






"""simple docstring"""



import argparse
import json
import os

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import ConvNextImageProcessor, ConvNextVaConfig, ConvNextVaForImageClassification
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


logging.set_verbosity_info()
_snake_case							:   List[Any]	  =       logging.get_logger(__name__)




def     lowerCamelCase_					(	__A	:		Dict      ):
					lowerCamelCase__					=ConvNextVaConfig()

					if "atto" in checkpoint_url:
										lowerCamelCase__					=[2, 2, 6, 2]
										lowerCamelCase__					=[40, 80, 160, 320]
					if "femto" in checkpoint_url:
										lowerCamelCase__					=[2, 2, 6, 2]
										lowerCamelCase__					=[48, 96, 192, 384]
					if "pico" in checkpoint_url:
										lowerCamelCase__					=[2, 2, 6, 2]
										lowerCamelCase__					=[64, 128, 256, 512]
					if "nano" in checkpoint_url:
										lowerCamelCase__					=[2, 2, 8, 2]
										lowerCamelCase__					=[80, 160, 320, 640]
					if "tiny" in checkpoint_url:
										lowerCamelCase__					=[3, 3, 9, 3]
										lowerCamelCase__					=[96, 192, 384, 768]
					if "base" in checkpoint_url:
										lowerCamelCase__					=[3, 3, 27, 3]
										lowerCamelCase__					=[128, 256, 512, 1024]
					if "large" in checkpoint_url:
										lowerCamelCase__					=[3, 3, 27, 3]
										lowerCamelCase__					=[192, 384, 768, 1536]
					if "huge" in checkpoint_url:
										lowerCamelCase__					=[3, 3, 27, 3]
										lowerCamelCase__					=[352, 704, 1408, 2816]

					lowerCamelCase__					=1000
					lowerCamelCase__					='imagenet-1k-id2label.json'
					lowerCamelCase__					=(1, 1000)

					lowerCamelCase__					='huggingface/label-files'
					lowerCamelCase__					=num_labels
					lowerCamelCase__					=json.load(open(hf_hub_download(__A      ,						__A      ,						repo_type='dataset'      )      ,						'r'      )      )
					lowerCamelCase__					={int(__A      ): v for k, v in idalabel.items()}

					lowerCamelCase__					=idalabel
					lowerCamelCase__					={v: k for k, v in idalabel.items()}
					lowerCamelCase__					=hidden_sizes
					lowerCamelCase__					=depths

					return config, expected_shape




def     lowerCamelCase_					(	__A	:		List[str]      ):
					if "downsample_layers.0.0" in name:
										lowerCamelCase__					=name.replace('downsample_layers.0.0'      ,						'embeddings.patch_embeddings'      )
					if "downsample_layers.0.1" in name:
										lowerCamelCase__					=name.replace('downsample_layers.0.1'      ,						'embeddings.norm'      )  # we rename to layernorm later on
					if "downsample_layers.1.0" in name:
										lowerCamelCase__					=name.replace('downsample_layers.1.0'      ,						'stages.1.downsampling_layer.0'      )
					if "downsample_layers.1.1" in name:
										lowerCamelCase__					=name.replace('downsample_layers.1.1'      ,						'stages.1.downsampling_layer.1'      )
					if "downsample_layers.2.0" in name:
										lowerCamelCase__					=name.replace('downsample_layers.2.0'      ,						'stages.2.downsampling_layer.0'      )
					if "downsample_layers.2.1" in name:
										lowerCamelCase__					=name.replace('downsample_layers.2.1'      ,						'stages.2.downsampling_layer.1'      )
					if "downsample_layers.3.0" in name:
										lowerCamelCase__					=name.replace('downsample_layers.3.0'      ,						'stages.3.downsampling_layer.0'      )
					if "downsample_layers.3.1" in name:
										lowerCamelCase__					=name.replace('downsample_layers.3.1'      ,						'stages.3.downsampling_layer.1'      )
					if "stages" in name and "downsampling_layer" not in name:
										# stages.0.0. for instance should be renamed to stages.0.layers.0.
										lowerCamelCase__					=name[: len('stages.0'      )] + '.layers' + name[len('stages.0'      ) :]
					if "gamma" in name:
										lowerCamelCase__					=name.replace('gamma'      ,						'weight'      )
					if "beta" in name:
										lowerCamelCase__					=name.replace('beta'      ,						'bias'      )
					if "stages" in name:
										lowerCamelCase__					=name.replace('stages'      ,						'encoder.stages'      )
					if "norm" in name:
										lowerCamelCase__					=name.replace('norm'      ,						'layernorm'      )
					if "head" in name:
										lowerCamelCase__					=name.replace('head'      ,						'classifier'      )

