




'''simple docstring'''

import colorsys
import io

import cva
import matplotlib as mpl
import matplotlib.colors as mplc
import matplotlib.figure as mplfigure
import numpy as np
import torch
from matplotlib.backends.backend_agg import FigureCanvasAgg

from utils import img_tensorize


a__         =     1_000





class __magic_name__:






						def __init__(   self						:						Dict					,   __UpperCamelCase						:						Tuple					,   __UpperCamelCase						:						Dict=1.2					,   __UpperCamelCase						:						Union[str, Any]="g"					,   __UpperCamelCase						:						Optional[Any]=0.5					,   __UpperCamelCase						:						Optional[Any]="-"					,   __UpperCamelCase						:						str="test_out.jpg"					,   __UpperCamelCase						:						Tuple=True					,   __UpperCamelCase						:						Dict=False					,   __UpperCamelCase						:						Optional[Any]=None					,   __UpperCamelCase						:						int=None					,   __UpperCamelCase						:						int=0.7					,   ):



									'''simple docstring'''






									if isinstance(__UpperCamelCase					,   torch.Tensor       ):
												snake_case__          =						img.numpy().astype("""np.uint8"""       )
									if isinstance(__UpperCamelCase					,   __UpperCamelCase       ):
												snake_case__          =						img_tensorize(__UpperCamelCase       )
									assert isinstance(__UpperCamelCase					,   np.ndarray       )

									snake_case__    ,			snake_case__          =						img.shape[1], img.shape[0]
									snake_case__          =						mplfigure.Figure(frameon=__UpperCamelCase       )
									snake_case__          =						fig.get_dpi()
									snake_case__          =						(width * scale + 1E-2) / dpi
									snake_case__          =						(height * scale + 1E-2) / dpi
									fig.set_size_inches(__UpperCamelCase					,   __UpperCamelCase       )
									snake_case__          =						fig.add_axes([0.0, 0.0, 1.0, 1.0]       )
									ax.axis("""off"""       )
									ax.set_xlim(0.0					,   __UpperCamelCase       )
									ax.set_ylim(__UpperCamelCase       )

									snake_case__          =						saveas
									snake_case__          =						rgb
									snake_case__          =						pynb
									snake_case__          =						img
									snake_case__          =						edgecolor
									snake_case__          =						0.5
									snake_case__          =						linestyle
									snake_case__          =						int(np.sqrt(min(__UpperCamelCase					,   __UpperCamelCase       )       ) * scale // 3       )
									snake_case__          =						width
									snake_case__          =						height
									snake_case__          =						scale
									snake_case__          =						fig
									snake_case__          =						ax
									snake_case__          =						pad
									snake_case__          =						idaobj
									snake_case__          =						idaattr
									snake_case__          =						FigureCanvasAgg(__UpperCamelCase       )






						def       __lowerCAmelCase(   self						:						Optional[int]					,   __UpperCamelCase						:						Optional[int]					,   __UpperCamelCase						:						Any=None       ):



									'''simple docstring'''






									if color is None:
												snake_case__          =						self.edgecolor
									((snake_case__)    ,			(snake_case__)    ,			(snake_case__)    ,			(snake_case__))          =						box
									snake_case__          =						xa - xa
									snake_case__          =						ya - ya
									self.ax.add_patch(
									    mpl.patches.Rectangle(
									        (xa, ya)					,   __UpperCamelCase					,   __UpperCamelCase					,   fill=__UpperCamelCase					,   edgecolor=__UpperCamelCase					,   linewidth=self.font_size // 3					,   alpha=self.alpha					,   linestyle=self.linestyle					,   )       )






						def       __lowerCAmelCase(   self						:						Optional[Any]					,   __UpperCamelCase						:						str					,   __UpperCamelCase						:						List[str]=None					,   __UpperCamelCase						:						Dict=None					,   __UpperCamelCase						:						Any=None					,   __UpperCamelCase						:						List[str]=None       ):



