


"""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()
UpperCamelCase__				:int			   =	logging.get_logger(__name__)





def 				A_       ( snake_case__		)					->  Any:
			# initialize config
			if "resnet-50" in model_name:
						_UpperCamelCase					:Any					     =	ResNetConfig.from_pretrained('''microsoft/resnet-50'''		)
			elif "resnet-101" in model_name:
						_UpperCamelCase					:int					     =	ResNetConfig.from_pretrained('''microsoft/resnet-101'''		)
			else:
						raise ValueError('''Model name should include either resnet50 or resnet101'''		)

			_UpperCamelCase					:int					     =	DetrConfig(use_timm_backbone=snake_case__       ,					backbone_config=snake_case__		)

			# set label attributes
			_UpperCamelCase					:Optional[int]					     =	'''panoptic''' in model_name
			if is_panoptic:
						_UpperCamelCase					:Dict					     =	2_50
			else:
						_UpperCamelCase					:Optional[Any]					     =	91
						_UpperCamelCase					:Union[str, Any]					     =	'''huggingface/label-files'''
						_UpperCamelCase					:Union[str, Any]					     =	'''coco-detection-id2label.json'''
						_UpperCamelCase					:str					     =	json.load(open(hf_hub_download(snake_case__       ,					snake_case__       ,					repo_type='''dataset'''		)       ,					'''r'''		)		)
						_UpperCamelCase					:List[Any]					     =	{int(snake_case__		): v for k, v in idalabel.items()}
						_UpperCamelCase					:Optional[Any]					     =	idalabel
						_UpperCamelCase					:Optional[int]					     =	{v: k for k, v in idalabel.items()}

			return config, is_panoptic





def 				A_       ( snake_case__		)					->  List[Any]:
			# here we list all keys to be renamed (original name on the left, our name on the right)
			_UpperCamelCase					:Optional[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 				A_       ( snake_case__       ,					snake_case__       ,					snake_case__		)					->  Optional[Any]:
			_UpperCamelCase					:int					     =	state_dict.pop(snake_case__		)
			_UpperCamelCase					:Dict					     =	val





def 				A_       ( snake_case__       ,					snake_case__=False		)					->  int:
			_UpperCamelCase					:List[Any]					     =	''''''
			if is_panoptic:
						_UpperCamelCase					:List[str]					     =	'''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					:List[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					:Any					     =	in_proj_weight[:2_56, :]
						_UpperCamelCase					:Union[str, Any]					     =	in_proj_bias[:2_56]
						_UpperCamelCase					:List[Any]					     =	in_proj_weight[2_56:5_12, :]
						_UpperCamelCase					:int					     =	in_proj_bias[2_56:5_12]
						_UpperCamelCase					:Optional[int]					     =	in_proj_weight[-2_56:, :]
						_UpperCamelCase					:int					     =	in_proj_bias[-2_56:]
			# 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[Any]					     =	state_dict.pop(f"{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight"		)
						_UpperCamelCase					:int					     =	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					:int					     =	in_proj_weight[:2_56, :]
						_UpperCamelCase					:Union[str, Any]					     =	in_proj_bias[:2_56]
						_UpperCamelCase					:Dict					     =	in_proj_weight[2_56:5_12, :]
						_UpperCamelCase					:List[Any]					     =	in_proj_bias[2_56:5_12]
						_UpperCamelCase					:Any					     =	in_proj_weight[-2_56:, :]
						_UpperCamelCase					:Dict					     =	in_proj_bias[-2_56:]
						# read in weights + bias of input projection layer of cross-attention
						_UpperCamelCase					:Dict					     =	state_dict.pop(
						    f"{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight"		)
						_UpperCamelCase					:Optional[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					:Optional[int]					     =	in_proj_weight_cross_attn[:2_56, :]
						_UpperCamelCase					:Union[str, Any]					     =	in_proj_bias_cross_attn[:2_56]
						_UpperCamelCase					:int					     =	in_proj_weight_cross_attn[2_56:5_12, :]
						_UpperCamelCase					:List[Any]					     =	in_proj_bias_cross_attn[2_56:5_12]
						_UpperCamelCase					:int					     =	in_proj_weight_cross_attn[-2_56:, :]
						_UpperCamelCase					:List[str]					     =	in_proj_bias_cross_attn[-2_56:]





def 				A_       ( )					->  List[str]:
			_UpperCamelCase					:Union[str, Any]					     =	'''http://images.cocodataset.org/val2017/000000039769.jpg'''
			_UpperCamelCase					:Optional[Any]					     =	Image.open(requests.get(snake_case__       ,					stream=snake_case__		).raw		)

			return im





@torch.no_grad()
def 				A_       ( snake_case__       ,					snake_case__=None       ,					snake_case__=False		)					->  Tuple:
			_UpperCamelCase     ,      _UpperCamelCase					:Dict					     =	get_detr_config(snake_case__		)

