import copy
import fnmatch
import json
import os
import pickle as pkl
import shutil
import sys
import tarfile
import tempfile
from collections import OrderedDict
from contextlib import contextmanager
from functools import partial
from hashlib import shaaaa
from io import BytesIO
from pathlib import Path
from urllib.parse import urlparse
from zipfile import ZipFile, is_zipfile

import cva
import numpy as np
import requests
import wget
from filelock import FileLock
from PIL import Image
from tqdm.auto import tqdm
from yaml import Loader, dump, load


try:
				import torch

				lowerCAmelCase__	:  str						=  True
except ImportError:
				lowerCAmelCase__	:  Optional[int]						=  False


try:
				from torch.hub import _get_torch_home

				lowerCAmelCase__	:  List[Any]						=  _get_torch_home()
except ImportError:
				lowerCAmelCase__	:  List[str]						=  os.path.expanduser(
				    os.getenv('''TORCH_HOME''', os.path.join(os.getenv('''XDG_CACHE_HOME''', '''~/.cache'''), '''torch'''))
				)

lowerCAmelCase__	:  Optional[Any]						=  os.path.join(torch_cache_home, '''transformers''')

lowerCAmelCase__	:  int						=  "https://cdn.huggingface.co"
lowerCAmelCase__	:  int						=  "https://s3.amazonaws.com/models.huggingface.co/bert"
lowerCAmelCase__	:  Optional[Any]						=  "/".join(str(Path(__file__).resolve()).split('''/''')[:-1])
lowerCAmelCase__	:  List[Any]						=  os.path.join(PATH, '''config.yaml''')
lowerCAmelCase__	:  Tuple						=  os.path.join(PATH, '''attributes.txt''')
lowerCAmelCase__	:  Dict						=  os.path.join(PATH, '''objects.txt''')
lowerCAmelCase__	:  Any						=  os.getenv('''PYTORCH_PRETRAINED_BERT_CACHE''', default_cache_path)
lowerCAmelCase__	:  Dict						=  os.getenv('''PYTORCH_TRANSFORMERS_CACHE''', PYTORCH_PRETRAINED_BERT_CACHE)
lowerCAmelCase__	:  List[Any]						=  os.getenv('''TRANSFORMERS_CACHE''', PYTORCH_TRANSFORMERS_CACHE)
lowerCAmelCase__	:  Any						=  "pytorch_model.bin"
lowerCAmelCase__	:  int						=  "config.yaml"
def 					UpperCamelCase__		(							A__=OBJECTS     ,					A__=ATTRIBUTES       )  ->   Optional[Any]:
				snake_case__   :      List[Any]   				= []
				with open(_snake_case       ) as f:
								for object in f.readlines():
												vg_classes.append(object.split(','       )[0].lower().strip()       )

				snake_case__   :      str   				= []
				with open(_snake_case       ) as f:
								for object in f.readlines():
												vg_attrs.append(object.split(','       )[0].lower().strip()       )
				return vg_classes, vg_attrs
def 					UpperCamelCase__		(							A__       )  ->   str:
				snake_case__   :      List[Any]   				= OrderedDict()
				with open(_snake_case     ,					'rb'       ) as f:
								snake_case__   :      List[Any]   				= pkl.load(_snake_case       )['model']
				for k in copy.deepcopy(list(ckp.keys()       )       ):
								snake_case__   :      Union[str, Any]   				= ckp.pop(_snake_case       )
								if isinstance(_snake_case     ,					np.ndarray       ):
												snake_case__   :      Tuple   				= torch.tensor(_snake_case       )
								else:
												assert isinstance(_snake_case     ,					torch.tensor       ), type(_snake_case       )
								snake_case__   :      Tuple   				= v
				return r





class 					__snake_case    :
					__lowerCamelCase			   =  {}




					def __init__(  self       ,			__UpperCamelCase       ,			__UpperCamelCase = "root"       ,			__UpperCamelCase=0						)	->    int:



									'''simple docstring'''







									snake_case__   :      Dict   				= name
									snake_case__   :      List[Any]   				= level
									snake_case__   :      int   				= {}
									for k, v in dictionary.items():
													if v is None:
																	raise ValueError()
													snake_case__   :      Union[str, Any]   				= copy.deepcopy(_lowercase						)
													snake_case__   :      Union[str, Any]   				= copy.deepcopy(_lowercase						)
													if isinstance(_lowercase       ,			_lowercase						):
																	snake_case__   :      Optional[Any]   				= Config(_lowercase       ,			name=_lowercase       ,			level=level + 1						)
													snake_case__   :      Optional[int]   				= v
													setattr(self       ,			_lowercase       ,			_lowercase						)

									snake_case__   :      str   				= d




					def __repr__(  self						)	->    Tuple:



									'''simple docstring'''







									return str(list((self._pointer.keys())						)						)




					def __setattr__(  self       ,			__UpperCamelCase       ,			__UpperCamelCase						)	->    List[str]:



									'''simple docstring'''







									snake_case__   :      str   				= val
									snake_case__   :      List[Any]   				= val
									snake_case__   :      Union[str, Any]   				= key.split('.'						)
									snake_case__   :      int   				= len(_lowercase						) - 1
									snake_case__   :      Dict   				= self._pointer
									if len(_lowercase						) > 1:
													for i, l in enumerate(_lowercase						):
																	if hasattr(self       ,			_lowercase						) and isinstance(getattr(self       ,			_lowercase						)       ,			_lowercase						):
																					setattr(getattr(self       ,			_lowercase						)       ,			'.'.join(levels[i:]						)       ,			_lowercase						)
																	if l == last_level:
																					snake_case__   :      Optional[int]   				= val
																	else:
																					snake_case__   :      Tuple   				= pointer[l]




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



									'''simple docstring'''







									return self._pointer




					def      __a     (  self       ,			__UpperCamelCase       ,			__UpperCamelCase						)	->    Tuple:



									'''simple docstring'''







									with open(F"""{file_name}"""       ,			'w'						) as stream:
													dump(_lowercase       ,			_lowercase						)




					def      __a     (  self       ,			__UpperCamelCase       ,			__UpperCamelCase						)	->    str:



									'''simple docstring'''







									with open(F"""{file_name}"""       ,			'w'						) as stream:
													json.dump(_lowercase       ,			_lowercase						)




					@staticmethod
					def      __a     (  __UpperCamelCase						)	->    Optional[Any]:



									'''simple docstring'''







									with open(_lowercase						) as stream:
													snake_case__   :      Any   				= load(_lowercase       ,			Loader=_lowercase						)
									return data




					def __str__(  self						)	->    List[str]:



									'''simple docstring'''







									snake_case__   :      Union[str, Any]   				= '    '
									if self._name != "root":
													snake_case__   :      Dict   				= F"""{t * (self._level-1)}{self._name}:\n"""
									else:
													snake_case__   :      Union[str, Any]   				= ''
									snake_case__   :      Any   				= self._level
									for i, (k, v) in enumerate(self._pointer.items()						):
													if isinstance(_lowercase       ,			_lowercase						):
																	r += F"""{t * (self._level)}{v}\n"""
																	self._level += 1
													else:
																	r += F"""{t * (self._level)}{k}: {v} ({type(_lowercase						).__name__})\n"""
													snake_case__   :      Optional[int]   				= level
									return r[:-1]




					@classmethod
					def      __a     (  cls       ,			__UpperCamelCase       ,			**__UpperCamelCase						)	->    Tuple:



									'''simple docstring'''







									snake_case__				,    snake_case__   :      Union[str, Any]   				= cls.get_config_dict(_lowercase       ,			**_lowercase						)
									return cls(_lowercase						)




					@classmethod
					def      __a     (  cls       ,			__UpperCamelCase       ,			**__UpperCamelCase						)	->    Any:



									'''simple docstring'''







									snake_case__   :      Any   				= kwargs.pop('cache_dir'       ,			_lowercase						)
									snake_case__   :      Optional[int]   				= kwargs.pop('force_download'       ,			_lowercase						)
									snake_case__   :      int   				= kwargs.pop('resume_download'       ,			_lowercase						)
									snake_case__   :      List[str]   				= kwargs.pop('proxies'       ,			_lowercase						)
									snake_case__   :      int   				= kwargs.pop('local_files_only'       ,			_lowercase						)

									if os.path.isdir(_lowercase						):
													snake_case__   :      List[str]   				= os.path.join(_lowercase       ,			_lowercase						)
									elif os.path.isfile(_lowercase						) or is_remote_url(_lowercase						):
													snake_case__   :      str   				= pretrained_model_name_or_path
									else:
													snake_case__   :      Tuple   				= hf_bucket_url(_lowercase       ,			filename=_lowercase       ,			use_cdn=_lowercase						)

