



"""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()
lowercase  							=					logging.get_logger(__name__)




def 	UpperCAmelCase     (		A	:						Any       ):

		'''simple docstring'''


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

		_UpperCAmelCase							 =		DetrConfig(use_timm_backbone=_lowerCAmelCase     ,				backbone_config=_lowerCAmelCase       )

		# set label attributes
		_UpperCAmelCase							 =		'panoptic' in model_name
		if is_panoptic:
				_UpperCAmelCase							 =		250
		else:
				_UpperCAmelCase							 =		91
				_UpperCAmelCase							 =		'huggingface/label-files'
				_UpperCAmelCase							 =		'coco-detection-id2label.json'
				_UpperCAmelCase							 =		json.load(open(hf_hub_download(_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()}

		return config, is_panoptic




def 	UpperCAmelCase     (		A	:						List[Any]       ):

		'''simple docstring'''


		_UpperCAmelCase							 =		[]

		# 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 	UpperCAmelCase     (		A	:						Optional[Any]     ,				A	:						List[str]     ,				A	:						int       ):

		'''simple docstring'''


		_UpperCAmelCase							 =		state_dict.pop(_lowerCAmelCase       )
		_UpperCAmelCase							 =		val




def 	UpperCAmelCase     (		A	:						Any     ,				A	:						Union[str, Any]=False       ):

		'''simple docstring'''


		_UpperCAmelCase							 =		''
		if is_panoptic:
				_UpperCAmelCase							 =		'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							 =		state_dict.pop(f'{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight'       )
				_UpperCAmelCase							 =		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							 =		in_proj_weight[:256, :]
				_UpperCAmelCase							 =		in_proj_bias[:256]
				_UpperCAmelCase							 =		in_proj_weight[256:512, :]
				_UpperCAmelCase							 =		in_proj_bias[256:512]
				_UpperCAmelCase							 =		in_proj_weight[-256:, :]
				_UpperCAmelCase							 =		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
				_UpperCAmelCase							 =		state_dict.pop(f'{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight'       )
				_UpperCAmelCase							 =		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							 =		in_proj_weight[:256, :]
				_UpperCAmelCase							 =		in_proj_bias[:256]
				_UpperCAmelCase							 =		in_proj_weight[256:512, :]
				_UpperCAmelCase							 =		in_proj_bias[256:512]
				_UpperCAmelCase							 =		in_proj_weight[-256:, :]
				_UpperCAmelCase							 =		in_proj_bias[-256:]
				# read in weights + bias of input projection layer of cross-attention
				_UpperCAmelCase							 =		state_dict.pop(
				    f'{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight'       )
				_UpperCAmelCase							 =		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							 =		in_proj_weight_cross_attn[:256, :]
				_UpperCAmelCase							 =		in_proj_bias_cross_attn[:256]
				_UpperCAmelCase							 =		in_proj_weight_cross_attn[256:512, :]
				_UpperCAmelCase							 =		in_proj_bias_cross_attn[256:512]
				_UpperCAmelCase							 =		in_proj_weight_cross_attn[-256:, :]
				_UpperCAmelCase							 =		in_proj_bias_cross_attn[-256:]




def 	UpperCAmelCase     (		):

		'''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 	UpperCAmelCase     (		A	:						str     ,				A	:						Dict=None     ,				A	:						Optional[int]=False       ):

		'''simple docstring'''


		_UpperCAmelCase ,					_UpperCAmelCase							 =		get_detr_config(_lowerCAmelCase       )

		# load original model from torch hub
		_UpperCAmelCase							 =		{
		    'detr-resnet-50': 'detr_resnet50',
		    'detr-resnet-101': 'detr_resnet101',
		}
		logger.info(f'Converting model {model_name}...'       )
		_UpperCAmelCase							 =		torch.hub.load('facebookresearch/detr'     ,				model_name_to_original_name[model_name]     ,				pretrained=_lowerCAmelCase       ).eval()
		_UpperCAmelCase							 =		detr.state_dict()
		# rename keys
		for src, dest in create_rename_keys(_lowerCAmelCase       ):
				if is_panoptic:
						_UpperCAmelCase							 =		'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
		_UpperCAmelCase							 =		'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							 =		state_dict.pop(_lowerCAmelCase       )
								_UpperCAmelCase							 =		val
						elif "class_labels_classifier" in key or "bbox_predictor" in key:
								_UpperCAmelCase							 =		state_dict.pop(_lowerCAmelCase       )
								_UpperCAmelCase							 =		val
						elif key.startswith('bbox_attention'       ) or key.startswith('mask_head'       ):
								continue
						else:
								_UpperCAmelCase							 =		state_dict.pop(_lowerCAmelCase       )
								_UpperCAmelCase							 =		val
				else:
						if not key.startswith('class_labels_classifier'       ) and not key.startswith('bbox_predictor'       ):
								_UpperCAmelCase							 =		state_dict.pop(_lowerCAmelCase       )
								_UpperCAmelCase							 =		val

