






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


__UpperCAmelCase      				=							logging.get_logger(__name__)


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

# See all MVP models at https://huggingface.co/models?filter=mvp
__UpperCAmelCase      				=							{
    "vocab_file": {
        "RUCAIBox/mvp": "https://huggingface.co/RUCAIBox/mvp/resolve/main/vocab.json",
    },
    "added_tokens.json": {
        "RUCAIBox/mvp": "https://huggingface.co/RUCAIBox/mvp/resolve/main/added_tokens.json",
    },
    "merges_file": {
        "RUCAIBox/mvp": "https://huggingface.co/RUCAIBox/mvp/resolve/main/merges.txt",
    },
}

__UpperCAmelCase      				=							{
    "RUCAIBox/mvp": 1_0_2_4,
}





@lru_cache()
def 		A_			(		)							->Any:







 """simple docstring"""

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





def 		A_			(		lowercase_	)							->List[str]:







 """simple docstring"""

 SCREAMING_SNAKE_CASE							  =						set()
 SCREAMING_SNAKE_CASE							  =						word[0]
 for char in word[1:]:
  pairs.add((prev_char, char)	)
  SCREAMING_SNAKE_CASE							  =						char
 return pairs
class 					a_(						lowercase__					):



     """simple docstring"""



     __snake_case						:							Optional[Any]       			=VOCAB_FILES_NAMES
     __snake_case						:							Union[str, Any]       			=PRETRAINED_VOCAB_FILES_MAP
     __snake_case						:							Optional[Any]       			=PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     __snake_case						:							str       			=['''input_ids''', '''attention_mask''']





     def __init__(						self			:					Optional[int]       ,       lowerCAmelCase__			:					Union[str, Any]       ,       lowerCAmelCase__			:					Any       ,       lowerCAmelCase__			:					Dict="replace"       ,       lowerCAmelCase__			:					Optional[Any]="<s>"       ,       lowerCAmelCase__			:					List[Any]="</s>"       ,       lowerCAmelCase__			:					Dict="</s>"       ,       lowerCAmelCase__			:					Optional[Any]="<s>"       ,       lowerCAmelCase__			:					Optional[int]="<unk>"       ,       lowerCAmelCase__			:					Tuple="<pad>"       ,       lowerCAmelCase__			:					Any="<mask>"       ,       lowerCAmelCase__			:					Tuple=False       ,       **lowerCAmelCase__			:					Optional[int]       ,       )    ->					Tuple:

      """simple docstring"""
      SCREAMING_SNAKE_CASE							  =						AddedToken(lowerCAmelCase__       ,       lstrip=lowerCAmelCase__       ,       rstrip=lowerCAmelCase__) if isinstance(lowerCAmelCase__       ,       lowerCAmelCase__) else bos_token
      SCREAMING_SNAKE_CASE							  =						AddedToken(lowerCAmelCase__       ,       lstrip=lowerCAmelCase__       ,       rstrip=lowerCAmelCase__) if isinstance(lowerCAmelCase__       ,       lowerCAmelCase__) else eos_token
      SCREAMING_SNAKE_CASE							  =						AddedToken(lowerCAmelCase__       ,       lstrip=lowerCAmelCase__       ,       rstrip=lowerCAmelCase__) if isinstance(lowerCAmelCase__       ,       lowerCAmelCase__) else sep_token
      SCREAMING_SNAKE_CASE							  =						AddedToken(lowerCAmelCase__       ,       lstrip=lowerCAmelCase__       ,       rstrip=lowerCAmelCase__) if isinstance(lowerCAmelCase__       ,       lowerCAmelCase__) else cls_token
      SCREAMING_SNAKE_CASE							  =						AddedToken(lowerCAmelCase__       ,       lstrip=lowerCAmelCase__       ,       rstrip=lowerCAmelCase__) if isinstance(lowerCAmelCase__       ,       lowerCAmelCase__) else unk_token
      SCREAMING_SNAKE_CASE							  =						AddedToken(lowerCAmelCase__       ,       lstrip=lowerCAmelCase__       ,       rstrip=lowerCAmelCase__) if isinstance(lowerCAmelCase__       ,       lowerCAmelCase__) else pad_token

