
import glob
import os
from typing import Dict, List, Union

import torch

from diffusers.utils import is_safetensors_available


if is_safetensors_available():
       import safetensors.torch

from huggingface_hub import snapshot_download

from diffusers import DiffusionPipeline, __version__
from diffusers.schedulers.scheduling_utils import SCHEDULER_CONFIG_NAME
from diffusers.utils import CONFIG_NAME, DIFFUSERS_CACHE, ONNX_WEIGHTS_NAME, WEIGHTS_NAME

class __snake_case	(   __lowerCamelCase  ):
    def __init__(						self   ):



           '''simple docstring'''







           self.register_to_config()
           super().__init__()
    def    _SCREAMING_SNAKE_CASE (						self	,snake_case	,snake_case   ):



           '''simple docstring'''







           if dicta == dicta:
                  return True
           else:
                  lowercase			,    lowercase							:  str 			=   self._remove_meta_keys(snake_case   )
                  lowercase			,    lowercase							:  str 			=   self._remove_meta_keys(snake_case   )
                  if configa == configa:
                         print(f"Warning !: Mismatch in keys {meta_keysa} and {meta_keysa}."   )
                         return True
           return False
    def    _SCREAMING_SNAKE_CASE (						self	,snake_case   ):



           '''simple docstring'''







           lowercase							:  Optional[int] 			=   []
           lowercase							:  Optional[Any] 			=   config_dict.copy()
           for key in config_dict.keys():
                  if key.startswith("""_"""   ):
                         temp_dict.pop(snake_case   )
                         meta_keys.append(snake_case   )
           return (temp_dict, meta_keys)
    @torch.no_grad()
    def    _SCREAMING_SNAKE_CASE (						self	,snake_case	,**snake_case   ):



           '''simple docstring'''







           lowercase							:  str 			=   kwargs.pop("""cache_dir"""	,snake_case   )
           lowercase							:  Any 			=   kwargs.pop("""resume_download"""	,snake_case   )
           lowercase							:  Dict 			=   kwargs.pop("""force_download"""	,snake_case   )
           lowercase							:  Any 			=   kwargs.pop("""proxies"""	,snake_case   )
           lowercase							:  Tuple 			=   kwargs.pop("""local_files_only"""	,snake_case   )
           lowercase							:  int 			=   kwargs.pop("""use_auth_token"""	,snake_case   )
           lowercase							:  str 			=   kwargs.pop("""revision"""	,snake_case   )
           lowercase							:  Any 			=   kwargs.pop("""torch_dtype"""	,snake_case   )
           lowercase							:  int 			=   kwargs.pop("""device_map"""	,snake_case   )

           lowercase							:  List[Any] 			=   kwargs.pop("""alpha"""	,0.5   )
           lowercase							:  Dict 			=   kwargs.pop("""interp"""	,snake_case   )

           print("""Received list"""	,snake_case   )
           print(f"Combining with alpha={alpha}, interpolation mode={interp}"   )

           lowercase							:  Dict 			=   len(snake_case   )
           # Ignore result from model_index_json comparision of the two checkpoints
           lowercase							:  int 			=   kwargs.pop("""force"""	,snake_case   )

           # If less than 2 checkpoints, nothing to merge. If more than 3, not supported for now.
           if checkpoint_count > 3 or checkpoint_count < 2:
                  raise ValueError(
                      """Received incorrect number of checkpoints to merge. Ensure that either 2 or 3 checkpoints are being"""
                      """ passed."""   )

           print("""Received the right number of checkpoints"""   )
           # chkpt0, chkpt1 = pretrained_model_name_or_path_list[0:2]
           # chkpt2 = pretrained_model_name_or_path_list[2] if checkpoint_count == 3 else None

