


'''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()
_UpperCamelCase          =     logging.get_logger(__name__)


def 		_lowercase   (SCREAMING_SNAKE_CASE       ):




     '''simple docstring'''





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

     return orig_key


def 		_lowercase   (SCREAMING_SNAKE_CASE       ):




     '''simple docstring'''





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


def 		_lowercase   ():




     '''simple docstring'''





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

     return im


@torch.no_grad()
def 		_lowercase   (SCREAMING_SNAKE_CASE					,				SCREAMING_SNAKE_CASE					,				SCREAMING_SNAKE_CASE					,				SCREAMING_SNAKE_CASE					,				SCREAMING_SNAKE_CASE					,				SCREAMING_SNAKE_CASE					,				SCREAMING_SNAKE_CASE					,				):




     '''simple docstring'''





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

     # load image processor
     __A  :					Dict						     =       DeformableDetrImageProcessor(format="coco_detection"       )

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

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

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

     __A  :					str						     =       "cuda" if torch.cuda.is_available() else "cpu"
     model.to(SCREAMING_SNAKE_CASE       )
     # verify our conversion
     __A  :					int						     =       model(pixel_values.to(SCREAMING_SNAKE_CASE       )       )

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

     if single_scale:
          __A  :					str						     =       torch.tensor(
              [[-9.9051, -4.2541, -6.4852], [-9.6947, -4.0854, -6.8033], [-10.0665, -5.8470, -7.7003]]       )
          __A  :					Optional[int]						     =       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  :					Dict						     =       torch.tensor(
              [[-8.9652, -4.1074, -5.6635], [-9.0596, -4.9447, -6.6075], [-10.1178, -4.5275, -6.2671]]       )
          __A  :					List[Any]						     =       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  :					int						     =       torch.tensor(
              [[-8.8895, -5.4187, -6.8153], [-8.4706, -6.1668, -7.6184], [-9.0042, -5.5359, -6.9141]]       )
          __A  :					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:
          __A  :					Optional[int]						     =       torch.tensor(
              [[-6.7108, -4.3213, -6.3777], [-8.9014, -6.1799, -6.7240], [-6.9315, -4.4735, -6.2298]]       )
          __A  :					Optional[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(SCREAMING_SNAKE_CASE       )					,				atol=1E-4       )
     assert torch.allclose(outputs.pred_boxes[0, :3, :3]					,				expected_boxes.to(SCREAMING_SNAKE_CASE       )					,				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(SCREAMING_SNAKE_CASE       ).mkdir(exist_ok=SCREAMING_SNAKE_CASE       )
     model.save_pretrained(SCREAMING_SNAKE_CASE       )
     image_processor.save_pretrained(SCREAMING_SNAKE_CASE       )

     # Push to hub
     if push_to_hub:
          __A  :					Union[str, Any]						     =       "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=SCREAMING_SNAKE_CASE					,				organization="nielsr"					,				commit_message="Add model"       )


if __name__ == "__main__":
      _UpperCamelCase          =     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."""
      )
      _UpperCamelCase          =     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'''


# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch

from dataclasses import dataclass
from typing import Optional, Tuple, Union

import flax
import jax.numpy as jnp
from jax import random

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import FlaxSchedulerMixin, FlaxSchedulerOutput, broadcast_to_shape_from_left







@flax.struct.dataclass
class        __magic_name__  :

     """simple docstring"""






     lowerCamelCase__						=							None
     lowerCamelCase__						=							None
     lowerCamelCase__						=							None


     @classmethod
     def lowerCAmelCase__     (      cls		):


          '''simple docstring'''





          return cls()







@dataclass
class        __magic_name__  (					lowerCAmelCase   ):

     """simple docstring"""






     lowerCamelCase__						=							42
     lowerCamelCase__						=							42
     lowerCamelCase__						=							None







class        __magic_name__  (					lowerCAmelCase						,		lowerCAmelCase   ):

     """simple docstring"""






     @property
     def lowerCAmelCase__     (      self		):


          '''simple docstring'''





          return True


     @register_to_config
     def __init__(      self ,    lowerCamelCase = 2000 ,    lowerCamelCase = 0.15 ,    lowerCamelCase = 0.01 ,    lowerCamelCase = 1348.0 ,    lowerCamelCase = 1E-5 ,    lowerCamelCase = 1 ,    ):


          '''simple docstring'''





          pass


     def lowerCAmelCase__     (      self		):


          '''simple docstring'''





          __A  :					List[Any]						     =       ScoreSdeVeSchedulerState.create()
          return self.set_sigmas(
              lowerCamelCase ,    self.config.num_train_timesteps ,    self.config.sigma_min ,    self.config.sigma_max ,    self.config.sampling_eps ,    )


     def lowerCAmelCase__     (      self ,    lowerCamelCase ,    lowerCamelCase ,    lowerCamelCase = () ,    lowerCamelCase = None		):


          '''simple docstring'''





          __A  :					Tuple						     =       sampling_eps if sampling_eps is not None else self.config.sampling_eps

          __A  :					Optional[int]						     =       jnp.linspace(1 ,    lowerCamelCase ,    lowerCamelCase		)
          return state.replace(timesteps=lowerCamelCase		)


     def lowerCAmelCase__     (      self ,    lowerCamelCase ,    lowerCamelCase ,    lowerCamelCase = None ,    lowerCamelCase = None ,    lowerCamelCase = None ,    ):


