



'''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()
UpperCamelCase_       :  Optional[int]					=  logging.get_logger(__name__)






def 		_lowerCAmelCase				(_lowercase			):

       """simple docstring"""

       a__								= SwinConfig(
           embed_dim=1_92   ,					depths=(2, 2, 18, 2)   ,					num_heads=(6, 12, 24, 48)   ,					window_size=12   ,					out_features=["stage2", "stage3", "stage4"]   ,					)

       a__								= DetaConfig(
           backbone_config=_lowerCamelCase   ,					num_queries=9_00   ,					encoder_ffn_dim=20_48   ,					decoder_ffn_dim=20_48   ,					num_feature_levels=5   ,					assign_first_stage=_lowerCamelCase   ,					with_box_refine=_lowerCamelCase   ,					two_stage=_lowerCamelCase   ,					)

       # set labels
       a__								= "huggingface/label-files"
       if "o365" in model_name:
              a__								= 3_66
              a__								= "object365-id2label.json"
       else:
              a__								= 91
              a__								= "coco-detection-id2label.json"

       a__								= num_labels
       a__								= json.load(open(cached_download(hf_hub_url(_lowerCamelCase   ,					_lowerCamelCase   ,					repo_type="dataset"			)			)   ,					"r"			)			)
       a__								= {int(_lowerCamelCase			): v for k, v in idalabel.items()}
       a__								= idalabel
       a__								= {v: k for k, v in idalabel.items()}

       return config






def 		_lowerCAmelCase				(_lowercase			):

       """simple docstring"""

       a__								= []

       # 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 		_lowerCAmelCase				(_lowercase   ,					_lowercase   ,					_lowercase			):

       """simple docstring"""

       a__								= dct.pop(_lowerCamelCase			)
       a__								= val






def 		_lowerCAmelCase				(_lowercase   ,					_lowercase			):

       """simple docstring"""

       a__								= [int(backbone_config.embed_dim * 2**i			) for i in range(len(backbone_config.depths			)			)]
       for i in range(len(backbone_config.depths			)			):
              a__								= 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)
                     a__								= state_dict.pop(F'backbone.0.body.layers.{i}.blocks.{j}.attn.qkv.weight'			)
                     a__								= 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
                     a__								= in_proj_weight[:dim, :]
                     a__								= in_proj_bias[: dim]
                     a__								= in_proj_weight[
                         dim : dim * 2, :
                     ]
                     a__								= in_proj_bias[
                         dim : dim * 2
                     ]
                     a__								= in_proj_weight[
                         -dim :, :
                     ]
                     a__								= in_proj_bias[-dim :]
                     # fmt: on






def 		_lowerCAmelCase				(_lowercase   ,					_lowercase			):

       """simple docstring"""

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






def 		_lowerCAmelCase				():

       """simple docstring"""

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

       return im






@torch.no_grad()
def 		_lowerCAmelCase				(_lowercase   ,					_lowercase   ,					_lowercase			):

       """simple docstring"""

       a__								= get_deta_config(_lowerCamelCase			)

       # load original state dict
       if model_name == "deta-swin-large":
              a__								= hf_hub_download(repo_id="nielsr/deta-checkpoints"   ,					filename="adet_swin_ft.pth"			)
       elif model_name == "deta-swin-large-o365":
              a__								= 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'			)

       a__								= torch.load(_lowerCamelCase   ,					map_location="cpu"			)["model"]

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

       # rename keys
       a__								= create_rename_keys(_lowerCamelCase			)
       for src, dest in rename_keys:
              rename_key(_lowerCamelCase   ,					_lowerCamelCase   ,					_lowerCamelCase			)
       read_in_swin_q_k_v(_lowerCamelCase   ,					config.backbone_config			)
       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:
                     a__								= state_dict.pop(_lowerCamelCase			)
                     a__								= val
              if "input_proj" in key:
                     a__								= state_dict.pop(_lowerCamelCase			)
                     a__								= val
              if "level_embed" in key or "pos_trans" in key or "pix_trans" in key or "enc_output" in key:
                     a__								= state_dict.pop(_lowerCamelCase			)
                     a__								= val

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

       a__								= "cuda" if torch.cuda.is_available() else "cpu"
       model.to(_lowerCamelCase			)

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

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

       # verify logits
       print("Logits:"   ,					outputs.logits[0, :3, :3]			)
       print("Boxes:"   ,					outputs.pred_boxes[0, :3, :3]			)
       if model_name == "deta-swin-large":
              a__								= torch.tensor(
                  [[-7.6308, -2.8485, -5.3737], [-7.2037, -4.5505, -4.8027], [-7.2943, -4.2611, -4.6617]]			)
              a__								= 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":
              a__								= torch.tensor(
                  [[-8.0122, -3.5720, -4.9717], [-8.1547, -3.6886, -4.6389], [-7.6610, -3.6194, -5.0134]]			)
              a__								= 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(_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__":
    UpperCamelCase_       :  Optional[int]					=  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."""
    )
    UpperCamelCase_       :  Optional[int]					=  parser.parse_args()
    convert_deta_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)






'''simple docstring'''



def       _UpperCamelCase			(_lowerCamelCase				:  int ,						_lowerCamelCase				:  int ,						_lowerCamelCase				:  int						)-> float:




     '''simple docstring'''





     __snake_case            =	(num_of_terms / 2) * (2 * first_term + (num_of_terms - 1) * common_diff)
     # formula for sum of series
     return total







def       _UpperCamelCase			()-> str:




     '''simple docstring'''





     print(sum_of_series(1 ,						1 ,						10						)						)


if __name__ == "__main__":
     import doctest

     doctest.testmod()






