





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 DeiTImageProcessor, ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel
from transformers.utils import logging


logging.set_verbosity_info()
UpperCAmelCase_  							=				logging.get_logger(__name__)




def 	lowerCAmelCase_					(						__UpperCAmelCase:    List[Any]	,     __UpperCAmelCase:    List[Any]=False   )   ->			Dict:
    UpperCamelCase__							:       str      =   []
    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"vit.encoder.layer.{i}.layernorm_before.weight")   )
        rename_keys.append((f"blocks.{i}.norm1.bias", f"vit.encoder.layer.{i}.layernorm_before.bias")   )
        rename_keys.append((f"blocks.{i}.attn.proj.weight", f"vit.encoder.layer.{i}.attention.output.dense.weight")   )
        rename_keys.append((f"blocks.{i}.attn.proj.bias", f"vit.encoder.layer.{i}.attention.output.dense.bias")   )
        rename_keys.append((f"blocks.{i}.norm2.weight", f"vit.encoder.layer.{i}.layernorm_after.weight")   )
        rename_keys.append((f"blocks.{i}.norm2.bias", f"vit.encoder.layer.{i}.layernorm_after.bias")   )
        rename_keys.append((f"blocks.{i}.mlp.fc1.weight", f"vit.encoder.layer.{i}.intermediate.dense.weight")   )
        rename_keys.append((f"blocks.{i}.mlp.fc1.bias", f"vit.encoder.layer.{i}.intermediate.dense.bias")   )
        rename_keys.append((f"blocks.{i}.mlp.fc2.weight", f"vit.encoder.layer.{i}.output.dense.weight")   )
        rename_keys.append((f"blocks.{i}.mlp.fc2.bias", f"vit.encoder.layer.{i}.output.dense.bias")   )

    # projection layer + position embeddings
    rename_keys.extend(
        [
            ('''cls_token''', '''vit.embeddings.cls_token'''),
            ('''patch_embed.proj.weight''', '''vit.embeddings.patch_embeddings.projection.weight'''),
            ('''patch_embed.proj.bias''', '''vit.embeddings.patch_embeddings.projection.bias'''),
            ('''pos_embed''', '''vit.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 "vit" from all keys that start with "vit"
        UpperCamelCase__							:       Dict      =   [(pair[0], pair[1][4:]) if pair[1].startswith('''vit'''   ) else pair for pair in rename_keys]
    else:
        # layernorm + classification head
        rename_keys.extend(
            [
                ('''norm.weight''', '''vit.layernorm.weight'''),
                ('''norm.bias''', '''vit.layernorm.bias'''),
                ('''head.weight''', '''classifier.weight'''),
                ('''head.bias''', '''classifier.bias'''),
            ]   )

    return rename_keys




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




def 	lowerCAmelCase_					(						__UpperCAmelCase:    List[str]   )   ->			List[Any]:
    UpperCamelCase__							:       str      =   ['''head.weight''', '''head.bias''']
    for k in ignore_keys:
        state_dict.pop(__UpperCAmelCase	,     __UpperCAmelCase   )




def 	lowerCAmelCase_					(						__UpperCAmelCase:    Union[str, Any]	,     __UpperCAmelCase:    List[str]	,     __UpperCAmelCase:    int   )   ->			str:
    UpperCamelCase__							:       Optional[Any]      =   dct.pop(__UpperCAmelCase   )
    UpperCamelCase__							:       Optional[Any]      =   val




def 	lowerCAmelCase_					(						)   ->			int:
    UpperCamelCase__							:       Any      =   '''http://images.cocodataset.org/val2017/000000039769.jpg'''
    UpperCamelCase__							:       Union[str, Any]      =   Image.open(requests.get(__UpperCAmelCase	,     stream=__UpperCAmelCase   ).raw   )
    return im




