




"""simple docstring"""

import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


__lowerCamelCase								=						logging.get_logger(__name__)

__lowerCamelCase								=						{
    "vocab_file": "vocab.json",
    "merges_file": "merges.txt",
}

__lowerCamelCase								=						{
    "vocab_file": {
        "roberta-base": "https://huggingface.co/roberta-base/resolve/main/vocab.json",
        "roberta-large": "https://huggingface.co/roberta-large/resolve/main/vocab.json",
        "roberta-large-mnli": "https://huggingface.co/roberta-large-mnli/resolve/main/vocab.json",
        "distilroberta-base": "https://huggingface.co/distilroberta-base/resolve/main/vocab.json",
        "roberta-base-openai-detector": "https://huggingface.co/roberta-base-openai-detector/resolve/main/vocab.json",
        "roberta-large-openai-detector": (
            "https://huggingface.co/roberta-large-openai-detector/resolve/main/vocab.json"
        ),
    },
    "merges_file": {
        "roberta-base": "https://huggingface.co/roberta-base/resolve/main/merges.txt",
        "roberta-large": "https://huggingface.co/roberta-large/resolve/main/merges.txt",
        "roberta-large-mnli": "https://huggingface.co/roberta-large-mnli/resolve/main/merges.txt",
        "distilroberta-base": "https://huggingface.co/distilroberta-base/resolve/main/merges.txt",
        "roberta-base-openai-detector": "https://huggingface.co/roberta-base-openai-detector/resolve/main/merges.txt",
        "roberta-large-openai-detector": (
            "https://huggingface.co/roberta-large-openai-detector/resolve/main/merges.txt"
        ),
    },
}

__lowerCamelCase								=						{
    "roberta-base": 5_12,
    "roberta-large": 5_12,
    "roberta-large-mnli": 5_12,
    "distilroberta-base": 5_12,
    "roberta-base-openai-detector": 5_12,
    "roberta-large-openai-detector": 5_12,
}



@lru_cache()
def 				a (						):



				'''simple docstring'''





				UpperCAmelCase_					:Union[str, Any]      			=      (
				    list(range(ord('''!'''  ),    ord('''~'''  ) + 1  )  ) + list(range(ord('''¡'''  ),    ord('''¬'''  ) + 1  )  ) + list(range(ord('''®'''  ),    ord('''ÿ'''  ) + 1  )  )
				)
				UpperCAmelCase_					:Union[str, Any]      			=      bs[:]
				UpperCAmelCase_					:Union[str, Any]      			=      0
				for b in range(2**8  ):
								if b not in bs:
												bs.append(__snake_case  )
												cs.append(2**8 + n  )
												n += 1
				UpperCAmelCase_					:str      			=      [chr(__snake_case  ) for n in cs]
				return dict(zip(__snake_case,    __snake_case  )  )



def 				a (						__snake_case     :				Any  ):



				'''simple docstring'''





				UpperCAmelCase_					:List[str]      			=      set()
				UpperCAmelCase_					:Dict      			=      word[0]
				for char in word[1:]:
								pairs.add((prev_char, char)  )
								UpperCAmelCase_					:int      			=      char
				return pairs





class  _snake_case				(							A__       ):

				'''simple docstring'''




				UpperCamelCase__		     =VOCAB_FILES_NAMES
				UpperCamelCase__		     =PRETRAINED_VOCAB_FILES_MAP
				UpperCamelCase__		     =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				UpperCamelCase__		     =["""input_ids""", """attention_mask"""]





