code: 
  
  
  
  
  '''simple docstring'''
  
  
  
  
  
  import json
  import os
  import re
  import warnings
  from pathlib import Path
  from shutil import copyfile
  from typing import Any, Dict, List, Optional, Tuple, Union
  
  import sentencepiece
  
  from ...tokenization_utils import PreTrainedTokenizer
  from ...utils import logging
  
  
  lowercase__			=	logging.get_logger(__name__)
  
  lowercase__			=	{
      '''source_spm''': '''source.spm''',
      '''target_spm''': '''target.spm''',
      '''vocab''': '''vocab.json''',
      '''target_vocab_file''': '''target_vocab.json''',
      '''tokenizer_config_file''': '''tokenizer_config.json''',
  }
  
  lowercase__			=	{
      '''source_spm''': {
          '''Helsinki-NLP/opus-mt-en-de''': '''https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/source.spm'''
      },
      '''target_spm''': {
          '''Helsinki-NLP/opus-mt-en-de''': '''https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/target.spm'''
      },
      '''vocab''': {
          '''Helsinki-NLP/opus-mt-en-de''': '''https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/vocab.json'''
      },
      '''tokenizer_config_file''': {
          '''Helsinki-NLP/opus-mt-en-de''': (
              '''https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/tokenizer_config.json'''
          )
      },
  }
  
  lowercase__			=	{'''Helsinki-NLP/opus-mt-en-de''': 5_1_2}
  lowercase__			=	{}
  
  
  
  
  
  
  class 						lowercase     (   UpperCAmelCase_):
        __lowercase 		=  VOCAB_FILES_NAMES
        __lowercase 		=  PRETRAINED_VOCAB_FILES_MAP
        __lowercase 		=  PRETRAINED_INIT_CONFIGURATION
        __lowercase 		=  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
        __lowercase 		=  ['''input_ids''', '''attention_mask''']
        __lowercase 		=  re.compile('''>>.+<<''')  # type: re.Pattern
  
  
  
  
  
  
        def __init__(self ,  a__ ,  a__ ,  a__ ,  a__=None ,  a__=None ,  a__=None ,  a__="<unk>" ,  a__="</s>" ,  a__="<pad>" ,  a__=5_1_2 ,  a__ = None ,  a__=False ,  **a__ ,  )       ->   None:
           SCREAMING_SNAKE_CASE__			:				List[Any]       						= {} if sp_model_kwargs is None else sp_model_kwargs
  
           super().__init__(
               # bos_token=bos_token,  unused. Start decoding with config.decoder_start_token_id
               source_lang=a__ ,  target_lang=a__ ,  unk_token=a__ ,  eos_token=a__ ,  pad_token=a__ ,  model_max_length=a__ ,  sp_model_kwargs=self.sp_model_kwargs ,  target_vocab_file=a__ ,  separate_vocabs=a__ ,  **a__ ,  )
           assert Path(a__					).exists(), f"""cannot find spm source {source_spm}"""
  
           SCREAMING_SNAKE_CASE__			:				Union[str, Any]       						= separate_vocabs
           SCREAMING_SNAKE_CASE__			:				int       						= load_json(a__					)
           if self.unk_token not in self.encoder:
              raise KeyError('''<unk> token must be in vocab'''					)
           assert self.pad_token in self.encoder
  
           if separate_vocabs:
              SCREAMING_SNAKE_CASE__			:				List[Any]       						= load_json(a__					)
              SCREAMING_SNAKE_CASE__			:				Union[str, Any]       						= {v: k for k, v in self.target_encoder.items()}
              SCREAMING_SNAKE_CASE__			:				str       						= []
           else:
              SCREAMING_SNAKE_CASE__			:				Optional[Any]       						= {v: k for k, v in self.encoder.items()}
              SCREAMING_SNAKE_CASE__			:				list       						= [k for k in self.encoder if k.startswith('''>>'''					) and k.endswith('''<<'''					)]
  
           SCREAMING_SNAKE_CASE__			:				Tuple       						= source_lang
           SCREAMING_SNAKE_CASE__			:				Tuple       						= target_lang
           SCREAMING_SNAKE_CASE__			:				int       						= [source_spm, target_spm]
  
