
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 						UpperCAmelCase	(		A_				):



   def __init__(self	:      Any				)	->			Optional[int]:


          '''simple docstring'''


          self.register_to_config()
          super().__init__()



   def        _SCREAMING_SNAKE_CASE     (self	:      int					,		snake_case__	:      List[Any]					,		snake_case__	:      List[str]				)	->			int:


          '''simple docstring'''


          if dicta == dicta:
                 return True
          else:
                 snake_case       ,      snake_case :     Any           =   self._remove_meta_keys(snake_case__				)
                 snake_case       ,      snake_case :     int           =   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	:      List[str]					,		snake_case__	:      Dict				)	->			List[Any]:


          '''simple docstring'''


          snake_case :     Optional[Any]           =   []
          snake_case :     List[str]           =   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	:      int					,		snake_case__	:      List[Union[str, os.PathLike]]					,		**snake_case__	:      Optional[Any]				)	->			List[Any]:


          '''simple docstring'''


          snake_case :     Any           =   kwargs.pop("cache_dir"					,		snake_case__				)
          snake_case :     Tuple           =   kwargs.pop("resume_download"					,		snake_case__				)
          snake_case :     Any           =   kwargs.pop("force_download"					,		snake_case__				)
          snake_case :     Optional[Any]           =   kwargs.pop("proxies"					,		snake_case__				)
          snake_case :     Tuple           =   kwargs.pop("local_files_only"					,		snake_case__				)
          snake_case :     int           =   kwargs.pop("use_auth_token"					,		snake_case__				)
          snake_case :     List[str]           =   kwargs.pop("revision"					,		snake_case__				)
          snake_case :     List[Any]           =   kwargs.pop("torch_dtype"					,		snake_case__				)
          snake_case :     Optional[Any]           =   kwargs.pop("device_map"					,		snake_case__				)

          snake_case :     Union[str, Any]           =   kwargs.pop("alpha"					,		0.5				)
          snake_case :     Dict           =   kwargs.pop("interp"					,		snake_case__				)

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

          snake_case :     Dict           =   len(snake_case__				)
          # Ignore result from model_index_json comparision of the two checkpoints
          snake_case :     str           =   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 '_'
          snake_case :     Optional[int]           =   []
          for pretrained_model_name_or_path in pretrained_model_name_or_path_list:
                 snake_case :     Dict           =   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__				)

          snake_case :     Optional[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.
          snake_case :     Union[str, Any]           =   []
          for pretrained_model_name_or_path, config_dict in zip(snake_case__					,		snake_case__				):
                 snake_case :     Tuple           =   [k for k in config_dict.keys() if not k.startswith("_"				)]
                 snake_case :     Optional[int]           =   [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,
                 ]
                 snake_case :     Any           =   config_dict.get("_class_name"				)
                 snake_case :     Optional[Any]           =   {"diffusers": __version__, "pipeline_class": requested_pipeline_class}

                 snake_case :     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
          snake_case :     str           =   DiffusionPipeline.from_pretrained(
              cached_folders[0]					,		torch_dtype=snake_case__					,		device_map=snake_case__				)
          final_pipe.to(self.device				)

          snake_case :     Optional[Any]           =   None
          if len(snake_case__				) > 2:
                 snake_case :     Tuple           =   os.path.join(cached_folders[2]				)

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

          # Find each module's state dict.
          for attr in final_pipe.config.keys():
                 if not attr.startswith("_"				):
                        snake_case :     Tuple           =   os.path.join(cached_folders[1]					,		snake_case__				)
                        if os.path.exists(snake_case__				):
                               snake_case :     Optional[int]           =   [
                                   *glob.glob(os.path.join(snake_case__					,		"*.safetensors"				)				),
                                   *glob.glob(os.path.join(snake_case__					,		"*.bin"				)				),
                               ]
                               snake_case :     Any           =   files[0] if len(snake_case__				) > 0 else None
                        if len(snake_case__				) < 3:
                               snake_case :     int           =   None
                        else:
                               snake_case :     int           =   os.path.join(cached_folders[2]					,		snake_case__				)
                               if os.path.exists(snake_case__				):
                                      snake_case :     Dict           =   [
                                          *glob.glob(os.path.join(snake_case__					,		"*.safetensors"				)				),
                                          *glob.glob(os.path.join(snake_case__					,		"*.bin"				)				),
                                      ]
                                      snake_case :     Optional[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:
                               snake_case :     Optional[Any]           =   getattr(snake_case__					,		snake_case__				)
                               if isinstance(snake_case__					,		snake_case__				):  # ignore requires_safety_checker boolean
                                      continue
                               snake_case :     Optional[int]           =   getattr(snake_case__					,		"state_dict"				)
                               snake_case :     Union[str, Any]           =   theta_a()

