





import logging
import os
import threading
import time


try:
  import warnings
except ImportError:
  UpperCAmelCase_  							=				None

try:
  import msvcrt
except ImportError:
  UpperCAmelCase_  							=				None

try:
  import fcntl
except ImportError:
  UpperCAmelCase_  							=				None


# Backward compatibility
# ------------------------------------------------
try:
  TimeoutError
except NameError:
  UpperCAmelCase_  							=				OSError


# Data
# ------------------------------------------------
UpperCAmelCase_  							=				[
    'Timeout',
    'BaseFileLock',
    'WindowsFileLock',
    'UnixFileLock',
    'SoftFileLock',
    'FileLock',
]

UpperCAmelCase_  							=				'3.0.12'


UpperCAmelCase_  							=				None




def 	lowerCAmelCase_					(						)   ->			List[str]:
    global _logger
    UpperCamelCase__							:       Union[str, Any]      =   _logger or logging.getLogger(__name__   )
    return _logger






class 					lowercase__ (			__lowerCamelCase       ):

   '''simple docstring'''


   def __init__(					self,	__magic_name__							)				->   str:






       """simple docstring"""
       UpperCamelCase__							:       Tuple      =   lock_file
       return None




   def __str__(					self							)				->   int:






       """simple docstring"""
       UpperCamelCase__							:       Dict      =   f"The file lock '{self.lock_file}' could not be acquired."
       return temp






class 					lowercase__ :

   '''simple docstring'''


   def __init__(					self,	__magic_name__							)				->   Any:






       """simple docstring"""
       UpperCamelCase__							:       int      =   lock
       return None




   def __enter__(					self							)				->   Any:






       """simple docstring"""
       return self.lock




   def __exit__(					self,	__magic_name__,	__magic_name__,	__magic_name__							)				->   Optional[Any]:






       """simple docstring"""
       self.lock.release()
       return None






class 					lowercase__ :

   '''simple docstring'''


   def __init__(					self,	__magic_name__,	__magic_name__=-1,	__magic_name__=None							)				->   Tuple:






       """simple docstring"""
       UpperCamelCase__							:       Tuple      =   max_filename_length if max_filename_length is not None else 255
       # Hash the filename if it's too long
       UpperCamelCase__							:       Dict      =   self.hash_filename_if_too_long(__magic_name__,	__magic_name__							)
       # The path to the lock file.
       UpperCamelCase__							:       List[str]      =   lock_file

       # The file descriptor for the *_lock_file* as it is returned by the
       # os.open() function.
       # This file lock is only NOT None, if the object currently holds the
       # lock.
       UpperCamelCase__							:       List[str]      =   None

       # The default timeout value.
       UpperCamelCase__							:       Optional[int]      =   timeout

       # We use this lock primarily for the lock counter.
       UpperCamelCase__							:       Any      =   threading.Lock()

       # The lock counter is used for implementing the nested locking
       # mechanism. Whenever the lock is acquired, the counter is increased and
       # the lock is only released, when this value is 0 again.
       UpperCamelCase__							:       Any      =   0
       return None




   @property
   def    UpperCamelCase__						(					self							)				->   List[Any]:






       """simple docstring"""
       return self._lock_file




   @property
   def    UpperCamelCase__						(					self							)				->   List[str]:






       """simple docstring"""
       return self._timeout




   @timeout.setter
   def    UpperCamelCase__						(					self,	__magic_name__							)				->   List[str]:






       """simple docstring"""
       UpperCamelCase__							:       str      =   float(__magic_name__							)
       return None




   def    UpperCamelCase__						(					self							)				->   Optional[Any]:






       """simple docstring"""
       raise NotImplementedError()




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






       """simple docstring"""
       raise NotImplementedError()




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






       """simple docstring"""
       return self._lock_file_fd is not None




   def    UpperCamelCase__						(					self,	__magic_name__=None,	__magic_name__=0.05							)				->   int:






       """simple docstring"""
       # Use the default timeout, if no timeout is provided.
       if timeout is None:
           UpperCamelCase__							:       Union[str, Any]      =   self.timeout

       # Increment the number right at the beginning.
       # We can still undo it, if something fails.
       with self._thread_lock:
           self._lock_counter += 1

