



'''simple docstring'''



import json
import os
import re
import unicodedata
from typing import Optional, Tuple

from ...tokenization_utils import PreTrainedTokenizer, _is_control, _is_punctuation, _is_whitespace
from ...utils import logging


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

_lowercase       :						Optional[Any]						={
    "vocab_file": "vocab.json",
    "merges_file": "merges.txt",
}

_lowercase       :						Tuple						={
    "vocab_file": {"openai-gpt": "https://huggingface.co/openai-gpt/resolve/main/vocab.json"},
    "merges_file": {"openai-gpt": "https://huggingface.co/openai-gpt/resolve/main/merges.txt"},
}

_lowercase       :						Tuple						={
    "openai-gpt": 512,
}


def 							__UpperCAmelCase				(				UpperCamelCase__				:Union[str, Any]			)		->						Optional[int]:
				snake_case__      :   Optional[int]      =    text.strip()
				if not text:
								return []
				snake_case__      :   Union[str, Any]      =    text.split()
				return tokens






class 			_SCREAMING_SNAKE_CASE				(lowercase__      ):



			def __init__(     self		:						List[str]    ,			__UpperCamelCase		:						Union[str, Any]=True    ,			__UpperCamelCase		:						Optional[Any]=None    ,			__UpperCamelCase		:						Optional[int]=True    ,			__UpperCamelCase		:						Optional[Any]=None    ,			__UpperCamelCase		:						Dict=True    ,			)      ->      Any:
							"""simple docstring"""





							if never_split is None:
											snake_case__      :   int      =    []
							snake_case__      :   Optional[Any]      =    do_lower_case
							snake_case__      :   Dict      =    set(__UpperCamelCase			)
							snake_case__      :   List[str]      =    tokenize_chinese_chars
							snake_case__      :   Any      =    strip_accents
							snake_case__      :   Union[str, Any]      =    do_split_on_punc



			def 				lowerCAmelCase (     self		:						Any    ,			__UpperCamelCase		:						Any    ,			__UpperCamelCase		:						Any=None			)      ->      int:
							"""simple docstring"""





							snake_case__      :   int      =    self.never_split.union(set(__UpperCamelCase			)			) if never_split else self.never_split
							snake_case__      :   Optional[Any]      =    self._clean_text(__UpperCamelCase			)

							# This was added on November 1st, 2018 for the multilingual and Chinese
							# models. This is also applied to the English models now, but it doesn't
							# matter since the English models were not trained on any Chinese data
							# and generally don't have any Chinese data in them (there are Chinese
							# characters in the vocabulary because Wikipedia does have some Chinese
							# words in the English Wikipedia.).
							if self.tokenize_chinese_chars:
											snake_case__      :   Union[str, Any]      =    self._tokenize_chinese_chars(__UpperCamelCase			)
							# prevents treating the same character with different unicode codepoints as different characters
							snake_case__      :   str      =    unicodedata.normalize('''NFC'''    ,			__UpperCamelCase			)
							snake_case__      :   Any      =    whitespace_tokenize(__UpperCamelCase			)
							snake_case__      :   List[str]      =    []
							for token in orig_tokens:
											if token not in never_split:
															if self.do_lower_case:
																			snake_case__      :   Any      =    token.lower()
																			if self.strip_accents is not False:
																							snake_case__      :   List[Any]      =    self._run_strip_accents(__UpperCamelCase			)
															elif self.strip_accents:
																			snake_case__      :   Dict      =    self._run_strip_accents(__UpperCamelCase			)
											split_tokens.extend(self._run_split_on_punc(__UpperCamelCase    ,			__UpperCamelCase			)			)

							snake_case__      :   Dict      =    whitespace_tokenize(''' '''.join(__UpperCamelCase			)			)
							return output_tokens



			def 				lowerCAmelCase (     self		:						str    ,			__UpperCamelCase		:						List[Any]			)      ->      Union[str, Any]:
							"""simple docstring"""





							snake_case__      :   Dict      =    unicodedata.normalize('''NFD'''    ,			__UpperCamelCase			)
							snake_case__      :   Union[str, Any]      =    []
							for char in text:
											snake_case__      :   int      =    unicodedata.category(__UpperCamelCase			)
											if cat == "Mn":
															continue
											output.append(__UpperCamelCase			)
							return "".join(__UpperCamelCase			)



