

'''simple docstring'''







import os
from glob import glob

import imageio
import torch
import torchvision
import wandb
from img_processing import custom_to_pil, loop_post_process, preprocess, preprocess_vqgan
from loaders import load_vqgan
from PIL import Image
from torch import nn

from transformers import CLIPModel, CLIPTokenizerFast
from utils import get_device, get_timestamp, show_pil


class        A__   :



    def __init__(       self		,					UpperCamelCase__ = "cpu"		,					UpperCamelCase__ = "openai/clip-vit-large-patch14"							)    ->   List[str]:





           '''simple docstring'''
           A_						       =   device
           A_						       =   CLIPTokenizerFast.from_pretrained(UpperCamelCase__							)
           A_						       =   [0.48145466, 0.4578275, 0.40821073]
           A_						       =   [0.26862954, 0.26130258, 0.27577711]
           A_						       =   torchvision.transforms.Normalize(self.image_mean		,					self.image_std							)
           A_						       =   torchvision.transforms.Resize(224							)
           A_						       =   torchvision.transforms.CenterCrop(224							)



    def        snake_case_      (       self		,					UpperCamelCase__							)    ->   List[Any]:





           '''simple docstring'''
           A_						       =   self.resize(UpperCamelCase__							)
           A_						       =   self.center_crop(UpperCamelCase__							)
           A_						       =   self.normalize(UpperCamelCase__							)
           return images







    def __call__(       self		,					UpperCamelCase__=None		,					UpperCamelCase__=None		,					**UpperCamelCase__							)    ->   Any:





           '''simple docstring'''
           A_						       =   self.tokenizer(text=UpperCamelCase__		,					**UpperCamelCase__							)
           A_						       =   self.preprocess_img(UpperCamelCase__							)
           A_						       =   {key: value.to(self.device							) for (key, value) in encoding.items()}
           return encoding


class        A__   (   nn.Module      ):



    def __init__(       self		,					UpperCamelCase__=10		,					UpperCamelCase__=0.01		,					UpperCamelCase__=None		,					UpperCamelCase__=None		,					UpperCamelCase__=None		,					UpperCamelCase__=None		,					UpperCamelCase__=None		,					UpperCamelCase__=None		,					UpperCamelCase__=False		,					UpperCamelCase__=True		,					UpperCamelCase__="image"		,					UpperCamelCase__=True		,					UpperCamelCase__=False		,					UpperCamelCase__=False		,					UpperCamelCase__=False		,					)    ->   Any:





           '''simple docstring'''
           super().__init__()
           A_						       =   None
           A_						       =   device if device else get_device()
           if vqgan:
                  A_						       =   vqgan
           else:
                  A_						       =   load_vqgan(self.device		,					conf_path=UpperCamelCase__		,					ckpt_path=UpperCamelCase__							)
           self.vqgan.eval()
           if clip:
                  A_						       =   clip
           else:
                  A_						       =   CLIPModel.from_pretrained("""openai/clip-vit-base-patch32"""							)
           self.clip.to(self.device							)
           A_						       =   ProcessorGradientFlow(device=self.device							)

           A_						       =   iterations
           A_						       =   lr
           A_						       =   log
           A_						       =   make_grid
           A_						       =   return_val
           A_						       =   quantize
           A_						       =   self.vqgan.decoder.z_shape



    def        snake_case_      (       self		,					UpperCamelCase__=None		,					UpperCamelCase__=None		,					UpperCamelCase__=5		,					UpperCamelCase__=True							)    ->   str:





           '''simple docstring'''
           A_						       =   []
           if output_path is None:
                  A_						       =   """./animation.gif"""
           if input_path is None:
                  A_						       =   self.save_path
           A_						       =   sorted(glob(input_path + """/*"""							)							)
           if not len(UpperCamelCase__							):
                  raise ValueError(
                      """No images found in save path, aborting (did you pass save_intermediate=True to the generate"""
                      """ function?)"""							)
           if len(UpperCamelCase__							) == 1:
                  print("""Only one image found in save path, (did you pass save_intermediate=True to the generate function?)"""							)
           A_						       =   total_duration / len(UpperCamelCase__							)
           A_						       =   [frame_duration] * len(UpperCamelCase__							)
           if extend_frames:
                  A_						       =   1.5
                  A_						       =   3
           for file_name in paths:
                  if file_name.endswith(""".png"""							):
                         images.append(imageio.imread(UpperCamelCase__							)							)
           imageio.mimsave(UpperCamelCase__		,					UpperCamelCase__		,					duration=UpperCamelCase__							)
           print(f'''gif saved to {output_path}'''							)



    def        snake_case_      (       self		,					UpperCamelCase__=None		,					UpperCamelCase__=None							)    ->   Optional[Any]:





           '''simple docstring'''

           if not (path or img):
                  raise ValueError("""Input either path or tensor"""							)
           if img is not None:
                  raise NotImplementedError
           A_						       =   preprocess(Image.open(UpperCamelCase__							)		,					target_image_size=256							).to(self.device							)
           A_						       =   preprocess_vqgan(UpperCamelCase__							)
           A_       ,      *A_						       =   self.vqgan.encode(UpperCamelCase__							)
           return z



    def        snake_case_      (       self		,					UpperCamelCase__							)    ->   Dict:





           '''simple docstring'''
           A_						       =   self.latent.detach().requires_grad_()
           A_						       =   base_latent + transform_vector
           if self.quantize:
                  A_       ,      *A_						       =   self.vqgan.quantize(UpperCamelCase__							)
           else:
                  A_						       =   trans_latent
           return self.vqgan.decode(UpperCamelCase__							)



    def        snake_case_      (       self		,					UpperCamelCase__		,					UpperCamelCase__		,					UpperCamelCase__=None							)    ->   Optional[Any]:





           '''simple docstring'''
           A_						       =   self.clip_preprocessor(text=UpperCamelCase__		,					images=UpperCamelCase__		,					return_tensors="""pt"""		,					padding=UpperCamelCase__							)
           A_						       =   self.clip(**UpperCamelCase__							)
           A_						       =   clip_outputs.logits_per_image
           if weights is not None:
                  A_						       =   similarity_logits * weights
           return similarity_logits.sum()



    def        snake_case_      (       self		,					UpperCamelCase__		,					UpperCamelCase__		,					UpperCamelCase__							)    ->   Dict:





           '''simple docstring'''
           A_						       =   self._get_clip_similarity(pos_prompts["""prompts"""]		,					UpperCamelCase__		,					weights=(1 / pos_prompts["""weights"""])							)
           if neg_prompts:
                  A_						       =   self._get_clip_similarity(neg_prompts["""prompts"""]		,					UpperCamelCase__		,					weights=neg_prompts["""weights"""]							)
           else:
                  A_						       =   torch.tensor([1]		,					device=self.device							)
           A_						       =   -torch.log(UpperCamelCase__							) + torch.log(UpperCamelCase__							)
           return loss



    def        snake_case_      (       self		,					UpperCamelCase__		,					UpperCamelCase__		,					UpperCamelCase__							)    ->   Tuple:





           '''simple docstring'''
           A_						       =   torch.randn_like(self.latent		,					requires_grad=UpperCamelCase__		,					device=self.device							)
           A_						       =   torch.optim.Adam([vector]		,					lr=self.lr							)

           for i in range(self.iterations							):
                  optim.zero_grad()
                  A_						       =   self._add_vector(UpperCamelCase__							)
                  A_						       =   loop_post_process(UpperCamelCase__							)
                  A_						       =   self._get_CLIP_loss(UpperCamelCase__		,					UpperCamelCase__		,					UpperCamelCase__							)
                  print("""CLIP loss"""		,					UpperCamelCase__							)
                  if self.log:
                         wandb.log({"""CLIP Loss""": clip_loss}							)
                  clip_loss.backward(retain_graph=UpperCamelCase__							)
                  optim.step()
                  if self.return_val == "image":
                         yield custom_to_pil(transformed_img[0]							)
                  else:
                         yield vector



    def        snake_case_      (       self		,					UpperCamelCase__		,					UpperCamelCase__		,					UpperCamelCase__							)    ->   Union[str, Any]:





           '''simple docstring'''
           wandb.init(reinit=UpperCamelCase__		,					project="""face-editor"""							)
           wandb.config.update({"""Positive Prompts""": positive_prompts}							)
           wandb.config.update({"""Negative Prompts""": negative_prompts}							)
           wandb.config.update({"""lr""": self.lr, """iterations""": self.iterations}							)
           if image_path:
                  A_						       =   Image.open(UpperCamelCase__							)
                  A_						       =   image.resize((256, 256)							)
                  wandb.log("""Original Image"""		,					wandb.Image(UpperCamelCase__							)							)



    def        snake_case_      (       self		,					UpperCamelCase__							)    ->   Dict:





           '''simple docstring'''

           if not prompts:
                  return []
           A_						       =   []
           A_						       =   []
           if isinstance(UpperCamelCase__		,					UpperCamelCase__							):
                  A_						       =   [prompt.strip() for prompt in prompts.split("""|"""							)]
           for prompt in prompts:
                  if isinstance(UpperCamelCase__		,					(tuple, list)							):
                         A_						       =   prompt[0]
                         A_						       =   float(prompt[1]							)
                  elif ":" in prompt:
                         A_       ,      A_						       =   prompt.split(""":"""							)
                         A_						       =   float(UpperCamelCase__							)
                  else:
                         A_						       =   prompt
                         A_						       =   1.0
                  processed_prompts.append(UpperCamelCase__							)
                  weights.append(UpperCamelCase__							)
           return {
               "prompts": processed_prompts,
               "weights": torch.tensor(UpperCamelCase__		,					device=self.device							),
           }







    def        snake_case_      (       self		,					UpperCamelCase__		,					UpperCamelCase__=None		,					UpperCamelCase__=None		,					UpperCamelCase__=True		,					UpperCamelCase__=False		,					UpperCamelCase__=True		,					UpperCamelCase__=True		,					UpperCamelCase__=None		,					)    ->   Union[str, Any]:





