code: 
  
  
  
  
  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
  
  
  _A							    =	logging.get_logger(__name__)
  
  _A							    =	{
      """vocab_file""": """vocab.json""",
      """merges_file""": """merges.txt""",
  }
  
  _A							    =	{
      """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""",
      },
  }
  
  _A							    =	{
      """Salesforce/codegen-350M-mono""": 2048,
  }
  @lru_cache()
  def 						lowerCAmelCase_       (	)       ->			Dict:
  
  
  
  
  
  							"""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 						lowerCAmelCase_       (	__a )       ->			Tuple:
  
  
  
  
  
  							"""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 							_lowerCAmelCase		(     UpperCamelCase__	):
  				lowerCamelCase__									=    VOCAB_FILES_NAMES
  				lowerCamelCase__									=    PRETRAINED_VOCAB_FILES_MAP
  				lowerCamelCase__									=    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  				lowerCamelCase__									=    ['input_ids', 'attention_mask']
  
  
  
  
  				def __init__( self		,		snake_case_		,		snake_case_		,		snake_case_="replace"		,		snake_case_="<|endoftext|>"		,		snake_case_="<|endoftext|>"		,		snake_case_="<|endoftext|>"		,		snake_case_=None		,		snake_case_=False		,		snake_case_=False		,		**snake_case_		,		)    ->      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		,		snake_case_							)    ->      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 snake_case_							: self.bpe_ranks.get(snake_case_		,		float('''inf'''							)							)							)
  																		if bigram not in self.bpe_ranks:
  																									break
  																		SCREAMING_SNAKE_CASE     ,							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		,		snake_case_		,		snake_case_=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		,		snake_case_							)    ->      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		,		snake_case_							)    ->      Dict:
  											return self.encoder.get(snake_case_		,		self.encoder.get(self.unk_token							)							)
  
  
  
  
  				def     __a		( self		,		snake_case_							)    ->      Any:
  											return self.decoder.get(snake_case_							)
  
  
  
  
  				def     __a		( self		,		snake_case_							)    ->      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		,		snake_case_		,		snake_case_ = 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 snake_case_							: kv[1]							):
  																									if index != token_index:
  																																logger.warning(
  																																    F'Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'
  																																    ''' Please check that the tokenizer is not corrupted!'''							)
  																																SCREAMING_SNAKE_CASE			: int	     =token_index
  																									writer.write(''' '''.join(snake_case_							) + '''\n'''							)
  																									index += 1
  
  											return vocab_file, merge_file
  
  
  
  
  				def     __a		( self		,		snake_case_		,		snake_case_=False		,		**snake_case_							)    ->      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		,		snake_case_		,		snake_case_ = False		,		snake_case_ = None		,		snake_case_ = None		,		**snake_case_		,		)    ->      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		,		snake_case_		,		snake_case_							)    ->      Union[str, Any]:
  											def find_re(snake_case_		,		snake_case_		,		snake_case_							):
  																		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: 
  
  
  
  
  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()
  _A							    =	logging.get_logger(__name__)
  def 						lowerCAmelCase_       (	)       ->			Any:
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  
  							SCREAMING_SNAKE_CASE			: Dict	     =DetaConfig(
  							    num_queries=900	,	encoder_ffn_dim=2048	,	decoder_ffn_dim=2048	,	num_feature_levels=5	,	assign_first_stage=__a	,	with_box_refine=__a	,	two_stage=__a	,	)
  
  							# set labels
  							SCREAMING_SNAKE_CASE			: Dict	     =91
  							SCREAMING_SNAKE_CASE			: Dict	     ='''huggingface/label-files'''
  							SCREAMING_SNAKE_CASE			: List[str]	     ='''coco-detection-id2label.json'''
  							SCREAMING_SNAKE_CASE			: Tuple	     =json.load(open(cached_download(hf_hub_url(__a	,	__a	,	repo_type='''dataset''' ) )	,	'''r''' ) )
  							SCREAMING_SNAKE_CASE			: int	     ={int(__a ): v for k, v in idalabel.items()}
  							SCREAMING_SNAKE_CASE			: Union[str, Any]	     =idalabel
  							SCREAMING_SNAKE_CASE			: Any	     ={v: k for k, v in idalabel.items()}
  
  							return config
  def 						lowerCAmelCase_       (	__a )       ->			Union[str, Any]:
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  
  							SCREAMING_SNAKE_CASE			: int	     =[]
  
