




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 	UpperCAmelCase_    (	__UpperCamelCase	):
    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 	UpperCAmelCase_    (	__UpperCamelCase	):
    # 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 	UpperCAmelCase_    (	):
    SCREAMING_SNAKE_CASE__								="""http://images.cocodataset.org/val2017/000000039769.jpg"""
    SCREAMING_SNAKE_CASE__								=Image.open(requests.get(__UpperCamelCase,				stream=__UpperCamelCase	).raw	)

    return im



@torch.no_grad()
def 	UpperCAmelCase_    (	__UpperCamelCase,				__UpperCamelCase,				__UpperCamelCase,				__UpperCamelCase,				__UpperCamelCase,				__UpperCamelCase,				__UpperCamelCase,				):
    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(__UpperCamelCase,				__UpperCamelCase,				repo_type="""dataset"""	)	),				"""r"""	)	)
    SCREAMING_SNAKE_CASE__								={int(__UpperCamelCase	): 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=__UpperCamelCase,				return_tensors="""pt"""	)
    SCREAMING_SNAKE_CASE__								=encoding["""pixel_values"""]

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

    # load original state dict
    SCREAMING_SNAKE_CASE__								=torch.load(__UpperCamelCase,				map_location="""cpu"""	)["""model"""]
    # rename keys
    for key in state_dict.copy().keys():
        SCREAMING_SNAKE_CASE__								=state_dict.pop(__UpperCamelCase	)
        SCREAMING_SNAKE_CASE__								=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
    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(__UpperCamelCase	)
            SCREAMING_SNAKE_CASE__								=val
    # finally, create HuggingFace model and load state dict
    SCREAMING_SNAKE_CASE__								=DeformableDetrForObjectDetection(__UpperCamelCase	)
    model.load_state_dict(__UpperCamelCase	)
    model.eval()

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

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

    if single_scale:
        SCREAMING_SNAKE_CASE__								=torch.tensor(
            [[-9.9051, -4.2541, -6.4852], [-9.6947, -4.0854, -6.8033], [-10.0665, -5.8470, -7.7003]]	)
        SCREAMING_SNAKE_CASE__								=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:
        SCREAMING_SNAKE_CASE__								=torch.tensor(
            [[-8.9652, -4.1074, -5.6635], [-9.0596, -4.9447, -6.6075], [-10.1178, -4.5275, -6.2671]]	)
        SCREAMING_SNAKE_CASE__								=torch.tensor([[0.7665, 0.4130, 0.4769], [0.8364, 0.1841, 0.3391], [0.6261, 0.3895, 0.7978]]	)

    if with_box_refine:
        SCREAMING_SNAKE_CASE__								=torch.tensor(
            [[-8.8895, -5.4187, -6.8153], [-8.4706, -6.1668, -7.6184], [-9.0042, -5.5359, -6.9141]]	)
        SCREAMING_SNAKE_CASE__								=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:
        SCREAMING_SNAKE_CASE__								=torch.tensor(
            [[-6.7108, -4.3213, -6.3777], [-8.9014, -6.1799, -6.7240], [-6.9315, -4.4735, -6.2298]]	)
        SCREAMING_SNAKE_CASE__								=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(__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:
        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=__UpperCamelCase,				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,
    )












from __future__ import annotations

from typing import Generic, TypeVar

lowerCamelCase_  						=   TypeVar("T")
class       __a					( Generic[T]     ):


     """simple docstring"""


     def __init__(	self			: int				,_UpperCamelCase			: T						)		->   str:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__								=data  # Assign data
         SCREAMING_SNAKE_CASE__								=None  # Initialize next as null
         SCREAMING_SNAKE_CASE__								=None  # Initialize prev as null






class       __a					( Generic[T]     ):


     """simple docstring"""


     def __init__(	self			: Tuple						)		->   None:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__								=None




     def   __A				(	self			: Dict				,_UpperCamelCase			: T						)		->   None:


         '''simple docstring'''





         if self.head is None:
             SCREAMING_SNAKE_CASE__								=Node(_UpperCamelCase						)
         else:
             SCREAMING_SNAKE_CASE__								=Node(_UpperCamelCase						)
             SCREAMING_SNAKE_CASE__								=new_node
             SCREAMING_SNAKE_CASE__								=self.head
             SCREAMING_SNAKE_CASE__								=None
             SCREAMING_SNAKE_CASE__								=new_node




     def   __A				(	self			: List[str]						)		->   T | None:


         '''simple docstring'''





         if self.head is None:
             return None
         else:
             assert self.head is not None
             SCREAMING_SNAKE_CASE__								=self.head.data
             SCREAMING_SNAKE_CASE__								=self.head.next
             if self.head is not None:
                 SCREAMING_SNAKE_CASE__								=None
             return temp




     def   __A				(	self			: List[str]						)		->   T | None:


         '''simple docstring'''





         return self.head.data if self.head is not None else None




     def __len__(	self			: Optional[int]						)		->   int:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__								=self.head
         SCREAMING_SNAKE_CASE__								=0
         while temp is not None:
             count += 1
             SCREAMING_SNAKE_CASE__								=temp.next
         return count




     def   __A				(	self			: List[Any]						)		->   bool:


         '''simple docstring'''





         return self.head is None




     def   __A				(	self			: int						)		->   None:


         '''simple docstring'''





         print("""stack elements are:"""						)
         SCREAMING_SNAKE_CASE__								=self.head
         while temp is not None:
             print(temp.data				,end="""->"""						)
             SCREAMING_SNAKE_CASE__								=temp.next


# Code execution starts here
if __name__ == "__main__":
    # Start with the empty stack
    lowerCamelCase_  						=   Stack()

    # Insert 4 at the beginning. So stack becomes 4->None
    print("Stack operations using Doubly LinkedList")
    stack.push(4)

    # Insert 5 at the beginning. So stack becomes 4->5->None
    stack.push(5)

    # Insert 6 at the beginning. So stack becomes 4->5->6->None
    stack.push(6)

    # Insert 7 at the beginning. So stack becomes 4->5->6->7->None
    stack.push(7)

    # Print the stack
    stack.print_stack()

    # Print the top element
    print("\nTop element is ", stack.top())

    # Print the stack size
    print("Size of the stack is ", len(stack))

    # pop the top element
    stack.pop()

    # pop the top element
    stack.pop()

    # two elements have now been popped off
    stack.print_stack()

    # Print True if the stack is empty else False
    print("\nstack is empty:", stack.is_empty())






