



'''simple docstring'''

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	( lowerCAmelCase_    )     -> Union[str, Any]:
		# initialize config
		if "resnet-50" in model_name:
				_a	: List[Any]								=				ResNetConfig.from_pretrained('microsoft/resnet-50'    )
		elif "resnet-101" in model_name:
				_a	: int								=				ResNetConfig.from_pretrained('microsoft/resnet-101'    )
		else:
				raise ValueError('Model name should include either resnet50 or resnet101'    )

		_a	: Dict								=				DetrConfig(use_timm_backbone=lowerCAmelCase_		,     backbone_config=lowerCAmelCase_    )

		# set label attributes
		_a	: str								=				'panoptic' in model_name
		if is_panoptic:
				_a	: List[str]								=				250
		else:
				_a	: Union[str, Any]								=				91
				_a	: Tuple								=				'huggingface/label-files'
				_a	: Union[str, Any]								=				'coco-detection-id2label.json'
				_a	: Any								=				json.load(open(hf_hub_download(lowerCAmelCase_		,     lowerCAmelCase_		,     repo_type='dataset'    )		,     'r'    )    )
				_a	: Tuple								=				{int(lowerCAmelCase_    ): v for k, v in idalabel.items()}
				_a	: List[Any]								=				idalabel
				_a	: Any								=				{v: k for k, v in idalabel.items()}

		return config, is_panoptic



def 	__lowerCamelCase	( lowerCAmelCase_    )     -> Union[str, Any]:
		# here we list all keys to be renamed (original name on the left, our name on the right)
		_a	: Union[str, Any]								=				[]

		# 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	( lowerCAmelCase_		,     lowerCAmelCase_		,     lowerCAmelCase_    )     -> int:
		_a	: List[Any]								=				state_dict.pop(lowerCAmelCase_    )
		_a	: str								=				val



def 	__lowerCamelCase	( lowerCAmelCase_		,     lowerCAmelCase_=False    )     -> Optional[Any]:
		_a	: Optional[Any]								=				''
		if is_panoptic:
				_a	: Tuple								=				'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)
				_a	: List[str]								=				state_dict.pop(f"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight"""    )
				_a	: Union[str, 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
				_a	: Optional[Any]								=				in_proj_weight[:256, :]
				_a	: List[str]								=				in_proj_bias[:256]
				_a	: Any								=				in_proj_weight[256:512, :]
				_a	: Any								=				in_proj_bias[256:512]
				_a	: Any								=				in_proj_weight[-256:, :]
				_a	: Dict								=				in_proj_bias[-256:]
		# 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
				_a	: Optional[int]								=				state_dict.pop(f"""{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight"""    )
				_a	: str								=				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
				_a	: Dict								=				in_proj_weight[:256, :]
				_a	: Tuple								=				in_proj_bias[:256]
				_a	: Any								=				in_proj_weight[256:512, :]
				_a	: List[str]								=				in_proj_bias[256:512]
				_a	: Union[str, Any]								=				in_proj_weight[-256:, :]
				_a	: Dict								=				in_proj_bias[-256:]
				# read in weights + bias of input projection layer of cross-attention
				_a	: List[Any]								=				state_dict.pop(
				    f"""{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight"""    )
				_a	: 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
				_a	: int								=				in_proj_weight_cross_attn[:256, :]
				_a	: Optional[Any]								=				in_proj_bias_cross_attn[:256]
				_a	: str								=				in_proj_weight_cross_attn[256:512, :]
				_a	: Optional[Any]								=				in_proj_bias_cross_attn[256:512]
				_a	: str								=				in_proj_weight_cross_attn[-256:, :]
				_a	: int								=				in_proj_bias_cross_attn[-256:]



def 	__lowerCamelCase	( )     -> Tuple:
		_a	: Optional[int]								=				'http://images.cocodataset.org/val2017/000000039769.jpg'
		_a	: Optional[int]								=				Image.open(requests.get(lowerCAmelCase_		,     stream=lowerCAmelCase_    ).raw    )

		return im



@torch.no_grad()
def 	__lowerCamelCase	( lowerCAmelCase_		,     lowerCAmelCase_=None		,     lowerCAmelCase_=False    )     -> int:
		_a			,   _a	: List[str]								=				get_detr_config(lowerCAmelCase_    )

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

    # finally, create HuggingFace model and load state dict
		_a	: int								=				DetrForSegmentation(lowerCAmelCase_    ) if is_panoptic else DetrForObjectDetection(lowerCAmelCase_    )
		model.load_state_dict(lowerCAmelCase_    )
		model.eval()

		# verify our conversion on an image
		_a	: int								=				'coco_panoptic' if is_panoptic else 'coco_detection'
		_a	: List[Any]								=				DetrImageProcessor(format=lowerCAmelCase_    )

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

		_a	: List[Any]								=				detr(lowerCAmelCase_    )
		_a	: Optional[int]								=				model(lowerCAmelCase_    )

		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(lowerCAmelCase_    ).mkdir(exist_ok=lowerCAmelCase_    )
				model.save_pretrained(lowerCAmelCase_    )
				processor.save_pretrained(lowerCAmelCase_    )

		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)









'''simple docstring'''

import warnings

from ...utils import logging
from .image_processing_deit import DeiTImageProcessor


__lowerCAmelCase		     =  logging.get_logger(__name__)

class       __magic_name__		(       _UpperCamelCase  ):







							def __init__(				self   :			Tuple      ,*_UpperCAmelCase   :			str      ,**_UpperCAmelCase   :			int						):
									warnings.warn(
									    'The class DeiTFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'
									    ' use DeiTImageProcessor instead.'      ,_UpperCAmelCase      ,)
									super().__init__(*_UpperCAmelCase      ,**_UpperCAmelCase						)