       UpperCamelCase__							:       Any      =   id(self							)
       UpperCamelCase__							:       str      =   self._lock_file
       UpperCamelCase__							:       List[str]      =   time.time()
       try:
           while True:
               with self._thread_lock:
                   if not self.is_locked:
                       logger().debug(f"Attempting to acquire lock {lock_id} on {lock_filename}"							)
                       self._acquire()

               if self.is_locked:
                   logger().debug(f"Lock {lock_id} acquired on {lock_filename}"							)
                   break
               elif timeout >= 0 and time.time() - start_time > timeout:
                   logger().debug(f"Timeout on acquiring lock {lock_id} on {lock_filename}"							)
                   raise Timeout(self._lock_file							)
               else:
                   logger().debug(
                       f"Lock {lock_id} not acquired on {lock_filename}, waiting {poll_intervall} seconds ..."							)
                   time.sleep(__magic_name__							)
       except:  # noqa
           # Something did go wrong, so decrement the counter.
           with self._thread_lock:
               UpperCamelCase__							:       Tuple      =   max(0,	self._lock_counter - 1							)

           raise
       return _Acquire_ReturnProxy(lock=self							)




   def    UpperCamelCase__						(					self,	__magic_name__=False							)				->   Union[str, Any]:






       """simple docstring"""
       with self._thread_lock:
           if self.is_locked:
               self._lock_counter -= 1

               if self._lock_counter == 0 or force:
                   UpperCamelCase__							:       Tuple      =   id(self							)
                   UpperCamelCase__							:       Tuple      =   self._lock_file

                   logger().debug(f"Attempting to release lock {lock_id} on {lock_filename}"							)
                   self._release()
                   UpperCamelCase__							:       Dict      =   0
                   logger().debug(f"Lock {lock_id} released on {lock_filename}"							)

       return None




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






       """simple docstring"""
       self.acquire()
       return self




   def __exit__(					self,	__magic_name__,	__magic_name__,	__magic_name__							)				->   str:






       """simple docstring"""
       self.release()
       return None




   def __del__(					self							)				->   Tuple:






       """simple docstring"""
       self.release(force=__magic_name__							)
       return None




   def    UpperCamelCase__						(					self,	__magic_name__,	__magic_name__							)				->   str:






       """simple docstring"""
       UpperCamelCase__							:       str      =   os.path.basename(__magic_name__							)
       if len(__magic_name__							) > max_length and max_length > 0:
           UpperCamelCase__							:       List[Any]      =   os.path.dirname(__magic_name__							)
           UpperCamelCase__							:       Optional[Any]      =   str(hash(__magic_name__							)							)
           UpperCamelCase__							:       int      =   filename[: max_length - len(__magic_name__							) - 8] + '''...''' + hashed_filename + '''.lock'''
           return os.path.join(__magic_name__,	__magic_name__							)
       else:
           return path






class 					lowercase__ (			__lowerCamelCase       ):

   '''simple docstring'''


   def __init__(					self,	__magic_name__,	__magic_name__=-1,	__magic_name__=None							)				->   Optional[Any]:






       """simple docstring"""
       from .file_utils import relative_to_absolute_path

       super().__init__(__magic_name__,	timeout=__magic_name__,	max_filename_length=__magic_name__							)
       UpperCamelCase__							:       Union[str, Any]      =   '''\\\\?\\''' + relative_to_absolute_path(self.lock_file							)




   def    UpperCamelCase__						(					self							)				->   Optional[Any]:






       """simple docstring"""
       UpperCamelCase__							:       int      =   os.O_RDWR | os.O_CREAT | os.O_TRUNC

       try:
           UpperCamelCase__							:       Any      =   os.open(self._lock_file,	__magic_name__							)
       except OSError:
           pass
       else:
           try:
               msvcrt.locking(__magic_name__,	msvcrt.LK_NBLCK,	1							)
           except OSError:
               os.close(__magic_name__							)
           else:
               UpperCamelCase__							:       Union[str, Any]      =   fd
       return None




   def    UpperCamelCase__						(					self							)				->   str:






       """simple docstring"""
       UpperCamelCase__							:       int      =   self._lock_file_fd
       UpperCamelCase__							:       str      =   None
       msvcrt.locking(__magic_name__,	msvcrt.LK_UNLCK,	1							)
       os.close(__magic_name__							)

       try:
           os.remove(self._lock_file							)
       # Probably another instance of the application
       # that acquired the file lock.
       except OSError:
           pass
       return None






class 					lowercase__ (			__lowerCamelCase       ):