                               snake_case :     Optional[int]           =   getattr(snake_case__					,		"load_state_dict"				)
                               snake_case :     Dict           =   (
                                   safetensors.torch.load_file(snake_case__				)
                                   if (is_safetensors_available() and checkpoint_path_a.endswith(".safetensors"				))
                                   else torch.load(snake_case__					,		map_location="cpu"				)
                               )
                               snake_case :     List[Any]           =   None
                               if checkpoint_path_a:
                                      snake_case :     Tuple           =   (
                                          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:
                                      snake_case :     Tuple           =   theta_func(theta_a[key]					,		theta_a[key]					,		theta_a[key]					,		snake_case__				)
                               else:
                                      snake_case :     Optional[Any]           =   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__	:      str					,		snake_case__	:      int					,		snake_case__	:      List[str]					,		snake_case__	:      Optional[int]				)	->			int:


          '''simple docstring'''


          return ((1 - alpha) * thetaa) + (alpha * thetaa)



   @staticmethod
   def        _SCREAMING_SNAKE_CASE     (snake_case__	:      List[Any]					,		snake_case__	:      Optional[int]					,		snake_case__	:      int					,		snake_case__	:      Optional[int]				)	->			List[str]:


          '''simple docstring'''


          snake_case :     Any           =   alpha * alpha * (3 - (2 * alpha))
          return thetaa + ((thetaa - thetaa) * alpha)



   @staticmethod
   def        _SCREAMING_SNAKE_CASE     (snake_case__	:      Any					,		snake_case__	:      Tuple					,		snake_case__	:      Optional[Any]					,		snake_case__	:      Optional[int]				)	->			int:


          '''simple docstring'''


          import math

          snake_case :     Dict           =   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__	:      Tuple					,		snake_case__	:      Any					,		snake_case__	:      Optional[Any]					,		snake_case__	:      List[Any]				)	->			Dict:


          '''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()
__lowerCamelCase							= logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
__lowerCamelCase							= []
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     UpperCamelCase      (					__lowerCamelCase  :				Dict       ,      __lowerCamelCase  :				List[str]       ,      __lowerCamelCase  :				Any					):
       snake_case :     int           =   state_dict.pop(__lowerCamelCase					)
       snake_case :     int           =   val


def     UpperCamelCase      (					__lowerCamelCase  :				List[Any]					):
       snake_case :     Optional[int]           =   OrderedDict()
       for key, value in state_dict.items():
              if "backbone.0.body" in key:
                     snake_case :     List[str]           =   key.replace("backbone.0.body"       ,      "backbone.conv_encoder.model"					)
                     snake_case :     str           =   value
              else:
                     snake_case :     Any           =   value

