'''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()
__UpperCAmelCase  							=logging.get_logger(__name__)






def __lowerCAmelCase				(							)			->					List[str]:
    __lowerCamelCase						=						DetaConfig(
        num_queries=9_00       ,						encoder_ffn_dim=20_48       ,						decoder_ffn_dim=20_48       ,						num_feature_levels=5       ,						assign_first_stage=UpperCamelCase__       ,						with_box_refine=UpperCamelCase__       ,						two_stage=UpperCamelCase__       ,						)

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

    return config






def __lowerCAmelCase				(							UpperCamelCase__       )			->					Union[str, Any]:
    __lowerCamelCase						=						[]

    # 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 __lowerCAmelCase				(							UpperCamelCase__       ,						UpperCamelCase__       ,						UpperCamelCase__       )			->					str:
    __lowerCamelCase						=						dct.pop(UpperCamelCase__       )
    __lowerCamelCase						=						val






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






def __lowerCAmelCase				(							)			->					Union[str, Any]:
    __lowerCamelCase						=						'''http://images.cocodataset.org/val2017/000000039769.jpg'''
    __lowerCamelCase						=						Image.open(requests.get(UpperCamelCase__       ,						stream=UpperCamelCase__       ).raw       )

    return im






@torch.no_grad()
def __lowerCAmelCase				(							UpperCamelCase__       ,						UpperCamelCase__       ,						UpperCamelCase__       )			->					str:
    __lowerCamelCase						=						get_deta_config()

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

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

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

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

    __lowerCamelCase						=						'''cuda''' if torch.cuda.is_available() else '''cpu'''
    model.to(UpperCamelCase__       )

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

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

    # verify logits
    if model_name == "deta-resnet-50":
        __lowerCamelCase						=						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]]       )
        __lowerCamelCase						=						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":
        __lowerCamelCase						=						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]]       )
        __lowerCamelCase						=						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(UpperCamelCase__       )       ,						atol=1E-4       )
    assert torch.allclose(outputs.pred_boxes[0, :3, :3]       ,						expected_boxes.to(UpperCamelCase__       )       ,						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(UpperCamelCase__       ).mkdir(exist_ok=UpperCamelCase__       )
        model.save_pretrained(UpperCamelCase__       )
        processor.save_pretrained(UpperCamelCase__       )

    # 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  							=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."
  )
  __UpperCAmelCase  							=parser.parse_args()
  convert_deta_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)

'''simple docstring'''




from __future__ import annotations






def __lowerCAmelCase				(							UpperCamelCase__       ,						UpperCamelCase__       )			->					list:
    __lowerCamelCase						=						[None] * (len(UpperCamelCase__       ) + len(UpperCamelCase__       ))

    __lowerCamelCase						=						0  # pointer to current index for left Half
    __lowerCamelCase						=						0  # pointer to current index for the right Half
    __lowerCamelCase						=						0  # pointer to current index for the sorted array Half

    while pointera < len(UpperCamelCase__       ) and pointera < len(UpperCamelCase__       ):
        if left_half[pointera] < right_half[pointera]:
            __lowerCamelCase						=						left_half[pointera]
            pointera += 1
            index += 1
        else:
            __lowerCamelCase						=						right_half[pointera]
            pointera += 1
            index += 1
    while pointera < len(UpperCamelCase__       ):
        __lowerCamelCase						=						left_half[pointera]
        pointera += 1
        index += 1

    while pointera < len(UpperCamelCase__       ):
        __lowerCamelCase						=						right_half[pointera]
        pointera += 1
        index += 1

    return sorted_array






def __lowerCAmelCase				(							UpperCamelCase__       )			->					list:
    if len(UpperCamelCase__       ) <= 1:
        return array
    # the actual formula to calculate the middle element = left + (right - left) // 2
    # this avoids integer overflow in case of large N
    __lowerCamelCase						=						0 + (len(UpperCamelCase__       ) - 0) // 2

    # Split the array into halves till the array length becomes equal to One
    # merge the arrays of single length returned by mergeSort function and
    # pass them into the merge arrays function which merges the array
    __lowerCamelCase						=						array[:middle]
    __lowerCamelCase						=						array[middle:]

    return merge(merge_sort(UpperCamelCase__       )       ,						merge_sort(UpperCamelCase__       )       )


if __name__ == "__main__":
  import doctest

  doctest.testmod()
