

'''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, SwinConfig
from transformers.utils import logging


logging.set_verbosity_info()
A_       						=							logging.get_logger(__name__)





def    A_  ( snake_case ):
  SCREAMING_SNAKE_CASE:List[str]						      =					SwinConfig(
      embed_dim=192      ,       depths=(2, 2, 18, 2)      ,       num_heads=(6, 12, 24, 48)      ,       window_size=12      ,       out_features=["stage2", "stage3", "stage4"]      ,       )

  SCREAMING_SNAKE_CASE:List[Any]						      =					DetaConfig(
      backbone_config=lowercase_      ,       num_queries=900      ,       encoder_ffn_dim=2048      ,       decoder_ffn_dim=2048      ,       num_feature_levels=5      ,       assign_first_stage=lowercase_      ,       with_box_refine=lowercase_      ,       two_stage=lowercase_      ,       )

  # set labels
  SCREAMING_SNAKE_CASE:int						      =					"huggingface/label-files"
  if "o365" in model_name:
    SCREAMING_SNAKE_CASE:Optional[Any]						      =					366
    SCREAMING_SNAKE_CASE:List[str]						      =					"object365-id2label.json"
  else:
    SCREAMING_SNAKE_CASE:List[str]						      =					91
    SCREAMING_SNAKE_CASE:List[str]						      =					"coco-detection-id2label.json"

  SCREAMING_SNAKE_CASE:Union[str, Any]						      =					num_labels
  SCREAMING_SNAKE_CASE:Optional[Any]						      =					json.load(open(cached_download(hf_hub_url(lowercase_      ,       lowercase_      ,       repo_type="dataset" ) )      ,       "r" ) )
  SCREAMING_SNAKE_CASE:Any						      =					{int(lowercase_ ): v for k, v in idalabel.items()}
  SCREAMING_SNAKE_CASE:Tuple						      =					idalabel
  SCREAMING_SNAKE_CASE:List[Any]						      =					{v: k for k, v in idalabel.items()}

  return config





def    A_  ( snake_case ):
  SCREAMING_SNAKE_CASE:List[str]						      =					[]

  # stem
  # fmt: off
  rename_keys.append(("backbone.0.body.patch_embed.proj.weight", "model.backbone.model.embeddings.patch_embeddings.projection.weight") )
  rename_keys.append(("backbone.0.body.patch_embed.proj.bias", "model.backbone.model.embeddings.patch_embeddings.projection.bias") )
  rename_keys.append(("backbone.0.body.patch_embed.norm.weight", "model.backbone.model.embeddings.norm.weight") )
  rename_keys.append(("backbone.0.body.patch_embed.norm.bias", "model.backbone.model.embeddings.norm.bias") )
  # stages
  for i in range(len(config.backbone_config.depths ) ):
    for j in range(config.backbone_config.depths[i] ):
      rename_keys.append((F'''backbone.0.body.layers.{i}.blocks.{j}.norm1.weight''', F'''model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_before.weight''') )
      rename_keys.append((F'''backbone.0.body.layers.{i}.blocks.{j}.norm1.bias''', F'''model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_before.bias''') )
      rename_keys.append((F'''backbone.0.body.layers.{i}.blocks.{j}.attn.relative_position_bias_table''', F'''model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table''') )
      rename_keys.append((F'''backbone.0.body.layers.{i}.blocks.{j}.attn.relative_position_index''', F'''model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index''') )
      rename_keys.append((F'''backbone.0.body.layers.{i}.blocks.{j}.attn.proj.weight''', F'''model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight''') )
      rename_keys.append((F'''backbone.0.body.layers.{i}.blocks.{j}.attn.proj.bias''', F'''model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias''') )
      rename_keys.append((F'''backbone.0.body.layers.{i}.blocks.{j}.norm2.weight''', F'''model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_after.weight''') )
      rename_keys.append((F'''backbone.0.body.layers.{i}.blocks.{j}.norm2.bias''', F'''model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_after.bias''') )
      rename_keys.append((F'''backbone.0.body.layers.{i}.blocks.{j}.mlp.fc1.weight''', F'''model.backbone.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight''') )
      rename_keys.append((F'''backbone.0.body.layers.{i}.blocks.{j}.mlp.fc1.bias''', F'''model.backbone.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias''') )
      rename_keys.append((F'''backbone.0.body.layers.{i}.blocks.{j}.mlp.fc2.weight''', F'''model.backbone.model.encoder.layers.{i}.blocks.{j}.output.dense.weight''') )
      rename_keys.append((F'''backbone.0.body.layers.{i}.blocks.{j}.mlp.fc2.bias''', F'''model.backbone.model.encoder.layers.{i}.blocks.{j}.output.dense.bias''') )

