

import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import DetrConfig, DetrForObjectDetection, DetrForSegmentation, DetrImageProcessor, ResNetConfig
from transformers.utils import logging


logging.set_verbosity_info()
lowerCAmelCase__								=		logging.get_logger(__name__)






def 					lowerCamelCase_     (							UpperCAmelCase_							:     Tuple	)						->				List[str]:




	'''simple docstring'''




	if "resnet-50" in model_name:
		_UpperCamelCase   :		List[Any]			=						ResNetConfig.from_pretrained('microsoft/resnet-50'	)
	elif "resnet-101" in model_name:
		_UpperCamelCase   :		Any			=						ResNetConfig.from_pretrained('microsoft/resnet-101'	)
	else:
		raise ValueError('Model name should include either resnet50 or resnet101'	)

	_UpperCamelCase   :		List[str]			=						DetrConfig(use_timm_backbone=UpperCAmelCase_							,       backbone_config=UpperCAmelCase_	)

	# set label attributes
	_UpperCamelCase   :		int			=						'panoptic' in model_name
	if is_panoptic:
		_UpperCamelCase   :		List[str]			=						2_5_0
	else:
		_UpperCamelCase   :		Tuple			=						9_1
		_UpperCamelCase   :		List[Any]			=						'huggingface/label-files'
		_UpperCamelCase   :		Tuple			=						'coco-detection-id2label.json'
		_UpperCamelCase   :		int			=						json.load(open(hf_hub_download(UpperCAmelCase_							,       UpperCAmelCase_							,       repo_type='dataset'	)							,       'r'	)	)
		_UpperCamelCase   :		Dict			=						{int(UpperCAmelCase_	): v for k, v in idalabel.items()}
		_UpperCamelCase   :		Any			=						idalabel
		_UpperCamelCase   :		List[str]			=						{v: k for k, v in idalabel.items()}

	return config, is_panoptic






def 					lowerCamelCase_     (							UpperCAmelCase_							:     List[Any]	)						->				Dict:




	'''simple docstring'''




	_UpperCamelCase   :		int			=						[]

	# stem
	# fmt: off
	rename_keys.append(('backbone.0.body.conv1.weight', 'backbone.conv_encoder.model.embedder.embedder.convolution.weight')	)
	rename_keys.append(('backbone.0.body.bn1.weight', 'backbone.conv_encoder.model.embedder.embedder.normalization.weight')	)
	rename_keys.append(('backbone.0.body.bn1.bias', 'backbone.conv_encoder.model.embedder.embedder.normalization.bias')	)
	rename_keys.append(('backbone.0.body.bn1.running_mean', 'backbone.conv_encoder.model.embedder.embedder.normalization.running_mean')	)
	rename_keys.append(('backbone.0.body.bn1.running_var', 'backbone.conv_encoder.model.embedder.embedder.normalization.running_var')	)
	# stages
	for stage_idx in range(len(config.backbone_config.depths	)	):
		for layer_idx in range(config.backbone_config.depths[stage_idx]	):
			# shortcut
			if layer_idx == 0:
				rename_keys.append(
				    (
				        F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.0.weight''',
				        F'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.convolution.weight''',
				    )	)
				rename_keys.append(
				    (
				        F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.weight''',
				        F'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.weight''',
				    )	)
				rename_keys.append(
				    (
				        F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.bias''',
				        F'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.bias''',
				    )	)
				rename_keys.append(
				    (
				        F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_mean''',
				        F'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_mean''',
				    )	)
				rename_keys.append(
				    (
				        F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_var''',
				        F'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_var''',
				    )	)
			# 3 convs
			for i in range(3	):
				rename_keys.append(
				    (
				        F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.conv{i+1}.weight''',
				        F'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.convolution.weight''',
				    )	)
				rename_keys.append(
				    (
				        F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.weight''',
				        F'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.weight''',
				    )	)
				rename_keys.append(
				    (
				        F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.bias''',
				        F'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.bias''',
				    )	)
				rename_keys.append(
				    (
				        F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_mean''',
				        F'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_mean''',
				    )	)
				rename_keys.append(
				    (
				        F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_var''',
				        F'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_var''',
				    )	)
    # fmt: on