   '''simple docstring'''


   def __init__(					self,	__magic_name__,	__magic_name__=-1,	__magic_name__=None							)				->   Any:






       """simple docstring"""
       UpperCamelCase__							:       Dict      =   os.statvfs(os.path.dirname(__magic_name__							)							).f_namemax
       super().__init__(__magic_name__,	timeout=__magic_name__,	max_filename_length=__magic_name__							)




   def    UpperCamelCase__						(					self							)				->   Optional[Any]:






       """simple docstring"""
       UpperCamelCase__							:       str      =   os.O_RDWR | os.O_CREAT | os.O_TRUNC
       UpperCamelCase__							:       int      =   os.open(self._lock_file,	__magic_name__							)

       try:
           fcntl.flock(__magic_name__,	fcntl.LOCK_EX | fcntl.LOCK_NB							)
       except OSError:
           os.close(__magic_name__							)
       else:
           UpperCamelCase__							:       List[str]      =   fd
       return None




   def    UpperCamelCase__						(					self							)				->   Optional[int]:






       """simple docstring"""
       # Do not remove the lockfile:
       #
       #   https://github.com/benediktschmitt/py-filelock/issues/31
       #   https://stackoverflow.com/questions/17708885/flock-removing-locked-file-without-race-condition
       UpperCamelCase__							:       List[str]      =   self._lock_file_fd
       UpperCamelCase__							:       List[Any]      =   None
       fcntl.flock(__magic_name__,	fcntl.LOCK_UN							)
       os.close(__magic_name__							)
       return None






class 					lowercase__ (			__lowerCamelCase       ):

   '''simple docstring'''


   def    UpperCamelCase__						(					self							)				->   Tuple:






       """simple docstring"""
       UpperCamelCase__							:       List[str]      =   os.O_WRONLY | os.O_CREAT | os.O_EXCL | os.O_TRUNC
       try:
           UpperCamelCase__							:       Tuple      =   os.open(self._lock_file,	__magic_name__							)
       except OSError:
           pass
       else:
           UpperCamelCase__							:       List[str]      =   fd
       return None




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






       """simple docstring"""
       os.close(self._lock_file_fd							)
       UpperCamelCase__							:       Optional[Any]      =   None

       try:
           os.remove(self._lock_file							)
       # The file is already deleted and that's what we want.
       except OSError:
           pass
       return None




UpperCAmelCase_  							=				None

if msvcrt:
  UpperCAmelCase_  							=				WindowsFileLock
elif fcntl:
  UpperCAmelCase_  							=				UnixFileLock
else:
  UpperCAmelCase_  							=				SoftFileLock

  if warnings is not None:
    warnings.warn('only soft file lock is available')