									'''simple docstring'''






									if len(boxes.shape       ) > 2:
												snake_case__          =						boxes[0]
									if len(obj_ids.shape       ) > 1:
												snake_case__          =						obj_ids[0]
									if len(obj_scores.shape       ) > 1:
												snake_case__          =						obj_scores[0]
									if len(attr_ids.shape       ) > 1:
												snake_case__          =						attr_ids[0]
									if len(attr_scores.shape       ) > 1:
												snake_case__          =						attr_scores[0]
									if isinstance(__UpperCamelCase					,   torch.Tensor       ):
												snake_case__          =						boxes.numpy()
									if isinstance(__UpperCamelCase					,   __UpperCamelCase       ):
												snake_case__          =						np.array(__UpperCamelCase       )
									assert isinstance(__UpperCamelCase					,   np.ndarray       )
									snake_case__          =						np.prod(boxes[:, 2:] - boxes[:, :2]					,   axis=1       )
									snake_case__          =						np.argsort(-areas       ).tolist()
									snake_case__          =						boxes[sorted_idxs] if boxes is not None else None
									snake_case__          =						obj_ids[sorted_idxs] if obj_ids is not None else None
									snake_case__          =						obj_scores[sorted_idxs] if obj_scores is not None else None
									snake_case__          =						attr_ids[sorted_idxs] if attr_ids is not None else None
									snake_case__          =						attr_scores[sorted_idxs] if attr_scores is not None else None

									snake_case__          =						[self._random_color(maximum=1       ) for _ in range(len(__UpperCamelCase       )       )]
									snake_case__          =						[assigned_colors[idx] for idx in sorted_idxs]
									if obj_ids is not None:
												snake_case__          =						self._create_text_labels_attr(__UpperCamelCase					,   __UpperCamelCase					,   __UpperCamelCase					,   __UpperCamelCase       )
												for i in range(len(__UpperCamelCase       )       ):
															snake_case__          =						assigned_colors[i]
															self.add_box(boxes[i]					,   __UpperCamelCase       )
															self.draw_labels(labels[i]					,   boxes[i]					,   __UpperCamelCase       )






						def       __lowerCAmelCase(   self						:						Any					,   __UpperCamelCase						:						List[str]					,   __UpperCamelCase						:						List[Any]					,   __UpperCamelCase						:						List[str]       ):



									'''simple docstring'''






									snake_case__    ,			snake_case__    ,			snake_case__    ,			snake_case__          =						box
									snake_case__          =						(xa, ya)
									snake_case__          =						(ya - ya) * (xa - xa)
									snake_case__          =						_SMALL_OBJ * self.scale
									if instance_area < small or ya - ya < 4_0 * self.scale:
												if ya >= self.height - 5:
															snake_case__          =						(xa, ya)
												else:
															snake_case__          =						(xa, ya)

									snake_case__          =						(ya - ya) / np.sqrt(self.height * self.width       )
									snake_case__          =						self._change_color_brightness(__UpperCamelCase					,   brightness_factor=0.7       )
									snake_case__          =						np.clip((height_ratio - 0.02) / 0.08 + 1					,   1.2					,   2       )
									font_size *= 0.75 * self.font_size

									self.draw_text(
									    text=__UpperCamelCase					,   position=__UpperCamelCase					,   color=__UpperCamelCase					,   )






						def       __lowerCAmelCase(   self						:						int					,   __UpperCamelCase						:						int					,   __UpperCamelCase						:						Any					,   __UpperCamelCase						:						Any="g"					,   __UpperCamelCase						:						Dict="left"					,   ):



