import argparse
import json
from collections import OrderedDict
from pathlib import Path

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

from transformers import (
    ConditionalDetrConfig,
    ConditionalDetrForObjectDetection,
    ConditionalDetrForSegmentation,
    ConditionalDetrImageProcessor,
)
from transformers.utils import logging


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

# here we list all keys to be renamed (original name on the left, our name on the right)
lowerCAmelCase__     						=			[]
for i in range(6):
    # 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}.cross_attn.out_proj.weight',
            F'decoder.layers.{i}.encoder_attn.out_proj.weight',
        )
    )
    rename_keys.append(
        (
            F'transformer.decoder.layers.{i}.cross_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'))

    # q, k, v projections in self/cross-attention in decoder for conditional DETR
    rename_keys.append(
        (F'transformer.decoder.layers.{i}.sa_qcontent_proj.weight', F'decoder.layers.{i}.sa_qcontent_proj.weight')
    )
    rename_keys.append(
        (F'transformer.decoder.layers.{i}.sa_kcontent_proj.weight', F'decoder.layers.{i}.sa_kcontent_proj.weight')
    )
    rename_keys.append(
        (F'transformer.decoder.layers.{i}.sa_qpos_proj.weight', F'decoder.layers.{i}.sa_qpos_proj.weight')
    )
    rename_keys.append(
        (F'transformer.decoder.layers.{i}.sa_kpos_proj.weight', F'decoder.layers.{i}.sa_kpos_proj.weight')
    )
    rename_keys.append((F'transformer.decoder.layers.{i}.sa_v_proj.weight', F'decoder.layers.{i}.sa_v_proj.weight'))
    rename_keys.append(
        (F'transformer.decoder.layers.{i}.ca_qcontent_proj.weight', F'decoder.layers.{i}.ca_qcontent_proj.weight')
    )
    # rename_keys.append((f"transformer.decoder.layers.{i}.ca_qpos_proj.weight", f"decoder.layers.{i}.ca_qpos_proj.weight"))
    rename_keys.append(
        (F'transformer.decoder.layers.{i}.ca_kcontent_proj.weight', F'decoder.layers.{i}.ca_kcontent_proj.weight')
    )
    rename_keys.append(
        (F'transformer.decoder.layers.{i}.ca_kpos_proj.weight', F'decoder.layers.{i}.ca_kpos_proj.weight')
    )
    rename_keys.append((F'transformer.decoder.layers.{i}.ca_v_proj.weight', F'decoder.layers.{i}.ca_v_proj.weight'))
    rename_keys.append(
        (F'transformer.decoder.layers.{i}.ca_qpos_sine_proj.weight', F'decoder.layers.{i}.ca_qpos_sine_proj.weight')
    )

    rename_keys.append(
        (F'transformer.decoder.layers.{i}.sa_qcontent_proj.bias', F'decoder.layers.{i}.sa_qcontent_proj.bias')
    )
    rename_keys.append(
        (F'transformer.decoder.layers.{i}.sa_kcontent_proj.bias', F'decoder.layers.{i}.sa_kcontent_proj.bias')
    )
    rename_keys.append((F'transformer.decoder.layers.{i}.sa_qpos_proj.bias', F'decoder.layers.{i}.sa_qpos_proj.bias'))
    rename_keys.append((F'transformer.decoder.layers.{i}.sa_kpos_proj.bias', F'decoder.layers.{i}.sa_kpos_proj.bias'))
    rename_keys.append((F'transformer.decoder.layers.{i}.sa_v_proj.bias', F'decoder.layers.{i}.sa_v_proj.bias'))
    rename_keys.append(
        (F'transformer.decoder.layers.{i}.ca_qcontent_proj.bias', F'decoder.layers.{i}.ca_qcontent_proj.bias')
    )
    # rename_keys.append((f"transformer.decoder.layers.{i}.ca_qpos_proj.bias", f"decoder.layers.{i}.ca_qpos_proj.bias"))
    rename_keys.append(
        (F'transformer.decoder.layers.{i}.ca_kcontent_proj.bias', F'decoder.layers.{i}.ca_kcontent_proj.bias')
    )
    rename_keys.append((F'transformer.decoder.layers.{i}.ca_kpos_proj.bias', F'decoder.layers.{i}.ca_kpos_proj.bias'))
    rename_keys.append((F'transformer.decoder.layers.{i}.ca_v_proj.bias', F'decoder.layers.{i}.ca_v_proj.bias'))
    rename_keys.append(
        (F'transformer.decoder.layers.{i}.ca_qpos_sine_proj.bias', F'decoder.layers.{i}.ca_qpos_sine_proj.bias')
    )

