





'''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_     			=						logging.get_logger(__name__)
def 				_lowerCamelCase(	UpperCamelCase__     :			Dict		)		->	Optional[int]:
    A							:							Dict				=    ConvNextVaConfig()

    if "atto" in checkpoint_url:
        A							:							str				=    [2, 2, 6, 2]
        A							:							List[Any]				=    [40, 80, 160, 320]
    if "femto" in checkpoint_url:
        A							:							int				=    [2, 2, 6, 2]
        A							:							str				=    [48, 96, 192, 384]
    if "pico" in checkpoint_url:
        A							:							Any				=    [2, 2, 6, 2]
        A							:							Union[str, Any]				=    [64, 128, 256, 512]
    if "nano" in checkpoint_url:
        A							:							Optional[Any]				=    [2, 2, 8, 2]
        A							:							Dict				=    [80, 160, 320, 640]
    if "tiny" in checkpoint_url:
        A							:							int				=    [3, 3, 9, 3]
        A							:							Optional[Any]				=    [96, 192, 384, 768]
    if "base" in checkpoint_url:
        A							:							Optional[int]				=    [3, 3, 27, 3]
        A							:							Optional[int]				=    [128, 256, 512, 1_024]
    if "large" in checkpoint_url:
        A							:							List[str]				=    [3, 3, 27, 3]
        A							:							Dict				=    [192, 384, 768, 1_536]
    if "huge" in checkpoint_url:
        A							:							int				=    [3, 3, 27, 3]
        A							:							int				=    [352, 704, 1_408, 2_816]

    A							:							int				=    1_000
    A							:							Any				=    '''imagenet-1k-id2label.json'''
    A							:							Any				=    (1, 1_000)

    A							:							str				=    '''huggingface/label-files'''
    A							:							List[Any]				=    num_labels
    A							:							Tuple				=    json.load(open(hf_hub_download(UpperCamelCase__     ,							UpperCamelCase__     ,							repo_type='''dataset'''		)     ,							'''r'''		)		)
    A							:							Optional[Any]				=    {int(UpperCamelCase__		): v for k, v in idalabel.items()}

    A							:							List[Any]				=    idalabel
    A							:							Tuple				=    {v: k for k, v in idalabel.items()}
    A							:							Any				=    hidden_sizes
    A							:							Dict				=    depths

    return config, expected_shape
def 				_lowerCamelCase(	UpperCamelCase__     :			int		)		->	Optional[int]:
    if "downsample_layers.0.0" in name:
        A							:							int				=    name.replace('''downsample_layers.0.0'''     ,							'''embeddings.patch_embeddings'''		)
    if "downsample_layers.0.1" in name:
        A							:							Optional[Any]				=    name.replace('''downsample_layers.0.1'''     ,							'''embeddings.norm'''		)  # we rename to layernorm later on
    if "downsample_layers.1.0" in name:
        A							:							Any				=    name.replace('''downsample_layers.1.0'''     ,							'''stages.1.downsampling_layer.0'''		)
    if "downsample_layers.1.1" in name:
        A							:							int				=    name.replace('''downsample_layers.1.1'''     ,							'''stages.1.downsampling_layer.1'''		)
    if "downsample_layers.2.0" in name:
        A							:							Tuple				=    name.replace('''downsample_layers.2.0'''     ,							'''stages.2.downsampling_layer.0'''		)
    if "downsample_layers.2.1" in name:
        A							:							List[Any]				=    name.replace('''downsample_layers.2.1'''     ,							'''stages.2.downsampling_layer.1'''		)
    if "downsample_layers.3.0" in name:
        A							:							Union[str, Any]				=    name.replace('''downsample_layers.3.0'''     ,							'''stages.3.downsampling_layer.0'''		)
    if "downsample_layers.3.1" in name:
        A							:							Tuple				=    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.
        A							:							Union[str, Any]				=    name[: len('''stages.0'''		)] + '''.layers''' + name[len('''stages.0'''		) :]
    if "gamma" in name:
        A							:							Union[str, Any]				=    name.replace('''gamma'''     ,							'''weight'''		)
    if "beta" in name:
        A							:							Optional[int]				=    name.replace('''beta'''     ,							'''bias'''		)
    if "stages" in name:
        A							:							str				=    name.replace('''stages'''     ,							'''encoder.stages'''		)
    if "norm" in name:
        A							:							Union[str, Any]				=    name.replace('''norm'''     ,							'''layernorm'''		)
    if "head" in name:
        A							:							Union[str, Any]				=    name.replace('''head'''     ,							'''classifier'''		)

