



'''simple docstring'''




import os
import re
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


a     :    int      						=					logging.get_logger(__name__)

a     :    str      						=					{
    """vocab_file""": """vocab.txt""",
    """merges_file""": """bpe.codes""",
}

a     :    Dict      						=					{
    """vocab_file""": {
        """vinai/phobert-base""": """https://huggingface.co/vinai/phobert-base/resolve/main/vocab.txt""",
        """vinai/phobert-large""": """https://huggingface.co/vinai/phobert-large/resolve/main/vocab.txt""",
    },
    """merges_file""": {
        """vinai/phobert-base""": """https://huggingface.co/vinai/phobert-base/resolve/main/bpe.codes""",
        """vinai/phobert-large""": """https://huggingface.co/vinai/phobert-large/resolve/main/bpe.codes""",
    },
}

a     :    Tuple      						=					{
    """vinai/phobert-base""": 2_5_6,
    """vinai/phobert-large""": 2_5_6,
}



def    __lowerCamelCase						(      _lowercase	)   ->    int:
							UpperCAmelCase  :	List[str]							  =	set()
							UpperCAmelCase  :	Union[str, Any]							  =	word[0]
							for char in word[1:]:
														pairs.add((prev_char, char)	)
														UpperCAmelCase  :	Union[str, Any]							  =	char

							UpperCAmelCase  :	str							  =	set(_lowercase	)
							return pairs


class     UpperCamelCase_				(							__magic_name__    ):
	lowercase    =				VOCAB_FILES_NAMES
	lowercase    =				PRETRAINED_VOCAB_FILES_MAP
	lowercase    =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES





	def __init__(						self	,	A	,	A	,	A="<s>"	,	A="</s>"	,	A="</s>"	,	A="<s>"	,	A="<unk>"	,	A="<pad>"	,	A="<mask>"	,	**A	,	)    ->    List[str]:
								super().__init__(
								    bos_token=A	,	eos_token=A	,	unk_token=A	,	sep_token=A	,	cls_token=A	,	pad_token=A	,	mask_token=A	,	**A	,	)

								UpperCAmelCase  :	str							  =	vocab_file
								UpperCAmelCase  :	Any							  =	merges_file

								UpperCAmelCase  :	Optional[int]							  =	{}
								UpperCAmelCase  :	Dict							  =	0
								UpperCAmelCase  :	List[Any]							  =	1
								UpperCAmelCase  :	int							  =	2
								UpperCAmelCase  :	Tuple							  =	3

								self.add_from_file(A					)

								UpperCAmelCase  :	Optional[Any]							  =	{v: k for k, v in self.encoder.items()}

								with open(A	,	encoding="""utf-8"""					) as merges_handle:
															UpperCAmelCase  :	str							  =	merges_handle.read().split("""\n"""					)[:-1]
								UpperCAmelCase  :	List[Any]							  =	[tuple(merge.split()[:-1]					) for merge in merges]
								UpperCAmelCase  :	Any							  =	dict(zip(A	,	range(len(A					)					)					)					)
								UpperCAmelCase  :	Union[str, Any]							  =	{}





	def 				_lowercase(						self	,	A	,	A = None					)    ->    List[int]:

								if token_ids_a is None:
															return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
								UpperCAmelCase  :	Optional[Any]							  =	[self.cls_token_id]
								UpperCAmelCase  :	Tuple							  =	[self.sep_token_id]
								return cls + token_ids_a + sep + sep + token_ids_a + sep





	def 				_lowercase(						self	,	A	,	A = None	,	A = False					)    ->    List[int]:

								if already_has_special_tokens:
															return super().get_special_tokens_mask(
															    token_ids_a=A	,	token_ids_a=A	,	already_has_special_tokens=A					)

								if token_ids_a is None:
															return [1] + ([0] * len(A					)) + [1]
								return [1] + ([0] * len(A					)) + [1, 1] + ([0] * len(A					)) + [1]





	def 				_lowercase(						self	,	A	,	A = None					)    ->    List[int]:
								UpperCAmelCase  :	Tuple							  =	[self.sep_token_id]
								UpperCAmelCase  :	str							  =	[self.cls_token_id]

								if token_ids_a is None:
															return len(cls + token_ids_a + sep					) * [0]
								return len(cls + token_ids_a + sep + sep + token_ids_a + sep					) * [0]





