






"""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()
__A		:	Optional[int]      =		logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
__A		:	Dict      =		[]
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    :			Tuple		, __snake_case    :			List[str]		, __snake_case    :			List[str]     ):
		lowercase_			:			Union[str, Any]								=       state_dict.pop(__UpperCamelCase     )
		lowercase_			:			Any								=       val



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

		return new_state_dict



def 						lowercase   (    __snake_case    :			Optional[int]		, __snake_case    :			Any=False     ):
		lowercase_			:			Optional[int]								=       ''''''
		if is_panoptic:
				lowercase_			:			List[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)
				lowercase_			:			Optional[Any]								=       state_dict.pop(F'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight'''     )
				lowercase_			:			List[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
				lowercase_			:			List[str]								=       in_proj_weight[:2_5_6, :]
				lowercase_			:			List[str]								=       in_proj_bias[:2_5_6]
				lowercase_			:			List[str]								=       in_proj_weight[2_5_6:5_1_2, :]
				lowercase_			:			int								=       in_proj_bias[2_5_6:5_1_2]
				lowercase_			:			str								=       in_proj_weight[-2_5_6:, :]
				lowercase_			:			int								=       in_proj_bias[-2_5_6:]
		# 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_			:			Union[str, Any]								=       state_dict.pop(F'''{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight'''     )
				lowercase_			:			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_			:			Optional[Any]								=       in_proj_weight[:2_5_6, :]
				lowercase_			:			List[str]								=       in_proj_bias[:2_5_6]
				lowercase_			:			Optional[Any]								=       in_proj_weight[2_5_6:5_1_2, :]
				lowercase_			:			List[str]								=       in_proj_bias[2_5_6:5_1_2]
				lowercase_			:			List[str]								=       in_proj_weight[-2_5_6:, :]
				lowercase_			:			Optional[Any]								=       in_proj_bias[-2_5_6:]
				# read in weights + bias of input projection layer of cross-attention
				lowercase_			:			Union[str, Any]								=       state_dict.pop(
				    F'''{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight'''     )
				lowercase_			:			Dict								=       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_			:			Optional[int]								=       in_proj_weight_cross_attn[:2_5_6, :]
				lowercase_			:			int								=       in_proj_bias_cross_attn[:2_5_6]
				lowercase_			:			str								=       in_proj_weight_cross_attn[2_5_6:5_1_2, :]
				lowercase_			:			Tuple								=       in_proj_bias_cross_attn[2_5_6:5_1_2]
				lowercase_			:			Union[str, Any]								=       in_proj_weight_cross_attn[-2_5_6:, :]
				lowercase_			:			Union[str, Any]								=       in_proj_bias_cross_attn[-2_5_6:]



def 						lowercase   (    ):
		lowercase_			:			Union[str, Any]								=       '''http://images.cocodataset.org/val2017/000000039769.jpg'''
		lowercase_			:			List[Any]								=       Image.open(requests.get(__UpperCamelCase		, stream=__UpperCamelCase     ).raw     )

		return im



@torch.no_grad()
def 						lowercase   (    __snake_case    :			Any		, __snake_case    :			Tuple     ):
		lowercase_			:			Optional[Any]								=       DetrConfig()
		# set backbone and dilation attributes
		if "resnet101" in model_name:
				lowercase_			:			List[str]								=       '''resnet101'''
		if "dc5" in model_name:
				lowercase_			:			Optional[Any]								=       True
		lowercase_			:			Any								=       '''panoptic''' in model_name
		if is_panoptic:
				lowercase_			:			Union[str, Any]								=       2_5_0
		else:
				lowercase_			:			Union[str, Any]								=       9_1
				lowercase_			:			str								=       '''huggingface/label-files'''
				lowercase_			:			List[str]								=       '''coco-detection-id2label.json'''
				lowercase_			:			Optional[int]								=       json.load(open(hf_hub_download(__UpperCamelCase		, __UpperCamelCase		, repo_type='''dataset'''     )		, '''r'''     )     )
				lowercase_			:			Optional[int]								=       {int(__UpperCamelCase     ): v for k, v in idalabel.items()}
				lowercase_			:			Optional[int]								=       idalabel
				lowercase_			:			Optional[int]								=       {v: k for k, v in idalabel.items()}

		# load image processor
		lowercase_			:			str								=       '''coco_panoptic''' if is_panoptic else '''coco_detection'''
		lowercase_			:			Any								=       DetrImageProcessor(format=__UpperCamelCase     )

