





"""simple docstring"""






import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import DetrConfig, DetrForObjectDetection, DetrForSegmentation, DetrImageProcessor, ResNetConfig
from transformers.utils import logging


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

def       __A (     a_  :Dict)						->						Tuple:
    # initialize config
    if "resnet-50" in model_name:
        __a	:  Any         =					ResNetConfig.from_pretrained('''microsoft/resnet-50''')
    elif "resnet-101" in model_name:
        __a	:  str         =					ResNetConfig.from_pretrained('''microsoft/resnet-101''')
    else:
        raise ValueError('''Model name should include either resnet50 or resnet101''')

    __a	:  Union[str, Any]         =					DetrConfig(use_timm_backbone=a_				,    backbone_config=a_)

    # set label attributes
    __a	:  Tuple         =					"""panoptic""" in model_name
    if is_panoptic:
        __a	:  List[Any]         =					2_50
    else:
        __a	:  Tuple         =					91
        __a	:  str         =					"""huggingface/label-files"""
        __a	:  Dict         =					"""coco-detection-id2label.json"""
        __a	:  str         =					json.load(open(hf_hub_download(a_				,    a_				,    repo_type='''dataset''')				,    '''r'''))
        __a	:  Optional[Any]         =					{int(a_): v for k, v in idalabel.items()}
        __a	:  Optional[Any]         =					idalabel
        __a	:  Dict         =					{v: k for k, v in idalabel.items()}

    return config, is_panoptic

def       __A (     a_  :Union[str, Any])						->						Union[str, Any]:
    # here we list all keys to be renamed (original name on the left, our name on the right)
    __a	:  Dict         =					[]

    # stem
    # fmt: off
    rename_keys.append(('''backbone.0.body.conv1.weight''', '''backbone.conv_encoder.model.embedder.embedder.convolution.weight'''))
    rename_keys.append(('''backbone.0.body.bn1.weight''', '''backbone.conv_encoder.model.embedder.embedder.normalization.weight'''))
    rename_keys.append(('''backbone.0.body.bn1.bias''', '''backbone.conv_encoder.model.embedder.embedder.normalization.bias'''))
    rename_keys.append(('''backbone.0.body.bn1.running_mean''', '''backbone.conv_encoder.model.embedder.embedder.normalization.running_mean'''))
    rename_keys.append(('''backbone.0.body.bn1.running_var''', '''backbone.conv_encoder.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"""backbone.conv_encoder.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"""backbone.conv_encoder.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"""backbone.conv_encoder.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"""backbone.conv_encoder.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"""backbone.conv_encoder.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"""backbone.conv_encoder.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"""backbone.conv_encoder.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"""backbone.conv_encoder.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"""backbone.conv_encoder.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"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_var""",
                    ))
                # fmt: on

