



'''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  :     str       =       1_0_0_0

class      UpperCamelCase_					:





	def __init__(    self						,						A						,						A=1.2						,						A="g"						,						A=0.5						,						A="-"						,						A="test_out.jpg"						,						A=True						,						A=False						,						A=None						,						A=None						,						A=0.7						,						)     ->     List[str]:
								if isinstance(A						,						torch.Tensor       ):
															UpperCAmelCase	:						Optional[Any]					=						img.numpy().astype("""np.uint8"""       )
								if isinstance(A						,						A       ):
															UpperCAmelCase	:						Optional[int]					=						img_tensorize(A       )
								assert isinstance(A						,						np.ndarray       )

								UpperCAmelCase	,	UpperCAmelCase	:						Union[str, Any]					=						img.shape[1], img.shape[0]
								UpperCAmelCase	:						Tuple					=						mplfigure.Figure(frameon=A       )
								UpperCAmelCase	:						Optional[Any]					=						fig.get_dpi()
								UpperCAmelCase	:						List[str]					=						(width * scale + 1e-2) / dpi
								UpperCAmelCase	:						Any					=						(height * scale + 1e-2) / dpi
								fig.set_size_inches(A						,						A       )
								UpperCAmelCase	:						Dict					=						fig.add_axes([0.0, 0.0, 1.0, 1.0]       )
								ax.axis("""off"""       )
								ax.set_xlim(0.0						,						A       )
								ax.set_ylim(A       )

								UpperCAmelCase	:						List[str]					=						saveas
								UpperCAmelCase	:						Optional[int]					=						rgb
								UpperCAmelCase	:						Dict					=						pynb
								UpperCAmelCase	:						Optional[int]					=						img
								UpperCAmelCase	:						Optional[Any]					=						edgecolor
								UpperCAmelCase	:						Any					=						0.5
								UpperCAmelCase	:						Tuple					=						linestyle
								UpperCAmelCase	:						int					=						int(np.sqrt(min(A						,						A       )       ) * scale // 3       )
								UpperCAmelCase	:						Dict					=						width
								UpperCAmelCase	:						str					=						height
								UpperCAmelCase	:						int					=						scale
								UpperCAmelCase	:						Any					=						fig
								UpperCAmelCase	:						List[str]					=						ax
								UpperCAmelCase	:						List[Any]					=						pad
								UpperCAmelCase	:						List[Any]					=						idaobj
								UpperCAmelCase	:						Optional[Any]					=						idaattr
								UpperCAmelCase	:						List[Any]					=						FigureCanvasAgg(A       )





	def 					_lowercase(    self						,						A						,						A=None       )     ->     Optional[int]:
								if color is None:
															UpperCAmelCase	:						List[Any]					=						self.edgecolor
								((UpperCAmelCase)	,	(UpperCAmelCase)	,	(UpperCAmelCase)	,	(UpperCAmelCase))	:						str					=						box
								UpperCAmelCase	:						int					=						xa - xa
								UpperCAmelCase	:						Dict					=						ya - ya
								self.ax.add_patch(
								    mpl.patches.Rectangle(
								        (xa, ya)						,						A						,						A						,						fill=A						,						edgecolor=A						,						linewidth=self.font_size // 3						,						alpha=self.alpha						,						linestyle=self.linestyle						,						)       )