           '''simple docstring'''

           if image_path:
                  A_						       =   self._get_latent(UpperCamelCase__							)
           else:
                  A_						       =   torch.randn(self.latent_dim		,					device=self.device							)
           if self.log:
                  self._init_logging(UpperCamelCase__		,					UpperCamelCase__		,					UpperCamelCase__							)

           assert pos_prompts, "You must provide at least one positive prompt."
           A_						       =   self.process_prompts(UpperCamelCase__							)
           A_						       =   self.process_prompts(UpperCamelCase__							)
           if save_final and save_path is None:
                  A_						       =   os.path.join("""./outputs/"""		,					"""_""".join(pos_prompts["""prompts"""]							)							)
           if not os.path.exists(UpperCamelCase__							):
                  os.makedirs(UpperCamelCase__							)
           else:
                  A_						       =   save_path + """_""" + get_timestamp()
                  os.makedirs(UpperCamelCase__							)
           A_						       =   save_path

           A_						       =   self.vqgan.decode(self.latent							)[0]
           if show_intermediate:
                  print("""Original Image"""							)
                  show_pil(custom_to_pil(UpperCamelCase__							)							)

           A_						       =   loop_post_process(UpperCamelCase__							)
           for iter, transformed_img in enumerate(self._optimize_CLIP(UpperCamelCase__		,					UpperCamelCase__		,					UpperCamelCase__							)							):
                  if show_intermediate:
                         show_pil(UpperCamelCase__							)
                  if save_intermediate:
                         transformed_img.save(os.path.join(self.save_path		,					f'''iter_{iter:03d}.png'''							)							)
                  if self.log:
                         wandb.log({"""Image""": wandb.Image(UpperCamelCase__							)}							)
           if show_final:
                  show_pil(UpperCamelCase__							)
           if save_final:
                  transformed_img.save(os.path.join(self.save_path		,					f'''iter_{iter:03d}_final.png'''							)							)








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()
_SCREAMING_SNAKE_CASE   =  logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
_SCREAMING_SNAKE_CASE   =  []
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[int]      ,    _snake_case				:       Dict      ,    _snake_case				:       Dict)					->	str:
   _lowercase              =state_dict.pop(_snake_case)
   _lowercase              =val





def        _snake_case   (_snake_case				:       Any)					->	Union[str, Any]:
   _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        _snake_case   (_snake_case				:       Tuple      ,    _snake_case				:       Tuple=False)					->	str:
   _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        _snake_case   ()					->	List[Any]:
   _lowercase              ='http://images.cocodataset.org/val2017/000000039769.jpg'
   _lowercase              =Image.open(requests.get(_snake_case      ,    stream=_snake_case).raw)

   return im





@torch.no_grad()
def        _snake_case   (_snake_case				:       Any      ,    _snake_case				:       int)					->	Union[str, Any]:
   _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(_snake_case      ,    _snake_case      ,    repo_type='dataset')      ,    'r'))
      _lowercase              ={int(_snake_case): 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=_snake_case)

   # prepare image
   _lowercase              =prepare_img()
   _lowercase              =image_processor(images=_snake_case      ,    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'      ,    _snake_case      ,    pretrained=_snake_case).eval()
   _lowercase              =conditional_detr.state_dict()
   # rename keys
   for src, dest in rename_keys:
      if is_panoptic:
         _lowercase              ='conditional_detr.' + src
      rename_key(_snake_case      ,    _snake_case      ,    _snake_case)
   _lowercase              =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
   _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(_snake_case)
            _lowercase              =val
         elif "class_labels_classifier" in key or "bbox_predictor" in key:
            _lowercase              =state_dict.pop(_snake_case)
            _lowercase              =val
         elif key.startswith('bbox_attention') or key.startswith('mask_head'):
            continue
         else:
            _lowercase              =state_dict.pop(_snake_case)
            _lowercase              =val
      else:
         if not key.startswith('class_labels_classifier') and not key.startswith('bbox_predictor'):
            _lowercase              =state_dict.pop(_snake_case)
            _lowercase              =val
    # finally, create HuggingFace model and load state dict
   _lowercase              =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
   _lowercase              =conditional_detr(_snake_case)
   _lowercase              =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__":
    _SCREAMING_SNAKE_CASE   =  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."
    )
    _SCREAMING_SNAKE_CASE   =  parser.parse_args()
    convert_conditional_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)