    for i in range(config.encoder_layers):
        # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
        rename_keys.append(
            (
                F"""transformer.encoder.layers.{i}.self_attn.out_proj.weight""",
                F"""encoder.layers.{i}.self_attn.out_proj.weight""",
            ))
        rename_keys.append(
            (F"""transformer.encoder.layers.{i}.self_attn.out_proj.bias""", F"""encoder.layers.{i}.self_attn.out_proj.bias"""))
        rename_keys.append((F"""transformer.encoder.layers.{i}.linear1.weight""", F"""encoder.layers.{i}.fc1.weight"""))
        rename_keys.append((F"""transformer.encoder.layers.{i}.linear1.bias""", F"""encoder.layers.{i}.fc1.bias"""))
        rename_keys.append((F"""transformer.encoder.layers.{i}.linear2.weight""", F"""encoder.layers.{i}.fc2.weight"""))
        rename_keys.append((F"""transformer.encoder.layers.{i}.linear2.bias""", F"""encoder.layers.{i}.fc2.bias"""))
        rename_keys.append(
            (F"""transformer.encoder.layers.{i}.norm1.weight""", F"""encoder.layers.{i}.self_attn_layer_norm.weight"""))
        rename_keys.append(
            (F"""transformer.encoder.layers.{i}.norm1.bias""", F"""encoder.layers.{i}.self_attn_layer_norm.bias"""))
        rename_keys.append(
            (F"""transformer.encoder.layers.{i}.norm2.weight""", F"""encoder.layers.{i}.final_layer_norm.weight"""))
        rename_keys.append((F"""transformer.encoder.layers.{i}.norm2.bias""", F"""encoder.layers.{i}.final_layer_norm.bias"""))
        # decoder layers: 2 times output projection, 2 feedforward neural networks and 3 layernorms
        rename_keys.append(
            (
                F"""transformer.decoder.layers.{i}.self_attn.out_proj.weight""",
                F"""decoder.layers.{i}.self_attn.out_proj.weight""",
            ))
        rename_keys.append(
            (F"""transformer.decoder.layers.{i}.self_attn.out_proj.bias""", F"""decoder.layers.{i}.self_attn.out_proj.bias"""))
        rename_keys.append(
            (
                F"""transformer.decoder.layers.{i}.multihead_attn.out_proj.weight""",
                F"""decoder.layers.{i}.encoder_attn.out_proj.weight""",
            ))
        rename_keys.append(
            (
                F"""transformer.decoder.layers.{i}.multihead_attn.out_proj.bias""",
                F"""decoder.layers.{i}.encoder_attn.out_proj.bias""",
            ))
        rename_keys.append((F"""transformer.decoder.layers.{i}.linear1.weight""", F"""decoder.layers.{i}.fc1.weight"""))
        rename_keys.append((F"""transformer.decoder.layers.{i}.linear1.bias""", F"""decoder.layers.{i}.fc1.bias"""))
        rename_keys.append((F"""transformer.decoder.layers.{i}.linear2.weight""", F"""decoder.layers.{i}.fc2.weight"""))
        rename_keys.append((F"""transformer.decoder.layers.{i}.linear2.bias""", F"""decoder.layers.{i}.fc2.bias"""))
        rename_keys.append(
            (F"""transformer.decoder.layers.{i}.norm1.weight""", F"""decoder.layers.{i}.self_attn_layer_norm.weight"""))
        rename_keys.append(
            (F"""transformer.decoder.layers.{i}.norm1.bias""", F"""decoder.layers.{i}.self_attn_layer_norm.bias"""))
        rename_keys.append(
            (F"""transformer.decoder.layers.{i}.norm2.weight""", F"""decoder.layers.{i}.encoder_attn_layer_norm.weight"""))
        rename_keys.append(
            (F"""transformer.decoder.layers.{i}.norm2.bias""", F"""decoder.layers.{i}.encoder_attn_layer_norm.bias"""))
        rename_keys.append(
            (F"""transformer.decoder.layers.{i}.norm3.weight""", F"""decoder.layers.{i}.final_layer_norm.weight"""))
        rename_keys.append((F"""transformer.decoder.layers.{i}.norm3.bias""", F"""decoder.layers.{i}.final_layer_norm.bias"""))

    # convolutional projection + query embeddings + layernorm of decoder + class and bounding box heads
    rename_keys.extend(
        [
            ('''input_proj.weight''', '''input_projection.weight'''),
            ('''input_proj.bias''', '''input_projection.bias'''),
            ('''query_embed.weight''', '''query_position_embeddings.weight'''),
            ('''transformer.decoder.norm.weight''', '''decoder.layernorm.weight'''),
            ('''transformer.decoder.norm.bias''', '''decoder.layernorm.bias'''),
            ('''class_embed.weight''', '''class_labels_classifier.weight'''),
            ('''class_embed.bias''', '''class_labels_classifier.bias'''),
            ('''bbox_embed.layers.0.weight''', '''bbox_predictor.layers.0.weight'''),
            ('''bbox_embed.layers.0.bias''', '''bbox_predictor.layers.0.bias'''),
            ('''bbox_embed.layers.1.weight''', '''bbox_predictor.layers.1.weight'''),
            ('''bbox_embed.layers.1.bias''', '''bbox_predictor.layers.1.bias'''),
            ('''bbox_embed.layers.2.weight''', '''bbox_predictor.layers.2.weight'''),
            ('''bbox_embed.layers.2.bias''', '''bbox_predictor.layers.2.bias'''),
        ])

