


from copy import deepcopy
from unittest.case import TestCase

import pytest

from datasets.arrow_dataset import Dataset
from datasets.features import Audio, ClassLabel, Features, Image, Sequence, Value
from datasets.info import DatasetInfo
from datasets.tasks import (
    AudioClassification,
    AutomaticSpeechRecognition,
    ImageClassification,
    LanguageModeling,
    QuestionAnsweringExtractive,
    Summarization,
    TextClassification,
    task_template_from_dict,
)
from datasets.utils.py_utils import asdict


snake_case			:      Union[str, Any] 							=   {
    'id': '5733be284776f41900661182',
    'title': 'University_of_Notre_Dame',
    'context': 'Architecturally, the school has a Catholic character. Atop the Main Building\'s gold dome is a golden statue of the Virgin Mary. Immediately in front of the Main Building and facing it, is a copper statue of Christ with arms upraised with the legend "Venite Ad Me Omnes". Next to the Main Building is the Basilica of the Sacred Heart. Immediately behind the basilica is the Grotto, a Marian place of prayer and reflection. It is a replica of the grotto at Lourdes, France where the Virgin Mary reputedly appeared to Saint Bernadette Soubirous in 1858. At the end of the main drive (and in a direct line that connects through 3 statues and the Gold Dome), is a simple, modern stone statue of Mary.',
    'question': 'To whom did the Virgin Mary allegedly appear in 1858 in Lourdes France?',
    'answers': {'text': ['Saint Bernadette Soubirous'], 'answer_start': [5_15]},
}


@pytest.mark.parametrize(
    'task_cls'   ,[
        AudioClassification,
        AutomaticSpeechRecognition,
        ImageClassification,
        LanguageModeling,
        QuestionAnsweringExtractive,
        Summarization,
        TextClassification,
    ]   ,)
def 				SCREAMING_SNAKE_CASE (     UpperCAmelCase__							):

				"""simple docstring"""






				_SCREAMING_SNAKE_CASE      = task_cls()
				_SCREAMING_SNAKE_CASE      = asdict(UpperCAmelCase__							)
				_SCREAMING_SNAKE_CASE      = task_template_from_dict(UpperCAmelCase__							)
				assert task == reloaded



class    __lowercase  :

							"""simple docstring"""


							def 		__magic_name__			(    self	)->	Dict:
											_SCREAMING_SNAKE_CASE      = LanguageModeling(text_column='input_text'	)
											assert {"input_text": "text"} == task.column_mapping





							def 		__magic_name__			(    self	)->	Union[str, Any]:
											_SCREAMING_SNAKE_CASE      = Features({'text': Value('string'	)}	)
											_SCREAMING_SNAKE_CASE      = {'text_column': 'input_text'}
											_SCREAMING_SNAKE_CASE      = LanguageModeling.from_dict(A_	)
											assert "language-modeling" == task.task
											assert input_schema == task.input_schema







class    __lowercase  (       UpperCamelCase							):

							"""simple docstring"""


							def 		__magic_name__			(    self	)->	Union[str, Any]:
											_SCREAMING_SNAKE_CASE      = sorted(['pos', 'neg']	)





							def 		__magic_name__			(    self	)->	List[Any]:
											_SCREAMING_SNAKE_CASE      = TextClassification(text_column='input_text'				, label_column='input_label'	)
											self.assertDictEqual({'input_text': 'text', 'input_label': 'labels'}				, task.column_mapping	)





							def 		__magic_name__			(    self	)->	List[Any]:
											_SCREAMING_SNAKE_CASE      = Features({'text': Value('string'	)}	)
											# Labels are cast to tuple during `TextClassification.__post_init__`, so we do the same here
											_SCREAMING_SNAKE_CASE      = Features({'labels': ClassLabel}	)
											_SCREAMING_SNAKE_CASE      = {'text_column': 'input_text', 'label_column': 'input_labels'}
											_SCREAMING_SNAKE_CASE      = TextClassification.from_dict(A_	)
											self.assertEqual('text-classification'				, task.task	)
											self.assertEqual(A_				, task.input_schema	)
											self.assertEqual(A_				, task.label_schema	)





