


'''simple docstring'''






import argparse
import json
from collections import OrderedDict
from functools import partial
from pathlib import Path

import timm
import torch
from huggingface_hub import hf_hub_download

from transformers import LevitConfig, LevitForImageClassificationWithTeacher, LevitImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
__UpperCAmelCase					       =							logging.get_logger()


def       _snake_case     (  A				,					A				,					A				,					A				,					A = True )		->					Tuple:
							print(F"""Converting {name}...""" )

							with torch.no_grad():
														if hidden_sizes == 128:
																					if name[-1] == "S":
																												lowerCAmelCase__      =			timm.create_model('''levit_128s'''				,					pretrained=lowerCamelCase__ )
																					else:
																												lowerCAmelCase__      =			timm.create_model('''levit_128'''				,					pretrained=lowerCamelCase__ )
														if hidden_sizes == 192:
																					lowerCAmelCase__      =			timm.create_model('''levit_192'''				,					pretrained=lowerCamelCase__ )
														if hidden_sizes == 256:
																					lowerCAmelCase__      =			timm.create_model('''levit_256'''				,					pretrained=lowerCamelCase__ )
														if hidden_sizes == 384:
																					lowerCAmelCase__      =			timm.create_model('''levit_384'''				,					pretrained=lowerCamelCase__ )

														from_model.eval()
														lowerCAmelCase__      =			LevitForImageClassificationWithTeacher(lowerCamelCase__ ).eval()
														lowerCAmelCase__      =			OrderedDict()

														lowerCAmelCase__      =			from_model.state_dict()
														lowerCAmelCase__      =			list(from_model.state_dict().keys() )
														lowerCAmelCase__      =			list(our_model.state_dict().keys() )
														print(len(lowerCamelCase__ )				,					len(lowerCamelCase__ ) )
														for i in range(len(lowerCamelCase__ ) ):
																					lowerCAmelCase__      =			weights[og_keys[i]]
														our_model.load_state_dict(lowerCamelCase__ )

														lowerCAmelCase__      =			torch.randn((2, 3, 224, 224) )
														lowerCAmelCase__      =			from_model(lowerCamelCase__ )
														lowerCAmelCase__      =			our_model(lowerCamelCase__ ).logits

							assert torch.allclose(lowerCamelCase__				,					lowerCamelCase__ ), "The model logits don't match the original one."

							lowerCAmelCase__      =			name
							print(lowerCamelCase__ )

							if push_to_hub:
														our_model.save_pretrained(save_directory / checkpoint_name )
														lowerCAmelCase__      =			LevitImageProcessor()
														image_processor.save_pretrained(save_directory / checkpoint_name )

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


def       _snake_case     (  A				,					A = None				,					A = True )		->					Union[str, Any]:
							lowerCAmelCase__      =			'''imagenet-1k-id2label.json'''
							lowerCAmelCase__      =			1000
							lowerCAmelCase__      =			(1, num_labels)

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

							lowerCAmelCase__      =			idalabel
							lowerCAmelCase__      =			{v: k for k, v in idalabel.items()}

							lowerCAmelCase__      =			partial(lowerCamelCase__				,					num_labels=lowerCamelCase__				,					idalabel=lowerCamelCase__				,					labelaid=lowerCamelCase__ )

							lowerCAmelCase__      =			{
							    '''levit-128S''': 128,
							    '''levit-128''': 128,
							    '''levit-192''': 192,
							    '''levit-256''': 256,
							    '''levit-384''': 384,
							}

