'''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()
lowerCAmelCase_			:			Union[str, Any]    	=   logging.get_logger(__name__)


def __a			(						)						->				Optional[int]:
      '''simple docstring'''


      lowercase_     			=   DetaConfig(
          num_queries=900			,		encoder_ffn_dim=2_048			,		decoder_ffn_dim=2_048			,		num_feature_levels=5			,		assign_first_stage=__lowerCamelCase			,		with_box_refine=__lowerCamelCase			,		two_stage=__lowerCamelCase			,		)

      # set labels
      lowercase_     			=   91
      lowercase_     			=   "huggingface/label-files"
      lowercase_     			=   "coco-detection-id2label.json"
      lowercase_     			=   json.load(open(cached_download(hf_hub_url(__lowerCamelCase			,		__lowerCamelCase			,		repo_type="dataset"					)					)			,		"r"					)					)
      lowercase_     			=   {int(__lowerCamelCase					): v for k, v in idalabel.items()}
      lowercase_     			=   idalabel
      lowercase_     			=   {v: k for k, v in idalabel.items()}

      return config


def __a			(						__lowerCamelCase      :    List[Any]					)						->				List[Any]:
      '''simple docstring'''


      lowercase_     			=   []

      # 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 __a			(						__lowerCamelCase      :    int			,		__lowerCamelCase      :    Union[str, Any]			,		__lowerCamelCase      :    Optional[Any]					)						->				int:
      '''simple docstring'''


      lowercase_     			=   dct.pop(__lowerCamelCase					)
      lowercase_     			=   val


def __a			(						__lowerCamelCase      :    str			,		__lowerCamelCase      :    Union[str, Any]					)						->				Optional[Any]:
      '''simple docstring'''


      lowercase_     			=   config.d_model
      for i in range(config.decoder_layers					):
            # read in weights + bias of input projection layer of self-attention
            lowercase_     			=   state_dict.pop(f'transformer.decoder.layers.{i}.self_attn.in_proj_weight'					)
            lowercase_     			=   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
            lowercase_     			=   in_proj_weight[:hidden_size, :]
            lowercase_     			=   in_proj_bias[:hidden_size]
            lowercase_     			=   in_proj_weight[
                hidden_size : hidden_size * 2, :
            ]
            lowercase_     			=   in_proj_bias[hidden_size : hidden_size * 2]
            lowercase_     			=   in_proj_weight[-hidden_size:, :]
            lowercase_     			=   in_proj_bias[-hidden_size:]


def __a			(						)						->				Optional[Any]:
      '''simple docstring'''


      lowercase_     			=   "http://images.cocodataset.org/val2017/000000039769.jpg"
      lowercase_     			=   Image.open(requests.get(__lowerCamelCase			,		stream=__lowerCamelCase					).raw					)

      return im


@torch.no_grad()
def __a			(						__lowerCamelCase      :    Any			,		__lowerCamelCase      :    Optional[int]			,		__lowerCamelCase      :    Optional[int]					)						->				Optional[Any]:
      '''simple docstring'''


      lowercase_     			=   get_deta_config()

      # load original state dict
      if model_name == "deta-resnet-50":
            lowercase_     			=   "adet_checkpoint0011.pth"
      elif model_name == "deta-resnet-50-24-epochs":
            lowercase_     			=   "adet_2x_checkpoint0023.pth"
      else:
            raise ValueError(f'Model name {model_name} not supported'					)
      lowercase_     			=   hf_hub_download(repo_id="nielsr/deta-checkpoints"			,		filename=__lowerCamelCase					)
      lowercase_     			=   torch.load(__lowerCamelCase			,		map_location="cpu"					)["model"]

      # rename keys
      lowercase_     			=   create_rename_keys(__lowerCamelCase					)
      for src, dest in rename_keys:
            rename_key(__lowerCamelCase			,		__lowerCamelCase			,		__lowerCamelCase					)
      read_in_decoder_q_k_v(__lowerCamelCase			,		__lowerCamelCase					)

