


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()
SCREAMING_SNAKE_CASE       :      Tuple												= logging.get_logger(__name__)



def 				UpperCamelCase_(      lowerCamelCase_ )    ->						int:
    # initialize config
    if "resnet-50" in model_name:
        _lowercase       :							Tuple      		=		ResNetConfig.from_pretrained('microsoft/resnet-50' )
    elif "resnet-101" in model_name:
        _lowercase       :							List[str]      		=		ResNetConfig.from_pretrained('microsoft/resnet-101' )
    else:
        raise ValueError('Model name should include either resnet50 or resnet101' )

    _lowercase       :							List[Any]      		=		DetrConfig(use_timm_backbone=lowerCamelCase_							, backbone_config=lowerCamelCase_ )

    # set label attributes
    _lowercase       :							Optional[Any]      		=		'panoptic' in model_name
    if is_panoptic:
        _lowercase       :							List[Any]      		=		250
    else:
        _lowercase       :							Optional[int]      		=		91
        _lowercase       :							Optional[Any]      		=		'huggingface/label-files'
        _lowercase       :							Any      		=		'coco-detection-id2label.json'
        _lowercase       :							str      		=		json.load(open(hf_hub_download(lowerCamelCase_							, lowerCamelCase_							, repo_type='dataset' )							, 'r' ) )
        _lowercase       :							Dict      		=		{int(lowerCamelCase_ ): v for k, v in idalabel.items()}
        _lowercase       :							str      		=		idalabel
        _lowercase       :							str      		=		{v: k for k, v in idalabel.items()}

    return config, is_panoptic



def 				UpperCamelCase_(      lowerCamelCase_ )    ->						List[Any]:
    # here we list all keys to be renamed (original name on the left, our name on the right)
    _lowercase       :							List[str]      		=		[]

    # 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 				UpperCamelCase_(      lowerCamelCase_							, lowerCamelCase_							, lowerCamelCase_ )    ->						Union[str, Any]:
    _lowercase       :							List[Any]      		=		state_dict.pop(lowerCamelCase_ )
    _lowercase       :							Optional[Any]      		=		val



def 				UpperCamelCase_(      lowerCamelCase_							, lowerCamelCase_=False )    ->						int:
    _lowercase       :							Dict      		=		''
    if is_panoptic:
        _lowercase       :							List[Any]      		=		'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)
        _lowercase       :							int      		=		state_dict.pop(F'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight''' )
        _lowercase       :							Optional[Any]      		=		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
        _lowercase       :							List[Any]      		=		in_proj_weight[:256, :]
        _lowercase       :							Any      		=		in_proj_bias[:256]
        _lowercase       :							int      		=		in_proj_weight[256:512, :]
        _lowercase       :							List[str]      		=		in_proj_bias[256:512]
        _lowercase       :							List[str]      		=		in_proj_weight[-256:, :]
        _lowercase       :							Dict      		=		in_proj_bias[-256:]
    # 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
        _lowercase       :							str      		=		state_dict.pop(F'''{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight''' )
        _lowercase       :							int      		=		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
        _lowercase       :							Optional[int]      		=		in_proj_weight[:256, :]
        _lowercase       :							Optional[int]      		=		in_proj_bias[:256]
        _lowercase       :							List[Any]      		=		in_proj_weight[256:512, :]
        _lowercase       :							List[Any]      		=		in_proj_bias[256:512]
        _lowercase       :							Dict      		=		in_proj_weight[-256:, :]
        _lowercase       :							Any      		=		in_proj_bias[-256:]
        # read in weights + bias of input projection layer of cross-attention
        _lowercase       :							List[str]      		=		state_dict.pop(
            F'''{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight''' )
        _lowercase       :							Dict      		=		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
        _lowercase       :							int      		=		in_proj_weight_cross_attn[:256, :]
        _lowercase       :							str      		=		in_proj_bias_cross_attn[:256]
        _lowercase       :							Union[str, Any]      		=		in_proj_weight_cross_attn[256:512, :]
        _lowercase       :							Tuple      		=		in_proj_bias_cross_attn[256:512]
        _lowercase       :							List[str]      		=		in_proj_weight_cross_attn[-256:, :]
        _lowercase       :							str      		=		in_proj_bias_cross_attn[-256:]



def 				UpperCamelCase_(      )    ->						int:
    _lowercase       :							Tuple      		=		'http://images.cocodataset.org/val2017/000000039769.jpg'
    _lowercase       :							Optional[Any]      		=		Image.open(requests.get(lowerCamelCase_							, stream=lowerCamelCase_ ).raw )

    return im



@torch.no_grad()
def 				UpperCamelCase_(      lowerCamelCase_							, lowerCamelCase_=None							, lowerCamelCase_=False )    ->						Optional[Any]:
    _lowercase		,				_lowercase       :							Optional[Any]      		=		get_detr_config(lowerCamelCase_ )

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

    # finally, create HuggingFace model and load state dict
    _lowercase       :							str      		=		DetrForSegmentation(lowerCamelCase_ ) if is_panoptic else DetrForObjectDetection(lowerCamelCase_ )
    model.load_state_dict(lowerCamelCase_ )
    model.eval()

    # verify our conversion on an image
    _lowercase       :							Optional[int]      		=		'coco_panoptic' if is_panoptic else 'coco_detection'
    _lowercase       :							List[Any]      		=		DetrImageProcessor(format=lowerCamelCase_ )

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

    _lowercase       :							int      		=		detr(lowerCamelCase_ )
    _lowercase       :							List[str]      		=		model(lowerCamelCase_ )

    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(lowerCamelCase_ ).mkdir(exist_ok=lowerCamelCase_ )
        model.save_pretrained(lowerCamelCase_ )
        processor.save_pretrained(lowerCamelCase_ )

    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__":
 SCREAMING_SNAKE_CASE       :      Dict												= 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.")
 SCREAMING_SNAKE_CASE       :      List[str]												= parser.parse_args()
 convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)






def 				UpperCamelCase_(      lowerCamelCase_							, lowerCamelCase_							, lowerCamelCase_ )    ->						float:
    _lowercase       :							Tuple      		=		(num_of_terms / 2) * (2 * first_term + (num_of_terms - 1) * common_diff)
    # formula for sum of series
    return total



def 				UpperCamelCase_(      )    ->						Optional[int]:
    print(sum_of_series(1							, 1							, 10 ) )


if __name__ == "__main__":
 import doctest

 doctest.testmod()


