
"""simple docstring"""

import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import cached_download, hf_hub_url
from PIL import Image

from transformers import DeformableDetrConfig, DeformableDetrForObjectDetection, DeformableDetrImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
UpperCamelCase      :							Optional[int]          =					logging.get_logger(__name__)






def 				A		(							snake_case :Dict						) ->      Tuple:
 if "backbone.0.body" in orig_key:
  __UpperCamelCase							    =			orig_key.replace('backbone.0.body'							,   'backbone.conv_encoder.model'						)
 if "transformer" in orig_key:
  __UpperCamelCase							    =			orig_key.replace('transformer.'							,   ''						)
 if "norm1" in orig_key:
  if "encoder" in orig_key:
   __UpperCamelCase							    =			orig_key.replace('norm1'							,   'self_attn_layer_norm'						)
  else:
   __UpperCamelCase							    =			orig_key.replace('norm1'							,   'encoder_attn_layer_norm'						)
 if "norm2" in orig_key:
  if "encoder" in orig_key:
   __UpperCamelCase							    =			orig_key.replace('norm2'							,   'final_layer_norm'						)
  else:
   __UpperCamelCase							    =			orig_key.replace('norm2'							,   'self_attn_layer_norm'						)
 if "norm3" in orig_key:
  __UpperCamelCase							    =			orig_key.replace('norm3'							,   'final_layer_norm'						)
 if "linear1" in orig_key:
  __UpperCamelCase							    =			orig_key.replace('linear1'							,   'fc1'						)
 if "linear2" in orig_key:
  __UpperCamelCase							    =			orig_key.replace('linear2'							,   'fc2'						)
 if "query_embed" in orig_key:
  __UpperCamelCase							    =			orig_key.replace('query_embed'							,   'query_position_embeddings'						)
 if "cross_attn" in orig_key:
  __UpperCamelCase							    =			orig_key.replace('cross_attn'							,   'encoder_attn'						)

 return orig_key






def 				A		(							snake_case :Dict						) ->      Optional[int]:
 # transformer decoder self-attention layers
 for i in range(6						):
  # read in weights + bias of input projection layer of self-attention
  __UpperCamelCase							    =			state_dict.pop(f'decoder.layers.{i}.self_attn.in_proj_weight'						)
  __UpperCamelCase							    =			state_dict.pop(f'decoder.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		(							) ->      Union[str, Any]:
 __UpperCamelCase							    =			'http://images.cocodataset.org/val2017/000000039769.jpg'
 __UpperCamelCase							    =			Image.open(requests.get(snake_case							,   stream=snake_case						).raw						)

 return im






@torch.no_grad()
def 				A		(							snake_case :List[str]							,   snake_case :List[str]							,   snake_case :Optional[int]							,   snake_case :Tuple							,   snake_case :Optional[int]							,   snake_case :Dict							,   snake_case :str							,   ) ->      int:
 __UpperCamelCase							    =			DeformableDetrConfig()
 # set config attributes
 if single_scale:
  __UpperCamelCase							    =			1
 __UpperCamelCase							    =			dilation
 __UpperCamelCase							    =			with_box_refine
 __UpperCamelCase							    =			two_stage
 # set labels
 __UpperCamelCase							    =			9_1
 __UpperCamelCase							    =			'huggingface/label-files'
 __UpperCamelCase							    =			'coco-detection-id2label.json'
 __UpperCamelCase							    =			json.load(open(cached_download(hf_hub_url(snake_case							,   snake_case							,   repo_type='dataset'						)						)							,   'r'						)						)
 __UpperCamelCase							    =			{int(snake_case						): v for k, v in idalabel.items()}
 __UpperCamelCase							    =			idalabel
 __UpperCamelCase							    =			{v: k for k, v in idalabel.items()}

 # load image processor
 __UpperCamelCase							    =			DeformableDetrImageProcessor(format='coco_detection'						)

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

 logger.info('Converting model...'						)