									'''simple docstring'''






									snake_case__          =						0
									snake_case__          =						self.font_size
									snake_case__          =						np.maximum(list(mplc.to_rgb(__UpperCamelCase       )       )					,   0.2       )
									snake_case__          =						max(0.8					,   np.max(__UpperCamelCase       )       )
									snake_case__          =						{
									    """facecolor""": """black""",
									    """alpha""": self.alpha,
									    """pad""": self.pad,
									    """edgecolor""": """none""",
									}
									snake_case__    ,			snake_case__          =						position
									self.ax.text(
									    __UpperCamelCase					,   __UpperCamelCase					,   __UpperCamelCase					,   size=font_size * self.scale					,   family="""sans-serif"""					,   bbox=__UpperCamelCase					,   verticalalignment="""top"""					,   horizontalalignment=__UpperCamelCase					,   color=__UpperCamelCase					,   zorder=1_0					,   rotation=__UpperCamelCase					,   )






						def       __lowerCAmelCase(   self						:						Any					,   __UpperCamelCase						:						Optional[int]=None       ):



									'''simple docstring'''






									if saveas is None:
												snake_case__          =						self.saveas
									if saveas.lower().endswith(""".jpg"""       ) or saveas.lower().endswith(""".png"""       ):
												cva.imwrite(
												    __UpperCamelCase					,   self._get_buffer()[:, :, ::-1]					,   )
									else:
												self.fig.savefig(__UpperCamelCase       )






						def       __lowerCAmelCase(   self						:						Optional[int]					,   __UpperCamelCase						:						Any					,   __UpperCamelCase						:						Optional[Any]					,   __UpperCamelCase						:						int					,   __UpperCamelCase						:						Union[str, Any]       ):



									'''simple docstring'''






									snake_case__          =						[self.idaobj[i] for i in classes]
									snake_case__          =						[self.idaattr[i] for i in attr_classes]
									snake_case__          =						[
									    f"""{label} {score:.2f} {attr} {attr_score:.2f}"""
									    for label, score, attr, attr_score in zip(__UpperCamelCase					,   __UpperCamelCase					,   __UpperCamelCase					,   __UpperCamelCase       )
									]
									return labels






						def       __lowerCAmelCase(   self						:						Tuple					,   __UpperCamelCase						:						Tuple					,   __UpperCamelCase						:						Optional[Any]       ):



									'''simple docstring'''






									snake_case__          =						[self.idaobj[i] for i in classes]
									if scores is not None:
												if labels is None:
															snake_case__          =						["""{:.0f}%""".format(s * 1_0_0       ) for s in scores]
												else:
															snake_case__          =						["""{} {:.0f}%""".format(__UpperCamelCase					,   s * 1_0_0       ) for li, s in zip(__UpperCamelCase					,   __UpperCamelCase       )]
									return labels






						def       __lowerCAmelCase(   self						:						List[Any]					,   __UpperCamelCase						:						int=2_5_5       ):



									'''simple docstring'''






									snake_case__          =						np.random.randint(0					,   len(_COLORS       )       )
									snake_case__          =						_COLORS[idx] * maximum
									if not self.rgb:
												snake_case__          =						ret[::-1]
									return ret






						def       __lowerCAmelCase(   self						:						Optional[int]       ):



									'''simple docstring'''






									if not self.pynb:
												snake_case__    ,			snake_case__          =						self.canvas.print_to_buffer()
												if (width, height) != (self.width, self.height):
															snake_case__          =						cva.resize(self.img					,   (width, height)       )
												else:
															snake_case__          =						self.img
									else:
												snake_case__          =						io.BytesIO()  # works for cairo backend
												self.canvas.print_rgba(__UpperCamelCase       )
												snake_case__    ,			snake_case__          =						self.width, self.height
												snake_case__          =						buf.getvalue()
												snake_case__          =						self.img

									snake_case__          =						np.frombuffer(__UpperCamelCase					,   dtype="""uint8"""       )
									snake_case__          =						buffer.reshape(__UpperCamelCase					,   __UpperCamelCase					,   4       )
									snake_case__    ,			snake_case__          =						np.split(__UpperCamelCase					,   [3]					,   axis=2       )

									try:
												import numexpr as ne  # fuse them with numexpr

												snake_case__          =						ne.evaluate("""img * (1 - alpha / 255.0) + rgb * (alpha / 255.0)"""       )
									except ImportError:
												snake_case__          =						alpha.astype("""float32"""       ) / 2_55.0
												snake_case__          =						img * (1 - alpha) + rgb * alpha

									return visualized_image.astype("""uint8"""       )







						def       __lowerCAmelCase(   self						:						Union[str, Any]					,   __UpperCamelCase						:						List[Any]					,   __UpperCamelCase						:						Optional[int]       ):