				def __init__(							self      :  str					,							snake_case      :  Dict					,							snake_case      :  int					,							snake_case      :  Tuple="replace"					,							snake_case      :  List[Any]="<s>"					,							snake_case      :  List[Any]="</s>"					,							snake_case      :  List[str]="</s>"					,							snake_case      :  Any="<s>"					,							snake_case      :  List[str]="<unk>"					,							snake_case      :  List[Any]="<pad>"					,							snake_case      :  List[Any]="<mask>"					,							snake_case      :  Optional[int]=False					,							**snake_case      :  Optional[Any]					,							):
								UpperCAmelCase_					:Optional[Any]      			=      AddedToken(snake_case					,							lstrip=snake_case					,							rstrip=snake_case     ) if isinstance(snake_case					,							snake_case     ) else bos_token
								UpperCAmelCase_					:List[Any]      			=      AddedToken(snake_case					,							lstrip=snake_case					,							rstrip=snake_case     ) if isinstance(snake_case					,							snake_case     ) else eos_token
								UpperCAmelCase_					:List[str]      			=      AddedToken(snake_case					,							lstrip=snake_case					,							rstrip=snake_case     ) if isinstance(snake_case					,							snake_case     ) else sep_token
								UpperCAmelCase_					:Optional[int]      			=      AddedToken(snake_case					,							lstrip=snake_case					,							rstrip=snake_case     ) if isinstance(snake_case					,							snake_case     ) else cls_token
								UpperCAmelCase_					:Optional[int]      			=      AddedToken(snake_case					,							lstrip=snake_case					,							rstrip=snake_case     ) if isinstance(snake_case					,							snake_case     ) else unk_token
								UpperCAmelCase_					:Dict      			=      AddedToken(snake_case					,							lstrip=snake_case					,							rstrip=snake_case     ) if isinstance(snake_case					,							snake_case     ) else pad_token

								# Mask token behave like a normal word, i.e. include the space before it
								UpperCAmelCase_					:str      			=      AddedToken(snake_case					,							lstrip=snake_case					,							rstrip=snake_case     ) if isinstance(snake_case					,							snake_case     ) else mask_token

								super().__init__(
								    errors=snake_case					,							bos_token=snake_case					,							eos_token=snake_case					,							unk_token=snake_case					,							sep_token=snake_case					,							cls_token=snake_case					,							pad_token=snake_case					,							mask_token=snake_case					,							add_prefix_space=snake_case					,							**snake_case					,							)

								with open(snake_case					,							encoding='''utf-8'''     ) as vocab_handle:
												UpperCAmelCase_					:Optional[Any]      			=      json.load(snake_case     )
								UpperCAmelCase_					:List[Any]      			=      {v: k for k, v in self.encoder.items()}
								UpperCAmelCase_					:str      			=      errors  # how to handle errors in decoding
								UpperCAmelCase_					:Optional[Any]      			=      bytes_to_unicode()
								UpperCAmelCase_					:Dict      			=      {v: k for k, v in self.byte_encoder.items()}
								with open(snake_case					,							encoding='''utf-8'''     ) as merges_handle:
												UpperCAmelCase_					:Tuple      			=      merges_handle.read().split('''\n'''     )[1:-1]
								UpperCAmelCase_					:List[Any]      			=      [tuple(merge.split()     ) for merge in bpe_merges]
								UpperCAmelCase_					:List[str]      			=      dict(zip(snake_case					,							range(len(snake_case     )     )     )     )
								UpperCAmelCase_					:List[str]      			=      {}
								UpperCAmelCase_					:Any      			=      add_prefix_space

								# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
								UpperCAmelCase_					:Tuple      			=      re.compile(r'''\'s|\'t|\'re|\'ve|\'m|\'ll|\'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+'''     )





				@property
				def 				snake_case_ (							self      :  List[str]     ):
								return len(self.encoder     )





				def 				snake_case_ (							self      :  Any     ):
								return dict(self.encoder					,							**self.added_tokens_encoder     )





				def 				snake_case_ (							self      :  Any					,							snake_case      :  Optional[Any]     ):
								if token in self.cache:
												return self.cache[token]
								UpperCAmelCase_					:Dict      			=      tuple(snake_case     )
								UpperCAmelCase_					:Optional[int]      			=      get_pairs(snake_case     )

								if not pairs:
												return token

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

																if word[i] == first and i < len(snake_case     ) - 1 and word[i + 1] == second:
																				new_word.append(first + second     )
																				i += 2
																else:
																				new_word.append(word[i]     )
																				i += 1
												UpperCAmelCase_					:Tuple      			=      tuple(snake_case     )
												UpperCAmelCase_					:List[str]      			=      new_word
												if len(snake_case     ) == 1:
																break
												else:
																UpperCAmelCase_					:int      			=      get_pairs(snake_case     )
								UpperCAmelCase_					:Union[str, Any]      			=      ''' '''.join(snake_case     )
								UpperCAmelCase_					:Optional[int]      			=      word
								return word





