






'''simple docstring'''




import argparse
import json
from collections import OrderedDict
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
from transformers.utils import logging


logging.set_verbosity_info()
UpperCamelCase__	:     Any    =			logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
UpperCamelCase__	:     List[str]    =			[]
for i in range(6):
						# 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'''),
    ]
)


def     lowerCAmelCase_       (     _lowerCamelCase:       Dict	,				_lowerCamelCase:       int	,				_lowerCamelCase:       List[Any] ):
		__SCREAMING_SNAKE_CASE	:		List[str]   	=		state_dict.pop(_lowerCamelCase )
		__SCREAMING_SNAKE_CASE	:		Dict   	=		val


def     lowerCAmelCase_       (     _lowerCamelCase:       Any ):
		__SCREAMING_SNAKE_CASE	:		Any   	=		OrderedDict()
		for key, value in state_dict.items():
				if "backbone.0.body" in key:
						__SCREAMING_SNAKE_CASE	:		int   	=		key.replace("""backbone.0.body"""	,				"""backbone.conv_encoder.model""" )
						__SCREAMING_SNAKE_CASE	:		List[str]   	=		value
				else:
						__SCREAMING_SNAKE_CASE	:		Dict   	=		value

		return new_state_dict


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


def     lowerCAmelCase_       (     ):
		__SCREAMING_SNAKE_CASE	:		str   	=		"""http://images.cocodataset.org/val2017/000000039769.jpg"""
		__SCREAMING_SNAKE_CASE	:		Any   	=		Image.open(requests.get(_lowerCamelCase	,				stream=_lowerCamelCase ).raw )

		return im


@torch.no_grad()
def     lowerCAmelCase_       (     _lowerCamelCase:       Optional[int]	,				_lowerCamelCase:       Optional[Any] ):
		__SCREAMING_SNAKE_CASE	:		int   	=		DetrConfig()
		# set backbone and dilation attributes
		if "resnet101" in model_name:
				__SCREAMING_SNAKE_CASE	:		Any   	=		"""resnet101"""
		if "dc5" in model_name:
				__SCREAMING_SNAKE_CASE	:		Union[str, Any]   	=		True
		__SCREAMING_SNAKE_CASE	:		Any   	=		"""panoptic""" in model_name
		if is_panoptic:
				__SCREAMING_SNAKE_CASE	:		Dict   	=		2_50
		else:
				__SCREAMING_SNAKE_CASE	:		Any   	=		91
				__SCREAMING_SNAKE_CASE	:		Dict   	=		"""huggingface/label-files"""
				__SCREAMING_SNAKE_CASE	:		str   	=		"""coco-detection-id2label.json"""
				__SCREAMING_SNAKE_CASE	:		Union[str, Any]   	=		json.load(open(hf_hub_download(_lowerCamelCase	,				_lowerCamelCase	,				repo_type="""dataset""" )	,				"""r""" ) )
				__SCREAMING_SNAKE_CASE	:		Optional[int]   	=		{int(_lowerCamelCase ): v for k, v in idalabel.items()}
				__SCREAMING_SNAKE_CASE	:		Dict   	=		idalabel
				__SCREAMING_SNAKE_CASE	:		Any   	=		{v: k for k, v in idalabel.items()}

		# load image processor
		__SCREAMING_SNAKE_CASE	:		Optional[int]   	=		"""coco_panoptic""" if is_panoptic else """coco_detection"""
		__SCREAMING_SNAKE_CASE	:		Dict   	=		DetrImageProcessor(format=_lowerCamelCase )

		# prepare image
		__SCREAMING_SNAKE_CASE	:		Any   	=		prepare_img()
		__SCREAMING_SNAKE_CASE	:		int   	=		image_processor(images=_lowerCamelCase	,				return_tensors="""pt""" )
		__SCREAMING_SNAKE_CASE	:		List[Any]   	=		encoding["""pixel_values"""]

		logger.info(F"Converting model {model_name}..." )

