




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:    str											=   logging.get_logger(__name__)


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

  return orig_key


def 				_a       (				lowerCAmelCase   )->  Any:
  # transformer decoder self-attention layers
  for i in range(6   ):
    # read in weights + bias of input projection layer of self-attention
    SCREAMING_SNAKE_CASE_							=       state_dict.pop(F'''decoder.layers.{i}.self_attn.in_proj_weight'''   )
    SCREAMING_SNAKE_CASE_							=       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
    SCREAMING_SNAKE_CASE_							=       in_proj_weight[:256, :]
    SCREAMING_SNAKE_CASE_							=       in_proj_bias[:256]
    SCREAMING_SNAKE_CASE_							=       in_proj_weight[256:512, :]
    SCREAMING_SNAKE_CASE_							=       in_proj_bias[256:512]
    SCREAMING_SNAKE_CASE_							=       in_proj_weight[-256:, :]
    SCREAMING_SNAKE_CASE_							=       in_proj_bias[-256:]


def 				_a       (				)->  List[Any]:
  SCREAMING_SNAKE_CASE_							=       'http://images.cocodataset.org/val2017/000000039769.jpg'
  SCREAMING_SNAKE_CASE_							=       Image.open(requests.get(lowerCAmelCase						,	stream=lowerCAmelCase   ).raw   )

  return im


@torch.no_grad()
def 				_a       (				lowerCAmelCase						,	lowerCAmelCase						,	lowerCAmelCase						,	lowerCAmelCase						,	lowerCAmelCase						,	lowerCAmelCase						,	lowerCAmelCase						,	)->  Dict:
  SCREAMING_SNAKE_CASE_							=       DeformableDetrConfig()
  # set config attributes
  if single_scale:
    SCREAMING_SNAKE_CASE_							=       1
  SCREAMING_SNAKE_CASE_							=       dilation
  SCREAMING_SNAKE_CASE_							=       with_box_refine
  SCREAMING_SNAKE_CASE_							=       two_stage
  # set labels
  SCREAMING_SNAKE_CASE_							=       91
  SCREAMING_SNAKE_CASE_							=       'huggingface/label-files'
  SCREAMING_SNAKE_CASE_							=       'coco-detection-id2label.json'
  SCREAMING_SNAKE_CASE_							=       json.load(open(cached_download(hf_hub_url(lowerCAmelCase						,	lowerCAmelCase						,	repo_type='dataset'   )   )						,	'r'   )   )
  SCREAMING_SNAKE_CASE_							=       {int(lowerCAmelCase   ): v for k, v in idalabel.items()}
  SCREAMING_SNAKE_CASE_							=       idalabel
  SCREAMING_SNAKE_CASE_							=       {v: k for k, v in idalabel.items()}

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

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

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

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

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

  SCREAMING_SNAKE_CASE_							=       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], [-10.5634, -5.3_3_7_9, -7.5_1_1_6]]   )
  SCREAMING_SNAKE_CASE_							=       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:
    SCREAMING_SNAKE_CASE_							=       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], [-10.0665, -5.8_4_7_0, -7.7_0_0_3]]   )
    SCREAMING_SNAKE_CASE_							=       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:
    SCREAMING_SNAKE_CASE_							=       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], [-10.1178, -4.5_2_7_5, -6.2_6_7_1]]   )
    SCREAMING_SNAKE_CASE_							=       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:
    SCREAMING_SNAKE_CASE_							=       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]]   )
    SCREAMING_SNAKE_CASE_							=       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:
    SCREAMING_SNAKE_CASE_							=       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]]   )
    SCREAMING_SNAKE_CASE_							=       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(lowerCAmelCase   )						,	atol=1E-4   )
  assert torch.allclose(outputs.pred_boxes[0, :3, :3]						,	expected_boxes.to(lowerCAmelCase   )						,	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(lowerCAmelCase   ).mkdir(exist_ok=lowerCAmelCase   )
  model.save_pretrained(lowerCAmelCase   )
  image_processor.save_pretrained(lowerCAmelCase   )

  # Push to hub
  if push_to_hub:
    SCREAMING_SNAKE_CASE_							=       '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=lowerCAmelCase						,	organization='nielsr'						,	commit_message='Add model'   )


if __name__ == "__main__":
      SCREAMING_SNAKE_CASE:    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.'''
      )
      SCREAMING_SNAKE_CASE:    Union[str, Any]											=   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,
      )





from __future__ import annotations

import random
import string



class       lowercase_		:



						def __init__( self  :      str				,       snake_case__  :      str | None = None     ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_							=       passcode or self.__passcode_creator()
								SCREAMING_SNAKE_CASE_							=       self.__make_key_list()
								SCREAMING_SNAKE_CASE_							=       self.__make_shift_key()



						def __str__( self  :      List[Any]     ):

								"""simple docstring"""

								return "".join(self.__passcode     )



						def   __a				( self  :      Union[str, Any]				,       snake_case__  :      list[int]     ):

								"""simple docstring"""

								for i in range(1				,       len(snake_case__     )				,       2     ):
										iterlist[i] *= -1
								return iterlist



						def   __a				( self  :      str     ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_							=       string.ascii_letters + string.digits
								SCREAMING_SNAKE_CASE_							=       [random.choice(snake_case__     ) for _ in range(random.randint(10				,       20     )     )]
								return password



						def   __a				( self  :      str     ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_							=       (
								    string.ascii_letters + string.digits + string.punctuation + ' \t\n'
								)

								SCREAMING_SNAKE_CASE_							=       []

								# creates points known as breakpoints to break the key_list_options at those
								# points and pivot each substring
								SCREAMING_SNAKE_CASE_							=       sorted(set(self.__passcode     )     )
								SCREAMING_SNAKE_CASE_							=       []

								# algorithm for creating a new shuffled list, keys_l, out of key_list_options
								for i in key_list_options:
										temp_list.extend(snake_case__     )

										# checking breakpoints at which to pivot temporary sublist and add it into
										# keys_l
										if i in breakpoints or i == key_list_options[-1]:
												keys_l.extend(temp_list[::-1]     )
												temp_list.clear()

        # returning a shuffled keys_l to prevent brute force guessing of shift key
								return keys_l



						def   __a				( self  :      Union[str, Any]     ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_							=       sum(self.__neg_pos([ord(snake_case__     ) for x in self.__passcode]     )     )
								return num if num > 0 else len(self.__passcode     )



						def   __a				( self  :      str				,       snake_case__  :      str     ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_							=       ''

								# decoding shift like Caesar cipher algorithm implementing negative shift or
								# reverse shift or left shift
								for i in encoded_message:
										SCREAMING_SNAKE_CASE_							=       self.__key_list.index(snake_case__     )
										decoded_message += self.__key_list[
										    (position - self.__shift_key) % -len(self.__key_list     )
										]

								return decoded_message






						def   __a				( self  :      Optional[int]				,       snake_case__  :      str     ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_							=       ''

								# encoding shift like Caesar cipher algorithm implementing positive shift or
								# forward shift or right shift
								for i in plaintext:
										SCREAMING_SNAKE_CASE_							=       self.__key_list.index(snake_case__     )
										encoded_message += self.__key_list[
										    (position + self.__shift_key) % len(self.__key_list     )
										]

								return encoded_message


def 				_a       (				lowerCAmelCase = "Hello, this is a modified Caesar cipher"   )->  str:
		SCREAMING_SNAKE_CASE_							=       ShuffledShiftCipher()
		return cipa.decrypt(cipa.encrypt(lowerCAmelCase   )   )


if __name__ == "__main__":
						import doctest

						doctest.testmod()