



'''simple docstring'''





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

			lowercase__					  =				DetrConfig(use_timm_backbone=SCREAMING_SNAKE_CASE_						,  backbone_config=SCREAMING_SNAKE_CASE_  )

			# set label attributes
			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(SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_						,  repo_type="dataset"  )						,  "r"  )  )
						lowercase__					  =				{int(SCREAMING_SNAKE_CASE_  ): v for k, v in idalabel.items()}
						lowercase__					  =				idalabel
						lowercase__					  =				{v: k for k, v in idalabel.items()}

			return config, is_panoptic



def __lowerCAmelCase (     SCREAMING_SNAKE_CASE_  ):
			# here we list all keys to be renamed (original name on the left, our name on the right)
			lowercase__					  =				[]

			# 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 (     SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_  ):
			lowercase__					  =				state_dict.pop(SCREAMING_SNAKE_CASE_  )
			lowercase__					  =				val



def __lowerCAmelCase (     SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_=False  ):
			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 __lowerCAmelCase (     ):
			lowercase__					  =				"http://images.cocodataset.org/val2017/000000039769.jpg"
			lowercase__					  =				Image.open(requests.get(SCREAMING_SNAKE_CASE_						,  stream=SCREAMING_SNAKE_CASE_  ).raw  )

			return im



@torch.no_grad()
def __lowerCAmelCase (     SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_=None						,  SCREAMING_SNAKE_CASE_=False  ):
			lowercase__  ,       lowercase__					  =				get_detr_config(SCREAMING_SNAKE_CASE_  )

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

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

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

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

			lowercase__					  =				detr(SCREAMING_SNAKE_CASE_  )
			lowercase__					  =				model(SCREAMING_SNAKE_CASE_  )

			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(SCREAMING_SNAKE_CASE_  ).mkdir(exist_ok=SCREAMING_SNAKE_CASE_  )
						model.save_pretrained(SCREAMING_SNAKE_CASE_  )
						processor.save_pretrained(SCREAMING_SNAKE_CASE_  )

			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 (     SCREAMING_SNAKE_CASE_  ):
			lowercase__					  =				len(SCREAMING_SNAKE_CASE_  )
			for i in range(SCREAMING_SNAKE_CASE_  ):
						for j in range(i + 1						,  SCREAMING_SNAKE_CASE_  ):
									if numbers[j] < numbers[i]:
												lowercase__  ,       lowercase__					  =				numbers[j], numbers[i]
			return numbers


if __name__ == "__main__":
							lowercase_								=     input("""Enter numbers separated by a comma:\n""").strip()
							lowercase_								=     [int(item) for item in user_input.split(""",""")]
							print(exchange_sort(unsorted))