									try:
													# Load from URL or cache if already cached
													snake_case__   :      Any   				= cached_path(
													    _lowercase       ,			cache_dir=_lowercase       ,			force_download=_lowercase       ,			proxies=_lowercase       ,			resume_download=_lowercase       ,			local_files_only=_lowercase       ,			)
													# Load config dict
													if resolved_config_file is None:
																	raise EnvironmentError

													snake_case__   :      Tuple   				= Config.load_yaml(_lowercase						)

									except EnvironmentError:
													snake_case__   :      List[str]   				= 'Can\'t load config for'
													raise EnvironmentError(_lowercase						)

									if resolved_config_file == config_file:
													print('loading configuration file from path'						)
									else:
													print('loading configuration file cache'						)

									return Config.load_yaml(_lowercase						), kwargs




def 					UpperCamelCase__		(							A__       )  ->   Optional[int]:
				snake_case__   :      List[Any]   				= torch.load('dump.pt'     ,					map_location=in_tensor.device       )
				snake_case__   :      List[Any]   				= in_tensor.numpy()
				snake_case__   :      List[str]   				= out_tensor.numpy()[0]
				print(na.shape     ,					na[0, 0, :5]       )
				print(na.shape     ,					na[0, 0, :5]       )
				assert np.allclose(_snake_case     ,					_snake_case     ,					rtol=0.0_1     ,					atol=0.1       ), (
				    F"""{sum([1 for x in np.isclose(_snake_case     ,					_snake_case     ,					rtol=0.0_1     ,					atol=0.1       ).flatten() if x is False]       )/len(na.flatten()       )*100:.4f} %"""
				    " element-wise mismatch"
				)
				raise Exception('tensors are all good'       )

				# Hugging face functions below
def 					UpperCamelCase__		(							A__       )  ->   Optional[Any]:
				snake_case__   :      Dict   				= urlparse(_snake_case       )
				return parsed.scheme in ("http", "https")
def 					UpperCamelCase__		(							A__     ,					A__     ,					A__=True       )  ->   List[str]:
				snake_case__   :      int   				= CLOUDFRONT_DISTRIB_PREFIX if use_cdn else S3_BUCKET_PREFIX
				snake_case__   :      Optional[int]   				= '/' not in model_id
				if legacy_format:
								return F"""{endpoint}/{model_id}-{filename}"""
				else:
								return F"""{endpoint}/{model_id}/{filename}"""
def 					UpperCamelCase__		(							A__     ,					A__     ,					A__=None     ,					A__=0     ,					A__=None     ,					)  ->   Dict:
				snake_case__   :      int   				= 'python/{}'.format(sys.version.split()[0]       )
				if _torch_available:
								ua += "; torch/{}".format(torch.__version__       )
				if isinstance(_snake_case     ,					_snake_case       ):
								ua += "; " + "; ".join('{}/{}'.format(_snake_case     ,					_snake_case       ) for k, v in user_agent.items()       )
				elif isinstance(_snake_case     ,					_snake_case       ):
								ua += "; " + user_agent
				snake_case__   :      Optional[Any]   				= {'user-agent': ua}
				if resume_size > 0:
								snake_case__   :      Optional[Any]   				= 'bytes=%d-' % (resume_size,)
				snake_case__   :      int   				= requests.get(_snake_case     ,					stream=_snake_case     ,					proxies=_snake_case     ,					headers=_snake_case       )
				if response.status_code == 416:  # Range not satisfiable
								return
				snake_case__   :      str   				= response.headers.get('Content-Length'       )
				snake_case__   :      Optional[Any]   				= resume_size + int(_snake_case       ) if content_length is not None else None
				snake_case__   :      Tuple   				= tqdm(
				    unit='B'     ,					unit_scale=_snake_case     ,					total=_snake_case     ,					initial=_snake_case     ,					desc='Downloading'     ,					)
				for chunk in response.iter_content(chunk_size=1024       ):
								if chunk:  # filter out keep-alive new chunks
												progress.update(len(_snake_case       )       )
												temp_file.write(_snake_case       )
				progress.close()
def 					UpperCamelCase__		(							A__     ,					A__=None     ,					A__=False     ,					A__=None     ,					A__=10     ,					A__=False     ,					A__=None     ,					A__=False     ,					)  ->   Any:
				if cache_dir is None:
								snake_case__   :      str   				= TRANSFORMERS_CACHE
				if isinstance(_snake_case     ,					_snake_case       ):
								snake_case__   :      Union[str, Any]   				= str(_snake_case       )

				os.makedirs(_snake_case     ,					exist_ok=_snake_case       )

				snake_case__   :      List[Any]   				= None
				if not local_files_only:
								try:
												snake_case__   :      int   				= requests.head(_snake_case     ,					allow_redirects=_snake_case     ,					proxies=_snake_case     ,					timeout=_snake_case       )
												if response.status_code == 200:
																snake_case__   :      int   				= response.headers.get('ETag'       )
								except (EnvironmentError, requests.exceptions.Timeout):
												# etag is already None
												pass

				snake_case__   :      int   				= url_to_filename(_snake_case     ,					_snake_case       )

