"""simple docstring"""






import argparse
import json
from pathlib import Path

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

from transformers import ConvNextConfig, ConvNextForImageClassification, ConvNextImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
lowerCAmelCase__  :  List[str]											=     logging.get_logger(__name__)



def   a_						( lowerCamelCase	):
    UpperCAmelCase__					=							ConvNextConfig()

    if "tiny" in checkpoint_url:
        UpperCAmelCase__					=							[3, 3, 9, 3]
        UpperCAmelCase__					=							[9_6, 1_9_2, 3_8_4, 7_6_8]
    if "small" in checkpoint_url:
        UpperCAmelCase__					=							[3, 3, 2_7, 3]
        UpperCAmelCase__					=							[9_6, 1_9_2, 3_8_4, 7_6_8]
    if "base" in checkpoint_url:
        UpperCAmelCase__					=							[3, 3, 2_7, 3]
        UpperCAmelCase__					=							[1_2_8, 2_5_6, 5_1_2, 1_0_2_4]
    if "large" in checkpoint_url:
        UpperCAmelCase__					=							[3, 3, 2_7, 3]
        UpperCAmelCase__					=							[1_9_2, 3_8_4, 7_6_8, 1_5_3_6]
    if "xlarge" in checkpoint_url:
        UpperCAmelCase__					=							[3, 3, 2_7, 3]
        UpperCAmelCase__					=							[2_5_6, 5_1_2, 1_0_2_4, 2_0_4_8]

    if "1k" in checkpoint_url:
        UpperCAmelCase__					=							1_0_0_0
        UpperCAmelCase__					=							'imagenet-1k-id2label.json'
        UpperCAmelCase__					=							(1, 1_0_0_0)
    else:
        UpperCAmelCase__					=							2_1_8_4_1
        UpperCAmelCase__					=							'imagenet-22k-id2label.json'
        UpperCAmelCase__					=							(1, 2_1_8_4_1)

    UpperCAmelCase__					=							'huggingface/label-files'
    UpperCAmelCase__					=							num_labels
    UpperCAmelCase__					=							json.load(open(hf_hub_download(lowerCamelCase ,      lowerCamelCase ,      repo_type='dataset'	) ,      'r'	)	)
    UpperCAmelCase__					=							{int(lowerCamelCase	): v for k, v in idalabel.items()}
    if "1k" not in checkpoint_url:
        # this dataset contains 21843 labels but the model only has 21841
        # we delete the classes as mentioned in https://github.com/google-research/big_transfer/issues/18
        del idalabel[9_2_0_5]
        del idalabel[1_5_0_2_7]
    UpperCAmelCase__					=							idalabel
    UpperCAmelCase__					=							{v: k for k, v in idalabel.items()}
    UpperCAmelCase__					=							hidden_sizes
    UpperCAmelCase__					=							depths

    return config, expected_shape



def   a_						( lowerCamelCase	):
    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 "stages" in name:
        UpperCAmelCase__					=							name.replace('stages' ,      'encoder.stages'	)
    if "norm" in name:
        UpperCAmelCase__					=							name.replace('norm' ,      'layernorm'	)
    if "gamma" in name:
        UpperCAmelCase__					=							name.replace('gamma' ,      'layer_scale_parameter'	)
    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(lowerCamelCase ,      stream=lowerCamelCase	).raw	)
    return im



@torch.no_grad()
def   a_						( lowerCamelCase ,      lowerCamelCase	):
    UpperCAmelCase__				,     UpperCAmelCase__					=							get_convnext_config(lowerCamelCase	)
    # load original state_dict from URL
    UpperCAmelCase__					=							torch.hub.load_state_dict_from_url(lowerCamelCase	)['model']
    # rename keys
    for key in state_dict.copy().keys():
        UpperCAmelCase__					=							state_dict.pop(lowerCamelCase	)
        UpperCAmelCase__					=							val
    # add prefix to all keys expect classifier head
    for key in state_dict.copy().keys():
        UpperCAmelCase__					=							state_dict.pop(lowerCamelCase	)
        if not key.startswith('classifier'	):
            UpperCAmelCase__					=							'convnext.' + key
        UpperCAmelCase__					=							val

    # load HuggingFace model
    UpperCAmelCase__					=							ConvNextForImageClassification(lowerCamelCase	)
    model.load_state_dict(lowerCamelCase	)
    model.eval()

    # Check outputs on an image, prepared by ConvNextImageProcessor
    UpperCAmelCase__					=							2_2_4 if '224' in checkpoint_url else 3_8_4
    UpperCAmelCase__					=							ConvNextImageProcessor(size=lowerCamelCase	)
    UpperCAmelCase__					=							image_processor(images=prepare_img() ,      return_tensors='pt'	).pixel_values

