




'''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()
A							:				Dict			     =	logging.get_logger(__name__)
def     snake_case_  (       a__		:     Optional[Any]      ):



    """simple docstring"""




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

    return orig_key
def     snake_case_  (       a__		:     Optional[Any]      ):



    """simple docstring"""




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



    """simple docstring"""




    __lowercase          =						"""http://images.cocodataset.org/val2017/000000039769.jpg"""
    __lowercase          =						Image.open(requests.get(a__   ,stream=a__      ).raw      )

    return im
@torch.no_grad()
def     snake_case_  (       a__		:     Tuple   ,a__		:     Tuple   ,a__		:     Dict   ,a__		:     int   ,a__		:     int   ,a__		:     Optional[Any]   ,a__		:     Tuple   ,):



    """simple docstring"""




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

    # load image processor
    __lowercase          =						DeformableDetrImageProcessor(format="""coco_detection"""      )

    # prepare image
    __lowercase          =						prepare_img()
    __lowercase          =						image_processor(images=a__   ,return_tensors="""pt"""      )
    __lowercase          =						encoding["""pixel_values"""]

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

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

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

    __lowercase          =						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]]      )
    __lowercase          =						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:
        __lowercase          =						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]]      )
        __lowercase          =						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:
        __lowercase          =						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]]      )
        __lowercase          =						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:
        __lowercase          =						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]]      )
        __lowercase          =						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:
        __lowercase          =						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]]      )
        __lowercase          =						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(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:
        __lowercase          =						"""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							:				Dict			     =	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							:				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,
  )










'''simple docstring'''
import random
from typing import List, Optional, Union

import numpy as np
import torch

from ..state import AcceleratorState
from .constants import CUDA_DISTRIBUTED_TYPES
from .dataclasses import DistributedType, RNGType
from .imports import is_npu_available, is_tpu_available, is_xpu_available


if is_tpu_available(check_device=False):
  import torch_xla.core.xla_model as xm
def     snake_case_  (       a__		:     int   ,a__		:     bool = False      ):



    """simple docstring"""




    if device_specific:
        seed += AcceleratorState().process_index
    random.seed(a__      )
    np.random.seed(a__      )
    torch.manual_seed(a__      )
    if is_xpu_available():
        torch.xpu.manual_seed_all(a__      )
    elif is_npu_available():
        torch.npu.manual_seed_all(a__      )
    else:
        torch.cuda.manual_seed_all(a__      )
    # ^^ safe to call this function even if cuda is not available
    if is_tpu_available():
        xm.set_rng_state(a__      )
def     snake_case_  (       a__		:     Optional[RNGType] = None   ,a__		:     Optional[torch.Generator] = None      ):



    """simple docstring"""




    if rng_type == RNGType.TORCH:
        __lowercase          =						torch.get_rng_state()
    elif rng_type == RNGType.CUDA:
        __lowercase          =						torch.cuda.get_rng_state()
    elif rng_type == RNGType.XLA:
        assert is_tpu_available(), "Can't synchronize XLA seeds on an environment without TPUs."
        __lowercase          =						torch.tensor(xm.get_rng_state()      )
    elif rng_type == RNGType.NPU:
        assert is_npu_available(), "Can't synchronize NPU seeds on an environment without NPUs."
        __lowercase          =						torch.npu.get_rng_state()
    elif rng_type == RNGType.XPU:
        assert is_xpu_available(), "Can't synchronize XPU seeds on an environment without XPUs."
        __lowercase          =						torch.xpu.get_rng_state()
    elif rng_type == RNGType.GENERATOR:
        assert generator is not None, "Need a generator to synchronize its seed."
        __lowercase          =						generator.get_state()

    # Broadcast the rng state from device 0 to other devices
    __lowercase          =						AcceleratorState()
    if state.distributed_type == DistributedType.TPU:
        __lowercase          =						rng_state.to(xm.xla_device()      )
        xm.collective_broadcast([rng_state]      )
        xm.mark_step()
        __lowercase          =						rng_state.cpu()
    elif (
        state.distributed_type in CUDA_DISTRIBUTED_TYPES
        or state.distributed_type == DistributedType.MULTI_NPU
        or state.distributed_type == DistributedType.MULTI_XPU
    ):
        __lowercase          =						rng_state.to(state.device      )
        torch.distributed.broadcast(a__   ,0      )
        __lowercase          =						rng_state.cpu()
    elif state.distributed_type == DistributedType.MULTI_CPU:
        torch.distributed.broadcast(a__   ,0      )

    # Set the broadcast rng state
    if rng_type == RNGType.TORCH:
        torch.set_rng_state(a__      )
    elif rng_type == RNGType.CUDA:
        torch.cuda.set_rng_state(a__      )
    elif rng_type == RNGType.NPU:
        torch.npu.set_rng_state(a__      )
    elif rng_type == RNGType.XPU:
        torch.xpu.set_rng_state(a__      )
    elif rng_type == RNGType.XLA:
        xm.set_rng_state(rng_state.item()      )
    elif rng_type == RNGType.GENERATOR:
        generator.set_state(a__      )



def     snake_case_  (       a__		:     List[Union[str, RNGType]]   ,a__		:     Optional[torch.Generator] = None      ):



    """simple docstring"""




    for rng_type in rng_types:
        synchronize_rng_state(RNGType(a__      )   ,generator=a__      )