    return rename_keys

def       __A (     a_  :Union[str, Any]				,    a_  :Any				,    a_  :List[str])						->						List[str]:
    __a	:  Union[str, Any]         =					state_dict.pop(a_)
    __a	:  Tuple         =					val

def       __A (     a_  :str				,    a_  :Optional[int]=False)						->						Optional[int]:
    __a	:  Optional[int]         =					""""""
    if is_panoptic:
        __a	:  Tuple         =					"""detr."""

    # first: transformer encoder
    for i in range(6):
        # read in weights + bias of input projection layer (in PyTorch's MultiHeadAttention, this is a single matrix + bias)
        __a	:  List[str]         =					state_dict.pop(F"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight""")
        __a	:  Tuple         =					state_dict.pop(F"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_bias""")
        # next, add query, keys and values (in that order) to the state dict
        __a	:  int         =					in_proj_weight[:2_56, :]
        __a	:  Optional[int]         =					in_proj_bias[:2_56]
        __a	:  Optional[Any]         =					in_proj_weight[2_56:5_12, :]
        __a	:  Any         =					in_proj_bias[2_56:5_12]
        __a	:  List[Any]         =					in_proj_weight[-2_56:, :]
        __a	:  Optional[Any]         =					in_proj_bias[-2_56:]
    # next: transformer decoder (which is a bit more complex because it also includes cross-attention)
    for i in range(6):
        # read in weights + bias of input projection layer of self-attention
        __a	:  Union[str, Any]         =					state_dict.pop(F"""{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight""")
        __a	:  List[str]         =					state_dict.pop(F"""{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_bias""")
        # next, add query, keys and values (in that order) to the state dict
        __a	:  str         =					in_proj_weight[:2_56, :]
        __a	:  Optional[int]         =					in_proj_bias[:2_56]
        __a	:  List[Any]         =					in_proj_weight[2_56:5_12, :]
        __a	:  List[Any]         =					in_proj_bias[2_56:5_12]
        __a	:  str         =					in_proj_weight[-2_56:, :]
        __a	:  List[Any]         =					in_proj_bias[-2_56:]
        # read in weights + bias of input projection layer of cross-attention
        __a	:  List[str]         =					state_dict.pop(
            F"""{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight""")
        __a	:  str         =					state_dict.pop(F"""{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_bias""")
        # next, add query, keys and values (in that order) of cross-attention to the state dict
        __a	:  Optional[int]         =					in_proj_weight_cross_attn[:2_56, :]
        __a	:  Dict         =					in_proj_bias_cross_attn[:2_56]
        __a	:  int         =					in_proj_weight_cross_attn[2_56:5_12, :]
        __a	:  Tuple         =					in_proj_bias_cross_attn[2_56:5_12]
        __a	:  List[Any]         =					in_proj_weight_cross_attn[-2_56:, :]
        __a	:  Tuple         =					in_proj_bias_cross_attn[-2_56:]

def       __A (     )						->						Dict:
    __a	:  Optional[int]         =					"""http://images.cocodataset.org/val2017/000000039769.jpg"""
    __a	:  Dict         =					Image.open(requests.get(a_				,    stream=a_).raw)

    return im

@torch.no_grad()
def       __A (     a_  :Optional[int]				,    a_  :str=None				,    a_  :int=False)						->						Optional[Any]:
    __a	:  Any         =					get_detr_config(a_)

