
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()
lowercase__						:str					    =       logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
lowercase__						: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 			UpperCamelCase       (				lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__						):
					'''simple docstring'''
					lowercase		      =		state_dict.pop(lowerCAmelCase__						)
					lowercase		      =		val



def 			UpperCamelCase       (				lowerCAmelCase__						):
					'''simple docstring'''
					lowercase		      =		OrderedDict()
					for key, value in state_dict.items():
										if "backbone.0.body" in key:
															lowercase		      =		key.replace('''backbone.0.body'''					,     '''backbone.conv_encoder.model'''						)
															lowercase		      =		value
										else:
															lowercase		      =		value

					return new_state_dict



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



def 			UpperCamelCase       (				):
					'''simple docstring'''
					lowercase		      =		"""http://images.cocodataset.org/val2017/000000039769.jpg"""
					lowercase		      =		Image.open(requests.get(lowerCAmelCase__					,     stream=lowerCAmelCase__						).raw						)

					return im



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

					# load image processor
					lowercase		      =		"""coco_panoptic""" if is_panoptic else """coco_detection"""
					lowercase		      =		DetrImageProcessor(format=lowerCAmelCase__						)

					# prepare image
					lowercase		      =		prepare_img()
					lowercase		      =		image_processor(images=lowerCAmelCase__					,     return_tensors='''pt'''						)
					lowercase		      =		encoding["""pixel_values"""]

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

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








_A		:					Optional[int]          =							{
    '''+''': lambda x, y: x + y,
    '''-''': lambda x, y: x - y,
    '''*''': lambda x, y: x * y,
    '''/''': lambda x, y: x / y,
}



def      __lowerCAmelCase    (				snake_case						:     Union[str, Any]			)							->				str:
   return c.isdigit()



def      __lowerCAmelCase    (				snake_case						:     Optional[Any]			)							->				str:
   __lowerCamelCase:					List[str]	 =						[]

   # iterate over the string in reverse order
   for c in expression.split()[::-1]:
      # push operand to stack
      if is_operand(snake_case			):
         stack.append(int(snake_case			)			)

      else:
         # pop values from stack can calculate the result
         # push the result onto the stack again
         __lowerCamelCase:					Any	 =						stack.pop()
         __lowerCamelCase:					int	 =						stack.pop()
         stack.append(calc[c](snake_case    ,   snake_case			)			)

   return stack.pop()


# Driver code
if __name__ == "__main__":
   _A		:					int          =							'''+ 9 * 2 6'''
   print(evaluate(test_expression))

   _A		:					str          =							'''/ * 10 2 + 4 1 '''
   print(evaluate(test_expression))