 # load original state dict
 __UpperCamelCase							    =			torch.load(snake_case							,   map_location='cpu'						)['model']
 # rename keys
 for key in state_dict.copy().keys():
  __UpperCamelCase							    =			state_dict.pop(snake_case						)
  __UpperCamelCase							    =			val
 # query, key and value matrices need special treatment
 read_in_q_k_v(snake_case						)
 # important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
 __UpperCamelCase							    =			'model.'
 for key in state_dict.copy().keys():
  if not key.startswith('class_embed'						) and not key.startswith('bbox_embed'						):
   __UpperCamelCase							    =			state_dict.pop(snake_case						)
   __UpperCamelCase							    =			val
    # finally, create HuggingFace model and load state dict
 __UpperCamelCase							    =			DeformableDetrForObjectDetection(snake_case						)
 model.load_state_dict(snake_case						)
 model.eval()

 __UpperCamelCase							    =			'cuda' if torch.cuda.is_available() else 'cpu'
 model.to(snake_case						)
 # verify our conversion
 __UpperCamelCase							    =			model(pixel_values.to(snake_case						)						)

 __UpperCamelCase							    =			torch.tensor(
     [[-9.6_645, -4.3_449, -5.8_705], [-9.7_035, -3.8_504, -5.0_724], [-10.5_634, -5.3_379, -7.5_116]]						)
 __UpperCamelCase							    =			torch.tensor([[0.8_693, 0.2_289, 0.2_492], [0.3_150, 0.5_489, 0.5_845], [0.5_563, 0.7_580, 0.8_518]]						)

 if single_scale:
  __UpperCamelCase							    =			torch.tensor(
      [[-9.9_051, -4.2_541, -6.4_852], [-9.6_947, -4.0_854, -6.8_033], [-10.0_665, -5.8_470, -7.7_003]]						)
  __UpperCamelCase							    =			torch.tensor([[0.7_292, 0.4_991, 0.5_532], [0.7_959, 0.2_426, 0.4_236], [0.7_582, 0.3_518, 0.4_451]]						)

 if single_scale and dilation:
  __UpperCamelCase							    =			torch.tensor(
      [[-8.9_652, -4.1_074, -5.6_635], [-9.0_596, -4.9_447, -6.6_075], [-10.1_178, -4.5_275, -6.2_671]]						)
  __UpperCamelCase							    =			torch.tensor([[0.7_665, 0.4_130, 0.4_769], [0.8_364, 0.1_841, 0.3_391], [0.6_261, 0.3_895, 0.7_978]]						)

 if with_box_refine:
  __UpperCamelCase							    =			torch.tensor(
      [[-8.8_895, -5.4_187, -6.8_153], [-8.4_706, -6.1_668, -7.6_184], [-9.0_042, -5.5_359, -6.9_141]]						)
  __UpperCamelCase							    =			torch.tensor([[0.7_828, 0.2_208, 0.4_323], [0.0_892, 0.5_996, 0.1_319], [0.5_524, 0.6_389, 0.8_914]]						)

 if with_box_refine and two_stage:
  __UpperCamelCase							    =			torch.tensor(
      [[-6.7_108, -4.3_213, -6.3_777], [-8.9_014, -6.1_799, -6.7_240], [-6.9_315, -4.4_735, -6.2_298]]						)
  __UpperCamelCase							    =			torch.tensor([[0.2_583, 0.5_499, 0.4_683], [0.7_652, 0.9_068, 0.4_882], [0.5_490, 0.2_763, 0.0_564]]						)

 print('Logits:'							,   outputs.logits[0, :3, :3]						)

 assert torch.allclose(outputs.logits[0, :3, :3]							,   expected_logits.to(snake_case						)							,   atol=1e-4						)
 assert torch.allclose(outputs.pred_boxes[0, :3, :3]							,   expected_boxes.to(snake_case						)							,   atol=1e-4						)

 print('Everything ok!'						)

 # Save model and image processor
 logger.info(f'Saving PyTorch model and image processor to {pytorch_dump_folder_path}...'						)
 Path(snake_case						).mkdir(exist_ok=snake_case						)
 model.save_pretrained(snake_case						)
 image_processor.save_pretrained(snake_case						)