	@property
	def 				_lowercase(						self					)    ->    Tuple:
								return len(self.encoder					)





	def 				_lowercase(						self					)    ->    Tuple:
								return dict(self.encoder	,	**self.added_tokens_encoder					)





	def 				_lowercase(						self	,	A					)    ->    Any:
								if token in self.cache:
															return self.cache[token]
								UpperCAmelCase  :	Optional[int]							  =	tuple(A					)
								UpperCAmelCase  :	List[str]							  =	tuple(list(word[:-1]					) + [word[-1] + """</w>"""]					)
								UpperCAmelCase  :	Union[str, Any]							  =	get_pairs(A					)

								if not pairs:
															return token

								while True:
															UpperCAmelCase  :	Dict							  =	min(A	,	key=lambda A					: self.bpe_ranks.get(A	,	float("""inf"""					)					)					)
															if bigram not in self.bpe_ranks:
																						break
															UpperCAmelCase  :	int							  =	bigram
															UpperCAmelCase  :	List[Any]							  =	[]
															UpperCAmelCase  :	Union[str, Any]							  =	0
															while i < len(A					):
																						try:
																													UpperCAmelCase  :	Any							  =	word.index(A	,	A					)
																						except ValueError:
																													new_word.extend(word[i:]					)
																													break
																						else:
																													new_word.extend(word[i:j]					)
																													UpperCAmelCase  :	Optional[int]							  =	j

																						if word[i] == first and i < len(A					) - 1 and word[i + 1] == second:
																													new_word.append(first + second					)
																													i += 2
																						else:
																													new_word.append(word[i]					)
																													i += 1
															UpperCAmelCase  :	Optional[Any]							  =	tuple(A					)
															UpperCAmelCase  :	List[Any]							  =	new_word
															if len(A					) == 1:
																						break
															else:
																						UpperCAmelCase  :	Optional[int]							  =	get_pairs(A					)
								UpperCAmelCase  :	List[str]							  =	"""@@ """.join(A					)
								UpperCAmelCase  :	List[str]							  =	word[:-4]
								UpperCAmelCase  :	int							  =	word
								return word





	def 				_lowercase(						self	,	A					)    ->    List[Any]:
								UpperCAmelCase  :	Dict							  =	[]

								UpperCAmelCase  :	Optional[Any]							  =	re.findall(r"""\S+\n?"""	,	A					)

								for token in words:
															split_tokens.extend(list(self.bpe(A					).split(""" """					)					)					)
								return split_tokens





	def 				_lowercase(						self	,	A					)    ->    Dict:
								return self.encoder.get(A	,	self.encoder.get(self.unk_token					)					)





	def 				_lowercase(						self	,	A					)    ->    Dict:
								return self.decoder.get(A	,	self.unk_token					)





	def 				_lowercase(						self	,	A					)    ->    Optional[Any]:
								UpperCAmelCase  :	List[Any]							  =	""" """.join(A					).replace("""@@ """	,	""""""					).strip()
								return out_string





	def 				_lowercase(						self	,	A	,	A = None					)    ->    Tuple[str]:
								if not os.path.isdir(A					):
															logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''					)
															return
								UpperCAmelCase  :	List[Any]							  =	os.path.join(
								    A	,	(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]					)
								UpperCAmelCase  :	Optional[Any]							  =	os.path.join(
								    A	,	(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""merges_file"""]					)

								if os.path.abspath(self.vocab_file					) != os.path.abspath(A					):
															copyfile(self.vocab_file	,	A					)

								if os.path.abspath(self.merges_file					) != os.path.abspath(A					):
															copyfile(self.merges_file	,	A					)

								return out_vocab_file, out_merge_file






	def 				_lowercase(						self	,	A					)    ->    List[str]:
								if isinstance(A	,	A					):
															try:
																						with open(A	,	"""r"""	,	encoding="""utf-8"""					) as fd:
																													self.add_from_file(A					)
															except FileNotFoundError as fnfe:
																						raise fnfe
															except UnicodeError:
																						raise Exception(f'''Incorrect encoding detected in {f}, please rebuild the dataset'''					)
															return

								UpperCAmelCase  :	int							  =	f.readlines()
								for lineTmp in lines:
															UpperCAmelCase  :	int							  =	lineTmp.strip()
															UpperCAmelCase  :	Optional[Any]							  =	line.rfind(""" """					)
															if idx == -1:
																						raise ValueError("""Incorrect dictionary format, expected '<token> <cnt>'"""					)
															UpperCAmelCase  :	int							  =	line[:idx]
															UpperCAmelCase  :	Union[str, Any]							  =	len(self.encoder					)






'''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     :    List[Any]      						=					logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
a     :    List[str]      						=					[]
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						(      _lowercase			,       _lowercase			,       _lowercase	)   ->    Optional[Any]:
							UpperCAmelCase  :	List[str]							  =	state_dict.pop(_lowercase	)
							UpperCAmelCase  :	List[str]							  =	val



def    __lowerCamelCase						(      _lowercase	)   ->    Any:
							UpperCAmelCase  :	Union[str, Any]							  =	OrderedDict()
							for key, value in state_dict.items():
														if "backbone.0.body" in key:
																					UpperCAmelCase  :	List[str]							  =	key.replace("""backbone.0.body"""			,       """backbone.conv_encoder.model"""	)
																					UpperCAmelCase  :	Dict							  =	value
														else:
																					UpperCAmelCase  :	List[Any]							  =	value