							lowerCAmelCase__      =			{
							    '''levit-128S''': ImageNetPreTrainedConfig(
							        hidden_sizes=[128, 256, 384]				,					num_attention_heads=[4, 6, 8]				,					depths=[2, 3, 4]				,					key_dim=[16, 16, 16]				,					drop_path_rate=0				,					),
							    '''levit-128''': ImageNetPreTrainedConfig(
							        hidden_sizes=[128, 256, 384]				,					num_attention_heads=[4, 8, 12]				,					depths=[4, 4, 4]				,					key_dim=[16, 16, 16]				,					drop_path_rate=0				,					),
							    '''levit-192''': ImageNetPreTrainedConfig(
							        hidden_sizes=[192, 288, 384]				,					num_attention_heads=[3, 5, 6]				,					depths=[4, 4, 4]				,					key_dim=[32, 32, 32]				,					drop_path_rate=0				,					),
							    '''levit-256''': ImageNetPreTrainedConfig(
							        hidden_sizes=[256, 384, 512]				,					num_attention_heads=[4, 6, 8]				,					depths=[4, 4, 4]				,					key_dim=[32, 32, 32]				,					drop_path_rate=0				,					),
							    '''levit-384''': ImageNetPreTrainedConfig(
							        hidden_sizes=[384, 512, 768]				,					num_attention_heads=[6, 9, 12]				,					depths=[4, 4, 4]				,					key_dim=[32, 32, 32]				,					drop_path_rate=0.1				,					),
							}

							if model_name:
														convert_weight_and_push(
														    names_to_hidden_sizes[model_name]				,					lowerCamelCase__				,					names_to_config[model_name]				,					lowerCamelCase__				,					lowerCamelCase__ )
							else:
														for model_name, config in names_to_config.items():
																					convert_weight_and_push(names_to_hidden_sizes[model_name]				,					lowerCamelCase__				,					lowerCamelCase__				,					lowerCamelCase__				,					lowerCamelCase__ )
							return config, expected_shape


if __name__ == "__main__":
		__UpperCAmelCase					       =							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 Levit* architecture,''',
		)
		parser.add_argument(
		    '''--pytorch_dump_folder_path''',
		    default='''levit-dump-folder/''',
		    type=Path,
		    required=False,
		    help='''Path to the output PyTorch model directory.''',
		)
		parser.add_argument('''--push_to_hub''', action='''store_true''', help='''Push model and image processor to the hub''')
		parser.add_argument(
		    '''--no-push_to_hub''',
		    dest='''push_to_hub''',
		    action='''store_false''',
		    help='''Do not push model and image processor to the hub''',
		)

		__UpperCAmelCase					       =							parser.parse_args()
		__UpperCAmelCase					       =							args.pytorch_dump_folder_path
		pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
		convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)










'''simple docstring'''






from manim import *




class 	a__       (							a__						):





							'''simple docstring'''






							def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							List[str]:
														lowerCAmelCase__      =			Rectangle(height=0.5      ,					width=0.5     )
														lowerCAmelCase__      =			Rectangle(height=0.46      ,					width=0.46     ).set_stroke(width=0     )

														lowerCAmelCase__      =			[mem.copy() for i in range(6     )]
														lowerCAmelCase__      =			[mem.copy() for i in range(6     )]
														lowerCAmelCase__      =			VGroup(*lowerCamelCase_     ).arrange(lowerCamelCase_      ,					buff=0     )
														lowerCAmelCase__      =			VGroup(*lowerCamelCase_     ).arrange(lowerCamelCase_      ,					buff=0     )
														lowerCAmelCase__      =			VGroup(lowerCamelCase_      ,					lowerCamelCase_     ).arrange(lowerCamelCase_      ,					buff=0     )
														lowerCAmelCase__      =			Text('''CPU'''      ,					font_size=24     )
														lowerCAmelCase__      =			Group(lowerCamelCase_      ,					lowerCamelCase_     ).arrange(lowerCamelCase_      ,					buff=0.5      ,					aligned_edge=lowerCamelCase_     )
														cpu.move_to([-2.5, -0.5, 0]     )
														self.add(lowerCamelCase_     )