					return name




def     lowerCamelCase_					(	):
					lowerCamelCase__					='http://images.cocodataset.org/val2017/000000039769.jpg'
					lowerCamelCase__					=Image.open(requests.get(__A      ,						stream=__A      ).raw      )
					return im




def     lowerCamelCase_					(	__A	:		int      ):
					if "224" in checkpoint_url:
										lowerCamelCase__					=224
										lowerCamelCase__					=224 / 256
					elif "384" in checkpoint_url:
										lowerCamelCase__					=384
										lowerCamelCase__					=None
					else:
										lowerCamelCase__					=512
										lowerCamelCase__					=None

					return ConvNextImageProcessor(
					    size=__A      ,						crop_pct=__A      ,						image_mean=[0.4_8_5, 0.4_5_6, 0.4_0_6]      ,						image_std=[0.2_2_9, 0.2_2_4, 0.2_2_5]      ,						resample=PILImageResampling.BICUBIC      ,						)




@torch.no_grad()
def     lowerCamelCase_					(	__A	:		Tuple      ,						__A	:		Any      ,						__A	:		str      ,						__A	:		List[str]      ):
					print('Downloading original model from checkpoint...'      )
					# define ConvNeXTV2 configuration based on URL
					lowerCamelCase__					,  lowerCamelCase__					=get_convnextva_config(__A      )
					# load original state_dict from URL
					lowerCamelCase__					=torch.hub.load_state_dict_from_url(__A      )['model']

					print('Converting model parameters...'      )
					# rename keys
					for key in state_dict.copy().keys():
										lowerCamelCase__					=state_dict.pop(__A      )
										lowerCamelCase__					=val
					# add prefix to all keys expect classifier head
					for key in state_dict.copy().keys():
										lowerCamelCase__					=state_dict.pop(__A      )
										if not key.startswith('classifier'      ):
															lowerCamelCase__					='convnextv2.' + key
										lowerCamelCase__					=val

					# load HuggingFace model
					lowerCamelCase__					=ConvNextVaForImageClassification(__A      )
					model.load_state_dict(__A      )
					model.eval()

					# Check outputs on an image, prepared by ConvNextImageProcessor
					lowerCamelCase__					=convert_preprocessor(__A      )
					lowerCamelCase__					=preprocessor(images=prepare_img()      ,						return_tensors='pt'      )
					lowerCamelCase__					=model(**__A      ).logits

