



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 DetrConfig, DetrForObjectDetection, DetrForSegmentation, DetrImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
A_		:  List[Any]										=  logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
A_		:  int										=  []
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}.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'''),
    ]
)


def 						UpperCAmelCase__   (   UpperCAmelCase__			:int			,       UpperCAmelCase__			:int			,       UpperCAmelCase__			:Optional[Any]       ):






 '''simple docstring'''

 a							=		state_dict.pop(_a       )
 a							=		val


def 						UpperCAmelCase__   (   UpperCAmelCase__			:Tuple       ):






 '''simple docstring'''

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

 return new_state_dict


def 						UpperCAmelCase__   (   UpperCAmelCase__			:Optional[Any]			,       UpperCAmelCase__			:Optional[int]=False       ):






 '''simple docstring'''

 a							=		""
 if is_panoptic:
  a							=		"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)
  a							=		state_dict.pop(F"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight"""       )
  a							=		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
  a							=		in_proj_weight[:2_56, :]
  a							=		in_proj_bias[:2_56]
  a							=		in_proj_weight[2_56:5_12, :]
  a							=		in_proj_bias[2_56:5_12]
  a							=		in_proj_weight[-2_56:, :]
  a							=		in_proj_bias[-2_56:]
 # 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
  a							=		state_dict.pop(F"""{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight"""       )
  a							=		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
  a							=		in_proj_weight[:2_56, :]
  a							=		in_proj_bias[:2_56]
  a							=		in_proj_weight[2_56:5_12, :]
  a							=		in_proj_bias[2_56:5_12]
  a							=		in_proj_weight[-2_56:, :]
  a							=		in_proj_bias[-2_56:]
  # read in weights + bias of input projection layer of cross-attention
  a							=		state_dict.pop(
      F"""{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight"""       )
  a							=		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
  a							=		in_proj_weight_cross_attn[:2_56, :]
  a							=		in_proj_bias_cross_attn[:2_56]
  a							=		in_proj_weight_cross_attn[2_56:5_12, :]
  a							=		in_proj_bias_cross_attn[2_56:5_12]
  a							=		in_proj_weight_cross_attn[-2_56:, :]
  a							=		in_proj_bias_cross_attn[-2_56:]


def 						UpperCAmelCase__   (   ):






 '''simple docstring'''

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

 return im


@torch.no_grad()
def 						UpperCAmelCase__   (   UpperCAmelCase__			:List[Any]			,       UpperCAmelCase__			:List[str]       ):






 '''simple docstring'''

 a							=		DetrConfig()
 # set backbone and dilation attributes
 if "resnet101" in model_name:
  a							=		"resnet101"
 if "dc5" in model_name:
  a							=		True
 a							=		"panoptic" in model_name
 if is_panoptic:
  a							=		2_50
 else:
  a							=		91
  a							=		"huggingface/label-files"
  a							=		"coco-detection-id2label.json"
  a							=		json.load(open(hf_hub_download(_a			,       _a			,       repo_type="dataset"       )			,       "r"       )       )
  a							=		{int(_a       ): v for k, v in idalabel.items()}
  a							=		idalabel
  a							=		{v: k for k, v in idalabel.items()}

 # load image processor
 a							=		"coco_panoptic" if is_panoptic else "coco_detection"
 a							=		DetrImageProcessor(format=_a       )

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

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

 # load original model from torch hub
 a							=		torch.hub.load("facebookresearch/detr"			,       _a			,       pretrained=_a       ).eval()
 a							=		detr.state_dict()
 # rename keys
 for src, dest in rename_keys:
  if is_panoptic:
   a							=		"detr." + src
  rename_key(_a			,       _a			,       _a       )
 a							=		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
 a							=		"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"       )
   ):
    a							=		state_dict.pop(_a       )
    a							=		val
   elif "class_labels_classifier" in key or "bbox_predictor" in key:
    a							=		state_dict.pop(_a       )
    a							=		val
   elif key.startswith("bbox_attention"       ) or key.startswith("mask_head"       ):
    continue
   else:
    a							=		state_dict.pop(_a       )
    a							=		val
  else:
   if not key.startswith("class_labels_classifier"       ) and not key.startswith("bbox_predictor"       ):
    a							=		state_dict.pop(_a       )
    a							=		val
    # finally, create HuggingFace model and load state dict
 a							=		DetrForSegmentation(_a       ) if is_panoptic else DetrForObjectDetection(_a       )
 model.load_state_dict(_a       )
 model.eval()
 # verify our conversion
 a							=		detr(_a       )
 a							=		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_		:  str										=  argparse.ArgumentParser()

     parser.add_argument(
         '''--model_name''', default='''detr_resnet50''', type=str, 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.'''
     )
     A_		:  List[Any]										=  parser.parse_args()
     convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)







'''simple docstring'''







from ...configuration_utils import PretrainedConfig
from ...utils import logging


A					       =logging.get_logger(__name__)

A					       ={
    'facebook/regnet-y-040': 'https://huggingface.co/facebook/regnet-y-040/blob/main/config.json',
}


class      _a    (      __a					):
       __a							:	Optional[int]  							=   """regnet"""
       __a							:	Optional[Any]  							=   ["""x""", """y"""]
       def __init__(      self		:      int  ,							lowercase		:      List[Any]=3  ,							lowercase		:      Union[str, Any]=32  ,							lowercase		:      Tuple=[128, 192, 512, 1_088]  ,							lowercase		:      List[Any]=[2, 6, 12, 2]  ,							lowercase		:      Optional[Any]=64  ,							lowercase		:      Optional[int]="y"  ,							lowercase		:      int="relu"  ,							**lowercase		:      int  ,							):



           '''simple docstring'''

           super().__init__(**lowercase							)
           if layer_type not in self.layer_types:
               raise ValueError(f"layer_type={layer_type} is not one of {','.join(self.layer_types							)}"							)
           UpperCAmelCase     			=						num_channels
           UpperCAmelCase     			=						embedding_size
           UpperCAmelCase     			=						hidden_sizes
           UpperCAmelCase     			=						depths
           UpperCAmelCase     			=						groups_width
           UpperCAmelCase     			=						layer_type
           UpperCAmelCase     			=						hidden_act
           # always downsample in the first stage
           UpperCAmelCase     			=						True