import argparse
import json
from collections import OrderedDict
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    ConditionalDetrConfig,
    ConditionalDetrForObjectDetection,
    ConditionalDetrForSegmentation,
    ConditionalDetrImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
UpperCAmelCase_  							=				logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
UpperCAmelCase_  							=				[]
for i in range(6):
  # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
  rename_keys.append(
      (F'''transformer.encoder.layers.{i}.self_attn.out_proj.weight''', F'''encoder.layers.{i}.self_attn.out_proj.weight''')
  )
  rename_keys.append(
      (F'''transformer.encoder.layers.{i}.self_attn.out_proj.bias''', F'''encoder.layers.{i}.self_attn.out_proj.bias''')
  )
  rename_keys.append((F'''transformer.encoder.layers.{i}.linear1.weight''', F'''encoder.layers.{i}.fc1.weight'''))
  rename_keys.append((F'''transformer.encoder.layers.{i}.linear1.bias''', F'''encoder.layers.{i}.fc1.bias'''))
  rename_keys.append((F'''transformer.encoder.layers.{i}.linear2.weight''', F'''encoder.layers.{i}.fc2.weight'''))
  rename_keys.append((F'''transformer.encoder.layers.{i}.linear2.bias''', F'''encoder.layers.{i}.fc2.bias'''))
  rename_keys.append(
      (F'''transformer.encoder.layers.{i}.norm1.weight''', F'''encoder.layers.{i}.self_attn_layer_norm.weight''')
  )
  rename_keys.append((F'''transformer.encoder.layers.{i}.norm1.bias''', F'''encoder.layers.{i}.self_attn_layer_norm.bias'''))
  rename_keys.append((F'''transformer.encoder.layers.{i}.norm2.weight''', F'''encoder.layers.{i}.final_layer_norm.weight'''))
  rename_keys.append((F'''transformer.encoder.layers.{i}.norm2.bias''', F'''encoder.layers.{i}.final_layer_norm.bias'''))
  # decoder layers: 2 times output projection, 2 feedforward neural networks and 3 layernorms
  rename_keys.append(
      (F'''transformer.decoder.layers.{i}.self_attn.out_proj.weight''', F'''decoder.layers.{i}.self_attn.out_proj.weight''')
  )
  rename_keys.append(
      (F'''transformer.decoder.layers.{i}.self_attn.out_proj.bias''', F'''decoder.layers.{i}.self_attn.out_proj.bias''')
  )
  rename_keys.append(
      (
          F'''transformer.decoder.layers.{i}.cross_attn.out_proj.weight''',
          F'''decoder.layers.{i}.encoder_attn.out_proj.weight''',
      )
  )
  rename_keys.append(
      (
          F'''transformer.decoder.layers.{i}.cross_attn.out_proj.bias''',
          F'''decoder.layers.{i}.encoder_attn.out_proj.bias''',
      )
  )
  rename_keys.append((F'''transformer.decoder.layers.{i}.linear1.weight''', F'''decoder.layers.{i}.fc1.weight'''))
  rename_keys.append((F'''transformer.decoder.layers.{i}.linear1.bias''', F'''decoder.layers.{i}.fc1.bias'''))
  rename_keys.append((F'''transformer.decoder.layers.{i}.linear2.weight''', F'''decoder.layers.{i}.fc2.weight'''))
  rename_keys.append((F'''transformer.decoder.layers.{i}.linear2.bias''', F'''decoder.layers.{i}.fc2.bias'''))
  rename_keys.append(
      (F'''transformer.decoder.layers.{i}.norm1.weight''', F'''decoder.layers.{i}.self_attn_layer_norm.weight''')
  )
  rename_keys.append((F'''transformer.decoder.layers.{i}.norm1.bias''', F'''decoder.layers.{i}.self_attn_layer_norm.bias'''))
  rename_keys.append(
      (F'''transformer.decoder.layers.{i}.norm2.weight''', F'''decoder.layers.{i}.encoder_attn_layer_norm.weight''')
  )
  rename_keys.append(
      (F'''transformer.decoder.layers.{i}.norm2.bias''', F'''decoder.layers.{i}.encoder_attn_layer_norm.bias''')
  )
  rename_keys.append((F'''transformer.decoder.layers.{i}.norm3.weight''', F'''decoder.layers.{i}.final_layer_norm.weight'''))
  rename_keys.append((F'''transformer.decoder.layers.{i}.norm3.bias''', F'''decoder.layers.{i}.final_layer_norm.bias'''))

  # q, k, v projections in self/cross-attention in decoder for conditional DETR
  rename_keys.append(
      (F'''transformer.decoder.layers.{i}.sa_qcontent_proj.weight''', F'''decoder.layers.{i}.sa_qcontent_proj.weight''')
  )
  rename_keys.append(
      (F'''transformer.decoder.layers.{i}.sa_kcontent_proj.weight''', F'''decoder.layers.{i}.sa_kcontent_proj.weight''')
  )
  rename_keys.append(
      (F'''transformer.decoder.layers.{i}.sa_qpos_proj.weight''', F'''decoder.layers.{i}.sa_qpos_proj.weight''')
  )
  rename_keys.append(
      (F'''transformer.decoder.layers.{i}.sa_kpos_proj.weight''', F'''decoder.layers.{i}.sa_kpos_proj.weight''')
  )
  rename_keys.append((F'''transformer.decoder.layers.{i}.sa_v_proj.weight''', F'''decoder.layers.{i}.sa_v_proj.weight'''))
  rename_keys.append(
      (F'''transformer.decoder.layers.{i}.ca_qcontent_proj.weight''', F'''decoder.layers.{i}.ca_qcontent_proj.weight''')
  )
  # rename_keys.append((f"transformer.decoder.layers.{i}.ca_qpos_proj.weight", f"decoder.layers.{i}.ca_qpos_proj.weight"))
  rename_keys.append(
      (F'''transformer.decoder.layers.{i}.ca_kcontent_proj.weight''', F'''decoder.layers.{i}.ca_kcontent_proj.weight''')
  )
  rename_keys.append(
      (F'''transformer.decoder.layers.{i}.ca_kpos_proj.weight''', F'''decoder.layers.{i}.ca_kpos_proj.weight''')
  )
  rename_keys.append((F'''transformer.decoder.layers.{i}.ca_v_proj.weight''', F'''decoder.layers.{i}.ca_v_proj.weight'''))
  rename_keys.append(
      (F'''transformer.decoder.layers.{i}.ca_qpos_sine_proj.weight''', F'''decoder.layers.{i}.ca_qpos_sine_proj.weight''')
  )