			def 				lowerCAmelCase (     self		:						str    ,			__UpperCamelCase		:						Optional[int]    ,			__UpperCamelCase		:						List[Any]=None			)      ->      Union[str, Any]:
							"""simple docstring"""





							if not self.do_split_on_punc or (never_split is not None and text in never_split):
											return [text]
							snake_case__      :   Optional[Any]      =    list(__UpperCamelCase			)
							snake_case__      :   str      =    0
							snake_case__      :   str      =    True
							snake_case__      :   Dict      =    []
							while i < len(__UpperCamelCase			):
											snake_case__      :   List[str]      =    chars[i]
											if _is_punctuation(__UpperCamelCase			):
															output.append([char]			)
															snake_case__      :   Optional[int]      =    True
											else:
															if start_new_word:
																			output.append([]			)
															snake_case__      :   Any      =    False
															output[-1].append(__UpperCamelCase			)
											i += 1

							return ["".join(__UpperCamelCase			) for x in output]



			def 				lowerCAmelCase (     self		:						Optional[Any]    ,			__UpperCamelCase		:						Tuple			)      ->      Optional[int]:
							"""simple docstring"""





							snake_case__      :   Optional[int]      =    []
							for char in text:
											snake_case__      :   str      =    ord(__UpperCamelCase			)
											if self._is_chinese_char(__UpperCamelCase			):
															output.append(''' '''			)
															output.append(__UpperCamelCase			)
															output.append(''' '''			)
											else:
															output.append(__UpperCamelCase			)
							return "".join(__UpperCamelCase			)



			def 				lowerCAmelCase (     self		:						Union[str, Any]    ,			__UpperCamelCase		:						Union[str, Any]			)      ->      str:
							"""simple docstring"""





							if (
							    (cp >= 0X4E_00 and cp <= 0X9F_FF)
							    or (cp >= 0X34_00 and cp <= 0X4D_BF)  #
							    or (cp >= 0X2_00_00 and cp <= 0X2_A6_DF)  #
							    or (cp >= 0X2_A7_00 and cp <= 0X2_B7_3F)  #
							    or (cp >= 0X2_B7_40 and cp <= 0X2_B8_1F)  #
							    or (cp >= 0X2_B8_20 and cp <= 0X2_CE_AF)  #
							    or (cp >= 0XF9_00 and cp <= 0XFA_FF)
							    or (cp >= 0X2_F8_00 and cp <= 0X2_FA_1F)  #
							):  #
											return True

							return False







			def 				lowerCAmelCase (     self		:						List[str]    ,			__UpperCamelCase		:						str			)      ->      Any:
							"""simple docstring"""





							snake_case__      :   str      =    []
							for char in text:
											snake_case__      :   List[Any]      =    ord(__UpperCamelCase			)
											if cp == 0 or cp == 0XFF_FD or _is_control(__UpperCamelCase			):
															continue
											if _is_whitespace(__UpperCamelCase			):
															output.append(''' '''			)
											else:
															output.append(__UpperCamelCase			)
							return "".join(__UpperCamelCase			)



def 							__UpperCAmelCase				(				UpperCamelCase__				:Optional[int]			)		->						Union[str, Any]:
				snake_case__      :   Dict      =    set()
				snake_case__      :   Tuple      =    word[0]
				for char in word[1:]:
								pairs.add((prev_char, char)			)
								snake_case__      :   Dict      =    char
				return pairs


def 							__UpperCAmelCase				(				UpperCamelCase__				:Tuple			)		->						Dict:
				snake_case__      :   int      =    text.replace('''—'''   ,				'''-'''			)
				snake_case__      :   Optional[int]      =    text.replace('''–'''   ,				'''-'''			)
				snake_case__      :   Union[str, Any]      =    text.replace('''―'''   ,				'''-'''			)
				snake_case__      :   Any      =    text.replace('''…'''   ,				'''...'''			)
				snake_case__      :   Tuple      =    text.replace('''´'''   ,				'''\''''			)
				snake_case__      :   int      =    re.sub(R'''(-+|~+|!+|"+|;+|\?+|\++|,+|\)+|\(+|\\+|\/+|\*+|\[+|\]+|}+|{+|\|+|_+)'''   ,				R''' \1 '''   ,				UpperCamelCase__			)
				snake_case__      :   Optional[int]      =    re.sub(R'''\s*\n\s*'''   ,				''' \n '''   ,				UpperCamelCase__			)
				snake_case__      :   List[Any]      =    re.sub(R'''[^\S\n]+'''   ,				''' '''   ,				UpperCamelCase__			)
				return text.strip()