							def 		__magic_name__			(    self	)->	int:
											_SCREAMING_SNAKE_CASE      = TextClassification(text_column='input_text'				, label_column='input_label'	)
											self.assertEqual(task.label_schema['labels']				, A_	)
											_SCREAMING_SNAKE_CASE      = task.align_with_features(Features({'input_label': ClassLabel(names=self.labels	)}	)	)
											self.assertEqual(task.label_schema['labels']				, ClassLabel(names=self.labels	)	)







class    __lowercase  (       UpperCamelCase							):

							"""simple docstring"""


							def 		__magic_name__			(    self	)->	Union[str, Any]:
											_SCREAMING_SNAKE_CASE      = QuestionAnsweringExtractive(
											    context_column='input_context'				, question_column='input_question'				, answers_column='input_answers'	)
											self.assertDictEqual(
											    {'input_context': 'context', 'input_question': 'question', 'input_answers': 'answers'}				, task.column_mapping	)





							def 		__magic_name__			(    self	)->	Optional[Any]:
											_SCREAMING_SNAKE_CASE      = Features({'question': Value('string'	), 'context': Value('string'	)}	)
											_SCREAMING_SNAKE_CASE      = Features(
											    {
											        'answers': Sequence(
											            {
											                'text': Value('string'	),
											                'answer_start': Value('int32'	),
											            }	)
											    }	)
											_SCREAMING_SNAKE_CASE      = {
											    'context_column': 'input_input_context',
											    'question_column': 'input_question',
											    'answers_column': 'input_answers',
											}
											_SCREAMING_SNAKE_CASE      = QuestionAnsweringExtractive.from_dict(A_	)
											self.assertEqual('question-answering-extractive'				, task.task	)
											self.assertEqual(A_				, task.input_schema	)
											self.assertEqual(A_				, task.label_schema	)







class    __lowercase  (       UpperCamelCase							):

							"""simple docstring"""


							def 		__magic_name__			(    self	)->	Any:
											_SCREAMING_SNAKE_CASE      = Summarization(text_column='input_text'				, summary_column='input_summary'	)
											self.assertDictEqual({'input_text': 'text', 'input_summary': 'summary'}				, task.column_mapping	)





							def 		__magic_name__			(    self	)->	Union[str, Any]:
											_SCREAMING_SNAKE_CASE      = Features({'text': Value('string'	)}	)
											_SCREAMING_SNAKE_CASE      = Features({'summary': Value('string'	)}	)
											_SCREAMING_SNAKE_CASE      = {'text_column': 'input_text', 'summary_column': 'input_summary'}
											_SCREAMING_SNAKE_CASE      = Summarization.from_dict(A_	)
											self.assertEqual('summarization'				, task.task	)
											self.assertEqual(A_				, task.input_schema	)
											self.assertEqual(A_				, task.label_schema	)







class    __lowercase  (       UpperCamelCase							):

							"""simple docstring"""


							def 		__magic_name__			(    self	)->	Tuple:
											_SCREAMING_SNAKE_CASE      = AutomaticSpeechRecognition(audio_column='input_audio'				, transcription_column='input_transcription'	)
											self.assertDictEqual({'input_audio': 'audio', 'input_transcription': 'transcription'}				, task.column_mapping	)





							def 		__magic_name__			(    self	)->	Tuple:
											_SCREAMING_SNAKE_CASE      = Features({'audio': Audio()}	)
											_SCREAMING_SNAKE_CASE      = Features({'transcription': Value('string'	)}	)
											_SCREAMING_SNAKE_CASE      = {
											    'audio_column': 'input_audio',
											    'transcription_column': 'input_transcription',
											}
											_SCREAMING_SNAKE_CASE      = AutomaticSpeechRecognition.from_dict(A_	)
											self.assertEqual('automatic-speech-recognition'				, task.task	)
											self.assertEqual(A_				, task.input_schema	)
											self.assertEqual(A_				, task.label_schema	)







class    __lowercase  (       UpperCamelCase							):