  rename_keys.append(
      (F'''transformer.decoder.layers.{i}.sa_qcontent_proj.bias''', F'''decoder.layers.{i}.sa_qcontent_proj.bias''')
  )
  rename_keys.append(
      (F'''transformer.decoder.layers.{i}.sa_kcontent_proj.bias''', F'''decoder.layers.{i}.sa_kcontent_proj.bias''')
  )
  rename_keys.append((F'''transformer.decoder.layers.{i}.sa_qpos_proj.bias''', F'''decoder.layers.{i}.sa_qpos_proj.bias'''))
  rename_keys.append((F'''transformer.decoder.layers.{i}.sa_kpos_proj.bias''', F'''decoder.layers.{i}.sa_kpos_proj.bias'''))
  rename_keys.append((F'''transformer.decoder.layers.{i}.sa_v_proj.bias''', F'''decoder.layers.{i}.sa_v_proj.bias'''))
  rename_keys.append(
      (F'''transformer.decoder.layers.{i}.ca_qcontent_proj.bias''', F'''decoder.layers.{i}.ca_qcontent_proj.bias''')
  )
  # rename_keys.append((f"transformer.decoder.layers.{i}.ca_qpos_proj.bias", f"decoder.layers.{i}.ca_qpos_proj.bias"))
  rename_keys.append(
      (F'''transformer.decoder.layers.{i}.ca_kcontent_proj.bias''', F'''decoder.layers.{i}.ca_kcontent_proj.bias''')
  )
  rename_keys.append((F'''transformer.decoder.layers.{i}.ca_kpos_proj.bias''', F'''decoder.layers.{i}.ca_kpos_proj.bias'''))
  rename_keys.append((F'''transformer.decoder.layers.{i}.ca_v_proj.bias''', F'''decoder.layers.{i}.ca_v_proj.bias'''))
  rename_keys.append(
      (F'''transformer.decoder.layers.{i}.ca_qpos_sine_proj.bias''', F'''decoder.layers.{i}.ca_qpos_sine_proj.bias''')
  )

# convolutional projection + query embeddings + layernorm of decoder + class and bounding box heads
# for conditional DETR, also convert reference point head and query scale MLP
rename_keys.extend(
    [
        ('input_proj.weight', 'input_projection.weight'),
        ('input_proj.bias', 'input_projection.bias'),
        ('query_embed.weight', 'query_position_embeddings.weight'),
        ('transformer.decoder.norm.weight', 'decoder.layernorm.weight'),
        ('transformer.decoder.norm.bias', 'decoder.layernorm.bias'),
        ('class_embed.weight', 'class_labels_classifier.weight'),
        ('class_embed.bias', 'class_labels_classifier.bias'),
        ('bbox_embed.layers.0.weight', 'bbox_predictor.layers.0.weight'),
        ('bbox_embed.layers.0.bias', 'bbox_predictor.layers.0.bias'),
        ('bbox_embed.layers.1.weight', 'bbox_predictor.layers.1.weight'),
        ('bbox_embed.layers.1.bias', 'bbox_predictor.layers.1.bias'),
        ('bbox_embed.layers.2.weight', 'bbox_predictor.layers.2.weight'),
        ('bbox_embed.layers.2.bias', 'bbox_predictor.layers.2.bias'),
        ('transformer.decoder.ref_point_head.layers.0.weight', 'decoder.ref_point_head.layers.0.weight'),
        ('transformer.decoder.ref_point_head.layers.0.bias', 'decoder.ref_point_head.layers.0.bias'),
        ('transformer.decoder.ref_point_head.layers.1.weight', 'decoder.ref_point_head.layers.1.weight'),
        ('transformer.decoder.ref_point_head.layers.1.bias', 'decoder.ref_point_head.layers.1.bias'),
        ('transformer.decoder.query_scale.layers.0.weight', 'decoder.query_scale.layers.0.weight'),
        ('transformer.decoder.query_scale.layers.0.bias', 'decoder.query_scale.layers.0.bias'),
        ('transformer.decoder.query_scale.layers.1.weight', 'decoder.query_scale.layers.1.weight'),
        ('transformer.decoder.query_scale.layers.1.bias', 'decoder.query_scale.layers.1.bias'),
        ('transformer.decoder.layers.0.ca_qpos_proj.weight', 'decoder.layers.0.ca_qpos_proj.weight'),
        ('transformer.decoder.layers.0.ca_qpos_proj.bias', 'decoder.layers.0.ca_qpos_proj.bias'),
    ]
)




