


"""simple docstring"""


import asyncio
import os
import shutil
import subprocess
import sys
import tempfile
import unittest
from distutils.util import strtobool
from functools import partial
from pathlib import Path
from typing import List, Union
from unittest import mock

import torch

from ..state import AcceleratorState, PartialState
from ..utils import (
    gather,
    is_bnb_available,
    is_comet_ml_available,
    is_datasets_available,
    is_deepspeed_available,
    is_mps_available,
    is_safetensors_available,
    is_tensorboard_available,
    is_torch_version,
    is_tpu_available,
    is_transformers_available,
    is_wandb_available,
    is_xpu_available,
)






def      a__ (				__lowercase     ,			__lowercase=False   )	->	List[str]:
				try:
								_A					  =			os.environ[key]
				except KeyError:
								# KEY isn't set, default to `default`.
								_A					  =			default
				else:
								# KEY is set, convert it to True or False.
								try:
												_A					  =			strtobool(__lowercase   )
								except ValueError:
												# More values are supported, but let's keep the message simple.
												raise ValueError(f"""If set, {key} must be yes or no."""   )
				return _value


a_       							=    parse_flag_from_env("RUN_SLOW", default=False)






def      a__ (				__lowercase   )	->	List[str]:
				return unittest.skip("Test was skipped"   )(__lowercase   )






def      a__ (				__lowercase   )	->	Dict:
				return unittest.skipUnless(_run_slow_tests     ,			"test is slow"   )(__lowercase   )






def      a__ (				__lowercase   )	->	Union[str, Any]:
				return unittest.skipUnless(not torch.cuda.is_available()     ,			"test requires only a CPU"   )(__lowercase   )






def      a__ (				__lowercase   )	->	List[Any]:
				return unittest.skipUnless(torch.cuda.is_available()     ,			"test requires a GPU"   )(__lowercase   )






def      a__ (				__lowercase   )	->	Tuple:
				return unittest.skipUnless(is_xpu_available()     ,			"test requires a XPU"   )(__lowercase   )






def      a__ (				__lowercase   )	->	List[str]:
				return unittest.skipUnless(is_mps_available()     ,			"test requires a `mps` backend support in `torch`"   )(__lowercase   )






def      a__ (				__lowercase   )	->	Optional[int]:
				return unittest.skipUnless(
				    is_transformers_available() and is_datasets_available()     ,			"test requires the Hugging Face suite"   )(__lowercase   )






def      a__ (				__lowercase   )	->	Any:
				return unittest.skipUnless(is_bnb_available()     ,			"test requires the bitsandbytes library"   )(__lowercase   )






def      a__ (				__lowercase   )	->	Optional[Any]:
				return unittest.skipUnless(is_tpu_available()     ,			"test requires TPU"   )(__lowercase   )






def      a__ (				__lowercase   )	->	List[str]:
				return unittest.skipUnless(torch.cuda.device_count() == 1     ,			"test requires a GPU"   )(__lowercase   )






def      a__ (				__lowercase   )	->	List[Any]:
				return unittest.skipUnless(torch.xpu.device_count() == 1     ,			"test requires a XPU"   )(__lowercase   )






def      a__ (				__lowercase   )	->	Union[str, Any]:
				return unittest.skipUnless(torch.cuda.device_count() > 1     ,			"test requires multiple GPUs"   )(__lowercase   )






def      a__ (				__lowercase   )	->	Optional[Any]:
				return unittest.skipUnless(torch.xpu.device_count() > 1     ,			"test requires multiple XPUs"   )(__lowercase   )






def      a__ (				__lowercase   )	->	Optional[int]:
				return unittest.skipUnless(is_safetensors_available()     ,			"test requires safetensors"   )(__lowercase   )






def      a__ (				__lowercase   )	->	str:
				return unittest.skipUnless(is_deepspeed_available()     ,			"test requires DeepSpeed"   )(__lowercase   )






def      a__ (				__lowercase   )	->	Any:
				return unittest.skipUnless(is_torch_version(">="     ,			"1.12.0"   )     ,			"test requires torch version >= 1.12.0"   )(__lowercase   )






def      a__ (				__lowercase=None     ,			__lowercase=None   )	->	List[Any]:
				if test_case is None:
								return partial(__lowercase     ,			version=__lowercase   )
				return unittest.skipUnless(is_torch_version(">="     ,			__lowercase   )     ,			f"""test requires torch version >= {version}"""   )(__lowercase   )






def      a__ (				__lowercase   )	->	int:
				return unittest.skipUnless(is_tensorboard_available()     ,			"test requires Tensorboard"   )(__lowercase   )