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

      super().__init__(
          errors=lowerCAmelCase__       ,       bos_token=lowerCAmelCase__       ,       eos_token=lowerCAmelCase__       ,       unk_token=lowerCAmelCase__       ,       sep_token=lowerCAmelCase__       ,       cls_token=lowerCAmelCase__       ,       pad_token=lowerCAmelCase__       ,       mask_token=lowerCAmelCase__       ,       add_prefix_space=lowerCAmelCase__       ,       **lowerCAmelCase__       ,       )
      with open(lowerCAmelCase__       ,       encoding='utf-8') as vocab_handle:
       SCREAMING_SNAKE_CASE							  =						json.load(lowerCAmelCase__)
      SCREAMING_SNAKE_CASE							  =						{v: k for k, v in self.encoder.items()}
      SCREAMING_SNAKE_CASE							  =						errors  # how to handle errors in decoding
      SCREAMING_SNAKE_CASE							  =						bytes_to_unicode()
      SCREAMING_SNAKE_CASE							  =						{v: k for k, v in self.byte_encoder.items()}
      with open(lowerCAmelCase__       ,       encoding='utf-8') as merges_handle:
       SCREAMING_SNAKE_CASE							  =						merges_handle.read().split('\n')[1:-1]
      SCREAMING_SNAKE_CASE							  =						[tuple(merge.split()) for merge in bpe_merges]
      SCREAMING_SNAKE_CASE							  =						dict(zip(lowerCAmelCase__       ,       range(len(lowerCAmelCase__))))
      SCREAMING_SNAKE_CASE							  =						{}
      SCREAMING_SNAKE_CASE							  =						add_prefix_space

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





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

      """simple docstring"""
      return len(self.encoder)





     def   __UpperCamelCase		(						self			:					List[Any])    ->					Optional[Any]:

      """simple docstring"""
      return dict(self.encoder       ,       **self.added_tokens_encoder)





     def   __UpperCamelCase		(						self			:					List[Any]       ,       lowerCAmelCase__			:					int)    ->					Any:

      """simple docstring"""
      if token in self.cache:
       return self.cache[token]
      SCREAMING_SNAKE_CASE							  =						tuple(lowerCAmelCase__)
      SCREAMING_SNAKE_CASE							  =						get_pairs(lowerCAmelCase__)

      if not pairs:
       return token

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

        if word[i] == first and i < len(lowerCAmelCase__) - 1 and word[i + 1] == second:
         new_word.append(first + second)
         i += 2
        else:
         new_word.append(word[i])
         i += 1
       SCREAMING_SNAKE_CASE							  =						tuple(lowerCAmelCase__)
       SCREAMING_SNAKE_CASE							  =						new_word
       if len(lowerCAmelCase__) == 1:
        break
       else:
        SCREAMING_SNAKE_CASE							  =						get_pairs(lowerCAmelCase__)
      SCREAMING_SNAKE_CASE							  =						' '.join(lowerCAmelCase__)
      SCREAMING_SNAKE_CASE							  =						word
      return word





     def   __UpperCamelCase		(						self			:					Optional[Any]       ,       lowerCAmelCase__			:					Any)    ->					Dict:

      """simple docstring"""
      SCREAMING_SNAKE_CASE							  =						[]
      for token in re.findall(self.pat       ,       lowerCAmelCase__):
       SCREAMING_SNAKE_CASE							  =						''.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(lowerCAmelCase__).split(' '))
      return bpe_tokens





     def   __UpperCamelCase		(						self			:					Union[str, Any]       ,       lowerCAmelCase__			:					Optional[Any])    ->					List[str]:

      """simple docstring"""
      return self.encoder.get(lowerCAmelCase__       ,       self.encoder.get(self.unk_token))





     def   __UpperCamelCase		(						self			:					Optional[Any]       ,       lowerCAmelCase__			:					List[Any])    ->					List[Any]:

      """simple docstring"""
      return self.decoder.get(lowerCAmelCase__)





     def   __UpperCamelCase		(						self			:					Dict       ,       lowerCAmelCase__			:					str)    ->					int:

      """simple docstring"""
      SCREAMING_SNAKE_CASE							  =						''.join(lowerCAmelCase__)
      SCREAMING_SNAKE_CASE							  =						bytearray([self.byte_decoder[c] for c in text]).decode('utf-8'       ,       errors=self.errors)
      return text





     def   __UpperCamelCase		(						self			:					Union[str, Any]       ,       lowerCAmelCase__			:					str       ,       lowerCAmelCase__			:					Optional[str] = None)    ->					Tuple[str]:

      """simple docstring"""
      if not os.path.isdir(lowerCAmelCase__):
       logger.error(f'''Vocabulary path ({save_directory}) should be a directory''')
       return
      SCREAMING_SNAKE_CASE							  =						os.path.join(
          lowerCAmelCase__       ,       (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'])
      SCREAMING_SNAKE_CASE							  =						os.path.join(
          lowerCAmelCase__       ,       (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['merges_file'])

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

      SCREAMING_SNAKE_CASE							  =						0
      with open(lowerCAmelCase__       ,       '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 lowerCAmelCase__: 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!')
         SCREAMING_SNAKE_CASE							  =						token_index
        writer.write(' '.join(lowerCAmelCase__) + '\n')
        index += 1

      return vocab_file, merge_file





     def   __UpperCamelCase		(						self			:					Tuple       ,       lowerCAmelCase__			:					List[int]       ,       lowerCAmelCase__			:					Optional[List[int]] = None)    ->					List[int]:

      """simple docstring"""
      if token_ids_a is None:
       return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
      SCREAMING_SNAKE_CASE							  =						[self.cls_token_id]
      SCREAMING_SNAKE_CASE							  =						[self.sep_token_id]
      return cls + token_ids_a + sep + sep + token_ids_a + sep





     def   __UpperCamelCase		(						self			:					int       ,       lowerCAmelCase__			:					List[int]       ,       lowerCAmelCase__			:					Optional[List[int]] = None       ,       lowerCAmelCase__			:					bool = False)    ->					List[int]:

      """simple docstring"""
      if already_has_special_tokens:
       return super().get_special_tokens_mask(
           token_ids_a=lowerCAmelCase__       ,       token_ids_a=lowerCAmelCase__       ,       already_has_special_tokens=lowerCAmelCase__)

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





     def   __UpperCamelCase		(						self			:					Optional[int]       ,       lowerCAmelCase__			:					List[int]       ,       lowerCAmelCase__			:					Optional[List[int]] = None)    ->					List[int]:

      """simple docstring"""
      SCREAMING_SNAKE_CASE							  =						[self.sep_token_id]
      SCREAMING_SNAKE_CASE							  =						[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   __UpperCamelCase		(						self			:					Dict       ,       lowerCAmelCase__			:					int       ,       lowerCAmelCase__			:					Union[str, Any]=False       ,       **lowerCAmelCase__			:					Optional[int])    ->					Optional[int]:

      """simple docstring"""
      SCREAMING_SNAKE_CASE							  =						kwargs.pop('add_prefix_space'       ,       self.add_prefix_space)
      if (is_split_into_words or add_prefix_space) and (len(lowerCAmelCase__) > 0 and not text[0].isspace()):
       SCREAMING_SNAKE_CASE							  =						' ' + text
      return (text, kwargs)












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()
__UpperCAmelCase      				=							logging.get_logger(__name__)





def 		A_			(		lowercase_	)							->Tuple:







 """simple docstring"""

 if "backbone.0.body" in orig_key:
  SCREAMING_SNAKE_CASE							  =						orig_key.replace('backbone.0.body'					,       'backbone.conv_encoder.model'	)
 if "transformer" in orig_key:
  SCREAMING_SNAKE_CASE							  =						orig_key.replace('transformer.'					,       ''	)
 if "norm1" in orig_key:
  if "encoder" in orig_key:
   SCREAMING_SNAKE_CASE							  =						orig_key.replace('norm1'					,       'self_attn_layer_norm'	)
  else:
   SCREAMING_SNAKE_CASE							  =						orig_key.replace('norm1'					,       'encoder_attn_layer_norm'	)
 if "norm2" in orig_key:
  if "encoder" in orig_key:
   SCREAMING_SNAKE_CASE							  =						orig_key.replace('norm2'					,       'final_layer_norm'	)
  else:
   SCREAMING_SNAKE_CASE							  =						orig_key.replace('norm2'					,       'self_attn_layer_norm'	)
 if "norm3" in orig_key:
  SCREAMING_SNAKE_CASE							  =						orig_key.replace('norm3'					,       'final_layer_norm'	)
 if "linear1" in orig_key:
  SCREAMING_SNAKE_CASE							  =						orig_key.replace('linear1'					,       'fc1'	)
 if "linear2" in orig_key:
  SCREAMING_SNAKE_CASE							  =						orig_key.replace('linear2'					,       'fc2'	)
 if "query_embed" in orig_key:
  SCREAMING_SNAKE_CASE							  =						orig_key.replace('query_embed'					,       'query_position_embeddings'	)
 if "cross_attn" in orig_key:
  SCREAMING_SNAKE_CASE							  =						orig_key.replace('cross_attn'					,       'encoder_attn'	)