	for i in range(config.encoder_layers	):
		# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
		rename_keys.append(
		    (
		        F'''transformer.encoder.layers.{i}.self_attn.out_proj.weight''',
		        F'''encoder.layers.{i}.self_attn.out_proj.weight''',
		    )	)
		rename_keys.append(
		    (F'''transformer.encoder.layers.{i}.self_attn.out_proj.bias''', F'''encoder.layers.{i}.self_attn.out_proj.bias''')	)
		rename_keys.append((F'''transformer.encoder.layers.{i}.linear1.weight''', F'''encoder.layers.{i}.fc1.weight''')	)
		rename_keys.append((F'''transformer.encoder.layers.{i}.linear1.bias''', F'''encoder.layers.{i}.fc1.bias''')	)
		rename_keys.append((F'''transformer.encoder.layers.{i}.linear2.weight''', F'''encoder.layers.{i}.fc2.weight''')	)
		rename_keys.append((F'''transformer.encoder.layers.{i}.linear2.bias''', F'''encoder.layers.{i}.fc2.bias''')	)
		rename_keys.append(
		    (F'''transformer.encoder.layers.{i}.norm1.weight''', F'''encoder.layers.{i}.self_attn_layer_norm.weight''')	)
		rename_keys.append(
		    (F'''transformer.encoder.layers.{i}.norm1.bias''', F'''encoder.layers.{i}.self_attn_layer_norm.bias''')	)
		rename_keys.append(
		    (F'''transformer.encoder.layers.{i}.norm2.weight''', F'''encoder.layers.{i}.final_layer_norm.weight''')	)
		rename_keys.append((F'''transformer.encoder.layers.{i}.norm2.bias''', F'''encoder.layers.{i}.final_layer_norm.bias''')	)
		# decoder layers: 2 times output projection, 2 feedforward neural networks and 3 layernorms
		rename_keys.append(
		    (
		        F'''transformer.decoder.layers.{i}.self_attn.out_proj.weight''',
		        F'''decoder.layers.{i}.self_attn.out_proj.weight''',
		    )	)
		rename_keys.append(
		    (F'''transformer.decoder.layers.{i}.self_attn.out_proj.bias''', F'''decoder.layers.{i}.self_attn.out_proj.bias''')	)
		rename_keys.append(
		    (
		        F'''transformer.decoder.layers.{i}.multihead_attn.out_proj.weight''',
		        F'''decoder.layers.{i}.encoder_attn.out_proj.weight''',
		    )	)
		rename_keys.append(
		    (
		        F'''transformer.decoder.layers.{i}.multihead_attn.out_proj.bias''',
		        F'''decoder.layers.{i}.encoder_attn.out_proj.bias''',
		    )	)
		rename_keys.append((F'''transformer.decoder.layers.{i}.linear1.weight''', F'''decoder.layers.{i}.fc1.weight''')	)
		rename_keys.append((F'''transformer.decoder.layers.{i}.linear1.bias''', F'''decoder.layers.{i}.fc1.bias''')	)
		rename_keys.append((F'''transformer.decoder.layers.{i}.linear2.weight''', F'''decoder.layers.{i}.fc2.weight''')	)
		rename_keys.append((F'''transformer.decoder.layers.{i}.linear2.bias''', F'''decoder.layers.{i}.fc2.bias''')	)
		rename_keys.append(
		    (F'''transformer.decoder.layers.{i}.norm1.weight''', F'''decoder.layers.{i}.self_attn_layer_norm.weight''')	)
		rename_keys.append(
		    (F'''transformer.decoder.layers.{i}.norm1.bias''', F'''decoder.layers.{i}.self_attn_layer_norm.bias''')	)
		rename_keys.append(
		    (F'''transformer.decoder.layers.{i}.norm2.weight''', F'''decoder.layers.{i}.encoder_attn_layer_norm.weight''')	)
		rename_keys.append(
		    (F'''transformer.decoder.layers.{i}.norm2.bias''', F'''decoder.layers.{i}.encoder_attn_layer_norm.bias''')	)
		rename_keys.append(
		    (F'''transformer.decoder.layers.{i}.norm3.weight''', F'''decoder.layers.{i}.final_layer_norm.weight''')	)
		rename_keys.append((F'''transformer.decoder.layers.{i}.norm3.bias''', F'''decoder.layers.{i}.final_layer_norm.bias''')	)