def      a__ (				__lowercase   )	->	Tuple:
				return unittest.skipUnless(is_wandb_available()     ,			"test requires wandb"   )(__lowercase   )






def      a__ (				__lowercase   )	->	Tuple:
				return unittest.skipUnless(is_comet_ml_available()     ,			"test requires comet_ml"   )(__lowercase   )


a_       							=    (
    any([is_wandb_available(), is_tensorboard_available()]) and not is_comet_ml_available()
)






def      a__ (				__lowercase   )	->	Any:
				return unittest.skipUnless(
				    _atleast_one_tracker_available     ,			"test requires at least one tracker to be available and for `comet_ml` to not be installed"     ,			)(__lowercase   )
class snake_case      (  unittest.TestCase):
		__UpperCamelCase           =			True







		@classmethod
		def  a_ (					cls      :       List[Any]		)       ->						Any:





						'''simple docstring'''







						_A					  =			tempfile.mkdtemp()







		@classmethod
		def  a_ (					cls      :       Dict		)       ->						List[str]:





						'''simple docstring'''







						if os.path.exists(cls.tmpdir		):
										shutil.rmtree(cls.tmpdir		)







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





						'''simple docstring'''







						if self.clear_on_setup:
										for path in Path(self.tmpdir		).glob("**/*"		):
														if path.is_file():
																		path.unlink()
														elif path.is_dir():
																		shutil.rmtree(a__		)





class snake_case      (  unittest.TestCase):







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





						'''simple docstring'''







						super().tearDown()
						# Reset the state of the AcceleratorState singleton.
						AcceleratorState._reset_state()
						PartialState._reset_state()





class snake_case      (  unittest.TestCase):







		def  a_ (					self      :       str    ,   a__      :       Union[mock.Mock, List[mock.Mock]]		)       ->						List[str]:





						'''simple docstring'''







						_A					  =			mocks if isinstance(a__    ,   (tuple, list)		) else [mocks]
						for m in self.mocks:
										m.start()
										self.addCleanup(m.stop		)






def      a__ (				__lowercase   )	->	str:
				_A					  =			AcceleratorState()
				_A					  =			tensor[None].clone().to(state.device   )
				_A					  =			gather(__lowercase   ).cpu()
				_A					  =			tensor[0].cpu()
				for i in range(tensors.shape[0]   ):
								if not torch.equal(tensors[i]     ,			__lowercase   ):
												return False
				return True
class snake_case      :







		def __init__(					self      :       int    ,   a__      :       Optional[Any]    ,   a__      :       List[str]    ,   a__      :       Optional[int]		)       ->						Union[str, Any]:





						'''simple docstring'''







						_A					  =			returncode
						_A					  =			stdout
						_A					  =			stderr






async def      a__ (				__lowercase     ,			__lowercase   )	->	Union[str, Any]:
				while True:
								_A					  =			await stream.readline()
								if line:
												callback(__lowercase   )
								else:
												break






async def      a__ (				__lowercase     ,			__lowercase=None     ,			__lowercase=None     ,			__lowercase=None     ,			__lowercase=False     ,			__lowercase=False   )	->	_RunOutput:
				if echo:
								print("\nRunning: "     ,			" ".join(__lowercase   )   )

				_A					  =			await asyncio.create_subprocess_exec(
				    cmd[0]     ,			*cmd[1:]     ,			stdin=__lowercase     ,			stdout=asyncio.subprocess.PIPE     ,			stderr=asyncio.subprocess.PIPE     ,			env=__lowercase     ,			)

				# note: there is a warning for a possible deadlock when using `wait` with huge amounts of data in the pipe
				# https://docs.python.org/3/library/asyncio-subprocess.html#asyncio.asyncio.subprocess.Process.wait
				#
				# If it starts hanging, will need to switch to the following code. The problem is that no data
				# will be seen until it's done and if it hangs for example there will be no debug info.
				# out, err = await p.communicate()
				# return _RunOutput(p.returncode, out, err)

				_A					  =			[]
				_A					  =			[]

				def tee(__lowercase     ,			__lowercase     ,			__lowercase     ,			__lowercase=""   ):
								_A					  =			line.decode("utf-8"   ).rstrip()
								sink.append(__lowercase   )
								if not quiet:
												print(__lowercase     ,			__lowercase     ,			file=__lowercase   )

