
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			:   str        =     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			:   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     UpperCAmelCase__							(       __magic_name__						:     Dict							,  __magic_name__						:     Optional[int]							,  __magic_name__						:     Union[str, Any]  ):


					'''simple docstring'''

					lowerCAmelCase		:					Union[str, Any]  	=     state_dict.pop(_UpperCAmelCase  )
					lowerCAmelCase		:					Tuple  	=     val
def     UpperCAmelCase__							(       __magic_name__						:     Dict  ):


					'''simple docstring'''

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

					return new_state_dict
def     UpperCAmelCase__							(       __magic_name__						:     Tuple							,  __magic_name__						:     Optional[int]=False  ):


					'''simple docstring'''

					lowerCAmelCase		:					List[str]  	=     ''''''
					if is_panoptic:
										lowerCAmelCase		:					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)
										lowerCAmelCase		:					Dict  	=     state_dict.pop(f'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight'''  )
										lowerCAmelCase		:					Optional[int]  	=     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
										lowerCAmelCase		:					Any  	=     in_proj_weight[:2_56, :]
										lowerCAmelCase		:					Union[str, Any]  	=     in_proj_bias[:2_56]
										lowerCAmelCase		:					Optional[Any]  	=     in_proj_weight[2_56:5_12, :]
										lowerCAmelCase		:					str  	=     in_proj_bias[2_56:5_12]
										lowerCAmelCase		:					Dict  	=     in_proj_weight[-2_56:, :]
										lowerCAmelCase		:					int  	=     in_proj_bias[-2_56:]
					# 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
										lowerCAmelCase		:					int  	=     state_dict.pop(f'''{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight'''  )
										lowerCAmelCase		:					str  	=     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
										lowerCAmelCase		:					Optional[int]  	=     in_proj_weight[:2_56, :]
										lowerCAmelCase		:					Optional[int]  	=     in_proj_bias[:2_56]
										lowerCAmelCase		:					List[Any]  	=     in_proj_weight[2_56:5_12, :]
										lowerCAmelCase		:					Optional[Any]  	=     in_proj_bias[2_56:5_12]
										lowerCAmelCase		:					List[str]  	=     in_proj_weight[-2_56:, :]
										lowerCAmelCase		:					str  	=     in_proj_bias[-2_56:]
										# read in weights + bias of input projection layer of cross-attention
										lowerCAmelCase		:					Dict  	=     state_dict.pop(
										    f'''{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight'''  )
										lowerCAmelCase		:					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
										lowerCAmelCase		:					str  	=     in_proj_weight_cross_attn[:2_56, :]
										lowerCAmelCase		:					Dict  	=     in_proj_bias_cross_attn[:2_56]
										lowerCAmelCase		:					Optional[int]  	=     in_proj_weight_cross_attn[2_56:5_12, :]
										lowerCAmelCase		:					str  	=     in_proj_bias_cross_attn[2_56:5_12]
										lowerCAmelCase		:					Optional[Any]  	=     in_proj_weight_cross_attn[-2_56:, :]
										lowerCAmelCase		:					List[str]  	=     in_proj_bias_cross_attn[-2_56:]
def     UpperCAmelCase__							(       ):


					'''simple docstring'''

					lowerCAmelCase		:					Tuple  	=     '''http://images.cocodataset.org/val2017/000000039769.jpg'''
					lowerCAmelCase		:					List[str]  	=     Image.open(requests.get(_UpperCAmelCase							,  stream=_UpperCAmelCase  ).raw  )

					return im
@torch.no_grad()
def     UpperCAmelCase__							(       __magic_name__						:     Union[str, Any]							,  __magic_name__						:     Optional[Any]  ):