	# convolutional projection + query embeddings + layernorm of decoder + class and bounding box heads
	rename_keys.extend(
	    [
	        ('input_proj.weight', 'input_projection.weight'),
	        ('input_proj.bias', 'input_projection.bias'),
	        ('query_embed.weight', 'query_position_embeddings.weight'),
	        ('transformer.decoder.norm.weight', 'decoder.layernorm.weight'),
	        ('transformer.decoder.norm.bias', 'decoder.layernorm.bias'),
	        ('class_embed.weight', 'class_labels_classifier.weight'),
	        ('class_embed.bias', 'class_labels_classifier.bias'),
	        ('bbox_embed.layers.0.weight', 'bbox_predictor.layers.0.weight'),
	        ('bbox_embed.layers.0.bias', 'bbox_predictor.layers.0.bias'),
	        ('bbox_embed.layers.1.weight', 'bbox_predictor.layers.1.weight'),
	        ('bbox_embed.layers.1.bias', 'bbox_predictor.layers.1.bias'),
	        ('bbox_embed.layers.2.weight', 'bbox_predictor.layers.2.weight'),
	        ('bbox_embed.layers.2.bias', 'bbox_predictor.layers.2.bias'),
	    ]	)

	return rename_keys






def 					lowerCamelCase_     (							UpperCAmelCase_							:     Optional[int]							,       UpperCAmelCase_							:     Tuple							,       UpperCAmelCase_							:     str	)						->				str:




	'''simple docstring'''




	_UpperCamelCase   :		List[str]			=						state_dict.pop(UpperCAmelCase_	)
	_UpperCamelCase   :		Dict			=						val






def 					lowerCamelCase_     (							UpperCAmelCase_							:     Dict							,       UpperCAmelCase_							:     Union[str, Any]=False	)						->				Tuple:




	'''simple docstring'''




	_UpperCamelCase   :		str			=						''
	if is_panoptic:
		_UpperCamelCase   :		Any			=						'detr.'

	# first: transformer encoder
	for i in range(6	):
		# read in weights + bias of input projection layer (in PyTorch's MultiHeadAttention, this is a single matrix + bias)
		_UpperCamelCase   :		Optional[int]			=						state_dict.pop(F'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight'''	)
		_UpperCamelCase   :		Any			=						state_dict.pop(F'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_bias'''	)
		# next, add query, keys and values (in that order) to the state dict
		_UpperCamelCase   :		str			=						in_proj_weight[:2_5_6, :]
		_UpperCamelCase   :		List[str]			=						in_proj_bias[:2_5_6]
		_UpperCamelCase   :		List[str]			=						in_proj_weight[2_5_6:5_1_2, :]
		_UpperCamelCase   :		List[Any]			=						in_proj_bias[2_5_6:5_1_2]
		_UpperCamelCase   :		str			=						in_proj_weight[-2_5_6:, :]
		_UpperCamelCase   :		str			=						in_proj_bias[-2_5_6:]
	# next: transformer decoder (which is a bit more complex because it also includes cross-attention)
	for i in range(6	):
		# read in weights + bias of input projection layer of self-attention
		_UpperCamelCase   :		Optional[int]			=						state_dict.pop(F'''{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight'''	)
		_UpperCamelCase   :		Optional[Any]			=						state_dict.pop(F'''{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_bias'''	)
		# next, add query, keys and values (in that order) to the state dict
		_UpperCamelCase   :		List[str]			=						in_proj_weight[:2_5_6, :]
		_UpperCamelCase   :		Tuple			=						in_proj_bias[:2_5_6]
		_UpperCamelCase   :		Union[str, Any]			=						in_proj_weight[2_5_6:5_1_2, :]
		_UpperCamelCase   :		List[Any]			=						in_proj_bias[2_5_6:5_1_2]
		_UpperCamelCase   :		List[Any]			=						in_proj_weight[-2_5_6:, :]
		_UpperCamelCase   :		List[Any]			=						in_proj_bias[-2_5_6:]
		# read in weights + bias of input projection layer of cross-attention
		_UpperCamelCase   :		List[str]			=						state_dict.pop(
		    F'''{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight'''	)
		_UpperCamelCase   :		Any			=						state_dict.pop(F'''{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_bias'''	)
		# next, add query, keys and values (in that order) of cross-attention to the state dict
		_UpperCamelCase   :		List[str]			=						in_proj_weight_cross_attn[:2_5_6, :]
		_UpperCamelCase   :		str			=						in_proj_bias_cross_attn[:2_5_6]
		_UpperCamelCase   :		Tuple			=						in_proj_weight_cross_attn[2_5_6:5_1_2, :]
		_UpperCamelCase   :		List[str]			=						in_proj_bias_cross_attn[2_5_6:5_1_2]
		_UpperCamelCase   :		Union[str, Any]			=						in_proj_weight_cross_attn[-2_5_6:, :]
		_UpperCamelCase   :		str			=						in_proj_bias_cross_attn[-2_5_6:]






