


import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import cached_download, hf_hub_url
from PIL import Image

from transformers import DeformableDetrConfig, DeformableDetrForObjectDetection, DeformableDetrImageProcessor
from transformers.utils import logging


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







def 	A (		A__			):







     if "backbone.0.body" in orig_key:
          UpperCAmelCase    :					Optional[int]			=      orig_key.replace("backbone.0.body"		,		"backbone.conv_encoder.model"			)
     if "transformer" in orig_key:
          UpperCAmelCase    :					Union[str, Any]			=      orig_key.replace("transformer."		,		""			)
     if "norm1" in orig_key:
          if "encoder" in orig_key:
               UpperCAmelCase    :					List[str]			=      orig_key.replace("norm1"		,		"self_attn_layer_norm"			)
          else:
               UpperCAmelCase    :					Any			=      orig_key.replace("norm1"		,		"encoder_attn_layer_norm"			)
     if "norm2" in orig_key:
          if "encoder" in orig_key:
               UpperCAmelCase    :					List[str]			=      orig_key.replace("norm2"		,		"final_layer_norm"			)
          else:
               UpperCAmelCase    :					Union[str, Any]			=      orig_key.replace("norm2"		,		"self_attn_layer_norm"			)
     if "norm3" in orig_key:
          UpperCAmelCase    :					Tuple			=      orig_key.replace("norm3"		,		"final_layer_norm"			)
     if "linear1" in orig_key:
          UpperCAmelCase    :					Any			=      orig_key.replace("linear1"		,		"fc1"			)
     if "linear2" in orig_key:
          UpperCAmelCase    :					List[str]			=      orig_key.replace("linear2"		,		"fc2"			)
     if "query_embed" in orig_key:
          UpperCAmelCase    :					Union[str, Any]			=      orig_key.replace("query_embed"		,		"query_position_embeddings"			)
     if "cross_attn" in orig_key:
          UpperCAmelCase    :					List[Any]			=      orig_key.replace("cross_attn"		,		"encoder_attn"			)

     return orig_key







def 	A (		A__			):







     for i in range(6			):
          # read in weights + bias of input projection layer of self-attention
          UpperCAmelCase    :					int			=      state_dict.pop(f'decoder.layers.{i}.self_attn.in_proj_weight'			)
          UpperCAmelCase    :					Optional[int]			=      state_dict.pop(f'decoder.layers.{i}.self_attn.in_proj_bias'			)
          # next, add query, keys and values (in that order) to the state dict
          UpperCAmelCase    :					Any			=      in_proj_weight[:2_5_6, :]
          UpperCAmelCase    :					Any			=      in_proj_bias[:2_5_6]
          UpperCAmelCase    :					int			=      in_proj_weight[2_5_6:5_1_2, :]
          UpperCAmelCase    :					str			=      in_proj_bias[2_5_6:5_1_2]
          UpperCAmelCase    :					Optional[int]			=      in_proj_weight[-2_5_6:, :]
          UpperCAmelCase    :					str			=      in_proj_bias[-2_5_6:]







def 	A (		):
     UpperCAmelCase    :					Union[str, Any]			=      "http://images.cocodataset.org/val2017/000000039769.jpg"
     UpperCAmelCase    :					str			=      Image.open(requests.get(UpperCamelCase_		,		stream=UpperCamelCase_			).raw			)

     return im







@torch.no_grad()
def 	A (		A__		,		A__		,		A__		,		A__		,		A__		,		A__		,		A__		,		):
     UpperCAmelCase    :					Any			=      DeformableDetrConfig()
     # set config attributes
     if single_scale:
          UpperCAmelCase    :					List[Any]			=      1
     UpperCAmelCase    :					Optional[Any]			=      dilation
     UpperCAmelCase    :					Tuple			=      with_box_refine
     UpperCAmelCase    :					Any			=      two_stage
     # set labels
     UpperCAmelCase    :					int			=      9_1
     UpperCAmelCase    :					List[str]			=      "huggingface/label-files"
     UpperCAmelCase    :					Optional[Any]			=      "coco-detection-id2label.json"
     UpperCAmelCase    :					str			=      json.load(open(cached_download(hf_hub_url(UpperCamelCase_		,		UpperCamelCase_		,		repo_type="dataset"			)			)		,		"r"			)			)
     UpperCAmelCase    :					List[Any]			=      {int(UpperCamelCase_			): v for k, v in idalabel.items()}
     UpperCAmelCase    :					Dict			=      idalabel
     UpperCAmelCase    :					List[str]			=      {v: k for k, v in idalabel.items()}

