


import matplotlib.pyplot as plt
import numpy as np


def 							SCREAMING_SNAKE_CASE					(  _UpperCAmelCase							,     _UpperCAmelCase							,     _UpperCAmelCase				)    ->  np.ndarray:
						_a     =			len(_UpperCAmelCase				)  # Number of training samples
						_a     =			np.eye(_UpperCAmelCase				)  # Initialize weights as identity matrix
						for j in range(_UpperCAmelCase				):
												_a     =			point - x_train[j]
												_a     =			np.exp(diff @ diff.T / (-2.0 * tau**2)				)

						return weights


def 							SCREAMING_SNAKE_CASE					(  _UpperCAmelCase							,     _UpperCAmelCase							,     _UpperCAmelCase							,     _UpperCAmelCase				)    ->  np.ndarray:
						_a     =			weight_matrix(_UpperCAmelCase							,     _UpperCAmelCase							,     _UpperCAmelCase				)
						_a     =			np.linalg.inv(x_train.T @ weight_mat @ x_train				) @ (
						    x_train.T @ weight_mat @ y_train.T
						)

						return weight


def 							SCREAMING_SNAKE_CASE					(  _UpperCAmelCase							,     _UpperCAmelCase							,     _UpperCAmelCase				)    ->  np.ndarray:
						_a     =			np.zeros(len(_UpperCAmelCase				)				)  # Initialize array of predictions
						for i, item in enumerate(_UpperCAmelCase				):
												_a     =			item @ local_weight(_UpperCAmelCase							,     _UpperCAmelCase							,     _UpperCAmelCase							,     _UpperCAmelCase				)

						return y_pred


def 							SCREAMING_SNAKE_CASE					(  _UpperCAmelCase							,     _UpperCAmelCase							,     _UpperCAmelCase				)    ->  tuple[np.ndarray, np.ndarray, np.ndarray]:
						import seaborn as sns

						_a     =			sns.load_dataset(_UpperCAmelCase				)
						_a     =			np.array(data[x_name]				)
						_a     =			np.array(data[y_name]				)

						_a     =			np.ones(len(_UpperCAmelCase				)				)

						# pairing elements of one and x_data
						_a     =			np.column_stack((one, x_data)				)

						return x_train, x_data, y_data


def 							SCREAMING_SNAKE_CASE					(  _UpperCAmelCase							,     _UpperCAmelCase							,     _UpperCAmelCase							,     _UpperCAmelCase							,     _UpperCAmelCase							,     _UpperCAmelCase							,     )    ->  None:
						_a     =			np.sort(_UpperCAmelCase							,     axis=0				)
						plt.scatter(_UpperCAmelCase							,     _UpperCAmelCase							,     color='blue'				)
						plt.plot(
						    x_train_sorted[:, 1]							,     preds[x_train[:, 1].argsort(0				)]							,     color='yellow'							,     linewidth=5							,     )
						plt.title('Local Weighted Regression'				)
						plt.xlabel(_UpperCAmelCase				)
						plt.ylabel(_UpperCAmelCase				)
						plt.show()


if __name__ == "__main__":
			import doctest

			doctest.testmod()

			# Demo with a dataset from the seaborn module
			lowercase_		,      lowercase_		,      lowercase_								=    load_data('tips', 'total_bill', 'tip')
			lowercase_								=    local_weight_regression(training_data_x, tip, 5)
			plot_preds(training_data_x, predictions, total_bill, tip, 'total_bill', 'tip')






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()
lowercase_								=    logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
lowercase_								=    []
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 							SCREAMING_SNAKE_CASE					(  _UpperCAmelCase							,     _UpperCAmelCase							,     _UpperCAmelCase				)    ->  Union[str, Any]:
						_a     =			state_dict.pop(_UpperCAmelCase				)
						_a     =			val


def 							SCREAMING_SNAKE_CASE					(  _UpperCAmelCase				)    ->  List[str]:
						_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 							SCREAMING_SNAKE_CASE					(  _UpperCAmelCase							,     _UpperCAmelCase=False				)    ->  Tuple:
						_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 							SCREAMING_SNAKE_CASE					(  )    ->  Union[str, Any]:
						_a     =			'http://images.cocodataset.org/val2017/000000039769.jpg'
						_a     =			Image.open(requests.get(_UpperCAmelCase							,     stream=_UpperCAmelCase				).raw				)

						return im


@torch.no_grad()
def 							SCREAMING_SNAKE_CASE					(  _UpperCAmelCase							,     _UpperCAmelCase				)    ->  List[str]:
						_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__":
		lowercase_								=    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.'
		)
		lowercase_								=    parser.parse_args()
		convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)


