


"""simple docstring"""







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_  :  int     		=    logging.get_logger(__name__)
def _A		(		lowercase						):







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

    return orig_key
def _A		(		lowercase						):







    """simple docstring"""
    # transformer decoder self-attention layers
    for i in range(6						):
        # read in weights + bias of input projection layer of self-attention
        a											=state_dict.pop(f'''decoder.layers.{i}.self_attn.in_proj_weight'''						)
        a											=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
        a											=in_proj_weight[:2_56, :]
        a											=in_proj_bias[:2_56]
        a											=in_proj_weight[2_56:5_12, :]
        a											=in_proj_bias[2_56:5_12]
        a											=in_proj_weight[-2_56:, :]
        a											=in_proj_bias[-2_56:]
def _A		(		):







    """simple docstring"""
    a											='''http://images.cocodataset.org/val2017/000000039769.jpg'''
    a											=Image.open(requests.get(lowercase			,       stream=lowercase						).raw						)

    return im
@torch.no_grad()
def _A		(		lowercase			,       lowercase			,       lowercase			,       lowercase			,       lowercase			,       lowercase			,       lowercase			,       ):







    """simple docstring"""
    a											=DeformableDetrConfig()
    # set config attributes
    if single_scale:
        a											=1
    a											=dilation
    a											=with_box_refine
    a											=two_stage
    # set labels
    a											=91
    a											='''huggingface/label-files'''
    a											='''coco-detection-id2label.json'''
    a											=json.load(open(cached_download(hf_hub_url(lowercase			,       lowercase			,       repo_type='''dataset'''						)						)			,       '''r'''						)						)
    a											={int(lowercase						): v for k, v in idalabel.items()}
    a											=idalabel
    a											={v: k for k, v in idalabel.items()}

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

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

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

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

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

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

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

    if with_box_refine:
        a											=torch.tensor(
            [[-8.8895, -5.4187, -6.8153], [-8.4706, -6.1668, -7.6184], [-9.0042, -5.5359, -6.9141]]						)
        a											=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:
        a											=torch.tensor(
            [[-6.7108, -4.3213, -6.3777], [-8.9014, -6.1799, -6.7240], [-6.9315, -4.4735, -6.2298]]						)
        a											=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(lowercase						)			,       atol=1E-4						)
    assert torch.allclose(outputs.pred_boxes[0, :3, :3]			,       expected_boxes.to(lowercase						)			,       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(lowercase						).mkdir(exist_ok=lowercase						)
    model.save_pretrained(lowercase						)
    image_processor.save_pretrained(lowercase						)

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


if __name__ == "__main__":
       lowerCamelCase_  :  Union[str, Any]     		=    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_  :  List[str]     		=    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

import random
import string






class  __A							:






 """simple docstring"""




 def __init__(						self					,       __A = None		)   ->	None:
     a											=passcode or self.__passcode_creator()
     a											=self.__make_key_list()
     a											=self.__make_shift_key()


 def __str__(						self		)   ->	str:
     return "".join(self.__passcode		)


 def  SCREAMING_SNAKE_CASE				(						self					,       __A		)   ->	list[int]:
     for i in range(1					,       len(__A		)					,       2		):
         iterlist[i] *= -1
     return iterlist


 def  SCREAMING_SNAKE_CASE				(						self		)   ->	list[str]:
     a											=string.ascii_letters + string.digits
     a											=[random.choice(__A		) for _ in range(random.randint(10					,       20		)		)]
     return password


 def  SCREAMING_SNAKE_CASE				(						self		)   ->	list[str]:
     a											=(
         string.ascii_letters + string.digits + string.punctuation + ''' \t\n'''
     )

     a											=[]

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

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

         # 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  SCREAMING_SNAKE_CASE				(						self		)   ->	int:
     a											=sum(self.__neg_pos([ord(__A		) for x in self.__passcode]		)		)
     return num if num > 0 else len(self.__passcode		)


 def  SCREAMING_SNAKE_CASE				(						self					,       __A		)   ->	str:
     a											=''''''

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

     return decoded_message





 def  SCREAMING_SNAKE_CASE				(						self					,       __A		)   ->	str:
     a											=''''''

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

     return encoded_message

def _A		(		lowercase = "Hello, this is a modified Caesar cipher"						):







    """simple docstring"""
    a											=ShuffledShiftCipher()
    return cipa.decrypt(cipa.encrypt(lowercase						)						)


if __name__ == "__main__":
       import doctest

       doctest.testmod()