def 					lowerCamelCase_     (							)						->				Any:




	'''simple docstring'''




	_UpperCamelCase   :		Dict			=						'http://images.cocodataset.org/val2017/000000039769.jpg'
	_UpperCamelCase   :		Union[str, Any]			=						Image.open(requests.get(UpperCAmelCase_							,       stream=UpperCAmelCase_	).raw	)

	return im






@torch.no_grad()
def 					lowerCamelCase_     (							UpperCAmelCase_							:     Union[str, Any]							,       UpperCAmelCase_							:     int=None							,       UpperCAmelCase_							:     Optional[Any]=False	)						->				Optional[Any]:




	'''simple docstring'''




	_UpperCamelCase	,		_UpperCamelCase   :		Union[str, Any]			=						get_detr_config(UpperCAmelCase_	)

	# load original model from torch hub
	_UpperCamelCase   :		Dict			=						{
	    'detr-resnet-50': 'detr_resnet50',
	    'detr-resnet-101': 'detr_resnet101',
	}
	logger.info(F'''Converting model {model_name}...'''	)
	_UpperCamelCase   :		List[str]			=						torch.hub.load('facebookresearch/detr'							,       model_name_to_original_name[model_name]							,       pretrained=UpperCAmelCase_	).eval()
	_UpperCamelCase   :		Optional[Any]			=						detr.state_dict()
	# rename keys
	for src, dest in create_rename_keys(UpperCAmelCase_	):
		if is_panoptic:
			_UpperCamelCase   :		List[Any]			=						'detr.' + src
		rename_key(UpperCAmelCase_							,       UpperCAmelCase_							,       UpperCAmelCase_	)
	# query, key and value matrices need special treatment
	read_in_q_k_v(UpperCAmelCase_							,       is_panoptic=UpperCAmelCase_	)
	# important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
	_UpperCamelCase   :		Dict			=						'detr.model.' if is_panoptic else 'model.'
	for key in state_dict.copy().keys():
		if is_panoptic:
			if (
			    key.startswith('detr'	)
			    and not key.startswith('class_labels_classifier'	)
			    and not key.startswith('bbox_predictor'	)
			):
				_UpperCamelCase   :		Union[str, Any]			=						state_dict.pop(UpperCAmelCase_	)
				_UpperCamelCase   :		Optional[Any]			=						val
			elif "class_labels_classifier" in key or "bbox_predictor" in key:
				_UpperCamelCase   :		List[Any]			=						state_dict.pop(UpperCAmelCase_	)
				_UpperCamelCase   :		Optional[int]			=						val
			elif key.startswith('bbox_attention'	) or key.startswith('mask_head'	):
				continue
			else:
				_UpperCamelCase   :		str			=						state_dict.pop(UpperCAmelCase_	)
				_UpperCamelCase   :		Optional[int]			=						val
		else:
			if not key.startswith('class_labels_classifier'	) and not key.startswith('bbox_predictor'	):
				_UpperCamelCase   :		Union[str, Any]			=						state_dict.pop(UpperCAmelCase_	)
				_UpperCamelCase   :		Union[str, Any]			=						val

    # finally, create HuggingFace model and load state dict
	_UpperCamelCase   :		Tuple			=						DetrForSegmentation(UpperCAmelCase_	) if is_panoptic else DetrForObjectDetection(UpperCAmelCase_	)
	model.load_state_dict(UpperCAmelCase_	)
	model.eval()

	# verify our conversion on an image
	_UpperCamelCase   :		int			=						'coco_panoptic' if is_panoptic else 'coco_detection'
	_UpperCamelCase   :		Optional[Any]			=						DetrImageProcessor(format=UpperCAmelCase_	)

