






"""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 :    Optional[int]								=				logging.get_logger(__name__)



def      _SCREAMING_SNAKE_CASE  (	__lowercase				:     List[str]      )  -> str:





    """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      _SCREAMING_SNAKE_CASE  (	__lowercase				:     Any      )  -> List[str]:





    """simple docstring"""

    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_5_6, :]
        __A             =		in_proj_bias[:2_5_6]
        __A             =		in_proj_weight[2_5_6:5_1_2, :]
        __A             =		in_proj_bias[2_5_6:5_1_2]
        __A             =		in_proj_weight[-2_5_6:, :]
        __A             =		in_proj_bias[-2_5_6:]



def      _SCREAMING_SNAKE_CASE  (	)  -> int:





    """simple docstring"""

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

    return im



@torch.no_grad()
def      _SCREAMING_SNAKE_CASE  (	__lowercase				:     int     ,			__lowercase				:     Any     ,			__lowercase				:     int     ,			__lowercase				:     List[Any]     ,			__lowercase				:     List[Any]     ,			__lowercase				:     Optional[Any]     ,			__lowercase				:     Union[str, Any]     ,			)  -> Union[str, Any]:





    """simple docstring"""

    __A             =		DeformableDetrConfig()
    # set config attributes
    if single_scale:
        __A             =		1
    __A             =		dilation
    __A             =		with_box_refine
    __A             =		two_stage
    # set labels
    __A             =		9_1
    __A             =		"huggingface/label-files"
    __A             =		"coco-detection-id2label.json"
    __A             =		json.load(open(cached_download(hf_hub_url(A_     ,			A_     ,			repo_type="""dataset"""      )      )     ,			"""r"""      )      )
    __A             =		{int(A_      ): 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=A_     ,			return_tensors="""pt"""      )
    __A             =		encoding["pixel_values"]

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

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

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

    __A             =		torch.tensor(
        [[-9.6_645, -4.3_449, -5.8_705], [-9.7_035, -3.8_504, -5.0_724], [-10.5_634, -5.3_379, -7.5_116]]      )
    __A             =		torch.tensor([[0.8_693, 0.2_289, 0.2_492], [0.3_150, 0.5_489, 0.5_845], [0.5_563, 0.7_580, 0.8_518]]      )

    if single_scale:
        __A             =		torch.tensor(
            [[-9.9_051, -4.2_541, -6.4_852], [-9.6_947, -4.0_854, -6.8_033], [-10.0_665, -5.8_470, -7.7_003]]      )
        __A             =		torch.tensor([[0.7_292, 0.4_991, 0.5_532], [0.7_959, 0.2_426, 0.4_236], [0.7_582, 0.3_518, 0.4_451]]      )

    if single_scale and dilation:
        __A             =		torch.tensor(
            [[-8.9_652, -4.1_074, -5.6_635], [-9.0_596, -4.9_447, -6.6_075], [-10.1_178, -4.5_275, -6.2_671]]      )
        __A             =		torch.tensor([[0.7_665, 0.4_130, 0.4_769], [0.8_364, 0.1_841, 0.3_391], [0.6_261, 0.3_895, 0.7_978]]      )

    if with_box_refine:
        __A             =		torch.tensor(
            [[-8.8_895, -5.4_187, -6.8_153], [-8.4_706, -6.1_668, -7.6_184], [-9.0_042, -5.5_359, -6.9_141]]      )
        __A             =		torch.tensor([[0.7_828, 0.2_208, 0.4_323], [0.0_892, 0.5_996, 0.1_319], [0.5_524, 0.6_389, 0.8_914]]      )

    if with_box_refine and two_stage:
        __A             =		torch.tensor(
            [[-6.7_108, -4.3_213, -6.3_777], [-8.9_014, -6.1_799, -6.7_240], [-6.9_315, -4.4_735, -6.2_298]]      )
        __A             =		torch.tensor([[0.2_583, 0.5_499, 0.4_683], [0.7_652, 0.9_068, 0.4_882], [0.5_490, 0.2_763, 0.0_564]]      )

    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:
        __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=A_     ,			organization="""nielsr"""     ,			commit_message="""Add model"""      )


if __name__ == "__main__":
  __a :    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."
  )
  __a :    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,
  )












# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
from typing import List



def      _SCREAMING_SNAKE_CASE  (	__lowercase				:     List[str]     ,			__lowercase				:     str      )  -> Optional[int]:





    """simple docstring"""

    if name != "training_function" and name != "main":
        raise ValueError(f"Incorrect function name passed: {name}, choose either 'main' or 'training_function'"      )
    __A      ,		__A             =		[], False
    for line in lines:
        if not found_start and f"def {name}" in line:
            __A             =		True
            good_lines.append(__lowercase      )
            continue
        if found_start:
            if name == "training_function" and "def main" in line:
                return good_lines
            if name == "main" and "if __name__" in line:
                return good_lines
            good_lines.append(__lowercase      )



def      _SCREAMING_SNAKE_CASE  (	__lowercase				:     List[str]      )  -> Optional[int]:





    """simple docstring"""

    return [line for line in lines if not line.lstrip().startswith("""#"""      ) and line != "\n"]






def      _SCREAMING_SNAKE_CASE  (	__lowercase				:     str     ,			__lowercase				:     str     ,			__lowercase				:     bool     ,			__lowercase				:     str = None      )  -> List[Any]:





    """simple docstring"""

    with open(__lowercase     ,			"""r"""      ) as f:
        __A             =		f.readlines()
    with open(os.path.abspath(os.path.join("""examples"""     ,			"""nlp_example.py"""      )      )     ,			"""r"""      ) as f:
        __A             =		f.readlines()
    with open(__lowercase     ,			"""r"""      ) as f:
        __A             =		f.readlines()
    if secondary_filename is not None:
        with open(__lowercase     ,			"""r"""      ) as f:
            __A             =		f.readlines()

    # This is our base, we remove all the code from here in our `full_filename` and `feature_filename` to find the new content
    if parser_only:
        __A             =		clean_lines(get_function_contents_by_name(__lowercase     ,			"""main"""      )      )
        __A             =		clean_lines(get_function_contents_by_name(__lowercase     ,			"""main"""      )      )
        __A             =		clean_lines(get_function_contents_by_name(__lowercase     ,			"""main"""      )      )
        if secondary_filename is not None:
            __A             =		clean_lines(get_function_contents_by_name(__lowercase     ,			"""main"""      )      )
    else:
        __A             =		clean_lines(get_function_contents_by_name(__lowercase     ,			"""training_function"""      )      )
        __A             =		clean_lines(get_function_contents_by_name(__lowercase     ,			"""training_function"""      )      )
        __A             =		clean_lines(get_function_contents_by_name(__lowercase     ,			"""training_function"""      )      )
        if secondary_filename is not None:
            __A             =		clean_lines(
                get_function_contents_by_name(__lowercase     ,			"""training_function"""      )      )

    __A             =		"""train_dataloader, eval_dataloader = get_dataloaders(accelerator, batch_size)\n"""

    # Specific code in our script that differs from the full version, aka what is new
    __A             =		[]
    __A             =		[]  # We keep track of the idxs just in case it's a repeated statement
    __A             =		iter(__lowercase      )
    for i in range(len(__lowercase      ) - 1      ):
        if i not in passed_idxs:
            __A             =		next(__lowercase      )
            if (line not in full_file_func) and (line.lstrip() != _dl_line):
                if "TESTING_MOCKED_DATALOADERS" not in line:
                    new_feature_code.append(__lowercase      )
                    passed_idxs.append(__lowercase      )
                else:
                    # Skip over the `config['num_epochs'] = 2` statement
                    __A             =		next(__lowercase      )

    # Extract out just the new parts from the full_file_training_func
    __A             =		[]
    __A             =		[]  # We keep track of the idxs just in case it's a repeated statement
    for i, line in enumerate(__lowercase      ):
        if i not in passed_idxs:
            if (line not in full_file_func) and (line.lstrip() != _dl_line):
                if "TESTING_MOCKED_DATALOADERS" not in line:
                    new_full_example_parts.append(__lowercase      )
                    passed_idxs.append(__lowercase      )

    # Finally, get the overall diff
    __A             =		[line for line in new_feature_code if line not in new_full_example_parts]
    if secondary_filename is not None:
        __A             =		[line for line in full_file_contents if line not in secondary_file_func]
        __A             =		[line for line in diff_from_example if line not in diff_from_two]

    return diff_from_example