	def 					_lowercase(    self						,						A						,						A=None						,						A=None						,						A=None						,						A=None       )     ->     Union[str, Any]:
								if len(boxes.shape       ) > 2:
															UpperCAmelCase	:						Tuple					=						boxes[0]
								if len(obj_ids.shape       ) > 1:
															UpperCAmelCase	:						int					=						obj_ids[0]
								if len(obj_scores.shape       ) > 1:
															UpperCAmelCase	:						Optional[Any]					=						obj_scores[0]
								if len(attr_ids.shape       ) > 1:
															UpperCAmelCase	:						Union[str, Any]					=						attr_ids[0]
								if len(attr_scores.shape       ) > 1:
															UpperCAmelCase	:						List[str]					=						attr_scores[0]
								if isinstance(A						,						torch.Tensor       ):
															UpperCAmelCase	:						str					=						boxes.numpy()
								if isinstance(A						,						A       ):
															UpperCAmelCase	:						Any					=						np.array(A       )
								assert isinstance(A						,						np.ndarray       )
								UpperCAmelCase	:						str					=						np.prod(boxes[:, 2:] - boxes[:, :2]						,						axis=1       )
								UpperCAmelCase	:						Dict					=						np.argsort(-areas       ).tolist()
								UpperCAmelCase	:						int					=						boxes[sorted_idxs] if boxes is not None else None
								UpperCAmelCase	:						Tuple					=						obj_ids[sorted_idxs] if obj_ids is not None else None
								UpperCAmelCase	:						List[Any]					=						obj_scores[sorted_idxs] if obj_scores is not None else None
								UpperCAmelCase	:						List[Any]					=						attr_ids[sorted_idxs] if attr_ids is not None else None
								UpperCAmelCase	:						Dict					=						attr_scores[sorted_idxs] if attr_scores is not None else None

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





	def 					_lowercase(    self						,						A						,						A						,						A       )     ->     Any:
								UpperCAmelCase	,	UpperCAmelCase	,	UpperCAmelCase	,	UpperCAmelCase	:						List[Any]					=						box
								UpperCAmelCase	:						List[Any]					=						(xa, ya)
								UpperCAmelCase	:						List[Any]					=						(ya - ya) * (xa - xa)
								UpperCAmelCase	:						Any					=						_SMALL_OBJ * self.scale
								if instance_area < small or ya - ya < 40 * self.scale:
															if ya >= self.height - 5:
																						UpperCAmelCase	:						str					=						(xa, ya)
															else:
																						UpperCAmelCase	:						Union[str, Any]					=						(xa, ya)

								UpperCAmelCase	:						Tuple					=						(ya - ya) / np.sqrt(self.height * self.width       )
								UpperCAmelCase	:						List[Any]					=						self._change_color_brightness(A						,						brightness_factor=0.7       )
								UpperCAmelCase	:						int					=						np.clip((height_ratio - 0.0_2) / 0.0_8 + 1						,						1.2						,						2       )
								font_size *= 0.7_5 * self.font_size

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





	def 					_lowercase(    self						,						A						,						A						,						A="g"						,						A="left"						,						)     ->     Optional[int]:
								UpperCAmelCase	:						List[str]					=						0
								UpperCAmelCase	:						Any					=						self.font_size
								UpperCAmelCase	:						List[Any]					=						np.maximum(list(mplc.to_rgb(A       )       )						,						0.2       )
								UpperCAmelCase	:						Any					=						max(0.8						,						np.max(A       )       )
								UpperCAmelCase	:						int					=						{
								    """facecolor""": """black""",
								    """alpha""": self.alpha,
								    """pad""": self.pad,
								    """edgecolor""": """none""",
								}
								UpperCAmelCase	,	UpperCAmelCase	:						Any					=						position
								self.ax.text(
								    A						,						A						,						A						,						size=font_size * self.scale						,						family="""sans-serif"""						,						bbox=A						,						verticalalignment="""top"""						,						horizontalalignment=A						,						color=A						,						zorder=10						,						rotation=A						,						)





	def 					_lowercase(    self						,						A=None       )     ->     Union[str, Any]:
								if saveas is None:
															UpperCAmelCase	:						Any					=						self.saveas
								if saveas.lower().endswith(""".jpg"""       ) or saveas.lower().endswith(""".png"""       ):
															cva.imwrite(
															    A						,						self._get_buffer()[:, :, ::-1]						,						)
								else:
															self.fig.savefig(A       )





	def 					_lowercase(    self						,						A						,						A						,						A						,						A       )     ->     str:
								UpperCAmelCase	:						Union[str, Any]					=						[self.idaobj[i] for i in classes]
								UpperCAmelCase	:						int					=						[self.idaattr[i] for i in attr_classes]
								UpperCAmelCase	:						str					=						[
								    f'''{label} {score:.2f} {attr} {attr_score:.2f}'''
								    for label, score, attr, attr_score in zip(A						,						A						,						A						,						A       )
								]
								return labels





