


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

							return orig_key


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


def 					_snake_case   (     )						->    Optional[Any]:
							lowerCAmelCase__          =	'''http://images.cocodataset.org/val2017/000000039769.jpg'''
							lowerCAmelCase__          =	Image.open(requests.get(A ,    stream=A       ).raw       )

							return im


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

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

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

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

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

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

							lowerCAmelCase__          =	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]]       )
							lowerCAmelCase__          =	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:
														lowerCAmelCase__          =	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]]       )
														lowerCAmelCase__          =	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:
														lowerCAmelCase__          =	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]]       )
														lowerCAmelCase__          =	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:
														lowerCAmelCase__          =	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]]       )
														lowerCAmelCase__          =	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:
														lowerCAmelCase__          =	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]]       )
														lowerCAmelCase__          =	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:
														lowerCAmelCase__          =	'''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__":
		__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'''






from __future__ import annotations

from collections.abc import Callable
from typing import Generic, TypeVar

__UpperCAmelCase    				=							TypeVar('''T''')
__UpperCAmelCase    				=							TypeVar('''U''')




class 		a__				(							Generic[T, U]  ):





							'''simple docstring'''






							def __init__(		self					,    lowerCamelCase_					,    lowerCamelCase_   )       ->							Union[str, Any]:
														lowerCAmelCase__          =	key
														lowerCAmelCase__          =	val
														lowerCAmelCase__          =	0
														lowerCAmelCase__          =	None
														lowerCAmelCase__          =	None







							def __repr__(		self   )       ->							str:
														return "Node: key: {}, val: {}, freq: {}, has next: {}, has prev: {}".format(
														    self.key					,    self.val					,    self.freq					,    self.next is not None					,    self.prev is not None   )




class 		a__				(							Generic[T, U]  ):





							'''simple docstring'''






							def __init__(		self   )       ->							None:
														lowerCAmelCase__          =	DoubleLinkedListNode(lowerCamelCase_					,    lowerCamelCase_   )
														lowerCAmelCase__          =	DoubleLinkedListNode(lowerCamelCase_					,    lowerCamelCase_   )
														lowerCAmelCase__   ,					lowerCAmelCase__          =	self.rear, self.head







							def __repr__(		self   )       ->							str:
														lowerCAmelCase__          =	['''DoubleLinkedList''']
														lowerCAmelCase__          =	self.head
														while node.next is not None:
																					rep.append(str(lowerCamelCase_   )   )
																					lowerCAmelCase__          =	node.next
														rep.append(str(self.rear   )   )
														return ",\n    ".join(lowerCamelCase_   )







							def   __SCREAMING_SNAKE_CASE							(		self					,    lowerCamelCase_   )       ->							None:
														lowerCAmelCase__          =	self.rear.prev

														# All nodes other than self.head are guaranteed to have non-None previous
														assert previous is not None

														lowerCAmelCase__          =	node
														lowerCAmelCase__          =	previous
														lowerCAmelCase__          =	node
														lowerCAmelCase__          =	self.rear
														node.freq += 1
														self._position_node(lowerCamelCase_   )







							def   __SCREAMING_SNAKE_CASE							(		self					,    lowerCamelCase_   )       ->							None:

														while node.prev is not None and node.prev.freq > node.freq:
																					# swap node with previous node
																					lowerCAmelCase__          =	node.prev

																					lowerCAmelCase__          =	previous_node.prev
																					lowerCAmelCase__          =	node.prev
																					lowerCAmelCase__          =	previous_node
																					lowerCAmelCase__          =	node







							def   __SCREAMING_SNAKE_CASE							(		self					,    lowerCamelCase_   )       ->							DoubleLinkedListNode[T, U] | None:

														if node.prev is None or node.next is None:
																					return None

														lowerCAmelCase__          =	node.next
														lowerCAmelCase__          =	node.prev
														lowerCAmelCase__          =	None
														lowerCAmelCase__          =	None
														return node




class 		a__				(							Generic[T, U]  ):





							'''simple docstring'''






							lowercase__			:   dict[Callable[[T], U], LFUCache[T, U]]							=      {}







							def __init__(		self					,    lowerCamelCase_   )       ->							Any:
														lowerCAmelCase__          =	DoubleLinkedList()
														lowerCAmelCase__          =	capacity
														lowerCAmelCase__          =	0
														lowerCAmelCase__          =	0
														lowerCAmelCase__          =	0
														lowerCAmelCase__          =	{}







							def __repr__(		self   )       ->							str:
														return (
														    F"""CacheInfo(hits={self.hits}, misses={self.miss}, """
														    F"""capacity={self.capacity}, current_size={self.num_keys})"""
														)







							def __contains__(		self					,    lowerCamelCase_   )       ->							bool:
														return key in self.cache







							def   __SCREAMING_SNAKE_CASE							(		self					,    lowerCamelCase_   )       ->							U | None:

														if key in self.cache:
																					self.hits += 1
																					lowerCAmelCase__          =	self.cache[key]
																					lowerCAmelCase__          =	self.list.remove(self.cache[key]   )
																					assert node == value_node

																					# node is guaranteed not None because it is in self.cache
																					assert node is not None
																					self.list.add(lowerCamelCase_   )
																					return node.val
														self.miss += 1
														return None







							def   __SCREAMING_SNAKE_CASE							(		self					,    lowerCamelCase_					,    lowerCamelCase_   )       ->							None:

														if key not in self.cache:
																					if self.num_keys >= self.capacity:
																												# delete first node when over capacity
																												lowerCAmelCase__          =	self.list.head.next

																												# guaranteed to have a non-None first node when num_keys > 0
																												# explain to type checker via assertions
																												assert first_node is not None
																												assert first_node.key is not None
																												assert self.list.remove(lowerCamelCase_   ) is not None
																												# first_node guaranteed to be in list

																												del self.cache[first_node.key]
																												self.num_keys -= 1
																					lowerCAmelCase__          =	DoubleLinkedListNode(lowerCamelCase_					,    lowerCamelCase_   )
																					self.list.add(self.cache[key]   )
																					self.num_keys += 1

														else:
																					lowerCAmelCase__          =	self.list.remove(self.cache[key]   )
																					assert node is not None  # node guaranteed to be in list
																					lowerCAmelCase__          =	value
																					self.list.add(lowerCamelCase_   )







							@classmethod
							def   __SCREAMING_SNAKE_CASE							(		cls					,    lowerCamelCase_ = 1_28   )       ->							Callable[[Callable[[T], U]], Callable[..., U]]:

														def cache_decorator_inner(lowerCamelCase_   ) -> Callable[..., U]:
																					def cache_decorator_wrapper(*lowerCamelCase_   ) -> U:
																												if func not in cls.decorator_function_to_instance_map:
																																			lowerCAmelCase__          =	LFUCache(lowerCamelCase_   )

																												lowerCAmelCase__          =	cls.decorator_function_to_instance_map[func].get(args[0]   )
																												if result is None:
																																			lowerCAmelCase__          =	func(*lowerCamelCase_   )
																																			cls.decorator_function_to_instance_map[func].put(args[0]					,    lowerCamelCase_   )
																												return result

																					def cache_info() -> LFUCache[T, U]:
																												return cls.decorator_function_to_instance_map[func]

																					setattr(lowerCamelCase_					,    '''cache_info'''					,    lowerCamelCase_   )  # noqa: B010

																					return cache_decorator_wrapper

														return cache_decorator_inner


if __name__ == "__main__":
		import doctest

		doctest.testmod()