           # Validate that the checkpoints can be merged
           # Step 1: Load the model config and compare the checkpoints. We'll compare the model_index.json first while ignoring the keys starting with '_'
           lowercase							:  List[str] 			=   []
           for pretrained_model_name_or_path in pretrained_model_name_or_path_list:
                  lowercase							:  str 			=   DiffusionPipeline.load_config(
                      snake_case	,cache_dir=snake_case	,resume_download=snake_case	,force_download=snake_case	,proxies=snake_case	,local_files_only=snake_case	,use_auth_token=snake_case	,revision=snake_case	,)
                  config_dicts.append(snake_case   )

           lowercase							:  List[Any] 			=   True
           for idx in range(1	,len(snake_case   )   ):
                  comparison_result &= self._compare_model_configs(config_dicts[idx - 1]	,config_dicts[idx]   )
                  if not force and comparison_result is False:
                         raise ValueError("""Incompatible checkpoints. Please check model_index.json for the models."""   )
                         print(config_dicts[0]	,config_dicts[1]   )
           print("""Compatible model_index.json files found"""   )
           # Step 2: Basic Validation has succeeded. Let's download the models and save them into our local files.
           lowercase							:  Tuple 			=   []
           for pretrained_model_name_or_path, config_dict in zip(snake_case	,snake_case   ):
                  lowercase							:  List[Any] 			=   [k for k in config_dict.keys() if not k.startswith("""_"""   )]
                  lowercase							:  Tuple 			=   [os.path.join(snake_case	,"""*"""   ) for k in folder_names]
                  allow_patterns += [
                      WEIGHTS_NAME,
                      SCHEDULER_CONFIG_NAME,
                      CONFIG_NAME,
                      ONNX_WEIGHTS_NAME,
                      DiffusionPipeline.config_name,
                  ]
                  lowercase							:  Union[str, Any] 			=   config_dict.get("""_class_name"""   )
                  lowercase							:  Optional[int] 			=   {"""diffusers""": __version__, """pipeline_class""": requested_pipeline_class}

                  lowercase							:  Optional[int] 			=   (
                      pretrained_model_name_or_path
                      if os.path.isdir(snake_case   )
                      else snapshot_download(
                          snake_case	,cache_dir=snake_case	,resume_download=snake_case	,proxies=snake_case	,local_files_only=snake_case	,use_auth_token=snake_case	,revision=snake_case	,allow_patterns=snake_case	,user_agent=snake_case	,)
                  )
                  print("""Cached Folder"""	,snake_case   )
                  cached_folders.append(snake_case   )

           # Step 3:-
           # Load the first checkpoint as a diffusion pipeline and modify its module state_dict in place
           lowercase							:  Optional[int] 			=   DiffusionPipeline.from_pretrained(
               cached_folders[0]	,torch_dtype=snake_case	,device_map=snake_case   )
           final_pipe.to(self.device   )

           lowercase							:  int 			=   None
           if len(snake_case   ) > 2:
                  lowercase							:  Optional[int] 			=   os.path.join(cached_folders[2]   )

           if interp == "sigmoid":
                  lowercase							:  List[str] 			=   CheckpointMergerPipeline.sigmoid
           elif interp == "inv_sigmoid":
                  lowercase							:  Tuple 			=   CheckpointMergerPipeline.inv_sigmoid
           elif interp == "add_diff":
                  lowercase							:  str 			=   CheckpointMergerPipeline.add_difference
           else:
                  lowercase							:  Union[str, Any] 			=   CheckpointMergerPipeline.weighted_sum