           # load SentencePiece model for pre-processing
           SCREAMING_SNAKE_CASE__			:				str       						= load_spm(a__ ,  self.sp_model_kwargs					)
           SCREAMING_SNAKE_CASE__			:				str       						= load_spm(a__ ,  self.sp_model_kwargs					)
           SCREAMING_SNAKE_CASE__			:				Optional[Any]       						= self.spm_source
           SCREAMING_SNAKE_CASE__			:				List[Any]       						= self.encoder
  
           # Multilingual target side: default to using first supported language code.
  
           self._setup_normalizer()
  
  
  
  
  
  
        def    A__(self					)       ->   List[str]:
           try:
              from sacremoses import MosesPunctNormalizer
  
              SCREAMING_SNAKE_CASE__			:				Dict       						= MosesPunctNormalizer(self.source_lang					).normalize
           except (ImportError, FileNotFoundError):
              warnings.warn('''Recommended: pip install sacremoses.'''					)
              SCREAMING_SNAKE_CASE__			:				str       						= lambda a__					: x
  
  
  
  
  
  
        def    A__(self ,  a__					)       ->   str:
           return self.punc_normalizer(a__					) if x else ""
  
  
  
  
  
  
        def    A__(self ,  a__					)       ->   Union[str, Any]:
           return self.current_encoder.get(a__ ,  self.current_encoder[self.unk_token]					)
  
  
  
  
  
  
        def    A__(self ,  a__					)       ->   Optional[int]:
           SCREAMING_SNAKE_CASE__			:				Union[str, Any]       						= self.language_code_re.match(a__					)
           SCREAMING_SNAKE_CASE__			:				list       						= [match.group(0					)] if match else []
           return code, self.language_code_re.sub('''''' ,  a__					)
  
  
  
  
  
  
        def    A__(self ,  a__					)       ->   List[str]:
           SCREAMING_SNAKE_CASE__						,				SCREAMING_SNAKE_CASE__			:				Optional[int]       						= self.remove_language_code(a__					)
           SCREAMING_SNAKE_CASE__			:				List[Any]       						= self.current_spm.encode(a__ ,  out_type=a__					)
           return code + pieces
  
  
  
  
  
  
        def    A__(self ,  a__					)       ->   str:
           return self.decoder.get(a__ ,  self.unk_token					)
  
  
  
  
  
  
        def    A__(self ,  a__ ,  **a__					)       ->   List[Any]:
           return super().batch_decode(a__ ,  **a__					)
  
  
  
  
  
  
        def    A__(self ,  a__ ,  **a__					)       ->   Tuple:
           return super().decode(a__ ,  **a__					)
  
  
  
  
  
  
        def    A__(self ,  a__					)       ->   str:
           SCREAMING_SNAKE_CASE__			:				List[str]       						= self.spm_source if self._decode_use_source_tokenizer else self.spm_target
           SCREAMING_SNAKE_CASE__			:				str       						= []
           SCREAMING_SNAKE_CASE__			:				Tuple       						= ''''''
           for token in tokens:
              # make sure that special tokens are not decoded using sentencepiece model
              if token in self.all_special_tokens:
                 out_string += sp_model.decode_pieces(a__					) + token + " "
                 SCREAMING_SNAKE_CASE__			:				str       						= []
              else:
                 current_sub_tokens.append(a__					)
           out_string += sp_model.decode_pieces(a__					)
           return out_string.strip()
  
  
  
  
  
  
        def    A__(self ,  a__ ,  a__=None					)       ->   List[int]:
           if token_ids_a is None:
              return token_ids_a + [self.eos_token_id]
           # We don't expect to process pairs, but leave the pair logic for API consistency
           return token_ids_a + token_ids_a + [self.eos_token_id]
  
  
  
  
  
  
        def    A__(self					)       ->   List[Any]:
           SCREAMING_SNAKE_CASE__			:				Union[str, Any]       						= self.spm_source
           SCREAMING_SNAKE_CASE__			:				Optional[Any]       						= self.encoder
  
