'''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_        =   logging.get_logger(__name__)


def 					UpperCAmelCase__      (							_lowerCAmelCase						: Tuple)						->		int:



      """simple docstring"""

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

      return orig_key


def 					UpperCAmelCase__      (							_lowerCAmelCase						: Optional[int])						->		Tuple:



      """simple docstring"""

      for i in range(6):
            # read in weights + bias of input projection layer of self-attention
            snake_case   :					str							=						state_dict.pop(f"""decoder.layers.{i}.self_attn.in_proj_weight""")
            snake_case   :					Optional[Any]							=						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
            snake_case   :					Tuple							=						in_proj_weight[:256, :]
            snake_case   :					Dict							=						in_proj_bias[:256]
            snake_case   :					List[str]							=						in_proj_weight[256:512, :]
            snake_case   :					Optional[Any]							=						in_proj_bias[256:512]
            snake_case   :					Optional[Any]							=						in_proj_weight[-256:, :]
            snake_case   :					List[Any]							=						in_proj_bias[-256:]


def 					UpperCAmelCase__      (							)						->		str:



      """simple docstring"""

      snake_case   :					Union[str, Any]							=						"http://images.cocodataset.org/val2017/000000039769.jpg"
      snake_case   :					Optional[Any]							=						Image.open(requests.get(_lowerCAmelCase,			stream=_lowerCAmelCase).raw)

      return im


@torch.no_grad()
def 					UpperCAmelCase__      (							_lowerCAmelCase						: List[str],			_lowerCAmelCase						: int,			_lowerCAmelCase						: Any,			_lowerCAmelCase						: Optional[int],			_lowerCAmelCase						: List[Any],			_lowerCAmelCase						: str,			_lowerCAmelCase						: int,			)						->		Optional[Any]:



      """simple docstring"""

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

      # load image processor
      snake_case   :					Union[str, Any]							=						DeformableDetrImageProcessor(format="coco_detection")

      # prepare image
      snake_case   :					List[Any]							=						prepare_img()
      snake_case   :					Optional[int]							=						image_processor(images=_lowerCAmelCase,			return_tensors="pt")
      snake_case   :					List[Any]							=						encoding["pixel_values"]

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

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

      snake_case   :					str							=						"cuda" if torch.cuda.is_available() else "cpu"
      model.to(_lowerCAmelCase)
      # verify our conversion
      snake_case   :					Any							=						model(pixel_values.to(_lowerCAmelCase))

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

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

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

      # Push to hub
      if push_to_hub:
            snake_case   :					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=_lowerCAmelCase,			organization="nielsr",			commit_message="Add model")


if __name__ == "__main__":
      A_        =   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_        =   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 collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


A_        =   logging.get_logger(__name__)

A_        =   {
    "apple/mobilevit-small": "https://huggingface.co/apple/mobilevit-small/resolve/main/config.json",
    "apple/mobilevit-x-small": "https://huggingface.co/apple/mobilevit-x-small/resolve/main/config.json",
    "apple/mobilevit-xx-small": "https://huggingface.co/apple/mobilevit-xx-small/resolve/main/config.json",
    "apple/deeplabv3-mobilevit-small": (
        "https://huggingface.co/apple/deeplabv3-mobilevit-small/resolve/main/config.json"
    ),
    "apple/deeplabv3-mobilevit-x-small": (
        "https://huggingface.co/apple/deeplabv3-mobilevit-x-small/resolve/main/config.json"
    ),
    "apple/deeplabv3-mobilevit-xx-small": (
        "https://huggingface.co/apple/deeplabv3-mobilevit-xx-small/resolve/main/config.json"
    ),
    # See all MobileViT models at https://huggingface.co/models?filter=mobilevit
}




class 		SCREAMING_SNAKE_CASE_				(_UpperCAmelCase			):



			"""simple docstring"""






			__magic_name__   :							Optional[Any]             =					"""mobilevit"""



			def __init__(       self							, snake_case_=3							, snake_case_=256							, snake_case_=2							, snake_case_=[144, 192, 240]							, snake_case_=[16, 32, 64, 96, 128, 160, 640]							, snake_case_=4							, snake_case_=2.0							, snake_case_=4.0							, snake_case_="silu"							, snake_case_=3							, snake_case_=32							, snake_case_=0.1							, snake_case_=0.0							, snake_case_=0.1							, snake_case_=0.02							, snake_case_=1e-5							, snake_case_=True							, snake_case_=256							, snake_case_=[6, 12, 18]							, snake_case_=0.1							, snake_case_=255							, **snake_case_							, ):
									super().__init__(**snake_case_				)

									snake_case   :					Any							=						num_channels
									snake_case   :					Optional[Any]							=						image_size
									snake_case   :					Union[str, Any]							=						patch_size
									snake_case   :					Union[str, Any]							=						hidden_sizes
									snake_case   :					List[Any]							=						neck_hidden_sizes
									snake_case   :					Optional[Any]							=						num_attention_heads
									snake_case   :					Any							=						mlp_ratio
									snake_case   :					List[str]							=						expand_ratio
									snake_case   :					Tuple							=						hidden_act
									snake_case   :					Tuple							=						conv_kernel_size
									snake_case   :					int							=						output_stride
									snake_case   :					Dict							=						hidden_dropout_prob
									snake_case   :					List[str]							=						attention_probs_dropout_prob
									snake_case   :					int							=						classifier_dropout_prob
									snake_case   :					str							=						initializer_range
									snake_case   :					Optional[int]							=						layer_norm_eps
									snake_case   :					Dict							=						qkv_bias

									# decode head attributes for semantic segmentation
									snake_case   :					Optional[int]							=						aspp_out_channels
									snake_case   :					Dict							=						atrous_rates
									snake_case   :					Any							=						aspp_dropout_prob
									snake_case   :					Any							=						semantic_loss_ignore_index







class 		SCREAMING_SNAKE_CASE_				(_UpperCAmelCase			):



			"""simple docstring"""






			__magic_name__   :							Union[str, Any]             =					version.parse("""1.11"""			)



			@property
			def      lowerCamelCase   (       self				):
									return OrderedDict([("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"})]				)



			@property
			def      lowerCamelCase   (       self				):
									if self.task == "image-classification":
															return OrderedDict([("logits", {0: "batch"})]				)
									else:
															return OrderedDict([("last_hidden_state", {0: "batch"}), ("pooler_output", {0: "batch"})]				)






			@property
			def      lowerCamelCase   (       self				):
									return 1e-4