	def 					_lowercase(    self						,						A						,						A       )     ->     str:
								UpperCAmelCase	:						Tuple					=						[self.idaobj[i] for i in classes]
								if scores is not None:
															if labels is None:
																						UpperCAmelCase	:						Dict					=						["""{:.0f}%""".format(s * 100       ) for s in scores]
															else:
																						UpperCAmelCase	:						Any					=						["""{} {:.0f}%""".format(A						,						s * 100       ) for li, s in zip(A						,						A       )]
								return labels





	def 					_lowercase(    self						,						A=255       )     ->     List[Any]:
								UpperCAmelCase	:						Tuple					=						np.random.randint(0						,						len(_COLORS       )       )
								UpperCAmelCase	:						str					=						_COLORS[idx] * maximum
								if not self.rgb:
															UpperCAmelCase	:						Dict					=						ret[::-1]
								return ret





	def 					_lowercase(    self       )     ->     Tuple:
								if not self.pynb:
															UpperCAmelCase	,	UpperCAmelCase	:						Dict					=						self.canvas.print_to_buffer()
															if (width, height) != (self.width, self.height):
																						UpperCAmelCase	:						Tuple					=						cva.resize(self.img						,						(width, height)       )
															else:
																						UpperCAmelCase	:						int					=						self.img
								else:
															UpperCAmelCase	:						List[Any]					=						io.BytesIO()  # works for cairo backend
															self.canvas.print_rgba(A       )
															UpperCAmelCase	,	UpperCAmelCase	:						Union[str, Any]					=						self.width, self.height
															UpperCAmelCase	:						Any					=						buf.getvalue()
															UpperCAmelCase	:						Union[str, Any]					=						self.img

								UpperCAmelCase	:						str					=						np.frombuffer(A						,						dtype="""uint8"""       )
								UpperCAmelCase	:						Any					=						buffer.reshape(A						,						A						,						4       )
								UpperCAmelCase	,	UpperCAmelCase	:						Dict					=						np.split(A						,						[3]						,						axis=2       )

								try:
															import numexpr as ne  # fuse them with numexpr

															UpperCAmelCase	:						List[str]					=						ne.evaluate("""img * (1 - alpha / 255.0) + rgb * (alpha / 255.0)"""       )
								except ImportError:
															UpperCAmelCase	:						Dict					=						alpha.astype("""float32"""       ) / 2_5_5.0
															UpperCAmelCase	:						Dict					=						img * (1 - alpha) + rgb * alpha

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






	def 					_lowercase(    self						,						A						,						A       )     ->     List[Any]:
								assert brightness_factor >= -1.0 and brightness_factor <= 1.0
								UpperCAmelCase	:						str					=						mplc.to_rgb(A       )
								UpperCAmelCase	:						List[str]					=						colorsys.rgb_to_hls(*mplc.to_rgb(A       )       )
								UpperCAmelCase	:						List[Any]					=						polygon_color[1] + (brightness_factor * polygon_color[1])
								UpperCAmelCase	:						int					=						0.0 if modified_lightness < 0.0 else modified_lightness
								UpperCAmelCase	:						List[str]					=						1.0 if modified_lightness > 1.0 else modified_lightness
								UpperCAmelCase	:						Optional[Any]					=						colorsys.hls_to_rgb(polygon_color[0]						,						A						,						polygon_color[2]       )
								return modified_color