				def 				snake_case_ (							self      :  List[Any]					,							snake_case      :  Tuple     ):
								UpperCAmelCase_					:Optional[Any]      			=      []
								for token in re.findall(self.pat					,							snake_case     ):
												UpperCAmelCase_					:List[Any]      			=      ''''''.join(
												    self.byte_encoder[b] for b in token.encode('''utf-8'''     )     )  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
												bpe_tokens.extend(bpe_token for bpe_token in self.bpe(snake_case     ).split(''' '''     )     )
								return bpe_tokens





				def 				snake_case_ (							self      :  List[Any]					,							snake_case      :  Union[str, Any]     ):
								return self.encoder.get(snake_case					,							self.encoder.get(self.unk_token     )     )





				def 				snake_case_ (							self      :  Optional[int]					,							snake_case      :  int     ):
								return self.decoder.get(snake_case     )





				def 				snake_case_ (							self      :  Optional[Any]					,							snake_case      :  Union[str, Any]     ):
								UpperCAmelCase_					:str      			=      ''''''.join(snake_case     )
								UpperCAmelCase_					:Dict      			=      bytearray([self.byte_decoder[c] for c in text]     ).decode('''utf-8'''					,							errors=self.errors     )
								return text





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

								with open(snake_case					,							'''w'''					,							encoding='''utf-8'''     ) as f:
												f.write(json.dumps(self.encoder					,							indent=2					,							sort_keys=snake_case					,							ensure_ascii=snake_case     ) + '''\n'''     )

								UpperCAmelCase_					:List[Any]      			=      0
								with open(snake_case					,							'''w'''					,							encoding='''utf-8'''     ) as writer:
												writer.write('''#version: 0.2\n'''     )
												for bpe_tokens, token_index in sorted(self.bpe_ranks.items()					,							key=lambda snake_case     : kv[1]     ):
																if index != token_index:
																				logger.warning(
																				    f'Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'
																				    ''' Please check that the tokenizer is not corrupted!'''     )
																				UpperCAmelCase_					:int      			=      token_index
																writer.write(''' '''.join(snake_case     ) + '''\n'''     )
																index += 1

								return vocab_file, merge_file





				def 				snake_case_ (							self      :  Optional[int]					,							snake_case      :  List[int]					,							snake_case      :  Optional[List[int]] = None     ):
								if token_ids_a is None:
												return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
								UpperCAmelCase_					:Any      			=      [self.cls_token_id]
								UpperCAmelCase_					:Any      			=      [self.sep_token_id]
								return cls + token_ids_a + sep + sep + token_ids_a + sep





				def 				snake_case_ (							self      :  Union[str, Any]					,							snake_case      :  List[int]					,							snake_case      :  Optional[List[int]] = None					,							snake_case      :  bool = False     ):
								if already_has_special_tokens:
												return super().get_special_tokens_mask(
												    token_ids_a=snake_case					,							token_ids_a=snake_case					,							already_has_special_tokens=snake_case     )

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





				def 				snake_case_ (							self      :  Dict					,							snake_case      :  List[int]					,							snake_case      :  Optional[List[int]] = None     ):
								UpperCAmelCase_					:int      			=      [self.sep_token_id]
								UpperCAmelCase_					:Tuple      			=      [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]





				def 				snake_case_ (							self      :  List[str]					,							snake_case      :  List[str]					,							snake_case      :  Optional[Any]=False					,							**snake_case      :  Dict     ):
								UpperCAmelCase_					:int      			=      kwargs.pop('''add_prefix_space'''					,							self.add_prefix_space     )
								if (is_split_into_words or add_prefix_space) and (len(snake_case     ) > 0 and not text[0].isspace()):
												UpperCAmelCase_					:Any      			=      ''' ''' + text
								return (text, kwargs)