					# note: the logits below were obtained without center cropping
					if checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_atto_1k_224_ema.pt":
										lowerCamelCase__					=torch.tensor([-0.3_9_3_0, 0.1_7_4_7, -0.5_2_4_6, 0.4_1_7_7, 0.4_2_9_5]      )
					elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_femto_1k_224_ema.pt":
										lowerCamelCase__					=torch.tensor([-0.1_7_2_7, -0.5_3_4_1, -0.7_8_1_8, -0.4_7_4_5, -0.6_5_6_6]      )
					elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_pico_1k_224_ema.pt":
										lowerCamelCase__					=torch.tensor([-0.0_3_3_3, 0.1_5_6_3, -0.9_1_3_7, 0.1_0_5_4, 0.0_3_8_1]      )
					elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_nano_1k_224_ema.pt":
										lowerCamelCase__					=torch.tensor([-0.1_7_4_4, -0.1_5_5_5, -0.0_7_1_3, 0.0_9_5_0, -0.1_4_3_1]      )
					elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_tiny_1k_224_ema.pt":
										lowerCamelCase__					=torch.tensor([0.9_9_9_6, 0.1_9_6_6, -0.4_3_8_6, -0.3_4_7_2, 0.6_6_6_1]      )
					elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_base_1k_224_ema.pt":
										lowerCamelCase__					=torch.tensor([-0.2_5_5_3, -0.6_7_0_8, -0.1_3_5_9, 0.2_5_1_8, -0.2_4_8_8]      )
					elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_large_1k_224_ema.pt":
										lowerCamelCase__					=torch.tensor([-0.0_6_7_3, -0.5_6_2_7, -0.3_7_5_3, -0.2_7_2_2, 0.0_1_7_8]      )
					elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_huge_1k_224_ema.pt":
										lowerCamelCase__					=torch.tensor([-0.6_3_7_7, -0.7_4_5_8, -0.2_1_5_0, 0.1_1_8_4, -0.0_5_9_7]      )
					elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_nano_22k_224_ema.pt":
										lowerCamelCase__					=torch.tensor([1.0_7_9_9, 0.2_3_2_2, -0.8_8_6_0, 1.0_2_1_9, 0.6_2_3_1]      )
					elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_nano_22k_384_ema.pt":
										lowerCamelCase__					=torch.tensor([0.3_7_6_6, 0.4_9_1_7, -1.1_4_2_6, 0.9_9_4_2, 0.6_0_2_4]      )
					elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_tiny_22k_224_ema.pt":
										lowerCamelCase__					=torch.tensor([0.4_2_2_0, -0.6_9_1_9, -0.4_3_1_7, -0.2_8_8_1, -0.6_6_0_9]      )
					elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_tiny_22k_384_ema.pt":
										lowerCamelCase__					=torch.tensor([0.1_0_8_2, -0.8_2_8_6, -0.5_0_9_5, 0.4_6_8_1, -0.8_0_8_5]      )
					elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_base_22k_224_ema.pt":
										lowerCamelCase__					=torch.tensor([-0.2_4_1_9, -0.6_2_2_1, 0.2_1_7_6, -0.0_9_8_0, -0.7_5_2_7]      )
					elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_base_22k_384_ema.pt":
										lowerCamelCase__					=torch.tensor([0.0_3_9_1, -0.4_3_7_1, 0.3_7_8_6, 0.1_2_5_1, -0.2_7_8_4]      )
					elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_large_22k_224_ema.pt":
										lowerCamelCase__					=torch.tensor([-0.0_5_0_4, 0.5_6_3_6, -0.1_7_2_9, -0.6_5_0_7, -0.3_9_4_9]      )
					elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_large_22k_384_ema.pt":
										lowerCamelCase__					=torch.tensor([0.3_5_6_0, 0.9_4_8_6, 0.3_1_4_9, -0.2_6_6_7, -0.5_1_3_8]      )
					elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_huge_22k_384_ema.pt":
										lowerCamelCase__					=torch.tensor([-0.2_4_6_9, -0.4_5_5_0, -0.5_8_5_3, -0.0_8_1_0, 0.0_3_0_9]      )
					elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_huge_22k_512_ema.pt":
										lowerCamelCase__					=torch.tensor([-0.3_0_9_0, 0.0_8_0_2, -0.0_6_8_2, -0.1_9_7_9, -0.2_8_2_6]      )
					else:
										raise ValueError(f"Unknown URL: {checkpoint_url}"      )

					assert torch.allclose(logits[0, :5]      ,						__A      ,						atol=1E-3      )
					assert logits.shape == expected_shape
					print('Model outputs match the original results!'      )