    UpperCAmelCase__					=							model(lowerCamelCase	).logits

    # note: the logits below were obtained without center cropping
    if checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_tiny_1k_224_ema.pth":
        UpperCAmelCase__					=							torch.tensor([-0.1210, -0.6605, 0.1918]	)
    elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_small_1k_224_ema.pth":
        UpperCAmelCase__					=							torch.tensor([-0.4473, -0.1847, -0.6365]	)
    elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_1k_224_ema.pth":
        UpperCAmelCase__					=							torch.tensor([0.4525, 0.7539, 0.0308]	)
    elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_1k_384.pth":
        UpperCAmelCase__					=							torch.tensor([0.3561, 0.6350, -0.0384]	)
    elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_1k_224_ema.pth":
        UpperCAmelCase__					=							torch.tensor([0.4174, -0.0989, 0.1489]	)
    elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_1k_384.pth":
        UpperCAmelCase__					=							torch.tensor([0.2513, -0.1349, -0.1613]	)
    elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_22k_224.pth":
        UpperCAmelCase__					=							torch.tensor([1.2980, 0.3631, -0.1198]	)
    elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_22k_224.pth":
        UpperCAmelCase__					=							torch.tensor([1.2963, 0.1227, 0.1723]	)
    elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_xlarge_22k_224.pth":
        UpperCAmelCase__					=							torch.tensor([1.7956, 0.8390, 0.2820]	)
    elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_22k_1k_224.pth":
        UpperCAmelCase__					=							torch.tensor([-0.2822, -0.0502, -0.0878]	)
    elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_22k_1k_384.pth":
        UpperCAmelCase__					=							torch.tensor([-0.5672, -0.0730, -0.4348]	)
    elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_22k_1k_224.pth":
        UpperCAmelCase__					=							torch.tensor([0.2681, 0.2365, 0.6246]	)
    elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_22k_1k_384.pth":
        UpperCAmelCase__					=							torch.tensor([-0.2642, 0.3931, 0.5116]	)
    elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_xlarge_22k_1k_224_ema.pth":
        UpperCAmelCase__					=							torch.tensor([-0.6677, -0.1873, -0.8379]	)
    elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_xlarge_22k_1k_384_ema.pth":
        UpperCAmelCase__					=							torch.tensor([-0.7749, -0.2967, -0.6444]	)
    else:
        raise ValueError(f'''Unknown URL: {checkpoint_url}'''	)

    assert torch.allclose(logits[0, :3] ,      lowerCamelCase ,      atol=1e-3	)
    assert logits.shape == expected_shape

    Path(lowerCamelCase	).mkdir(exist_ok=lowerCamelCase	)
    print(f'''Saving model to {pytorch_dump_folder_path}'''	)
    model.save_pretrained(lowerCamelCase	)
    print(f'''Saving image processor to {pytorch_dump_folder_path}'''	)
    image_processor.save_pretrained(lowerCamelCase	)

    print('Pushing model to the hub...'	)
    UpperCAmelCase__					=							'convnext'
    if "tiny" in checkpoint_url:
        model_name += "-tiny"
    elif "small" in checkpoint_url:
        model_name += "-small"
    elif "base" in checkpoint_url:
        model_name += "-base"
    elif "xlarge" in checkpoint_url:
        model_name += "-xlarge"
    elif "large" in checkpoint_url:
        model_name += "-large"
    if "224" in checkpoint_url:
        model_name += "-224"
    elif "384" in checkpoint_url:
        model_name += "-384"
    if "22k" in checkpoint_url and "1k" not in checkpoint_url:
        model_name += "-22k"
    if "22k" in checkpoint_url and "1k" in checkpoint_url:
        model_name += "-22k-1k"

    model.push_to_hub(
        repo_path_or_name=Path(lowerCamelCase ,      lowerCamelCase	) ,      organization='nielsr' ,      commit_message='Add model' ,      )


if __name__ == "__main__":
      lowerCAmelCase__  :  Tuple											=     argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          '--checkpoint_url',
          default='https://dl.fbaipublicfiles.com/convnext/convnext_tiny_1k_224_ema.pth',
          type=str,
          help='URL of the original ConvNeXT checkpoint you\'d like to convert.',
      )
      parser.add_argument(
          '--pytorch_dump_folder_path',
          default=None,
          type=str,
          required=True,
          help='Path to the output PyTorch model directory.',
      )

      lowerCAmelCase__  :  Tuple											=     parser.parse_args()
      convert_convnext_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)




"""simple docstring"""






from typing import SupportsIndex

import numpy as np
from scipy.ndimage import convolve



def   a_						( lowerCamelCase ,      lowerCamelCase ,      lowerCamelCase	):
				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_						( lowerCamelCase ,      lowerCamelCase ,      lowerCamelCase ,      lowerCamelCase = None ,      ):
				if alpha is None:
								UpperCAmelCase__					=							0.1

				# Initialize flow
				UpperCAmelCase__					=							np.zeros_like(lowerCamelCase	)
				UpperCAmelCase__					=							np.zeros_like(lowerCamelCase	)

				# 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 / 1_2, 1 / 6, 1 / 1_2], [1 / 6, 0, 1 / 6], [1 / 1_2, 1 / 6, 1 / 1_2]]	)

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

								UpperCAmelCase__					=							convolve(lowerCamelCase ,      lowerCamelCase	)
								UpperCAmelCase__					=							convolve(lowerCamelCase ,      lowerCamelCase	)

								# 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()