    if i < 3:
      rename_keys.append((F'''backbone.0.body.layers.{i}.downsample.reduction.weight''', F'''model.backbone.model.encoder.layers.{i}.downsample.reduction.weight''') )
      rename_keys.append((F'''backbone.0.body.layers.{i}.downsample.norm.weight''', F'''model.backbone.model.encoder.layers.{i}.downsample.norm.weight''') )
      rename_keys.append((F'''backbone.0.body.layers.{i}.downsample.norm.bias''', F'''model.backbone.model.encoder.layers.{i}.downsample.norm.bias''') )

  rename_keys.append(("backbone.0.body.norm1.weight", "model.backbone.model.hidden_states_norms.stage2.weight") )
  rename_keys.append(("backbone.0.body.norm1.bias", "model.backbone.model.hidden_states_norms.stage2.bias") )
  rename_keys.append(("backbone.0.body.norm2.weight", "model.backbone.model.hidden_states_norms.stage3.weight") )
  rename_keys.append(("backbone.0.body.norm2.bias", "model.backbone.model.hidden_states_norms.stage3.bias") )
  rename_keys.append(("backbone.0.body.norm3.weight", "model.backbone.model.hidden_states_norms.stage4.weight") )
  rename_keys.append(("backbone.0.body.norm3.bias", "model.backbone.model.hidden_states_norms.stage4.bias") )

  # 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    A_  ( snake_case      ,       snake_case      ,       snake_case ):
  SCREAMING_SNAKE_CASE:List[Any]						      =					dct.pop(lowercase_ )
  SCREAMING_SNAKE_CASE:Optional[Any]						      =					val





def    A_  ( snake_case      ,       snake_case ):
  SCREAMING_SNAKE_CASE:Dict						      =					[int(backbone_config.embed_dim * 2**i ) for i in range(len(backbone_config.depths ) )]
  for i in range(len(backbone_config.depths ) ):
    SCREAMING_SNAKE_CASE:List[str]						      =					num_features[i]
    for j in range(backbone_config.depths[i] ):
      # fmt: off
      # read in weights + bias of input projection layer (in original implementation, this is a single matrix + bias)
      SCREAMING_SNAKE_CASE:Optional[Any]						      =					state_dict.pop(F'''backbone.0.body.layers.{i}.blocks.{j}.attn.qkv.weight''' )
      SCREAMING_SNAKE_CASE:Optional[int]						      =					state_dict.pop(F'''backbone.0.body.layers.{i}.blocks.{j}.attn.qkv.bias''' )
      # next, add query, keys and values (in that order) to the state dict
      SCREAMING_SNAKE_CASE:int						      =					in_proj_weight[:dim, :]
      SCREAMING_SNAKE_CASE:Any						      =					in_proj_bias[: dim]
      SCREAMING_SNAKE_CASE:List[str]						      =					in_proj_weight[
          dim : dim * 2, :
      ]
      SCREAMING_SNAKE_CASE:Union[str, Any]						      =					in_proj_bias[
          dim : dim * 2
      ]
      SCREAMING_SNAKE_CASE:List[Any]						      =					in_proj_weight[
          -dim :, :
      ]
      SCREAMING_SNAKE_CASE:Optional[int]						      =					in_proj_bias[-dim :]
      # fmt: on