							"""simple docstring"""


							def 		__magic_name__			(    self	)->	Tuple:
											_SCREAMING_SNAKE_CASE      = sorted(['pos', 'neg']	)





							def 		__magic_name__			(    self	)->	Tuple:
											_SCREAMING_SNAKE_CASE      = AudioClassification(audio_column='input_audio'				, label_column='input_label'	)
											self.assertDictEqual({'input_audio': 'audio', 'input_label': 'labels'}				, task.column_mapping	)





							def 		__magic_name__			(    self	)->	Optional[Any]:
											_SCREAMING_SNAKE_CASE      = Features({'audio': Audio()}	)
											_SCREAMING_SNAKE_CASE      = Features({'labels': ClassLabel}	)
											_SCREAMING_SNAKE_CASE      = {
											    'audio_column': 'input_image',
											    'label_column': 'input_label',
											}
											_SCREAMING_SNAKE_CASE      = AudioClassification.from_dict(A_	)
											self.assertEqual('audio-classification'				, task.task	)
											self.assertEqual(A_				, task.input_schema	)
											self.assertEqual(A_				, task.label_schema	)





							def 		__magic_name__			(    self	)->	List[str]:
											_SCREAMING_SNAKE_CASE      = AudioClassification(audio_column='input_audio'				, label_column='input_label'	)
											self.assertEqual(task.label_schema['labels']				, A_	)
											_SCREAMING_SNAKE_CASE      = task.align_with_features(Features({'input_label': ClassLabel(names=self.labels	)}	)	)
											self.assertEqual(task.label_schema['labels']				, ClassLabel(names=self.labels	)	)







class    __lowercase  (       UpperCamelCase							):

							"""simple docstring"""


							def 		__magic_name__			(    self	)->	Dict:
											_SCREAMING_SNAKE_CASE      = sorted(['pos', 'neg']	)





							def 		__magic_name__			(    self	)->	int:
											_SCREAMING_SNAKE_CASE      = ImageClassification(image_column='input_image'				, label_column='input_label'	)
											self.assertDictEqual({'input_image': 'image', 'input_label': 'labels'}				, task.column_mapping	)





							def 		__magic_name__			(    self	)->	List[str]:
											_SCREAMING_SNAKE_CASE      = Features({'image': Image()}	)
											_SCREAMING_SNAKE_CASE      = Features({'labels': ClassLabel}	)
											_SCREAMING_SNAKE_CASE      = {
											    'image_column': 'input_image',
											    'label_column': 'input_label',
											}
											_SCREAMING_SNAKE_CASE      = ImageClassification.from_dict(A_	)
											self.assertEqual('image-classification'				, task.task	)
											self.assertEqual(A_				, task.input_schema	)
											self.assertEqual(A_				, task.label_schema	)





							def 		__magic_name__			(    self	)->	Optional[int]:
											_SCREAMING_SNAKE_CASE      = ImageClassification(image_column='input_image'				, label_column='input_label'	)
											self.assertEqual(task.label_schema['labels']				, A_	)
											_SCREAMING_SNAKE_CASE      = task.align_with_features(Features({'input_label': ClassLabel(names=self.labels	)}	)	)
											self.assertEqual(task.label_schema['labels']				, ClassLabel(names=self.labels	)	)







class    __lowercase  (       UpperCamelCase							):