    return name
def 				_lowerCamelCase(	)		->	List[Any]:
    A							:							Any				=    '''http://images.cocodataset.org/val2017/000000039769.jpg'''
    A							:							Tuple				=    Image.open(requests.get(UpperCamelCase__     ,							stream=UpperCamelCase__		).raw		)
    return im
def 				_lowerCamelCase(	UpperCamelCase__     :			Dict		)		->	Any:
    if "224" in checkpoint_url:
        A							:							str				=    224
        A							:							str				=    224 / 256
    elif "384" in checkpoint_url:
        A							:							int				=    384
        A							:							List[Any]				=    None
    else:
        A							:							Optional[int]				=    512
        A							:							str				=    None

    return ConvNextImageProcessor(
        size=UpperCamelCase__     ,							crop_pct=UpperCamelCase__     ,							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(	UpperCamelCase__     :			Optional[Any]     ,							UpperCamelCase__     :			List[Any]     ,							UpperCamelCase__     :			Tuple     ,							UpperCamelCase__     :			Tuple		)		->	Union[str, Any]:
    print('''Downloading original model from checkpoint...'''		)
    # define ConvNeXTV2 configuration based on URL
    A,						A							:							Dict				=    get_convnextva_config(UpperCamelCase__		)
    # load original state_dict from URL
    A							:							Union[str, Any]				=    torch.hub.load_state_dict_from_url(UpperCamelCase__		)['''model''']

    print('''Converting model parameters...'''		)
    # rename keys
    for key in state_dict.copy().keys():
        A							:							Optional[int]				=    state_dict.pop(UpperCamelCase__		)
        A							:							List[Any]				=    val
    # add prefix to all keys expect classifier head
    for key in state_dict.copy().keys():
        A							:							List[Any]				=    state_dict.pop(UpperCamelCase__		)
        if not key.startswith('''classifier'''		):
            A							:							str				=    '''convnextv2.''' + key
        A							:							Optional[Any]				=    val

    # load HuggingFace model
    A							:							List[Any]				=    ConvNextVaForImageClassification(UpperCamelCase__		)
    model.load_state_dict(UpperCamelCase__		)
    model.eval()

    # Check outputs on an image, prepared by ConvNextImageProcessor
    A							:							str				=    convert_preprocessor(UpperCamelCase__		)
    A							:							Union[str, Any]				=    preprocessor(images=prepare_img()     ,							return_tensors='''pt'''		)
    A							:							int				=    model(**UpperCamelCase__		).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":
        A							:							Union[str, Any]				=    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":
        A							:							Union[str, Any]				=    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":
        A							:							Optional[int]				=    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":
        A							:							List[str]				=    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":
        A							:							Any				=    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":
        A							:							str				=    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":
        A							:							List[Any]				=    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":
        A							:							Dict				=    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":
        A							:							Optional[Any]				=    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":
        A							:							Optional[int]				=    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":
        A							:							Optional[Any]				=    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":
        A							:							Tuple				=    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":
        A							:							int				=    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":
        A							:							Any				=    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":
        A							:							int				=    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":
        A							:							Any				=    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":
        A							:							str				=    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":
        A							:							Union[str, Any]				=    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]     ,							UpperCamelCase__     ,							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(UpperCamelCase__		):
            os.mkdir(UpperCamelCase__		)

        model.save_pretrained(UpperCamelCase__		)
        preprocessor.save_pretrained(UpperCamelCase__		)