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

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

# convolutional projection + query embeddings + layernorm of decoder + class and bounding box heads
rename_keys.extend(
    [
        ("input_proj.weight", "input_projection.weight"),
        ("input_proj.bias", "input_projection.bias"),
        ("query_embed.weight", "query_position_embeddings.weight"),
        ("transformer.decoder.norm.weight", "decoder.layernorm.weight"),
        ("transformer.decoder.norm.bias", "decoder.layernorm.bias"),
        ("class_embed.weight", "class_labels_classifier.weight"),
        ("class_embed.bias", "class_labels_classifier.bias"),
        ("bbox_embed.layers.0.weight", "bbox_predictor.layers.0.weight"),
        ("bbox_embed.layers.0.bias", "bbox_predictor.layers.0.bias"),
        ("bbox_embed.layers.1.weight", "bbox_predictor.layers.1.weight"),
        ("bbox_embed.layers.1.bias", "bbox_predictor.layers.1.bias"),
        ("bbox_embed.layers.2.weight", "bbox_predictor.layers.2.weight"),
        ("bbox_embed.layers.2.bias", "bbox_predictor.layers.2.bias"),
    ]
)



def 				a (						__snake_case     :				Optional[Any],    __snake_case     :				Any,    __snake_case     :				Tuple  ):



				'''simple docstring'''





				UpperCAmelCase_					:Optional[Any]      			=      state_dict.pop(__snake_case  )
				UpperCAmelCase_					:List[str]      			=      val



def 				a (						__snake_case     :				int  ):



				'''simple docstring'''





				UpperCAmelCase_					:int      			=      OrderedDict()
				for key, value in state_dict.items():
								if "backbone.0.body" in key:
												UpperCAmelCase_					:List[Any]      			=      key.replace('''backbone.0.body''',    '''backbone.conv_encoder.model'''  )
												UpperCAmelCase_					:Union[str, Any]      			=      value
								else:
												UpperCAmelCase_					:Any      			=      value

				return new_state_dict



def 				a (						__snake_case     :				Tuple,    __snake_case     :				Optional[Any]=False  ):



				'''simple docstring'''





				UpperCAmelCase_					:Union[str, Any]      			=      ''''''
				if is_panoptic:
								UpperCAmelCase_					:Tuple      			=      '''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_					:Optional[Any]      			=      state_dict.pop(f'{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight'  )
								UpperCAmelCase_					:Tuple      			=      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_					:List[str]      			=      in_proj_weight[:256, :]
								UpperCAmelCase_					:Union[str, Any]      			=      in_proj_bias[:256]
								UpperCAmelCase_					:List[Any]      			=      in_proj_weight[256:512, :]
								UpperCAmelCase_					:List[Any]      			=      in_proj_bias[256:512]
								UpperCAmelCase_					:str      			=      in_proj_weight[-256:, :]
								UpperCAmelCase_					:Optional[int]      			=      in_proj_bias[-256:]
				# next: transformer decoder (which is a bit more complex because it also includes cross-attention)
				for i in range(6  ):
								# read in weights + bias of input projection layer of self-attention
								UpperCAmelCase_					:List[str]      			=      state_dict.pop(f'{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight'  )
								UpperCAmelCase_					:str      			=      state_dict.pop(f'{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_bias'  )
								# next, add query, keys and values (in that order) to the state dict
								UpperCAmelCase_					:Dict      			=      in_proj_weight[:256, :]
								UpperCAmelCase_					:Optional[int]      			=      in_proj_bias[:256]
								UpperCAmelCase_					:List[Any]      			=      in_proj_weight[256:512, :]
								UpperCAmelCase_					:List[str]      			=      in_proj_bias[256:512]
								UpperCAmelCase_					:int      			=      in_proj_weight[-256:, :]
								UpperCAmelCase_					:List[str]      			=      in_proj_bias[-256:]
								# read in weights + bias of input projection layer of cross-attention
								UpperCAmelCase_					:int      			=      state_dict.pop(
								    f'{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight'  )
								UpperCAmelCase_					:Optional[int]      			=      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
								UpperCAmelCase_					:int      			=      in_proj_weight_cross_attn[:256, :]
								UpperCAmelCase_					:str      			=      in_proj_bias_cross_attn[:256]
								UpperCAmelCase_					:Tuple      			=      in_proj_weight_cross_attn[256:512, :]
								UpperCAmelCase_					:Any      			=      in_proj_bias_cross_attn[256:512]
								UpperCAmelCase_					:str      			=      in_proj_weight_cross_attn[-256:, :]
								UpperCAmelCase_					:List[Any]      			=      in_proj_bias_cross_attn[-256:]



