





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



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

    return orig_key



def 							lowerCAmelCase_				(    A_):
    # transformer decoder self-attention layers
    for i in range(6):
        # read in weights + bias of input projection layer of self-attention
        UpperCamelCase__:					Optional[int]												=      state_dict.pop(F"decoder.layers.{i}.self_attn.in_proj_weight")
        UpperCamelCase__:					str												=      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__:					List[str]												=      in_proj_weight[:2_56, :]
        UpperCamelCase__:					List[Any]												=      in_proj_bias[:2_56]
        UpperCamelCase__:					List[Any]												=      in_proj_weight[2_56:5_12, :]
        UpperCamelCase__:					Union[str, Any]												=      in_proj_bias[2_56:5_12]
        UpperCamelCase__:					List[Any]												=      in_proj_weight[-2_56:, :]
        UpperCamelCase__:					int												=      in_proj_bias[-2_56:]



def 							lowerCAmelCase_				(    ):
    UpperCamelCase__:					List[Any]												=      "http://images.cocodataset.org/val2017/000000039769.jpg"
    UpperCamelCase__:					Optional[int]												=      Image.open(requests.get(A_  ,stream=A_).raw)

    return im



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

    # load image processor
    UpperCamelCase__:					Tuple												=      DeformableDetrImageProcessor(format="coco_detection")

    # prepare image
    UpperCamelCase__:					Dict												=      prepare_img()
    UpperCamelCase__:					int												=      image_processor(images=A_  ,return_tensors="pt")
    UpperCamelCase__:					Any												=      encoding["pixel_values"]

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

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

    UpperCamelCase__:					Optional[int]												=      "cuda" if torch.cuda.is_available() else "cpu"
    model.to(A_)
    # verify our conversion
    UpperCamelCase__:					Optional[int]												=      model(pixel_values.to(A_))

    UpperCamelCase__:					List[Any]												=      torch.tensor(
        [[-9.6645, -4.3449, -5.8705], [-9.7035, -3.8504, -5.0724], [-10.5634, -5.3379, -7.5116]])
    UpperCamelCase__:					Union[str, Any]												=      torch.tensor([[0.8693, 0.2289, 0.2492], [0.3150, 0.5489, 0.5845], [0.5563, 0.7580, 0.8518]])

    if single_scale:
        UpperCamelCase__:					Optional[int]												=      torch.tensor(
            [[-9.9051, -4.2541, -6.4852], [-9.6947, -4.0854, -6.8033], [-10.0665, -5.8470, -7.7003]])
        UpperCamelCase__:					int												=      torch.tensor([[0.7292, 0.4991, 0.5532], [0.7959, 0.2426, 0.4236], [0.7582, 0.3518, 0.4451]])

    if single_scale and dilation:
        UpperCamelCase__:					Tuple												=      torch.tensor(
            [[-8.9652, -4.1074, -5.6635], [-9.0596, -4.9447, -6.6075], [-10.1178, -4.5275, -6.2671]])
        UpperCamelCase__:					Union[str, Any]												=      torch.tensor([[0.7665, 0.4130, 0.4769], [0.8364, 0.1841, 0.3391], [0.6261, 0.3895, 0.7978]])

    if with_box_refine:
        UpperCamelCase__:					Optional[Any]												=      torch.tensor(
            [[-8.8895, -5.4187, -6.8153], [-8.4706, -6.1668, -7.6184], [-9.0042, -5.5359, -6.9141]])
        UpperCamelCase__:					str												=      torch.tensor([[0.7828, 0.2208, 0.4323], [0.0892, 0.5996, 0.1319], [0.5524, 0.6389, 0.8914]])

    if with_box_refine and two_stage:
        UpperCamelCase__:					str												=      torch.tensor(
            [[-6.7108, -4.3213, -6.3777], [-8.9014, -6.1799, -6.7240], [-6.9315, -4.4735, -6.2298]])
        UpperCamelCase__:					Any												=      torch.tensor([[0.2583, 0.5499, 0.4683], [0.7652, 0.9068, 0.4882], [0.5490, 0.2763, 0.0564]])

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

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

    # Push to hub
    if push_to_hub:
        UpperCamelCase__:					int												=      "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=A_  ,organization="nielsr"  ,commit_message="Add model")


if __name__ == "__main__":
   A__:  str       					=					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__:  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

from PIL import Image

# Define the first generation of cells
# fmt: off
A__:  Tuple       					=					[[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,
          0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]



def 							lowerCAmelCase_				(    A_):
    return [int(A_) for c in F"{ruleset:08}"[:8]]



def 							lowerCAmelCase_				(    A_  ,A_  ,A_):
    UpperCamelCase__:					List[str]												=      len(cells[0])  # 31
    UpperCamelCase__:					Union[str, Any]												=      []
    for i in range(A_):
        # Get the neighbors of each cell
        # Handle neighbours outside bounds by using 0 as their value
        UpperCamelCase__:					str												=      0 if i == 0 else cells[time][i - 1]
        UpperCamelCase__:					Union[str, Any]												=      0 if i == population - 1 else cells[time][i + 1]
        # Define a new cell and add it to the new generation
        UpperCamelCase__:					List[Any]												=      7 - int(F"{left_neighbor}{cells[time][i]}{right_neighbor}"  ,2)
        next_generation.append(rule[situation])
    return next_generation



def 							lowerCAmelCase_				(    A_):
    UpperCamelCase__:					int												=      Image.new("RGB"  ,(len(cells[0]), len(A_)))
    UpperCamelCase__:					Dict												=      img.load()
    # Generates image
    for w in range(img.width):
        for h in range(img.height):
            UpperCamelCase__:					List[str]												=      2_55 - int(2_55 * cells[h][w])
            UpperCamelCase__:					Union[str, Any]												=      (color, color, color)
    return img


if __name__ == "__main__":
   A__:  Any       					=					bin(int(input('''Rule:\n''').strip()))[2:]
   A__:  Dict       					=					format_ruleset(int(rule_num))
   for time in range(16):
      CELLS.append(new_generation(CELLS, rule, time))
   A__:  int       					=					generate_image(CELLS)
   # Uncomment to save the image
   # img.save(f"rule_{rule_num}.png")
   img.show()