					if save_model:
										print('Saving model to local...'      )
										# Create folder to save model
										if not os.path.isdir(__A      ):
															os.mkdir(__A      )

										model.save_pretrained(__A      )
										preprocessor.save_pretrained(__A      )

					lowerCamelCase__					='convnextv2'
					if "atto" in checkpoint_url:
										model_name += "-atto"
					if "femto" in checkpoint_url:
										model_name += "-femto"
					if "pico" in checkpoint_url:
										model_name += "-pico"
					if "nano" in checkpoint_url:
										model_name += "-nano"
					elif "tiny" in checkpoint_url:
										model_name += "-tiny"
					elif "base" in checkpoint_url:
										model_name += "-base"
					elif "large" in checkpoint_url:
										model_name += "-large"
					elif "huge" in checkpoint_url:
										model_name += "-huge"
					if "22k" in checkpoint_url and "1k" not in checkpoint_url:
										model_name += "-22k"
					elif "22k" in checkpoint_url and "1k" in checkpoint_url:
										model_name += "-22k-1k"
					elif "1k" in checkpoint_url:
										model_name += "-1k"
					if "224" in checkpoint_url:
										model_name += "-224"
					elif "384" in checkpoint_url:
										model_name += "-384"
					elif "512" in checkpoint_url:
										model_name += "-512"

					if push_to_hub:
										print(f"Pushing {model_name} to the hub..."      )
										model.push_to_hub(__A      )
										preprocessor.push_to_hub(__A      )


if __name__ == "__main__":
						_snake_case							:   int	  =       argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    "--checkpoint_url",
						    default="https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_atto_1k_224_ema.pt",
						    type=str,
						    help="URL of the original ConvNeXTV2 checkpoint you'd like to convert.",
						)
						parser.add_argument(
						    "--pytorch_dump_folder_path",
						    default="model",
						    type=str,
						    help="Path to the output PyTorch model directory.",
						)
						parser.add_argument("--save_model", action="store_true", help="Save model to local")
						parser.add_argument("--push_to_hub", action="store_true", help="Push model and image preprocessor to the hub")

						_snake_case							:   Dict	  =       parser.parse_args()
						convert_convnextva_checkpoint(
						    args.checkpoint_url, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub
						)







"""simple docstring"""



import random




class 							__lowercase:






						def __init__(       self     ,    _SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE = 0.01     ,    _SCREAMING_SNAKE_CASE = 10_00     ,    _SCREAMING_SNAKE_CASE = -1     ,    )      ->	None:




											"""simple docstring"""





											lowerCamelCase__					=sample
											if len(self.sample) == 0:
																raise ValueError('Sample data can not be empty')
											lowerCamelCase__					=target
											if len(self.target) == 0:
																raise ValueError('Target data can not be empty')
											if len(self.sample) != len(self.target):
																raise ValueError('Sample data and Target data do not have matching lengths')
											lowerCamelCase__					=learning_rate
											lowerCamelCase__					=epoch_number
											lowerCamelCase__					=bias
											lowerCamelCase__					=len(_SCREAMING_SNAKE_CASE)
											lowerCamelCase__					=len(sample[0])  # number of columns in dataset
											lowerCamelCase__					=[]






						def 		lowerCAmelCase (       self)      ->	None:




											"""simple docstring"""





											for sample in self.sample:
																sample.insert(0     ,    self.bias)

											for _ in range(self.col_sample):
																self.weight.append(random.random())

											self.weight.insert(0     ,    self.bias)

											lowerCamelCase__					=0

											while True:
																lowerCamelCase__					=False
																for i in range(self.number_sample):
																					lowerCamelCase__					=0
																					for j in range(self.col_sample + 1):
																										lowerCamelCase__					=u + self.weight[j] * self.sample[i][j]
																					lowerCamelCase__					=self.sign(_SCREAMING_SNAKE_CASE)
																					if y != self.target[i]:
																										for j in range(self.col_sample + 1):
																															lowerCamelCase__					=(
																															    self.weight[j]
																															    + self.learning_rate
																															    * (self.target[i] - y)
																															    * self.sample[i][j]
																															)
																										lowerCamelCase__					=True
            # print('Epoch: \n',epoch_count)
																lowerCamelCase__					=epoch_count + 1
																# if you want control the epoch or just by error
																if not has_misclassified:
																					print(('\nEpoch:\n', epoch_count))
																					print('------------------------\n')
																					# if epoch_count > self.epoch_number or not error:
																					break






