


'''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()
lowerCAmelCase__						:Tuple         =							logging.get_logger(__name__)

def  lowerCAmelCase__				(    a__:	int )						->	List[Any]:


							'''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  lowerCAmelCase__				(    a__:	int )						->	Tuple:


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

def  lowerCAmelCase__				(    )						->	Union[str, Any]:


							'''simple docstring'''





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

							return im

@torch.no_grad()
def  lowerCAmelCase__				(    a__:	Dict    ,						a__:	Union[str, Any]    ,						a__:	Any    ,						a__:	Optional[Any]    ,						a__:	List[str]    ,						a__:	int    ,						a__:	int    ,						)						->	Optional[Any]:


							'''simple docstring'''





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

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

							# load original state dict
							_UpperCAmelCase					=    torch.load(__lowerCAmelCase    ,						map_location='cpu' )["model"]
							# rename keys
							for key in state_dict.copy().keys():
														_UpperCAmelCase					=    state_dict.pop(__lowerCAmelCase )
														_UpperCAmelCase					=    val
							# query, key and value matrices need special treatment
							read_in_q_k_v(__lowerCAmelCase )
							# 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(__lowerCAmelCase )
																					_UpperCAmelCase					=    val
    # finally, create HuggingFace model and load state dict
							_UpperCAmelCase					=    DeformableDetrForObjectDetection(__lowerCAmelCase )
							model.load_state_dict(__lowerCAmelCase )
							model.eval()

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

							_UpperCAmelCase					=    torch.tensor(
							    [[-9.6_645, -4.3_449, -5.8_705], [-9.7_035, -3.8_504, -5.0_724], [-1_0.5_6_3_4, -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], [-1_0.0_6_6_5, -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], [-1_0.1_1_7_8, -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(__lowerCAmelCase )    ,						atol=1e-4 )
							assert torch.allclose(outputs.pred_boxes[0, :3, :3]    ,						expected_boxes.to(__lowerCAmelCase )    ,						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(__lowerCAmelCase ).mkdir(exist_ok=__lowerCAmelCase )
							model.save_pretrained(__lowerCAmelCase )
							image_processor.save_pretrained(__lowerCAmelCase )

							# 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=__lowerCAmelCase    ,						organization='nielsr'    ,						commit_message='Add model' )


if __name__ == "__main__":
		lowerCAmelCase__						:Optional[int]         =							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.'''
		)
		lowerCAmelCase__						:Dict         =							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,
		)




import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCAmelCase__						:int         =							logging.get_logger(__name__)

lowerCAmelCase__						:Optional[int]         =							{
    '''facebook/sam-vit-huge''': '''https://huggingface.co/facebook/sam-vit-huge/resolve/main/config.json''',
    '''facebook/sam-vit-large''': '''https://huggingface.co/facebook/sam-vit-large/resolve/main/config.json''',
    '''facebook/sam-vit-base''': '''https://huggingface.co/facebook/sam-vit-base/resolve/main/config.json''',
}

class 	__a   (       UpperCAmelCase    ):
		def __init__(    self    ,					_SCREAMING_SNAKE_CASE=256    ,					_SCREAMING_SNAKE_CASE=1024    ,					_SCREAMING_SNAKE_CASE=16    ,					_SCREAMING_SNAKE_CASE=16    ,					_SCREAMING_SNAKE_CASE=4    ,					_SCREAMING_SNAKE_CASE="gelu"    ,					_SCREAMING_SNAKE_CASE=1e-6    ,					**_SCREAMING_SNAKE_CASE    ,					)				-> Tuple:




									"""simple docstring"""





									super().__init__(**_SCREAMING_SNAKE_CASE		)
									_UpperCAmelCase					=    hidden_size
									_UpperCAmelCase					=    image_size
									_UpperCAmelCase					=    patch_size
									_UpperCAmelCase					=    image_size // patch_size
									_UpperCAmelCase					=    mask_input_channels
									_UpperCAmelCase					=    num_point_embeddings
									_UpperCAmelCase					=    hidden_act
									_UpperCAmelCase					=    layer_norm_eps



class 	__a   (       UpperCAmelCase    ):
		def __init__(    self    ,					_SCREAMING_SNAKE_CASE=256    ,					_SCREAMING_SNAKE_CASE="relu"    ,					_SCREAMING_SNAKE_CASE=2048    ,					_SCREAMING_SNAKE_CASE=2    ,					_SCREAMING_SNAKE_CASE=8    ,					_SCREAMING_SNAKE_CASE=2    ,					_SCREAMING_SNAKE_CASE=3    ,					_SCREAMING_SNAKE_CASE=3    ,					_SCREAMING_SNAKE_CASE=256    ,					_SCREAMING_SNAKE_CASE=1e-6    ,					**_SCREAMING_SNAKE_CASE    ,					)				-> List[Any]:




									"""simple docstring"""





									super().__init__(**_SCREAMING_SNAKE_CASE		)
									_UpperCAmelCase					=    hidden_size
									_UpperCAmelCase					=    hidden_act
									_UpperCAmelCase					=    mlp_dim
									_UpperCAmelCase					=    num_hidden_layers
									_UpperCAmelCase					=    num_attention_heads
									_UpperCAmelCase					=    attention_downsample_rate
									_UpperCAmelCase					=    num_multimask_outputs
									_UpperCAmelCase					=    iou_head_depth
									_UpperCAmelCase					=    iou_head_hidden_dim
									_UpperCAmelCase					=    layer_norm_eps



class 	__a   (       UpperCAmelCase    ):
		def __init__(    self    ,					_SCREAMING_SNAKE_CASE=768    ,					_SCREAMING_SNAKE_CASE=256    ,					_SCREAMING_SNAKE_CASE=12    ,					_SCREAMING_SNAKE_CASE=12    ,					_SCREAMING_SNAKE_CASE=3    ,					_SCREAMING_SNAKE_CASE=1024    ,					_SCREAMING_SNAKE_CASE=16    ,					_SCREAMING_SNAKE_CASE="gelu"    ,					_SCREAMING_SNAKE_CASE=1e-0_6    ,					_SCREAMING_SNAKE_CASE=0.0    ,					_SCREAMING_SNAKE_CASE=1e-1_0    ,					_SCREAMING_SNAKE_CASE=True    ,					_SCREAMING_SNAKE_CASE=4.0    ,					_SCREAMING_SNAKE_CASE=True    ,					_SCREAMING_SNAKE_CASE=True    ,					_SCREAMING_SNAKE_CASE=14    ,					_SCREAMING_SNAKE_CASE=[2, 5, 8, 11]    ,					_SCREAMING_SNAKE_CASE=128    ,					_SCREAMING_SNAKE_CASE=None    ,					**_SCREAMING_SNAKE_CASE    ,					)				-> List[str]:




									"""simple docstring"""





									super().__init__(**_SCREAMING_SNAKE_CASE		)

									_UpperCAmelCase					=    hidden_size
									_UpperCAmelCase					=    output_channels
									_UpperCAmelCase					=    num_hidden_layers
									_UpperCAmelCase					=    num_attention_heads
									_UpperCAmelCase					=    num_channels
									_UpperCAmelCase					=    image_size
									_UpperCAmelCase					=    patch_size
									_UpperCAmelCase					=    hidden_act
									_UpperCAmelCase					=    layer_norm_eps
									_UpperCAmelCase					=    attention_dropout
									_UpperCAmelCase					=    initializer_range
									_UpperCAmelCase					=    qkv_bias
									_UpperCAmelCase					=    mlp_ratio
									_UpperCAmelCase					=    use_abs_pos
									_UpperCAmelCase					=    use_rel_pos
									_UpperCAmelCase					=    window_size
									_UpperCAmelCase					=    global_attn_indexes
									_UpperCAmelCase					=    num_pos_feats
									_UpperCAmelCase					=    int(hidden_size * mlp_ratio		) if mlp_dim is None else mlp_dim



class 	__a   (       UpperCAmelCase    ):
		_a			:						Any    			=			'sam'
		_a			:						Any    			=			True
		def __init__(    self    ,					_SCREAMING_SNAKE_CASE=None    ,					_SCREAMING_SNAKE_CASE=None    ,					_SCREAMING_SNAKE_CASE=None    ,					_SCREAMING_SNAKE_CASE=0.02    ,					**_SCREAMING_SNAKE_CASE    ,					)				-> Tuple:




									"""simple docstring"""





									super().__init__(**_SCREAMING_SNAKE_CASE		)
									_UpperCAmelCase					=    vision_config if vision_config is not None else {}
									_UpperCAmelCase					=    prompt_encoder_config if prompt_encoder_config is not None else {}
									_UpperCAmelCase					=    mask_decoder_config if mask_decoder_config is not None else {}

									if isinstance(_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE		):
																_UpperCAmelCase					=    vision_config.to_dict()
									if isinstance(_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE		):
																_UpperCAmelCase					=    prompt_encoder_config.to_dict()
									if isinstance(_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE		):
																_UpperCAmelCase					=    mask_decoder_config.to_dict()

									_UpperCAmelCase					=    SamVisionConfig(**_SCREAMING_SNAKE_CASE		)
									_UpperCAmelCase					=    SamPromptEncoderConfig(**_SCREAMING_SNAKE_CASE		)
									_UpperCAmelCase					=    SamMaskDecoderConfig(**_SCREAMING_SNAKE_CASE		)
									_UpperCAmelCase					=    initializer_range






		def 			UpperCAmelCase__       (    self		)				-> str:




									"""simple docstring"""





									_UpperCAmelCase					=    copy.deepcopy(self.__dict__		)
									_UpperCAmelCase					=    self.vision_config.to_dict()
									_UpperCAmelCase					=    self.prompt_encoder_config.to_dict()
									_UpperCAmelCase					=    self.mask_decoder_config.to_dict()
									_UpperCAmelCase					=    self.__class__.model_type
									return output