							"""simple docstring"""


							def 		__magic_name__			(    self	)->	List[Any]:
											_SCREAMING_SNAKE_CASE      = DatasetInfo(task_templates=QuestionAnsweringExtractive()	)
											_SCREAMING_SNAKE_CASE      = Dataset.from_dict({k: [v] for k, v in SAMPLE_QUESTION_ANSWERING_EXTRACTIVE.items()}				, info=A_	)
											assert isinstance(dataset.info.task_templates				, A_	)
											assert len(dataset.info.task_templates	) == 1

											def keep_task(A_	):
															return x

											def dont_keep_task(A_	):
															_SCREAMING_SNAKE_CASE      = deepcopy(A_	)
															_SCREAMING_SNAKE_CASE      = 0
															return out

											_SCREAMING_SNAKE_CASE      = dataset.map(A_	)
											assert mapped_dataset.info.task_templates == dataset.info.task_templates
											# reload from cache
											_SCREAMING_SNAKE_CASE      = dataset.map(A_	)
											assert mapped_dataset.info.task_templates == dataset.info.task_templates

											_SCREAMING_SNAKE_CASE      = dataset.map(A_	)
											assert mapped_dataset.info.task_templates == []
											# reload from cache
											_SCREAMING_SNAKE_CASE      = dataset.map(A_	)
											assert mapped_dataset.info.task_templates == []





							def 		__magic_name__			(    self	)->	Optional[int]:
											_SCREAMING_SNAKE_CASE      = Features({'text': Value('string'	), 'label': ClassLabel(names=('pos', 'neg')	)}	)
											_SCREAMING_SNAKE_CASE      = TextClassification(text_column='text'				, label_column='label'	)
											_SCREAMING_SNAKE_CASE      = DatasetInfo(features=A_				, task_templates=A_	)
											_SCREAMING_SNAKE_CASE      = Dataset.from_dict({'text': ['A sentence.'], 'label': ['pos']}				, info=A_	)

											def process(A_	):
															return example

											_SCREAMING_SNAKE_CASE      = dataset.remove_columns('label'	)
											_SCREAMING_SNAKE_CASE      = modified_dataset.map(A_	)
											assert mapped_dataset.info.task_templates == []




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			:      Dict 							=   logging.get_logger(__name__)


def 				SCREAMING_SNAKE_CASE (     UpperCAmelCase__							):

				"""simple docstring"""






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

				return orig_key


def 				SCREAMING_SNAKE_CASE (     UpperCAmelCase__							):

				"""simple docstring"""






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


def 				SCREAMING_SNAKE_CASE (     ):

				"""simple docstring"""






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

				return im


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

				"""simple docstring"""






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

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

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

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

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

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

				_SCREAMING_SNAKE_CASE      = 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]]							)
				_SCREAMING_SNAKE_CASE      = 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:
								_SCREAMING_SNAKE_CASE      = 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]]							)
								_SCREAMING_SNAKE_CASE      = 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:
								_SCREAMING_SNAKE_CASE      = 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]]							)
								_SCREAMING_SNAKE_CASE      = 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:
								_SCREAMING_SNAKE_CASE      = 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]]							)
								_SCREAMING_SNAKE_CASE      = 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:
								_SCREAMING_SNAKE_CASE      = 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]]							)
								_SCREAMING_SNAKE_CASE      = 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(UpperCAmelCase__							)   ,atol=1e-4							)
				assert torch.allclose(outputs.pred_boxes[0, :3, :3]   ,expected_boxes.to(UpperCAmelCase__							)   ,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(UpperCAmelCase__							).mkdir(exist_ok=UpperCAmelCase__							)
				model.save_pretrained(UpperCAmelCase__							)
				image_processor.save_pretrained(UpperCAmelCase__							)

				# Push to hub
				if push_to_hub:
								_SCREAMING_SNAKE_CASE      = '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=UpperCAmelCase__   ,organization='nielsr'   ,commit_message='Add model'							)


if __name__ == "__main__":
							snake_case			:      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.'
							)
							snake_case			:      List[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,
							)