           # Find each module's state dict.
           for attr in final_pipe.config.keys():
                  if not attr.startswith("""_"""   ):
                         lowercase							:  str 			=   os.path.join(cached_folders[1]	,snake_case   )
                         if os.path.exists(snake_case   ):
                                lowercase							:  str 			=   [
                                    *glob.glob(os.path.join(snake_case	,"""*.safetensors"""   )   ),
                                    *glob.glob(os.path.join(snake_case	,"""*.bin"""   )   ),
                                ]
                                lowercase							:  str 			=   files[0] if len(snake_case   ) > 0 else None
                         if len(snake_case   ) < 3:
                                lowercase							:  Union[str, Any] 			=   None
                         else:
                                lowercase							:  Tuple 			=   os.path.join(cached_folders[2]	,snake_case   )
                                if os.path.exists(snake_case   ):
                                       lowercase							:  Optional[Any] 			=   [
                                           *glob.glob(os.path.join(snake_case	,"""*.safetensors"""   )   ),
                                           *glob.glob(os.path.join(snake_case	,"""*.bin"""   )   ),
                                       ]
                                       lowercase							:  Union[str, Any] 			=   files[0] if len(snake_case   ) > 0 else None
                # For an attr if both checkpoint_path_1 and 2 are None, ignore.
                # If atleast one is present, deal with it according to interp method, of course only if the state_dict keys match.
                         if checkpoint_path_a is None and checkpoint_path_a is None:
                                print(f"Skipping {attr}: not present in 2nd or 3d model"   )
                                continue
                         try:
                                lowercase							:  int 			=   getattr(snake_case	,snake_case   )
                                if isinstance(snake_case	,snake_case   ):  # ignore requires_safety_checker boolean
                                       continue
                                lowercase							:  Any 			=   getattr(snake_case	,"""state_dict"""   )
                                lowercase							:  Optional[Any] 			=   theta_a()

                                lowercase							:  List[Any] 			=   getattr(snake_case	,"""load_state_dict"""   )
                                lowercase							:  str 			=   (
                                    safetensors.torch.load_file(snake_case   )
                                    if (is_safetensors_available() and checkpoint_path_a.endswith(""".safetensors"""   ))
                                    else torch.load(snake_case	,map_location="""cpu"""   )
                                )
                                lowercase							:  Dict 			=   None
                                if checkpoint_path_a:
                                       lowercase							:  Any 			=   (
                                           safetensors.torch.load_file(snake_case   )
                                           if (is_safetensors_available() and checkpoint_path_a.endswith(""".safetensors"""   ))
                                           else torch.load(snake_case	,map_location="""cpu"""   )
                                       )

                                if not theta_a.keys() == theta_a.keys():
                                       print(f"Skipping {attr}: key mismatch"   )
                                       continue
                                if theta_a and not theta_a.keys() == theta_a.keys():
                                       print(f"Skipping {attr}:y mismatch"   )
                         except Exception as e:
                                print(f"Skipping {attr} do to an unexpected error: {str(snake_case   )}"   )
                                continue
                         print(f"MERGING {attr}"   )

                         for key in theta_a.keys():
                                if theta_a:
                                       lowercase							:  Optional[int] 			=   theta_func(theta_a[key]	,theta_a[key]	,theta_a[key]	,snake_case   )
                                else:
                                       lowercase							:  Tuple 			=   theta_func(theta_a[key]	,theta_a[key]	,snake_case	,snake_case   )

                         del theta_a
                         del theta_a
                         update_theta_a(snake_case   )

                         del theta_a
           return final_pipe
    @staticmethod
    def    _SCREAMING_SNAKE_CASE (						snake_case	,snake_case	,snake_case	,snake_case   ):



           '''simple docstring'''







           return ((1 - alpha) * thetaa) + (alpha * thetaa)
    @staticmethod
    def    _SCREAMING_SNAKE_CASE (						snake_case	,snake_case	,snake_case	,snake_case   ):



           '''simple docstring'''







           lowercase							:  Optional[Any] 			=   alpha * alpha * (3 - (2 * alpha))
           return thetaa + ((thetaa - thetaa) * alpha)
    @staticmethod
    def    _SCREAMING_SNAKE_CASE (						snake_case	,snake_case	,snake_case	,snake_case   ):



           '''simple docstring'''







           import math

           lowercase							:  str 			=   0.5 - math.sin(math.asin(1.0 - 2.0 * alpha   ) / 3.0   )
           return thetaa + ((thetaa - thetaa) * alpha)
    @staticmethod
    def    _SCREAMING_SNAKE_CASE (						snake_case	,snake_case	,snake_case	,snake_case   ):



           '''simple docstring'''







           return thetaa + (thetaa - thetaa) * (1.0 - alpha)


