






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

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

from transformers import PoolFormerConfig, PoolFormerForImageClassification, PoolFormerImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
__magic_name__   :  Union[str, Any]        =							logging.get_logger(__name__)
def 		lowercase__  (				_UpperCamelCase       ,							_UpperCamelCase       ,							_UpperCamelCase       ,							_UpperCamelCase) ->			List[Any]:
      """simple docstring"""

      UpperCamelCase					       =  original_name.split('.')[0]
      UpperCamelCase					       =  key.split('.')
      UpperCamelCase					       =  int(key_list[key_list.index(_UpperCamelCase) - 2])
      UpperCamelCase					       =  int(key_list[key_list.index(_UpperCamelCase) - 1])
      UpperCamelCase					       =  orig_block_num - offset

      UpperCamelCase					       =  key.replace(F'{orig_block_num}.{layer_num}.{original_name}'       ,							F'block.{new_block_num}.{layer_num}.{new_name}')
      return key
def 		lowercase__  (				_UpperCamelCase) ->			List[Any]:
      """simple docstring"""

      UpperCamelCase					       =  OrderedDict()
      UpperCamelCase       ,  UpperCamelCase					       =  0, 0
      for key, value in state_dict.items():
            if key.startswith('network'):
                  UpperCamelCase					       =  key.replace('network'       ,							'poolformer.encoder')
            if "proj" in key:
                  # Works for the first embedding as well as the internal embedding layers
                  if key.endswith('bias') and "patch_embed" not in key:
                        patch_emb_offset += 1
                  UpperCamelCase					       =  key[: key.find('proj')]
                  UpperCamelCase					       =  key.replace(_UpperCamelCase       ,							F'patch_embeddings.{total_embed_found}.')
                  UpperCamelCase					       =  key.replace('proj'       ,							'projection')
                  if key.endswith('bias'):
                        total_embed_found += 1
            if "patch_embeddings" in key:
                  UpperCamelCase					       =  'poolformer.encoder.' + key
            if "mlp.fc1" in key:
                  UpperCamelCase					       =  replace_key_with_offset(_UpperCamelCase       ,							_UpperCamelCase       ,							'mlp.fc1'       ,							'output.conv1')
            if "mlp.fc2" in key:
                  UpperCamelCase					       =  replace_key_with_offset(_UpperCamelCase       ,							_UpperCamelCase       ,							'mlp.fc2'       ,							'output.conv2')
            if "norm1" in key:
                  UpperCamelCase					       =  replace_key_with_offset(_UpperCamelCase       ,							_UpperCamelCase       ,							'norm1'       ,							'before_norm')
            if "norm2" in key:
                  UpperCamelCase					       =  replace_key_with_offset(_UpperCamelCase       ,							_UpperCamelCase       ,							'norm2'       ,							'after_norm')
            if "layer_scale_1" in key:
                  UpperCamelCase					       =  replace_key_with_offset(_UpperCamelCase       ,							_UpperCamelCase       ,							'layer_scale_1'       ,							'layer_scale_1')
            if "layer_scale_2" in key:
                  UpperCamelCase					       =  replace_key_with_offset(_UpperCamelCase       ,							_UpperCamelCase       ,							'layer_scale_2'       ,							'layer_scale_2')
            if "head" in key:
                  UpperCamelCase					       =  key.replace('head'       ,							'classifier')
            UpperCamelCase					       =  value
      return new_state_dict
def 		lowercase__  (				) ->			Optional[int]:
      """simple docstring"""

      UpperCamelCase					       =  'http://images.cocodataset.org/val2017/000000039769.jpg'
      UpperCamelCase					       =  Image.open(requests.get(_UpperCamelCase       ,							stream=_UpperCamelCase).raw)

      return image
@torch.no_grad()
def 		lowercase__  (				_UpperCamelCase       ,							_UpperCamelCase       ,							_UpperCamelCase) ->			Dict:
      """simple docstring"""

      UpperCamelCase					       =  PoolFormerConfig()

      # set attributes based on model_name
      UpperCamelCase					       =  'huggingface/label-files'
      UpperCamelCase					       =  model_name[-3:]
      UpperCamelCase					       =  10_00
      UpperCamelCase					       =  'imagenet-1k-id2label.json'
      UpperCamelCase					       =  (1, 10_00)