									'''simple docstring'''






									assert brightness_factor >= -1.0 and brightness_factor <= 1.0
									snake_case__          =						mplc.to_rgb(__UpperCamelCase       )
									snake_case__          =						colorsys.rgb_to_hls(*mplc.to_rgb(__UpperCamelCase       )       )
									snake_case__          =						polygon_color[1] + (brightness_factor * polygon_color[1])
									snake_case__          =						0.0 if modified_lightness < 0.0 else modified_lightness
									snake_case__          =						1.0 if modified_lightness > 1.0 else modified_lightness
									snake_case__          =						colorsys.hls_to_rgb(polygon_color[0]					,   __UpperCamelCase					,   polygon_color[2]       )
									return modified_color







a__         =     (
    np.array(
        [
            0.000,
            0.447,
            0.741,
            0.850,
            0.325,
            0.098,
            0.929,
            0.694,
            0.125,
            0.494,
            0.184,
            0.556,
            0.466,
            0.674,
            0.188,
            0.301,
            0.745,
            0.933,
            0.635,
            0.078,
            0.184,
            0.300,
            0.300,
            0.300,
            0.600,
            0.600,
            0.600,
            1.000,
            0.000,
            0.000,
            1.000,
            0.500,
            0.000,
            0.749,
            0.749,
            0.000,
            0.000,
            1.000,
            0.000,
            0.000,
            0.000,
            1.000,
            0.667,
            0.000,
            1.000,
            0.333,
            0.333,
            0.000,
            0.333,
            0.667,
            0.000,
            0.333,
            1.000,
            0.000,
            0.667,
            0.333,
            0.000,
            0.667,
            0.667,
            0.000,
            0.667,
            1.000,
            0.000,
            1.000,
            0.333,
            0.000,
            1.000,
            0.667,
            0.000,
            1.000,
            1.000,
            0.000,
            0.000,
            0.333,
            0.500,
            0.000,
            0.667,
            0.500,
            0.000,
            1.000,
            0.500,
            0.333,
            0.000,
            0.500,
            0.333,
            0.333,
            0.500,
            0.333,
            0.667,
            0.500,
            0.333,
            1.000,
            0.500,
            0.667,
            0.000,
            0.500,
            0.667,
            0.333,
            0.500,
            0.667,
            0.667,
            0.500,
            0.667,
            1.000,
            0.500,
            1.000,
            0.000,
            0.500,
            1.000,
            0.333,
            0.500,
            1.000,
            0.667,
            0.500,
            1.000,
            1.000,
            0.500,
            0.000,
            0.333,
            1.000,
            0.000,
            0.667,
            1.000,
            0.000,
            1.000,
            1.000,
            0.333,
            0.000,
            1.000,
            0.333,
            0.333,
            1.000,
            0.333,
            0.667,
            1.000,
            0.333,
            1.000,
            1.000,
            0.667,
            0.000,
            1.000,
            0.667,
            0.333,
            1.000,
            0.667,
            0.667,
            1.000,
            0.667,
            1.000,
            1.000,
            1.000,
            0.000,
            1.000,
            1.000,
            0.333,
            1.000,
            1.000,
            0.667,
            1.000,
            0.333,
            0.000,
            0.000,
            0.500,
            0.000,
            0.000,
            0.667,
            0.000,
            0.000,
            0.833,
            0.000,
            0.000,
            1.000,
            0.000,
            0.000,
            0.000,
            0.167,
            0.000,
            0.000,
            0.333,
            0.000,
            0.000,
            0.500,
            0.000,
            0.000,
            0.667,
            0.000,
            0.000,
            0.833,
            0.000,
            0.000,
            1.000,
            0.000,
            0.000,
            0.000,
            0.167,
            0.000,
            0.000,
            0.333,
            0.000,
            0.000,
            0.500,
            0.000,
            0.000,
            0.667,
            0.000,
            0.000,
            0.833,
            0.000,
            0.000,
            1.000,
            0.000,
            0.000,
            0.000,
            0.143,
            0.143,
            0.143,
            0.857,
            0.857,
            0.857,
            1.000,
            1.000,
            1.000,
        ]
    )
    .astype(np.floataa)
    .reshape(-1, 3)
)




