

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()
lowercase_									=    logging.get_logger(__name__)
def 		_snake_case(       )				-> Optional[int]:





    '''simple docstring'''






    A__        =  DetaConfig(
        num_queries=900       ,     encoder_ffn_dim=2048       ,     decoder_ffn_dim=2048       ,     num_feature_levels=5       ,     assign_first_stage=SCREAMING_SNAKE_CASE__       ,     with_box_refine=SCREAMING_SNAKE_CASE__       ,     two_stage=SCREAMING_SNAKE_CASE__       ,     )

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

    return config
def 		_snake_case(       SCREAMING_SNAKE_CASE__     :      int )				-> Tuple:





    '''simple docstring'''






    A__        =  []

    # 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 		_snake_case(       SCREAMING_SNAKE_CASE__     :      Union[str, Any]       ,     SCREAMING_SNAKE_CASE__     :      Dict       ,     SCREAMING_SNAKE_CASE__     :      Any )				-> str:





    '''simple docstring'''






    A__        =  dct.pop(SCREAMING_SNAKE_CASE__ )
    A__        =  val
def 		_snake_case(       SCREAMING_SNAKE_CASE__     :      Tuple       ,     SCREAMING_SNAKE_CASE__     :      Tuple )				-> Union[str, Any]:





    '''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 		_snake_case(       )				-> Optional[Any]:





    '''simple docstring'''






    A__        =  'http://images.cocodataset.org/val2017/000000039769.jpg'
    A__        =  Image.open(requests.get(SCREAMING_SNAKE_CASE__       ,     stream=SCREAMING_SNAKE_CASE__ ).raw )

    return im
@torch.no_grad()
def 		_snake_case(       SCREAMING_SNAKE_CASE__     :      Optional[Any]       ,     SCREAMING_SNAKE_CASE__     :      Optional[int]       ,     SCREAMING_SNAKE_CASE__     :      Dict )				-> List[str]:





    '''simple docstring'''






    A__        =  get_deta_config()

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

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

    # 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(SCREAMING_SNAKE_CASE__ )
            A__        =  val
        if "input_proj" in key:
            A__        =  state_dict.pop(SCREAMING_SNAKE_CASE__ )
            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(SCREAMING_SNAKE_CASE__ )
            A__        =  val

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

    A__        =  'cuda' if torch.cuda.is_available() else 'cpu'
    model.to(SCREAMING_SNAKE_CASE__ )

    # load image processor
    A__        =  DetaImageProcessor(format='coco_detection' )

    # verify our conversion on image
    A__        =  prepare_img()
    A__        =  processor(images=SCREAMING_SNAKE_CASE__       ,     return_tensors='pt' )
    A__        =  encoding['pixel_values']
    A__        =  model(pixel_values.to(SCREAMING_SNAKE_CASE__ ) )

    # verify logits
    if model_name == "deta-resnet-50":
        A__        =  torch.tensor(
            [[-7.3978, -2.5406, -4.1668], [-8.2684, -3.9933, -3.8096], [-7.0515, -3.7973, -5.8516]] )
        A__        =  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":
        A__        =  torch.tensor(
            [[-7.1688, -2.4857, -4.8669], [-7.8630, -3.8154, -4.2674], [-7.2730, -4.1865, -5.5323]] )
        A__        =  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(SCREAMING_SNAKE_CASE__ )       ,     atol=1E-4 )
    assert torch.allclose(outputs.pred_boxes[0, :3, :3]       ,     expected_boxes.to(SCREAMING_SNAKE_CASE__ )       ,     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(SCREAMING_SNAKE_CASE__ ).mkdir(exist_ok=SCREAMING_SNAKE_CASE__ )
        model.save_pretrained(SCREAMING_SNAKE_CASE__ )
        processor.save_pretrained(SCREAMING_SNAKE_CASE__ )

    # 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__":
     lowercase_									=    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."
     )
     lowercase_									=    parser.parse_args()
     convert_deta_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)



"""simple docstring"""

import copy
from typing import Dict, Optional

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


UpperCamelCase      :							List[str]          =					logging.get_logger(__name__)

UpperCamelCase      :							Dict          =					{
    "shi-labs/oneformer_ade20k_swin_tiny": (
        "https://huggingface.co/shi-labs/oneformer_ade20k_swin_tiny/blob/main/config.json"
    ),
    # See all OneFormer models at https://huggingface.co/models?filter=oneformer
}

class __lowerCAmelCase						( __SCREAMING_SNAKE_CASE     ):
     lowercase		  =		"oneformer"
     lowercase		  =		{"hidden_size": "hidden_dim"}