@torch.no_grad()
def 	lowerCAmelCase_					(						__UpperCAmelCase:    Tuple	,     __UpperCAmelCase:    Any   )   ->			Tuple:
    UpperCamelCase__							:       Dict      =   ViTConfig()
    UpperCamelCase__							:       Tuple      =   False
    # dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
    if vit_name[-5:] == "in21k":
        UpperCamelCase__							:       Any      =   True
        UpperCamelCase__							:       str      =   int(vit_name[-12:-10]   )
        UpperCamelCase__							:       List[Any]      =   int(vit_name[-9:-6]   )
    else:
        UpperCamelCase__							:       Tuple      =   1000
        UpperCamelCase__							:       Optional[int]      =   '''huggingface/label-files'''
        UpperCamelCase__							:       str      =   '''imagenet-1k-id2label.json'''
        UpperCamelCase__							:       List[str]      =   json.load(open(hf_hub_download(__UpperCAmelCase	,     __UpperCAmelCase	,     repo_type='''dataset'''   )	,     '''r'''   )   )
        UpperCamelCase__							:       Tuple      =   {int(__UpperCAmelCase   ): v for k, v in idalabel.items()}
        UpperCamelCase__							:       List[Any]      =   idalabel
        UpperCamelCase__							:       Union[str, Any]      =   {v: k for k, v in idalabel.items()}
        UpperCamelCase__							:       Dict      =   int(vit_name[-6:-4]   )
        UpperCamelCase__							:       Optional[int]      =   int(vit_name[-3:]   )
    # size of the architecture
    if "deit" in vit_name:
        if vit_name[9:].startswith('''tiny'''   ):
            UpperCamelCase__							:       List[str]      =   192
            UpperCamelCase__							:       int      =   768
            UpperCamelCase__							:       Any      =   12
            UpperCamelCase__							:       Optional[int]      =   3
        elif vit_name[9:].startswith('''small'''   ):
            UpperCamelCase__							:       List[Any]      =   384
            UpperCamelCase__							:       Union[str, Any]      =   1536
            UpperCamelCase__							:       Optional[int]      =   12
            UpperCamelCase__							:       List[Any]      =   6
        else:
            pass
    else:
        if vit_name[4:].startswith('''small'''   ):
            UpperCamelCase__							:       str      =   768
            UpperCamelCase__							:       int      =   2304
            UpperCamelCase__							:       List[str]      =   8
            UpperCamelCase__							:       Optional[int]      =   8
        elif vit_name[4:].startswith('''base'''   ):
            pass
        elif vit_name[4:].startswith('''large'''   ):
            UpperCamelCase__							:       Optional[Any]      =   1024
            UpperCamelCase__							:       int      =   4096
            UpperCamelCase__							:       List[Any]      =   24
            UpperCamelCase__							:       Any      =   16
        elif vit_name[4:].startswith('''huge'''   ):
            UpperCamelCase__							:       Any      =   1280
            UpperCamelCase__							:       int      =   5120
            UpperCamelCase__							:       Union[str, Any]      =   32
            UpperCamelCase__							:       Optional[int]      =   16

    # load original model from timm
    UpperCamelCase__							:       Union[str, Any]      =   timm.create_model(__UpperCAmelCase	,     pretrained=__UpperCAmelCase   )
    timm_model.eval()

    # load state_dict of original model, remove and rename some keys
    UpperCamelCase__							:       Dict      =   timm_model.state_dict()
    if base_model:
        remove_classification_head_(__UpperCAmelCase   )
    UpperCamelCase__							:       Tuple      =   create_rename_keys(__UpperCAmelCase	,     __UpperCAmelCase   )
    for src, dest in rename_keys:
        rename_key(__UpperCAmelCase	,     __UpperCAmelCase	,     __UpperCAmelCase   )
    read_in_q_k_v(__UpperCAmelCase	,     __UpperCAmelCase	,     __UpperCAmelCase   )

    # load HuggingFace model
    if vit_name[-5:] == "in21k":
        UpperCamelCase__							:       List[Any]      =   ViTModel(__UpperCAmelCase   ).eval()
    else:
        UpperCamelCase__							:       List[Any]      =   ViTForImageClassification(__UpperCAmelCase   ).eval()
    model.load_state_dict(__UpperCAmelCase   )