    A							:							int				=    '''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(UpperCamelCase__		)
        preprocessor.push_to_hub(UpperCamelCase__		)


if __name__ == "__main__":
     snake_case_     			=						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_     			=						parser.parse_args()
     convert_convnextva_checkpoint(
         args.checkpoint_url, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub
     )







'''simple docstring'''






import timeit


class 	_lowercase    :


   def __init__(			self     , _UpperCAmelCase       ):
       if isinstance(_UpperCAmelCase     , _UpperCAmelCase       ):  # Initializes a matrix identical to the one provided.
           A							:							List[Any]				=    arg
           A							:							Dict				=    len(_UpperCAmelCase       )
       else:  # Initializes a square matrix of the given size and set values to zero.
           A							:							str				=    arg
           A							:							Union[str, Any]				=    [[0 for _ in range(self.n       )] for _ in range(self.n       )]


   def __mul__(			self     , _UpperCAmelCase       ):
       A							:							List[str]				=    Matrix(self.n       )
       for i in range(self.n       ):
           for j in range(self.n       ):
               for k in range(self.n       ):
                   matrix.t[i][j] += self.t[i][k] * b.t[k][j]
       return matrix







def 				_lowerCamelCase(	UpperCamelCase__     :			Union[str, Any]     ,							UpperCamelCase__     :			int		)		->	Union[str, Any]:
    A							:							Tuple				=    Matrix([[1, 0], [0, 1]]		)
    while b > 0:
        if b & 1:
            matrix *= a
        a *= a
        b >>= 1
    return matrix
def 				_lowerCamelCase(	UpperCamelCase__     :			int     ,							UpperCamelCase__     :			Tuple     ,							UpperCamelCase__     :			Any		)		->	Any:
    # Trivial Cases
    if n == 1:
        return fa
    elif n == 2:
        return fa
    A							:							Optional[Any]				=    Matrix([[1, 1], [1, 0]]		)
    A							:							int				=    modular_exponentiation(UpperCamelCase__     ,							n - 2		)
    return fa * matrix.t[0][0] + fa * matrix.t[0][1]
def 				_lowerCamelCase(	UpperCamelCase__     :			str     ,							UpperCamelCase__     :			Optional[Any]     ,							UpperCamelCase__     :			Any		)		->	List[Any]:
    # Trivial Cases
    if n == 1:
        return fa
    elif n == 2:
        return fa

    A							:							str				=    fa
    A							:							List[Any]				=    fa
    n -= 2

    while n > 0:
        A,						A							:							List[str]				=    fn_a + fn_a, fn_a
        n -= 1

    return fn_a
def 				_lowerCamelCase(	)		->	Union[str, Any]:
    A							:							Any				=    '''
from random import randint
from __main__ import fibonacci_with_matrix_exponentiation
'''
    A							:							Optional[int]				=    '''fibonacci_with_matrix_exponentiation(randint(1,70000), 1, 1)'''
    A							:							List[Any]				=    timeit.timeit(setup=UpperCamelCase__     ,							stmt=UpperCamelCase__     ,							number=100		)
    print('''With matrix exponentiation the average execution time is '''     ,							exec_time / 100		)
    return exec_time
def 				_lowerCamelCase(	)		->	List[Any]:
    A							:							str				=    '''
from random import randint
from __main__ import simple_fibonacci
'''
    A							:							Any				=    '''simple_fibonacci(randint(1,70000), 1, 1)'''
    A							:							str				=    timeit.timeit(setup=UpperCamelCase__     ,							stmt=UpperCamelCase__     ,							number=100		)
    print(
        '''Without matrix exponentiation the average execution time is '''     ,							exec_time / 100		)
    return exec_time
def 				_lowerCamelCase(	)		->	Optional[Any]:
    matrix_exponentiation_time()
    simple_fibonacci_time()


if __name__ == "__main__":
     main()
