


import argparse
import json
from pathlib import Path

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

from transformers import DeiTConfig, DeiTForImageClassificationWithTeacher, DeiTImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
snake_case							: int      =   logging.get_logger(__name__)






def 							__lowercase     (				__lowerCAmelCase							:       Dict      ,					__lowerCAmelCase							:       Optional[Any]=False     ):
							a__    =  []
							for i in range(config.num_hidden_layers     ):
														# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
														rename_keys.append((F'blocks.{i}.norm1.weight', F'deit.encoder.layer.{i}.layernorm_before.weight')     )
														rename_keys.append((F'blocks.{i}.norm1.bias', F'deit.encoder.layer.{i}.layernorm_before.bias')     )
														rename_keys.append((F'blocks.{i}.attn.proj.weight', F'deit.encoder.layer.{i}.attention.output.dense.weight')     )
														rename_keys.append((F'blocks.{i}.attn.proj.bias', F'deit.encoder.layer.{i}.attention.output.dense.bias')     )
														rename_keys.append((F'blocks.{i}.norm2.weight', F'deit.encoder.layer.{i}.layernorm_after.weight')     )
														rename_keys.append((F'blocks.{i}.norm2.bias', F'deit.encoder.layer.{i}.layernorm_after.bias')     )
														rename_keys.append((F'blocks.{i}.mlp.fc1.weight', F'deit.encoder.layer.{i}.intermediate.dense.weight')     )
														rename_keys.append((F'blocks.{i}.mlp.fc1.bias', F'deit.encoder.layer.{i}.intermediate.dense.bias')     )
														rename_keys.append((F'blocks.{i}.mlp.fc2.weight', F'deit.encoder.layer.{i}.output.dense.weight')     )
														rename_keys.append((F'blocks.{i}.mlp.fc2.bias', F'deit.encoder.layer.{i}.output.dense.bias')     )

							# projection layer + position embeddings
							rename_keys.extend(
							    [
							        ('cls_token', 'deit.embeddings.cls_token'),
							        ('dist_token', 'deit.embeddings.distillation_token'),
							        ('patch_embed.proj.weight', 'deit.embeddings.patch_embeddings.projection.weight'),
							        ('patch_embed.proj.bias', 'deit.embeddings.patch_embeddings.projection.bias'),
							        ('pos_embed', 'deit.embeddings.position_embeddings'),
							    ]     )

							if base_model:
														# layernorm + pooler
														rename_keys.extend(
														    [
														        ('norm.weight', 'layernorm.weight'),
														        ('norm.bias', 'layernorm.bias'),
														        ('pre_logits.fc.weight', 'pooler.dense.weight'),
														        ('pre_logits.fc.bias', 'pooler.dense.bias'),
														    ]     )

														# if just the base model, we should remove "deit" from all keys that start with "deit"
														a__    =  [(pair[0], pair[1][4:]) if pair[1].startswith('deit'     ) else pair for pair in rename_keys]
							else:
														# layernorm + classification heads
														rename_keys.extend(
														    [
														        ('norm.weight', 'deit.layernorm.weight'),
														        ('norm.bias', 'deit.layernorm.bias'),
														        ('head.weight', 'cls_classifier.weight'),
														        ('head.bias', 'cls_classifier.bias'),
														        ('head_dist.weight', 'distillation_classifier.weight'),
														        ('head_dist.bias', 'distillation_classifier.bias'),
														    ]     )

							return rename_keys






def 							__lowercase     (				__lowerCAmelCase							:       Optional[int]      ,					__lowerCAmelCase							:       List[str]      ,					__lowerCAmelCase							:       List[str]=False     ):
							for i in range(config.num_hidden_layers     ):
														if base_model:
																					a__    =  ''
														else:
																					a__    =  'deit.'
														# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
														a__    =  state_dict.pop(F'blocks.{i}.attn.qkv.weight'     )
														a__    =  state_dict.pop(F'blocks.{i}.attn.qkv.bias'     )
														# next, add query, keys and values (in that order) to the state dict
														a__    =  in_proj_weight[
														    : config.hidden_size, :
														]
														a__    =  in_proj_bias[: config.hidden_size]
														a__    =  in_proj_weight[
														    config.hidden_size : config.hidden_size * 2, :
														]
														a__    =  in_proj_bias[
														    config.hidden_size : config.hidden_size * 2
														]
														a__    =  in_proj_weight[
														    -config.hidden_size :, :
														]
														a__    =  in_proj_bias[-config.hidden_size :]