    # Check outputs on an image, prepared by ViTImageProcessor/DeiTImageProcessor
    if "deit" in vit_name:
        UpperCamelCase__							:       List[Any]      =   DeiTImageProcessor(size=config.image_size   )
    else:
        UpperCamelCase__							:       Any      =   ViTImageProcessor(size=config.image_size   )
    UpperCamelCase__							:       Optional[int]      =   image_processor(images=prepare_img()	,     return_tensors='''pt'''   )
    UpperCamelCase__							:       Any      =   encoding['''pixel_values''']
    UpperCamelCase__							:       Dict      =   model(__UpperCAmelCase   )

    if base_model:
        UpperCamelCase__							:       List[Any]      =   timm_model.forward_features(__UpperCAmelCase   )
        assert timm_pooled_output.shape == outputs.pooler_output.shape
        assert torch.allclose(__UpperCAmelCase	,     outputs.pooler_output	,     atol=1e-3   )
    else:
        UpperCamelCase__							:       Optional[int]      =   timm_model(__UpperCAmelCase   )
        assert timm_logits.shape == outputs.logits.shape
        assert torch.allclose(__UpperCAmelCase	,     outputs.logits	,     atol=1e-3   )

    Path(__UpperCAmelCase   ).mkdir(exist_ok=__UpperCAmelCase   )
    print(f"Saving model {vit_name} to {pytorch_dump_folder_path}"   )
    model.save_pretrained(__UpperCAmelCase   )
    print(f"Saving image processor to {pytorch_dump_folder_path}"   )
    image_processor.save_pretrained(__UpperCAmelCase   )


if __name__ == "__main__":
  UpperCAmelCase_  							=				argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      '--vit_name',
      default='vit_base_patch16_224',
      type=str,
      help='Name of the ViT 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.'
  )

  UpperCAmelCase_  							=				parser.parse_args()
  convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path)








from manim import *






class 					lowercase__ (			__lowerCamelCase       ):

   '''simple docstring'''


   def    UpperCamelCase__						(					self							)				->   List[str]:






       """simple docstring"""
       UpperCamelCase__							:       Any      =   Rectangle(height=0.5,	width=0.5							)
       UpperCamelCase__							:       Any      =   Rectangle(height=0.25,	width=0.25							)
       UpperCamelCase__							:       Dict      =   Rectangle(height=0.46,	width=0.46							).set_stroke(width=0							)

       UpperCamelCase__							:       List[Any]      =   [mem.copy() for i in range(6							)]
       UpperCamelCase__							:       Optional[int]      =   [mem.copy() for i in range(6							)]
       UpperCamelCase__							:       str      =   VGroup(*__magic_name__							).arrange(__magic_name__,	buff=0							)
       UpperCamelCase__							:       List[str]      =   VGroup(*__magic_name__							).arrange(__magic_name__,	buff=0							)
       UpperCamelCase__							:       Tuple      =   VGroup(__magic_name__,	__magic_name__							).arrange(__magic_name__,	buff=0							)
       UpperCamelCase__							:       List[str]      =   Text('''CPU''',	font_size=24							)
       UpperCamelCase__							:       Any      =   Group(__magic_name__,	__magic_name__							).arrange(__magic_name__,	buff=0.5,	aligned_edge=__magic_name__							)
       cpu.move_to([-2.5, -0.5, 0]							)
       self.add(__magic_name__							)

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

       UpperCamelCase__							:       str      =   [mem.copy() for i in range(6							)]
       UpperCamelCase__							:       Tuple      =   VGroup(*__magic_name__							).arrange(__magic_name__,	buff=0							)

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

       UpperCamelCase__							:       Union[str, Any]      =   []
       UpperCamelCase__							:       Any      =   []
       UpperCamelCase__							:       List[Any]      =   []

       for i, rect in enumerate(__magic_name__							):
           rect.set_stroke(__magic_name__							)

           UpperCamelCase__							:       Tuple      =   Rectangle(height=0.46 / 4,	width=0.46 / 3							).set_stroke(width=0.0							).set_fill(__magic_name__,	opacity=0.7							)