	_UpperCamelCase   :		List[Any]			=						processor(images=prepare_img()							,       return_tensors='pt'	)
	_UpperCamelCase   :		str			=						encoding['pixel_values']

	_UpperCamelCase   :		Optional[Any]			=						detr(UpperCAmelCase_	)
	_UpperCamelCase   :		Union[str, Any]			=						model(UpperCAmelCase_	)

	assert torch.allclose(outputs.logits							,       original_outputs['pred_logits']							,       atol=1e-3	)
	assert torch.allclose(outputs.pred_boxes							,       original_outputs['pred_boxes']							,       atol=1e-3	)
	if is_panoptic:
		assert torch.allclose(outputs.pred_masks							,       original_outputs['pred_masks']							,       atol=1e-4	)
	print('Looks ok!'	)

	if pytorch_dump_folder_path is not None:
		# 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_	)
		processor.save_pretrained(UpperCAmelCase_	)

	if push_to_hub:
		# Upload model and image processor to the hub
		logger.info('Uploading PyTorch model and image processor to the hub...'	)
		model.push_to_hub(F'''nielsr/{model_name}'''	)
		processor.push_to_hub(F'''nielsr/{model_name}'''	)


if __name__ == "__main__":
							lowerCAmelCase__								=		argparse.ArgumentParser()

							parser.add_argument(
							    """--model_name""",
							    default="""detr-resnet-50""",
							    type=str,
							    choices=["""detr-resnet-50""", """detr-resnet-101"""],
							    help="""Name of the DETR model you'd like to convert.""",
							)
							parser.add_argument(
							    """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the folder to output PyTorch model."""
							)
							parser.add_argument("""--push_to_hub""", action="""store_true""", help="""Whether to push the model to the hub or not.""")
							lowerCAmelCase__								=		parser.parse_args()
							convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)







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


lowerCAmelCase__								=		logging.get_logger(__name__)

lowerCAmelCase__								=		{
    """facebook/vit-mae-base""": """https://huggingface.co/facebook/vit-mae-base/resolve/main/config.json""",
    # See all ViT MAE models at https://huggingface.co/models?filter=vit-mae
}







class 	lowercase    (				_lowercase							):


		"""simple docstring"""




		a__					       =  "vit_mae"


		def __init__(				self	,       __snake_case=7_68	,       __snake_case=12	,       __snake_case=12	,       __snake_case=30_72	,       __snake_case="gelu"	,       __snake_case=0.0	,       __snake_case=0.0	,       __snake_case=0.0_2	,       __snake_case=1e-12	,       __snake_case=2_24	,       __snake_case=16	,       __snake_case=3	,       __snake_case=True	,       __snake_case=16	,       __snake_case=5_12	,       __snake_case=8	,       __snake_case=20_48	,       __snake_case=0.7_5	,       __snake_case=False	,       **__snake_case	,       ):
			super().__init__(**__snake_case)

			_UpperCamelCase   :		Optional[int]			=						hidden_size
			_UpperCamelCase   :		Optional[int]			=						num_hidden_layers
			_UpperCamelCase   :		Tuple			=						num_attention_heads
			_UpperCamelCase   :		List[str]			=						intermediate_size
			_UpperCamelCase   :		str			=						hidden_act
			_UpperCamelCase   :		List[str]			=						hidden_dropout_prob
			_UpperCamelCase   :		List[Any]			=						attention_probs_dropout_prob
			_UpperCamelCase   :		str			=						initializer_range
			_UpperCamelCase   :		Any			=						layer_norm_eps
			_UpperCamelCase   :		int			=						image_size
			_UpperCamelCase   :		Any			=						patch_size
			_UpperCamelCase   :		List[Any]			=						num_channels
			_UpperCamelCase   :		Union[str, Any]			=						qkv_bias
			_UpperCamelCase   :		str			=						decoder_num_attention_heads
			_UpperCamelCase   :		Union[str, Any]			=						decoder_hidden_size
			_UpperCamelCase   :		Union[str, Any]			=						decoder_num_hidden_layers
			_UpperCamelCase   :		Any			=						decoder_intermediate_size
			_UpperCamelCase   :		int			=						mask_ratio
			_UpperCamelCase   :		List[Any]			=						norm_pix_loss