class 			_SCREAMING_SNAKE_CASE				(lowercase__      ):
			A__								=  VOCAB_FILES_NAMES
			A__								=  PRETRAINED_VOCAB_FILES_MAP
			A__								=  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			A__								=  ['input_ids', 'attention_mask']



			def __init__(     self		:						Optional[Any]    ,			__UpperCamelCase		:						Any    ,			__UpperCamelCase		:						Any    ,			__UpperCamelCase		:						int="<unk>"    ,			**__UpperCamelCase		:						str			)      ->      Optional[Any]:
							"""simple docstring"""





							super().__init__(unk_token=__UpperCamelCase    ,			**__UpperCamelCase			)

							try:
											import ftfy
											from spacy.lang.en import English

											snake_case__      :   Union[str, Any]      =    English()
											snake_case__      :   List[str]      =    _nlp.tokenizer
											snake_case__      :   Tuple      =    ftfy.fix_text
							except ImportError:
											logger.warning('''ftfy or spacy is not installed using BERT BasicTokenizer instead of SpaCy & ftfy.'''			)
											snake_case__      :   List[Any]      =    BasicTokenizer(do_lower_case=__UpperCamelCase			)
											snake_case__      :   Optional[int]      =    None

							with open(__UpperCamelCase    ,			encoding='''utf-8'''			) as vocab_handle:
											snake_case__      :   Optional[Any]      =    json.load(__UpperCamelCase			)
							snake_case__      :   str      =    {v: k for k, v in self.encoder.items()}
							with open(__UpperCamelCase    ,			encoding='''utf-8'''			) as merges_handle:
											snake_case__      :   Optional[int]      =    merges_handle.read().split('''\n'''			)[1:-1]
							snake_case__      :   int      =    [tuple(merge.split()			) for merge in merges]
							snake_case__      :   Any      =    dict(zip(__UpperCamelCase    ,			range(len(__UpperCamelCase			)			)			)			)
							snake_case__      :   int      =    {}



			@property
			def 				lowerCAmelCase (     self		:						Dict			)      ->      Optional[int]:
							"""simple docstring"""





							return True



			@property
			def 				lowerCAmelCase (     self		:						str			)      ->      Any:
							"""simple docstring"""





							return len(self.encoder			)



			def 				lowerCAmelCase (     self		:						str			)      ->      Tuple:
							"""simple docstring"""





							return dict(self.encoder    ,			**self.added_tokens_encoder			)



			def 				lowerCAmelCase (     self		:						Dict    ,			__UpperCamelCase		:						Dict			)      ->      Union[str, Any]:
							"""simple docstring"""





							snake_case__      :   Optional[Any]      =    tuple(token[:-1]			) + (token[-1] + '''</w>''',)
							if token in self.cache:
											return self.cache[token]
							snake_case__      :   Union[str, Any]      =    get_pairs(__UpperCamelCase			)

							if not pairs:
											return token + "</w>"

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

															if word[i] == first and i < len(__UpperCamelCase			) - 1 and word[i + 1] == second:
																			new_word.append(first + second			)
																			i += 2
															else:
																			new_word.append(word[i]			)
																			i += 1
											snake_case__      :   Optional[Any]      =    tuple(__UpperCamelCase			)
											snake_case__      :   Dict      =    new_word
											if len(__UpperCamelCase			) == 1:
															break
											else:
															snake_case__      :   List[Any]      =    get_pairs(__UpperCamelCase			)
							snake_case__      :   Union[str, Any]      =    ''' '''.join(__UpperCamelCase			)
							if word == "\n  </w>":
											snake_case__      :   Union[str, Any]      =    '''\n</w>'''
							snake_case__      :   List[Any]      =    word
							return word



			def 				lowerCAmelCase (     self		:						Optional[Any]    ,			__UpperCamelCase		:						Optional[int]			)      ->      List[Any]:
							"""simple docstring"""





							snake_case__      :   Any      =    []
							if self.fix_text is None:
											# Using BERT's BasicTokenizer
											snake_case__      :   Any      =    self.nlp.tokenize(__UpperCamelCase			)
											for token in text:
															split_tokens.extend(list(self.bpe(__UpperCamelCase			).split(''' '''			)			)			)
							else:
											# Using SpaCy & ftfy (original tokenization process of OpenAI GPT)
											snake_case__      :   int      =    self.nlp(text_standardize(self.fix_text(__UpperCamelCase			)			)			)
											for token in text:
															split_tokens.extend(list(self.bpe(token.text.lower()			).split(''' '''			)			)			)
							return split_tokens