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

       self.add(*__magic_name__,	*__magic_name__,	*__magic_name__							)

       UpperCamelCase__							:       Tuple      =   [mem.copy() for i in range(6							)]
       UpperCamelCase__							:       Dict      =   VGroup(*__magic_name__							).arrange(__magic_name__,	buff=0							)

       UpperCamelCase__							:       List[str]      =   Text('''Loaded Checkpoint''',	font_size=24							)
       UpperCamelCase__							:       Optional[Any]      =   Group(__magic_name__,	__magic_name__							).arrange(__magic_name__,	buff=0.5,	aligned_edge=__magic_name__							)
       checkpoint.move_to([3, 0.5, 0]							)

       self.add(__magic_name__							)

       UpperCamelCase__							:       Tuple      =   []
       UpperCamelCase__							:       List[str]      =   []

       for i, rect in enumerate(__magic_name__							):
           UpperCamelCase__							:       Optional[int]      =   fill.copy().set_fill(__magic_name__,	opacity=0.7							)
           target.move_to(__magic_name__							)
           ckpt_arr.append(__magic_name__							)

           UpperCamelCase__							:       int      =   target.copy()
           if i < 5:
               cpu_target.move_to(cpu_left_col_base[i + 1]							)
           else:
               cpu_target.move_to(cpu_right_col_base[i - 5]							)
           ckpt_cpu_arr.append(__magic_name__							)
       self.add(*__magic_name__,	*__magic_name__							)

       UpperCamelCase__							:       List[str]      =   Square(side_length=2.2							)
       key.move_to([-5, 2, 0]							)

       UpperCamelCase__							:       List[str]      =   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(__magic_name__,	__magic_name__							)

       UpperCamelCase__							:       Any      =   MarkupText(
           f"<span fgcolor='{BLUE}'>●</span> Checkpoint",	font_size=18,	)

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

       UpperCamelCase__							:       Dict      =   MarkupText(
           f"Based on the passed in configuration, weights are stored in\na variety of np.memmaps on disk or to a particular device.",	font_size=24,	)
       step_a.move_to([2, 2, 0]							)

       UpperCamelCase__							:       Any      =   [meta_mem.copy() for i in range(6							)]
       UpperCamelCase__							:       int      =   [meta_mem.copy() for i in range(6							)]
       UpperCamelCase__							:       Optional[int]      =   VGroup(*__magic_name__							).arrange(__magic_name__,	buff=0							)
       UpperCamelCase__							:       Union[str, Any]      =   VGroup(*__magic_name__							).arrange(__magic_name__,	buff=0							)
       UpperCamelCase__							:       Union[str, Any]      =   VGroup(__magic_name__,	__magic_name__							).arrange(__magic_name__,	buff=0							)
       UpperCamelCase__							:       Any      =   Text('''Disk''',	font_size=24							)
       UpperCamelCase__							:       List[Any]      =   Group(__magic_name__,	__magic_name__							).arrange(__magic_name__,	buff=0.5,	aligned_edge=__magic_name__							)
       disk.move_to([-4.0, -1.25, 0]							)
       self.play(Write(__magic_name__,	run_time=3							),	Write(__magic_name__,	run_time=1							),	Create(__magic_name__,	run_time=1							)							)

       UpperCamelCase__							:       Union[str, Any]      =   []
       for i, rect in enumerate(__magic_name__							):
           UpperCamelCase__							:       Union[str, Any]      =   rect.copy()
           target.generate_target()
           target.target.move_to(disk_left_col_base[i]							).scale(0.5							)
           animations.append(MoveToTarget(__magic_name__,	run_time=1.5							)							)
       self.play(*__magic_name__							)

       self.play(FadeOut(__magic_name__							)							)

       UpperCamelCase__							:       Optional[int]      =   MarkupText(f"Then, the checkpoint is removed from memory\nthrough garbage collection.",	font_size=24							)
       step_a.move_to([2, 2, 0]							)

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

       self.play(
           FadeOut(__magic_name__,	__magic_name__,	*__magic_name__,	*__magic_name__							),	)

       self.wait()