# convolutional projection + query embeddings + layernorm of decoder + class and bounding box heads
# for conditional DETR, also convert reference point head and query scale MLP
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'),
        ('transformer.decoder.ref_point_head.layers.0.weight', 'decoder.ref_point_head.layers.0.weight'),
        ('transformer.decoder.ref_point_head.layers.0.bias', 'decoder.ref_point_head.layers.0.bias'),
        ('transformer.decoder.ref_point_head.layers.1.weight', 'decoder.ref_point_head.layers.1.weight'),
        ('transformer.decoder.ref_point_head.layers.1.bias', 'decoder.ref_point_head.layers.1.bias'),
        ('transformer.decoder.query_scale.layers.0.weight', 'decoder.query_scale.layers.0.weight'),
        ('transformer.decoder.query_scale.layers.0.bias', 'decoder.query_scale.layers.0.bias'),
        ('transformer.decoder.query_scale.layers.1.weight', 'decoder.query_scale.layers.1.weight'),
        ('transformer.decoder.query_scale.layers.1.bias', 'decoder.query_scale.layers.1.bias'),
        ('transformer.decoder.layers.0.ca_qpos_proj.weight', 'decoder.layers.0.ca_qpos_proj.weight'),
        ('transformer.decoder.layers.0.ca_qpos_proj.bias', 'decoder.layers.0.ca_qpos_proj.bias'),
    ]
)






def 				__lowerCamelCase		(						lowerCAmelCase__     ,  lowerCAmelCase__     ,  lowerCAmelCase__     ):
       lowerCAmelCase__					     = state_dict.pop(lowerCAmelCase__     )
       lowerCAmelCase__					     = val






def 				__lowerCamelCase		(						lowerCAmelCase__     ):
       lowerCAmelCase__					     = OrderedDict()
       for key, value in state_dict.items():
              if "backbone.0.body" in key:
                     lowerCAmelCase__					     = key.replace('backbone.0.body'     ,  'backbone.conv_encoder.model'     )
                     lowerCAmelCase__					     = value
              else:
                     lowerCAmelCase__					     = value

       return new_state_dict






def 				__lowerCamelCase		(						lowerCAmelCase__     ,  lowerCAmelCase__=False     ):
       lowerCAmelCase__					     = ''''''
       if is_panoptic:
              lowerCAmelCase__					     = '''conditional_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)
              lowerCAmelCase__					     = state_dict.pop(F"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight"""     )
              lowerCAmelCase__					     = 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
              lowerCAmelCase__					     = in_proj_weight[:2_5_6, :]
              lowerCAmelCase__					     = in_proj_bias[:2_5_6]
              lowerCAmelCase__					     = in_proj_weight[2_5_6:5_1_2, :]
              lowerCAmelCase__					     = in_proj_bias[2_5_6:5_1_2]
              lowerCAmelCase__					     = in_proj_weight[-2_5_6:, :]
              lowerCAmelCase__					     = in_proj_bias[-2_5_6:]






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

       return im






@torch.no_grad()
def 				__lowerCamelCase		(						lowerCAmelCase__     ,  lowerCAmelCase__     ):
       lowerCAmelCase__					     = ConditionalDetrConfig()
       # set backbone and dilation attributes
       if "resnet101" in model_name:
              lowerCAmelCase__					     = '''resnet101'''
       if "dc5" in model_name:
              lowerCAmelCase__					     = True
       lowerCAmelCase__					     = '''panoptic''' in model_name
       if is_panoptic:
              lowerCAmelCase__					     = 2_5_0
       else:
              lowerCAmelCase__					     = 9_1
              lowerCAmelCase__					     = '''huggingface/label-files'''
              lowerCAmelCase__					     = '''coco-detection-id2label.json'''
              lowerCAmelCase__					     = json.load(open(hf_hub_download(lowerCAmelCase__     ,  lowerCAmelCase__     ,  repo_type='dataset'     )     ,  'r'     )     )
              lowerCAmelCase__					     = {int(lowerCAmelCase__     ): v for k, v in idalabel.items()}
              lowerCAmelCase__					     = idalabel
              lowerCAmelCase__					     = {v: k for k, v in idalabel.items()}