				# get cache path to put the file
				snake_case__   :      int   				= os.path.join(_snake_case     ,					_snake_case       )

				# etag is None = we don't have a connection, or url doesn't exist, or is otherwise inaccessible.
				# try to get the last downloaded one
				if etag is None:
								if os.path.exists(_snake_case       ):
												return cache_path
								else:
												snake_case__   :      List[str]   				= [
												    file
												    for file in fnmatch.filter(os.listdir(_snake_case       )     ,					filename + '.*'       )
												    if not file.endswith('.json'       ) and not file.endswith('.lock'       )
												]
												if len(_snake_case       ) > 0:
																return os.path.join(_snake_case     ,					matching_files[-1]       )
												else:
																# If files cannot be found and local_files_only=True,
																# the models might've been found if local_files_only=False
																# Notify the user about that
																if local_files_only:
																				raise ValueError(
																				    'Cannot find the requested files in the cached path and outgoing traffic has been'
																				    ' disabled. To enable model look-ups and downloads online, set \'local_files_only\''
																				    ' to False.'       )
																return None

    # From now on, etag is not None.
				if os.path.exists(_snake_case       ) and not force_download:
								return cache_path

				# Prevent parallel downloads of the same file with a lock.
				snake_case__   :      Optional[int]   				= cache_path + '.lock'
				with FileLock(_snake_case       ):
								# If the download just completed while the lock was activated.
								if os.path.exists(_snake_case       ) and not force_download:
												# Even if returning early like here, the lock will be released.
												return cache_path

								if resume_download:
												snake_case__   :      Any   				= cache_path + '.incomplete'

												@contextmanager
												def _resumable_file_manager():
																with open(_snake_case     ,					'a+b'       ) as f:
																				yield f

												snake_case__   :      str   				= _resumable_file_manager
												if os.path.exists(_snake_case       ):
																snake_case__   :      Any   				= os.stat(_snake_case       ).st_size
												else:
																snake_case__   :      Optional[int]   				= 0
								else:
												snake_case__   :      Union[str, Any]   				= partial(tempfile.NamedTemporaryFile     ,					dir=_snake_case     ,					delete=_snake_case       )
												snake_case__   :      Optional[Any]   				= 0

								# Download to temporary file, then copy to cache dir once finished.
								# Otherwise you get corrupt cache entries if the download gets interrupted.
								with temp_file_manager() as temp_file:
												print(
												    '%s not found in cache or force_download set to True, downloading to %s'     ,					_snake_case     ,					temp_file.name     ,					)

												http_get(
												    _snake_case     ,					_snake_case     ,					proxies=_snake_case     ,					resume_size=_snake_case     ,					user_agent=_snake_case     ,					)

								os.replace(temp_file.name     ,					_snake_case       )

								snake_case__   :      int   				= {'url': url, 'etag': etag}
								snake_case__   :      Dict   				= cache_path + '.json'
								with open(_snake_case     ,					'w'       ) as meta_file:
												json.dump(_snake_case     ,					_snake_case       )

				return cache_path
def 					UpperCamelCase__		(							A__     ,					A__=None       )  ->   Any:
				snake_case__   :      str   				= url.encode('utf-8'       )
				snake_case__   :      Optional[Any]   				= shaaaa(_snake_case       )
				snake_case__   :      str   				= url_hash.hexdigest()

				if etag:
								snake_case__   :      Union[str, Any]   				= etag.encode('utf-8'       )
								snake_case__   :      Optional[int]   				= shaaaa(_snake_case       )
								filename += "." + etag_hash.hexdigest()

				if url.endswith('.h5'       ):
								filename += ".h5"

