




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()
snake_case_					:					int       						=			logging.get_logger(__name__)


def   A		(__A  :       List[str]			)  -> int:







     """simple docstring"""


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

     return orig_key


def   A		(__A  :       Any			)  -> List[str]:







     """simple docstring"""


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


def   A		()  -> Any:







     """simple docstring"""


     UpperCAmelCase_		=				'http://images.cocodataset.org/val2017/000000039769.jpg'
     UpperCAmelCase_		=				Image.open(requests.get(a_  ,							stream=a_			).raw			)

     return im


@torch.no_grad()
def   A		(__A  :       List[str]  ,							__A  :       Dict  ,							__A  :       List[str]  ,							__A  :       Optional[Any]  ,							__A  :       Dict  ,							__A  :       str  ,							__A  :       List[Any]  ,							)  -> Optional[int]:







     """simple docstring"""


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

     # load image processor
     UpperCAmelCase_		=				DeformableDetrImageProcessor(format='''coco_detection'''			)

     # prepare image
     UpperCAmelCase_		=				prepare_img()
     UpperCAmelCase_		=				image_processor(images=a_  ,							return_tensors='''pt'''			)
     UpperCAmelCase_		=				encoding['pixel_values']

     logger.info('''Converting model...'''			)

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

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

     UpperCAmelCase_		=				torch.tensor(
         [[-9.6_645, -4.3_449, -5.8_705], [-9.7_035, -3.8_504, -5.0_724], [-10.5634, -5.3_379, -7.5_116]]			)
     UpperCAmelCase_		=				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:
          UpperCAmelCase_		=				torch.tensor(
              [[-9.9_051, -4.2_541, -6.4_852], [-9.6_947, -4.0_854, -6.8_033], [-10.0665, -5.8_470, -7.7_003]]			)
          UpperCAmelCase_		=				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:
          UpperCAmelCase_		=				torch.tensor(
              [[-8.9_652, -4.1_074, -5.6_635], [-9.0_596, -4.9_447, -6.6_075], [-10.1178, -4.5_275, -6.2_671]]			)
          UpperCAmelCase_		=				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:
          UpperCAmelCase_		=				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]]			)
          UpperCAmelCase_		=				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:
          UpperCAmelCase_		=				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]]			)
          UpperCAmelCase_		=				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:
          UpperCAmelCase_		=				'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__":
    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."
    )
    snake_case_					:					Tuple       						=			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 csv
import os

import pytest

from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.csv import CsvDatasetReader, CsvDatasetWriter

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases
def 						UpperCAmelCase   (						a_,		a_     ):





		'''simple docstring'''


		assert isinstance(a_,		a_     )
		assert dataset.num_rows == 4
		assert dataset.num_columns == 3
		assert dataset.column_names == ["col_1", "col_2", "col_3"]
		for feature, expected_dtype in expected_features.items():
				assert dataset.features[feature].dtype == expected_dtype
@pytest.mark.parametrize('keep_in_memory',		[False, True]     )
def 						UpperCAmelCase   (						a_,		a_,		a_     ):





		'''simple docstring'''


		lowerCamelCase	:     List[Any]            =       tmp_path / 'cache'
		lowerCamelCase	:     Optional[int]            =       {'col_1': 'int64', 'col_2': 'int64', 'col_3': 'float64'}
		with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
				lowerCamelCase	:     Tuple            =       CsvDatasetReader(a_,		cache_dir=a_,		keep_in_memory=a_     ).read()
		_check_csv_dataset(a_,		a_     )
@pytest.mark.parametrize(
    'features',		[
        None,
        {'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'},
        {'col_1': 'string', 'col_2': 'string', 'col_3': 'string'},
        {'col_1': 'int32', 'col_2': 'int32', 'col_3': 'int32'},
        {'col_1': 'float32', 'col_2': 'float32', 'col_3': 'float32'},
    ],		)
def 						UpperCAmelCase   (						a_,		a_,		a_     ):





		'''simple docstring'''


		lowerCamelCase	:     int            =       tmp_path / 'cache'
		# CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
		lowerCamelCase	:     List[str]            =       {'col_1': 'int64', 'col_2': 'int64', 'col_3': 'float64'}
		lowerCamelCase	:     str            =       features.copy() if features else default_expected_features
		lowerCamelCase	:     List[Any]            =       (
		    Features({feature: Value(a_     ) for feature, dtype in features.items()}     ) if features is not None else None
		)
		lowerCamelCase	:     Union[str, Any]            =       CsvDatasetReader(a_,		features=a_,		cache_dir=a_     ).read()
		_check_csv_dataset(a_,		a_     )
@pytest.mark.parametrize('split',		[None, NamedSplit('train'     ), 'train', 'test']     )
def 						UpperCAmelCase   (						a_,		a_,		a_     ):





		'''simple docstring'''


		lowerCamelCase	:     Any            =       tmp_path / 'cache'
		lowerCamelCase	:     Dict            =       {'col_1': 'int64', 'col_2': 'int64', 'col_3': 'float64'}
		lowerCamelCase	:     Dict            =       CsvDatasetReader(a_,		cache_dir=a_,		split=a_     ).read()
		_check_csv_dataset(a_,		a_     )
		assert dataset.split == split if split else "train"
@pytest.mark.parametrize('path_type',		[str, list]     )
def 						UpperCAmelCase   (						a_,		a_,		a_     ):





