




"""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 (
    ConditionalDetrConfig,
    ConditionalDetrForObjectDetection,
    ConditionalDetrForSegmentation,
    ConditionalDetrImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
UpperCamelCase__						=			logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
UpperCamelCase__						=			[]
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}.cross_attn.out_proj.weight",
				        F"decoder.layers.{i}.encoder_attn.out_proj.weight",
				    )
				)
				rename_keys.append(
				    (
				        F"transformer.decoder.layers.{i}.cross_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"))

				# q, k, v projections in self/cross-attention in decoder for conditional DETR
				rename_keys.append(
				    (F"transformer.decoder.layers.{i}.sa_qcontent_proj.weight", F"decoder.layers.{i}.sa_qcontent_proj.weight")
				)
				rename_keys.append(
				    (F"transformer.decoder.layers.{i}.sa_kcontent_proj.weight", F"decoder.layers.{i}.sa_kcontent_proj.weight")
				)
				rename_keys.append(
				    (F"transformer.decoder.layers.{i}.sa_qpos_proj.weight", F"decoder.layers.{i}.sa_qpos_proj.weight")
				)
				rename_keys.append(
				    (F"transformer.decoder.layers.{i}.sa_kpos_proj.weight", F"decoder.layers.{i}.sa_kpos_proj.weight")
				)
				rename_keys.append((F"transformer.decoder.layers.{i}.sa_v_proj.weight", F"decoder.layers.{i}.sa_v_proj.weight"))
				rename_keys.append(
				    (F"transformer.decoder.layers.{i}.ca_qcontent_proj.weight", F"decoder.layers.{i}.ca_qcontent_proj.weight")
				)
				# rename_keys.append((f"transformer.decoder.layers.{i}.ca_qpos_proj.weight", f"decoder.layers.{i}.ca_qpos_proj.weight"))
				rename_keys.append(
				    (F"transformer.decoder.layers.{i}.ca_kcontent_proj.weight", F"decoder.layers.{i}.ca_kcontent_proj.weight")
				)
				rename_keys.append(
				    (F"transformer.decoder.layers.{i}.ca_kpos_proj.weight", F"decoder.layers.{i}.ca_kpos_proj.weight")
				)
				rename_keys.append((F"transformer.decoder.layers.{i}.ca_v_proj.weight", F"decoder.layers.{i}.ca_v_proj.weight"))
				rename_keys.append(
				    (F"transformer.decoder.layers.{i}.ca_qpos_sine_proj.weight", F"decoder.layers.{i}.ca_qpos_sine_proj.weight")
				)

				rename_keys.append(
				    (F"transformer.decoder.layers.{i}.sa_qcontent_proj.bias", F"decoder.layers.{i}.sa_qcontent_proj.bias")
				)
				rename_keys.append(
				    (F"transformer.decoder.layers.{i}.sa_kcontent_proj.bias", F"decoder.layers.{i}.sa_kcontent_proj.bias")
				)
				rename_keys.append((F"transformer.decoder.layers.{i}.sa_qpos_proj.bias", F"decoder.layers.{i}.sa_qpos_proj.bias"))
				rename_keys.append((F"transformer.decoder.layers.{i}.sa_kpos_proj.bias", F"decoder.layers.{i}.sa_kpos_proj.bias"))
				rename_keys.append((F"transformer.decoder.layers.{i}.sa_v_proj.bias", F"decoder.layers.{i}.sa_v_proj.bias"))
				rename_keys.append(
				    (F"transformer.decoder.layers.{i}.ca_qcontent_proj.bias", F"decoder.layers.{i}.ca_qcontent_proj.bias")
				)
				# rename_keys.append((f"transformer.decoder.layers.{i}.ca_qpos_proj.bias", f"decoder.layers.{i}.ca_qpos_proj.bias"))
				rename_keys.append(
				    (F"transformer.decoder.layers.{i}.ca_kcontent_proj.bias", F"decoder.layers.{i}.ca_kcontent_proj.bias")
				)
				rename_keys.append((F"transformer.decoder.layers.{i}.ca_kpos_proj.bias", F"decoder.layers.{i}.ca_kpos_proj.bias"))
				rename_keys.append((F"transformer.decoder.layers.{i}.ca_v_proj.bias", F"decoder.layers.{i}.ca_v_proj.bias"))
				rename_keys.append(
				    (F"transformer.decoder.layers.{i}.ca_qpos_sine_proj.bias", F"decoder.layers.{i}.ca_qpos_sine_proj.bias")
				)