    # load original model from torch hub
    __a	:  Dict         =					{
        """detr-resnet-50""": """detr_resnet50""",
        """detr-resnet-101""": """detr_resnet101""",
    }
    logger.info(F"""Converting model {model_name}...""")
    __a	:  int         =					torch.hub.load('''facebookresearch/detr'''				,    model_name_to_original_name[model_name]				,    pretrained=a_).eval()
    __a	:  Union[str, Any]         =					detr.state_dict()
    # rename keys
    for src, dest in create_rename_keys(a_):
        if is_panoptic:
            __a	:  List[str]         =					"""detr.""" + src
        rename_key(a_				,    a_				,    a_)
    # query, key and value matrices need special treatment
    read_in_q_k_v(a_				,    is_panoptic=a_)
    # important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
    __a	:  Dict         =					"""detr.model.""" if is_panoptic else """model."""
    for key in state_dict.copy().keys():
        if is_panoptic:
            if (
                key.startswith('''detr''')
                and not key.startswith('''class_labels_classifier''')
                and not key.startswith('''bbox_predictor''')
            ):
                __a	:  Optional[Any]         =					state_dict.pop(a_)
                __a	:  Optional[Any]         =					val
            elif "class_labels_classifier" in key or "bbox_predictor" in key:
                __a	:  Optional[int]         =					state_dict.pop(a_)
                __a	:  Optional[int]         =					val
            elif key.startswith('''bbox_attention''') or key.startswith('''mask_head'''):
                continue
            else:
                __a	:  int         =					state_dict.pop(a_)
                __a	:  List[Any]         =					val
        else:
            if not key.startswith('''class_labels_classifier''') and not key.startswith('''bbox_predictor'''):
                __a	:  int         =					state_dict.pop(a_)
                __a	:  int         =					val

                # finally, create HuggingFace model and load state dict
    __a	:  Dict         =					DetrForSegmentation(a_) if is_panoptic else DetrForObjectDetection(a_)
    model.load_state_dict(a_)
    model.eval()

    # verify our conversion on an image
    __a	:  Union[str, Any]         =					"""coco_panoptic""" if is_panoptic else """coco_detection"""
    __a	:  Any         =					DetrImageProcessor(format=a_)

    __a	:  Any         =					processor(images=prepare_img()				,    return_tensors='''pt''')
    __a	:  Union[str, Any]         =					encoding["""pixel_values"""]

    __a	:  Optional[int]         =					detr(a_)
    __a	:  Any         =					model(a_)

    assert torch.allclose(outputs.logits				,    original_outputs['''pred_logits''']				,    atol=1e-3)
    assert torch.allclose(outputs.pred_boxes				,    original_outputs['''pred_boxes''']				,    atol=1e-3)
    if is_panoptic:
        assert torch.allclose(outputs.pred_masks				,    original_outputs['''pred_masks''']				,    atol=1e-4)
    print('''Looks ok!''')

    if pytorch_dump_folder_path is not None:
        # Save model and image processor
        logger.info(F"""Saving PyTorch model and image processor to {pytorch_dump_folder_path}...""")
        Path(a_).mkdir(exist_ok=a_)
        model.save_pretrained(a_)
        processor.save_pretrained(a_)

    if push_to_hub:
        # Upload model and image processor to the hub
        logger.info('''Uploading PyTorch model and image processor to the hub...''')
        model.push_to_hub(F"""nielsr/{model_name}""")
        processor.push_to_hub(F"""nielsr/{model_name}""")


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

  parser.add_argument(
      '''--model_name''',
      default='''detr-resnet-50''',
      type=str,
      choices=['''detr-resnet-50''', '''detr-resnet-101'''],
      help='''Name of the DETR 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 to push the model to the hub or not.''')
  A	     =					parser.parse_args()
  convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)






"""simple docstring"""






def       __A (     a_  :int = 1_00_00_00)						->						int:
				__a	:  Tuple         =					[i - 1 for i in range(limit + 1)]

				for i in range(2				,    limit + 1):
								if phi[i] == i - 1:
												for j in range(2 * i				,    limit + 1				,    a_):
																phi[j] -= phi[j] // i

				return sum(phi[2 : limit + 1])


if __name__ == "__main__":
		print(solution())