					'''simple docstring'''

					lowerCAmelCase		:					Union[str, Any]  	=     DetrConfig()
					# set backbone and dilation attributes
					if "resnet101" in model_name:
										lowerCAmelCase		:					List[Any]  	=     '''resnet101'''
					if "dc5" in model_name:
										lowerCAmelCase		:					Optional[Any]  	=     True
					lowerCAmelCase		:					str  	=     '''panoptic''' in model_name
					if is_panoptic:
										lowerCAmelCase		:					List[Any]  	=     2_50
					else:
										lowerCAmelCase		:					List[str]  	=     91
										lowerCAmelCase		:					Union[str, Any]  	=     '''huggingface/label-files'''
										lowerCAmelCase		:					Optional[int]  	=     '''coco-detection-id2label.json'''
										lowerCAmelCase		:					str  	=     json.load(open(hf_hub_download(_UpperCAmelCase							,  _UpperCAmelCase							,  repo_type='''dataset'''  )							,  '''r'''  )  )
										lowerCAmelCase		:					Union[str, Any]  	=     {int(_UpperCAmelCase  ): v for k, v in idalabel.items()}
										lowerCAmelCase		:					List[Any]  	=     idalabel
										lowerCAmelCase		:					int  	=     {v: k for k, v in idalabel.items()}

					# load image processor
					lowerCAmelCase		:					str  	=     '''coco_panoptic''' if is_panoptic else '''coco_detection'''
					lowerCAmelCase		:					Optional[Any]  	=     DetrImageProcessor(format=_UpperCAmelCase  )

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

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

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


def     UpperCAmelCase__							(       __magic_name__						:     Tuple							,  __magic_name__						:     int							,  __magic_name__						:     Dict							,  __magic_name__						:     str  ):


					'''simple docstring'''

					if start == end:
										return start if lst[start] > item else start + 1
					if start > end:
										return start

					lowerCAmelCase		:					Optional[Any]  	=     (start + end) // 2
					if lst[mid] < item:
										return binary_search(__magic_name__							,  __magic_name__							,  mid + 1							,  __magic_name__  )
					elif lst[mid] > item:
										return binary_search(__magic_name__							,  __magic_name__							,  __magic_name__							,  mid - 1  )
					else:
										return mid
def     UpperCAmelCase__							(       __magic_name__						:     List[Any]  ):


					'''simple docstring'''

					lowerCAmelCase		:					Union[str, Any]  	=     len(__magic_name__  )

					for index in range(1							,  __magic_name__  ):
										lowerCAmelCase		:					Optional[int]  	=     lst[index]
										lowerCAmelCase		:					Dict  	=     binary_search(__magic_name__							,  __magic_name__							,  0							,  index - 1  )
										lowerCAmelCase		:					Union[str, Any]  	=     lst[:pos] + [value] + lst[pos:index] + lst[index + 1 :]

					return lst
def     UpperCAmelCase__							(       __magic_name__						:     Optional[int]							,  __magic_name__						:     List[str]  ):


					'''simple docstring'''

					if not left:
										return right

					if not right:
										return left

					if left[0] < right[0]:
										return [left[0], *merge(left[1:]							,  __magic_name__  )]

					return [right[0], *merge(__magic_name__							,  right[1:]  )]
def     UpperCAmelCase__							(       __magic_name__						:     str  ):


					'''simple docstring'''

					lowerCAmelCase		:					str  	=     len(__magic_name__  )
					lowerCAmelCase	,							lowerCAmelCase		:					Union[str, Any]  	=     [], []
					lowerCAmelCase		:					Union[str, Any]  	=     [lst[0]]
					lowerCAmelCase		:					Optional[Any]  	=     []
					lowerCAmelCase		:					List[Any]  	=     1
					while i < length:
										if lst[i] < lst[i - 1]:
															runs.append(__magic_name__  )
															lowerCAmelCase		:					Any  	=     [lst[i]]
										else:
															new_run.append(lst[i]  )
										i += 1
					runs.append(__magic_name__  )

					for run in runs:
										sorted_runs.append(insertion_sort(__magic_name__  )  )
					for run in sorted_runs:
										lowerCAmelCase		:					int  	=     merge(__magic_name__							,  __magic_name__  )

					return sorted_array
def     UpperCAmelCase__							(       ):


					'''simple docstring'''

					lowerCAmelCase		:					Tuple  	=     [5, 9, 10, 3, -4, 5, 1_78, 92, 46, -18, 0, 7]
					lowerCAmelCase		:					Optional[Any]  	=     tim_sort(__magic_name__  )
					print(__magic_name__  )


if __name__ == "__main__":
		main()
