code: 
  
  
  
  
  '''simple docstring'''
  
  
  
  
  
  import json
  import os
  from functools import lru_cache
  from typing import TYPE_CHECKING, List, Optional, Tuple, Union
  
  import numpy as np
  import regex as re
  
  from ...utils import is_tf_available, is_torch_available, logging
  
  
  if TYPE_CHECKING:
         if is_torch_available():
                import torch
         if is_tf_available():
                import tensorflow as tf
  
  from ...tokenization_utils import AddedToken, PreTrainedTokenizer
  
  
  lowercase__			=	logging.get_logger(__name__)
  
  lowercase__			=	{
      '''vocab_file''': '''vocab.json''',
      '''merges_file''': '''merges.txt''',
  }
  
  lowercase__			=	{
      '''vocab_file''': {
          '''Salesforce/codegen-350M-mono''': '''https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/vocab.json''',
      },
      '''merges_file''': {
          '''Salesforce/codegen-350M-mono''': '''https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/merges.txt''',
      },
  }
  
  lowercase__			=	{
      '''Salesforce/codegen-350M-mono''': 2_0_4_8,
  }
  
  
  @lru_cache()
  def 				UpperCamelCase	(  ):
  
  
  
  
         '''simple docstring'''
  
         SCREAMING_SNAKE_CASE__			:				str       						= (
             list(range(ord('''!'''   )	,     ord('''~'''   ) + 1   )   ) + list(range(ord('''¡'''   )	,     ord('''¬'''   ) + 1   )   ) + list(range(ord('''®'''   )	,     ord('''ÿ'''   ) + 1   )   )
         )
         SCREAMING_SNAKE_CASE__			:				Optional[int]       						= bs[:]
         SCREAMING_SNAKE_CASE__			:				str       						= 0
         for b in range(2**8   ):
                if b not in bs:
                       bs.append(__a   )
                       cs.append(2**8 + n   )
                       n += 1
         SCREAMING_SNAKE_CASE__			:				List[str]       						= [chr(__a   ) for n in cs]
         return dict(zip(__a	,     __a   )   )
  
  
  def 				UpperCamelCase	(  snake_case   ):
  
  
  
  
         '''simple docstring'''
  
         SCREAMING_SNAKE_CASE__			:				int       						= set()
         SCREAMING_SNAKE_CASE__			:				Optional[int]       						= word[0]
         for char in word[1:]:
                pairs.add((prev_char, char)   )
                SCREAMING_SNAKE_CASE__			:				Optional[int]       						= char
         return pairs
  
  
  
  
  
  
  
  class 						lowercase     (   UpperCamelCase__):
        __lowercase 		=  VOCAB_FILES_NAMES
        __lowercase 		=  PRETRAINED_VOCAB_FILES_MAP
        __lowercase 		=  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
        __lowercase 		=  ['''input_ids''', '''attention_mask''']
  
  
  
  
  
  
        def __init__(self ,  a__ ,  a__ ,  a__="replace" ,  a__="<|endoftext|>" ,  a__="<|endoftext|>" ,  a__="<|endoftext|>" ,  a__=None ,  a__=False ,  a__=False ,  **a__ ,  )       ->   Optional[Any]:
               SCREAMING_SNAKE_CASE__			:				Dict       						= AddedToken(snake_case_ ,  lstrip=snake_case_ ,  rstrip=snake_case_					) if isinstance(snake_case_ ,  snake_case_					) else bos_token
               SCREAMING_SNAKE_CASE__			:				Optional[int]       						= AddedToken(snake_case_ ,  lstrip=snake_case_ ,  rstrip=snake_case_					) if isinstance(snake_case_ ,  snake_case_					) else eos_token
               SCREAMING_SNAKE_CASE__			:				List[Any]       						= AddedToken(snake_case_ ,  lstrip=snake_case_ ,  rstrip=snake_case_					) if isinstance(snake_case_ ,  snake_case_					) else unk_token
               SCREAMING_SNAKE_CASE__			:				Dict       						= AddedToken(snake_case_ ,  lstrip=snake_case_ ,  rstrip=snake_case_					) if isinstance(snake_case_ ,  snake_case_					) else pad_token
               super().__init__(
                   errors=snake_case_ ,  unk_token=snake_case_ ,  bos_token=snake_case_ ,  eos_token=snake_case_ ,  pad_token=snake_case_ ,  add_prefix_space=snake_case_ ,  add_bos_token=snake_case_ ,  **snake_case_ ,  )
               SCREAMING_SNAKE_CASE__			:				Optional[int]       						= add_bos_token
  