      # fix some prefixes
      for key in state_dict.copy().keys():
            if "transformer.decoder.class_embed" in key or "transformer.decoder.bbox_embed" in key:
                  lowercase_     			=   state_dict.pop(__lowerCamelCase					)
                  lowercase_     			=   val
            if "input_proj" in key:
                  lowercase_     			=   state_dict.pop(__lowerCamelCase					)
                  lowercase_     			=   val
            if "level_embed" in key or "pos_trans" in key or "pix_trans" in key or "enc_output" in key:
                  lowercase_     			=   state_dict.pop(__lowerCamelCase					)
                  lowercase_     			=   val

    # finally, create HuggingFace model and load state dict
      lowercase_     			=   DetaForObjectDetection(__lowerCamelCase					)
      model.load_state_dict(__lowerCamelCase					)
      model.eval()

      lowercase_     			=   "cuda" if torch.cuda.is_available() else "cpu"
      model.to(__lowerCamelCase					)

      # load image processor
      lowercase_     			=   DetaImageProcessor(format="coco_detection"					)

      # verify our conversion on image
      lowercase_     			=   prepare_img()
      lowercase_     			=   processor(images=__lowerCamelCase			,		return_tensors="pt"					)
      lowercase_     			=   encoding["pixel_values"]
      lowercase_     			=   model(pixel_values.to(__lowerCamelCase					)					)

      # verify logits
      if model_name == "deta-resnet-50":
            lowercase_     			=   torch.tensor(
                [[-7.3_9_7_8, -2.5_4_0_6, -4.1_6_6_8], [-8.2_6_8_4, -3.9_9_3_3, -3.8_0_9_6], [-7.0_5_1_5, -3.7_9_7_3, -5.8_5_1_6]]					)
            lowercase_     			=   torch.tensor([[0.5_0_4_3, 0.4_9_7_3, 0.9_9_9_8], [0.2_5_4_2, 0.5_4_8_9, 0.4_7_4_8], [0.5_4_9_0, 0.2_7_6_5, 0.0_5_7_0]]					)
      elif model_name == "deta-resnet-50-24-epochs":
            lowercase_     			=   torch.tensor(
                [[-7.1_6_8_8, -2.4_8_5_7, -4.8_6_6_9], [-7.8_6_3_0, -3.8_1_5_4, -4.2_6_7_4], [-7.2_7_3_0, -4.1_8_6_5, -5.5_3_2_3]]					)
            lowercase_     			=   torch.tensor([[0.5_0_2_1, 0.4_9_7_1, 0.9_9_9_4], [0.2_5_4_6, 0.5_4_8_6, 0.4_7_3_1], [0.1_6_8_6, 0.1_9_8_6, 0.2_1_4_2]]					)

      assert torch.allclose(outputs.logits[0, :3, :3]			,		expected_logits.to(__lowerCamelCase					)			,		atol=1e-4					)
      assert torch.allclose(outputs.pred_boxes[0, :3, :3]			,		expected_boxes.to(__lowerCamelCase					)			,		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(__lowerCamelCase					).mkdir(exist_ok=__lowerCamelCase					)
            model.save_pretrained(__lowerCamelCase					)
            processor.save_pretrained(__lowerCamelCase					)

      # 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__":
       lowerCAmelCase_			:			Dict    	=   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."
       )
       lowerCAmelCase_			:			Optional[int]    	=   parser.parse_args()
       convert_deta_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)







'''simple docstring'''

import unittest
from dataclasses import dataclass
from typing import Optional

from transformers.utils import ModelOutput





@dataclass
class 				lowercase    (    __lowerCamelCase			):
   lowerCamelCase_            =42
   lowerCamelCase_            =None
   lowerCamelCase_            =None





class 				lowercase    (    unittest.TestCase			):


   def __UpperCAmelCase							( self					:						Tuple)   ->       Dict:
         lowercase_     			=   ModelOutputTest(a=30)
         self.assertEqual(x.a ,		30)
         self.assertIsNone(x.b)
         self.assertIsNone(x.c)
         with self.assertRaises(__lowerCAmelCase):
               lowercase_     			=   x.d


   def __UpperCAmelCase							( self					:						List[str])   ->       Union[str, Any]:
         lowercase_     			=   ModelOutputTest(a=30 ,		b=10)
         self.assertEqual(x[0] ,		30)
         self.assertEqual(x[1] ,		10)
         self.assertEqual(x[:2] ,		(30, 10))
         self.assertEqual(x[:] ,		(30, 10))