def    A_  ( snake_case      ,       snake_case ):
  SCREAMING_SNAKE_CASE:Optional[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:str						      =					state_dict.pop(F'''transformer.decoder.layers.{i}.self_attn.in_proj_weight''' )
    SCREAMING_SNAKE_CASE:Optional[int]						      =					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:List[str]						      =					in_proj_weight[:hidden_size, :]
    SCREAMING_SNAKE_CASE:Dict						      =					in_proj_bias[:hidden_size]
    SCREAMING_SNAKE_CASE:Dict						      =					in_proj_weight[
        hidden_size : hidden_size * 2, :
    ]
    SCREAMING_SNAKE_CASE:Any						      =					in_proj_bias[hidden_size : hidden_size * 2]
    SCREAMING_SNAKE_CASE:int						      =					in_proj_weight[-hidden_size:, :]
    SCREAMING_SNAKE_CASE:int						      =					in_proj_bias[-hidden_size:]





def    A_  ( ):
  SCREAMING_SNAKE_CASE:List[str]						      =					"http://images.cocodataset.org/val2017/000000039769.jpg"
  SCREAMING_SNAKE_CASE:int						      =					Image.open(requests.get(lowercase_      ,       stream=lowercase_ ).raw )

  return im





@torch.no_grad()
def    A_  ( snake_case      ,       snake_case      ,       snake_case ):
  SCREAMING_SNAKE_CASE:List[Any]						      =					get_deta_config(lowercase_ )

  # load original state dict
  if model_name == "deta-swin-large":
    SCREAMING_SNAKE_CASE:Optional[int]						      =					hf_hub_download(repo_id="nielsr/deta-checkpoints"      ,       filename="adet_swin_ft.pth" )
  elif model_name == "deta-swin-large-o365":
    SCREAMING_SNAKE_CASE:Any						      =					hf_hub_download(repo_id="jozhang97/deta-swin-l-o365"      ,       filename="deta_swin_pt_o365.pth" )
  else:
    raise ValueError(F'''Model name {model_name} not supported''' )

  SCREAMING_SNAKE_CASE:Dict						      =					torch.load(lowercase_      ,       map_location="cpu" )["model"]

  # original state dict
  for name, param in state_dict.items():
    print(lowercase_      ,       param.shape )

  # rename keys
  SCREAMING_SNAKE_CASE:Dict						      =					create_rename_keys(lowercase_ )
  for src, dest in rename_keys:
    rename_key(lowercase_      ,       lowercase_      ,       lowercase_ )
  read_in_swin_q_k_v(lowercase_      ,       config.backbone_config )
  read_in_decoder_q_k_v(lowercase_      ,       lowercase_ )

  # 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(lowercase_ )
      SCREAMING_SNAKE_CASE:Tuple						      =					val
    if "input_proj" in key:
      SCREAMING_SNAKE_CASE:Union[str, Any]						      =					state_dict.pop(lowercase_ )
      SCREAMING_SNAKE_CASE:Union[str, 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[Any]						      =					state_dict.pop(lowercase_ )
      SCREAMING_SNAKE_CASE:Optional[Any]						      =					val

    # finally, create HuggingFace model and load state dict
  SCREAMING_SNAKE_CASE:Union[str, Any]						      =					DetaForObjectDetection(lowercase_ )
  model.load_state_dict(lowercase_ )
  model.eval()

  SCREAMING_SNAKE_CASE:List[str]						      =					"cuda" if torch.cuda.is_available() else "cpu"
  model.to(lowercase_ )

  # load image processor
  SCREAMING_SNAKE_CASE:int						      =					DetaImageProcessor(format="coco_detection" )

  # verify our conversion on image
  SCREAMING_SNAKE_CASE:Optional[int]						      =					prepare_img()
  SCREAMING_SNAKE_CASE:Dict						      =					processor(images=lowercase_      ,       return_tensors="pt" )
  SCREAMING_SNAKE_CASE:Optional[Any]						      =					encoding["pixel_values"]
  SCREAMING_SNAKE_CASE:Any						      =					model(pixel_values.to(lowercase_ ) )

