




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()
SCREAMING_SNAKE_CASE_    :       List[Any]       =							logging.get_logger(__name__)
def 							__lowerCAmelCase			(UpperCAmelCase_		):
    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 							__lowerCAmelCase			(UpperCAmelCase_		):
    # 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[:256, :]
        a__  			=							in_proj_bias[:256]
        a__  			=							in_proj_weight[256:512, :]
        a__  			=							in_proj_bias[256:512]
        a__  			=							in_proj_weight[-256:, :]
        a__  			=							in_proj_bias[-256:]
def 							__lowerCAmelCase			():
    a__  			=							"""http://images.cocodataset.org/val2017/000000039769.jpg"""
    a__  			=							Image.open(requests.get(UpperCAmelCase_ ,     stream=UpperCAmelCase_		).raw		)

    return im
@torch.no_grad()
def 							__lowerCAmelCase			(UpperCAmelCase_ ,     UpperCAmelCase_ ,     UpperCAmelCase_ ,     UpperCAmelCase_ ,     UpperCAmelCase_ ,     UpperCAmelCase_ ,     UpperCAmelCase_ ,     ):
    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(UpperCAmelCase_ ,     UpperCAmelCase_ ,     repo_type="""dataset"""		)		) ,     """r"""		)		)
    a__  			=							{int(UpperCAmelCase_		): 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=UpperCAmelCase_ ,     return_tensors="""pt"""		)
    a__  			=							encoding["""pixel_values"""]

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

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

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

    a__  			=							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]]		)
    a__  			=							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:
        a__  			=							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]]		)
        a__  			=							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:
        a__  			=							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]]		)
        a__  			=							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:
        a__  			=							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]]		)
        a__  			=							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:
        a__  			=							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]]		)
        a__  			=							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(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:
        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=UpperCAmelCase_ ,     organization="""nielsr""" ,     commit_message="""Add model"""		)


if __name__ == "__main__":
 SCREAMING_SNAKE_CASE_    :       List[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.'''
 )
 SCREAMING_SNAKE_CASE_    :       Union[str, 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

import random







class 		__UpperCAmelCase:





     """simple docstring"""



     __UpperCAmelCase						:			Optional[int]							=     6

     def __init__(   self			:							int     )      ->      Optional[Any]:






         """simple docstring"""
         a__  			=							list(range(1    ,					Dice.NUM_SIDES + 1     )     )






     def 					__SCREAMING_SNAKE_CASE		(   self			:							Union[str, Any]     )      ->      List[str]:






         """simple docstring"""
         return random.choice(self.sides     )






def 							__lowerCAmelCase			(UpperCAmelCase_ ,     UpperCAmelCase_ = 2		):
    a__  			=							[Dice() for i in range(UpperCAmelCase_		)]
    a__  			=							[0] * (len(UpperCAmelCase_		) * Dice.NUM_SIDES + 1)
    for _ in range(UpperCAmelCase_		):
        count_of_sum[sum(dice.roll() for dice in dices		)] += 1
    a__  			=							[round((count * 100) / num_throws ,     2		) for count in count_of_sum]
    return probability[num_dice:]  # remove probability of sums that never appear


if __name__ == "__main__":
 import doctest

 doctest.testmod()