		'''simple docstring'''


		if issubclass(a_,		a_     ):
				lowerCamelCase	:     Dict            =       csv_path
		elif issubclass(a_,		a_     ):
				lowerCamelCase	:     Tuple            =       [csv_path]
		lowerCamelCase	:     Optional[Any]            =       tmp_path / 'cache'
		lowerCamelCase	:     Tuple            =       {'col_1': 'int64', 'col_2': 'int64', 'col_3': 'float64'}
		lowerCamelCase	:     List[Any]            =       CsvDatasetReader(a_,		cache_dir=a_     ).read()
		_check_csv_dataset(a_,		a_     )
def 						UpperCAmelCase   (						a_,		a_,		a_=("train",)     ):





		'''simple docstring'''


		assert isinstance(a_,		a_     )
		for split in splits:
				lowerCamelCase	:     Any            =       dataset_dict[split]
				assert dataset.num_rows == 4
				assert dataset.num_columns == 3
				assert dataset.column_names == ["col_1", "col_2", "col_3"]
				for feature, expected_dtype in expected_features.items():
						assert dataset.features[feature].dtype == expected_dtype
@pytest.mark.parametrize('keep_in_memory',		[False, True]     )
def 						UpperCAmelCase   (						a_,		a_,		a_     ):





		'''simple docstring'''


		lowerCamelCase	:     Any            =       tmp_path / 'cache'
		lowerCamelCase	:     int            =       {'col_1': 'int64', 'col_2': 'int64', 'col_3': 'float64'}
		with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
				lowerCamelCase	:     int            =       CsvDatasetReader({'train': csv_path},		cache_dir=a_,		keep_in_memory=a_     ).read()
		_check_csv_datasetdict(a_,		a_     )
@pytest.mark.parametrize(
    'features',		[
        None,
        {'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'},
        {'col_1': 'string', 'col_2': 'string', 'col_3': 'string'},
        {'col_1': 'int32', 'col_2': 'int32', 'col_3': 'int32'},
        {'col_1': 'float32', 'col_2': 'float32', 'col_3': 'float32'},
    ],		)
def 						UpperCAmelCase   (						a_,		a_,		a_     ):





		'''simple docstring'''


		lowerCamelCase	:     str            =       tmp_path / 'cache'
		# CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
		lowerCamelCase	:     Dict            =       {'col_1': 'int64', 'col_2': 'int64', 'col_3': 'float64'}
		lowerCamelCase	:     str            =       features.copy() if features else default_expected_features
		lowerCamelCase	:     Any            =       (
		    Features({feature: Value(a_     ) for feature, dtype in features.items()}     ) if features is not None else None
		)
		lowerCamelCase	:     Optional[Any]            =       CsvDatasetReader({'train': csv_path},		features=a_,		cache_dir=a_     ).read()
		_check_csv_datasetdict(a_,		a_     )
@pytest.mark.parametrize('split',		[None, NamedSplit('train'     ), 'train', 'test']     )
def 						UpperCAmelCase   (						a_,		a_,		a_     ):





		'''simple docstring'''


		if split:
				lowerCamelCase	:     List[Any]            =       {split: csv_path}
		else:
				lowerCamelCase	:     Any            =       {'train': csv_path, 'test': csv_path}
		lowerCamelCase	:     List[Any]            =       tmp_path / 'cache'
		lowerCamelCase	:     Union[str, Any]            =       {'col_1': 'int64', 'col_2': 'int64', 'col_3': 'float64'}
		lowerCamelCase	:     Union[str, Any]            =       CsvDatasetReader(a_,		cache_dir=a_     ).read()
		_check_csv_datasetdict(a_,		a_,		splits=list(path.keys()     )     )
		assert all(dataset[split].split == split for split in path.keys()     )
def 						UpperCAmelCase   (						a_     ):





		'''simple docstring'''


		with open(a_,		encoding='utf-8'     ) as csvfile:
				yield from csv.reader(a_     )
def 						UpperCAmelCase   (						a_,		a_     ):





		'''simple docstring'''


		lowerCamelCase	:     Optional[int]            =       tmp_path / 'cache'
		lowerCamelCase	:     int            =       os.path.join(a_,		'tmp.csv'     )
		lowerCamelCase	:     List[str]            =       CsvDatasetReader({'train': csv_path},		cache_dir=a_     ).read()
		CsvDatasetWriter(dataset['train'],		a_,		num_proc=1     ).write()

		lowerCamelCase	:     Tuple            =       iter_csv_file(a_     )
		lowerCamelCase	:     Tuple            =       iter_csv_file(a_     )

		for rowa, rowa in zip(a_,		a_     ):
				assert rowa == rowa
def 						UpperCAmelCase   (						a_,		a_     ):





		'''simple docstring'''


		lowerCamelCase	:     Optional[Any]            =       tmp_path / 'cache'
		lowerCamelCase	:     Optional[Any]            =       os.path.join(a_,		'tmp.csv'     )
		lowerCamelCase	:     Dict            =       CsvDatasetReader({'train': csv_path},		cache_dir=a_     ).read()
		CsvDatasetWriter(dataset['train'],		a_,		num_proc=2     ).write()

		lowerCamelCase	:     int            =       iter_csv_file(a_     )
		lowerCamelCase	:     Optional[int]            =       iter_csv_file(a_     )

		for rowa, rowa in zip(a_,		a_     ):
				assert rowa == rowa





def 						UpperCAmelCase   (						a_,		a_     ):





		'''simple docstring'''


		lowerCamelCase	:     Tuple            =       tmp_path / 'cache'
		lowerCamelCase	:     Optional[Any]            =       os.path.join(a_,		'tmp.csv'     )
		lowerCamelCase	:     Optional[int]            =       CsvDatasetReader({'train': csv_path},		cache_dir=a_     ).read()
		with pytest.raises(a_     ):
				CsvDatasetWriter(dataset['train'],		a_,		num_proc=0     )