  
  
  
  
  
        def    A__(self					)       ->   Any:
           SCREAMING_SNAKE_CASE__			:				Optional[Any]       						= self.spm_target
           if self.separate_vocabs:
              SCREAMING_SNAKE_CASE__			:				Tuple       						= self.target_encoder
  
  
  
  
  
  
        @property
        def    A__(self					)       ->   int:
           return len(self.encoder					)
  
  
  
  
  
  
        def    A__(self ,  a__ ,  a__ = None					)       ->   Tuple[str]:
           if not os.path.isdir(a__					):
              logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""					)
              return
           SCREAMING_SNAKE_CASE__			:				Tuple       						= []
  
           if self.separate_vocabs:
              SCREAMING_SNAKE_CASE__			:				int       						= os.path.join(
                  a__ ,  (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab'''] ,  )
              SCREAMING_SNAKE_CASE__			:				Any       						= os.path.join(
                  a__ ,  (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''target_vocab_file'''] ,  )
              save_json(self.encoder ,  a__					)
              save_json(self.target_encoder ,  a__					)
              saved_files.append(a__					)
              saved_files.append(a__					)
           else:
              SCREAMING_SNAKE_CASE__			:				Tuple       						= os.path.join(
                  a__ ,  (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab''']					)
              save_json(self.encoder ,  a__					)
              saved_files.append(a__					)
  
           for spm_save_filename, spm_orig_path, spm_model in zip(
               [VOCAB_FILES_NAMES['''source_spm'''], VOCAB_FILES_NAMES['''target_spm''']] ,  self.spm_files ,  [self.spm_source, self.spm_target] ,  ):
              SCREAMING_SNAKE_CASE__			:				List[str]       						= os.path.join(
                  a__ ,  (filename_prefix + '''-''' if filename_prefix else '''''') + spm_save_filename					)
              if os.path.abspath(a__					) != os.path.abspath(a__					) and os.path.isfile(a__					):
                 copyfile(a__ ,  a__					)
                 saved_files.append(a__					)
              elif not os.path.isfile(a__					):
                 with open(a__ ,  '''wb'''					) as fi:
                    SCREAMING_SNAKE_CASE__			:				List[Any]       						= spm_model.serialized_model_proto()
                    fi.write(a__					)
                 saved_files.append(a__					)
  
           return tuple(a__					)
  
  
  
  
  
  
        def    A__(self					)       ->   Dict:
           return self.get_src_vocab()
  
  
  
  
  
  
        def    A__(self					)       ->   Tuple:
           return dict(self.encoder ,  **self.added_tokens_encoder					)
  
  
  
  
  
  
        def    A__(self					)       ->   int:
           return dict(self.target_encoder ,  **self.added_tokens_decoder					)
  
  
  
  
  
  
        def __getstate__(self					)       ->   Dict:
           SCREAMING_SNAKE_CASE__			:				Any       						= self.__dict__.copy()
           state.update(
               {k: None for k in ['''spm_source''', '''spm_target''', '''current_spm''', '''punc_normalizer''', '''target_vocab_file''']}					)
           return state
  
  
  
  
  
  
        def __setstate__(self ,  a__					)       ->   None:
           SCREAMING_SNAKE_CASE__			:				Union[str, Any]       						= d
  
           # for backward compatibility
           if not hasattr(self ,  '''sp_model_kwargs'''					):
              SCREAMING_SNAKE_CASE__			:				Optional[Any]       						= {}
  
           SCREAMING_SNAKE_CASE__						,				SCREAMING_SNAKE_CASE__			:				Dict       						= (load_spm(a__ ,  self.sp_model_kwargs					) for f in self.spm_files)
           SCREAMING_SNAKE_CASE__			:				List[str]       						= self.spm_source
           self._setup_normalizer()
  
  
  
  
  
  
        def    A__(self ,  *a__ ,  **a__					)       ->   List[str]:
           return 1
  
  
  
