



import argparse
import json
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, ResNetConfig
from transformers.utils import logging


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






def 					__lowerCAmelCase		(  __lowerCamelCase	:    Tuple	)  ->						int:
					# initialize config
					if "resnet-50" in model_name:
										__lowerCAmelCase	   =ResNetConfig.from_pretrained("""microsoft/resnet-50"""	)
					elif "resnet-101" in model_name:
										__lowerCAmelCase	   =ResNetConfig.from_pretrained("""microsoft/resnet-101"""	)
					else:
										raise ValueError("""Model name should include either resnet50 or resnet101"""	)

					__lowerCAmelCase	   =DetrConfig(use_timm_backbone=__lowerCamelCase				,   backbone_config=__lowerCamelCase	)

					# set label attributes
					__lowerCAmelCase	   ="""panoptic""" in model_name
					if is_panoptic:
										__lowerCAmelCase	   =250
					else:
										__lowerCAmelCase	   =91
										__lowerCAmelCase	   ="""huggingface/label-files"""
										__lowerCAmelCase	   ="""coco-detection-id2label.json"""
										__lowerCAmelCase	   =json.load(open(hf_hub_download(__lowerCamelCase				,   __lowerCamelCase				,   repo_type="""dataset"""	)				,   """r"""	)	)
										__lowerCAmelCase	   ={int(__lowerCamelCase	): v for k, v in idalabel.items()}
										__lowerCAmelCase	   =idalabel
										__lowerCAmelCase	   ={v: k for k, v in idalabel.items()}

					return config, is_panoptic






def 					__lowerCAmelCase		(  __lowerCamelCase	:    int	)  ->						List[Any]:
					# here we list all keys to be renamed (original name on the left, our name on the right)
					__lowerCAmelCase	   =[]

					# stem
					# fmt: off
					rename_keys.append(("""backbone.0.body.conv1.weight""", """backbone.conv_encoder.model.embedder.embedder.convolution.weight""")	)
					rename_keys.append(("""backbone.0.body.bn1.weight""", """backbone.conv_encoder.model.embedder.embedder.normalization.weight""")	)
					rename_keys.append(("""backbone.0.body.bn1.bias""", """backbone.conv_encoder.model.embedder.embedder.normalization.bias""")	)
					rename_keys.append(("""backbone.0.body.bn1.running_mean""", """backbone.conv_encoder.model.embedder.embedder.normalization.running_mean""")	)
					rename_keys.append(("""backbone.0.body.bn1.running_var""", """backbone.conv_encoder.model.embedder.embedder.normalization.running_var""")	)
					# stages
					for stage_idx in range(len(config.backbone_config.depths	)	):
										for layer_idx in range(config.backbone_config.depths[stage_idx]	):
															# shortcut
															if layer_idx == 0:
																				rename_keys.append(
																				    (
																				        f"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.0.weight""",
																				        f"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.convolution.weight""",
																				    )	)
																				rename_keys.append(
																				    (
																				        f"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.weight""",
																				        f"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.weight""",
																				    )	)
																				rename_keys.append(
																				    (
																				        f"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.bias""",
																				        f"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.bias""",
																				    )	)
																				rename_keys.append(
																				    (
																				        f"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_mean""",
																				        f"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_mean""",
																				    )	)
																				rename_keys.append(
																				    (
																				        f"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_var""",
																				        f"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_var""",
																				    )	)
															# 3 convs
															for i in range(3	):
																				rename_keys.append(
																				    (
																				        f"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.conv{i+1}.weight""",
																				        f"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.convolution.weight""",
																				    )	)
																				rename_keys.append(
																				    (
																				        f"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.weight""",
																				        f"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.weight""",
																				    )	)
																				rename_keys.append(
																				    (
																				        f"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.bias""",
																				        f"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.bias""",
																				    )	)
																				rename_keys.append(
																				    (
																				        f"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_mean""",
																				        f"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_mean""",
																				    )	)
																				rename_keys.append(
																				    (
																				        f"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_var""",
																				        f"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_var""",
																				    )	)
    # fmt: on

					for i in range(config.encoder_layers	):
										# 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"""),
					    ]	)

					return rename_keys






def 					__lowerCAmelCase		(  __lowerCamelCase	:    int				,   __lowerCamelCase	:    Any				,   __lowerCamelCase	:    Dict	)  ->						List[str]:
					__lowerCAmelCase	   =state_dict.pop(__lowerCamelCase	)
					__lowerCAmelCase	   =val