  # verify logits
  print("Logits:"      ,       outputs.logits[0, :3, :3] )
  print("Boxes:"      ,       outputs.pred_boxes[0, :3, :3] )
  if model_name == "deta-swin-large":
    SCREAMING_SNAKE_CASE:Tuple						      =					torch.tensor(
        [[-7.6308, -2.8485, -5.3737], [-7.2037, -4.5505, -4.8027], [-7.2943, -4.2611, -4.6617]] )
    SCREAMING_SNAKE_CASE:List[Any]						      =					torch.tensor([[0.4987, 0.4969, 0.9999], [0.2549, 0.5498, 0.4805], [0.5498, 0.2757, 0.0569]] )
  elif model_name == "deta-swin-large-o365":
    SCREAMING_SNAKE_CASE:Dict						      =					torch.tensor(
        [[-8.0122, -3.5720, -4.9717], [-8.1547, -3.6886, -4.6389], [-7.6610, -3.6194, -5.0134]] )
    SCREAMING_SNAKE_CASE:int						      =					torch.tensor([[0.2523, 0.5549, 0.4881], [0.7715, 0.4149, 0.4601], [0.5503, 0.2753, 0.0575]] )
  assert torch.allclose(outputs.logits[0, :3, :3]      ,       expected_logits.to(lowercase_ )      ,       atol=1e-4 )
  assert torch.allclose(outputs.pred_boxes[0, :3, :3]      ,       expected_boxes.to(lowercase_ )      ,       atol=1e-4 )
  print("Everything ok!" )

  if pytorch_dump_folder_path:
    # Save model and processor
    logger.info(F'''Saving PyTorch model and processor to {pytorch_dump_folder_path}...''' )
    Path(lowercase_ ).mkdir(exist_ok=lowercase_ )
    model.save_pretrained(lowercase_ )
    processor.save_pretrained(lowercase_ )

  # 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-swin-large",
      choices=["deta-swin-large", "deta-swin-large-o365"],
      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)









'''simple docstring'''

from importlib import import_module

from .logging import get_logger


A_       						=							get_logger(__name__)





class 							_snake_case :
   def __init__( self			:      Dict     ,SCREAMING_SNAKE_CASE__			:      Any     ,SCREAMING_SNAKE_CASE__			:      Dict=None							):
     SCREAMING_SNAKE_CASE:Any						      =					attrs or []
     if module is not None:
       for key in module.__dict__:
         if key in attrs or not key.startswith("__"							):
           setattr(self     ,SCREAMING_SNAKE_CASE__     ,getattr(SCREAMING_SNAKE_CASE__     ,SCREAMING_SNAKE_CASE__							)							)
     SCREAMING_SNAKE_CASE:Any						      =					module._original_module if isinstance(SCREAMING_SNAKE_CASE__     ,_PatchedModuleObj							) else module





class 							_snake_case :
   _A       :     Any       =					[]
   def __init__( self			:      Any     ,SCREAMING_SNAKE_CASE__			:      List[str]     ,SCREAMING_SNAKE_CASE__			:      str     ,SCREAMING_SNAKE_CASE__			:      str     ,SCREAMING_SNAKE_CASE__			:      Optional[Any]=None							):
     SCREAMING_SNAKE_CASE:Any						      =					obj
     SCREAMING_SNAKE_CASE:Any						      =					target
     SCREAMING_SNAKE_CASE:Union[str, Any]						      =					new
     SCREAMING_SNAKE_CASE:Any						      =					target.split("."							)[0]
     SCREAMING_SNAKE_CASE:str						      =					{}
     SCREAMING_SNAKE_CASE:List[Any]						      =					attrs or []
   def __enter__( self			:      Tuple							):
     *SCREAMING_SNAKE_CASE      ,    SCREAMING_SNAKE_CASE:int						      =					self.target.split("."							)