				return filename
def 					UpperCamelCase__		(							A__     ,					A__=None     ,					A__=False     ,					A__=None     ,					A__=False     ,					A__=None     ,					A__=False     ,					A__=False     ,					A__=False     ,					)  ->   Union[str, Any]:
				if cache_dir is None:
								snake_case__   :      Optional[Any]   				= TRANSFORMERS_CACHE
				if isinstance(_snake_case     ,					_snake_case       ):
								snake_case__   :      Tuple   				= str(_snake_case       )
				if isinstance(_snake_case     ,					_snake_case       ):
								snake_case__   :      str   				= str(_snake_case       )

				if is_remote_url(_snake_case       ):
								# URL, so get it from the cache (downloading if necessary)
								snake_case__   :      Tuple   				= get_from_cache(
								    _snake_case     ,					cache_dir=_snake_case     ,					force_download=_snake_case     ,					proxies=_snake_case     ,					resume_download=_snake_case     ,					user_agent=_snake_case     ,					local_files_only=_snake_case     ,					)
				elif os.path.exists(_snake_case       ):
								# File, and it exists.
								snake_case__   :      Tuple   				= url_or_filename
				elif urlparse(_snake_case       ).scheme == "":
								# File, but it doesn't exist.
								raise EnvironmentError('file {} not found'.format(_snake_case       )       )
				else:
								# Something unknown
								raise ValueError('unable to parse {} as a URL or as a local path'.format(_snake_case       )       )

				if extract_compressed_file:
								if not is_zipfile(_snake_case       ) and not tarfile.is_tarfile(_snake_case       ):
												return output_path

								# Path where we extract compressed archives
								# We avoid '.' in dir name and add "-extracted" at the end: "./model.zip" => "./model-zip-extracted/"
								snake_case__				,    snake_case__   :      List[str]   				= os.path.split(_snake_case       )
								snake_case__   :      List[Any]   				= output_file.replace('.'     ,					'-'       ) + '-extracted'
								snake_case__   :      Union[str, Any]   				= os.path.join(_snake_case     ,					_snake_case       )

								if os.path.isdir(_snake_case       ) and os.listdir(_snake_case       ) and not force_extract:
												return output_path_extracted

								# Prevent parallel extractions
								snake_case__   :      Any   				= output_path + '.lock'
								with FileLock(_snake_case       ):
												shutil.rmtree(_snake_case     ,					ignore_errors=_snake_case       )
												os.makedirs(_snake_case       )
												if is_zipfile(_snake_case       ):
																with ZipFile(_snake_case     ,					'r'       ) as zip_file:
																				zip_file.extractall(_snake_case       )
																				zip_file.close()
												elif tarfile.is_tarfile(_snake_case       ):
																snake_case__   :      Union[str, Any]   				= tarfile.open(_snake_case       )
																tar_file.extractall(_snake_case       )
																tar_file.close()
												else:
																raise EnvironmentError('Archive format of {} could not be identified'.format(_snake_case       )       )

								return output_path_extracted

				return output_path
def 					UpperCamelCase__		(							A__     ,					A__=","       )  ->   Union[str, Any]:
				assert isinstance(_snake_case     ,					_snake_case       )
				if os.path.isfile(_snake_case       ):
								with open(_snake_case       ) as f:
												snake_case__   :      int   				= eval(f.read()       )
				else:
								snake_case__   :      Union[str, Any]   				= requests.get(_snake_case       )
								try:
												snake_case__   :      str   				= requests.json()
								except Exception:
												snake_case__   :      List[Any]   				= req.content.decode()
												assert data is not None, "could not connect"
												try:
																snake_case__   :      List[str]   				= eval(_snake_case       )
												except Exception:
																snake_case__   :      Dict   				= data.split('\n'       )
								req.close()
				return data
def 					UpperCamelCase__		(							A__       )  ->   str:
				snake_case__   :      str   				= requests.get(_snake_case       )
				snake_case__   :      List[Any]   				= np.array(Image.open(BytesIO(response.content       )       )       )
				return img
def 					UpperCamelCase__		(							A__       )  ->   List[str]:
				snake_case__   :      int   				= url.split('/'       )[-1]
				if fn not in os.listdir(os.getcwd()       ):
								wget.download(_snake_case       )
				with open(_snake_case     ,					'rb'       ) as stream:
								snake_case__   :      str   				= pkl.load(_snake_case       )
				snake_case__   :      Dict   				= weights.pop('model'       )
				snake_case__   :      int   				= {}
				for k, v in model.items():
								snake_case__   :      List[Any]   				= torch.from_numpy(_snake_case       )
								if "running_var" in k:
												snake_case__   :      Optional[int]   				= torch.tensor([0]       )
												snake_case__   :      Dict   				= k.replace('running_var'     ,					'num_batches_tracked'       )
												snake_case__   :      Union[str, Any]   				= zero
				return new
def 					UpperCamelCase__		(							)  ->   Any:
				print(F"""{os.path.abspath(os.path.join(_snake_case     ,					os.pardir       )       )}/demo.ipynb"""       )
def 					UpperCamelCase__		(							A__     ,					A__="RGB"       )  ->   str:
				assert isinstance(_snake_case     ,					_snake_case       )
				if os.path.isfile(_snake_case       ):
								snake_case__   :      Tuple   				= cva.imread(_snake_case       )
				else:
								snake_case__   :      Any   				= get_image_from_url(_snake_case       )
								assert img is not None, F"""could not connect to: {im}"""
				snake_case__   :      List[Any]   				= cva.cvtColor(_snake_case     ,					cva.COLOR_BGR2RGB       )
				if input_format == "RGB":
								snake_case__   :      Optional[Any]   				= img[:, :, ::-1]
				return img