a  :     Union[str, Any]       =       (
    np.array(
        [
            0.0_0_0,
            0.4_4_7,
            0.7_4_1,
            0.8_5_0,
            0.3_2_5,
            0.0_9_8,
            0.9_2_9,
            0.6_9_4,
            0.1_2_5,
            0.4_9_4,
            0.1_8_4,
            0.5_5_6,
            0.4_6_6,
            0.6_7_4,
            0.1_8_8,
            0.3_0_1,
            0.7_4_5,
            0.9_3_3,
            0.6_3_5,
            0.0_7_8,
            0.1_8_4,
            0.3_0_0,
            0.3_0_0,
            0.3_0_0,
            0.6_0_0,
            0.6_0_0,
            0.6_0_0,
            1.0_0_0,
            0.0_0_0,
            0.0_0_0,
            1.0_0_0,
            0.5_0_0,
            0.0_0_0,
            0.7_4_9,
            0.7_4_9,
            0.0_0_0,
            0.0_0_0,
            1.0_0_0,
            0.0_0_0,
            0.0_0_0,
            0.0_0_0,
            1.0_0_0,
            0.6_6_7,
            0.0_0_0,
            1.0_0_0,
            0.3_3_3,
            0.3_3_3,
            0.0_0_0,
            0.3_3_3,
            0.6_6_7,
            0.0_0_0,
            0.3_3_3,
            1.0_0_0,
            0.0_0_0,
            0.6_6_7,
            0.3_3_3,
            0.0_0_0,
            0.6_6_7,
            0.6_6_7,
            0.0_0_0,
            0.6_6_7,
            1.0_0_0,
            0.0_0_0,
            1.0_0_0,
            0.3_3_3,
            0.0_0_0,
            1.0_0_0,
            0.6_6_7,
            0.0_0_0,
            1.0_0_0,
            1.0_0_0,
            0.0_0_0,
            0.0_0_0,
            0.3_3_3,
            0.5_0_0,
            0.0_0_0,
            0.6_6_7,
            0.5_0_0,
            0.0_0_0,
            1.0_0_0,
            0.5_0_0,
            0.3_3_3,
            0.0_0_0,
            0.5_0_0,
            0.3_3_3,
            0.3_3_3,
            0.5_0_0,
            0.3_3_3,
            0.6_6_7,
            0.5_0_0,
            0.3_3_3,
            1.0_0_0,
            0.5_0_0,
            0.6_6_7,
            0.0_0_0,
            0.5_0_0,
            0.6_6_7,
            0.3_3_3,
            0.5_0_0,
            0.6_6_7,
            0.6_6_7,
            0.5_0_0,
            0.6_6_7,
            1.0_0_0,
            0.5_0_0,
            1.0_0_0,
            0.0_0_0,
            0.5_0_0,
            1.0_0_0,
            0.3_3_3,
            0.5_0_0,
            1.0_0_0,
            0.6_6_7,
            0.5_0_0,
            1.0_0_0,
            1.0_0_0,
            0.5_0_0,
            0.0_0_0,
            0.3_3_3,
            1.0_0_0,
            0.0_0_0,
            0.6_6_7,
            1.0_0_0,
            0.0_0_0,
            1.0_0_0,
            1.0_0_0,
            0.3_3_3,
            0.0_0_0,
            1.0_0_0,
            0.3_3_3,
            0.3_3_3,
            1.0_0_0,
            0.3_3_3,
            0.6_6_7,
            1.0_0_0,
            0.3_3_3,
            1.0_0_0,
            1.0_0_0,
            0.6_6_7,
            0.0_0_0,
            1.0_0_0,
            0.6_6_7,
            0.3_3_3,
            1.0_0_0,
            0.6_6_7,
            0.6_6_7,
            1.0_0_0,
            0.6_6_7,
            1.0_0_0,
            1.0_0_0,
            1.0_0_0,
            0.0_0_0,
            1.0_0_0,
            1.0_0_0,
            0.3_3_3,
            1.0_0_0,
            1.0_0_0,
            0.6_6_7,
            1.0_0_0,
            0.3_3_3,
            0.0_0_0,
            0.0_0_0,
            0.5_0_0,
            0.0_0_0,
            0.0_0_0,
            0.6_6_7,
            0.0_0_0,
            0.0_0_0,
            0.8_3_3,
            0.0_0_0,
            0.0_0_0,
            1.0_0_0,
            0.0_0_0,
            0.0_0_0,
            0.0_0_0,
            0.1_6_7,
            0.0_0_0,
            0.0_0_0,
            0.3_3_3,
            0.0_0_0,
            0.0_0_0,
            0.5_0_0,
            0.0_0_0,
            0.0_0_0,
            0.6_6_7,
            0.0_0_0,
            0.0_0_0,
            0.8_3_3,
            0.0_0_0,
            0.0_0_0,
            1.0_0_0,
            0.0_0_0,
            0.0_0_0,
            0.0_0_0,
            0.1_6_7,
            0.0_0_0,
            0.0_0_0,
            0.3_3_3,
            0.0_0_0,
            0.0_0_0,
            0.5_0_0,
            0.0_0_0,
            0.0_0_0,
            0.6_6_7,
            0.0_0_0,
            0.0_0_0,
            0.8_3_3,
            0.0_0_0,
            0.0_0_0,
            1.0_0_0,
            0.0_0_0,
            0.0_0_0,
            0.0_0_0,
            0.1_4_3,
            0.1_4_3,
            0.1_4_3,
            0.8_5_7,
            0.8_5_7,
            0.8_5_7,
            1.0_0_0,
            1.0_0_0,
            1.0_0_0,
        ]
    )
    .astype(np.floataa)
    .reshape(-1, 3)
)