               with open(snake_case_ ,  encoding='''utf-8'''					) as vocab_handle:
                      SCREAMING_SNAKE_CASE__			:				List[str]       						= json.load(snake_case_					)
               SCREAMING_SNAKE_CASE__			:				Optional[Any]       						= {v: k for k, v in self.encoder.items()}
               SCREAMING_SNAKE_CASE__			:				str       						= errors  # how to handle errors in decoding
               SCREAMING_SNAKE_CASE__			:				List[str]       						= bytes_to_unicode()
               SCREAMING_SNAKE_CASE__			:				List[str]       						= {v: k for k, v in self.byte_encoder.items()}
               with open(snake_case_ ,  encoding='''utf-8'''					) as merges_handle:
                      SCREAMING_SNAKE_CASE__			:				Tuple       						= merges_handle.read().split('''\n'''					)[1:-1]
               SCREAMING_SNAKE_CASE__			:				List[str]       						= [tuple(merge.split()					) for merge in bpe_merges]
               SCREAMING_SNAKE_CASE__			:				Any       						= dict(zip(snake_case_ ,  range(len(snake_case_					)					)					)					)
               SCREAMING_SNAKE_CASE__			:				List[Any]       						= {}
               SCREAMING_SNAKE_CASE__			:				Optional[Any]       						= add_prefix_space
  
               # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
               SCREAMING_SNAKE_CASE__			:				str       						= re.compile(R'''\'s|\'t|\'re|\'ve|\'m|\'ll|\'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+'''					)
  
  
  
  
  
  
        @property
        def    A__(self					)       ->   Any:
               return len(self.encoder					)
  
  
  
  
  
  
        def    A__(self					)       ->   Tuple:
               return dict(self.encoder ,  **self.added_tokens_encoder					)
  
  
  
  
  
  
        def    A__(self ,  a__					)       ->   Optional[int]:
               if token in self.cache:
                      return self.cache[token]
               SCREAMING_SNAKE_CASE__			:				List[Any]       						= tuple(snake_case_					)
               SCREAMING_SNAKE_CASE__			:				Dict       						= get_pairs(snake_case_					)
  
               if not pairs:
                      return token
  
               while True:
                      SCREAMING_SNAKE_CASE__			:				Optional[Any]       						= min(snake_case_ ,  key=lambda a__					: self.bpe_ranks.get(snake_case_ ,  float('''inf'''					)					)					)
                      if bigram not in self.bpe_ranks:
                             break
                      SCREAMING_SNAKE_CASE__			:				List[Any]       						= bigram
                      SCREAMING_SNAKE_CASE__			:				List[Any]       						= []
                      SCREAMING_SNAKE_CASE__			:				Tuple       						= 0
                      while i < len(snake_case_					):
                             try:
                                    SCREAMING_SNAKE_CASE__			:				int       						= word.index(snake_case_ ,  snake_case_					)
                             except ValueError:
                                    new_word.extend(word[i:]					)
                                    break
                             else:
                                    new_word.extend(word[i:j]					)
                                    SCREAMING_SNAKE_CASE__			:				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
                      SCREAMING_SNAKE_CASE__			:				Tuple       						= tuple(snake_case_					)
                      SCREAMING_SNAKE_CASE__			:				Dict       						= new_word
                      if len(snake_case_					) == 1:
                             break
                      else:
                             SCREAMING_SNAKE_CASE__			:				Dict       						= get_pairs(snake_case_					)
               SCREAMING_SNAKE_CASE__			:				Any       						= ''' '''.join(snake_case_					)
               SCREAMING_SNAKE_CASE__			:				List[str]       						= word
               return word
  
  
  
  
  
  
        def    A__(self ,  a__ ,  a__=None					)       ->   Any:
               if self.add_bos_token:
                      SCREAMING_SNAKE_CASE__			:				Dict       						= [self.bos_token_id]
               else:
                      SCREAMING_SNAKE_CASE__			:				Optional[Any]       						= []
  
               SCREAMING_SNAKE_CASE__			:				Union[str, Any]       						= bos_token_ids + token_ids_a
  
               if token_ids_a is None:
                      return output
  
               return output + bos_token_ids + token_ids_a
  
  
  