def 					UpperCamelCase__		(							A__     ,					A__=1       )  ->   Union[str, Any]:
				return (images[i : i + batch] for i in range(0     ,					len(_snake_case       )     ,					_snake_case       ))



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


logging.set_verbosity_info()
__UpperCamelCase       :    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)
__UpperCamelCase       :    Optional[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}.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     (	_snake_case		:       List[str],				_snake_case		:       Tuple,				_snake_case		:       Any       ):
   _lowercase							     =       state_dict.pop(_snake_case       )
   _lowercase							     =       val


def   __UpperCAmelCase     (	_snake_case		:       Optional[int]       ):
   _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   __UpperCAmelCase     (	_snake_case		:       Optional[Any],				_snake_case		:       int=False       ):
   _lowercase							     =       ""
   if is_panoptic:
      _lowercase							     =       "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[:2_5_6, :]
      _lowercase							     =       in_proj_bias[:2_5_6]
      _lowercase							     =       in_proj_weight[2_5_6:5_1_2, :]
      _lowercase							     =       in_proj_bias[2_5_6:5_1_2]
      _lowercase							     =       in_proj_weight[-2_5_6:, :]
      _lowercase							     =       in_proj_bias[-2_5_6:]
   # 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							     =       state_dict.pop(f"""{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight"""       )
      _lowercase							     =       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							     =       in_proj_weight[:2_5_6, :]
      _lowercase							     =       in_proj_bias[:2_5_6]
      _lowercase							     =       in_proj_weight[2_5_6:5_1_2, :]
      _lowercase							     =       in_proj_bias[2_5_6:5_1_2]
      _lowercase							     =       in_proj_weight[-2_5_6:, :]
      _lowercase							     =       in_proj_bias[-2_5_6:]
      # read in weights + bias of input projection layer of cross-attention
      _lowercase							     =       state_dict.pop(
          f"""{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight"""       )
      _lowercase							     =       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							     =       in_proj_weight_cross_attn[:2_5_6, :]
      _lowercase							     =       in_proj_bias_cross_attn[:2_5_6]
      _lowercase							     =       in_proj_weight_cross_attn[2_5_6:5_1_2, :]
      _lowercase							     =       in_proj_bias_cross_attn[2_5_6:5_1_2]
      _lowercase							     =       in_proj_weight_cross_attn[-2_5_6:, :]
      _lowercase							     =       in_proj_bias_cross_attn[-2_5_6:]


def   __UpperCAmelCase     (	):
   _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   __UpperCAmelCase     (	_snake_case		:       Tuple,				_snake_case		:       str       ):
   _lowercase							     =       DetrConfig()
   # 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							     =       2_5_0
   else:
      _lowercase							     =       9_1
      _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							     =       DetrImageProcessor(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("facebookresearch/detr",				_snake_case,				pretrained=_snake_case       ).eval()
   _lowercase							     =       detr.state_dict()
   # rename keys
   for src, dest in rename_keys:
      if is_panoptic:
         _lowercase							     =       "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							     =       "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							     =       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							     =       DetrForSegmentation(_snake_case       ) if is_panoptic else DetrForObjectDetection(_snake_case       )
   model.load_state_dict(_snake_case       )
   model.eval()
   # verify our conversion
   _lowercase							     =       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__":
  __UpperCamelCase       :    int         =		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."
  )
  __UpperCamelCase       :    str         =		parser.parse_args()
  convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)