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()
_SCREAMING_SNAKE_CASE   		=			logging.get_logger(__name__)





def  lowercase(  UpperCamelCase_					)		->  Union[str, Any]:







 '''simple docstring'''
 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  lowercase(  UpperCamelCase_					)		->  Optional[int]:







 '''simple docstring'''
 # 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[:256, :]
  UpperCamelCase      =			in_proj_bias[:256]
  UpperCamelCase      =			in_proj_weight[256:512, :]
  UpperCamelCase      =			in_proj_bias[256:512]
  UpperCamelCase      =			in_proj_weight[-256:, :]
  UpperCamelCase      =			in_proj_bias[-256:]





def  lowercase(  )		->  Any:







 '''simple docstring'''
 UpperCamelCase      =			"""http://images.cocodataset.org/val2017/000000039769.jpg"""
 UpperCamelCase      =			Image.open(requests.get(UpperCamelCase_       ,				stream=UpperCamelCase_					).raw					)

 return im





@torch.no_grad()
def  lowercase(  UpperCamelCase_       ,				UpperCamelCase_       ,				UpperCamelCase_       ,				UpperCamelCase_       ,				UpperCamelCase_       ,				UpperCamelCase_       ,				UpperCamelCase_       ,				)		->  Dict:







 '''simple docstring'''
 UpperCamelCase      =			DeformableDetrConfig()
 # set config attributes
 if single_scale:
  UpperCamelCase      =			1
 UpperCamelCase      =			dilation
 UpperCamelCase      =			with_box_refine
 UpperCamelCase      =			two_stage
 # set labels
 UpperCamelCase      =			91
 UpperCamelCase      =			"""huggingface/label-files"""
 UpperCamelCase      =			"""coco-detection-id2label.json"""
 UpperCamelCase      =			json.load(open(cached_download(hf_hub_url(UpperCamelCase_       ,				UpperCamelCase_       ,				repo_type="""dataset"""					)					)       ,				"""r"""					)					)
 UpperCamelCase      =			{int(UpperCamelCase_					): 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=UpperCamelCase_       ,				return_tensors="""pt"""					)
 UpperCamelCase      =			encoding["""pixel_values"""]

 logger.info("""Converting model..."""					)

 # load original state dict
 UpperCamelCase      =			torch.load(UpperCamelCase_       ,				map_location="""cpu"""					)["""model"""]
 # rename keys
 for key in state_dict.copy().keys():
  UpperCamelCase      =			state_dict.pop(UpperCamelCase_					)
  UpperCamelCase      =			val
 # query, key and value matrices need special treatment
 read_in_q_k_v(UpperCamelCase_					)
 # 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(UpperCamelCase_					)
   UpperCamelCase      =			val
    # finally, create HuggingFace model and load state dict
 UpperCamelCase      =			DeformableDetrForObjectDetection(UpperCamelCase_					)
 model.load_state_dict(UpperCamelCase_					)
 model.eval()

 UpperCamelCase      =			"""cuda""" if torch.cuda.is_available() else """cpu"""
 model.to(UpperCamelCase_					)
 # verify our conversion
 UpperCamelCase      =			model(pixel_values.to(UpperCamelCase_					)					)

 UpperCamelCase      =			torch.tensor(
     [[-9.6_6_4_5, -4.3_4_4_9, -5.8_7_0_5], [-9.7_0_3_5, -3.8_5_0_4, -5.0_7_2_4], [-1_0.5_6_3_4, -5.3_3_7_9, -7.5_1_1_6]]					)
 UpperCamelCase      =			torch.tensor([[0.8_6_9_3, 0.2_2_8_9, 0.2_4_9_2], [0.3_1_5_0, 0.5_4_8_9, 0.5_8_4_5], [0.5_5_6_3, 0.7_5_8_0, 0.8_5_1_8]]					)

 if single_scale:
  UpperCamelCase      =			torch.tensor(
      [[-9.9_0_5_1, -4.2_5_4_1, -6.4_8_5_2], [-9.6_9_4_7, -4.0_8_5_4, -6.8_0_3_3], [-1_0.0_6_6_5, -5.8_4_7_0, -7.7_0_0_3]]					)
  UpperCamelCase      =			torch.tensor([[0.7_2_9_2, 0.4_9_9_1, 0.5_5_3_2], [0.7_9_5_9, 0.2_4_2_6, 0.4_2_3_6], [0.7_5_8_2, 0.3_5_1_8, 0.4_4_5_1]]					)

 if single_scale and dilation:
  UpperCamelCase      =			torch.tensor(
      [[-8.9_6_5_2, -4.1_0_7_4, -5.6_6_3_5], [-9.0_5_9_6, -4.9_4_4_7, -6.6_0_7_5], [-1_0.1_1_7_8, -4.5_2_7_5, -6.2_6_7_1]]					)
  UpperCamelCase      =			torch.tensor([[0.7_6_6_5, 0.4_1_3_0, 0.4_7_6_9], [0.8_3_6_4, 0.1_8_4_1, 0.3_3_9_1], [0.6_2_6_1, 0.3_8_9_5, 0.7_9_7_8]]					)

