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()
a_					      = logging.get_logger(__name__)





def      __lowercase  (		lowerCamelCase      :  int			):
 if "backbone.0.body" in orig_key:
  UpperCamelCase_      :		str				=      orig_key.replace('backbone.0.body'						,						'backbone.conv_encoder.model'			)
 if "transformer" in orig_key:
  UpperCamelCase_      :		Tuple				=      orig_key.replace('transformer.'						,						''			)
 if "norm1" in orig_key:
  if "encoder" in orig_key:
   UpperCamelCase_      :		Any				=      orig_key.replace('norm1'						,						'self_attn_layer_norm'			)
  else:
   UpperCamelCase_      :		Optional[Any]				=      orig_key.replace('norm1'						,						'encoder_attn_layer_norm'			)
 if "norm2" in orig_key:
  if "encoder" in orig_key:
   UpperCamelCase_      :		Any				=      orig_key.replace('norm2'						,						'final_layer_norm'			)
  else:
   UpperCamelCase_      :		Union[str, Any]				=      orig_key.replace('norm2'						,						'self_attn_layer_norm'			)
 if "norm3" in orig_key:
  UpperCamelCase_      :		Dict				=      orig_key.replace('norm3'						,						'final_layer_norm'			)
 if "linear1" in orig_key:
  UpperCamelCase_      :		str				=      orig_key.replace('linear1'						,						'fc1'			)
 if "linear2" in orig_key:
  UpperCamelCase_      :		str				=      orig_key.replace('linear2'						,						'fc2'			)
 if "query_embed" in orig_key:
  UpperCamelCase_      :		Union[str, Any]				=      orig_key.replace('query_embed'						,						'query_position_embeddings'			)
 if "cross_attn" in orig_key:
  UpperCamelCase_      :		Optional[Any]				=      orig_key.replace('cross_attn'						,						'encoder_attn'			)

 return orig_key





def      __lowercase  (		lowerCamelCase      :  Any			):
 # transformer decoder self-attention layers
 for i in range(6			):
  # read in weights + bias of input projection layer of self-attention
  UpperCamelCase_      :		List[str]				=      state_dict.pop(F"decoder.layers.{i}.self_attn.in_proj_weight"			)
  UpperCamelCase_      :		Tuple				=      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_      :		Optional[Any]				=      in_proj_weight[:256, :]
  UpperCamelCase_      :		Union[str, Any]				=      in_proj_bias[:256]
  UpperCamelCase_      :		Union[str, Any]				=      in_proj_weight[256:512, :]
  UpperCamelCase_      :		str				=      in_proj_bias[256:512]
  UpperCamelCase_      :		Union[str, Any]				=      in_proj_weight[-256:, :]
  UpperCamelCase_      :		Optional[int]				=      in_proj_bias[-256:]





def      __lowercase  (		):
 UpperCamelCase_      :		Union[str, Any]				=      "http://images.cocodataset.org/val2017/000000039769.jpg"
 UpperCamelCase_      :		Any				=      Image.open(requests.get(lowerCamelCase						,						stream=lowerCamelCase			).raw			)

 return im





@torch.no_grad()
def      __lowercase  (		lowerCamelCase      :  Tuple						,						lowerCamelCase      :  Union[str, Any]						,						lowerCamelCase      :  Dict						,						lowerCamelCase      :  str						,						lowerCamelCase      :  List[str]						,						lowerCamelCase      :  Any						,						lowerCamelCase      :  Optional[Any]						,						):
 UpperCamelCase_      :		Tuple				=      DeformableDetrConfig()
 # set config attributes
 if single_scale:
  UpperCamelCase_      :		Tuple				=      1
 UpperCamelCase_      :		Tuple				=      dilation
 UpperCamelCase_      :		List[str]				=      with_box_refine
 UpperCamelCase_      :		Tuple				=      two_stage
 # set labels
 UpperCamelCase_      :		Union[str, Any]				=      91
 UpperCamelCase_      :		List[str]				=      "huggingface/label-files"
 UpperCamelCase_      :		Tuple				=      "coco-detection-id2label.json"
 UpperCamelCase_      :		int				=      json.load(open(cached_download(hf_hub_url(lowerCamelCase						,						lowerCamelCase						,						repo_type='dataset'			)			)						,						'r'			)			)
 UpperCamelCase_      :		List[Any]				=      {int(lowerCamelCase			): v for k, v in idalabel.items()}
 UpperCamelCase_      :		Dict				=      idalabel
 UpperCamelCase_      :		Union[str, Any]				=      {v: k for k, v in idalabel.items()}

 # load image processor
 UpperCamelCase_      :		Optional[int]				=      DeformableDetrImageProcessor(format='coco_detection'			)

 # prepare image
 UpperCamelCase_      :		List[str]				=      prepare_img()
 UpperCamelCase_      :		Tuple				=      image_processor(images=lowerCamelCase						,						return_tensors='pt'			)
 UpperCamelCase_      :		int				=      encoding["pixel_values"]

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

 # load original state dict
 UpperCamelCase_      :		Optional[int]				=      torch.load(lowerCamelCase						,						map_location='cpu'			)["model"]
 # rename keys
 for key in state_dict.copy().keys():
  UpperCamelCase_      :		Dict				=      state_dict.pop(lowerCamelCase			)
  UpperCamelCase_      :		Union[str, Any]				=      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
 UpperCamelCase_      :		List[str]				=      "model."
 for key in state_dict.copy().keys():
  if not key.startswith('class_embed'			) and not key.startswith('bbox_embed'			):
   UpperCamelCase_      :		Optional[int]				=      state_dict.pop(lowerCamelCase			)
   UpperCamelCase_      :		Any				=      val
    # finally, create HuggingFace model and load state dict
 UpperCamelCase_      :		Optional[int]				=      DeformableDetrForObjectDetection(lowerCamelCase			)
 model.load_state_dict(lowerCamelCase			)
 model.eval()

 UpperCamelCase_      :		Dict				=      "cuda" if torch.cuda.is_available() else "cpu"
 model.to(lowerCamelCase			)
 # verify our conversion
 UpperCamelCase_      :		int				=      model(pixel_values.to(lowerCamelCase			)			)

 UpperCamelCase_      :		List[Any]				=      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_      :		List[str]				=      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_      :		Optional[Any]				=      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_      :		Tuple				=      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_      :		Optional[Any]				=      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_      :		Dict				=      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_      :		Any				=      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_      :		Tuple				=      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_      :		str				=      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_      :		int				=      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:
  UpperCamelCase_      :		Union[str, Any]				=      "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__":
   a_					      = 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.'
   )
   a_					      = 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 datetime import date

import requests

a_					      = 'https://api.carbonintensity.org.uk/intensity'





def      __lowercase  (		):
 UpperCamelCase_      :		Dict				=      requests.get(lowerCamelCase			).json()['data'][0]
 return last_half_hour["intensity"]["actual"]





def      __lowercase  (		lowerCamelCase      :  Union[str, Any]						,						lowerCamelCase      :  Union[str, Any]			):
 return requests.get(F"{BASE_URL}/{start}/{end}"			).json()["data"]


if __name__ == "__main__":
   for entry in fetch_from_to(start=date(2_020, 10, 1), end=date(2_020, 10, 3)):
      print('from {from} to {to}: {intensity[actual]}'.format(**entry))
   print(F"""{fetch_last_half_hour() = }""")