			def 				lowerCAmelCase (     self		:						Tuple    ,			__UpperCamelCase		:						Union[str, Any]			)      ->      List[Any]:
							"""simple docstring"""





							return self.encoder.get(__UpperCamelCase    ,			self.encoder.get(self.unk_token			)			)



			def 				lowerCAmelCase (     self		:						str    ,			__UpperCamelCase		:						Optional[Any]			)      ->      Optional[int]:
							"""simple docstring"""





							return self.decoder.get(__UpperCamelCase    ,			self.unk_token			)



			def 				lowerCAmelCase (     self		:						Optional[Any]    ,			__UpperCamelCase		:						int			)      ->      int:
							"""simple docstring"""





							snake_case__      :   Optional[int]      =    ''''''.join(__UpperCamelCase			).replace('''</w>'''    ,			''' '''			).strip()
							return out_string







			def 				lowerCAmelCase (     self		:						Optional[int]    ,			__UpperCamelCase		:						str    ,			__UpperCamelCase		:						Optional[str] = None			)      ->      Tuple[str]:
							"""simple docstring"""





							if not os.path.isdir(__UpperCamelCase			):
											logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''			)
											return
							snake_case__      :   Optional[Any]      =    os.path.join(
							    __UpperCamelCase    ,			(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']			)
							snake_case__      :   str      =    os.path.join(
							    __UpperCamelCase    ,			(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''merges_file''']			)

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

							snake_case__      :   Union[str, Any]      =    0
							with open(__UpperCamelCase    ,			'''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 __UpperCamelCase			: 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!'''			)
																			snake_case__      :   Optional[int]      =    token_index
															writer.write(''' '''.join(__UpperCamelCase			) + '''\n'''			)
															index += 1

							return vocab_file, merge_file






"""simple docstring"""
import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import cached_download, hf_hub_url
from PIL import Image

from transformers import DeformableDetrConfig, DeformableDetrForObjectDetection, DeformableDetrImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
lowercase		:    List[str]			=       logging.get_logger(__name__)


def      lowerCamelCase_     (						lowerCamelCase_			)  ->      str:


					'''simple docstring'''
					if "backbone.0.body" in orig_key:
										__a:    Any		     =       orig_key.replace("""backbone.0.body"""						,					"""backbone.conv_encoder.model"""			)
					if "transformer" in orig_key:
										__a:    str		     =       orig_key.replace("""transformer."""						,					""""""			)
					if "norm1" in orig_key:
										if "encoder" in orig_key:
															__a:    List[str]		     =       orig_key.replace("""norm1"""						,					"""self_attn_layer_norm"""			)
										else:
															__a:    Optional[int]		     =       orig_key.replace("""norm1"""						,					"""encoder_attn_layer_norm"""			)
					if "norm2" in orig_key:
										if "encoder" in orig_key:
															__a:    Union[str, Any]		     =       orig_key.replace("""norm2"""						,					"""final_layer_norm"""			)
										else:
															__a:    str		     =       orig_key.replace("""norm2"""						,					"""self_attn_layer_norm"""			)
					if "norm3" in orig_key:
										__a:    int		     =       orig_key.replace("""norm3"""						,					"""final_layer_norm"""			)
					if "linear1" in orig_key:
										__a:    Dict		     =       orig_key.replace("""linear1"""						,					"""fc1"""			)
					if "linear2" in orig_key:
										__a:    Any		     =       orig_key.replace("""linear2"""						,					"""fc2"""			)
					if "query_embed" in orig_key:
										__a:    Dict		     =       orig_key.replace("""query_embed"""						,					"""query_position_embeddings"""			)
					if "cross_attn" in orig_key:
										__a:    Dict		     =       orig_key.replace("""cross_attn"""						,					"""encoder_attn"""			)

					return orig_key


def      lowerCamelCase_     (						lowerCamelCase_			)  ->      List[str]:


					'''simple docstring'''
					# transformer decoder self-attention layers
					for i in range(6			):
										# read in weights + bias of input projection layer of self-attention
										__a:    Optional[Any]		     =       state_dict.pop(f"""decoder.layers.{i}.self_attn.in_proj_weight"""			)
										__a:    List[str]		     =       state_dict.pop(f"""decoder.layers.{i}.self_attn.in_proj_bias"""			)
										# next, add query, keys and values (in that order) to the state dict
										__a:    Optional[int]		     =       in_proj_weight[:2_56, :]
										__a:    int		     =       in_proj_bias[:2_56]
										__a:    Tuple		     =       in_proj_weight[2_56:5_12, :]
										__a:    Union[str, Any]		     =       in_proj_bias[2_56:5_12]
										__a:    Optional[int]		     =       in_proj_weight[-2_56:, :]
										__a:    Any		     =       in_proj_bias[-2_56:]


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


					'''simple docstring'''
					__a:    List[Any]		     =       """http://images.cocodataset.org/val2017/000000039769.jpg"""
					__a:    Tuple		     =       Image.open(requests.get(lowerCamelCase_						,					stream=lowerCamelCase_			).raw			)

					return im


@torch.no_grad()
def      lowerCamelCase_     (						lowerCamelCase_						,					lowerCamelCase_						,					lowerCamelCase_						,					lowerCamelCase_						,					lowerCamelCase_						,					lowerCamelCase_						,					lowerCamelCase_						,					)  ->      Union[str, Any]:


					'''simple docstring'''
					__a:    Optional[Any]		     =       DeformableDetrConfig()
					# set config attributes
					if single_scale:
										__a:    Optional[int]		     =       1
					__a:    Optional[Any]		     =       dilation
					__a:    Dict		     =       with_box_refine
					__a:    Any		     =       two_stage
					# set labels
					__a:    str		     =       91
					__a:    Dict		     =       """huggingface/label-files"""
					__a:    Tuple		     =       """coco-detection-id2label.json"""
					__a:    Optional[int]		     =       json.load(open(cached_download(hf_hub_url(lowerCamelCase_						,					lowerCamelCase_						,					repo_type="""dataset"""			)			)						,					"""r"""			)			)
					__a:    Union[str, Any]		     =       {int(lowerCamelCase_			): v for k, v in idalabel.items()}
					__a:    Dict		     =       idalabel
					__a:    List[Any]		     =       {v: k for k, v in idalabel.items()}

					# load image processor
					__a:    Tuple		     =       DeformableDetrImageProcessor(format="""coco_detection"""			)

					# prepare image
					__a:    Tuple		     =       prepare_img()
					__a:    Optional[int]		     =       image_processor(images=lowerCamelCase_						,					return_tensors="""pt"""			)
					__a:    Any		     =       encoding["""pixel_values"""]

					logger.info("""Converting model..."""			)

					# load original state dict
					__a:    List[Any]		     =       torch.load(lowerCamelCase_						,					map_location="""cpu"""			)["""model"""]
					# rename keys
					for key in state_dict.copy().keys():
										__a:    Dict		     =       state_dict.pop(lowerCamelCase_			)
										__a:    List[str]		     =       val
					# query, key and value matrices need special treatment
					read_in_q_k_v(lowerCamelCase_			)
					# important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
					__a:    Optional[int]		     =       """model."""
					for key in state_dict.copy().keys():
										if not key.startswith("""class_embed"""			) and not key.startswith("""bbox_embed"""			):
															__a:    List[Any]		     =       state_dict.pop(lowerCamelCase_			)
															__a:    Optional[int]		     =       val
    # finally, create HuggingFace model and load state dict
					__a:    List[str]		     =       DeformableDetrForObjectDetection(lowerCamelCase_			)
					model.load_state_dict(lowerCamelCase_			)
					model.eval()

					__a:    List[str]		     =       """cuda""" if torch.cuda.is_available() else """cpu"""
					model.to(lowerCamelCase_			)
					# verify our conversion
					__a:    str		     =       model(pixel_values.to(lowerCamelCase_			)			)

					__a:    Union[str, Any]		     =       torch.tensor(
					    [[-9.6_645, -4.3_449, -5.8_705], [-9.7_035, -3.8_504, -5.0_724], [-10.5_634, -5.3_379, -7.5_116]]			)
					__a:    Union[str, Any]		     =       torch.tensor([[0.8_693, 0.2_289, 0.2_492], [0.3_150, 0.5_489, 0.5_845], [0.5_563, 0.7_580, 0.8_518]]			)