 return orig_key





def 		A_			(		lowercase_	)							->Dict:







 """simple docstring"""

 for i in range(6	):
  # read in weights + bias of input projection layer of self-attention
  SCREAMING_SNAKE_CASE							  =						state_dict.pop(f'''decoder.layers.{i}.self_attn.in_proj_weight'''	)
  SCREAMING_SNAKE_CASE							  =						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
  SCREAMING_SNAKE_CASE							  =						in_proj_weight[:2_5_6, :]
  SCREAMING_SNAKE_CASE							  =						in_proj_bias[:2_5_6]
  SCREAMING_SNAKE_CASE							  =						in_proj_weight[2_5_6:5_1_2, :]
  SCREAMING_SNAKE_CASE							  =						in_proj_bias[2_5_6:5_1_2]
  SCREAMING_SNAKE_CASE							  =						in_proj_weight[-2_5_6:, :]
  SCREAMING_SNAKE_CASE							  =						in_proj_bias[-2_5_6:]





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







 """simple docstring"""

 SCREAMING_SNAKE_CASE							  =						'http://images.cocodataset.org/val2017/000000039769.jpg'
 SCREAMING_SNAKE_CASE							  =						Image.open(requests.get(lowercase_					,       stream=lowercase_	).raw	)

 return im





@torch.no_grad()
def 		A_			(		lowercase_					,       lowercase_					,       lowercase_					,       lowercase_					,       lowercase_					,       lowercase_					,       lowercase_					,       )							->int:







 """simple docstring"""

 SCREAMING_SNAKE_CASE							  =						DeformableDetrConfig()
 # set config attributes
 if single_scale:
  SCREAMING_SNAKE_CASE							  =						1
 SCREAMING_SNAKE_CASE							  =						dilation
 SCREAMING_SNAKE_CASE							  =						with_box_refine
 SCREAMING_SNAKE_CASE							  =						two_stage
 # set labels
 SCREAMING_SNAKE_CASE							  =						9_1
 SCREAMING_SNAKE_CASE							  =						'huggingface/label-files'
 SCREAMING_SNAKE_CASE							  =						'coco-detection-id2label.json'
 SCREAMING_SNAKE_CASE							  =						json.load(open(cached_download(hf_hub_url(lowercase_					,       lowercase_					,       repo_type='dataset'	)	)					,       'r'	)	)
 SCREAMING_SNAKE_CASE							  =						{int(lowercase_	): v for k, v in idalabel.items()}
 SCREAMING_SNAKE_CASE							  =						idalabel
 SCREAMING_SNAKE_CASE							  =						{v: k for k, v in idalabel.items()}

 # load image processor
 SCREAMING_SNAKE_CASE							  =						DeformableDetrImageProcessor(format='coco_detection'	)

 # prepare image
 SCREAMING_SNAKE_CASE							  =						prepare_img()
 SCREAMING_SNAKE_CASE							  =						image_processor(images=lowercase_					,       return_tensors='pt'	)
 SCREAMING_SNAKE_CASE							  =						encoding['pixel_values']

 logger.info('Converting model...'	)

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

 SCREAMING_SNAKE_CASE							  =						'cuda' if torch.cuda.is_available() else 'cpu'
 model.to(lowercase_	)
 # verify our conversion
 SCREAMING_SNAKE_CASE							  =						model(pixel_values.to(lowercase_	)	)

