


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

# here we list all keys to be renamed (original name on the left, our name on the right)
SCREAMING_SNAKE_CASE					:       List[Any]							   =							[]
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 						lowercase				(	_snake_case		: List[str]	,  _snake_case		: str	,  _snake_case		: List[Any]	)	->str:
	"""simple docstring"""





	__snake_case      :		List[Any]         =							state_dict.pop(_snake_case	)
	__snake_case      :		Dict         =							val






def 						lowercase				(	_snake_case		: int	)	->int:
	"""simple docstring"""





	__snake_case      :		List[Any]         =							OrderedDict()
	for key, value in state_dict.items():
		if "backbone.0.body" in key:
			__snake_case      :		Union[str, Any]         =							key.replace('''backbone.0.body'''	,  '''backbone.conv_encoder.model'''	)
			__snake_case      :		Optional[int]         =							value
		else:
			__snake_case      :		Optional[Any]         =							value

	return new_state_dict






def 						lowercase				(	_snake_case		: int	,  _snake_case		: Dict=False	)	->int:
	"""simple docstring"""





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






def 						lowercase				(	)	->List[str]:
	"""simple docstring"""





	__snake_case      :		Union[str, Any]         =							'''http://images.cocodataset.org/val2017/000000039769.jpg'''
	__snake_case      :		int         =							Image.open(requests.get(_snake_case	,  stream=_snake_case	).raw	)

	return im






@torch.no_grad()
def 						lowercase				(	_snake_case		: int	,  _snake_case		: Union[str, Any]	)	->Dict:
	"""simple docstring"""





	__snake_case      :		List[str]         =							DetrConfig()
	# set backbone and dilation attributes
	if "resnet101" in model_name:
		__snake_case      :		Union[str, Any]         =							'''resnet101'''
	if "dc5" in model_name:
		__snake_case      :		str         =							True
	__snake_case      :		Tuple         =							'''panoptic''' in model_name
	if is_panoptic:
		__snake_case      :		Any         =							250
	else:
		__snake_case      :		Any         =							91
		__snake_case      :		Tuple         =							'''huggingface/label-files'''
		__snake_case      :		Dict         =							'''coco-detection-id2label.json'''
		__snake_case      :		Optional[int]         =							json.load(open(hf_hub_download(_snake_case	,  _snake_case	,  repo_type='''dataset'''	)	,  '''r'''	)	)
		__snake_case      :		List[Any]         =							{int(_snake_case	): v for k, v in idalabel.items()}
		__snake_case      :		int         =							idalabel
		__snake_case      :		str         =							{v: k for k, v in idalabel.items()}

	# load image processor
	__snake_case      :		Any         =							'''coco_panoptic''' if is_panoptic else '''coco_detection'''
	__snake_case      :		List[str]         =							DetrImageProcessor(format=_snake_case	)

	# prepare image
	__snake_case      :		Optional[int]         =							prepare_img()
	__snake_case      :		Union[str, Any]         =							image_processor(images=_snake_case	,  return_tensors='''pt'''	)
	__snake_case      :		Any         =							encoding['''pixel_values''']

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

	# load original model from torch hub
	__snake_case      :		List[Any]         =							torch.hub.load('''facebookresearch/detr'''	,  _snake_case	,  pretrained=_snake_case	).eval()
	__snake_case      :		Optional[int]         =							detr.state_dict()
	# rename keys
	for src, dest in rename_keys:
		if is_panoptic:
			__snake_case      :		List[str]         =							'''detr.''' + src
		rename_key(_snake_case	,  _snake_case	,  _snake_case	)
	__snake_case      :		str         =							rename_backbone_keys(_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
	__snake_case      :		List[str]         =							'''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'''	)
			):
				__snake_case      :		int         =							state_dict.pop(_snake_case	)
				__snake_case      :		Any         =							val
			elif "class_labels_classifier" in key or "bbox_predictor" in key:
				__snake_case      :		Tuple         =							state_dict.pop(_snake_case	)
				__snake_case      :		Any         =							val
			elif key.startswith('''bbox_attention'''	) or key.startswith('''mask_head'''	):
				continue
			else:
				__snake_case      :		Any         =							state_dict.pop(_snake_case	)
				__snake_case      :		List[str]         =							val
		else:
			if not key.startswith('''class_labels_classifier'''	) and not key.startswith('''bbox_predictor'''	):
				__snake_case      :		List[Any]         =							state_dict.pop(_snake_case	)
				__snake_case      :		int         =							val
    # finally, create HuggingFace model and load state dict
	__snake_case      :		Union[str, Any]         =							DetrForSegmentation(_snake_case	) if is_panoptic else DetrForObjectDetection(_snake_case	)
	model.load_state_dict(_snake_case	)
	model.eval()
	# verify our conversion
	__snake_case      :		int         =							detr(_snake_case	)
	__snake_case      :		Any         =							model(_snake_case	)
	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(_snake_case	).mkdir(exist_ok=_snake_case	)
	model.save_pretrained(_snake_case	)
	image_processor.save_pretrained(_snake_case	)


if __name__ == "__main__":
			SCREAMING_SNAKE_CASE					:       Optional[int]							   =							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."""
			)
			SCREAMING_SNAKE_CASE					:       Any							   =							parser.parse_args()
			convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)












'''simple docstring'''


from math import pi




def  __a			(  UpperCAmelCase     )   ->float:



	"""simple docstring"""







	return degree / (180 / pi)


if __name__ == "__main__":
				from doctest import testmod

				testmod()