 if with_box_refine:
  UpperCamelCase      =			torch.tensor(
      [[-8.8_8_9_5, -5.4_1_8_7, -6.8_1_5_3], [-8.4_7_0_6, -6.1_6_6_8, -7.6_1_8_4], [-9.0_0_4_2, -5.5_3_5_9, -6.9_1_4_1]]					)
  UpperCamelCase      =			torch.tensor([[0.7_8_2_8, 0.2_2_0_8, 0.4_3_2_3], [0.0_8_9_2, 0.5_9_9_6, 0.1_3_1_9], [0.5_5_2_4, 0.6_3_8_9, 0.8_9_1_4]]					)

 if with_box_refine and two_stage:
  UpperCamelCase      =			torch.tensor(
      [[-6.7_1_0_8, -4.3_2_1_3, -6.3_7_7_7], [-8.9_0_1_4, -6.1_7_9_9, -6.7_2_4_0], [-6.9_3_1_5, -4.4_7_3_5, -6.2_2_9_8]]					)
  UpperCamelCase      =			torch.tensor([[0.2_5_8_3, 0.5_4_9_9, 0.4_6_8_3], [0.7_6_5_2, 0.9_0_6_8, 0.4_8_8_2], [0.5_4_9_0, 0.2_7_6_3, 0.0_5_6_4]]					)

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

 assert torch.allclose(outputs.logits[0, :3, :3]       ,				expected_logits.to(UpperCamelCase_					)       ,				atol=1E-4					)
 assert torch.allclose(outputs.pred_boxes[0, :3, :3]       ,				expected_boxes.to(UpperCamelCase_					)       ,				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(UpperCamelCase_					).mkdir(exist_ok=UpperCamelCase_					)
 model.save_pretrained(UpperCamelCase_					)
 image_processor.save_pretrained(UpperCamelCase_					)

 # 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=UpperCamelCase_       ,				organization="""nielsr"""       ,				commit_message="""Add model"""					)


if __name__ == "__main__":
     _SCREAMING_SNAKE_CASE   		=			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."""
     )
     _SCREAMING_SNAKE_CASE   		=			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,
     )

import numpy as np





def  lowercase(  UpperCamelCase_       ,				UpperCamelCase_       ,				UpperCamelCase_					)		->  List[str]:







 '''simple docstring'''
 UpperCamelCase      =			n_draws * class_counts / class_counts.sum()
 # floored means we don't overshoot n_samples, but probably undershoot
 UpperCamelCase      =			np.floor(UpperCamelCase_					)
 # we add samples according to how much "left over" probability
 # they had, until we arrive at n_samples
 UpperCamelCase      =			int(n_draws - floored.sum()					)
 if need_to_add > 0:
  UpperCamelCase      =			continuous - floored
  UpperCamelCase      =			np.sort(np.unique(UpperCamelCase_					)					)[::-1]
  # add according to remainder, but break ties
  # randomly to avoid biases
  for value in values:
   ((UpperCamelCase)     ,    )      =			np.where(remainder == value					)
   # if we need_to_add less than what's in inds
   # we draw randomly from them.
   # if we need to add more, we add them all and
   # go to the next value
   UpperCamelCase      =			min(len(UpperCamelCase_					)       ,				UpperCamelCase_					)
   UpperCamelCase      =			rng.choice(UpperCamelCase_       ,				size=UpperCamelCase_       ,				replace=UpperCamelCase_					)
   floored[inds] += 1
   need_to_add -= add_now
   if need_to_add == 0:
    break
 return floored.astype(np.intaa					)





def  lowercase(  UpperCamelCase_       ,				UpperCamelCase_       ,				UpperCamelCase_       ,				UpperCamelCase_       ,				UpperCamelCase_=10					)		->  Dict:







 '''simple docstring'''
 UpperCamelCase     ,    UpperCamelCase      =			np.unique(UpperCamelCase_       ,				return_inverse=UpperCamelCase_					)
 UpperCamelCase      =			classes.shape[0]
 UpperCamelCase      =			np.bincount(UpperCamelCase_					)
 if np.min(UpperCamelCase_					) < 2:
  raise ValueError("""Minimum class count error"""					)
 if n_train < n_classes:
  raise ValueError(
      """The train_size = %d should be greater or """ """equal to the number of classes = %d""" % (n_train, n_classes)					)
 if n_test < n_classes:
  raise ValueError(
      """The test_size = %d should be greater or """ """equal to the number of classes = %d""" % (n_test, n_classes)					)
 UpperCamelCase      =			np.split(np.argsort(UpperCamelCase_       ,				kind="""mergesort"""					)       ,				np.cumsum(UpperCamelCase_					)[:-1]					)
 for _ in range(UpperCamelCase_					):
  UpperCamelCase      =			approximate_mode(UpperCamelCase_       ,				UpperCamelCase_       ,				UpperCamelCase_					)
  UpperCamelCase      =			class_counts - n_i
  UpperCamelCase      =			approximate_mode(UpperCamelCase_       ,				UpperCamelCase_       ,				UpperCamelCase_					)

  UpperCamelCase      =			[]
  UpperCamelCase      =			[]

  for i in range(UpperCamelCase_					):
   UpperCamelCase      =			rng.permutation(class_counts[i]					)
   UpperCamelCase      =			class_indices[i].take(UpperCamelCase_       ,				mode="""clip"""					)
   train.extend(perm_indices_class_i[: n_i[i]]					)
   test.extend(perm_indices_class_i[n_i[i] : n_i[i] + t_i[i]]					)
  UpperCamelCase      =			rng.permutation(UpperCamelCase_					)
  UpperCamelCase      =			rng.permutation(UpperCamelCase_					)

  yield train, test