			# load original model from torch hub
			_UpperCamelCase					:Optional[int]					     =	{
			    '''detr-resnet-50''': '''detr_resnet50''',
			    '''detr-resnet-101''': '''detr_resnet101''',
			}
			logger.info(f"Converting model {model_name}..."		)
			_UpperCamelCase					:Tuple					     =	torch.hub.load('''facebookresearch/detr'''       ,					model_name_to_original_name[model_name]       ,					pretrained=snake_case__		).eval()
			_UpperCamelCase					:Optional[int]					     =	detr.state_dict()
			# rename keys
			for src, dest in create_rename_keys(snake_case__		):
						if is_panoptic:
									_UpperCamelCase					:List[str]					     =	'''detr.''' + src
						rename_key(snake_case__       ,					snake_case__       ,					snake_case__		)
			# query, key and value matrices need special treatment
			read_in_q_k_v(snake_case__       ,					is_panoptic=snake_case__		)
			# important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
			_UpperCamelCase					:Tuple					     =	'''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					:int					     =	state_dict.pop(snake_case__		)
												_UpperCamelCase					:Optional[Any]					     =	val
									elif "class_labels_classifier" in key or "bbox_predictor" in key:
												_UpperCamelCase					:Any					     =	state_dict.pop(snake_case__		)
												_UpperCamelCase					:Any					     =	val
									elif key.startswith('''bbox_attention'''		) or key.startswith('''mask_head'''		):
												continue
									else:
												_UpperCamelCase					:List[str]					     =	state_dict.pop(snake_case__		)
												_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(snake_case__		)
												_UpperCamelCase					:Optional[Any]					     =	val

    # finally, create HuggingFace model and load state dict
			_UpperCamelCase					:Union[str, Any]					     =	DetrForSegmentation(snake_case__		) if is_panoptic else DetrForObjectDetection(snake_case__		)
			model.load_state_dict(snake_case__		)
			model.eval()

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

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

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

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

			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__":
					UpperCamelCase__				:List[str]			   =	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.""")
					UpperCamelCase__				:List[Any]			   =	parser.parse_args()
					convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)





"""simple docstring"""






from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


UpperCamelCase__				:Union[str, Any]			   =	logging.get_logger(__name__)

UpperCamelCase__				:Union[str, Any]			   =	{
    """google/mobilenet_v2_1.4_224""": """https://huggingface.co/google/mobilenet_v2_1.4_224/resolve/main/config.json""",
    """google/mobilenet_v2_1.0_224""": """https://huggingface.co/google/mobilenet_v2_1.0_224/resolve/main/config.json""",
    """google/mobilenet_v2_0.75_160""": """https://huggingface.co/google/mobilenet_v2_0.75_160/resolve/main/config.json""",
    """google/mobilenet_v2_0.35_96""": """https://huggingface.co/google/mobilenet_v2_0.35_96/resolve/main/config.json""",
    # See all MobileNetV2 models at https://huggingface.co/models?filter=mobilenet_v2
}






class      A(  lowerCamelCase__		):

		"""simple docstring"""





		A    =							"mobilenet_v2"
		def __init__( self					,				SCREAMING_SNAKE_CASE__=3					,				SCREAMING_SNAKE_CASE__=2_24					,				SCREAMING_SNAKE_CASE__=1.0					,				SCREAMING_SNAKE_CASE__=8					,				SCREAMING_SNAKE_CASE__=8					,				SCREAMING_SNAKE_CASE__=6					,				SCREAMING_SNAKE_CASE__=32					,				SCREAMING_SNAKE_CASE__=True					,				SCREAMING_SNAKE_CASE__=True					,				SCREAMING_SNAKE_CASE__="relu6"					,				SCREAMING_SNAKE_CASE__=True					,				SCREAMING_SNAKE_CASE__=0.8					,				SCREAMING_SNAKE_CASE__=0.0_2					,				SCREAMING_SNAKE_CASE__=0.0_0_1					,				SCREAMING_SNAKE_CASE__=2_55					,				**SCREAMING_SNAKE_CASE__					,				)      ->	int:

					"""simple docstring"""



					super().__init__(**SCREAMING_SNAKE_CASE__   )

					if depth_multiplier <= 0:
								raise ValueError('''depth_multiplier must be greater than zero.'''   )

					_UpperCamelCase					:Optional[int]					     =	num_channels
					_UpperCamelCase					:Tuple					     =	image_size
					_UpperCamelCase					:Union[str, Any]					     =	depth_multiplier
					_UpperCamelCase					:Any					     =	depth_divisible_by
					_UpperCamelCase					:Optional[int]					     =	min_depth
					_UpperCamelCase					:Union[str, Any]					     =	expand_ratio
					_UpperCamelCase					:str					     =	output_stride
					_UpperCamelCase					:Tuple					     =	first_layer_is_expansion
					_UpperCamelCase					:Any					     =	finegrained_output
					_UpperCamelCase					:Union[str, Any]					     =	hidden_act
					_UpperCamelCase					:Any					     =	tf_padding
					_UpperCamelCase					:str					     =	classifier_dropout_prob
					_UpperCamelCase					:Optional[Any]					     =	initializer_range
					_UpperCamelCase					:int					     =	layer_norm_eps
					_UpperCamelCase					:Union[str, Any]					     =	semantic_loss_ignore_index






class      A(  lowerCamelCase__		):

		"""simple docstring"""





		A    =							version.parse("1.11"		)
		@property
		def   _UpperCamelCase( self   )      ->	Mapping[str, Mapping[int, str]]:

					"""simple docstring"""



					return OrderedDict([('''pixel_values''', {0: '''batch'''})]   )
		@property
		def   _UpperCamelCase( self   )      ->	Mapping[str, Mapping[int, str]]:

					"""simple docstring"""



					if self.task == "image-classification":
								return OrderedDict([('''logits''', {0: '''batch'''})]   )
					else:
								return OrderedDict([('''last_hidden_state''', {0: '''batch'''}), ('''pooler_output''', {0: '''batch'''})]   )





		@property
		def   _UpperCamelCase( self   )      ->	float:

					"""simple docstring"""



					return 1E-4