		# prepare image
		lowercase_			:			List[Any]								=       prepare_img()
		lowercase_			:			Dict								=       image_processor(images=__UpperCamelCase		, return_tensors='''pt'''     )
		lowercase_			:			Any								=       encoding['''pixel_values''']

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

		# load original model from torch hub
		lowercase_			:			Tuple								=       torch.hub.load('''facebookresearch/detr'''		, __UpperCamelCase		, pretrained=__UpperCamelCase     ).eval()
		lowercase_			:			Optional[int]								=       detr.state_dict()
		# rename keys
		for src, dest in rename_keys:
				if is_panoptic:
						lowercase_			:			Optional[int]								=       '''detr.''' + src
				rename_key(__UpperCamelCase		, __UpperCamelCase		, __UpperCamelCase     )
		lowercase_			:			Union[str, Any]								=       rename_backbone_keys(__UpperCamelCase     )
		# query, key and value matrices need special treatment
		read_in_q_k_v(__UpperCamelCase		, is_panoptic=__UpperCamelCase     )
		# important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
		lowercase_			:			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'''     )
						):
								lowercase_			:			int								=       state_dict.pop(__UpperCamelCase     )
								lowercase_			:			str								=       val
						elif "class_labels_classifier" in key or "bbox_predictor" in key:
								lowercase_			:			Optional[int]								=       state_dict.pop(__UpperCamelCase     )
								lowercase_			:			Tuple								=       val
						elif key.startswith('''bbox_attention'''     ) or key.startswith('''mask_head'''     ):
								continue
						else:
								lowercase_			:			Dict								=       state_dict.pop(__UpperCamelCase     )
								lowercase_			:			Union[str, Any]								=       val
				else:
						if not key.startswith('''class_labels_classifier'''     ) and not key.startswith('''bbox_predictor'''     ):
								lowercase_			:			List[Any]								=       state_dict.pop(__UpperCamelCase     )
								lowercase_			:			List[Any]								=       val
    # finally, create HuggingFace model and load state dict
		lowercase_			:			Tuple								=       DetrForSegmentation(__UpperCamelCase     ) if is_panoptic else DetrForObjectDetection(__UpperCamelCase     )
		model.load_state_dict(__UpperCamelCase     )
		model.eval()
		# verify our conversion
		lowercase_			:			Optional[int]								=       detr(__UpperCamelCase     )
		lowercase_			:			int								=       model(__UpperCamelCase     )
		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(__UpperCamelCase     ).mkdir(exist_ok=__UpperCamelCase     )
		model.save_pretrained(__UpperCamelCase     )
		image_processor.save_pretrained(__UpperCamelCase     )


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











"""simple docstring"""



import math
import os

import cva
import numpy as np

__A		:	int      =		255.0



def 						lowercase   (    __snake_case    :			float		, __snake_case    :			float     ):
		lowercase_			:			Any								=       np.mean((original - contrast) ** 2     )
		if mse == 0:
				return 1_0_0

		return 2_0 * math.logaa(PIXEL_MAX / math.sqrt(__snake_case     )     )



def 						lowercase   (    ):
		lowercase_			:			Any								=       os.path.dirname(os.path.realpath(__snake_case     )     )
		# Loading images (original image and compressed image)
		lowercase_			:			Union[str, Any]								=       cva.imread(os.path.join(__snake_case		, '''image_data/original_image.png'''     )     )
		lowercase_			:			List[Any]								=       cva.imread(os.path.join(__snake_case		, '''image_data/compressed_image.png'''     )		, 1     )

		lowercase_			:			Tuple								=       cva.imread(os.path.join(__snake_case		, '''image_data/PSNR-example-base.png'''     )     )
		lowercase_			:			List[str]								=       cva.imread(
		    os.path.join(__snake_case		, '''image_data/PSNR-example-comp-10.jpg'''     )		, 1     )

		# Value expected: 29.73dB
		print('''-- First Test --'''     )
		print(F'''PSNR value is {peak_signal_to_noise_ratio(__snake_case		, __snake_case     )} dB'''     )

		# # Value expected: 31.53dB (Wikipedia Example)
		print('''\n-- Second Test --'''     )
		print(F'''PSNR value is {peak_signal_to_noise_ratio(__snake_case		, __snake_case     )} dB'''     )


if __name__ == "__main__":
						main()