def 							__lowercase     (				__lowerCAmelCase							:       List[str]      ,					__lowerCAmelCase							:       str      ,					__lowerCAmelCase							:       List[str]     ):
							a__    =  dct.pop(__lowerCAmelCase     )
							a__    =  val






def 							__lowercase     (				):
							a__    =  'http://images.cocodataset.org/val2017/000000039769.jpg'
							a__    =  Image.open(requests.get(__lowerCAmelCase      ,					stream=__lowerCAmelCase     ).raw     )
							return im






@torch.no_grad()
def 							__lowercase     (				__lowerCAmelCase							:       str      ,					__lowerCAmelCase							:       Optional[Any]     ):
							a__    =  DeiTConfig()
							# all deit models have fine-tuned heads
							a__    =  False
							# dataset (fine-tuned on ImageNet 2012), patch_size and image_size
							a__    =  1_0_0_0
							a__    =  'huggingface/label-files'
							a__    =  'imagenet-1k-id2label.json'
							a__    =  json.load(open(hf_hub_download(__lowerCAmelCase      ,					__lowerCAmelCase      ,					repo_type='dataset'     )      ,					'r'     )     )
							a__    =  {int(__lowerCAmelCase     ): v for k, v in idalabel.items()}
							a__    =  idalabel
							a__    =  {v: k for k, v in idalabel.items()}
							a__    =  int(deit_name[-6:-4]     )
							a__    =  int(deit_name[-3:]     )
							# size of the architecture
							if deit_name[9:].startswith('tiny'     ):
														a__    =  1_9_2
														a__    =  7_6_8
														a__    =  1_2
														a__    =  3
							elif deit_name[9:].startswith('small'     ):
														a__    =  3_8_4
														a__    =  1_5_3_6
														a__    =  1_2
														a__    =  6
							if deit_name[9:].startswith('base'     ):
														pass
							elif deit_name[4:].startswith('large'     ):
														a__    =  1_0_2_4
														a__    =  4_0_9_6
														a__    =  2_4
														a__    =  1_6

							# load original model from timm
							a__    =  timm.create_model(__lowerCAmelCase      ,					pretrained=__lowerCAmelCase     )
							timm_model.eval()

							# load state_dict of original model, remove and rename some keys
							a__    =  timm_model.state_dict()
							a__    =  create_rename_keys(__lowerCAmelCase      ,					__lowerCAmelCase     )
							for src, dest in rename_keys:
														rename_key(__lowerCAmelCase      ,					__lowerCAmelCase      ,					__lowerCAmelCase     )
							read_in_q_k_v(__lowerCAmelCase      ,					__lowerCAmelCase      ,					__lowerCAmelCase     )

							# load HuggingFace model
							a__    =  DeiTForImageClassificationWithTeacher(__lowerCAmelCase     ).eval()
							model.load_state_dict(__lowerCAmelCase     )

							# Check outputs on an image, prepared by DeiTImageProcessor
							a__    =  int(
							    (2_5_6 / 2_2_4) * config.image_size     )  # to maintain same ratio w.r.t. 224 images, see https://github.com/facebookresearch/deit/blob/ab5715372db8c6cad5740714b2216d55aeae052e/datasets.py#L103
							a__    =  DeiTImageProcessor(size=__lowerCAmelCase      ,					crop_size=config.image_size     )
							a__    =  image_processor(images=prepare_img()      ,					return_tensors='pt'     )
							a__    =  encoding['pixel_values']
							a__    =  model(__lowerCAmelCase     )

							a__    =  timm_model(__lowerCAmelCase     )
							assert timm_logits.shape == outputs.logits.shape
							assert torch.allclose(__lowerCAmelCase      ,					outputs.logits      ,					atol=1E-3     )

