
'''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()
lowerCAmelCase :  Union[str, Any]      =      logging.get_logger(__name__)







def 						A_(			A		:						int):
							UpperCamelCase					       =			ConvNextVaConfig()

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

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

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

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

							return config, expected_shape







def 						A_(			A		:						int):
							if "downsample_layers.0.0" in name:
														UpperCamelCase					       =			name.replace('downsample_layers.0.0'						,			'embeddings.patch_embeddings')
							if "downsample_layers.0.1" in name:
														UpperCamelCase					       =			name.replace('downsample_layers.0.1'						,			'embeddings.norm')  # we rename to layernorm later on
							if "downsample_layers.1.0" in name:
														UpperCamelCase					       =			name.replace('downsample_layers.1.0'						,			'stages.1.downsampling_layer.0')
							if "downsample_layers.1.1" in name:
														UpperCamelCase					       =			name.replace('downsample_layers.1.1'						,			'stages.1.downsampling_layer.1')
							if "downsample_layers.2.0" in name:
														UpperCamelCase					       =			name.replace('downsample_layers.2.0'						,			'stages.2.downsampling_layer.0')
							if "downsample_layers.2.1" in name:
														UpperCamelCase					       =			name.replace('downsample_layers.2.1'						,			'stages.2.downsampling_layer.1')
							if "downsample_layers.3.0" in name:
														UpperCamelCase					       =			name.replace('downsample_layers.3.0'						,			'stages.3.downsampling_layer.0')
							if "downsample_layers.3.1" in name:
														UpperCamelCase					       =			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.
														UpperCamelCase					       =			name[: len('stages.0')] + '.layers' + name[len('stages.0') :]
							if "gamma" in name:
														UpperCamelCase					       =			name.replace('gamma'						,			'weight')
							if "beta" in name:
														UpperCamelCase					       =			name.replace('beta'						,			'bias')
							if "stages" in name:
														UpperCamelCase					       =			name.replace('stages'						,			'encoder.stages')
							if "norm" in name:
														UpperCamelCase					       =			name.replace('norm'						,			'layernorm')
							if "head" in name:
														UpperCamelCase					       =			name.replace('head'						,			'classifier')

							return name







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







def 						A_(			A		:						Tuple):
							if "224" in checkpoint_url:
														UpperCamelCase					       =			224
														UpperCamelCase					       =			224 / 256
							elif "384" in checkpoint_url:
														UpperCamelCase					       =			384
														UpperCamelCase					       =			None
							else:
														UpperCamelCase					       =			512
														UpperCamelCase					       =			None

							return ConvNextImageProcessor(
							    size=A						,			crop_pct=A						,			image_mean=[0.485, 0.456, 0.406]						,			image_std=[0.229, 0.224, 0.225]						,			resample=PILImageResampling.BICUBIC						,			)







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

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

							# load HuggingFace model
							UpperCamelCase					       =			ConvNextVaForImageClassification(A)
							model.load_state_dict(A)
							model.eval()

							# Check outputs on an image, prepared by ConvNextImageProcessor
							UpperCamelCase					       =			convert_preprocessor(A)
							UpperCamelCase					       =			preprocessor(images=prepare_img()						,			return_tensors='pt')
							UpperCamelCase					       =			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":
														UpperCamelCase					       =			torch.tensor([-0.3_930, 0.1_747, -0.5_246, 0.4_177, 0.4_295])
							elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_femto_1k_224_ema.pt":
														UpperCamelCase					       =			torch.tensor([-0.1_727, -0.5_341, -0.7_818, -0.4_745, -0.6_566])
							elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_pico_1k_224_ema.pt":
														UpperCamelCase					       =			torch.tensor([-0.0_333, 0.1_563, -0.9_137, 0.1_054, 0.0_381])
							elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_nano_1k_224_ema.pt":
														UpperCamelCase					       =			torch.tensor([-0.1_744, -0.1_555, -0.0_713, 0.0_950, -0.1_431])
							elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_tiny_1k_224_ema.pt":
														UpperCamelCase					       =			torch.tensor([0.9_996, 0.1_966, -0.4_386, -0.3_472, 0.6_661])
							elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_base_1k_224_ema.pt":
														UpperCamelCase					       =			torch.tensor([-0.2_553, -0.6_708, -0.1_359, 0.2_518, -0.2_488])
							elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_large_1k_224_ema.pt":
														UpperCamelCase					       =			torch.tensor([-0.0_673, -0.5_627, -0.3_753, -0.2_722, 0.0_178])
							elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_huge_1k_224_ema.pt":
														UpperCamelCase					       =			torch.tensor([-0.6_377, -0.7_458, -0.2_150, 0.1_184, -0.0_597])
							elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_nano_22k_224_ema.pt":
														UpperCamelCase					       =			torch.tensor([1.0_799, 0.2_322, -0.8_860, 1.0_219, 0.6_231])
							elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_nano_22k_384_ema.pt":
														UpperCamelCase					       =			torch.tensor([0.3_766, 0.4_917, -1.1_426, 0.9_942, 0.6_024])
							elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_tiny_22k_224_ema.pt":
														UpperCamelCase					       =			torch.tensor([0.4_220, -0.6_919, -0.4_317, -0.2_881, -0.6_609])
							elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_tiny_22k_384_ema.pt":
														UpperCamelCase					       =			torch.tensor([0.1_082, -0.8_286, -0.5_095, 0.4_681, -0.8_085])
							elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_base_22k_224_ema.pt":
														UpperCamelCase					       =			torch.tensor([-0.2_419, -0.6_221, 0.2_176, -0.0_980, -0.7_527])
							elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_base_22k_384_ema.pt":
														UpperCamelCase					       =			torch.tensor([0.0_391, -0.4_371, 0.3_786, 0.1_251, -0.2_784])
							elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_large_22k_224_ema.pt":
														UpperCamelCase					       =			torch.tensor([-0.0_504, 0.5_636, -0.1_729, -0.6_507, -0.3_949])
							elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_large_22k_384_ema.pt":
														UpperCamelCase					       =			torch.tensor([0.3_560, 0.9_486, 0.3_149, -0.2_667, -0.5_138])
							elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_huge_22k_384_ema.pt":
														UpperCamelCase					       =			torch.tensor([-0.2_469, -0.4_550, -0.5_853, -0.0_810, 0.0_309])
							elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_huge_22k_512_ema.pt":
														UpperCamelCase					       =			torch.tensor([-0.3_090, 0.0_802, -0.0_682, -0.1_979, -0.2_826])
							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)