     def __init__(      self      ,	__UpperCAmelCase = None      ,	__UpperCAmelCase = 255      ,	__UpperCAmelCase = 150      ,	__UpperCAmelCase = 0.1      ,	__UpperCAmelCase = 2.0      ,	__UpperCAmelCase = 5.0      ,	__UpperCAmelCase = 5.0      ,	__UpperCAmelCase = 0.5      ,	__UpperCAmelCase = 0.0_7      ,	__UpperCAmelCase = 1_2544      ,	__UpperCAmelCase = 3.0      ,	__UpperCAmelCase = 0.7_5      ,	__UpperCAmelCase = 0.0_2      ,	__UpperCAmelCase = 1.0      ,	__UpperCAmelCase = 1E-05      ,	__UpperCAmelCase = False      ,	__UpperCAmelCase = True      ,	__UpperCAmelCase = True      ,	__UpperCAmelCase = [4, 8, 16, 32]      ,	__UpperCAmelCase = 77      ,	__UpperCAmelCase = 256      ,	__UpperCAmelCase = 77      ,	__UpperCAmelCase = 6      ,	__UpperCAmelCase = 4_9408      ,	__UpperCAmelCase = 2      ,	__UpperCAmelCase = 16      ,	__UpperCAmelCase = 256      ,	__UpperCAmelCase = 256      ,	__UpperCAmelCase = 256      ,	__UpperCAmelCase = 1024      ,	__UpperCAmelCase = "GN"      ,	__UpperCAmelCase = 6      ,	__UpperCAmelCase = 10      ,	__UpperCAmelCase = True      ,	__UpperCAmelCase = 8      ,	__UpperCAmelCase = 0.1      ,	__UpperCAmelCase = 2048      ,	__UpperCAmelCase = False      ,	__UpperCAmelCase = False      ,	__UpperCAmelCase = 2      ,	__UpperCAmelCase = 4      ,	**__UpperCAmelCase      ,	):

      '''simple docstring'''




      if backbone_config is None:
       logger.info('`backbone_config` is unset. Initializing the config with the default `Swin` backbone.' )
       __UpperCamelCase							    =			CONFIG_MAPPING['swin'](
           image_size=224      ,	in_channels=3      ,	patch_size=4      ,	embed_dim=96      ,	depths=[2, 2, 6, 2]      ,	num_heads=[3, 6, 12, 24]      ,	window_size=7      ,	drop_path_rate=0.3      ,	use_absolute_embeddings=__UpperCAmelCase      ,	out_features=['stage1', 'stage2', 'stage3', 'stage4']      ,	)
      elif isinstance(__UpperCAmelCase      ,	__UpperCAmelCase ):
       __UpperCamelCase							    =			backbone_config.get('model_type' )
       __UpperCamelCase							    =			CONFIG_MAPPING[backbone_model_type]
       __UpperCamelCase							    =			config_class.from_dict(__UpperCAmelCase )

      __UpperCamelCase							    =			backbone_config

      __UpperCamelCase							    =			ignore_value
      __UpperCamelCase							    =			num_queries
      __UpperCamelCase							    =			no_object_weight
      __UpperCamelCase							    =			class_weight
      __UpperCamelCase							    =			mask_weight
      __UpperCamelCase							    =			dice_weight
      __UpperCamelCase							    =			contrastive_weight
      __UpperCamelCase							    =			contrastive_temperature
      __UpperCamelCase							    =			train_num_points
      __UpperCamelCase							    =			oversample_ratio
      __UpperCamelCase							    =			importance_sample_ratio
      __UpperCamelCase							    =			init_std
      __UpperCamelCase							    =			init_xavier_std
      __UpperCamelCase							    =			layer_norm_eps
      __UpperCamelCase							    =			is_training
      __UpperCamelCase							    =			use_auxiliary_loss
      __UpperCamelCase							    =			output_auxiliary_logits
      __UpperCamelCase							    =			strides
      __UpperCamelCase							    =			task_seq_len
      __UpperCamelCase							    =			text_encoder_width
      __UpperCamelCase							    =			text_encoder_context_length
      __UpperCamelCase							    =			text_encoder_num_layers
      __UpperCamelCase							    =			text_encoder_vocab_size
      __UpperCamelCase							    =			text_encoder_proj_layers
      __UpperCamelCase							    =			text_encoder_n_ctx
      __UpperCamelCase							    =			conv_dim
      __UpperCamelCase							    =			mask_dim
      __UpperCamelCase							    =			hidden_dim
      __UpperCamelCase							    =			encoder_feedforward_dim
      __UpperCamelCase							    =			norm
      __UpperCamelCase							    =			encoder_layers
      __UpperCamelCase							    =			decoder_layers
      __UpperCamelCase							    =			use_task_norm
      __UpperCamelCase							    =			num_attention_heads
      __UpperCamelCase							    =			dropout
      __UpperCamelCase							    =			dim_feedforward
      __UpperCamelCase							    =			pre_norm
      __UpperCamelCase							    =			enforce_input_proj
      __UpperCamelCase							    =			query_dec_layers
      __UpperCamelCase							    =			common_stride
      __UpperCamelCase							    =			decoder_layers

      super().__init__(**__UpperCAmelCase )







     def 		UpperCAmelCase   (      self ):

      '''simple docstring'''




      __UpperCamelCase							    =			copy.deepcopy(self.__dict__ )
      __UpperCamelCase							    =			self.backbone_config.to_dict()
      __UpperCamelCase							    =			self.__class__.model_type
      return output