							Path(__lowerCAmelCase     ).mkdir(exist_ok=__lowerCAmelCase     )
							print(F'Saving model {deit_name} to {pytorch_dump_folder_path}'     )
							model.save_pretrained(__lowerCAmelCase     )
							print(F'Saving image processor to {pytorch_dump_folder_path}'     )
							image_processor.save_pretrained(__lowerCAmelCase     )


if __name__ == "__main__":
						snake_case							: Optional[int]      =   argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    '''--deit_name''',
						    default='''vit_deit_base_distilled_patch16_224''',
						    type=str,
						    help='''Name of the DeiT timm model you\'d like to convert.''',
						)
						parser.add_argument(
						    '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
						)

						snake_case							: List[str]      =   parser.parse_args()
						convert_deit_checkpoint(args.deit_name, args.pytorch_dump_folder_path)








from manim import *







class  snake_case_     (lowerCamelCase_   ):
					def 				lowerCamelCase__(   self     :Optional[Any]   )      ->					Optional[int]:
												a__    =  Rectangle(height=0.5    ,width=0.5   )
												a__    =  Rectangle(height=0.46    ,width=0.46   ).set_stroke(width=0   )

												a__    =  Rectangle(height=0.25    ,width=0.25   )

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

												a__    =  [mem.copy() for i in range(4   )]
												a__    =  VGroup(*__snake_case   ).arrange(__snake_case    ,buff=0   )
												a__    =  Text('GPU'    ,font_size=24   )
												a__    =  Group(__snake_case    ,__snake_case   ).arrange(__snake_case    ,buff=0.5    ,aligned_edge=__snake_case   )
												gpu.move_to([-1, -1, 0]   )
												self.add(__snake_case   )

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

												a__    =  Text('Model'    ,font_size=24   )
												a__    =  Group(__snake_case    ,__snake_case   ).arrange(__snake_case    ,buff=0.5    ,aligned_edge=__snake_case   )
												model.move_to([3, -1.0, 0]   )
												self.add(__snake_case   )

												a__    =  []
												a__    =  []

												for i, rect in enumerate(__snake_case   ):
																			a__    =  fill.copy().set_fill(__snake_case    ,opacity=0.8   )
																			target.move_to(__snake_case   )
																			model_arr.append(__snake_case   )

																			a__    =  Rectangle(height=0.46    ,width=0.46   ).set_stroke(width=0.0   ).set_fill(__snake_case    ,opacity=0.8   )
																			cpu_target.move_to(cpu_left_col_base[i]   )
																			model_cpu_arr.append(__snake_case   )

												self.add(*__snake_case    ,*__snake_case   )

												a__    =  [meta_mem.copy() for i in range(6   )]
												a__    =  [meta_mem.copy() for i in range(6   )]
												a__    =  VGroup(*__snake_case   ).arrange(__snake_case    ,buff=0   )
												a__    =  VGroup(*__snake_case   ).arrange(__snake_case    ,buff=0   )
												a__    =  VGroup(__snake_case    ,__snake_case   ).arrange(__snake_case    ,buff=0   )
												a__    =  Text('Disk'    ,font_size=24   )
												a__    =  Group(__snake_case    ,__snake_case   ).arrange(__snake_case    ,buff=0.5    ,aligned_edge=__snake_case   )
												disk.move_to([-4, -1.25, 0]   )
												self.add(__snake_case    ,__snake_case   )

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

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

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

												self.add(__snake_case    ,__snake_case   )

												a__    =  MarkupText(
												    F'<span fgcolor=\'{BLUE}\'>●</span> Checkpoint'    ,font_size=18    ,)

												blue_text.next_to(__snake_case    ,DOWN * 2.4    ,aligned_edge=key_text.get_left()   )
												self.add(__snake_case   )

												a__    =  MarkupText(
												    F'Now watch as an input is passed through the model\nand how the memory is utilized and handled.'    ,font_size=24    ,)
												step_a.move_to([2, 2, 0]   )

												self.play(Write(__snake_case   )   )

												a__    =  Square(0.3   )
												input.set_fill(__snake_case    ,opacity=1.0   )
												input.set_stroke(width=0.0   )
												input.next_to(model_base[0]    ,__snake_case    ,buff=0.5   )

												self.play(Write(__snake_case   )   )

												input.generate_target()
												input.target.next_to(model_arr[0]    ,direction=__snake_case    ,buff=0.02   )
												self.play(MoveToTarget(__snake_case   )   )

												self.play(FadeOut(__snake_case   )   )

