





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()
A__:    Union[str, Any]  	=	logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
A__:    str  	=	[]
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_ (							A_    ,A_    ,A_):
    UpperCamelCase__:	Optional[int]					=     state_dict.pop(A_)
    UpperCamelCase__:	str					=     val



def 				lowerCAmelCase_ (							A_):
    UpperCamelCase__:	Union[str, Any]					=     OrderedDict()
    for key, value in state_dict.items():
        if "backbone.0.body" in key:
            UpperCamelCase__:	Union[str, Any]					=     key.replace("backbone.0.body"    ,"backbone.conv_encoder.model")
            UpperCamelCase__:	Optional[Any]					=     value
        else:
            UpperCamelCase__:	str					=     value

    return new_state_dict



def 				lowerCAmelCase_ (							A_    ,A_=False):
    UpperCamelCase__:	List[str]					=     ""
    if is_panoptic:
        UpperCamelCase__:	Union[str, Any]					=     "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)
        UpperCamelCase__:	List[str]					=     state_dict.pop(F"{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight")
        UpperCamelCase__:	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
        UpperCamelCase__:	List[Any]					=     in_proj_weight[:2_56, :]
        UpperCamelCase__:	Union[str, Any]					=     in_proj_bias[:2_56]
        UpperCamelCase__:	Any					=     in_proj_weight[2_56:5_12, :]
        UpperCamelCase__:	Dict					=     in_proj_bias[2_56:5_12]
        UpperCamelCase__:	str					=     in_proj_weight[-2_56:, :]
        UpperCamelCase__:	Any					=     in_proj_bias[-2_56:]



def 				lowerCAmelCase_ (							):
    UpperCamelCase__:	List[Any]					=     "http://images.cocodataset.org/val2017/000000039769.jpg"
    UpperCamelCase__:	Union[str, Any]					=     Image.open(requests.get(A_    ,stream=A_).raw)

    return im



@torch.no_grad()
def 				lowerCAmelCase_ (							A_    ,A_):
    UpperCamelCase__:	Optional[Any]					=     ConditionalDetrConfig()
    # set backbone and dilation attributes
    if "resnet101" in model_name:
        UpperCamelCase__:	List[str]					=     "resnet101"
    if "dc5" in model_name:
        UpperCamelCase__:	int					=     True
    UpperCamelCase__:	str					=     "panoptic" in model_name
    if is_panoptic:
        UpperCamelCase__:	Tuple					=     2_50
    else:
        UpperCamelCase__:	str					=     91
        UpperCamelCase__:	str					=     "huggingface/label-files"
        UpperCamelCase__:	List[Any]					=     "coco-detection-id2label.json"
        UpperCamelCase__:	Any					=     json.load(open(hf_hub_download(A_    ,A_    ,repo_type="dataset")    ,"r"))
        UpperCamelCase__:	List[Any]					=     {int(A_): v for k, v in idalabel.items()}
        UpperCamelCase__:	Union[str, Any]					=     idalabel
        UpperCamelCase__:	List[Any]					=     {v: k for k, v in idalabel.items()}

    # load image processor
    UpperCamelCase__:	Union[str, Any]					=     "coco_panoptic" if is_panoptic else "coco_detection"
    UpperCamelCase__:	List[Any]					=     ConditionalDetrImageProcessor(format=A_)

    # prepare image
    UpperCamelCase__:	Optional[int]					=     prepare_img()
    UpperCamelCase__:	int					=     image_processor(images=A_    ,return_tensors="pt")
    UpperCamelCase__:	Tuple					=     encoding["pixel_values"]