      # set config attributes
      UpperCamelCase					       =  json.load(open(hf_hub_download(_UpperCamelCase       ,							_UpperCamelCase       ,							repo_type='dataset')       ,							'r'))
      UpperCamelCase					       =  {int(_UpperCamelCase): v for k, v in idalabel.items()}
      UpperCamelCase					       =  idalabel
      UpperCamelCase					       =  {v: k for k, v in idalabel.items()}
      if size == "s12":
            UpperCamelCase					       =  [2, 2, 6, 2]
            UpperCamelCase					       =  [64, 1_28, 3_20, 5_12]
            UpperCamelCase					       =  4.0
            UpperCamelCase					       =  0.9
      elif size == "s24":
            UpperCamelCase					       =  [4, 4, 12, 4]
            UpperCamelCase					       =  [64, 1_28, 3_20, 5_12]
            UpperCamelCase					       =  4.0
            UpperCamelCase					       =  0.9
      elif size == "s36":
            UpperCamelCase					       =  [6, 6, 18, 6]
            UpperCamelCase					       =  [64, 1_28, 3_20, 5_12]
            UpperCamelCase					       =  4.0
            UpperCamelCase					       =  1e-6
            UpperCamelCase					       =  0.9
      elif size == "m36":
            UpperCamelCase					       =  [6, 6, 18, 6]
            UpperCamelCase					       =  [96, 1_92, 3_84, 7_68]
            UpperCamelCase					       =  4.0
            UpperCamelCase					       =  1e-6
            UpperCamelCase					       =  0.9_5
      elif size == "m48":
            UpperCamelCase					       =  [8, 8, 24, 8]
            UpperCamelCase					       =  [96, 1_92, 3_84, 7_68]
            UpperCamelCase					       =  4.0
            UpperCamelCase					       =  1e-6
            UpperCamelCase					       =  0.9_5
      else:
            raise ValueError(F'Size {size} not supported')

      # load image processor
      UpperCamelCase					       =  PoolFormerImageProcessor(crop_pct=_UpperCamelCase)

      # Prepare image
      UpperCamelCase					       =  prepare_img()
      UpperCamelCase					       =  image_processor(images=_UpperCamelCase       ,							return_tensors='pt').pixel_values

      logger.info(F'Converting model {model_name}...')

      # load original state dict
      UpperCamelCase					       =  torch.load(_UpperCamelCase       ,							map_location=torch.device('cpu'))

      # rename keys
      UpperCamelCase					       =  rename_keys(_UpperCamelCase)

      # create HuggingFace model and load state dict
      UpperCamelCase					       =  PoolFormerForImageClassification(_UpperCamelCase)
      model.load_state_dict(_UpperCamelCase)
      model.eval()

      # Define image processor
      UpperCamelCase					       =  PoolFormerImageProcessor(crop_pct=_UpperCamelCase)
      UpperCamelCase					       =  image_processor(images=prepare_img()       ,							return_tensors='pt').pixel_values

      # forward pass
      UpperCamelCase					       =  model(_UpperCamelCase)
      UpperCamelCase					       =  outputs.logits

      # define expected logit slices for different models
      if size == "s12":
            UpperCamelCase					       =  torch.tensor([-0.3_0_4_5, -0.6_7_5_8, -0.4_8_6_9])
      elif size == "s24":
            UpperCamelCase					       =  torch.tensor([0.4_4_0_2, -0.1_3_7_4, -0.8_0_4_5])
      elif size == "s36":
            UpperCamelCase					       =  torch.tensor([-0.6_0_8_0, -0.5_1_3_3, -0.5_8_9_8])
      elif size == "m36":
            UpperCamelCase					       =  torch.tensor([0.3_9_5_2, 0.2_2_6_3, -1.2_6_6_8])
      elif size == "m48":
            UpperCamelCase					       =  torch.tensor([0.1_1_6_7, -0.0_6_5_6, -0.3_4_2_3])
      else:
            raise ValueError(F'Size {size} not supported')

      # verify logits
      assert logits.shape == expected_shape
      assert torch.allclose(logits[0, :3]       ,							_UpperCamelCase       ,							atol=1e-2)

      # finally, save model and image processor
      logger.info(F'Saving PyTorch model and image processor to {pytorch_dump_folder_path}...')
      Path(_UpperCamelCase).mkdir(exist_ok=_UpperCamelCase)
      model.save_pretrained(_UpperCamelCase)
      print(F'Saving image processor to {pytorch_dump_folder_path}')
      image_processor.save_pretrained(_UpperCamelCase)


if __name__ == "__main__":
       __magic_name__   :  Dict        =							argparse.ArgumentParser()

       parser.add_argument(
           '''--model_name''',
           default='''poolformer_s12''',
           type=str,
           help='''Name of the model you\'d like to convert.''',
       )
       parser.add_argument(
           '''--checkpoint_path''', default=None, type=str, help='''Path to the original PyTorch checkpoint (.pth file).'''
       )
       parser.add_argument(
           '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the folder to output PyTorch model.'''
       )
       __magic_name__   :  Union[str, Any]        =							parser.parse_args()
       convert_poolformer_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path)











from manim import *





class 					A__     (     __snake_case ):

     '''simple docstring'''






     def   _SCREAMING_SNAKE_CASE     ( self      :    Union[str, Any] ):






           """simple docstring"""




           UpperCamelCase					       =  Rectangle(height=0.5   ,						width=0.5 )
           UpperCamelCase					       =  Rectangle(height=0.4_6   ,						width=0.4_6 ).set_stroke(width=0 )