  
  
  
        def    A__(self ,  a__					)       ->   Dict:
           SCREAMING_SNAKE_CASE__			:				Union[str, Any]       						= set(self.all_special_ids					)  # call it once instead of inside list comp
           all_special_ids.remove(self.unk_token_id					)  # <unk> is only sometimes special
           return [1 if x in all_special_ids else 0 for x in seq]
  
  
  
  
  
  
        def    A__(self ,  a__ ,  a__ = None ,  a__ = False					)       ->   List[int]:
           if already_has_special_tokens:
              return self._special_token_mask(a__					)
           elif token_ids_a is None:
              return self._special_token_mask(a__					) + [1]
           else:
              return self._special_token_mask(token_ids_a + token_ids_a					) + [1]
  
  
  def 				UpperCamelCase	(  snake_case	,     snake_case   ):
  
  
  
  
     '''simple docstring'''
  
     SCREAMING_SNAKE_CASE__			:				Dict       						= sentencepiece.SentencePieceProcessor(**snake_case   )
     spm.Load(snake_case   )
     return spm
  
  
  def 				UpperCamelCase	(  snake_case	,     snake_case   ):
  
  
  
  
     '''simple docstring'''
  
     with open(snake_case	,     '''w'''   ) as f:
        json.dump(snake_case	,     snake_case	,     indent=2   )
  
  
  
  
  
  
  
  def 				UpperCamelCase	(  snake_case   ):
  
  
  
  
     '''simple docstring'''
  
     with open(snake_case	,     '''r'''   ) as f:
        return json.load(snake_case   )
  
  
style_context: 
  
  
  
  
  '''simple docstring'''
  
  
  
  
  
  import argparse
  import json
  from pathlib import Path
  
  import requests
  import torch
  from huggingface_hub import cached_download, hf_hub_download, hf_hub_url
  from PIL import Image
  
  from transformers import DetaConfig, DetaForObjectDetection, DetaImageProcessor
  from transformers.utils import logging
  
  
  logging.set_verbosity_info()
  lowercase__			=	logging.get_logger(__name__)
  
  
  def 				UpperCamelCase	(  ):
  
  
  
  
     '''simple docstring'''
  
     SCREAMING_SNAKE_CASE__			:				Tuple       						= DetaConfig(
         num_queries=9_00	,     encoder_ffn_dim=20_48	,     decoder_ffn_dim=20_48	,     num_feature_levels=5	,     assign_first_stage=snake_case	,     with_box_refine=snake_case	,     two_stage=snake_case	,     )
  
     # set labels
     SCREAMING_SNAKE_CASE__			:				str       						= 91
     SCREAMING_SNAKE_CASE__			:				int       						= '''huggingface/label-files'''
     SCREAMING_SNAKE_CASE__			:				Optional[int]       						= '''coco-detection-id2label.json'''
     SCREAMING_SNAKE_CASE__			:				Optional[Any]       						= json.load(open(cached_download(hf_hub_url(snake_case	,     snake_case	,     repo_type='''dataset'''   )   )	,     '''r'''   )   )
     SCREAMING_SNAKE_CASE__			:				int       						= {int(snake_case   ): v for k, v in idalabel.items()}
     SCREAMING_SNAKE_CASE__			:				str       						= idalabel
     SCREAMING_SNAKE_CASE__			:				List[str]       						= {v: k for k, v in idalabel.items()}
  
     return config
  
  
  def 				UpperCamelCase	(  snake_case   ):
  
  
  
  
     '''simple docstring'''
  
     SCREAMING_SNAKE_CASE__			:				Optional[Any]       						= []
  