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

    # load original model from torch hub
    UpperCamelCase__:	Union[str, Any]					=     torch.hub.load("DeppMeng/ConditionalDETR"    ,A_    ,pretrained=A_).eval()
    UpperCamelCase__:	Dict					=     conditional_detr.state_dict()
    # rename keys
    for src, dest in rename_keys:
        if is_panoptic:
            UpperCamelCase__:	Union[str, Any]					=     "conditional_detr." + src
        rename_key(A_    ,A_    ,A_)
    UpperCamelCase__:	Any					=     rename_backbone_keys(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
    UpperCamelCase__:	Dict					=     "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")
            ):
                UpperCamelCase__:	Any					=     state_dict.pop(A_)
                UpperCamelCase__:	List[Any]					=     val
            elif "class_labels_classifier" in key or "bbox_predictor" in key:
                UpperCamelCase__:	str					=     state_dict.pop(A_)
                UpperCamelCase__:	Tuple					=     val
            elif key.startswith("bbox_attention") or key.startswith("mask_head"):
                continue
            else:
                UpperCamelCase__:	List[Any]					=     state_dict.pop(A_)
                UpperCamelCase__:	List[Any]					=     val
        else:
            if not key.startswith("class_labels_classifier") and not key.startswith("bbox_predictor"):
                UpperCamelCase__:	List[Any]					=     state_dict.pop(A_)
                UpperCamelCase__:	str					=     val
    # finally, create HuggingFace model and load state dict
    UpperCamelCase__:	Dict					=     ConditionalDetrForSegmentation(A_) if is_panoptic else ConditionalDetrForObjectDetection(A_)
    model.load_state_dict(A_)
    model.eval()
    model.push_to_hub(repo_id=A_    ,organization="DepuMeng"    ,commit_message="Add model")
    # verify our conversion
    UpperCamelCase__:	Optional[int]					=     conditional_detr(A_)
    UpperCamelCase__:	List[str]					=     model(A_)
    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(A_).mkdir(exist_ok=A_)
    model.save_pretrained(A_)
    image_processor.save_pretrained(A_)


if __name__ == "__main__":
   A__:    List[Any]  	=	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.'''
   )
   A__:    Optional[Any]  	=	parser.parse_args()
   convert_conditional_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)








import logging

from transformers import PretrainedConfig


A__:    Dict  	=	logging.getLogger(__name__)


A__:    List[Any]  	=	{
    '''bertabs-finetuned-cnndm''': '''https://huggingface.co/remi/bertabs-finetuned-cnndm-extractive-abstractive-summarization/resolve/main/config.json''',
}




class        _a    (    UpperCamelCase__):

      """simple docstring"""


      UpperCamelCase__  		=       """bertabs"""






      def __init__(		self:			Any	,    __lowerCamelCase:			Optional[int]=3_0522	,    __lowerCamelCase:			List[Any]=512	,    __lowerCamelCase:			Optional[Any]=6	,    __lowerCamelCase:			Optional[Any]=512	,    __lowerCamelCase:			List[str]=8	,    __lowerCamelCase:			List[Any]=512	,    __lowerCamelCase:			Optional[int]=0.2	,    __lowerCamelCase:			Optional[int]=6	,    __lowerCamelCase:			Optional[Any]=768	,    __lowerCamelCase:			int=8	,    __lowerCamelCase:			Union[str, Any]=2048	,    __lowerCamelCase:			Dict=0.2	,    **__lowerCamelCase:			Dict	,    ):
          '''simple docstring'''


          super().__init__(**__lowerCamelCase					)

          UpperCamelCase__:	str					=     vocab_size
          UpperCamelCase__:	Tuple					=     max_pos

          UpperCamelCase__:	Union[str, Any]					=     enc_layers
          UpperCamelCase__:	Optional[Any]					=     enc_hidden_size
          UpperCamelCase__:	Optional[Any]					=     enc_heads
          UpperCamelCase__:	Dict					=     enc_ff_size
          UpperCamelCase__:	Tuple					=     enc_dropout

          UpperCamelCase__:	Tuple					=     dec_layers
          UpperCamelCase__:	int					=     dec_hidden_size
          UpperCamelCase__:	int					=     dec_heads
          UpperCamelCase__:	str					=     dec_ff_size
          UpperCamelCase__:	Union[str, Any]					=     dec_dropout