       return new_state_dict


def     UpperCamelCase      (					__lowerCamelCase  :				List[str]       ,      __lowerCamelCase  :				Dict=False					):
       snake_case :     List[str]           =   ""
       if is_panoptic:
              snake_case :     Dict           =   "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)
              snake_case :     Optional[Any]           =   state_dict.pop(f"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight"""					)
              snake_case :     int           =   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
              snake_case :     Optional[Any]           =   in_proj_weight[:256, :]
              snake_case :     List[str]           =   in_proj_bias[:256]
              snake_case :     Tuple           =   in_proj_weight[256:512, :]
              snake_case :     Dict           =   in_proj_bias[256:512]
              snake_case :     Optional[int]           =   in_proj_weight[-256:, :]
              snake_case :     Optional[int]           =   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
              snake_case :     int           =   state_dict.pop(f"""{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight"""					)
              snake_case :     List[str]           =   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
              snake_case :     Dict           =   in_proj_weight[:256, :]
              snake_case :     Optional[int]           =   in_proj_bias[:256]
              snake_case :     Union[str, Any]           =   in_proj_weight[256:512, :]
              snake_case :     Optional[Any]           =   in_proj_bias[256:512]
              snake_case :     Dict           =   in_proj_weight[-256:, :]
              snake_case :     int           =   in_proj_bias[-256:]
              # read in weights + bias of input projection layer of cross-attention
              snake_case :     Dict           =   state_dict.pop(
                  f"""{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight"""					)
              snake_case :     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
              snake_case :     Any           =   in_proj_weight_cross_attn[:256, :]
              snake_case :     Dict           =   in_proj_bias_cross_attn[:256]
              snake_case :     Any           =   in_proj_weight_cross_attn[256:512, :]
              snake_case :     Optional[int]           =   in_proj_bias_cross_attn[256:512]
              snake_case :     List[Any]           =   in_proj_weight_cross_attn[-256:, :]
              snake_case :     Optional[Any]           =   in_proj_bias_cross_attn[-256:]


def     UpperCamelCase      (					):
       snake_case :     Optional[Any]           =   "http://images.cocodataset.org/val2017/000000039769.jpg"
       snake_case :     List[str]           =   Image.open(requests.get(__lowerCamelCase       ,      stream=__lowerCamelCase					).raw					)

       return im


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

       # load image processor
       snake_case :     Tuple           =   "coco_panoptic" if is_panoptic else "coco_detection"
       snake_case :     str           =   DetrImageProcessor(format=__lowerCamelCase					)

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

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

       # load original model from torch hub
       snake_case :     Dict           =   torch.hub.load("facebookresearch/detr"       ,      __lowerCamelCase       ,      pretrained=__lowerCamelCase					).eval()
       snake_case :     Any           =   detr.state_dict()
       # rename keys
       for src, dest in rename_keys:
              if is_panoptic:
                     snake_case :     List[str]           =   "detr." + src
              rename_key(__lowerCamelCase       ,      __lowerCamelCase       ,      __lowerCamelCase					)
       snake_case :     str           =   rename_backbone_keys(__lowerCamelCase					)
       # query, key and value matrices need special treatment
       read_in_q_k_v(__lowerCamelCase       ,      is_panoptic=__lowerCamelCase					)
       # important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
       snake_case :     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"					)
                     ):
                            snake_case :     Tuple           =   state_dict.pop(__lowerCamelCase					)
                            snake_case :     Any           =   val
                     elif "class_labels_classifier" in key or "bbox_predictor" in key:
                            snake_case :     Dict           =   state_dict.pop(__lowerCamelCase					)
                            snake_case :     Optional[Any]           =   val
                     elif key.startswith("bbox_attention"					) or key.startswith("mask_head"					):
                            continue
                     else:
                            snake_case :     Union[str, Any]           =   state_dict.pop(__lowerCamelCase					)
                            snake_case :     int           =   val
              else:
                     if not key.startswith("class_labels_classifier"					) and not key.startswith("bbox_predictor"					):
                            snake_case :     int           =   state_dict.pop(__lowerCamelCase					)
                            snake_case :     Optional[int]           =   val
    # finally, create HuggingFace model and load state dict
       snake_case :     Union[str, Any]           =   DetrForSegmentation(__lowerCamelCase					) if is_panoptic else DetrForObjectDetection(__lowerCamelCase					)
       model.load_state_dict(__lowerCamelCase					)
       model.eval()
       # verify our conversion
       snake_case :     List[Any]           =   detr(__lowerCamelCase					)
       snake_case :     Tuple           =   model(__lowerCamelCase					)
       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(__lowerCamelCase					).mkdir(exist_ok=__lowerCamelCase					)
       model.save_pretrained(__lowerCamelCase					)
       image_processor.save_pretrained(__lowerCamelCase					)


if __name__ == "__main__":
   __lowerCamelCase							= 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."""
   )
   __lowerCamelCase							= parser.parse_args()
   convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)





