

'''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_							=					logging.get_logger(__name__)






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

						lowercase   :       Union[str, Any]								=DetrConfig(use_timm_backbone=__magic_name__      ,   backbone_config=__magic_name__	)

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

						return config, is_panoptic






def 							_lowerCAmelCase (      __magic_name__	:    Tuple	)							->     str:
						# here we list all keys to be renamed (original name on the left, our name on the right)
						lowercase   :       Dict								=[]

						# 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 (      __magic_name__	:    Any      ,   __magic_name__	:    int      ,   __magic_name__	:    List[str]	)							->     List[str]:
						lowercase   :       List[str]								=state_dict.pop(__magic_name__	)
						lowercase   :       Tuple								=val






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






def 							_lowerCAmelCase (      )							->     Optional[int]:
						lowercase   :       List[Any]								='''http://images.cocodataset.org/val2017/000000039769.jpg'''
						lowercase   :       List[Any]								=Image.open(requests.get(__magic_name__      ,   stream=__magic_name__	).raw	)

						return im






@torch.no_grad()
def 							_lowerCAmelCase (      __magic_name__	:    Union[str, Any]      ,   __magic_name__	:    str=None      ,   __magic_name__	:    Optional[Any]=False	)							->     Dict:
						lowercase		,  lowercase   :       Tuple								=get_detr_config(__magic_name__	)

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

    # finally, create HuggingFace model and load state dict
						lowercase   :       str								=DetrForSegmentation(__magic_name__	) if is_panoptic else DetrForObjectDetection(__magic_name__	)
						model.load_state_dict(__magic_name__	)
						model.eval()

						# verify our conversion on an image
						lowercase   :       int								='''coco_panoptic''' if is_panoptic else '''coco_detection'''
						lowercase   :       Any								=DetrImageProcessor(format=__magic_name__	)

						lowercase   :       List[Any]								=processor(images=prepare_img()      ,   return_tensors='''pt'''	)
						lowercase   :       Optional[int]								=encoding['''pixel_values''']

						lowercase   :       Optional[int]								=detr(__magic_name__	)
						lowercase   :       int								=model(__magic_name__	)

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

						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_							=					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_							=					parser.parse_args()
							convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)








'''simple docstring'''





import math






def 							_lowerCAmelCase (      __magic_name__	:    float      ,   __magic_name__	:    float	)							->     float:
						if (
						    not isinstance(__magic_name__      ,   (int, float)	)
						    or power_factor < -1
						    or power_factor > 1
						):
												raise ValueError('''power_factor must be a valid float value between -1 and 1.'''	)
						return apparent_power * power_factor






def 							_lowerCAmelCase (      __magic_name__	:    float      ,   __magic_name__	:    float	)							->     float:
						if (
						    not isinstance(__magic_name__      ,   (int, float)	)
						    or power_factor < -1
						    or power_factor > 1
						):
												raise ValueError('''power_factor must be a valid float value between -1 and 1.'''	)
						return apparent_power * math.sqrt(1 - power_factor**2	)


if __name__ == "__main__":
							import doctest

							doctest.testmod()