def 				a (						):



				'''simple docstring'''





				UpperCAmelCase_					:Tuple      			=      '''http://images.cocodataset.org/val2017/000000039769.jpg'''
				UpperCAmelCase_					:List[str]      			=      Image.open(requests.get(__snake_case,    stream=__snake_case  ).raw  )

				return im



@torch.no_grad()
def 				a (						__snake_case     :				Union[str, Any],    __snake_case     :				List[Any]  ):



				'''simple docstring'''





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

				# load image processor
				UpperCAmelCase_					:Dict      			=      '''coco_panoptic''' if is_panoptic else '''coco_detection'''
				UpperCAmelCase_					:Optional[Any]      			=      DetrImageProcessor(format=__snake_case  )

				# prepare image
				UpperCAmelCase_					:Dict      			=      prepare_img()
				UpperCAmelCase_					:Union[str, Any]      			=      image_processor(images=__snake_case,    return_tensors='''pt'''  )
				UpperCAmelCase_					:Optional[Any]      			=      encoding['''pixel_values''']

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

				# load original model from torch hub
				UpperCAmelCase_					:int      			=      torch.hub.load('''facebookresearch/detr''',    __snake_case,    pretrained=__snake_case  ).eval()
				UpperCAmelCase_					:List[Any]      			=      detr.state_dict()
				# rename keys
				for src, dest in rename_keys:
								if is_panoptic:
												UpperCAmelCase_					:Optional[int]      			=      '''detr.''' + src
								rename_key(__snake_case,    __snake_case,    __snake_case  )
				UpperCAmelCase_					:int      			=      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
				UpperCAmelCase_					:Tuple      			=      '''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'''  )
												):
																UpperCAmelCase_					:List[str]      			=      state_dict.pop(__snake_case  )
																UpperCAmelCase_					:List[Any]      			=      val
												elif "class_labels_classifier" in key or "bbox_predictor" in key:
																UpperCAmelCase_					:int      			=      state_dict.pop(__snake_case  )
																UpperCAmelCase_					:Union[str, Any]      			=      val
												elif key.startswith('''bbox_attention'''  ) or key.startswith('''mask_head'''  ):
																continue
												else:
																UpperCAmelCase_					:Tuple      			=      state_dict.pop(__snake_case  )
																UpperCAmelCase_					:Dict      			=      val
								else:
												if not key.startswith('''class_labels_classifier'''  ) and not key.startswith('''bbox_predictor'''  ):
																UpperCAmelCase_					:Any      			=      state_dict.pop(__snake_case  )
																UpperCAmelCase_					:Any      			=      val
    # finally, create HuggingFace model and load state dict
				UpperCAmelCase_					:Optional[int]      			=      DetrForSegmentation(__snake_case  ) if is_panoptic else DetrForObjectDetection(__snake_case  )
				model.load_state_dict(__snake_case  )
				model.eval()
				# verify our conversion
				UpperCAmelCase_					:List[Any]      			=      detr(__snake_case  )
				UpperCAmelCase_					:Optional[int]      			=      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__":
						__lowerCamelCase								=						argparse.ArgumentParser()

						parser.add_argument(
						    "--model_name", default="detr_resnet50", type=str, help="Name of the DETR model you'd like to convert."
						)
						parser.add_argument(
						    "--pytorch_dump_folder_path", default=None, type=str, help="Path to the folder to output PyTorch model."
						)
						__lowerCamelCase								=						parser.parse_args()
						convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)