'''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()
lowercase											=      logging.get_logger(__name__)






def 			UpperCAmelCase_							(		lowercase__							):


     '''simple docstring'''







     a_									=ConvNextConfig()

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

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

     a_									="huggingface/label-files"
     a_									=num_labels
     a_									=json.load(open(hf_hub_download(lowercase__							,						lowercase__							,						repo_type="dataset"							)							,						"r"							)							)
     a_									={int(lowercase__							): 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]
     a_									=idalabel
     a_									={v: k for k, v in idalabel.items()}
     a_									=hidden_sizes
     a_									=depths

     return config, expected_shape






def 			UpperCAmelCase_							(		lowercase__							):


     '''simple docstring'''







     if "downsample_layers.0.0" in name:
          a_									=name.replace("downsample_layers.0.0"							,						"embeddings.patch_embeddings"							)
     if "downsample_layers.0.1" in name:
          a_									=name.replace("downsample_layers.0.1"							,						"embeddings.norm"							)  # we rename to layernorm later on
     if "downsample_layers.1.0" in name:
          a_									=name.replace("downsample_layers.1.0"							,						"stages.1.downsampling_layer.0"							)
     if "downsample_layers.1.1" in name:
          a_									=name.replace("downsample_layers.1.1"							,						"stages.1.downsampling_layer.1"							)
     if "downsample_layers.2.0" in name:
          a_									=name.replace("downsample_layers.2.0"							,						"stages.2.downsampling_layer.0"							)
     if "downsample_layers.2.1" in name:
          a_									=name.replace("downsample_layers.2.1"							,						"stages.2.downsampling_layer.1"							)
     if "downsample_layers.3.0" in name:
          a_									=name.replace("downsample_layers.3.0"							,						"stages.3.downsampling_layer.0"							)
     if "downsample_layers.3.1" in name:
          a_									=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_									=name[: len("stages.0"							)] + ".layers" + name[len("stages.0"							) :]
     if "stages" in name:
          a_									=name.replace("stages"							,						"encoder.stages"							)
     if "norm" in name:
          a_									=name.replace("norm"							,						"layernorm"							)
     if "gamma" in name:
          a_									=name.replace("gamma"							,						"layer_scale_parameter"							)
     if "head" in name:
          a_									=name.replace("head"							,						"classifier"							)

     return name






def 			UpperCAmelCase_							(		):


     '''simple docstring'''







     a_									="http://images.cocodataset.org/val2017/000000039769.jpg"
     a_									=Image.open(requests.get(lowercase__							,						stream=lowercase__							).raw							)
     return im






@torch.no_grad()
def 			UpperCAmelCase_							(		lowercase__							,						lowercase__							):


     '''simple docstring'''







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

     # load HuggingFace model
     a_									=ConvNextForImageClassification(lowercase__							)
     model.load_state_dict(lowercase__							)
     model.eval()

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

     a_									=model(lowercase__							).logits

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

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

     Path(lowercase__							).mkdir(exist_ok=lowercase__							)
     print(F"""Saving model to {pytorch_dump_folder_path}"""							)
     model.save_pretrained(lowercase__							)
     print(F"""Saving image processor to {pytorch_dump_folder_path}"""							)
     image_processor.save_pretrained(lowercase__							)

     print("Pushing model to the hub..."							)
     a_									="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(lowercase__							,						lowercase__							)							,						organization="nielsr"							,						commit_message="Add model"							,						)


if __name__ == "__main__":
  lowercase											=      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.''',
  )

  lowercase											=      parser.parse_args()
  convert_convnext_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)


