





from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


__lowercase  :   str						      =logging.get_logger(__name__)

__lowercase  :   List[Any]						      ={
    """facebook/deit-base-distilled-patch16-224""": (
        """https://huggingface.co/facebook/deit-base-patch16-224/resolve/main/config.json"""
    ),
    # See all DeiT models at https://huggingface.co/models?filter=deit
}

class      A       (	__lowercase						):
      _snake_case							     ='''deit'''



      def __init__(	self:						Optional[Any]		,  _lowerCAmelCase:						Any=768		,  _lowerCAmelCase:						str=12		,  _lowerCAmelCase:						List[str]=12		,  _lowerCAmelCase:						Optional[int]=3072		,  _lowerCAmelCase:						Tuple="gelu"		,  _lowerCAmelCase:						List[Any]=0.0		,  _lowerCAmelCase:						int=0.0		,  _lowerCAmelCase:						Dict=0.02		,  _lowerCAmelCase:						Union[str, Any]=1e-12		,  _lowerCAmelCase:						Any=224		,  _lowerCAmelCase:						str=16		,  _lowerCAmelCase:						int=3		,  _lowerCAmelCase:						List[str]=True		,  _lowerCAmelCase:						Dict=16		,  **_lowerCAmelCase:						Dict		,  )						->    Optional[Any]:
         '''simple docstring'''

         super().__init__(**_lowerCAmelCase       )

         UpperCAmelCase_											=hidden_size
         UpperCAmelCase_											=num_hidden_layers
         UpperCAmelCase_											=num_attention_heads
         UpperCAmelCase_											=intermediate_size
         UpperCAmelCase_											=hidden_act
         UpperCAmelCase_											=hidden_dropout_prob
         UpperCAmelCase_											=attention_probs_dropout_prob
         UpperCAmelCase_											=initializer_range
         UpperCAmelCase_											=layer_norm_eps
         UpperCAmelCase_											=image_size
         UpperCAmelCase_											=patch_size
         UpperCAmelCase_											=num_channels
         UpperCAmelCase_											=qkv_bias
         UpperCAmelCase_											=encoder_stride

class      A       (	__lowercase						):
      _snake_case							     =version.parse('''1.11'''						)



      @property
      def 	lowerCAmelCase__    (	self:						str       )						->    Mapping[str, Mapping[int, str]]:
         '''simple docstring'''

         return OrderedDict(
             [
                 ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
             ]       )






      @property
      def 	lowerCAmelCase__    (	self:						Any       )						->    float:
         '''simple docstring'''

         return 1e-4






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()
__lowercase  :   Optional[Any]						      =logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
__lowercase  :   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     a__ (					lowercase__  ,    lowercase__  ,    lowercase__  ):






   '''simple docstring'''






   UpperCAmelCase_											=state_dict.pop(lowercase__  )
   UpperCAmelCase_											=val




def     a__ (					lowercase__  ):






   '''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     a__ (					lowercase__  ,    lowercase__=False  ):






   '''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     a__ (					):






   '''simple docstring'''






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

   return im




@torch.no_grad()
def     a__ (					lowercase__  ,    lowercase__  ):






   '''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(lowercase__  ,    lowercase__  ,    repo_type="dataset"  )  ,    "r"  )  )
      UpperCAmelCase_											={int(lowercase__  ): 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=lowercase__  )

   # prepare image
   UpperCAmelCase_											=prepare_img()
   UpperCAmelCase_											=image_processor(images=lowercase__  ,    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"  ,    lowercase__  ,    pretrained=lowercase__  ).eval()
   UpperCAmelCase_											=conditional_detr.state_dict()
   # rename keys
   for src, dest in rename_keys:
      if is_panoptic:
         UpperCAmelCase_											="conditional_detr." + src
      rename_key(lowercase__  ,    lowercase__  ,    lowercase__  )
   UpperCAmelCase_											=rename_backbone_keys(lowercase__  )
   # query, key and value matrices need special treatment
   read_in_q_k_v(lowercase__  ,    is_panoptic=lowercase__  )
   # 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(lowercase__  )
            UpperCAmelCase_											=val
         elif "class_labels_classifier" in key or "bbox_predictor" in key:
            UpperCAmelCase_											=state_dict.pop(lowercase__  )
            UpperCAmelCase_											=val
         elif key.startswith("bbox_attention"  ) or key.startswith("mask_head"  ):
            continue
         else:
            UpperCAmelCase_											=state_dict.pop(lowercase__  )
            UpperCAmelCase_											=val
      else:
         if not key.startswith("class_labels_classifier"  ) and not key.startswith("bbox_predictor"  ):
            UpperCAmelCase_											=state_dict.pop(lowercase__  )
            UpperCAmelCase_											=val
    # finally, create HuggingFace model and load state dict
   UpperCAmelCase_											=ConditionalDetrForSegmentation(lowercase__  ) if is_panoptic else ConditionalDetrForObjectDetection(lowercase__  )
   model.load_state_dict(lowercase__  )
   model.eval()
   model.push_to_hub(repo_id=lowercase__  ,    organization="DepuMeng"  ,    commit_message="Add model"  )
   # verify our conversion
   UpperCAmelCase_											=conditional_detr(lowercase__  )
   UpperCAmelCase_											=model(lowercase__  )
   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(lowercase__  ).mkdir(exist_ok=lowercase__  )
   model.save_pretrained(lowercase__  )
   image_processor.save_pretrained(lowercase__  )


if __name__ == "__main__":
     __lowercase  :   Dict						      =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."""
     )
     __lowercase  :   Any						      =parser.parse_args()
     convert_conditional_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)