       # load image processor
       lowerCAmelCase__					     = '''coco_panoptic''' if is_panoptic else '''coco_detection'''
       lowerCAmelCase__					     = ConditionalDetrImageProcessor(format=lowerCAmelCase__     )

       # prepare image
       lowerCAmelCase__					     = prepare_img()
       lowerCAmelCase__					     = image_processor(images=lowerCAmelCase__     ,  return_tensors='pt'     )
       lowerCAmelCase__					     = encoding['''pixel_values''']

       logger.info(F"""Converting model {model_name}..."""     )

       # load original model from torch hub
       lowerCAmelCase__					     = torch.hub.load('DeppMeng/ConditionalDETR'     ,  lowerCAmelCase__     ,  pretrained=lowerCAmelCase__     ).eval()
       lowerCAmelCase__					     = conditional_detr.state_dict()
       # rename keys
       for src, dest in rename_keys:
              if is_panoptic:
                     lowerCAmelCase__					     = '''conditional_detr.''' + src
              rename_key(lowerCAmelCase__     ,  lowerCAmelCase__     ,  lowerCAmelCase__     )
       lowerCAmelCase__					     = rename_backbone_keys(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
       lowerCAmelCase__					     = '''conditional_detr.model.''' if is_panoptic else '''model.'''
       for key in state_dict.copy().keys():
              if is_panoptic:
                     if (
                         key.startswith('conditional_detr'     )
                         and not key.startswith('class_labels_classifier'     )
                         and not key.startswith('bbox_predictor'     )
                     ):
                            lowerCAmelCase__					     = state_dict.pop(lowerCAmelCase__     )
                            lowerCAmelCase__					     = val
                     elif "class_labels_classifier" in key or "bbox_predictor" in key:
                            lowerCAmelCase__					     = state_dict.pop(lowerCAmelCase__     )
                            lowerCAmelCase__					     = val
                     elif key.startswith('bbox_attention'     ) or key.startswith('mask_head'     ):
                            continue
                     else:
                            lowerCAmelCase__					     = state_dict.pop(lowerCAmelCase__     )
                            lowerCAmelCase__					     = val
              else:
                     if not key.startswith('class_labels_classifier'     ) and not key.startswith('bbox_predictor'     ):
                            lowerCAmelCase__					     = state_dict.pop(lowerCAmelCase__     )
                            lowerCAmelCase__					     = val
                            # finally, create HuggingFace model and load state dict
       lowerCAmelCase__					     = ConditionalDetrForSegmentation(lowerCAmelCase__     ) if is_panoptic else ConditionalDetrForObjectDetection(lowerCAmelCase__     )
       model.load_state_dict(lowerCAmelCase__     )
       model.eval()
       model.push_to_hub(repo_id=lowerCAmelCase__     ,  organization='DepuMeng'     ,  commit_message='Add model'     )
       # verify our conversion
       lowerCAmelCase__					     = conditional_detr(lowerCAmelCase__     )
       lowerCAmelCase__					     = model(lowerCAmelCase__     )
       assert torch.allclose(outputs.logits     ,  original_outputs['pred_logits']     ,  atol=1e-4     )
       assert torch.allclose(outputs.pred_boxes     ,  original_outputs['pred_boxes']     ,  atol=1e-4     )
       if is_panoptic:
              assert torch.allclose(outputs.pred_masks     ,  original_outputs['pred_masks']     ,  atol=1e-4     )

       # 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__     )
       image_processor.save_pretrained(lowerCAmelCase__     )


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

    parser.add_argument(
        '--model_name',
        default='conditional_detr_resnet50',
        type=str,
        help='Name of the CONDITIONAL_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.'
    )
    lowerCAmelCase__     						=			parser.parse_args()
    convert_conditional_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)

import numpy as np






def 				__lowerCamelCase		(						lowerCAmelCase__     ):
       return 1 / (1 + np.exp(-vector     ))






def 				__lowerCamelCase		(						lowerCAmelCase__     ):
       return vector * sigmoid(lowerCAmelCase__     )


if __name__ == "__main__":
    import doctest

    doctest.testmod()
