


'''simple docstring'''



from manim import *



class 			a       (	_a ):


     """simple docstring"""






     def 				lowerCamelCase__				(       self  :	str    )		->							Any:
          __UpperCAmelCase			:				Tuple													=    Rectangle(height=0.5							,				width=0.5    )
          __UpperCAmelCase			:				List[str]													=    Rectangle(height=0.25							,				width=0.25    )
          __UpperCAmelCase			:				Optional[int]													=    Rectangle(height=0.46							,				width=0.46    ).set_stroke(width=0    )

          __UpperCAmelCase			:				List[str]													=    [mem.copy() for i in range(6    )]
          __UpperCAmelCase			:				List[str]													=    [mem.copy() for i in range(6    )]
          __UpperCAmelCase			:				Dict													=    VGroup(*snake_case    ).arrange(snake_case							,				buff=0    )
          __UpperCAmelCase			:				int													=    VGroup(*snake_case    ).arrange(snake_case							,				buff=0    )
          __UpperCAmelCase			:				str													=    VGroup(snake_case							,				snake_case    ).arrange(snake_case							,				buff=0    )
          __UpperCAmelCase			:				Union[str, Any]													=    Text('''CPU'''							,				font_size=24    )
          __UpperCAmelCase			:				Dict													=    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    )

          __UpperCAmelCase			:				Dict													=    [mem.copy() for i in range(4    )]
          __UpperCAmelCase			:				Tuple													=    VGroup(*snake_case    ).arrange(snake_case							,				buff=0    )
          __UpperCAmelCase			:				Optional[Any]													=    Text('''GPU'''							,				font_size=24    )
          __UpperCAmelCase			:				Any													=    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    )

          __UpperCAmelCase			:				List[Any]													=    [mem.copy() for i in range(6    )]
          __UpperCAmelCase			:				Tuple													=    VGroup(*snake_case    ).arrange(snake_case							,				buff=0    )

          __UpperCAmelCase			:				Optional[Any]													=    Text('''Model'''							,				font_size=24    )
          __UpperCAmelCase			:				str													=    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    )

          __UpperCAmelCase			:				Any													=    []
          __UpperCAmelCase			:				Union[str, Any]													=    []
          __UpperCAmelCase			:				Any													=    []

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

               __UpperCAmelCase			:				Union[str, Any]													=    Rectangle(height=0.46 / 4							,				width=0.46 / 3    ).set_stroke(width=0.0    ).set_fill(snake_case							,				opacity=0.7    )

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

          self.add(*snake_case							,				*snake_case							,				*snake_case    )

          __UpperCAmelCase			:				Tuple													=    [mem.copy() for i in range(6    )]
          __UpperCAmelCase			:				Any													=    VGroup(*snake_case    ).arrange(snake_case							,				buff=0    )

          __UpperCAmelCase			:				Dict													=    Text('''Loaded Checkpoint'''							,				font_size=24    )
          __UpperCAmelCase			:				Tuple													=    Group(snake_case							,				snake_case    ).arrange(snake_case							,				buff=0.5							,				aligned_edge=snake_case    )
          checkpoint.move_to([3, 0.5, 0]    )

          self.add(snake_case    )

          __UpperCAmelCase			:				Union[str, Any]													=    []
          __UpperCAmelCase			:				int													=    []

          for i, rect in enumerate(snake_case    ):
               __UpperCAmelCase			:				List[Any]													=    fill.copy().set_fill(snake_case							,				opacity=0.7    )
               target.move_to(snake_case    )
               ckpt_arr.append(snake_case    )

               __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(snake_case    )
          self.add(*snake_case							,				*snake_case    )

          __UpperCAmelCase			:				Optional[Any]													=    Square(side_length=2.2    )
          key.move_to([-5, 2, 0]    )

          __UpperCAmelCase			:				Tuple													=    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    )

          __UpperCAmelCase			:				Optional[Any]													=    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    )

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

          __UpperCAmelCase			:				Optional[int]													=    []
          for i, rect in enumerate(snake_case    ):
               __UpperCAmelCase			:				Any													=    rect.copy()
               target.generate_target()
               target.target.move_to(disk_left_col_base[i]    ).scale(0.5    )
               animations.append(MoveToTarget(snake_case							,				run_time=1.5    )    )
          self.play(*snake_case    )

          self.play(FadeOut(snake_case    )    )

          __UpperCAmelCase			:				Union[str, Any]													=    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(snake_case							,				run_time=3    )    )

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

          self.wait()



'''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 timm import create_model
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform

from transformers import BitConfig, BitForImageClassification, BitImageProcessor
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


logging.set_verbosity_info()
__UpperCAmelCase    :List[Any]				       =     logging.get_logger(__name__)




def 			_a			(		_lowercase      :			Tuple						):





     '''simple docstring'''






     __UpperCAmelCase			:				Union[str, Any]													=    '''huggingface/label-files'''
     __UpperCAmelCase			:				str													=    '''imagenet-1k-id2label.json'''
     __UpperCAmelCase			:				Optional[Any]													=    json.load(open(hf_hub_download(_lowercase    ,      _lowercase    ,      repo_type='''dataset'''						)    ,      '''r'''						)						)
     __UpperCAmelCase			:				int													=    {int(_lowercase						): v for k, v in idalabel.items()}
     __UpperCAmelCase			:				int													=    {v: k for k, v in idalabel.items()}

