmelCase_			=      '''panoptic''' in model_name
   if is_panoptic:
      lowerCAmelCase_			=      250
   else:
      lowerCAmelCase_			=      91
      lowerCAmelCase_			=      '''huggingface/label-files'''
      lowerCAmelCase_			=      '''coco-detection-id2label.json'''
      lowerCAmelCase_			=      json.load(open(hf_hub_download(__snake_case	,   __snake_case	,   repo_type='''dataset''')	,   '''r'''))
      lowerCAmelCase_			=      {int(__snake_case): v for k, v in idalabel.items()}
      lowerCAmelCase_			=      idalabel
      lowerCAmelCase_			=      {v: k for k, v in idalabel.items()}

   # load image processor
   lowerCAmelCase_			=      '''coco_panoptic''' if is_panoptic else '''coco_detection'''
   lowerCAmelCase_			=      ConditionalDetrImageProcessor(format=__snake_case)

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

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

   # load original model from torch hub
   lowerCAmelCase_			=      torch.hub.load('''DeppMeng/ConditionalDETR'''	,   __snake_case	,   pretrained=__snake_case).eval()
   lowerCAmelCase_			=      conditional_detr.state_dict()
   # rename keys
   for src, dest in rename_keys:
      if is_panoptic:
         lowerCAmelCase_			=      '''conditional_detr.''' + src
      rename_key(__snake_case	,   __snake_case	,   __snake_case)
   lowerCAmelCase_			=      rename_backbone_keys(__snake_case)
   # query, key and value matrices need special treatment
   read_in_q_k_v(__snake_case	,   is_panoptic=__snake_case)
   # important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
   lowerCAmelCase_			=      '''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''')
         ):
            lowerCAmelCase_			=      state_dict.pop(__snake_case)
            lowerCAmelCase_			=      val
         elif "class_labels_classifier" in key or "bbox_predictor" in key:
            lowerCAmelCase_			=      state_dict.pop(__snake_case)
            lowerCAmelCase_			=      val
         elif key.startswith('''bbox_attention''') or key.startswith('''mask_head'''):
            continue
         else:
            lowerCAmelCase_			=      state_dict.pop(__snake_case)
            lowerCAmelCase_			=      val
      else:
         if not key.startswith('''class_labels_classifier''') and not key.startswith('''bbox_predictor'''):
            lowerCAmelCase_			=      state_dict.pop(__snake_case)
            lowerCAmelCase_			=      val
    # finally, create HuggingFace model and load state dict
   lowerCAmelCase_			=      ConditionalDetrForSegmentation(__snake_case) if is_panoptic else ConditionalDetrForObjectDetection(__snake_case)
   model.load_state_dict(__snake_case)
   model.eval()
   model.push_to_hub(repo_id=__snake_case	,   organization='''DepuMeng'''	,   commit_message='''Add model''')
   # verify our conversion
   lowerCAmelCase_			=      conditional_detr(__snake_case)
   lowerCAmelCase_			=      model(__snake_case)
   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(__snake_case).mkdir(exist_ok=__snake_case)
   model.save_pretrained(__snake_case)
   image_processor.save_pretrained(__snake_case)


if __name__ == "__main__":
 A_     :						List[str]    							=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.'''
 )
 A_     :						Tuple    							=parser.parse_args()
 convert_conditional_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)






'''simple docstring'''

import importlib

import torch
import yaml
from omegaconf import OmegaConf
from taming.models.vqgan import VQModel



def snake_case_      (					__snake_case  :							Union[str, Any]	,   __snake_case  :							Union[str, Any]=False)			->					Optional[int]:
   lowerCAmelCase_			=      OmegaConf.load(__snake_case)
   if display:
      print(yaml.dump(OmegaConf.to_container(__snake_case)))
   return config



def snake_case_      (					__snake_case  :							Any	,   __snake_case  :							Optional[int]=None	,   __snake_case  :							str=None)			->					str:
   if conf_path is None:
      lowerCAmelCase_			=      '''./model_checkpoints/vqgan_only.yaml'''
   lowerCAmelCase_			=      load_config(__snake_case	,   display=__snake_case)
   lowerCAmelCase_			=      VQModel(**config.model.params)
   if ckpt_path is None:
      lowerCAmelCase_			=      '''./model_checkpoints/vqgan_only.pt'''
   lowerCAmelCase_			=      torch.load(__snake_case	,   map_location=__snake_case)
   if ".ckpt" in ckpt_path:
      lowerCAmelCase_			=      sd['''state_dict''']
   model.load_state_dict(__snake_case	,   strict=__snake_case)
   model.to(__snake_case)
   del sd
   return model



def snake_case_      (					__snake_case  :							Union[str, Any]	,   __snake_case  :							str)			->					Optional[int]:
   lowerCAmelCase_	,lowerCAmelCase_	,lowerCAmelCase_			=      model.encode(__snake_case)
   print(F'''VQGAN --- {model.__class__.__name__}: latent shape: {z.shape[2:]}''')
   lowerCAmelCase_			=      model.decode(__snake_case)
   return xrec



def snake_case_      (					__snake_case  :							int	,   __snake_case  :							List[Any]=False)			->					Union[str, Any]:
   lowerCAmelCase_	,lowerCAmelCase_			=      string.rsplit('''.'''	,   1)
   if reload:
      lowerCAmelCase_			=      importlib.import_module(__snake_case)
      importlib.reload(__snake_case)
   return getattr(importlib.import_module(__snake_case	,   package=__snake_case)	,   cls)



def snake_case_      (					__snake_case  :							Tuple)			->					Union[str, Any]:
   if "target" not in config:
      raise KeyError('''Expected key `target` to instantiate.''')
   return get_obj_from_str(config['''target'''])(**config.get('''params'''	,   {}))



def snake_case_      (					__snake_case  :							Optional[int]	,   __snake_case  :							Any	,   __snake_case  :							Optional[int]=True	,   __snake_case  :							Any=True)			->					List[str]:
   lowerCAmelCase_			=      instantiate_from_config(__snake_case)
   if sd is not None:
      model.load_state_dict(__snake_case)
   if gpu:
      model.cuda()
   if eval_mode:
      model.eval()
   return {"model": model}






def snake_case_      (					__snake_case  :							Any	,   __snake_case  :							Optional[int]	,   __snake_case  :							Dict	,   __snake_case  :							str)			->					Any:
   # load the specified checkpoint
   if ckpt:
      lowerCAmelCase_			=      torch.load(__snake_case	,   map_location='''cpu''')
      lowerCAmelCase_			=      pl_sd['''global_step''']
      print(F'''loaded model from global step {global_step}.''')
   else:
      lowerCAmelCase_			=      {'''state_dict''': None}
      lowerCAmelCase_			=      None
   lowerCAmelCase_			=      load_model_from_config(config.model	,   pl_sd['''state_dict''']	,   gpu=__snake_case	,   eval_mode=__snake_case)['''model''']
   return model, global_step