							return new_state_dict



def    __lowerCamelCase						(      _lowercase			,       _lowercase=False	)   ->    Optional[int]:
							UpperCAmelCase  :	Dict							  =	""""""
							if is_panoptic:
														UpperCAmelCase  :	Tuple							  =	"""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  :	List[Any]							  =	state_dict.pop(F'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_bias'''	)
														# next, add query, keys and values (in that order) to the state dict
														UpperCAmelCase  :	Dict							  =	in_proj_weight[:2_5_6, :]
														UpperCAmelCase  :	Optional[Any]							  =	in_proj_bias[:2_5_6]
														UpperCAmelCase  :	List[Any]							  =	in_proj_weight[2_5_6:5_1_2, :]
														UpperCAmelCase  :	Tuple							  =	in_proj_bias[2_5_6:5_1_2]
														UpperCAmelCase  :	List[str]							  =	in_proj_weight[-2_5_6:, :]
														UpperCAmelCase  :	List[str]							  =	in_proj_bias[-2_5_6:]



def    __lowerCamelCase						(      )   ->    Dict:
							UpperCAmelCase  :	List[Any]							  =	"""http://images.cocodataset.org/val2017/000000039769.jpg"""
							UpperCAmelCase  :	Tuple							  =	Image.open(requests.get(_lowercase			,       stream=_lowercase	).raw	)

							return im



@torch.no_grad()
def    __lowerCamelCase						(      _lowercase			,       _lowercase	)   ->    str:
							UpperCAmelCase  :	str							  =	ConditionalDetrConfig()
							# set backbone and dilation attributes
							if "resnet101" in model_name:
														UpperCAmelCase  :	List[Any]							  =	"""resnet101"""
							if "dc5" in model_name:
														UpperCAmelCase  :	Optional[int]							  =	True
							UpperCAmelCase  :	List[Any]							  =	"""panoptic""" in model_name
							if is_panoptic:
														UpperCAmelCase  :	Union[str, Any]							  =	2_5_0
							else:
														UpperCAmelCase  :	int							  =	9_1
														UpperCAmelCase  :	Tuple							  =	"""huggingface/label-files"""
														UpperCAmelCase  :	List[Any]							  =	"""coco-detection-id2label.json"""
														UpperCAmelCase  :	Optional[int]							  =	json.load(open(hf_hub_download(_lowercase			,       _lowercase			,       repo_type="""dataset"""	)			,       """r"""	)	)
														UpperCAmelCase  :	Dict							  =	{int(_lowercase	): v for k, v in idalabel.items()}
														UpperCAmelCase  :	Optional[Any]							  =	idalabel
														UpperCAmelCase  :	List[Any]							  =	{v: k for k, v in idalabel.items()}

							# load image processor
							UpperCAmelCase  :	List[str]							  =	"""coco_panoptic""" if is_panoptic else """coco_detection"""
							UpperCAmelCase  :	List[Any]							  =	ConditionalDetrImageProcessor(format=_lowercase	)

							# prepare image
							UpperCAmelCase  :	Union[str, Any]							  =	prepare_img()
							UpperCAmelCase  :	Dict							  =	image_processor(images=_lowercase			,       return_tensors="""pt"""	)
							UpperCAmelCase  :	List[Any]							  =	encoding["""pixel_values"""]

							logger.info(F'''Converting model {model_name}...'''	)

							# load original model from torch hub
							UpperCAmelCase  :	int							  =	torch.hub.load("""DeppMeng/ConditionalDETR"""			,       _lowercase			,       pretrained=_lowercase	).eval()
							UpperCAmelCase  :	List[Any]							  =	conditional_detr.state_dict()
							# rename keys
							for src, dest in rename_keys:
														if is_panoptic:
																					UpperCAmelCase  :	List[Any]							  =	"""conditional_detr.""" + src
														rename_key(_lowercase			,       _lowercase			,       _lowercase	)
							UpperCAmelCase  :	List[Any]							  =	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
							UpperCAmelCase  :	int							  =	"""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  :	Union[str, Any]							  =	state_dict.pop(_lowercase	)
																												UpperCAmelCase  :	int							  =	val
																					elif "class_labels_classifier" in key or "bbox_predictor" in key:
																												UpperCAmelCase  :	Any							  =	state_dict.pop(_lowercase	)
																												UpperCAmelCase  :	Optional[Any]							  =	val
																					elif key.startswith("""bbox_attention"""	) or key.startswith("""mask_head"""	):
																												continue
																					else:
																												UpperCAmelCase  :	List[Any]							  =	state_dict.pop(_lowercase	)
																												UpperCAmelCase  :	str							  =	val
														else:
																					if not key.startswith("""class_labels_classifier"""	) and not key.startswith("""bbox_predictor"""	):
																												UpperCAmelCase  :	Optional[int]							  =	state_dict.pop(_lowercase	)
																												UpperCAmelCase  :	Union[str, Any]							  =	val
    # finally, create HuggingFace model and load state dict
							UpperCAmelCase  :	List[Any]							  =	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
							UpperCAmelCase  :	Union[str, Any]							  =	conditional_detr(_lowercase	)
							UpperCAmelCase  :	int							  =	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     :    Tuple      						=					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     :    Optional[Any]      						=					parser.parse_args()
							convert_conditional_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)

