




"""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()
_lowercase						   =     logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
_lowercase						   =     []
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   ,      lowerCamelCase   ,      lowerCamelCase		):
						'''simple docstring'''


						snake_case__							:       Dict				      =       state_dict.pop(lowerCamelCase		)
						snake_case__							:       Tuple				      =       val

def 		lowerCAmelCase				(			lowerCamelCase		):
						'''simple docstring'''


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

						return new_state_dict

def 		lowerCAmelCase				(			lowerCamelCase   ,      lowerCamelCase=False		):
						'''simple docstring'''


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

def 		lowerCAmelCase				(			):
						'''simple docstring'''


						snake_case__							:       Optional[Any]				      =       '''http://images.cocodataset.org/val2017/000000039769.jpg'''
						snake_case__							:       List[Any]				      =       Image.open(requests.get(lowerCamelCase   ,      stream=lowerCamelCase		).raw		)

						return im

@torch.no_grad()
def 		lowerCAmelCase				(			lowerCamelCase   ,      lowerCamelCase		):
						'''simple docstring'''


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

						# load image processor
						snake_case__							:       Union[str, Any]				      =       '''coco_panoptic''' if is_panoptic else '''coco_detection'''
						snake_case__							:       Dict				      =       DetrImageProcessor(format=lowerCamelCase		)

						# prepare image
						snake_case__							:       Tuple				      =       prepare_img()
						snake_case__							:       Tuple				      =       image_processor(images=lowerCamelCase   ,      return_tensors='''pt'''		)
						snake_case__							:       Optional[int]				      =       encoding['''pixel_values''']

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

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








"""simple docstring"""





_lowercase						   =     (1, 1_53, 3_70, 3_71, 16_34, 24_67_80_51, 1_15_13_22_19_01_87_63_99_25_65_09_55_97_97_39_71_52_24_01)
_lowercase						   =     (-1_53, -1, 0, 1.2, 2_00, "A", [], {}, None)

def 		lowerCAmelCase				(			lowerCamelCase		):
						'''simple docstring'''


						if not isinstance(lowerCamelCase   ,      lowerCamelCase		) or n < 1:
												return False

						# Initialization of sum and number of digits.
						snake_case__							:       Optional[Any]				      =       0
						snake_case__							:       Union[str, Any]				      =       0
						snake_case__							:       List[str]				      =       n
						# Calculation of digits of the number
						while temp > 0:
												number_of_digits += 1
												temp //= 10
						# Dividing number into separate digits and find Armstrong number
						snake_case__							:       Optional[int]				      =       n
						while temp > 0:
												snake_case__							:       Optional[int]				      =       temp % 10
												total += rem**number_of_digits
												temp //= 10
						return n == total

def 		lowerCAmelCase				(			lowerCamelCase		):
						'''simple docstring'''


						if not isinstance(lowerCamelCase   ,      lowerCamelCase		) or n < 1:
												return False

						# Init a "histogram" of the digits
						snake_case__							:       Union[str, Any]				      =       [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
						snake_case__							:       int				      =       0
						snake_case__							:       int				      =       0
						snake_case__							:       Dict				      =       n
						while temp > 0:
												snake_case__      ,						snake_case__							:       Optional[int]				      =       divmod(lowerCamelCase   ,      10		)
												digit_histogram[rem] += 1
												digit_total += 1

						for cnt, i in zip(lowerCamelCase   ,      range(len(lowerCamelCase		)		)		):
												total += cnt * i**digit_total

						return n == total

def 		lowerCAmelCase				(			lowerCamelCase		):
						'''simple docstring'''


						if not isinstance(lowerCamelCase   ,      lowerCamelCase		) or n < 1:
												return False
						snake_case__							:       Union[str, Any]				      =       len(str(lowerCamelCase		)		)  # the power that all digits will be raised to
						# check if sum of each digit multiplied expo times is equal to number
						return n == sum(int(lowerCamelCase		) ** expo for i in str(lowerCamelCase		)		)

def 		lowerCAmelCase				(			):
						'''simple docstring'''


						snake_case__							:       Dict				      =       int(input('''Enter an integer to see if it is an Armstrong number: '''		).strip()		)
						print(f'''{num} is {'' if armstrong_number(lowerCamelCase		) else 'not '}an Armstrong number.'''		)
						print(f'''{num} is {'' if narcissistic_number(lowerCamelCase		) else 'not '}an Armstrong number.'''		)
						print(f'''{num} is {'' if pluperfect_number(lowerCamelCase		) else 'not '}an Armstrong number.'''		)


if __name__ == "__main__":
							import doctest

							doctest.testmod()
							main()


