


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      = logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
A      = []
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 __UpperCAmelCase       (		__A ,						__A ,						__A			)						->					str:



       '''simple docstring'''





       UpperCAmelCase__										=     state_dict.pop(__A			)
       UpperCAmelCase__										=     val





def __UpperCAmelCase       (		__A			)						->					Tuple:



       '''simple docstring'''





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

       return new_state_dict





def __UpperCAmelCase       (		__A ,						__A=False			)						->					Optional[Any]:



       '''simple docstring'''





       UpperCAmelCase__										=     ""
       if is_panoptic:
              UpperCAmelCase__										=     "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__										=     state_dict.pop(F"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight"""			)
              UpperCAmelCase__										=     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__										=     in_proj_weight[:2_5_6, :]
              UpperCAmelCase__										=     in_proj_bias[:2_5_6]
              UpperCAmelCase__										=     in_proj_weight[2_5_6:5_1_2, :]
              UpperCAmelCase__										=     in_proj_bias[2_5_6:5_1_2]
              UpperCAmelCase__										=     in_proj_weight[-2_5_6:, :]
              UpperCAmelCase__										=     in_proj_bias[-2_5_6:]





def __UpperCAmelCase       (		)						->					List[str]:



       '''simple docstring'''





       UpperCAmelCase__										=     "http://images.cocodataset.org/val2017/000000039769.jpg"
       UpperCAmelCase__										=     Image.open(requests.get(__A ,						stream=__A			).raw			)

       return im





@torch.no_grad()
def __UpperCAmelCase       (		__A ,						__A			)						->					int:



       '''simple docstring'''





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

       # load image processor
       UpperCAmelCase__										=     "coco_panoptic" if is_panoptic else "coco_detection"
       UpperCAmelCase__										=     ConditionalDetrImageProcessor(format=__A			)

       # prepare image
       UpperCAmelCase__										=     prepare_img()
       UpperCAmelCase__										=     image_processor(images=__A ,						return_tensors="pt"			)
       UpperCAmelCase__										=     encoding["pixel_values"]

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

       # load original model from torch hub
       UpperCAmelCase__										=     torch.hub.load("DeppMeng/ConditionalDETR" ,						__A ,						pretrained=__A			).eval()
       UpperCAmelCase__										=     conditional_detr.state_dict()
       # rename keys
       for src, dest in rename_keys:
              if is_panoptic:
                     UpperCAmelCase__										=     "conditional_detr." + src
              rename_key(__A ,						__A ,						__A			)
       UpperCAmelCase__										=     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__										=     "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__										=     state_dict.pop(__A			)
                            UpperCAmelCase__										=     val
                     elif "class_labels_classifier" in key or "bbox_predictor" in key:
                            UpperCAmelCase__										=     state_dict.pop(__A			)
                            UpperCAmelCase__										=     val
                     elif key.startswith("bbox_attention"			) or key.startswith("mask_head"			):
                            continue
                     else:
                            UpperCAmelCase__										=     state_dict.pop(__A			)
                            UpperCAmelCase__										=     val
              else:
                     if not key.startswith("class_labels_classifier"			) and not key.startswith("bbox_predictor"			):
                            UpperCAmelCase__										=     state_dict.pop(__A			)
                            UpperCAmelCase__										=     val
    # finally, create HuggingFace model and load state dict
       UpperCAmelCase__										=     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__										=     conditional_detr(__A			)
       UpperCAmelCase__										=     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      = 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      = parser.parse_args()
    convert_conditional_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)









import logging
from dataclasses import dataclass, field
from typing import Optional

from seqaseq_trainer import arg_to_scheduler

from transformers import TrainingArguments


A      = logging.getLogger(__name__)


@dataclass
class     lowercase__  (					__SCREAMING_SNAKE_CASE		):
  A__=  field(
      default=0.0					,							metadata={'help': 'The label smoothing epsilon to apply (if not zero).'}		)
  A__=  field(default=__SCREAMING_SNAKE_CASE					,							metadata={'help': 'Whether to SortishSamler or not.'}		)
  A__=  field(
      default=__SCREAMING_SNAKE_CASE					,							metadata={'help': 'Whether to use generate to calculate generative metrics (ROUGE, BLEU).'}		)
  A__=  field(default=__SCREAMING_SNAKE_CASE					,							metadata={'help': 'whether to use adafactor'}		)
  A__=  field(
      default=__SCREAMING_SNAKE_CASE					,							metadata={'help': 'Encoder layer dropout probability. Goes into model.config.'}		)
  A__=  field(
      default=__SCREAMING_SNAKE_CASE					,							metadata={'help': 'Decoder layer dropout probability. Goes into model.config.'}		)
  A__=  field(default=__SCREAMING_SNAKE_CASE					,							metadata={'help': 'Dropout probability. Goes into model.config.'}		)
  A__=  field(
      default=__SCREAMING_SNAKE_CASE					,							metadata={'help': 'Attention dropout probability. Goes into model.config.'}		)
  A__=  field(
      default='linear'					,							metadata={'help': f'Which lr scheduler to use. Selected in {sorted(arg_to_scheduler.keys()		)}'}					,							)