							UpperCamelCase					       =			'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__":
						lowerCAmelCase :  List[Any]      =      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')

						lowerCAmelCase :  int      =      parser.parse_args()
						convert_convnextva_checkpoint(
						    args.checkpoint_url, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub
						)






'''simple docstring'''
from typing import SupportsIndex

import numpy as np
from scipy.ndimage import convolve







def 						A_(			A		:						np.ndarray						,			A		:						np.ndarray						,			A		:						np.ndarray):
							UpperCamelCase					       =			np.stack((horizontal_flow, vertical_flow)						,			2)

							# Create a grid of all pixel coordinates and subtract the flow to get the
							# target pixels coordinates
							UpperCamelCase					       =			np.stack(
							    np.meshgrid(np.arange(0						,			image.shape[1])						,			np.arange(0						,			image.shape[0]))						,			2)
							UpperCamelCase					       =			np.round(grid - flow).astype(np.intaa)

							# Find the locations outside of the original image
							UpperCamelCase					       =			(grid < 0) | (grid >= np.array([image.shape[1], image.shape[0]]))
							UpperCamelCase					       =			0

							UpperCamelCase					       =			image[grid[:, :, 1], grid[:, :, 0]]

							# Set pixels at invalid locations to 0
							UpperCamelCase					       =			0

							return warped







def 						A_(			A		:						np.ndarray						,			A		:						np.ndarray						,			A		:						SupportsIndex						,			A		:						float | None = None						,			):
							if alpha is None:
														UpperCamelCase					       =			0.1

							# Initialize flow
							UpperCamelCase					       =			np.zeros_like(A)
							UpperCamelCase					       =			np.zeros_like(A)

							# Prepare kernels for the calculation of the derivatives and the average velocity
							UpperCamelCase					       =			np.array([[-1, 1], [-1, 1]]) * 0.25
							UpperCamelCase					       =			np.array([[-1, -1], [1, 1]]) * 0.25
							UpperCamelCase					       =			np.array([[1, 1], [1, 1]]) * 0.25
							UpperCamelCase					       =			np.array(
							    [[1 / 12, 1 / 6, 1 / 12], [1 / 6, 0, 1 / 6], [1 / 12, 1 / 6, 1 / 12]])

							# Iteratively refine the flow
							for _ in range(A):
														UpperCamelCase					       =			warp(A						,			A						,			A)
														UpperCamelCase					       =			convolve(A						,			A) + convolve(A						,			A)
														UpperCamelCase					       =			convolve(A						,			A) + convolve(A						,			A)
														UpperCamelCase					       =			convolve(A						,			A) + convolve(A						,			-kernel_t)

														UpperCamelCase					       =			convolve(A						,			A)
														UpperCamelCase					       =			convolve(A						,			A)

														# This updates the flow as proposed in the paper (Step 12)
														UpperCamelCase					       =			(
														    derivative_x * avg_horizontal_velocity
														    + derivative_y * avg_vertical_velocity
														    + derivative_t
														)
														UpperCamelCase					       =			update / (alpha**2 + derivative_x**2 + derivative_y**2)

														UpperCamelCase					       =			avg_horizontal_velocity - derivative_x * update
														UpperCamelCase					       =			avg_vertical_velocity - derivative_y * update

							return horizontal_flow, vertical_flow


if __name__ == "__main__":
						import doctest

						doctest.testmod()




