





"""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()
_snake_case	:						Dict					=					logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
_snake_case	:						Optional[Any]					=					[]
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   ,    UpperCamelCase   ,    UpperCamelCase			):
							A						=    state_dict.pop(UpperCamelCase			)
							A						=    val






def    A__							(     UpperCamelCase			):
							A						=    OrderedDict()
							for key, value in state_dict.items():
														if "backbone.0.body" in key:
																					A						=    key.replace("backbone.0.body"   ,    "backbone.conv_encoder.model"			)
																					A						=    value
														else:
																					A						=    value

							return new_state_dict






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






def    A__							(     ):
							A						=    "http://images.cocodataset.org/val2017/000000039769.jpg"
							A						=    Image.open(requests.get(UpperCamelCase   ,    stream=UpperCamelCase			).raw			)

							return im






@torch.no_grad()
def    A__							(     UpperCamelCase   ,    UpperCamelCase			):
							A						=    DetrConfig()
							# set backbone and dilation attributes
							if "resnet101" in model_name:
														A						=    "resnet101"
							if "dc5" in model_name:
														A						=    True
							A						=    "panoptic" in model_name
							if is_panoptic:
														A						=    250
							else:
														A						=    91
														A						=    "huggingface/label-files"
														A						=    "coco-detection-id2label.json"
														A						=    json.load(open(hf_hub_download(UpperCamelCase   ,    UpperCamelCase   ,    repo_type="dataset"			)   ,    "r"			)			)
														A						=    {int(UpperCamelCase			): v for k, v in idalabel.items()}
														A						=    idalabel
														A						=    {v: k for k, v in idalabel.items()}

							# load image processor
							A						=    "coco_panoptic" if is_panoptic else "coco_detection"
							A						=    DetrImageProcessor(format=UpperCamelCase			)

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

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

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









"""simple docstring"""
from functools import lru_cache






@lru_cache
def    A__							(     UpperCamelCase			):
							if not isinstance(UpperCamelCase   ,    UpperCamelCase			):
														raise TypeError("k must be an integer."			)
							if k < 0:
														raise ValueError("k integer must be greater or equal to zero."			)
							if k == 0:
														return 0
							elif k == 1:
														return 1
							else:
														return fibonacci(k - 1			) + fibonacci(k - 2			)






def    A__							(     UpperCamelCase   ,    UpperCamelCase			):
							A						=    len(UpperCamelCase			)
							# Find m such that F_m >= n where F_i is the i_th fibonacci number.
							A						=    0
							while True:
														if fibonacci(UpperCamelCase			) >= len_list:
																					A						=    i
																					break
														i += 1
							A						=    0
							while fibb_k > 0:
														A						=    min(
														    offset + fibonacci(fibb_k - 1			)   ,    len_list - 1			)  # Prevent out of range
														A						=    arr[index_k]
														if item_k_1 == val:
																					return index_k
														elif val < item_k_1:
																					fibb_k -= 1
														elif val > item_k_1:
																					offset += fibonacci(fibb_k - 1			)
																					fibb_k -= 2
							else:
														return -1


if __name__ == "__main__":
		import doctest

		doctest.testmod()