    # finally, create HuggingFace model and load state dict
		_UpperCAmelCase							 =		DetrForSegmentation(_lowerCAmelCase       ) if is_panoptic else DetrForObjectDetection(_lowerCAmelCase       )
		model.load_state_dict(_lowerCAmelCase       )
		model.eval()

		# verify our conversion on an image
		_UpperCAmelCase							 =		'coco_panoptic' if is_panoptic else 'coco_detection'
		_UpperCAmelCase							 =		DetrImageProcessor(format=_lowerCAmelCase       )

		_UpperCAmelCase							 =		processor(images=prepare_img()     ,				return_tensors='pt'       )
		_UpperCAmelCase							 =		encoding['pixel_values']

		_UpperCAmelCase							 =		detr(_lowerCAmelCase       )
		_UpperCAmelCase							 =		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__":
					lowercase  							=					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.''')
					lowercase  							=					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


lowercase  							=					logging.get_logger(__name__)

lowercase  							=					{
    '''microsoft/beit-base-patch16-224-pt22k''': (
        '''https://huggingface.co/microsoft/beit-base-patch16-224-pt22k/resolve/main/config.json'''
    ),
    # See all BEiT models at https://huggingface.co/models?filter=beit
}




class 			lowercase__			(      A    ):
							'''simple docstring'''







							_UpperCAmelCase      				=					'''beit'''






							def __init__(  self	,       snake_case=8192	,       snake_case=768	,       snake_case=12	,       snake_case=12	,       snake_case=3072	,       snake_case="gelu"	,       snake_case=0.0	,       snake_case=0.0	,       snake_case=0.02	,       snake_case=1E-12	,       snake_case=224	,       snake_case=16	,       snake_case=3	,       snake_case=False	,       snake_case=False	,       snake_case=False	,       snake_case=False	,       snake_case=0.1	,       snake_case=0.1	,       snake_case=True	,       snake_case=[3, 5, 7, 11]	,       snake_case=[1, 2, 3, 6]	,       snake_case=True	,       snake_case=0.4	,       snake_case=256	,       snake_case=1	,       snake_case=False	,       snake_case=255	,       **snake_case	,       )			->							str:
									super().__init__(**snake_case						)

									_UpperCAmelCase							 =		vocab_size
									_UpperCAmelCase							 =		hidden_size
									_UpperCAmelCase							 =		num_hidden_layers
									_UpperCAmelCase							 =		num_attention_heads
									_UpperCAmelCase							 =		intermediate_size
									_UpperCAmelCase							 =		hidden_act
									_UpperCAmelCase							 =		hidden_dropout_prob
									_UpperCAmelCase							 =		attention_probs_dropout_prob
									_UpperCAmelCase							 =		initializer_range
									_UpperCAmelCase							 =		layer_norm_eps

									_UpperCAmelCase							 =		image_size
									_UpperCAmelCase							 =		patch_size
									_UpperCAmelCase							 =		num_channels
									_UpperCAmelCase							 =		use_mask_token
									_UpperCAmelCase							 =		use_absolute_position_embeddings
									_UpperCAmelCase							 =		use_relative_position_bias
									_UpperCAmelCase							 =		use_shared_relative_position_bias
									_UpperCAmelCase							 =		layer_scale_init_value
									_UpperCAmelCase							 =		drop_path_rate
									_UpperCAmelCase							 =		use_mean_pooling
									# decode head attributes (semantic segmentation)
									_UpperCAmelCase							 =		out_indices
									_UpperCAmelCase							 =		pool_scales
									# auxiliary head attributes (semantic segmentation)
									_UpperCAmelCase							 =		use_auxiliary_head
									_UpperCAmelCase							 =		auxiliary_loss_weight
									_UpperCAmelCase							 =		auxiliary_channels
									_UpperCAmelCase							 =		auxiliary_num_convs
									_UpperCAmelCase							 =		auxiliary_concat_input
									_UpperCAmelCase							 =		semantic_loss_ignore_index







class 			lowercase__			(      A    ):
							'''simple docstring'''







							_UpperCAmelCase      				=					version.parse('''1.11'''    )






							@property
							def        lowerCamelCase_   (  self						)			->							Mapping[str, Mapping[int, str]]:
									return OrderedDict(
									    [
									        ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
									    ]						)






							@property
							def        lowerCamelCase_   (  self						)			->							float:
									return 1E-4