         lowercase_     			=   ModelOutputTest(a=30 ,		c=10)
         self.assertEqual(x[0] ,		30)
         self.assertEqual(x[1] ,		10)
         self.assertEqual(x[:2] ,		(30, 10))
         self.assertEqual(x[:] ,		(30, 10))


   def __UpperCAmelCase							( self					:						Union[str, Any])   ->       List[str]:
         lowercase_     			=   ModelOutputTest(a=30 ,		b=10)
         self.assertEqual(x["a"] ,		30)
         self.assertEqual(x["b"] ,		10)
         with self.assertRaises(__lowerCAmelCase):
               lowercase_     			=   x["c"]

         lowercase_     			=   ModelOutputTest(a=30 ,		c=10)
         self.assertEqual(x["a"] ,		30)
         self.assertEqual(x["c"] ,		10)
         with self.assertRaises(__lowerCAmelCase):
               lowercase_     			=   x["b"]


   def __UpperCAmelCase							( self					:						List[str])   ->       List[str]:
         lowercase_     			=   ModelOutputTest(a=30)
         self.assertEqual(list(x.keys()) ,		["a"])
         self.assertEqual(list(x.values()) ,		[30])
         self.assertEqual(list(x.items()) ,		[("a", 30)])
         self.assertEqual(list(__lowerCAmelCase) ,		["a"])

         lowercase_     			=   ModelOutputTest(a=30 ,		b=10)
         self.assertEqual(list(x.keys()) ,		["a", "b"])
         self.assertEqual(list(x.values()) ,		[30, 10])
         self.assertEqual(list(x.items()) ,		[("a", 30), ("b", 10)])
         self.assertEqual(list(__lowerCAmelCase) ,		["a", "b"])

         lowercase_     			=   ModelOutputTest(a=30 ,		c=10)
         self.assertEqual(list(x.keys()) ,		["a", "c"])
         self.assertEqual(list(x.values()) ,		[30, 10])
         self.assertEqual(list(x.items()) ,		[("a", 30), ("c", 10)])
         self.assertEqual(list(__lowerCAmelCase) ,		["a", "c"])

         with self.assertRaises(__lowerCAmelCase):
               lowercase_     			=   x.update({"d": 20})
         with self.assertRaises(__lowerCAmelCase):
               del x["a"]
         with self.assertRaises(__lowerCAmelCase):
               lowercase_     			=   x.pop("a")
         with self.assertRaises(__lowerCAmelCase):
               lowercase_     			=   x.setdefault("d" ,		32)


   def __UpperCAmelCase							( self					:						List[Any])   ->       Optional[int]:
         lowercase_     			=   ModelOutputTest(a=30)
         lowercase_     			=   10
         self.assertEqual(x.a ,		10)
         self.assertEqual(x["a"] ,		10)


   def __UpperCAmelCase							( self					:						List[Any])   ->       Any:
         lowercase_     			=   ModelOutputTest(a=30)
         lowercase_     			=   10
         self.assertEqual(x.a ,		10)
         self.assertEqual(x["a"] ,		10)


   def __UpperCAmelCase							( self					:						Union[str, Any])   ->       List[Any]:
         lowercase_     			=   ModelOutputTest({"a": 30, "b": 10})
         self.assertEqual(list(x.keys()) ,		["a", "b"])
         self.assertEqual(x.a ,		30)
         self.assertEqual(x.b ,		10)


   def __UpperCAmelCase							( self					:						List[str])   ->       str:
         lowercase_     			=   ModelOutputTest([("a", 30), ("b", 10)])
         self.assertEqual(list(x.keys()) ,		["a", "b"])
         self.assertEqual(x.a ,		30)
         self.assertEqual(x.b ,		10)

         with self.assertRaises(__lowerCAmelCase):
               lowercase_     			=   ModelOutputTest([("a", 30), (10, 10)])

         lowercase_     			=   ModelOutputTest(a=(30, 30))
         self.assertEqual(list(x.keys()) ,		["a"])
         self.assertEqual(x.a ,		(30, 30))