    # XXX: the timeout doesn't seem to make any difference here
				await asyncio.wait(
				    [
				        asyncio.create_task(_read_stream(p.stdout     ,			lambda __lowercase   : tee(__lowercase     ,			__lowercase     ,			sys.stdout     ,			label="stdout:"   )   )   ),
				        asyncio.create_task(_read_stream(p.stderr     ,			lambda __lowercase   : tee(__lowercase     ,			__lowercase     ,			sys.stderr     ,			label="stderr:"   )   )   ),
				    ]     ,			timeout=__lowercase     ,			)
				return _RunOutput(await p.wait()     ,			__lowercase     ,			__lowercase   )






def      a__ (				__lowercase     ,			__lowercase=None     ,			__lowercase=None     ,			__lowercase=180     ,			__lowercase=False     ,			__lowercase=True   )	->	_RunOutput:
				_A					  =			asyncio.get_event_loop()
				_A					  =			loop.run_until_complete(
				    _stream_subprocess(__lowercase     ,			env=__lowercase     ,			stdin=__lowercase     ,			timeout=__lowercase     ,			quiet=__lowercase     ,			echo=__lowercase   )   )

				_A					  =			" ".join(__lowercase   )
				if result.returncode > 0:
								_A					  =			"\n".join(result.stderr   )
								raise RuntimeError(
								    f"""'{cmd_str}' failed with returncode {result.returncode}\n\n"""
								    f"""The combined stderr from workers follows:\n{stderr}"""   )

				return result
class snake_case      (  _UpperCamelCase):
		pass






def      a__ (				__lowercase     ,			__lowercase=False   )	->	Tuple:
				try:
								_A					  =			subprocess.check_output(__lowercase     ,			stderr=subprocess.STDOUT   )
								if return_stdout:
												if hasattr(__lowercase     ,			"decode"   ):
																_A					  =			output.decode("utf-8"   )
												return output
				except subprocess.CalledProcessError as e:
								raise SubprocessCallException(
								    f"""Command `{' '.join(__lowercase   )}` failed with the following error:\n\n{e.output.decode()}"""   ) from e










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


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

# here we list all keys to be renamed (original name on the left, our name on the right)
a_       							=    []
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      a__ (				__lowercase     ,			__lowercase     ,			__lowercase   )	->	List[str]:
				_A					  =			state_dict.pop(__lowercase   )
				_A					  =			val






def      a__ (				__lowercase   )	->	List[str]:
				_A					  =			OrderedDict()
				for key, value in state_dict.items():
								if "backbone.0.body" in key:
												_A					  =			key.replace("backbone.0.body"     ,			"backbone.conv_encoder.model"   )
												_A					  =			value
								else:
												_A					  =			value

				return new_state_dict






def      a__ (				__lowercase     ,			__lowercase=False   )	->	Any:
				_A					  =			""
				if is_panoptic:
								_A					  =			"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)
								_A					  =			state_dict.pop(f"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight"""   )
								_A					  =			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
								_A					  =			in_proj_weight[:256, :]
								_A					  =			in_proj_bias[:256]
								_A					  =			in_proj_weight[256:512, :]
								_A					  =			in_proj_bias[256:512]
								_A					  =			in_proj_weight[-256:, :]
								_A					  =			in_proj_bias[-256:]






def      a__ (				)	->	int:
				_A					  =			"http://images.cocodataset.org/val2017/000000039769.jpg"
				_A					  =			Image.open(requests.get(__lowercase     ,			stream=__lowercase   ).raw   )

				return im






@torch.no_grad()
def      a__ (				__lowercase     ,			__lowercase   )	->	Any:
				_A					  =			ConditionalDetrConfig()
				# set backbone and dilation attributes
				if "resnet101" in model_name:
								_A					  =			"resnet101"
				if "dc5" in model_name:
								_A					  =			True
				_A					  =			"panoptic" in model_name
				if is_panoptic:
								_A					  =			250
				else:
								_A					  =			91
								_A					  =			"huggingface/label-files"
								_A					  =			"coco-detection-id2label.json"
								_A					  =			json.load(open(hf_hub_download(__lowercase     ,			__lowercase     ,			repo_type="dataset"   )     ,			"r"   )   )
								_A					  =			{int(__lowercase   ): v for k, v in idalabel.items()}
								_A					  =			idalabel
								_A					  =			{v: k for k, v in idalabel.items()}

				# load image processor
				_A					  =			"coco_panoptic" if is_panoptic else "coco_detection"
				_A					  =			ConditionalDetrImageProcessor(format=__lowercase   )

				# prepare image
				_A					  =			prepare_img()
				_A					  =			image_processor(images=__lowercase     ,			return_tensors="pt"   )
				_A					  =			encoding["pixel_values"]

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

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


if __name__ == "__main__":
		a_       							=    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."
		)
		a_       							=    parser.parse_args()
		convert_conditional_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)