     # stem
     # fmt: off
     rename_keys.append(('''backbone.0.body.conv1.weight''', '''model.backbone.model.embedder.embedder.convolution.weight''')   )
     rename_keys.append(('''backbone.0.body.bn1.weight''', '''model.backbone.model.embedder.embedder.normalization.weight''')   )
     rename_keys.append(('''backbone.0.body.bn1.bias''', '''model.backbone.model.embedder.embedder.normalization.bias''')   )
     rename_keys.append(('''backbone.0.body.bn1.running_mean''', '''model.backbone.model.embedder.embedder.normalization.running_mean''')   )
     rename_keys.append(('''backbone.0.body.bn1.running_var''', '''model.backbone.model.embedder.embedder.normalization.running_var''')   )
     # stages
     for stage_idx in range(len(config.backbone_config.depths   )   ):
        for layer_idx in range(config.backbone_config.depths[stage_idx]   ):
           # shortcut
           if layer_idx == 0:
              rename_keys.append(
                  (
                      f"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.0.weight""",
                      f"""model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.convolution.weight""",
                  )   )
              rename_keys.append(
                  (
                      f"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.weight""",
                      f"""model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.weight""",
                  )   )
              rename_keys.append(
                  (
                      f"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.bias""",
                      f"""model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.bias""",
                  )   )
              rename_keys.append(
                  (
                      f"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_mean""",
                      f"""model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_mean""",
                  )   )
              rename_keys.append(
                  (
                      f"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_var""",
                      f"""model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_var""",
                  )   )
           # 3 convs
           for i in range(3   ):
              rename_keys.append(
                  (
                      f"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.conv{i+1}.weight""",
                      f"""model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.convolution.weight""",
                  )   )
              rename_keys.append(
                  (
                      f"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.weight""",
                      f"""model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.weight""",
                  )   )
              rename_keys.append(
                  (
                      f"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.bias""",
                      f"""model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.bias""",
                  )   )
              rename_keys.append(
                  (
                      f"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_mean""",
                      f"""model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_mean""",
                  )   )
              rename_keys.append(
                  (
                      f"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_var""",
                      f"""model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_var""",
                  )   )
      # transformer encoder
     for i in range(config.encoder_layers   ):
        rename_keys.append((f"""transformer.encoder.layers.{i}.self_attn.sampling_offsets.weight""", f"""model.encoder.layers.{i}.self_attn.sampling_offsets.weight""")   )
        rename_keys.append((f"""transformer.encoder.layers.{i}.self_attn.sampling_offsets.bias""", f"""model.encoder.layers.{i}.self_attn.sampling_offsets.bias""")   )
        rename_keys.append((f"""transformer.encoder.layers.{i}.self_attn.attention_weights.weight""", f"""model.encoder.layers.{i}.self_attn.attention_weights.weight""")   )
        rename_keys.append((f"""transformer.encoder.layers.{i}.self_attn.attention_weights.bias""", f"""model.encoder.layers.{i}.self_attn.attention_weights.bias""")   )
        rename_keys.append((f"""transformer.encoder.layers.{i}.self_attn.value_proj.weight""", f"""model.encoder.layers.{i}.self_attn.value_proj.weight""")   )
        rename_keys.append((f"""transformer.encoder.layers.{i}.self_attn.value_proj.bias""", f"""model.encoder.layers.{i}.self_attn.value_proj.bias""")   )
        rename_keys.append((f"""transformer.encoder.layers.{i}.self_attn.output_proj.weight""", f"""model.encoder.layers.{i}.self_attn.output_proj.weight""")   )
        rename_keys.append((f"""transformer.encoder.layers.{i}.self_attn.output_proj.bias""", f"""model.encoder.layers.{i}.self_attn.output_proj.bias""")   )
        rename_keys.append((f"""transformer.encoder.layers.{i}.norm1.weight""", f"""model.encoder.layers.{i}.self_attn_layer_norm.weight""")   )
        rename_keys.append((f"""transformer.encoder.layers.{i}.norm1.bias""", f"""model.encoder.layers.{i}.self_attn_layer_norm.bias""")   )
        rename_keys.append((f"""transformer.encoder.layers.{i}.linear1.weight""", f"""model.encoder.layers.{i}.fc1.weight""")   )
        rename_keys.append((f"""transformer.encoder.layers.{i}.linear1.bias""", f"""model.encoder.layers.{i}.fc1.bias""")   )
        rename_keys.append((f"""transformer.encoder.layers.{i}.linear2.weight""", f"""model.encoder.layers.{i}.fc2.weight""")   )
        rename_keys.append((f"""transformer.encoder.layers.{i}.linear2.bias""", f"""model.encoder.layers.{i}.fc2.bias""")   )
        rename_keys.append((f"""transformer.encoder.layers.{i}.norm2.weight""", f"""model.encoder.layers.{i}.final_layer_norm.weight""")   )
        rename_keys.append((f"""transformer.encoder.layers.{i}.norm2.bias""", f"""model.encoder.layers.{i}.final_layer_norm.bias""")   )
  