		# load original model from torch hub
		__SCREAMING_SNAKE_CASE	:		Optional[int]   	=		torch.hub.load("""facebookresearch/detr"""	,				_lowerCamelCase	,				pretrained=_lowerCamelCase ).eval()
		__SCREAMING_SNAKE_CASE	:		Union[str, Any]   	=		detr.state_dict()
		# rename keys
		for src, dest in rename_keys:
				if is_panoptic:
						__SCREAMING_SNAKE_CASE	:		List[str]   	=		"""detr.""" + src
				rename_key(_lowerCamelCase	,				_lowerCamelCase	,				_lowerCamelCase )
		__SCREAMING_SNAKE_CASE	:		List[str]   	=		rename_backbone_keys(_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
		__SCREAMING_SNAKE_CASE	:		Optional[int]   	=		"""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""" )
						):
								__SCREAMING_SNAKE_CASE	:		str   	=		state_dict.pop(_lowerCamelCase )
								__SCREAMING_SNAKE_CASE	:		Union[str, Any]   	=		val
						elif "class_labels_classifier" in key or "bbox_predictor" in key:
								__SCREAMING_SNAKE_CASE	:		List[str]   	=		state_dict.pop(_lowerCamelCase )
								__SCREAMING_SNAKE_CASE	:		List[Any]   	=		val
						elif key.startswith("""bbox_attention""" ) or key.startswith("""mask_head""" ):
								continue
						else:
								__SCREAMING_SNAKE_CASE	:		Union[str, Any]   	=		state_dict.pop(_lowerCamelCase )
								__SCREAMING_SNAKE_CASE	:		Tuple   	=		val
				else:
						if not key.startswith("""class_labels_classifier""" ) and not key.startswith("""bbox_predictor""" ):
								__SCREAMING_SNAKE_CASE	:		Optional[int]   	=		state_dict.pop(_lowerCamelCase )
								__SCREAMING_SNAKE_CASE	:		Tuple   	=		val
    # finally, create HuggingFace model and load state dict
		__SCREAMING_SNAKE_CASE	:		Optional[Any]   	=		DetrForSegmentation(_lowerCamelCase ) if is_panoptic else DetrForObjectDetection(_lowerCamelCase )
		model.load_state_dict(_lowerCamelCase )
		model.eval()
		# verify our conversion
		__SCREAMING_SNAKE_CASE	:		Union[str, Any]   	=		detr(_lowerCamelCase )
		__SCREAMING_SNAKE_CASE	:		Tuple   	=		model(_lowerCamelCase )
		assert torch.allclose(outputs.logits	,				original_outputs["""pred_logits"""]	,				atol=1E-4 )
		assert torch.allclose(outputs.pred_boxes	,				original_outputs["""pred_boxes"""]	,				atol=1E-4 )
		if is_panoptic:
				assert torch.allclose(outputs.pred_masks	,				original_outputs["""pred_masks"""]	,				atol=1E-4 )

		# 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 )
		image_processor.save_pretrained(_lowerCamelCase )


if __name__ == "__main__":
						UpperCamelCase__	:     List[str]    =			argparse.ArgumentParser()

						parser.add_argument(
						    '''--model_name''', default='''detr_resnet50''', type=str, 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.'''
						)
						UpperCamelCase__	:     List[Any]    =			parser.parse_args()
						convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)




'''simple docstring'''




from __future__ import annotations


def     lowerCAmelCase_       (     _lowerCamelCase:       list[int | float] ):
		if len(_lowerCamelCase ) == 0:
				raise ValueError("""find_min() arg is an empty sequence""" )
		__SCREAMING_SNAKE_CASE	:		List[str]   	=		nums[0]
		for num in nums:
				__SCREAMING_SNAKE_CASE	:		int   	=		min(_lowerCamelCase	,				_lowerCamelCase )
		return min_num


if __name__ == "__main__":
						import doctest

						doctest.testmod(verbose=True)