					if single_scale:
										__a:    Optional[Any]		     =       torch.tensor(
										    [[-9.9_051, -4.2_541, -6.4_852], [-9.6_947, -4.0_854, -6.8_033], [-10.0_665, -5.8_470, -7.7_003]]			)
										__a:    Any		     =       torch.tensor([[0.7_292, 0.4_991, 0.5_532], [0.7_959, 0.2_426, 0.4_236], [0.7_582, 0.3_518, 0.4_451]]			)

					if single_scale and dilation:
										__a:    Dict		     =       torch.tensor(
										    [[-8.9_652, -4.1_074, -5.6_635], [-9.0_596, -4.9_447, -6.6_075], [-10.1_178, -4.5_275, -6.2_671]]			)
										__a:    int		     =       torch.tensor([[0.7_665, 0.4_130, 0.4_769], [0.8_364, 0.1_841, 0.3_391], [0.6_261, 0.3_895, 0.7_978]]			)

					if with_box_refine:
										__a:    List[str]		     =       torch.tensor(
										    [[-8.8_895, -5.4_187, -6.8_153], [-8.4_706, -6.1_668, -7.6_184], [-9.0_042, -5.5_359, -6.9_141]]			)
										__a:    Optional[int]		     =       torch.tensor([[0.7_828, 0.2_208, 0.4_323], [0.0_892, 0.5_996, 0.1_319], [0.5_524, 0.6_389, 0.8_914]]			)

					if with_box_refine and two_stage:
										__a:    List[Any]		     =       torch.tensor(
										    [[-6.7_108, -4.3_213, -6.3_777], [-8.9_014, -6.1_799, -6.7_240], [-6.9_315, -4.4_735, -6.2_298]]			)
										__a:    Union[str, Any]		     =       torch.tensor([[0.2_583, 0.5_499, 0.4_683], [0.7_652, 0.9_068, 0.4_882], [0.5_490, 0.2_763, 0.0_564]]			)

					print("""Logits:"""						,					outputs.logits[0, :3, :3]			)

					assert torch.allclose(outputs.logits[0, :3, :3]						,					expected_logits.to(lowerCamelCase_			)						,					atol=1e-4			)
					assert torch.allclose(outputs.pred_boxes[0, :3, :3]						,					expected_boxes.to(lowerCamelCase_			)						,					atol=1e-4			)

					print("""Everything ok!"""			)

					# Save model and image processor
					logger.info(f"""Saving PyTorch model and image processor to {pytorch_dump_folder_path}..."""			)
					Path(lowerCamelCase_			).mkdir(exist_ok=lowerCamelCase_			)
					model.save_pretrained(lowerCamelCase_			)
					image_processor.save_pretrained(lowerCamelCase_			)

					# Push to hub
					if push_to_hub:
										__a:    Dict		     =       """deformable-detr"""
										model_name += "-single-scale" if single_scale else ""
										model_name += "-dc5" if dilation else ""
										model_name += "-with-box-refine" if with_box_refine else ""
										model_name += "-two-stage" if two_stage else ""
										print("""Pushing model to hub..."""			)
										model.push_to_hub(repo_path_or_name=lowerCamelCase_						,					organization="""nielsr"""						,					commit_message="""Add model"""			)


if __name__ == "__main__":
			lowercase		:    int			=       argparse.ArgumentParser()

			parser.add_argument(
			    '--checkpoint_path',
			    type=str,
			    default='/home/niels/checkpoints/deformable_detr/r50_deformable_detr-checkpoint.pth',
			    help='Path to Pytorch checkpoint (.pth file) you\'d like to convert.',
			)
			parser.add_argument('--single_scale', action='store_true', help='Whether to set config.num_features_levels = 1.')
			parser.add_argument('--dilation', action='store_true', help='Whether to set config.dilation=True.')
			parser.add_argument('--with_box_refine', action='store_true', help='Whether to set config.with_box_refine=True.')
			parser.add_argument('--two_stage', action='store_true', help='Whether to set config.two_stage=True.')
			parser.add_argument(
			    '--pytorch_dump_folder_path',
			    default=None,
			    type=str,
			    required=True,
			    help='Path to the folder to output PyTorch model.',
			)
			parser.add_argument(
			    '--push_to_hub', action='store_true', help='Whether or not to push the converted model to the 🤗 hub.'
			)
			lowercase		:    Union[str, Any]			=       parser.parse_args()
			convert_deformable_detr_checkpoint(
			    args.checkpoint_path,
			    args.single_scale,
			    args.dilation,
			    args.with_box_refine,
			    args.two_stage,
			    args.pytorch_dump_folder_path,
			    args.push_to_hub,
			)