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 DetrConfig, DetrForObjectDetection, DetrForSegmentation, DetrImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
lowercase						:      Union[str, Any]							=							logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
lowercase						:      Tuple							=							[]
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}.multihead_attn.out_proj.weight''',
          F'''decoder.layers.{i}.encoder_attn.out_proj.weight''',
      )
  )
  rename_keys.append(
      (
          F'''transformer.decoder.layers.{i}.multihead_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'''))

# convolutional projection + query embeddings + layernorm of decoder + class and bounding box heads
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"""),
    ]
)







def 		_snake_case(						SCREAMING_SNAKE_CASE__      ,			SCREAMING_SNAKE_CASE__      ,			SCREAMING_SNAKE_CASE__       )				->					Optional[int]:
       lowercase							:  Optional[int] 			=   state_dict.pop(SCREAMING_SNAKE_CASE__       )
       lowercase							:  str 			=   val







def 		_snake_case(						SCREAMING_SNAKE_CASE__       )				->					Optional[Any]:
       lowercase							:  int 			=   OrderedDict()
       for key, value in state_dict.items():
              if "backbone.0.body" in key:
                     lowercase							:  Optional[Any] 			=   key.replace("""backbone.0.body"""      ,			"""backbone.conv_encoder.model"""       )
                     lowercase							:  Optional[int] 			=   value
              else:
                     lowercase							:  Dict 			=   value

       return new_state_dict







def 		_snake_case(						SCREAMING_SNAKE_CASE__      ,			SCREAMING_SNAKE_CASE__=False       )				->					Optional[Any]:
       lowercase							:  str 			=   """"""
       if is_panoptic:
              lowercase							:  Union[str, Any] 			=   """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							:  Union[str, Any] 			=   state_dict.pop(f"{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight"       )
              lowercase							:  Any 			=   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							:  Union[str, Any] 			=   in_proj_weight[:256, :]
              lowercase							:  str 			=   in_proj_bias[:256]
              lowercase							:  Union[str, Any] 			=   in_proj_weight[256:512, :]
              lowercase							:  Optional[int] 			=   in_proj_bias[256:512]
              lowercase							:  List[Any] 			=   in_proj_weight[-256:, :]
              lowercase							:  List[Any] 			=   in_proj_bias[-256:]
       # next: transformer decoder (which is a bit more complex because it also includes cross-attention)
       for i in range(6       ):
              # read in weights + bias of input projection layer of self-attention
              lowercase							:  int 			=   state_dict.pop(f"{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight"       )
              lowercase							:  List[Any] 			=   state_dict.pop(f"{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_bias"       )
              # next, add query, keys and values (in that order) to the state dict
              lowercase							:  Any 			=   in_proj_weight[:256, :]
              lowercase							:  int 			=   in_proj_bias[:256]
              lowercase							:  Tuple 			=   in_proj_weight[256:512, :]
              lowercase							:  Optional[Any] 			=   in_proj_bias[256:512]
              lowercase							:  int 			=   in_proj_weight[-256:, :]
              lowercase							:  Optional[Any] 			=   in_proj_bias[-256:]
              # read in weights + bias of input projection layer of cross-attention
              lowercase							:  int 			=   state_dict.pop(
                  f"{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight"       )
              lowercase							:  Dict 			=   state_dict.pop(f"{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_bias"       )
              # next, add query, keys and values (in that order) of cross-attention to the state dict
              lowercase							:  List[str] 			=   in_proj_weight_cross_attn[:256, :]
              lowercase							:  Optional[Any] 			=   in_proj_bias_cross_attn[:256]
              lowercase							:  int 			=   in_proj_weight_cross_attn[256:512, :]
              lowercase							:  Optional[int] 			=   in_proj_bias_cross_attn[256:512]
              lowercase							:  Union[str, Any] 			=   in_proj_weight_cross_attn[-256:, :]
              lowercase							:  int 			=   in_proj_bias_cross_attn[-256:]







def 		_snake_case(						)				->					str:
       lowercase							:  Tuple 			=   """http://images.cocodataset.org/val2017/000000039769.jpg"""
       lowercase							:  int 			=   Image.open(requests.get(SCREAMING_SNAKE_CASE__      ,			stream=SCREAMING_SNAKE_CASE__       ).raw       )

       return im







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

       # load image processor
       lowercase							:  Any 			=   """coco_panoptic""" if is_panoptic else """coco_detection"""
       lowercase							:  Dict 			=   DetrImageProcessor(format=SCREAMING_SNAKE_CASE__       )