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






def 					__lowerCAmelCase		(  )  ->						str:
					__lowerCAmelCase	   ="""http://images.cocodataset.org/val2017/000000039769.jpg"""
					__lowerCAmelCase	   =Image.open(requests.get(__lowerCamelCase				,   stream=__lowerCamelCase	).raw	)

					return im






@torch.no_grad()
def 					__lowerCAmelCase		(  __lowerCamelCase	:    Union[str, Any]				,   __lowerCamelCase	:    int=None				,   __lowerCamelCase	:    List[str]=False	)  ->						Dict:
					__lowerCAmelCase   ,	__lowerCAmelCase	   =get_detr_config(__lowerCamelCase	)

					# load original model from torch hub
					__lowerCAmelCase	   ={
					    """detr-resnet-50""": """detr_resnet50""",
					    """detr-resnet-101""": """detr_resnet101""",
					}
					logger.info(f"""Converting model {model_name}..."""	)
					__lowerCAmelCase	   =torch.hub.load("""facebookresearch/detr"""				,   model_name_to_original_name[model_name]				,   pretrained=__lowerCamelCase	).eval()
					__lowerCAmelCase	   =detr.state_dict()
					# rename keys
					for src, dest in create_rename_keys(__lowerCamelCase	):
										if is_panoptic:
															__lowerCAmelCase	   ="""detr.""" + src
										rename_key(__lowerCamelCase				,   __lowerCamelCase				,   __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
					__lowerCAmelCase	   ="""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	   =state_dict.pop(__lowerCamelCase	)
																				__lowerCAmelCase	   =val
															elif "class_labels_classifier" in key or "bbox_predictor" in key:
																				__lowerCAmelCase	   =state_dict.pop(__lowerCamelCase	)
																				__lowerCAmelCase	   =val
															elif key.startswith("""bbox_attention"""	) or key.startswith("""mask_head"""	):
																				continue
															else:
																				__lowerCAmelCase	   =state_dict.pop(__lowerCamelCase	)
																				__lowerCAmelCase	   =val
										else:
															if not key.startswith("""class_labels_classifier"""	) and not key.startswith("""bbox_predictor"""	):
																				__lowerCAmelCase	   =state_dict.pop(__lowerCamelCase	)
																				__lowerCAmelCase	   =val

    # finally, create HuggingFace model and load state dict
					__lowerCAmelCase	   =DetrForSegmentation(__lowerCamelCase	) if is_panoptic else DetrForObjectDetection(__lowerCamelCase	)
					model.load_state_dict(__lowerCamelCase	)
					model.eval()

					# verify our conversion on an image
					__lowerCAmelCase	   ="""coco_panoptic""" if is_panoptic else """coco_detection"""
					__lowerCAmelCase	   =DetrImageProcessor(format=__lowerCamelCase	)

					__lowerCAmelCase	   =processor(images=prepare_img()				,   return_tensors="""pt"""	)
					__lowerCAmelCase	   =encoding["""pixel_values"""]

					__lowerCAmelCase	   =detr(__lowerCamelCase	)
					__lowerCAmelCase	   =model(__lowerCamelCase	)

					assert torch.allclose(outputs.logits				,   original_outputs["""pred_logits"""]				,   atol=1E-3	)
					assert torch.allclose(outputs.pred_boxes				,   original_outputs["""pred_boxes"""]				,   atol=1E-3	)
					if is_panoptic:
										assert torch.allclose(outputs.pred_masks				,   original_outputs["""pred_masks"""]				,   atol=1E-4	)
					print("""Looks ok!"""	)

					if pytorch_dump_folder_path is not None:
										# 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	)
										processor.save_pretrained(__lowerCamelCase	)

					if push_to_hub:
										# Upload model and image processor to the hub
										logger.info("""Uploading PyTorch model and image processor to the hub..."""	)
										model.push_to_hub(f"""nielsr/{model_name}"""	)
										processor.push_to_hub(f"""nielsr/{model_name}"""	)


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

				parser.add_argument(
				    '''--model_name''',
				    default='''detr-resnet-50''',
				    type=str,
				    choices=['''detr-resnet-50''', '''detr-resnet-101'''],
				    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.'''
				)
				parser.add_argument('''--push_to_hub''', action='''store_true''', help='''Whether to push the model to the hub or not.''')
				lowercase_	       =			parser.parse_args()
				convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)









def 					__lowerCAmelCase		(  __lowerCamelCase	:    int = 3				,   __lowerCamelCase	:    int = 7				,   __lowerCamelCase	:    int = 1000000	)  ->						int:
					__lowerCAmelCase	   =0
					__lowerCAmelCase	   =1

					for current_denominator in range(1				,   limit + 1	):
										__lowerCAmelCase	   =current_denominator * numerator // denominator
										if current_denominator % denominator == 0:
															current_numerator -= 1
										if current_numerator * max_denominator > current_denominator * max_numerator:
															__lowerCAmelCase	   =current_numerator
															__lowerCAmelCase	   =current_denominator
					return max_numerator


if __name__ == "__main__":
				print(solution(numerator=3, denominator=7, limit=1_00_00_00))