'''simple docstring'''




import argparse
import json
import sys
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import List

import torch
import torch.nn as nn
from huggingface_hub import cached_download, hf_hub_download
from torch import Tensor

from transformers import AutoImageProcessor, VanConfig, VanForImageClassification
from transformers.models.deprecated.van.modeling_van import VanLayerScaling
from transformers.utils import logging


logging.set_verbosity_info()
a  :     Any       =       logging.get_logger(__name__)

@dataclass
class      UpperCamelCase_					:
	lowercase		=					42
	lowercase		=					field(default_factory=__magic_name__     )
	lowercase		=					field(default_factory=__magic_name__     )





	def 					_lowercase(    self						,						A						,						A						,						A       )     ->     Optional[Any]:
								UpperCAmelCase	:						Tuple					=						len(list(m.modules()       )       ) == 1 or isinstance(A						,						nn.Convad       ) or isinstance(A						,						nn.BatchNormad       )
								if has_not_submodules:
															if not isinstance(A						,						A       ):
																						self.traced.append(A       )





	def __call__(    self						,						A       )     ->     Dict:
								for m in self.module.modules():
															self.handles.append(m.register_forward_hook(self._forward_hook       )       )
								self.module(A       )
								[x.remove() for x in self.handles]
								return self






	@property
	def 					_lowercase(    self       )     ->     Dict:
								# check the len of the state_dict keys to see if we have learnable params
								return list(filter(lambda A       : len(list(x.state_dict().keys()       )       ) > 0						,						self.traced       )       )




@dataclass
class      UpperCamelCase_					:
	lowercase		=					42
	lowercase		=					42
	lowercase		=					0
	lowercase		=					field(default_factory=__magic_name__     )
	lowercase		=					field(default_factory=__magic_name__     )





	def __call__(    self						,						A       )     ->     str:
								UpperCAmelCase	:						Dict					=						Tracker(self.dest       )(A       ).parametrized
								UpperCAmelCase	:						Union[str, Any]					=						Tracker(self.src       )(A       ).parametrized

								UpperCAmelCase	:						Optional[int]					=						list(filter(lambda A       : type(A       ) not in self.src_skip						,						A       )       )
								UpperCAmelCase	:						Optional[int]					=						list(filter(lambda A       : type(A       ) not in self.dest_skip						,						A       )       )

								if len(A       ) != len(A       ):
															raise Exception(
															    f'''Numbers of operations are different. Source module has {len(A       )} operations while'''
															    f''' destination module has {len(A       )}.'''       )

								for dest_m, src_m in zip(A						,						A       ):
															dest_m.load_state_dict(src_m.state_dict()       )
															if self.verbose == 1:
																						print(f'''Transfered from={src_m} to={dest_m}'''       )