     # load image processor
     UpperCAmelCase    :					int			=      DeformableDetrImageProcessor(format="coco_detection"			)

     # prepare image
     UpperCAmelCase    :					Optional[int]			=      prepare_img()
     UpperCAmelCase    :					Dict			=      image_processor(images=UpperCamelCase_		,		return_tensors="pt"			)
     UpperCAmelCase    :					Tuple			=      encoding["pixel_values"]

     logger.info("Converting model..."			)

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

     UpperCAmelCase    :					Optional[int]			=      "cuda" if torch.cuda.is_available() else "cpu"
     model.to(UpperCamelCase_			)
     # verify our conversion
     UpperCAmelCase    :					Optional[int]			=      model(pixel_values.to(UpperCamelCase_			)			)

     UpperCAmelCase    :					Tuple			=      torch.tensor(
         [[-9.6645, -4.3449, -5.8705], [-9.7035, -3.8504, -5.0724], [-10.5634, -5.3379, -7.5116]]			)
     UpperCAmelCase    :					Union[str, Any]			=      torch.tensor([[0.8693, 0.2289, 0.2492], [0.3150, 0.5489, 0.5845], [0.5563, 0.7580, 0.8518]]			)

     if single_scale:
          UpperCAmelCase    :					Any			=      torch.tensor(
              [[-9.9051, -4.2541, -6.4852], [-9.6947, -4.0854, -6.8033], [-10.0665, -5.8470, -7.7003]]			)
          UpperCAmelCase    :					int			=      torch.tensor([[0.7292, 0.4991, 0.5532], [0.7959, 0.2426, 0.4236], [0.7582, 0.3518, 0.4451]]			)

     if single_scale and dilation:
          UpperCAmelCase    :					int			=      torch.tensor(
              [[-8.9652, -4.1074, -5.6635], [-9.0596, -4.9447, -6.6075], [-10.1178, -4.5275, -6.2671]]			)
          UpperCAmelCase    :					Optional[Any]			=      torch.tensor([[0.7665, 0.4130, 0.4769], [0.8364, 0.1841, 0.3391], [0.6261, 0.3895, 0.7978]]			)

     if with_box_refine:
          UpperCAmelCase    :					Optional[Any]			=      torch.tensor(
              [[-8.8895, -5.4187, -6.8153], [-8.4706, -6.1668, -7.6184], [-9.0042, -5.5359, -6.9141]]			)
          UpperCAmelCase    :					Optional[Any]			=      torch.tensor([[0.7828, 0.2208, 0.4323], [0.0892, 0.5996, 0.1319], [0.5524, 0.6389, 0.8914]]			)

     if with_box_refine and two_stage:
          UpperCAmelCase    :					int			=      torch.tensor(
              [[-6.7108, -4.3213, -6.3777], [-8.9014, -6.1799, -6.7240], [-6.9315, -4.4735, -6.2298]]			)
          UpperCAmelCase    :					Union[str, Any]			=      torch.tensor([[0.2583, 0.5499, 0.4683], [0.7652, 0.9068, 0.4882], [0.5490, 0.2763, 0.0564]]			)

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

     assert torch.allclose(outputs.logits[0, :3, :3]		,		expected_logits.to(UpperCamelCase_			)		,		atol=1E-4			)
     assert torch.allclose(outputs.pred_boxes[0, :3, :3]		,		expected_boxes.to(UpperCamelCase_			)		,		atol=1E-4			)

     print("Everything ok!"			)

     # Save model and image processor
     logger.info(f'Saving PyTorch model and image processor to {pytorch_dump_folder_path}...'			)
     Path(UpperCamelCase_			).mkdir(exist_ok=UpperCamelCase_			)
     model.save_pretrained(UpperCamelCase_			)
     image_processor.save_pretrained(UpperCamelCase_			)