     # transformer decoder
     for i in range(config.decoder_layers   ):
        rename_keys.append((f"""transformer.decoder.layers.{i}.cross_attn.sampling_offsets.weight""", f"""model.decoder.layers.{i}.encoder_attn.sampling_offsets.weight""")   )
        rename_keys.append((f"""transformer.decoder.layers.{i}.cross_attn.sampling_offsets.bias""", f"""model.decoder.layers.{i}.encoder_attn.sampling_offsets.bias""")   )
        rename_keys.append((f"""transformer.decoder.layers.{i}.cross_attn.attention_weights.weight""", f"""model.decoder.layers.{i}.encoder_attn.attention_weights.weight""")   )
        rename_keys.append((f"""transformer.decoder.layers.{i}.cross_attn.attention_weights.bias""", f"""model.decoder.layers.{i}.encoder_attn.attention_weights.bias""")   )
        rename_keys.append((f"""transformer.decoder.layers.{i}.cross_attn.value_proj.weight""", f"""model.decoder.layers.{i}.encoder_attn.value_proj.weight""")   )
        rename_keys.append((f"""transformer.decoder.layers.{i}.cross_attn.value_proj.bias""", f"""model.decoder.layers.{i}.encoder_attn.value_proj.bias""")   )
        rename_keys.append((f"""transformer.decoder.layers.{i}.cross_attn.output_proj.weight""", f"""model.decoder.layers.{i}.encoder_attn.output_proj.weight""")   )
        rename_keys.append((f"""transformer.decoder.layers.{i}.cross_attn.output_proj.bias""", f"""model.decoder.layers.{i}.encoder_attn.output_proj.bias""")   )
        rename_keys.append((f"""transformer.decoder.layers.{i}.norm1.weight""", f"""model.decoder.layers.{i}.encoder_attn_layer_norm.weight""")   )
        rename_keys.append((f"""transformer.decoder.layers.{i}.norm1.bias""", f"""model.decoder.layers.{i}.encoder_attn_layer_norm.bias""")   )
        rename_keys.append((f"""transformer.decoder.layers.{i}.self_attn.out_proj.weight""", f"""model.decoder.layers.{i}.self_attn.out_proj.weight""")   )
        rename_keys.append((f"""transformer.decoder.layers.{i}.self_attn.out_proj.bias""", f"""model.decoder.layers.{i}.self_attn.out_proj.bias""")   )
        rename_keys.append((f"""transformer.decoder.layers.{i}.norm2.weight""", f"""model.decoder.layers.{i}.self_attn_layer_norm.weight""")   )
        rename_keys.append((f"""transformer.decoder.layers.{i}.norm2.bias""", f"""model.decoder.layers.{i}.self_attn_layer_norm.bias""")   )
        rename_keys.append((f"""transformer.decoder.layers.{i}.linear1.weight""", f"""model.decoder.layers.{i}.fc1.weight""")   )
        rename_keys.append((f"""transformer.decoder.layers.{i}.linear1.bias""", f"""model.decoder.layers.{i}.fc1.bias""")   )
        rename_keys.append((f"""transformer.decoder.layers.{i}.linear2.weight""", f"""model.decoder.layers.{i}.fc2.weight""")   )
        rename_keys.append((f"""transformer.decoder.layers.{i}.linear2.bias""", f"""model.decoder.layers.{i}.fc2.bias""")   )
        rename_keys.append((f"""transformer.decoder.layers.{i}.norm3.weight""", f"""model.decoder.layers.{i}.final_layer_norm.weight""")   )
        rename_keys.append((f"""transformer.decoder.layers.{i}.norm3.bias""", f"""model.decoder.layers.{i}.final_layer_norm.bias""")   )
  