     __UpperCAmelCase			:				Optional[Any]													=    '''std_conv''' if '''bit''' in model_name else False

     # note that when using BiT as backbone for ViT-hybrid checkpoints,
     # one needs to additionally set config.layer_type = "bottleneck", config.stem_type = "same",
     # config.conv_layer = "std_conv_same"
     __UpperCAmelCase			:				int													=    BitConfig(
         conv_layer=_lowercase    ,      num_labels=1000    ,      idalabel=_lowercase    ,      labelaid=_lowercase    ,      )

     return config




def 			_a			(		_lowercase      :			Tuple						):





     '''simple docstring'''






     if "stem.conv" in name:
          __UpperCAmelCase			:				Any													=    name.replace('''stem.conv'''    ,      '''bit.embedder.convolution'''						)
     if "blocks" in name:
          __UpperCAmelCase			:				Optional[int]													=    name.replace('''blocks'''    ,      '''layers'''						)
     if "head.fc" in name:
          __UpperCAmelCase			:				Dict													=    name.replace('''head.fc'''    ,      '''classifier.1'''						)
     if name.startswith('''norm'''						):
          __UpperCAmelCase			:				List[Any]													=    '''bit.''' + name
     if "bit" not in name and "classifier" not in name:
          __UpperCAmelCase			:				List[str]													=    '''bit.encoder.''' + name

     return name




def 			_a			(		):





     '''simple docstring'''






     __UpperCAmelCase			:				Optional[Any]													=    '''http://images.cocodataset.org/val2017/000000039769.jpg'''
     __UpperCAmelCase			:				Optional[Any]													=    Image.open(requests.get(_lowercase    ,      stream=_lowercase						).raw						)
     return im




@torch.no_grad()
def 			_a			(		_lowercase      :			Tuple    ,      _lowercase      :			Dict    ,      _lowercase      :			Tuple=False						):





     '''simple docstring'''






     __UpperCAmelCase			:				Any													=    get_config(_lowercase						)

     # load original model from timm
     __UpperCAmelCase			:				Tuple													=    create_model(_lowercase    ,      pretrained=_lowercase						)
     timm_model.eval()

     # load state_dict of original model
     __UpperCAmelCase			:				List[Any]													=    timm_model.state_dict()
     for key in state_dict.copy().keys():
          __UpperCAmelCase			:				Optional[Any]													=    state_dict.pop(_lowercase						)
          __UpperCAmelCase			:				int													=    val.squeeze() if '''head''' in key else val

     # load HuggingFace model
     __UpperCAmelCase			:				Any													=    BitForImageClassification(_lowercase						)
     model.eval()
     model.load_state_dict(_lowercase						)

     # create image processor
     __UpperCAmelCase			:				Optional[int]													=    create_transform(**resolve_data_config({}    ,      model=_lowercase						)						)
     __UpperCAmelCase			:				Union[str, Any]													=    transform.transforms

     __UpperCAmelCase			:				str													=    {
         '''bilinear''': PILImageResampling.BILINEAR,
         '''bicubic''': PILImageResampling.BICUBIC,
         '''nearest''': PILImageResampling.NEAREST,
     }

     __UpperCAmelCase			:				List[Any]													=    BitImageProcessor(
         do_resize=_lowercase    ,      size={'''shortest_edge''': timm_transforms[0].size}    ,      resample=pillow_resamplings[timm_transforms[0].interpolation.value]    ,      do_center_crop=_lowercase    ,      crop_size={'''height''': timm_transforms[1].size[0], '''width''': timm_transforms[1].size[1]}    ,      do_normalize=_lowercase    ,      image_mean=timm_transforms[-1].mean.tolist()    ,      image_std=timm_transforms[-1].std.tolist()    ,      )

     __UpperCAmelCase			:				Optional[Any]													=    prepare_img()
     __UpperCAmelCase			:				Any													=    transform(_lowercase						).unsqueeze(0						)
     __UpperCAmelCase			:				List[Any]													=    processor(_lowercase    ,      return_tensors='''pt'''						).pixel_values

     # verify pixel values
     assert torch.allclose(_lowercase    ,      _lowercase						)

     # verify logits
     with torch.no_grad():
          __UpperCAmelCase			:				Union[str, Any]													=    model(_lowercase						)
          __UpperCAmelCase			:				Union[str, Any]													=    outputs.logits

     print('''Logits:'''    ,      logits[0, :3]						)
     print('''Predicted class:'''    ,      model.config.idalabel[logits.argmax(-1						).item()]						)
     __UpperCAmelCase			:				Optional[int]													=    timm_model(_lowercase						)
     assert timm_logits.shape == outputs.logits.shape
     assert torch.allclose(_lowercase    ,      outputs.logits    ,      atol=1E-3						)
     print('''Looks ok!'''						)

     if pytorch_dump_folder_path is not None:
          Path(_lowercase						).mkdir(exist_ok=_lowercase						)
          print(F'Saving model {model_name} and processor to {pytorch_dump_folder_path}'						)
          model.save_pretrained(_lowercase						)
          processor.save_pretrained(_lowercase						)

     if push_to_hub:
          print(F'Pushing model {model_name} and processor to the hub'						)
          model.push_to_hub(F'ybelkada/{model_name}'						)
          processor.push_to_hub(F'ybelkada/{model_name}'						)


if __name__ == "__main__":
      __UpperCAmelCase    :Optional[int]				       =     argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          "--model_name",
          default="resnetv2_50x1_bitm",
          type=str,
          help="Name of the BiT 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."
      )
      parser.add_argument(
          "--push_to_hub",
          action="store_true",
          help="Whether to push the model to the hub.",
      )

      __UpperCAmelCase    :str				       =     parser.parse_args()
      convert_bit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)