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()
lowerCAmelCase__      		=					logging.get_logger(__name__)






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

       return orig_key






def  __lowerCamelCase						(		lowerCAmelCase__      ):
       # transformer decoder self-attention layers
       for i in range(6      ):
              # read in weights + bias of input projection layer of self-attention
              lowerCAmelCase__	      =   state_dict.pop(F"""decoder.layers.{i}.self_attn.in_proj_weight"""      )
              lowerCAmelCase__	      =   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
              lowerCAmelCase__	      =   in_proj_weight[:2_5_6, :]
              lowerCAmelCase__	      =   in_proj_bias[:2_5_6]
              lowerCAmelCase__	      =   in_proj_weight[2_5_6:5_1_2, :]
              lowerCAmelCase__	      =   in_proj_bias[2_5_6:5_1_2]
              lowerCAmelCase__	      =   in_proj_weight[-2_5_6:, :]
              lowerCAmelCase__	      =   in_proj_bias[-2_5_6:]






def  __lowerCamelCase						(		):
       lowerCAmelCase__	      =   'http://images.cocodataset.org/val2017/000000039769.jpg'
       lowerCAmelCase__	      =   Image.open(requests.get(snake_case_      ,    stream=snake_case_      ).raw      )

       return im






@torch.no_grad()
def  __lowerCamelCase						(		lowerCAmelCase__      ,    lowerCAmelCase__      ,    lowerCAmelCase__      ,    lowerCAmelCase__      ,    lowerCAmelCase__      ,    lowerCAmelCase__      ,    lowerCAmelCase__      ,    ):
       lowerCAmelCase__	      =   DeformableDetrConfig()
       # set config attributes
       if single_scale:
              lowerCAmelCase__	      =   1
       lowerCAmelCase__	      =   dilation
       lowerCAmelCase__	      =   with_box_refine
       lowerCAmelCase__	      =   two_stage
       # set labels
       lowerCAmelCase__	      =   9_1
       lowerCAmelCase__	      =   'huggingface/label-files'
       lowerCAmelCase__	      =   'coco-detection-id2label.json'
       lowerCAmelCase__	      =   json.load(open(cached_download(hf_hub_url(snake_case_      ,    snake_case_      ,    repo_type='dataset'      )      )      ,    'r'      )      )
       lowerCAmelCase__	      =   {int(snake_case_      ): v for k, v in idalabel.items()}
       lowerCAmelCase__	      =   idalabel
       lowerCAmelCase__	      =   {v: k for k, v in idalabel.items()}

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

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

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

       # load original state dict
       lowerCAmelCase__	      =   torch.load(snake_case_      ,    map_location='cpu'      )['model']
       # rename keys
       for key in state_dict.copy().keys():
              lowerCAmelCase__	      =   state_dict.pop(snake_case_      )
              lowerCAmelCase__	      =   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
       lowerCAmelCase__	      =   'model.'
       for key in state_dict.copy().keys():
              if not key.startswith('class_embed'      ) and not key.startswith('bbox_embed'      ):
                     lowerCAmelCase__	      =   state_dict.pop(snake_case_      )
                     lowerCAmelCase__	      =   val
    # finally, create HuggingFace model and load state dict
       lowerCAmelCase__	      =   DeformableDetrForObjectDetection(snake_case_      )
       model.load_state_dict(snake_case_      )
       model.eval()

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

       lowerCAmelCase__	      =   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]]      )
       lowerCAmelCase__	      =   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:
              lowerCAmelCase__	      =   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]]      )
              lowerCAmelCase__	      =   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:
              lowerCAmelCase__	      =   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]]      )
              lowerCAmelCase__	      =   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:
              lowerCAmelCase__	      =   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]]      )
              lowerCAmelCase__	      =   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:
              lowerCAmelCase__	      =   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]]      )
              lowerCAmelCase__	      =   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(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:
              lowerCAmelCase__	      =   '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__":
    lowerCAmelCase__      		=					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.'
    )
    lowerCAmelCase__      		=					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'''







from __future__ import annotations

from pathlib import Path






def 					_SCREAMING_SNAKE_CASE		(						snake_case_							,       snake_case_	):
	return pointa[0] * pointa[1] - pointa[1] * pointa[0]






def 					_SCREAMING_SNAKE_CASE		(						snake_case_							,       snake_case_							,       snake_case_							,       snake_case_							,       snake_case_							,       snake_case_	):
	_lowercase							=     (xa, ya)
	_lowercase							=     (xa - xa, ya - ya)
	_lowercase							=     (xa - xa, ya - ya)
	_lowercase							=     -vector_product(snake_case_							,       snake_case_	) / vector_product(
	    snake_case_							,       snake_case_	)
	_lowercase							=     +vector_product(snake_case_							,       snake_case_	) / vector_product(
	    snake_case_							,       snake_case_	)

	return a > 0 and b > 0 and a + b < 1






def 					_SCREAMING_SNAKE_CASE		(						snake_case_ = "p102_triangles.txt"	):
	_lowercase							=     Path(snake_case_	).parent.joinpath(snake_case_	).read_text(encoding="""utf-8"""	)

	_lowercase							=     []
	for line in data.strip().split("""\n"""	):
		triangles.append([int(snake_case_	) for number in line.split(""","""	)]	)

	_lowercase							=     0
	_lowercase							=     42

	for triangle in triangles:
		ret += contains_origin(*snake_case_	)

	return ret


if __name__ == "__main__":
							print(F"""{solution() = }""")