     # fmt: on
  
     return rename_keys
  
  
  def 				UpperCamelCase	(  snake_case	,     snake_case	,     snake_case   ):
  
  
  
  
     '''simple docstring'''
  
     SCREAMING_SNAKE_CASE__			:				Optional[Any]       						= dct.pop(snake_case   )
     SCREAMING_SNAKE_CASE__			:				Union[str, Any]       						= val
  
  
  def 				UpperCamelCase	(  snake_case	,     snake_case   ):
  
  
  
  
     '''simple docstring'''
  
     SCREAMING_SNAKE_CASE__			:				List[Any]       						= config.d_model
     for i in range(config.decoder_layers   ):
        # read in weights + bias of input projection layer of self-attention
        SCREAMING_SNAKE_CASE__			:				Optional[Any]       						= state_dict.pop(f"""transformer.decoder.layers.{i}.self_attn.in_proj_weight"""   )
        SCREAMING_SNAKE_CASE__			:				Any       						= state_dict.pop(f"""transformer.decoder.layers.{i}.self_attn.in_proj_bias"""   )
        # next, add query, keys and values (in that order) to the state dict
        SCREAMING_SNAKE_CASE__			:				Union[str, Any]       						= in_proj_weight[:hidden_size, :]
        SCREAMING_SNAKE_CASE__			:				Optional[int]       						= in_proj_bias[:hidden_size]
        SCREAMING_SNAKE_CASE__			:				Optional[Any]       						= in_proj_weight[
            hidden_size : hidden_size * 2, :
        ]
        SCREAMING_SNAKE_CASE__			:				List[Any]       						= in_proj_bias[hidden_size : hidden_size * 2]
        SCREAMING_SNAKE_CASE__			:				List[Any]       						= in_proj_weight[-hidden_size:, :]
        SCREAMING_SNAKE_CASE__			:				str       						= in_proj_bias[-hidden_size:]
  
  
  def 				UpperCamelCase	(  ):
  
  
  
  
     '''simple docstring'''
  
     SCREAMING_SNAKE_CASE__			:				Dict       						= '''http://images.cocodataset.org/val2017/000000039769.jpg'''
     SCREAMING_SNAKE_CASE__			:				List[Any]       						= Image.open(requests.get(snake_case	,     stream=snake_case   ).raw   )
  
     return im
  
  
  @torch.no_grad()
  def 				UpperCamelCase	(  snake_case	,     snake_case	,     snake_case   ):
  
  
  
  
     '''simple docstring'''
  
     SCREAMING_SNAKE_CASE__			:				List[Any]       						= get_deta_config()
  
     # load original state dict
     if model_name == "deta-resnet-50":
        SCREAMING_SNAKE_CASE__			:				Optional[Any]       						= '''adet_checkpoint0011.pth'''
     elif model_name == "deta-resnet-50-24-epochs":
        SCREAMING_SNAKE_CASE__			:				Any       						= '''adet_2x_checkpoint0023.pth'''
     else:
        raise ValueError(f"""Model name {model_name} not supported"""   )
     SCREAMING_SNAKE_CASE__			:				Optional[int]       						= hf_hub_download(repo_id='''nielsr/deta-checkpoints'''	,     filename=snake_case   )
     SCREAMING_SNAKE_CASE__			:				int       						= torch.load(snake_case	,     map_location='''cpu'''   )['''model''']
  
     # rename keys
     SCREAMING_SNAKE_CASE__			:				Dict       						= create_rename_keys(snake_case   )
     for src, dest in rename_keys:
        rename_key(snake_case	,     snake_case	,     snake_case   )
     read_in_decoder_q_k_v(snake_case	,     snake_case   )
  