 # Push to hub
 if push_to_hub:
  __UpperCamelCase							    =			'deformable-detr'
  model_name += "-single-scale" if single_scale else ""
  model_name += "-dc5" if dilation else ""
  model_name += "-with-box-refine" if with_box_refine else ""
  model_name += "-two-stage" if two_stage else ""
  print('Pushing model to hub...'						)
  model.push_to_hub(repo_path_or_name=snake_case							,   organization='nielsr'							,   commit_message='Add model'						)


if __name__ == "__main__":
      UpperCamelCase      :							int          =					argparse.ArgumentParser()

      parser.add_argument(
          "--checkpoint_path",
          type=str,
          default="/home/niels/checkpoints/deformable_detr/r50_deformable_detr-checkpoint.pth",
          help="Path to Pytorch checkpoint (.pth file) you'd like to convert.",
      )
      parser.add_argument("--single_scale", action="store_true", help="Whether to set config.num_features_levels = 1.")
      parser.add_argument("--dilation", action="store_true", help="Whether to set config.dilation=True.")
      parser.add_argument("--with_box_refine", action="store_true", help="Whether to set config.with_box_refine=True.")
      parser.add_argument("--two_stage", action="store_true", help="Whether to set config.two_stage=True.")
      parser.add_argument(
          "--pytorch_dump_folder_path",
          default=None,
          type=str,
          required=True,
          help="Path to the folder to output PyTorch model.",
      )
      parser.add_argument(
          "--push_to_hub", action="store_true", help="Whether or not to push the converted model to the 🤗 hub."
      )
      UpperCamelCase      :							List[str]          =					parser.parse_args()
      convert_deformable_detr_checkpoint(
          args.checkpoint_path,
          args.single_scale,
          args.dilation,
          args.with_box_refine,
          args.two_stage,
          args.pytorch_dump_folder_path,
          args.push_to_hub,
      )





"""simple docstring"""

import copy
from typing import Dict, Optional

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


UpperCamelCase      :							List[str]          =					logging.get_logger(__name__)

UpperCamelCase      :							Dict          =					{
    "shi-labs/oneformer_ade20k_swin_tiny": (
        "https://huggingface.co/shi-labs/oneformer_ade20k_swin_tiny/blob/main/config.json"
    ),
    # See all OneFormer models at https://huggingface.co/models?filter=oneformer
}

class __lowerCAmelCase						( __SCREAMING_SNAKE_CASE     ):
     lowercase		  =		"oneformer"
     lowercase		  =		{"hidden_size": "hidden_dim"}





     def __init__(      self      ,	__UpperCAmelCase = None      ,	__UpperCAmelCase = 255      ,	__UpperCAmelCase = 150      ,	__UpperCAmelCase = 0.1      ,	__UpperCAmelCase = 2.0      ,	__UpperCAmelCase = 5.0      ,	__UpperCAmelCase = 5.0      ,	__UpperCAmelCase = 0.5      ,	__UpperCAmelCase = 0.0_7      ,	__UpperCAmelCase = 1_2544      ,	__UpperCAmelCase = 3.0      ,	__UpperCAmelCase = 0.7_5      ,	__UpperCAmelCase = 0.0_2      ,	__UpperCAmelCase = 1.0      ,	__UpperCAmelCase = 1E-05      ,	__UpperCAmelCase = False      ,	__UpperCAmelCase = True      ,	__UpperCAmelCase = True      ,	__UpperCAmelCase = [4, 8, 16, 32]      ,	__UpperCAmelCase = 77      ,	__UpperCAmelCase = 256      ,	__UpperCAmelCase = 77      ,	__UpperCAmelCase = 6      ,	__UpperCAmelCase = 4_9408      ,	__UpperCAmelCase = 2      ,	__UpperCAmelCase = 16      ,	__UpperCAmelCase = 256      ,	__UpperCAmelCase = 256      ,	__UpperCAmelCase = 256      ,	__UpperCAmelCase = 1024      ,	__UpperCAmelCase = "GN"      ,	__UpperCAmelCase = 6      ,	__UpperCAmelCase = 10      ,	__UpperCAmelCase = True      ,	__UpperCAmelCase = 8      ,	__UpperCAmelCase = 0.1      ,	__UpperCAmelCase = 2048      ,	__UpperCAmelCase = False      ,	__UpperCAmelCase = False      ,	__UpperCAmelCase = 2      ,	__UpperCAmelCase = 4      ,	**__UpperCAmelCase      ,	):