           UpperCamelCase					       =  Rectangle(height=0.2_5   ,						width=0.2_5 )

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

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

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

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

           UpperCamelCase					       =  []
           UpperCamelCase					       =  []

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

                 UpperCamelCase					       =  Rectangle(height=0.4_6   ,						width=0.4_6 ).set_stroke(width=0.0 ).set_fill(_SCREAMING_SNAKE_CASE   ,						opacity=0.8 )
                 cpu_target.move_to(cpu_left_col_base[i] )
                 model_cpu_arr.append(_SCREAMING_SNAKE_CASE )

           self.add(*_SCREAMING_SNAKE_CASE   ,						*_SCREAMING_SNAKE_CASE )

           UpperCamelCase					       =  [meta_mem.copy() for i in range(6 )]
           UpperCamelCase					       =  [meta_mem.copy() for i in range(6 )]
           UpperCamelCase					       =  VGroup(*_SCREAMING_SNAKE_CASE ).arrange(_SCREAMING_SNAKE_CASE   ,						buff=0 )
           UpperCamelCase					       =  VGroup(*_SCREAMING_SNAKE_CASE ).arrange(_SCREAMING_SNAKE_CASE   ,						buff=0 )
           UpperCamelCase					       =  VGroup(_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE ).arrange(_SCREAMING_SNAKE_CASE   ,						buff=0 )
           UpperCamelCase					       =  Text('Disk'   ,						font_size=24 )
           UpperCamelCase					       =  Group(_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE ).arrange(_SCREAMING_SNAKE_CASE   ,						buff=0.5   ,						aligned_edge=_SCREAMING_SNAKE_CASE )
           disk.move_to([-4, -1.2_5, 0] )
           self.add(_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE )

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

           UpperCamelCase					       =  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(_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE )

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

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

           UpperCamelCase					       =  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(_SCREAMING_SNAKE_CASE ) )

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

           self.play(Write(_SCREAMING_SNAKE_CASE ) )

           input.generate_target()
           input.target.next_to(model_arr[0]   ,						direction=_SCREAMING_SNAKE_CASE   ,						buff=0.0_2 )
           self.play(MoveToTarget(_SCREAMING_SNAKE_CASE ) )

           self.play(FadeOut(_SCREAMING_SNAKE_CASE ) )

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

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

           UpperCamelCase					       =  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(_SCREAMING_SNAKE_CASE   ,						run_time=3 ) )

           UpperCamelCase					       =  {'run_time': 1, 'fade_in': True, 'fade_out': True, 'buff': 0.0_2}

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

           UpperCamelCase					       =  a.copy()
           for i in range(6 ):
                 a_c.next_to(model_arr[i].get_right() + 0.0_2   ,						_SCREAMING_SNAKE_CASE   ,						buff=0.2 )

                 input.generate_target()
                 input.target.move_to(model_arr[i].get_right() + 0.0_2 )

                 UpperCamelCase					       =  AnimationGroup(
                     FadeOut(_SCREAMING_SNAKE_CASE   ,						run_time=0.5 )   ,						MoveToTarget(_SCREAMING_SNAKE_CASE   ,						run_time=0.5 )   ,						FadeIn(_SCREAMING_SNAKE_CASE   ,						run_time=0.5 )   ,						lag_ratio=0.2 )

                 self.play(_SCREAMING_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:
                             UpperCamelCase					       =  0.7

                       self.play(
                           Circumscribe(model_arr[i]   ,						**_SCREAMING_SNAKE_CASE )   ,						Circumscribe(cpu_left_col_base[i]   ,						**_SCREAMING_SNAKE_CASE )   ,						Circumscribe(cpu_left_col_base[i + 1]   ,						color=_SCREAMING_SNAKE_CASE   ,						**_SCREAMING_SNAKE_CASE )   ,						Circumscribe(gpu_rect[0]   ,						color=_SCREAMING_SNAKE_CASE   ,						**_SCREAMING_SNAKE_CASE )   ,						Circumscribe(model_arr[i + 1]   ,						color=_SCREAMING_SNAKE_CASE   ,						**_SCREAMING_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 + 0.0_2   ,						buff=0.2 )

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

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

                 UpperCamelCase					       =  a_c
                 UpperCamelCase					       =  a_c.copy()

           input.generate_target()
           input.target.next_to(model_base[-1]   ,						RIGHT + 0.0_2   ,						buff=0.5 )
           self.play(
               FadeOut(_SCREAMING_SNAKE_CASE )   ,						FadeOut(_SCREAMING_SNAKE_CASE   ,						run_time=0.5 )   ,						)

           UpperCamelCase					       =  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(_SCREAMING_SNAKE_CASE   ,						run_time=3 )   ,						MoveToTarget(_SCREAMING_SNAKE_CASE ) )

           self.wait()