														lowerCAmelCase__      =			[mem.copy() for i in range(1     )]
														lowerCAmelCase__      =			VGroup(*lowerCamelCase_     ).arrange(lowerCamelCase_      ,					buff=0     )
														lowerCAmelCase__      =			Text('''GPU'''      ,					font_size=24     )
														lowerCAmelCase__      =			Group(lowerCamelCase_      ,					lowerCamelCase_     ).arrange(lowerCamelCase_      ,					buff=0.5      ,					aligned_edge=lowerCamelCase_     )
														gpu.align_to(lowerCamelCase_      ,					lowerCamelCase_     )
														gpu.set_x(gpu.get_x() - 1     )

														self.add(lowerCamelCase_     )

														lowerCAmelCase__      =			[mem.copy() for i in range(6     )]
														lowerCAmelCase__      =			VGroup(*lowerCamelCase_     ).arrange(lowerCamelCase_      ,					buff=0     )

														lowerCAmelCase__      =			Text('''Model'''      ,					font_size=24     )
														lowerCAmelCase__      =			Group(lowerCamelCase_      ,					lowerCamelCase_     ).arrange(lowerCamelCase_      ,					buff=0.5      ,					aligned_edge=lowerCamelCase_     )
														model.move_to([3, -1.0, 0]     )

														self.play(
														    Create(lowerCamelCase_      ,					run_time=1     )      ,					Create(lowerCamelCase_      ,					run_time=1     )      ,					Create(lowerCamelCase_      ,					run_time=1     )      ,					)

														lowerCAmelCase__      =			MarkupText(
														    F"""First, an empty model skeleton is loaded\ninto <span fgcolor='{YELLOW}'>memory</span> without using much RAM."""      ,					font_size=24      ,					)

														lowerCAmelCase__      =			Square(side_length=2.2     )
														key.move_to([-5, 2, 0]     )

														lowerCAmelCase__      =			MarkupText(
														    F"""<b>Key:</b>\n\n<span fgcolor='{YELLOW}'>●</span> Empty Model"""      ,					font_size=18      ,					)

														key_text.move_to([-5, 2.4, 0]     )

														step_a.move_to([2, 2, 0]     )
														self.play(Write(lowerCamelCase_      ,					run_time=2.5     )      ,					Write(lowerCamelCase_     )      ,					Write(lowerCamelCase_     )     )

														self.add(lowerCamelCase_     )

														lowerCAmelCase__      =			[]
														lowerCAmelCase__      =			[]
														lowerCAmelCase__      =			[]
														for i, rect in enumerate(lowerCamelCase_     ):
																					lowerCAmelCase__      =			Rectangle(height=0.46      ,					width=0.46     ).set_stroke(width=0.0     ).set_fill(lowerCamelCase_      ,					opacity=0.7     )
																					cpu_target.move_to(lowerCamelCase_     )
																					cpu_target.generate_target()
																					lowerCAmelCase__      =			0.46 / 4
																					lowerCAmelCase__      =			0.46 / 3

																					if i == 0:
																												cpu_target.target.next_to(cpu_left_col_base[0].get_corner(DOWN + LEFT     )      ,					buff=0.02      ,					direction=lowerCamelCase_     )
																												cpu_target.target.set_x(cpu_target.target.get_x() + 0.1     )
																					elif i == 3:
																												cpu_target.target.next_to(cpu_targs[0].target      ,					direction=lowerCamelCase_      ,					buff=0.0     )
																					else:
																												cpu_target.target.next_to(cpu_targs[i - 1].target      ,					direction=lowerCamelCase_      ,					buff=0.0     )
																					cpu_targs.append(lowerCamelCase_     )

																					first_animations.append(rect.animate(run_time=0.5     ).set_stroke(lowerCamelCase_     )     )
																					second_animations.append(MoveToTarget(lowerCamelCase_      ,					run_time=1.5     )     )

														self.play(*lowerCamelCase_     )
														self.play(*lowerCamelCase_     )

														self.wait()