



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


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

			__lowerCAmelCase      :  str												=       DetrConfig(use_timm_backbone=__snake_case					,backbone_config=__snake_case	)

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

			return config, is_panoptic


def 			_lowercase				(  __snake_case	)						->   Optional[Any]:
			# here we list all keys to be renamed (original name on the left, our name on the right)
			__lowerCAmelCase      :  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 			_lowercase				(  __snake_case					,__snake_case					,__snake_case	)						->   Any:
			__lowerCAmelCase      :  Union[str, Any]												=       state_dict.pop(__snake_case	)
			__lowerCAmelCase      :  Optional[Any]												=       val


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


def 			_lowercase				(  )						->   List[Any]:
			__lowerCAmelCase      :  Dict												=       "http://images.cocodataset.org/val2017/000000039769.jpg"
			__lowerCAmelCase      :  Union[str, Any]												=       Image.open(requests.get(__snake_case					,stream=__snake_case	).raw	)

			return im


@torch.no_grad()
def 			_lowercase				(  __snake_case					,__snake_case=None					,__snake_case=False	)						->   List[str]:
			__lowerCAmelCase						,	__lowerCAmelCase      :  Optional[Any]												=       get_detr_config(__snake_case	)

			# load original model from torch hub
			__lowerCAmelCase      :  List[Any]												=       {
			    "detr-resnet-50": "detr_resnet50",
			    "detr-resnet-101": "detr_resnet101",
			}
			logger.info(F"""Converting model {model_name}..."""	)
			__lowerCAmelCase      :  Tuple												=       torch.hub.load("facebookresearch/detr"					,model_name_to_original_name[model_name]					,pretrained=__snake_case	).eval()
			__lowerCAmelCase      :  List[str]												=       detr.state_dict()
			# rename keys
			for src, dest in create_rename_keys(__snake_case	):
						if is_panoptic:
									__lowerCAmelCase      :  Optional[Any]												=       "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
			__lowerCAmelCase      :  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"	)
									):
												__lowerCAmelCase      :  Tuple												=       state_dict.pop(__snake_case	)
												__lowerCAmelCase      :  Optional[int]												=       val
									elif "class_labels_classifier" in key or "bbox_predictor" in key:
												__lowerCAmelCase      :  List[str]												=       state_dict.pop(__snake_case	)
												__lowerCAmelCase      :  Union[str, Any]												=       val
									elif key.startswith("bbox_attention"	) or key.startswith("mask_head"	):
												continue
									else:
												__lowerCAmelCase      :  Dict												=       state_dict.pop(__snake_case	)
												__lowerCAmelCase      :  Tuple												=       val
						else:
									if not key.startswith("class_labels_classifier"	) and not key.startswith("bbox_predictor"	):
												__lowerCAmelCase      :  str												=       state_dict.pop(__snake_case	)
												__lowerCAmelCase      :  int												=       val

    # finally, create HuggingFace model and load state dict
			__lowerCAmelCase      :  Dict												=       DetrForSegmentation(__snake_case	) if is_panoptic else DetrForObjectDetection(__snake_case	)
			model.load_state_dict(__snake_case	)
			model.eval()

			# verify our conversion on an image
			__lowerCAmelCase      :  Optional[Any]												=       "coco_panoptic" if is_panoptic else "coco_detection"
			__lowerCAmelCase      :  Dict												=       DetrImageProcessor(format=__snake_case	)

			__lowerCAmelCase      :  List[str]												=       processor(images=prepare_img()					,return_tensors="pt"	)
			__lowerCAmelCase      :  int												=       encoding["pixel_values"]

			__lowerCAmelCase      :  Union[str, Any]												=       detr(__snake_case	)
			__lowerCAmelCase      :  Optional[Any]												=       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__":
	__snake_case							:   Optional[int]      =    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.')
	__snake_case							:   List[str]      =    parser.parse_args()
	convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)




"""simple docstring"""






from typing import TYPE_CHECKING

from ..utils import _LazyModule


__snake_case							:   Optional[int]      =    {
    'config': [
        'EXTERNAL_DATA_FORMAT_SIZE_LIMIT',
        'OnnxConfig',
        'OnnxConfigWithPast',
        'OnnxSeq2SeqConfigWithPast',
        'PatchingSpec',
    ],
    'convert': ['export', 'validate_model_outputs'],
    'features': ['FeaturesManager'],
    'utils': ['ParameterFormat', 'compute_serialized_parameters_size'],
}


if TYPE_CHECKING:
	from .config import (
	    EXTERNAL_DATA_FORMAT_SIZE_LIMIT,
	    OnnxConfig,
	    OnnxConfigWithPast,
	    OnnxSeqaSeqConfigWithPast,
	    PatchingSpec,
	)
	from .convert import export, validate_model_outputs
	from .features import FeaturesManager
	from .utils import ParameterFormat, compute_serialized_parameters_size

else:
	import sys

	__snake_case							:   Any      =    _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)