  
  
  
        def    A__(self ,  a__					)       ->   List[Any]:
               SCREAMING_SNAKE_CASE__			:				List[Any]       						= []
               for token in re.findall(self.pat ,  snake_case_					):
                      SCREAMING_SNAKE_CASE__			:				Optional[int]       						= ''''''.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    A__(self ,  a__					)       ->   Dict:
               return self.encoder.get(snake_case_ ,  self.encoder.get(self.unk_token					)					)
  
  
  
  
  
  
        def    A__(self ,  a__					)       ->   Any:
               return self.decoder.get(snake_case_					)
  
  
  
  
  
  
        def    A__(self ,  a__					)       ->   Any:
               SCREAMING_SNAKE_CASE__			:				Optional[int]       						= ''''''.join(snake_case_					)
               SCREAMING_SNAKE_CASE__			:				Optional[Any]       						= bytearray([self.byte_decoder[c] for c in text]					).decode('''utf-8''' ,  errors=self.errors					)
               return text
  
  
  
  
  
  
        def    A__(self ,  a__ ,  a__ = None					)       ->   Tuple[str]:
               if not os.path.isdir(snake_case_					):
                      logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""					)
                      return
               SCREAMING_SNAKE_CASE__			:				List[str]       						= os.path.join(
                   snake_case_ ,  (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']					)
               SCREAMING_SNAKE_CASE__			:				List[str]       						= 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'''					)
  
               SCREAMING_SNAKE_CASE__			:				Tuple       						= 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 a__					: 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__			:				int       						= token_index
                             writer.write(''' '''.join(snake_case_					) + '''\n'''					)
                             index += 1
  
               return vocab_file, merge_file
  
  
  
  
  
  
        def    A__(self ,  a__ ,  a__=False ,  **a__					)       ->   Dict:
               SCREAMING_SNAKE_CASE__			:				str       						= kwargs.pop('''add_prefix_space''' ,  self.add_prefix_space					)
               if is_split_into_words or add_prefix_space:
                      SCREAMING_SNAKE_CASE__			:				Optional[Any]       						= ''' ''' + text
               return (text, kwargs)
  
  
  
  
  
  
        def    A__(self ,  a__ ,  a__ = False ,  a__ = None ,  a__ = None ,  **a__ ,  )       ->   str:
               SCREAMING_SNAKE_CASE__			:				List[str]       						= super()._decode(
                   token_ids=snake_case_ ,  skip_special_tokens=snake_case_ ,  clean_up_tokenization_spaces=snake_case_ ,  **snake_case_ ,  )
  
               if truncate_before_pattern is not None and len(snake_case_					) > 0:
                      SCREAMING_SNAKE_CASE__			:				List[Any]       						= self.truncate(snake_case_ ,  snake_case_					)
  
               return decoded_text
  
  
  
  
  
  
        def    A__(self ,  a__ ,  a__					)       ->   Union[str, Any]:
               def find_re(a__ ,  a__ ,  a__					):
                      SCREAMING_SNAKE_CASE__			:				int       						= pattern.search(snake_case_ ,  snake_case_					)
                      return m.start() if m else -1
  
               SCREAMING_SNAKE_CASE__			:				Any       						= [re.compile(snake_case_ ,  re.MULTILINE					) for pattern in truncate_before_pattern]
  
               SCREAMING_SNAKE_CASE__			:				List[str]       						= list(re.finditer('''^print''' ,  snake_case_ ,  re.MULTILINE					)					)
  
               if len(snake_case_					) > 1:
                      SCREAMING_SNAKE_CASE__			:				Optional[int]       						= completion[: prints[1].start()]
  
               SCREAMING_SNAKE_CASE__			:				Union[str, Any]       						= list(re.finditer('''^def''' ,  snake_case_ ,  re.MULTILINE					)					)
  
               if len(snake_case_					) > 1:
                      SCREAMING_SNAKE_CASE__			:				List[Any]       						= completion[: defs[1].start()]
  
               SCREAMING_SNAKE_CASE__			:				Any       						= 0
  
               SCREAMING_SNAKE_CASE__			:				Any       						= [
                   pos for pos in [find_re(snake_case_ ,  snake_case_ ,  snake_case_					) for terminal in terminals] if pos != -1
               ]
  
               if len(snake_case_					) > 0:
                      return completion[: min(snake_case_					)]
               else:
                      return completion
  
  
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)
  
  