  							# 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 						lowerCAmelCase_       (	__a	,	__a	,	__a )       ->			Union[str, Any]:
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  
  							SCREAMING_SNAKE_CASE			: Optional[Any]	     =dct.pop(__a )
  							SCREAMING_SNAKE_CASE			: List[Any]	     =val
  def 						lowerCAmelCase_       (	__a	,	__a )       ->			Dict:
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  
  							SCREAMING_SNAKE_CASE			: Tuple	     =config.d_model
  							for i in range(config.decoder_layers ):
  														# read in weights + bias of input projection layer of self-attention
  														SCREAMING_SNAKE_CASE			: List[str]	     =state_dict.pop(f'transformer.decoder.layers.{i}.self_attn.in_proj_weight' )
  														SCREAMING_SNAKE_CASE			: Optional[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			: Dict	     =in_proj_weight[:hidden_size, :]
  														SCREAMING_SNAKE_CASE			: List[Any]	     =in_proj_bias[:hidden_size]
  														SCREAMING_SNAKE_CASE			: int	     =in_proj_weight[
  														    hidden_size : hidden_size * 2, :
  														]
  														SCREAMING_SNAKE_CASE			: Any	     =in_proj_bias[hidden_size : hidden_size * 2]
  														SCREAMING_SNAKE_CASE			: List[str]	     =in_proj_weight[-hidden_size:, :]
  														SCREAMING_SNAKE_CASE			: Dict	     =in_proj_bias[-hidden_size:]
  def 						lowerCAmelCase_       (	)       ->			int:
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  
  							SCREAMING_SNAKE_CASE			: Tuple	     ='''http://images.cocodataset.org/val2017/000000039769.jpg'''
  							SCREAMING_SNAKE_CASE			: Union[str, Any]	     =Image.open(requests.get(__a	,	stream=__a ).raw )
  
  							return im
  @torch.no_grad()
  def 						lowerCAmelCase_       (	__a	,	__a	,	__a )       ->			Optional[Any]:
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  
  							SCREAMING_SNAKE_CASE			: str	     =get_deta_config()
  
  							# load original state dict
  							if model_name == "deta-resnet-50":
  														SCREAMING_SNAKE_CASE			: int	     ='''adet_checkpoint0011.pth'''
  							elif model_name == "deta-resnet-50-24-epochs":
  														SCREAMING_SNAKE_CASE			: str	     ='''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=__a )
  							SCREAMING_SNAKE_CASE			: str	     =torch.load(__a	,	map_location='''cpu''' )['''model''']
  
  							# rename keys
  							SCREAMING_SNAKE_CASE			: int	     =create_rename_keys(__a )
  							for src, dest in rename_keys:
  														rename_key(__a	,	__a	,	__a )
  							read_in_decoder_q_k_v(__a	,	__a )
  
  							# 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			: str	     =state_dict.pop(__a )
  																					SCREAMING_SNAKE_CASE			: List[str]	     =val
  														if "input_proj" in key:
  																					SCREAMING_SNAKE_CASE			: Tuple	     =state_dict.pop(__a )
  																					SCREAMING_SNAKE_CASE			: Tuple	     =val
  														if "level_embed" in key or "pos_trans" in key or "pix_trans" in key or "enc_output" in key:
  																					SCREAMING_SNAKE_CASE			: Dict	     =state_dict.pop(__a )
  																					SCREAMING_SNAKE_CASE			: List[str]	     =val
  
      # finally, create HuggingFace model and load state dict
  							SCREAMING_SNAKE_CASE			: str	     =DetaForObjectDetection(__a )
  							model.load_state_dict(__a )
  							model.eval()
  
  							SCREAMING_SNAKE_CASE			: int	     ='''cuda''' if torch.cuda.is_available() else '''cpu'''
  							model.to(__a )
  
  							# load image processor
  							SCREAMING_SNAKE_CASE			: Optional[Any]	     =DetaImageProcessor(format='''coco_detection''' )
  
  							# verify our conversion on image
  							SCREAMING_SNAKE_CASE			: Union[str, Any]	     =prepare_img()
  							SCREAMING_SNAKE_CASE			: Any	     =processor(images=__a	,	return_tensors='''pt''' )
  							SCREAMING_SNAKE_CASE			: List[Any]	     =encoding['''pixel_values''']
  							SCREAMING_SNAKE_CASE			: Dict	     =model(pixel_values.to(__a ) )
  
  							# verify logits
  							if model_name == "deta-resnet-50":
  														SCREAMING_SNAKE_CASE			: Optional[int]	     =torch.tensor(
  														    [[-7.3978, -2.5406, -4.1668], [-8.2684, -3.9933, -3.8096], [-7.0515, -3.7973, -5.8516]] )
  														SCREAMING_SNAKE_CASE			: Tuple	     =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			: Any	     =torch.tensor(
  														    [[-7.1688, -2.4857, -4.8669], [-7.8630, -3.8154, -4.2674], [-7.2730, -4.1865, -5.5323]] )
  														SCREAMING_SNAKE_CASE			: List[Any]	     =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(__a )	,	atol=1E-4 )
  							assert torch.allclose(outputs.pred_boxes[0, :3, :3]	,	expected_boxes.to(__a )	,	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(__a ).mkdir(exist_ok=__a )
  														model.save_pretrained(__a )
  														processor.save_pretrained(__a )
  
  							# 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__":
  							_A							    =	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."""
  							)
  							_A							    =	parser.parse_args()
  							convert_deta_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
  
  
  
  
  
  
  