def  __lowerCamelCase    (   _lowercase      ,							_lowercase						) ->     nn.Module:
							# nn.Parameter cannot be tracked by the Tracker, thus we need to manually convert them
							UpperCAmelCase	:						Optional[Any]					=						from_model.state_dict()
							UpperCAmelCase	:						int					=						our_model.state_dict()
							UpperCAmelCase	:						Optional[int]					=						our_model.config
							UpperCAmelCase	:						str					=						[]
							for stage_idx in range(len(config.hidden_sizes						)						):
														for block_id in range(config.depths[stage_idx]						):
																					UpperCAmelCase	:						List[Any]					=						F'''block{stage_idx + 1}.{block_id}.layer_scale_1'''
																					UpperCAmelCase	:						Any					=						F'''van.encoder.stages.{stage_idx}.layers.{block_id}.attention_scaling.weight'''

																					all_keys.append((from_key, to_key)						)
																					UpperCAmelCase	:						Optional[int]					=						F'''block{stage_idx + 1}.{block_id}.layer_scale_2'''
																					UpperCAmelCase	:						str					=						F'''van.encoder.stages.{stage_idx}.layers.{block_id}.mlp_scaling.weight'''

																					all_keys.append((from_key, to_key)						)

							for from_key, to_key in all_keys:
														UpperCAmelCase	:						List[Any]					=						from_state_dict.pop(_lowercase						)

							our_model.load_state_dict(_lowercase						)
							return our_model



def  __lowerCamelCase    (   _lowercase      ,							_lowercase      ,							_lowercase      ,							_lowercase      ,							_lowercase      ,							_lowercase = True      ,							) ->     List[Any]:
							print(F'''Downloading weights for {name}...'''						)
							UpperCAmelCase	:						List[str]					=						cached_download(_lowercase						)
							print(F'''Converting {name}...'''						)
							UpperCAmelCase	:						int					=						torch.load(_lowercase						)["""state_dict"""]
							from_model.load_state_dict(_lowercase						)
							from_model.eval()
							with torch.no_grad():
														UpperCAmelCase	:						Tuple					=						VanForImageClassification(_lowercase						).eval()
														UpperCAmelCase	:						int					=						ModuleTransfer(src=_lowercase      ,							dest=_lowercase						)
														UpperCAmelCase	:						Any					=						torch.randn((1, 3, 2_2_4, 2_2_4)						)
														module_transfer(_lowercase						)
														UpperCAmelCase	:						Tuple					=						copy_parameters(_lowercase      ,							_lowercase						)

							if not torch.allclose(from_model(_lowercase						)      ,							our_model(_lowercase						).logits						):
														raise ValueError("""The model logits don't match the original one."""						)

							UpperCAmelCase	:						List[str]					=						name
							print(_lowercase						)

							if push_to_hub:
														our_model.push_to_hub(
														    repo_path_or_name=save_directory / checkpoint_name      ,							commit_message="""Add model"""      ,							use_temp_dir=_lowercase      ,							)

														# we can use the convnext one
														UpperCAmelCase	:						Dict					=						AutoImageProcessor.from_pretrained("""facebook/convnext-base-224-22k-1k"""						)
														image_processor.push_to_hub(
														    repo_path_or_name=save_directory / checkpoint_name      ,							commit_message="""Add image processor"""      ,							use_temp_dir=_lowercase      ,							)

														print(F'''Pushed {checkpoint_name}'''						)



def  __lowerCamelCase    (   _lowercase      ,							_lowercase = None      ,							_lowercase = True						) ->     List[Any]:
							UpperCAmelCase	:						int					=						"""imagenet-1k-id2label.json"""
							UpperCAmelCase	:						List[Any]					=						1_0_0_0

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

							UpperCAmelCase	:						Any					=						idalabel
							UpperCAmelCase	:						List[str]					=						{v: k for k, v in idalabel.items()}

							UpperCAmelCase	:						Any					=						partial(_lowercase      ,							num_labels=_lowercase      ,							idalabel=_lowercase      ,							labelaid=_lowercase						)