       # prepare image
       lowercase							:  Optional[Any] 			=   prepare_img()
       lowercase							:  int 			=   image_processor(images=SCREAMING_SNAKE_CASE__      ,			return_tensors="""pt"""       )
       lowercase							:  int 			=   encoding["""pixel_values"""]

       logger.info(f"Converting model {model_name}..."       )

       # load original model from torch hub
       lowercase							:  Optional[int] 			=   torch.hub.load("""facebookresearch/detr"""      ,			SCREAMING_SNAKE_CASE__      ,			pretrained=SCREAMING_SNAKE_CASE__       ).eval()
       lowercase							:  Optional[int] 			=   detr.state_dict()
       # rename keys
       for src, dest in rename_keys:
              if is_panoptic:
                     lowercase							:  Optional[Any] 			=   """detr.""" + src
              rename_key(SCREAMING_SNAKE_CASE__      ,			SCREAMING_SNAKE_CASE__      ,			SCREAMING_SNAKE_CASE__       )
       lowercase							:  str 			=   rename_backbone_keys(SCREAMING_SNAKE_CASE__       )
       # query, key and value matrices need special treatment
       read_in_q_k_v(SCREAMING_SNAKE_CASE__      ,			is_panoptic=SCREAMING_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							:  List[Any] 			=   """detr.model.""" if is_panoptic else """model."""
       for key in state_dict.copy().keys():
              if is_panoptic:
                     if (
                         key.startswith("""detr"""       )
                         and not key.startswith("""class_labels_classifier"""       )
                         and not key.startswith("""bbox_predictor"""       )
                     ):
                            lowercase							:  Optional[Any] 			=   state_dict.pop(SCREAMING_SNAKE_CASE__       )
                            lowercase							:  Optional[Any] 			=   val
                     elif "class_labels_classifier" in key or "bbox_predictor" in key:
                            lowercase							:  Tuple 			=   state_dict.pop(SCREAMING_SNAKE_CASE__       )
                            lowercase							:  str 			=   val
                     elif key.startswith("""bbox_attention"""       ) or key.startswith("""mask_head"""       ):
                            continue
                     else:
                            lowercase							:  Any 			=   state_dict.pop(SCREAMING_SNAKE_CASE__       )
                            lowercase							:  Dict 			=   val
              else:
                     if not key.startswith("""class_labels_classifier"""       ) and not key.startswith("""bbox_predictor"""       ):
                            lowercase							:  List[str] 			=   state_dict.pop(SCREAMING_SNAKE_CASE__       )
                            lowercase							:  Union[str, Any] 			=   val
    # finally, create HuggingFace model and load state dict
       lowercase							:  int 			=   DetrForSegmentation(SCREAMING_SNAKE_CASE__       ) if is_panoptic else DetrForObjectDetection(SCREAMING_SNAKE_CASE__       )
       model.load_state_dict(SCREAMING_SNAKE_CASE__       )
       model.eval()
       # verify our conversion
       lowercase							:  Union[str, Any] 			=   detr(SCREAMING_SNAKE_CASE__       )
       lowercase							:  int 			=   model(SCREAMING_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(SCREAMING_SNAKE_CASE__       ).mkdir(exist_ok=SCREAMING_SNAKE_CASE__       )
       model.save_pretrained(SCREAMING_SNAKE_CASE__       )
       image_processor.save_pretrained(SCREAMING_SNAKE_CASE__       )


if __name__ == "__main__":
  lowercase						:      Any							=							argparse.ArgumentParser()

  parser.add_argument(
      """--model_name""", default="""detr_resnet50""", type=str, help="""Name of the 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						:      List[Any]							=							parser.parse_args()
  convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)