     # fix some prefixes
     for key in state_dict.copy().keys():
        if "transformer.decoder.class_embed" in key or "transformer.decoder.bbox_embed" in key:
           SCREAMING_SNAKE_CASE__			:				Optional[int]       						= state_dict.pop(snake_case   )
           SCREAMING_SNAKE_CASE__			:				Dict       						= val
        if "input_proj" in key:
           SCREAMING_SNAKE_CASE__			:				Any       						= state_dict.pop(snake_case   )
           SCREAMING_SNAKE_CASE__			:				Optional[Any]       						= val
        if "level_embed" in key or "pos_trans" in key or "pix_trans" in key or "enc_output" in key:
           SCREAMING_SNAKE_CASE__			:				List[str]       						= state_dict.pop(snake_case   )
           SCREAMING_SNAKE_CASE__			:				Optional[int]       						= val
  
      # finally, create HuggingFace model and load state dict
     SCREAMING_SNAKE_CASE__			:				List[Any]       						= DetaForObjectDetection(snake_case   )
     model.load_state_dict(snake_case   )
     model.eval()
  
     SCREAMING_SNAKE_CASE__			:				Tuple       						= '''cuda''' if torch.cuda.is_available() else '''cpu'''
     model.to(snake_case   )
  
     # load image processor
     SCREAMING_SNAKE_CASE__			:				Union[str, Any]       						= DetaImageProcessor(format='''coco_detection'''   )
  
     # verify our conversion on image
     SCREAMING_SNAKE_CASE__			:				int       						= prepare_img()
     SCREAMING_SNAKE_CASE__			:				int       						= processor(images=snake_case	,     return_tensors='''pt'''   )
     SCREAMING_SNAKE_CASE__			:				Union[str, Any]       						= encoding['''pixel_values''']
     SCREAMING_SNAKE_CASE__			:				Union[str, Any]       						= model(pixel_values.to(snake_case   )   )
  
     # verify logits
     if model_name == "deta-resnet-50":
        SCREAMING_SNAKE_CASE__			:				str       						= torch.tensor(
            [[-7.3978, -2.5406, -4.1668], [-8.2684, -3.9933, -3.8096], [-7.0515, -3.7973, -5.8516]]   )
        SCREAMING_SNAKE_CASE__			:				List[Any]       						= torch.tensor([[0.5043, 0.4973, 0.9998], [0.2542, 0.5489, 0.4748], [0.5490, 0.2765, 0.0570]]   )
     elif model_name == "deta-resnet-50-24-epochs":
        SCREAMING_SNAKE_CASE__			:				List[str]       						= torch.tensor(
            [[-7.1688, -2.4857, -4.8669], [-7.8630, -3.8154, -4.2674], [-7.2730, -4.1865, -5.5323]]   )
        SCREAMING_SNAKE_CASE__			:				Tuple       						= torch.tensor([[0.5021, 0.4971, 0.9994], [0.2546, 0.5486, 0.4731], [0.1686, 0.1986, 0.2142]]   )
  
     assert torch.allclose(outputs.logits[0, :3, :3]	,     expected_logits.to(snake_case   )	,     atol=1e-4   )
     assert torch.allclose(outputs.pred_boxes[0, :3, :3]	,     expected_boxes.to(snake_case   )	,     atol=1e-4   )
     print('''Everything ok!'''   )
  
     if pytorch_dump_folder_path:
        # Save model and processor
        logger.info(f"""Saving PyTorch model and processor to {pytorch_dump_folder_path}..."""   )
        Path(snake_case   ).mkdir(exist_ok=snake_case   )
        model.save_pretrained(snake_case   )
        processor.save_pretrained(snake_case   )
  
     # Push to hub
     if push_to_hub:
        print('''Pushing model and processor to hub...'''   )
        model.push_to_hub(f"""jozhang97/{model_name}"""   )
        processor.push_to_hub(f"""jozhang97/{model_name}"""   )
  
  
  if __name__ == "__main__":
         lowercase__			=	argparse.ArgumentParser()
  
         parser.add_argument(
             '''--model_name''',
             type=str,
             default='''deta-resnet-50''',
             choices=['''deta-resnet-50''', '''deta-resnet-50-24-epochs'''],
             help='''Name of the 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.''',
         )
         parser.add_argument(
             '''--push_to_hub''', action='''store_true''', help='''Whether or not to push the converted model to the 🤗 hub.'''
         )
         lowercase__			=	parser.parse_args()
         convert_deta_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
  
  