												a__    =  Arrow(start=__snake_case    ,end=__snake_case    ,color=__snake_case    ,buff=0.5   )
												a.next_to(model_arr[0].get_left()    ,__snake_case    ,buff=0.2   )

												model_cpu_arr[0].generate_target()
												model_cpu_arr[0].target.move_to(gpu_rect[0]   )

												a__    =  MarkupText(
												    F'As the input reaches a layer, the hook triggers\nand weights are moved from the CPU\nto the GPU and back.'    ,font_size=24    ,)
												step_a.move_to([2, 2, 0]   )

												self.play(Write(__snake_case    ,run_time=3   )   )

												a__    =  {'run_time': 1, 'fade_in': True, 'fade_out': True, 'buff': 0.02}

												self.play(
												    Write(__snake_case   )    ,Circumscribe(model_arr[0]    ,color=__snake_case    ,**__snake_case   )    ,Circumscribe(model_cpu_arr[0]    ,color=__snake_case    ,**__snake_case   )    ,Circumscribe(gpu_rect[0]    ,color=__snake_case    ,**__snake_case   )    ,)
												self.play(MoveToTarget(model_cpu_arr[0]   )   )

												a__    =  a.copy()
												for i in range(6   ):
																			a_c.next_to(model_arr[i].get_right() + 0.02    ,__snake_case    ,buff=0.2   )

																			input.generate_target()
																			input.target.move_to(model_arr[i].get_right() + 0.02   )

																			a__    =  AnimationGroup(
																			    FadeOut(__snake_case    ,run_time=0.5   )    ,MoveToTarget(__snake_case    ,run_time=0.5   )    ,FadeIn(__snake_case    ,run_time=0.5   )    ,lag_ratio=0.2   )

																			self.play(__snake_case   )

																			model_cpu_arr[i].generate_target()
																			model_cpu_arr[i].target.move_to(cpu_left_col_base[i]   )

																			if i < 5:
																										model_cpu_arr[i + 1].generate_target()
																										model_cpu_arr[i + 1].target.move_to(gpu_rect[0]   )
																										if i >= 1:
																																	a__    =  0.7

																										self.play(
																										    Circumscribe(model_arr[i]    ,**__snake_case   )    ,Circumscribe(cpu_left_col_base[i]    ,**__snake_case   )    ,Circumscribe(cpu_left_col_base[i + 1]    ,color=__snake_case    ,**__snake_case   )    ,Circumscribe(gpu_rect[0]    ,color=__snake_case    ,**__snake_case   )    ,Circumscribe(model_arr[i + 1]    ,color=__snake_case    ,**__snake_case   )    ,)
																										if i < 1:
																																	self.play(
																																	    MoveToTarget(model_cpu_arr[i]   )    ,MoveToTarget(model_cpu_arr[i + 1]   )    ,)
																										else:
																																	self.play(
																																	    MoveToTarget(model_cpu_arr[i]    ,run_time=0.7   )    ,MoveToTarget(model_cpu_arr[i + 1]    ,run_time=0.7   )    ,)
																			else:
																										model_cpu_arr[i].generate_target()
																										model_cpu_arr[i].target.move_to(cpu_left_col_base[-1]   )
																										input.generate_target()
																										input.target.next_to(model_arr[-1].get_right()    ,RIGHT + 0.02    ,buff=0.2   )

																										self.play(
																										    Circumscribe(model_arr[-1]    ,color=__snake_case    ,**__snake_case   )    ,Circumscribe(cpu_left_col_base[-1]    ,color=__snake_case    ,**__snake_case   )    ,Circumscribe(gpu_rect[0]    ,color=__snake_case    ,**__snake_case   )    ,)

																										self.play(MoveToTarget(model_cpu_arr[i]   )   )

																			a__    =  a_c
																			a__    =  a_c.copy()

												input.generate_target()
												input.target.next_to(model_base[-1]    ,RIGHT + 0.02    ,buff=0.5   )
												self.play(
												    FadeOut(__snake_case   )    ,FadeOut(__snake_case    ,run_time=0.5   )    ,)

												a__    =  MarkupText(F'Inference on a model too large for GPU memory\nis successfully completed.'    ,font_size=24   )
												step_a.move_to([2, 2, 0]   )

												self.play(Write(__snake_case    ,run_time=3   )    ,MoveToTarget(__snake_case   )   )

												self.wait()