							UpperCAmelCase	:						Optional[Any]					=						{
							    """van-tiny""": ImageNetPreTrainedConfig(
							        hidden_sizes=[3_2, 6_4, 1_6_0, 2_5_6]      ,							depths=[3, 3, 5, 2]      ,							mlp_ratios=[8, 8, 4, 4]      ,							),
							    """van-small""": ImageNetPreTrainedConfig(
							        hidden_sizes=[6_4, 1_2_8, 3_2_0, 5_1_2]      ,							depths=[2, 2, 4, 2]      ,							mlp_ratios=[8, 8, 4, 4]      ,							),
							    """van-base""": ImageNetPreTrainedConfig(
							        hidden_sizes=[6_4, 1_2_8, 3_2_0, 5_1_2]      ,							depths=[3, 3, 1_2, 3]      ,							mlp_ratios=[8, 8, 4, 4]      ,							),
							    """van-large""": ImageNetPreTrainedConfig(
							        hidden_sizes=[6_4, 1_2_8, 3_2_0, 5_1_2]      ,							depths=[3, 5, 2_7, 3]      ,							mlp_ratios=[8, 8, 4, 4]      ,							),
							}

							UpperCAmelCase	:						Optional[Any]					=						{
							    """van-tiny""": van_tiny,
							    """van-small""": van_small,
							    """van-base""": van_base,
							    """van-large""": van_large,
							}

							UpperCAmelCase	:						Optional[int]					=						{
							    """van-tiny""": (
							        """https://huggingface.co/Visual-Attention-Network/VAN-Tiny-original/resolve/main/van_tiny_754.pth.tar"""
							    ),
							    """van-small""": (
							        """https://huggingface.co/Visual-Attention-Network/VAN-Small-original/resolve/main/van_small_811.pth.tar"""
							    ),
							    """van-base""": (
							        """https://huggingface.co/Visual-Attention-Network/VAN-Base-original/resolve/main/van_base_828.pth.tar"""
							    ),
							    """van-large""": (
							        """https://huggingface.co/Visual-Attention-Network/VAN-Large-original/resolve/main/van_large_839.pth.tar"""
							    ),
							}

							if model_name:
														convert_weight_and_push(
														    _lowercase      ,							names_to_config[model_name]      ,							checkpoint=names_to_original_checkpoints[model_name]      ,							from_model=names_to_original_models[model_name]()      ,							save_directory=_lowercase      ,							push_to_hub=_lowercase      ,							)
							else:
														for model_name, config in names_to_config.items():
																					convert_weight_and_push(
																					    _lowercase      ,							_lowercase      ,							checkpoint=names_to_original_checkpoints[model_name]      ,							from_model=names_to_original_models[model_name]()      ,							save_directory=_lowercase      ,							push_to_hub=_lowercase      ,							)


if __name__ == "__main__":
							a  :     int       =       argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    """--model-name""",
							    default=None,
							    type=str,
							    help=(
							        """The name of the model you wish to convert, it must be one of the supported resnet* architecture,"""
							        """ currently: van-tiny/small/base/large. If `None`, all of them will the converted."""
							    ),
							)
							parser.add_argument(
							    """--pytorch_dump_folder_path""",
							    default=None,
							    type=Path,
							    required=True,
							    help="""Path to the output PyTorch model directory.""",
							)
							parser.add_argument(
							    """--van_dir""",
							    required=True,
							    type=Path,
							    help=(
							        """A path to VAN's original implementation directory. You can download from here:"""
							        """ https://github.com/Visual-Attention-Network/VAN-Classification"""
							    ),
							)
							parser.add_argument(
							    """--push_to_hub""",
							    default=True,
							    type=bool,
							    required=False,
							    help="""If True, push model and image processor to the hub.""",
							)

							a  :     List[str]       =       parser.parse_args()
							a  :     Path       =       args.pytorch_dump_folder_path
							pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
							a  :     Union[str, Any]       =       args.van_dir
							# append the path to the parents to maskformer dir
							sys.path.append(str(van_dir.parent))
							from van.models.van import van_base, van_large, van_small, van_tiny

							convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)

