"""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()
UpperCamelCase_    :    Any											=						logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
UpperCamelCase_    :    Tuple											=						[]
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 					a__ (						UpperCAmelCase__    :     List[str]						,     UpperCAmelCase__    :     int						,     UpperCAmelCase__    :     List[Any]     )->		Dict:
						UpperCAmelCase__   		=						state_dict.pop(__lowercase     )
						UpperCAmelCase__   		=						val


def 					a__ (						UpperCAmelCase__    :     Optional[int]     )->		List[str]:
						UpperCAmelCase__   		=						OrderedDict()
						for key, value in state_dict.items():
												if "backbone.0.body" in key:
																		UpperCAmelCase__   		=						key.replace("""backbone.0.body"""						,     """backbone.conv_encoder.model"""     )
																		UpperCAmelCase__   		=						value
												else:
																		UpperCAmelCase__   		=						value

						return new_state_dict


def 					a__ (						UpperCAmelCase__    :     Dict						,     UpperCAmelCase__    :     Optional[int]=False     )->		int:
						UpperCAmelCase__   		=						""""""
						if is_panoptic:
												UpperCAmelCase__   		=						"""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)
												UpperCAmelCase__   		=						state_dict.pop(f'{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight'     )
												UpperCAmelCase__   		=						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
												UpperCAmelCase__   		=						in_proj_weight[:2_5_6, :]
												UpperCAmelCase__   		=						in_proj_bias[:2_5_6]
												UpperCAmelCase__   		=						in_proj_weight[2_5_6:5_1_2, :]
												UpperCAmelCase__   		=						in_proj_bias[2_5_6:5_1_2]
												UpperCAmelCase__   		=						in_proj_weight[-2_5_6:, :]
												UpperCAmelCase__   		=						in_proj_bias[-2_5_6:]
						# 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
												UpperCAmelCase__   		=						state_dict.pop(f'{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight'     )
												UpperCAmelCase__   		=						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
												UpperCAmelCase__   		=						in_proj_weight[:2_5_6, :]
												UpperCAmelCase__   		=						in_proj_bias[:2_5_6]
												UpperCAmelCase__   		=						in_proj_weight[2_5_6:5_1_2, :]
												UpperCAmelCase__   		=						in_proj_bias[2_5_6:5_1_2]
												UpperCAmelCase__   		=						in_proj_weight[-2_5_6:, :]
												UpperCAmelCase__   		=						in_proj_bias[-2_5_6:]
												# read in weights + bias of input projection layer of cross-attention
												UpperCAmelCase__   		=						state_dict.pop(
												    f'{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight'     )
												UpperCAmelCase__   		=						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
												UpperCAmelCase__   		=						in_proj_weight_cross_attn[:2_5_6, :]
												UpperCAmelCase__   		=						in_proj_bias_cross_attn[:2_5_6]
												UpperCAmelCase__   		=						in_proj_weight_cross_attn[2_5_6:5_1_2, :]
												UpperCAmelCase__   		=						in_proj_bias_cross_attn[2_5_6:5_1_2]
												UpperCAmelCase__   		=						in_proj_weight_cross_attn[-2_5_6:, :]
												UpperCAmelCase__   		=						in_proj_bias_cross_attn[-2_5_6:]


def 					a__ (						)->		Optional[int]:
						UpperCAmelCase__   		=						"""http://images.cocodataset.org/val2017/000000039769.jpg"""
						UpperCAmelCase__   		=						Image.open(requests.get(__lowercase						,     stream=__lowercase     ).raw     )

						return im


@torch.no_grad()
def 					a__ (						UpperCAmelCase__    :     Union[str, Any]						,     UpperCAmelCase__    :     List[Any]     )->		str:
						UpperCAmelCase__   		=						DetrConfig()
						# set backbone and dilation attributes
						if "resnet101" in model_name:
												UpperCAmelCase__   		=						"""resnet101"""
						if "dc5" in model_name:
												UpperCAmelCase__   		=						True
						UpperCAmelCase__   		=						"""panoptic""" in model_name
						if is_panoptic:
												UpperCAmelCase__   		=						2_5_0
						else:
												UpperCAmelCase__   		=						9_1
												UpperCAmelCase__   		=						"""huggingface/label-files"""
												UpperCAmelCase__   		=						"""coco-detection-id2label.json"""
												UpperCAmelCase__   		=						json.load(open(hf_hub_download(__lowercase						,     __lowercase						,     repo_type="""dataset"""     )						,     """r"""     )     )
												UpperCAmelCase__   		=						{int(__lowercase     ): v for k, v in idalabel.items()}
												UpperCAmelCase__   		=						idalabel
												UpperCAmelCase__   		=						{v: k for k, v in idalabel.items()}

						# load image processor
						UpperCAmelCase__   		=						"""coco_panoptic""" if is_panoptic else """coco_detection"""
						UpperCAmelCase__   		=						DetrImageProcessor(format=__lowercase     )

						# prepare image
						UpperCAmelCase__   		=						prepare_img()
						UpperCAmelCase__   		=						image_processor(images=__lowercase						,     return_tensors="""pt"""     )
						UpperCAmelCase__   		=						encoding["""pixel_values"""]

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

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


"""simple docstring"""







def 					a__ (						UpperCAmelCase__    :     List[Any]     )->		Dict:
						UpperCAmelCase__   		=						[7_0_1, 3_0_1, 1_3_2, 5_7, 2_3, 1_0, 4, 1]
						for gap in gaps:
												for i in range(UpperCAmelCase__						,     len(UpperCAmelCase__     )     ):
																		UpperCAmelCase__   		=						collection[i]
																		UpperCAmelCase__   		=						i
																		while j >= gap and collection[j - gap] > insert_value:
																								UpperCAmelCase__   		=						collection[j - gap]
																								j -= gap
																		if j != i:
																								UpperCAmelCase__   		=						insert_value
						return collection


if __name__ == "__main__":
						from doctest import testmod

						testmod()
						UpperCamelCase_    :    Union[str, Any]											=						input("""Enter numbers separated by a comma:\n""").strip()
						UpperCamelCase_    :    int											=						[int(item) for item in user_input.split(""",""")]
						print(shell_sort(unsorted))