# convolutional projection + query embeddings + layernorm of decoder + class and bounding box heads
# for conditional DETR, also convert reference point head and query scale MLP
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'''),
        ('''transformer.decoder.ref_point_head.layers.0.weight''', '''decoder.ref_point_head.layers.0.weight'''),
        ('''transformer.decoder.ref_point_head.layers.0.bias''', '''decoder.ref_point_head.layers.0.bias'''),
        ('''transformer.decoder.ref_point_head.layers.1.weight''', '''decoder.ref_point_head.layers.1.weight'''),
        ('''transformer.decoder.ref_point_head.layers.1.bias''', '''decoder.ref_point_head.layers.1.bias'''),
        ('''transformer.decoder.query_scale.layers.0.weight''', '''decoder.query_scale.layers.0.weight'''),
        ('''transformer.decoder.query_scale.layers.0.bias''', '''decoder.query_scale.layers.0.bias'''),
        ('''transformer.decoder.query_scale.layers.1.weight''', '''decoder.query_scale.layers.1.weight'''),
        ('''transformer.decoder.query_scale.layers.1.bias''', '''decoder.query_scale.layers.1.bias'''),
        ('''transformer.decoder.layers.0.ca_qpos_proj.weight''', '''decoder.layers.0.ca_qpos_proj.weight'''),
        ('''transformer.decoder.layers.0.ca_qpos_proj.bias''', '''decoder.layers.0.ca_qpos_proj.bias'''),
    ]
)





def 							UpperCAmelCase		(		snake_case			:					Dict      ,       snake_case			:					Dict      ,       snake_case			:					Optional[int]    ):
					_lowerCAmelCase:List[Any]									=state_dict.pop(lowercase_    )
					_lowerCAmelCase:Tuple									=val





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

					return new_state_dict





def 							UpperCAmelCase		(		snake_case			:					Optional[int]      ,       snake_case			:					Dict=False    ):
					_lowerCAmelCase:List[str]									=""""""
					if is_panoptic:
										_lowerCAmelCase:List[Any]									="""conditional_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:int									=state_dict.pop(F'{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight'    )
										_lowerCAmelCase: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
										_lowerCAmelCase:Dict									=in_proj_weight[:256, :]
										_lowerCAmelCase:List[Any]									=in_proj_bias[:256]
										_lowerCAmelCase:Optional[Any]									=in_proj_weight[256:512, :]
										_lowerCAmelCase:Tuple									=in_proj_bias[256:512]
										_lowerCAmelCase:Dict									=in_proj_weight[-256:, :]
										_lowerCAmelCase:Dict									=in_proj_bias[-256:]





def 							UpperCAmelCase		(		):
					_lowerCAmelCase:List[str]									="""http://images.cocodataset.org/val2017/000000039769.jpg"""
					_lowerCAmelCase:Optional[int]									=Image.open(requests.get(lowercase_      ,       stream=lowercase_    ).raw    )

					return im





@torch.no_grad()
def 							UpperCAmelCase		(		snake_case			:					str      ,       snake_case			:					int    ):
					_lowerCAmelCase:Optional[Any]									=ConditionalDetrConfig()
					# set backbone and dilation attributes
					if "resnet101" in model_name:
										_lowerCAmelCase:Optional[int]									="""resnet101"""
					if "dc5" in model_name:
										_lowerCAmelCase:str									=True
					_lowerCAmelCase:Union[str, Any]									="""panoptic""" in model_name
					if is_panoptic:
										_lowerCAmelCase:Tuple									=250
					else:
										_lowerCAmelCase:Tuple									=91
										_lowerCAmelCase:Union[str, Any]									="""huggingface/label-files"""
										_lowerCAmelCase:str									="""coco-detection-id2label.json"""
										_lowerCAmelCase:Tuple									=json.load(open(hf_hub_download(lowercase_      ,       lowercase_      ,       repo_type='''dataset'''    )      ,       '''r'''    )    )
										_lowerCAmelCase:List[Any]									={int(lowercase_    ): v for k, v in idalabel.items()}
										_lowerCAmelCase:Optional[Any]									=idalabel
										_lowerCAmelCase:Union[str, Any]									={v: k for k, v in idalabel.items()}

					# load image processor
					_lowerCAmelCase:str									="""coco_panoptic""" if is_panoptic else """coco_detection"""
					_lowerCAmelCase:List[Any]									=ConditionalDetrImageProcessor(format=lowercase_    )

					# prepare image
					_lowerCAmelCase:Union[str, Any]									=prepare_img()
					_lowerCAmelCase:Any									=image_processor(images=lowercase_      ,       return_tensors='''pt'''    )
					_lowerCAmelCase:int									=encoding["""pixel_values"""]

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

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


if __name__ == "__main__":
				UpperCamelCase__						=			argparse.ArgumentParser()

				parser.add_argument(
				    '''--model_name''',
				    default='''conditional_detr_resnet50''',
				    type=str,
				    help='''Name of the CONDITIONAL_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.'''
				)
				UpperCamelCase__						=			parser.parse_args()
				convert_conditional_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)












"""simple docstring"""






def 							UpperCAmelCase		(		snake_case			:					int      ,       snake_case			:					int    ):
					return x if y == 0 else greatest_common_divisor(snake_case      ,       x % y    )





def 							UpperCAmelCase		(		snake_case			:					int      ,       snake_case			:					int    ):
					return (x * y) // greatest_common_divisor(snake_case      ,       snake_case    )





def 							UpperCAmelCase		(		snake_case			:					int = 20    ):
					_lowerCAmelCase:List[Any]									=  1
					for i in range(1      ,       n + 1    ):
										_lowerCAmelCase:List[str]									=  lcm(snake_case      ,       snake_case    )
					return g


if __name__ == "__main__":
				print(F"{solution() = }")