          '''simple docstring'''





          __A  :					str						     =       sigma_min if sigma_min is not None else self.config.sigma_min
          __A  :					Union[str, Any]						     =       sigma_max if sigma_max is not None else self.config.sigma_max
          __A  :					Dict						     =       sampling_eps if sampling_eps is not None else self.config.sampling_eps
          if state.timesteps is None:
               __A  :					Union[str, Any]						     =       self.set_timesteps(lowerCamelCase ,    lowerCamelCase ,    lowerCamelCase		)

          __A  :					Dict						     =       jnp.exp(jnp.linspace(jnp.log(lowerCamelCase		) ,    jnp.log(lowerCamelCase		) ,    lowerCamelCase		)		)
          __A  :					int						     =       jnp.array([sigma_min * (sigma_max / sigma_min) ** t for t in state.timesteps]		)

          return state.replace(discrete_sigmas=lowerCamelCase ,    sigmas=lowerCamelCase		)


     def lowerCAmelCase__     (      self ,    lowerCamelCase ,    lowerCamelCase ,    lowerCamelCase		):


          '''simple docstring'''





          return jnp.where(timesteps == 0 ,    jnp.zeros_like(lowerCamelCase		) ,    state.discrete_sigmas[timesteps - 1]		)


     def lowerCAmelCase__     (      self ,    lowerCamelCase ,    lowerCamelCase ,    lowerCamelCase ,    lowerCamelCase ,    lowerCamelCase ,    lowerCamelCase = True ,    ):


          '''simple docstring'''





          if state.timesteps is None:
               raise ValueError(
                   "`state.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"		)

          __A  :					Any						     =       timestep * jnp.ones(
              sample.shape[0] ,    )
          __A  :					Any						     =       (timestep * (len(state.timesteps		) - 1)).long()

          __A  :					Any						     =       state.discrete_sigmas[timesteps]
          __A  :					Union[str, Any]						     =       self.get_adjacent_sigma(lowerCamelCase ,    lowerCamelCase ,    lowerCamelCase		)
          __A  :					List[str]						     =       jnp.zeros_like(lowerCamelCase		)
          __A  :					Union[str, Any]						     =       (sigma**2 - adjacent_sigma**2) ** 0.5

          # equation 6 in the paper: the model_output modeled by the network is grad_x log pt(x)
          # also equation 47 shows the analog from SDE models to ancestral sampling methods
          __A  :					List[Any]						     =       diffusion.flatten()
          __A  :					List[Any]						     =       broadcast_to_shape_from_left(lowerCamelCase ,    sample.shape		)
          __A  :					str						     =       drift - diffusion**2 * model_output

          #  equation 6: sample noise for the diffusion term of
          __A  :					Any						     =       random.split(lowerCamelCase ,    num=1		)
          __A  :					Union[str, Any]						     =       random.normal(key=lowerCamelCase ,    shape=sample.shape		)
          __A  :					Dict						     =       sample - drift  # subtract because `dt` is a small negative timestep
          # TODO is the variable diffusion the correct scaling term for the noise?
          __A  :					int						     =       prev_sample_mean + diffusion * noise  # add impact of diffusion field g

          if not return_dict:
               return (prev_sample, prev_sample_mean, state)

          return FlaxSdeVeOutput(prev_sample=lowerCamelCase ,    prev_sample_mean=lowerCamelCase ,    state=lowerCamelCase		)


     def lowerCAmelCase__     (      self ,    lowerCamelCase ,    lowerCamelCase ,    lowerCamelCase ,    lowerCamelCase ,    lowerCamelCase = True ,    ):


          '''simple docstring'''





          if state.timesteps is None:
               raise ValueError(
                   "`state.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"		)

          # For small batch sizes, the paper "suggest replacing norm(z) with sqrt(d), where d is the dim. of z"
          # sample noise for correction
          __A  :					int						     =       random.split(lowerCamelCase ,    num=1		)
          __A  :					int						     =       random.normal(key=lowerCamelCase ,    shape=sample.shape		)

          # compute step size from the model_output, the noise, and the snr
          __A  :					Optional[Any]						     =       jnp.linalg.norm(lowerCamelCase		)
          __A  :					Dict						     =       jnp.linalg.norm(lowerCamelCase		)
          __A  :					Any						     =       (self.config.snr * noise_norm / grad_norm) ** 2 * 2
          __A  :					List[str]						     =       step_size * jnp.ones(sample.shape[0]		)

          # compute corrected sample: model_output term and noise term
          __A  :					Optional[Any]						     =       step_size.flatten()
          __A  :					Optional[int]						     =       broadcast_to_shape_from_left(lowerCamelCase ,    sample.shape		)
          __A  :					Dict						     =       sample + step_size * model_output
          __A  :					List[Any]						     =       prev_sample_mean + ((step_size * 2) ** 0.5) * noise

          if not return_dict:
               return (prev_sample, state)

          return FlaxSdeVeOutput(prev_sample=lowerCamelCase ,    state=lowerCamelCase		)


     def __len__(      self		):


          '''simple docstring'''





          return self.config.num_train_timesteps