 SCREAMING_SNAKE_CASE							  =						torch.tensor(
     [[-9.6_6_4_5, -4.3_4_4_9, -5.8_7_0_5], [-9.7_0_3_5, -3.8_5_0_4, -5.0_7_2_4], [-1_0.5_6_3_4, -5.3_3_7_9, -7.5_1_1_6]]	)
 SCREAMING_SNAKE_CASE							  =						torch.tensor([[0.8_6_9_3, 0.2_2_8_9, 0.2_4_9_2], [0.3_1_5_0, 0.5_4_8_9, 0.5_8_4_5], [0.5_5_6_3, 0.7_5_8_0, 0.8_5_1_8]]	)

 if single_scale:
  SCREAMING_SNAKE_CASE							  =						torch.tensor(
      [[-9.9_0_5_1, -4.2_5_4_1, -6.4_8_5_2], [-9.6_9_4_7, -4.0_8_5_4, -6.8_0_3_3], [-1_0.0_6_6_5, -5.8_4_7_0, -7.7_0_0_3]]	)
  SCREAMING_SNAKE_CASE							  =						torch.tensor([[0.7_2_9_2, 0.4_9_9_1, 0.5_5_3_2], [0.7_9_5_9, 0.2_4_2_6, 0.4_2_3_6], [0.7_5_8_2, 0.3_5_1_8, 0.4_4_5_1]]	)

 if single_scale and dilation:
  SCREAMING_SNAKE_CASE							  =						torch.tensor(
      [[-8.9_6_5_2, -4.1_0_7_4, -5.6_6_3_5], [-9.0_5_9_6, -4.9_4_4_7, -6.6_0_7_5], [-1_0.1_1_7_8, -4.5_2_7_5, -6.2_6_7_1]]	)
  SCREAMING_SNAKE_CASE							  =						torch.tensor([[0.7_6_6_5, 0.4_1_3_0, 0.4_7_6_9], [0.8_3_6_4, 0.1_8_4_1, 0.3_3_9_1], [0.6_2_6_1, 0.3_8_9_5, 0.7_9_7_8]]	)

 if with_box_refine:
  SCREAMING_SNAKE_CASE							  =						torch.tensor(
      [[-8.8_8_9_5, -5.4_1_8_7, -6.8_1_5_3], [-8.4_7_0_6, -6.1_6_6_8, -7.6_1_8_4], [-9.0_0_4_2, -5.5_3_5_9, -6.9_1_4_1]]	)
  SCREAMING_SNAKE_CASE							  =						torch.tensor([[0.7_8_2_8, 0.2_2_0_8, 0.4_3_2_3], [0.0_8_9_2, 0.5_9_9_6, 0.1_3_1_9], [0.5_5_2_4, 0.6_3_8_9, 0.8_9_1_4]]	)

 if with_box_refine and two_stage:
  SCREAMING_SNAKE_CASE							  =						torch.tensor(
      [[-6.7_1_0_8, -4.3_2_1_3, -6.3_7_7_7], [-8.9_0_1_4, -6.1_7_9_9, -6.7_2_4_0], [-6.9_3_1_5, -4.4_7_3_5, -6.2_2_9_8]]	)
  SCREAMING_SNAKE_CASE							  =						torch.tensor([[0.2_5_8_3, 0.5_4_9_9, 0.4_6_8_3], [0.7_6_5_2, 0.9_0_6_8, 0.4_8_8_2], [0.5_4_9_0, 0.2_7_6_3, 0.0_5_6_4]]	)

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

 assert torch.allclose(outputs.logits[0, :3, :3]					,       expected_logits.to(lowercase_	)					,       atol=1e-4	)
 assert torch.allclose(outputs.pred_boxes[0, :3, :3]					,       expected_boxes.to(lowercase_	)					,       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(lowercase_	).mkdir(exist_ok=lowercase_	)
 model.save_pretrained(lowercase_	)
 image_processor.save_pretrained(lowercase_	)

 # Push to hub
 if push_to_hub:
  SCREAMING_SNAKE_CASE							  =						'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=lowercase_					,       organization='nielsr'					,       commit_message='Add model'	)


if __name__ == "__main__":
      __UpperCAmelCase      				=							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."
      )
      __UpperCAmelCase      				=							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,
      )