def 	lowerCAmelCase_					(						__UpperCAmelCase:    Tuple	,     __UpperCAmelCase:    List[Any]	,     __UpperCAmelCase:    Dict   )   ->			Dict:
    UpperCamelCase__							:       List[str]      =   state_dict.pop(__UpperCAmelCase   )
    UpperCamelCase__							:       Union[str, Any]      =   val




def 	lowerCAmelCase_					(						__UpperCAmelCase:    Dict   )   ->			List[str]:
    UpperCamelCase__							:       List[str]      =   OrderedDict()
    for key, value in state_dict.items():
        if "backbone.0.body" in key:
            UpperCamelCase__							:       Optional[Any]      =   key.replace('''backbone.0.body'''	,     '''backbone.conv_encoder.model'''   )
            UpperCamelCase__							:       List[str]      =   value
        else:
            UpperCamelCase__							:       Optional[int]      =   value

    return new_state_dict




def 	lowerCAmelCase_					(						__UpperCAmelCase:    Optional[int]	,     __UpperCAmelCase:    Optional[int]=False   )   ->			Optional[int]:
    UpperCamelCase__							:       List[str]      =   ''''''
    if is_panoptic:
        UpperCamelCase__							:       List[Any]      =   '''conditional_detr.'''

    # first: transformer encoder
    for i in range(6   ):
        # read in weights + bias of input projection layer (in PyTorch's MultiHeadAttention, this is a single matrix + bias)
        UpperCamelCase__							:       List[Any]      =   state_dict.pop(f"{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight"   )
        UpperCamelCase__							:       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
        UpperCamelCase__							:       Any      =   in_proj_weight[:256, :]
        UpperCamelCase__							:       List[str]      =   in_proj_bias[:256]
        UpperCamelCase__							:       Optional[int]      =   in_proj_weight[256:512, :]
        UpperCamelCase__							:       List[str]      =   in_proj_bias[256:512]
        UpperCamelCase__							:       Tuple      =   in_proj_weight[-256:, :]
        UpperCamelCase__							:       List[Any]      =   in_proj_bias[-256:]




def 	lowerCAmelCase_					(						)   ->			int:
    UpperCamelCase__							:       Any      =   '''http://images.cocodataset.org/val2017/000000039769.jpg'''
    UpperCamelCase__							:       Optional[int]      =   Image.open(requests.get(__UpperCAmelCase	,     stream=__UpperCAmelCase   ).raw   )

    return im




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

    # load image processor
    UpperCamelCase__							:       int      =   '''coco_panoptic''' if is_panoptic else '''coco_detection'''
    UpperCamelCase__							:       str      =   ConditionalDetrImageProcessor(format=__UpperCAmelCase   )

    # prepare image
    UpperCamelCase__							:       Tuple      =   prepare_img()
    UpperCamelCase__							:       List[str]      =   image_processor(images=__UpperCAmelCase	,     return_tensors='''pt'''   )
    UpperCamelCase__							:       Union[str, Any]      =   encoding['''pixel_values''']

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

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


if __name__ == "__main__":
  UpperCAmelCase_  							=				argparse.ArgumentParser()

  parser.add_argument(
      '--model_name',
      default='conditional_detr_resnet50',
      type=str,
      help='Name of the CONDITIONAL_DETR model you\'d like to convert.',
  )
  parser.add_argument(
      '--pytorch_dump_folder_path', default=None, type=str, help='Path to the folder to output PyTorch model.'
  )
  UpperCAmelCase_  							=				parser.parse_args()
  convert_conditional_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)