     # Patch modules:
     # it's used to patch attributes of submodules like "os.path.join";
     # in this case we need to patch "os" and "os.path"

     for i in range(len(SCREAMING_SNAKE_CASE__							)							):
       try:
         SCREAMING_SNAKE_CASE:int						      =					import_module(".".join(submodules[: i + 1]							)							)
       except ModuleNotFoundError:
         continue
       # We iterate over all the globals in self.obj in case we find "os" or "os.path"
       for attr in self.obj.__dir__():
         SCREAMING_SNAKE_CASE:str						      =					getattr(self.obj     ,SCREAMING_SNAKE_CASE__							)
         # We don't check for the name of the global, but rather if its value *is* "os" or "os.path".
         # This allows to patch renamed modules like "from os import path as ospath".
         if obj_attr is submodule or (
             (isinstance(SCREAMING_SNAKE_CASE__     ,_PatchedModuleObj							) and obj_attr._original_module is submodule)
         ):
           SCREAMING_SNAKE_CASE:Union[str, Any]						      =					obj_attr
           # patch at top level
           setattr(self.obj     ,SCREAMING_SNAKE_CASE__     ,_PatchedModuleObj(SCREAMING_SNAKE_CASE__     ,attrs=self.attrs							)							)
           SCREAMING_SNAKE_CASE:int						      =					getattr(self.obj     ,SCREAMING_SNAKE_CASE__							)
           # construct lower levels patches
           for key in submodules[i + 1 :]:
             setattr(SCREAMING_SNAKE_CASE__     ,SCREAMING_SNAKE_CASE__     ,_PatchedModuleObj(getattr(SCREAMING_SNAKE_CASE__     ,SCREAMING_SNAKE_CASE__     ,SCREAMING_SNAKE_CASE__							)     ,attrs=self.attrs							)							)
             SCREAMING_SNAKE_CASE:Dict						      =					getattr(SCREAMING_SNAKE_CASE__     ,SCREAMING_SNAKE_CASE__							)
           # finally set the target attribute
           setattr(SCREAMING_SNAKE_CASE__     ,SCREAMING_SNAKE_CASE__     ,self.new							)

        # Patch attribute itself:
        # it's used for builtins like "open",
        # and also to patch "os.path.join" we may also need to patch "join"
        # itself if it was imported as "from os.path import join".

     if submodules:  # if it's an attribute of a submodule like "os.path.join"
       try:
         SCREAMING_SNAKE_CASE:Any						      =					getattr(import_module(".".join(SCREAMING_SNAKE_CASE__							)							)     ,SCREAMING_SNAKE_CASE__							)
       except (AttributeError, ModuleNotFoundError):
         return
       # We iterate over all the globals in self.obj in case we find "os.path.join"
       for attr in self.obj.__dir__():
         # We don't check for the name of the global, but rather if its value *is* "os.path.join".
         # This allows to patch renamed attributes like "from os.path import join as pjoin".
         if getattr(self.obj     ,SCREAMING_SNAKE_CASE__							) is attr_value:
           SCREAMING_SNAKE_CASE:Union[str, Any]						      =					getattr(self.obj     ,SCREAMING_SNAKE_CASE__							)
           setattr(self.obj     ,SCREAMING_SNAKE_CASE__     ,self.new							)
     elif target_attr in globals()["__builtins__"]:  # if it'a s builtin like "open"
       SCREAMING_SNAKE_CASE:int						      =					globals()["__builtins__"][target_attr]
       setattr(self.obj     ,SCREAMING_SNAKE_CASE__     ,self.new							)
     else:
       raise RuntimeError(F'''Tried to patch attribute {target_attr} instead of a submodule.'''							)
   def __exit__( self			:      List[Any]     ,*SCREAMING_SNAKE_CASE__			:      Union[str, Any]							):
     for attr in list(self.original							):
       setattr(self.obj     ,SCREAMING_SNAKE_CASE__     ,self.original.pop(SCREAMING_SNAKE_CASE__							)							)
   def     __UpperCamelCase						( self			:      Optional[int]							):
     self.__enter__()
     self._active_patches.append(self							)







   def     __UpperCamelCase						( self			:      Dict							):
     try:
       self._active_patches.remove(self							)
     except ValueError:
       # If the patch hasn't been started this will fail
       return None

     return self.__exit__()