      '''simple docstring'''




      if backbone_config is None:
       logger.info('`backbone_config` is unset. Initializing the config with the default `Swin` backbone.' )
       __UpperCamelCase							    =			CONFIG_MAPPING['swin'](
           image_size=224      ,	in_channels=3      ,	patch_size=4      ,	embed_dim=96      ,	depths=[2, 2, 6, 2]      ,	num_heads=[3, 6, 12, 24]      ,	window_size=7      ,	drop_path_rate=0.3      ,	use_absolute_embeddings=__UpperCAmelCase      ,	out_features=['stage1', 'stage2', 'stage3', 'stage4']      ,	)
      elif isinstance(__UpperCAmelCase      ,	__UpperCAmelCase ):
       __UpperCamelCase							    =			backbone_config.get('model_type' )
       __UpperCamelCase							    =			CONFIG_MAPPING[backbone_model_type]
       __UpperCamelCase							    =			config_class.from_dict(__UpperCAmelCase )

      __UpperCamelCase							    =			backbone_config

      __UpperCamelCase							    =			ignore_value
      __UpperCamelCase							    =			num_queries
      __UpperCamelCase							    =			no_object_weight
      __UpperCamelCase							    =			class_weight
      __UpperCamelCase							    =			mask_weight
      __UpperCamelCase							    =			dice_weight
      __UpperCamelCase							    =			contrastive_weight
      __UpperCamelCase							    =			contrastive_temperature
      __UpperCamelCase							    =			train_num_points
      __UpperCamelCase							    =			oversample_ratio
      __UpperCamelCase							    =			importance_sample_ratio
      __UpperCamelCase							    =			init_std
      __UpperCamelCase							    =			init_xavier_std
      __UpperCamelCase							    =			layer_norm_eps
      __UpperCamelCase							    =			is_training
      __UpperCamelCase							    =			use_auxiliary_loss
      __UpperCamelCase							    =			output_auxiliary_logits
      __UpperCamelCase							    =			strides
      __UpperCamelCase							    =			task_seq_len
      __UpperCamelCase							    =			text_encoder_width
      __UpperCamelCase							    =			text_encoder_context_length
      __UpperCamelCase							    =			text_encoder_num_layers
      __UpperCamelCase							    =			text_encoder_vocab_size
      __UpperCamelCase							    =			text_encoder_proj_layers
      __UpperCamelCase							    =			text_encoder_n_ctx
      __UpperCamelCase							    =			conv_dim
      __UpperCamelCase							    =			mask_dim
      __UpperCamelCase							    =			hidden_dim
      __UpperCamelCase							    =			encoder_feedforward_dim
      __UpperCamelCase							    =			norm
      __UpperCamelCase							    =			encoder_layers
      __UpperCamelCase							    =			decoder_layers
      __UpperCamelCase							    =			use_task_norm
      __UpperCamelCase							    =			num_attention_heads
      __UpperCamelCase							    =			dropout
      __UpperCamelCase							    =			dim_feedforward
      __UpperCamelCase							    =			pre_norm
      __UpperCamelCase							    =			enforce_input_proj
      __UpperCamelCase							    =			query_dec_layers
      __UpperCamelCase							    =			common_stride
      __UpperCamelCase							    =			decoder_layers

      super().__init__(**__UpperCAmelCase )







     def 		UpperCAmelCase   (      self ):

      '''simple docstring'''




      __UpperCamelCase							    =			copy.deepcopy(self.__dict__ )
      __UpperCamelCase							    =			self.backbone_config.to_dict()
      __UpperCamelCase							    =			self.__class__.model_type
      return output