     # Push to hub
     if push_to_hub:
          UpperCAmelCase    :					Union[str, Any]			=      "deformable-detr"
          model_name += "-single-scale" if single_scale else ""
          model_name += "-dc5" if dilation else ""
          model_name += "-with-box-refine" if with_box_refine else ""
          model_name += "-two-stage" if two_stage else ""
          print("Pushing model to hub..."			)
          model.push_to_hub(repo_path_or_name=UpperCamelCase_		,		organization="nielsr"		,		commit_message="Add model"			)


if __name__ == "__main__":
      _snake_case				=			argparse.ArgumentParser()

      parser.add_argument(
          "--checkpoint_path",
          type=str,
          default="/home/niels/checkpoints/deformable_detr/r50_deformable_detr-checkpoint.pth",
          help="Path to Pytorch checkpoint (.pth file) you'd like to convert.",
      )
      parser.add_argument("--single_scale", action="store_true", help="Whether to set config.num_features_levels = 1.")
      parser.add_argument("--dilation", action="store_true", help="Whether to set config.dilation=True.")
      parser.add_argument("--with_box_refine", action="store_true", help="Whether to set config.with_box_refine=True.")
      parser.add_argument("--two_stage", action="store_true", help="Whether to set config.two_stage=True.")
      parser.add_argument(
          "--pytorch_dump_folder_path",
          default=None,
          type=str,
          required=True,
          help="Path to the folder to output PyTorch model.",
      )
      parser.add_argument(
          "--push_to_hub", action="store_true", help="Whether or not to push the converted model to the 🤗 hub."
      )
      _snake_case				=			parser.parse_args()
      convert_deformable_detr_checkpoint(
          args.checkpoint_path,
          args.single_scale,
          args.dilation,
          args.with_box_refine,
          args.two_stage,
          args.pytorch_dump_folder_path,
          args.push_to_hub,
      )











"""simple docstring"""




import argparse

import torch

from transformers import ImageGPTConfig, ImageGPTForCausalLM, load_tf_weights_in_imagegpt
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()






def 							lowerCAmelCase			(   UpperCamelCase_:				Dict  ,			UpperCamelCase_:				Dict  ,			UpperCamelCase_:				Any       )							->				Tuple:







  '''simple docstring'''







  _a					      =						{"small": (512, 8, 24), "medium": (1024, 8, 36), "large": (1536, 16, 48)}
  _a      ,						_a      ,						_a					      =						MODELS[model_size]  # set model hyperparameters
  _a					      =						ImageGPTConfig(n_embd=UpperCamelCase_  ,			n_layer=UpperCamelCase_  ,			n_head=UpperCamelCase_       )
  _a					      =						ImageGPTForCausalLM(UpperCamelCase_       )

  # Load weights from numpy
  load_tf_weights_in_imagegpt(UpperCamelCase_  ,			UpperCamelCase_  ,			UpperCamelCase_       )

  # Save pytorch-model
  _a					      =						pytorch_dump_folder_path + "/" + WEIGHTS_NAME
  _a					      =						pytorch_dump_folder_path + "/" + CONFIG_NAME
  print(f'''Save PyTorch model to {pytorch_weights_dump_path}'''       )
  torch.save(model.state_dict()  ,			UpperCamelCase_       )
  print(f'''Save configuration file to {pytorch_config_dump_path}'''       )
  with open(UpperCamelCase_  ,			"w"  ,			encoding="utf-8"       ) as f:
    f.write(config.to_json_string()       )


if __name__ == "__main__":
     UpperCamelCase											=     argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         """--imagegpt_checkpoint_path""",
         default=None,
         type=str,
         required=True,
         help="""Path to the TensorFlow checkpoint path.""",
     )
     parser.add_argument(
         """--model_size""",
         default=None,
         type=str,
         required=True,
         help="""Size of the model (can be either 'small', 'medium' or 'large').""",
     )
     parser.add_argument(
         """--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
     )
     UpperCamelCase											=     parser.parse_args()
     convert_imagegpt_checkpoint_to_pytorch(
         args.imagegpt_checkpoint_path, args.model_size, args.pytorch_dump_folder_path
     )