						def 		lowerCAmelCase (       self     ,    _SCREAMING_SNAKE_CASE)      ->	None:




											"""simple docstring"""





											if len(self.sample) == 0:
																raise ValueError('Sample data can not be empty')
											sample.insert(0     ,    self.bias)
											lowerCamelCase__					=0
											for i in range(self.col_sample + 1):
																lowerCamelCase__					=u + self.weight[i] * sample[i]

											lowerCamelCase__					=self.sign(_SCREAMING_SNAKE_CASE)

											if y == -1:
																print(('Sample: ', sample))
																print('classification: P1')
											else:
																print(('Sample: ', sample))
																print('classification: P2')






						def 		lowerCAmelCase (       self     ,    _SCREAMING_SNAKE_CASE)      ->	int:




											"""simple docstring"""





											return 1 if u >= 0 else -1



_snake_case							:   int	  =       [
    [-0.6508, 0.1097, 4.0009],
    [-1.4492, 0.8896, 4.4005],
    [2.0850, 0.6876, 12.0710],
    [0.2626, 1.1476, 7.7985],
    [0.6418, 1.0234, 7.0427],
    [0.2569, 0.6730, 8.3265],
    [1.1155, 0.6043, 7.4446],
    [0.0914, 0.3399, 7.0677],
    [0.0121, 0.5256, 4.6316],
    [-0.0429, 0.4660, 5.4323],
    [0.4340, 0.6870, 8.2287],
    [0.2735, 1.0287, 7.1934],
    [0.4839, 0.4851, 7.4850],
    [0.4089, -0.1267, 5.5019],
    [1.4391, 0.1614, 8.5843],
    [-0.9115, -0.1973, 2.1962],
    [0.3654, 1.0475, 7.4858],
    [0.2144, 0.7515, 7.1699],
    [0.2013, 1.0014, 6.5489],
    [0.6483, 0.2183, 5.8991],
    [-0.1147, 0.2242, 7.2435],
    [-0.7970, 0.8795, 3.8762],
    [-1.0625, 0.6366, 2.4707],
    [0.5307, 0.1285, 5.6883],
    [-1.2200, 0.7777, 1.7252],
    [0.3957, 0.1076, 5.6623],
    [-0.1013, 0.5989, 7.1812],
    [2.4482, 0.9455, 11.2095],
    [2.0149, 0.6192, 10.9263],
    [0.2012, 0.2611, 5.4631],
]

_snake_case							:   List[str]	  =       [
    -1,
    -1,
    -1,
    1,
    1,
    -1,
    1,
    -1,
    1,
    1,
    -1,
    1,
    -1,
    -1,
    -1,
    -1,
    1,
    1,
    1,
    1,
    -1,
    1,
    1,
    1,
    1,
    -1,
    -1,
    1,
    -1,
    1,
]


if __name__ == "__main__":
						import doctest

						doctest.testmod()

						_snake_case							:   int	  =       Perceptron(
						    sample=samples, target=target, learning_rate=0.01, epoch_number=1_000, bias=-1
						)
						network.training()
						print("Finished training perceptron")
						print("Enter values to predict or q to exit")
						while True:
												_snake_case							:   list	  =       []
												for i in range(len(samples[0])):
																		_snake_case							:   List[str]	  =       input("value: ").strip()
																		if user_input == "q":
																								break
																		_snake_case							:   Optional[Any]	  =       float(user_input)
																		sample.insert(i, observation)
												network.sort(sample)