
"""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()
__lowerCAmelCase							:  Dict 				=		logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
__lowerCAmelCase							:  Optional[int] 				=		[]
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 __snake_case (      UpperCamelCase						,					UpperCamelCase						,					UpperCamelCase    )       ->    str:


		"""simple docstring"""





		a__   		=     state_dict.pop(_lowerCAmelCase    )
		a__   		=     val

def __snake_case (      UpperCamelCase    )       ->    int:


		"""simple docstring"""





		a__   		=     OrderedDict()
		for key, value in state_dict.items():
				if "backbone.0.body" in key:
						a__   		=     key.replace('''backbone.0.body'''						,					'''backbone.conv_encoder.model'''    )
						a__   		=     value
				else:
						a__   		=     value

		return new_state_dict

def __snake_case (      UpperCamelCase						,					UpperCamelCase=False    )       ->    int:


		"""simple docstring"""





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

def __snake_case (      )       ->    int:


		"""simple docstring"""





		a__   		=     '''http://images.cocodataset.org/val2017/000000039769.jpg'''
		a__   		=     Image.open(requests.get(_lowerCAmelCase						,					stream=_lowerCAmelCase    ).raw    )

		return im

@torch.no_grad()
def __snake_case (      UpperCamelCase						,					UpperCamelCase    )       ->    Union[str, Any]:


		"""simple docstring"""





		a__   		=     DetrConfig()
		# set backbone and dilation attributes
		if "resnet101" in model_name:
				a__   		=     '''resnet101'''
		if "dc5" in model_name:
				a__   		=     True
		a__   		=     '''panoptic''' in model_name
		if is_panoptic:
				a__   		=     250
		else:
				a__   		=     91
				a__   		=     '''huggingface/label-files'''
				a__   		=     '''coco-detection-id2label.json'''
				a__   		=     json.load(open(hf_hub_download(_lowerCAmelCase						,					_lowerCAmelCase						,					repo_type='''dataset'''    )						,					'''r'''    )    )
				a__   		=     {int(_lowerCAmelCase    ): v for k, v in idalabel.items()}
				a__   		=     idalabel
				a__   		=     {v: k for k, v in idalabel.items()}

		# load image processor
		a__   		=     '''coco_panoptic''' if is_panoptic else '''coco_detection'''
		a__   		=     DetrImageProcessor(format=_lowerCAmelCase    )

		# prepare image
		a__   		=     prepare_img()
		a__   		=     image_processor(images=_lowerCAmelCase						,					return_tensors='''pt'''    )
		a__   		=     encoding['''pixel_values''']

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

		# load original model from torch hub
		a__   		=     torch.hub.load('''facebookresearch/detr'''						,					_lowerCAmelCase						,					pretrained=_lowerCAmelCase    ).eval()
		a__   		=     detr.state_dict()
		# rename keys
		for src, dest in rename_keys:
				if is_panoptic:
						a__   		=     '''detr.''' + src
				rename_key(_lowerCAmelCase						,					_lowerCAmelCase						,					_lowerCAmelCase    )
		a__   		=     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
		a__   		=     '''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'''    )
						):
								a__   		=     state_dict.pop(_lowerCAmelCase    )
								a__   		=     val
						elif "class_labels_classifier" in key or "bbox_predictor" in key:
								a__   		=     state_dict.pop(_lowerCAmelCase    )
								a__   		=     val
						elif key.startswith('''bbox_attention'''    ) or key.startswith('''mask_head'''    ):
								continue
						else:
								a__   		=     state_dict.pop(_lowerCAmelCase    )
								a__   		=     val
				else:
						if not key.startswith('''class_labels_classifier'''    ) and not key.startswith('''bbox_predictor'''    ):
								a__   		=     state_dict.pop(_lowerCAmelCase    )
								a__   		=     val
								# finally, create HuggingFace model and load state dict
		a__   		=     DetrForSegmentation(_lowerCAmelCase    ) if is_panoptic else DetrForObjectDetection(_lowerCAmelCase    )
		model.load_state_dict(_lowerCAmelCase    )
		model.eval()
		# verify our conversion
		a__   		=     detr(_lowerCAmelCase    )
		a__   		=     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__":
					__lowerCAmelCase							:  Tuple 				=		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.'''
					)
					__lowerCAmelCase							:  List[Any] 				=		parser.parse_args()
					convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)










from math import cos, sin



def     lowerCAmelCase		(		_lowerCAmelCase				: float						):




 """simple docstring"""







 UpperCAmelCase__							=    float(_lowerCAmelCase						)
 return [[scaling_factor * int(x == y						) for x in range(2						)] for y in range(2						)]



def     lowerCAmelCase		(		_lowerCAmelCase				: float						):




 """simple docstring"""







 UpperCAmelCase__			,			UpperCAmelCase__							=    cos(_lowerCAmelCase						), sin(_lowerCAmelCase						)
 return [[c, -s], [s, c]]



def     lowerCAmelCase		(		_lowerCAmelCase				: float						):




 """simple docstring"""







 UpperCAmelCase__			,			UpperCAmelCase__							=    cos(_lowerCAmelCase						), sin(_lowerCAmelCase						)
 UpperCAmelCase__							=    c * s
 return [[c * c, cs], [cs, s * s]]



def     lowerCAmelCase		(		_lowerCAmelCase				: float						):




 """simple docstring"""







 UpperCAmelCase__			,			UpperCAmelCase__							=    cos(_lowerCAmelCase						), sin(_lowerCAmelCase						)
 UpperCAmelCase__							=    c * s
 return [[2 * c - 1, 2 * cs], [2 * cs, 2 * s - 1]]


print(F'''    {scaling(5) = }''')
print(F'''  {rotation(4_5) = }''')
print(F'''{projection(4_5) = }''')


print(F'''{reflection(4_5) = }''')





