



'''simple docstring'''





import argparse
import re
from pathlib import Path

import requests
import torch
from PIL import Image
from torchvision.transforms import CenterCrop, Compose, Normalize, Resize, ToTensor

from transformers import (
    EfficientFormerConfig,
    EfficientFormerForImageClassificationWithTeacher,
    EfficientFormerImageProcessor,
)
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, PILImageResampling





def 						__snake_case(				_lowerCAmelCase     ,   _lowerCAmelCase		)		-> str:
      snake_case__  :    Optional[int]    						=      old_name

      if "patch_embed" in old_name:
            snake_case__						,				snake_case__						,				snake_case__  :    Optional[int]    						=      old_name.split("""."""		)

            if layer == "0":
                  snake_case__  :    List[Any]    						=      old_name.replace("""0"""     ,   """convolution1"""		)
            elif layer == "1":
                  snake_case__  :    Tuple    						=      old_name.replace("""1"""     ,   """batchnorm_before"""		)
            elif layer == "3":
                  snake_case__  :    Dict    						=      old_name.replace("""3"""     ,   """convolution2"""		)
            else:
                  snake_case__  :    Optional[int]    						=      old_name.replace("""4"""     ,   """batchnorm_after"""		)

      if "network" in old_name and re.search(r"""\d\.\d"""     ,   _lowerCAmelCase		):
            snake_case__  :    Tuple    						=      r"""\b\d{2}\b"""
            if bool(re.search(_lowerCAmelCase     ,   _lowerCAmelCase		)		):
                  snake_case__  :    Optional[Any]    						=      re.search(r"""\d\.\d\d."""     ,   _lowerCAmelCase		).group()
            else:
                  snake_case__  :    List[Any]    						=      re.search(r"""\d\.\d."""     ,   _lowerCAmelCase		).group()
            if int(match[0]		) < 6:
                  snake_case__  :    Optional[Any]    						=      old_name.replace(_lowerCAmelCase     ,   """"""		)
                  snake_case__  :    Any    						=      trimmed_name.replace("""network"""     ,   match[0] + """.meta4D_layers.blocks.""" + match[2:-1]		)
                  snake_case__  :    Tuple    						=      """intermediate_stages.""" + trimmed_name
            else:
                  snake_case__  :    Optional[int]    						=      old_name.replace(_lowerCAmelCase     ,   """"""		)
                  if int(match[2]		) < num_meta4D_last_stage:
                        snake_case__  :    Dict    						=      trimmed_name.replace("""network"""     ,   """meta4D_layers.blocks.""" + match[2]		)
                  else:
                        snake_case__  :    Tuple    						=      str(int(match[2]		) - num_meta4D_last_stage		)
                        snake_case__  :    List[str]    						=      trimmed_name.replace("""network"""     ,   """meta3D_layers.blocks.""" + layer_index		)
                        if "norm1" in old_name:
                              snake_case__  :    Tuple    						=      trimmed_name.replace("""norm1"""     ,   """layernorm1"""		)
                        elif "norm2" in old_name:
                              snake_case__  :    Union[str, Any]    						=      trimmed_name.replace("""norm2"""     ,   """layernorm2"""		)
                        elif "fc1" in old_name:
                              snake_case__  :    Optional[int]    						=      trimmed_name.replace("""fc1"""     ,   """linear_in"""		)
                        elif "fc2" in old_name:
                              snake_case__  :    Dict    						=      trimmed_name.replace("""fc2"""     ,   """linear_out"""		)

                  snake_case__  :    Any    						=      """last_stage.""" + trimmed_name

      elif "network" in old_name and re.search(r""".\d."""     ,   _lowerCAmelCase		):
            snake_case__  :    Dict    						=      old_name.replace("""network"""     ,   """intermediate_stages"""		)

      if "fc" in new_name:
            snake_case__  :    Optional[Any]    						=      new_name.replace("""fc"""     ,   """convolution"""		)
      elif ("norm1" in new_name) and ("layernorm1" not in new_name):
            snake_case__  :    Optional[Any]    						=      new_name.replace("""norm1"""     ,   """batchnorm_before"""		)
      elif ("norm2" in new_name) and ("layernorm2" not in new_name):
            snake_case__  :    Optional[int]    						=      new_name.replace("""norm2"""     ,   """batchnorm_after"""		)
      if "proj" in new_name:
            snake_case__  :    str    						=      new_name.replace("""proj"""     ,   """projection"""		)
      if "dist_head" in new_name:
            snake_case__  :    Any    						=      new_name.replace("""dist_head"""     ,   """distillation_classifier"""		)
      elif "head" in new_name:
            snake_case__  :    Any    						=      new_name.replace("""head"""     ,   """classifier"""		)
      elif "patch_embed" in new_name:
            snake_case__  :    List[Any]    						=      """efficientformer.""" + new_name
      elif new_name == "norm.weight" or new_name == "norm.bias":
            snake_case__  :    Union[str, Any]    						=      new_name.replace("""norm"""     ,   """layernorm"""		)
            snake_case__  :    int    						=      """efficientformer.""" + new_name
      else:
            snake_case__  :    Optional[int]    						=      """efficientformer.encoder.""" + new_name

      return new_name





def 						__snake_case(				_lowerCAmelCase     ,   _lowerCAmelCase		)		-> Optional[Any]:
      for key in checkpoint.copy().keys():
            snake_case__  :    Dict    						=      checkpoint.pop(_lowerCAmelCase		)
            snake_case__  :    int    						=      val

      return checkpoint





def 						__snake_case(				)		-> Any:
      snake_case__  :    int    						=      """http://images.cocodataset.org/val2017/000000039769.jpg"""
      snake_case__  :    List[str]    						=      Image.open(requests.get(_lowerCAmelCase     ,   stream=_lowerCAmelCase		).raw		)

      return image





def 						__snake_case(				_lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase		)		-> str:
      snake_case__  :    Dict    						=      torch.load(_lowerCAmelCase     ,   map_location="""cpu"""		)["""model"""]
      snake_case__  :    Any    						=      EfficientFormerConfig.from_json_file(_lowerCAmelCase		)
      snake_case__  :    Any    						=      EfficientFormerForImageClassificationWithTeacher(_lowerCAmelCase		)
      snake_case__  :    List[Any]    						=      """_""".join(checkpoint_path.split("""/"""		)[-1].split("""."""		)[0].split("""_"""		)[:-1]		)

      snake_case__  :    Optional[Any]    						=      config.depths[-1] - config.num_metaad_blocks + 1
      snake_case__  :    Optional[int]    						=      convert_torch_checkpoint(_lowerCAmelCase     ,   _lowerCAmelCase		)

      model.load_state_dict(_lowerCAmelCase		)
      model.eval()

      snake_case__  :    Any    						=      {
          """bilinear""": PILImageResampling.BILINEAR,
          """bicubic""": PILImageResampling.BICUBIC,
          """nearest""": PILImageResampling.NEAREST,
      }

      # prepare image
      snake_case__  :    List[Any]    						=      prepare_img()
      snake_case__  :    Dict    						=      256
      snake_case__  :    Any    						=      224
      snake_case__  :    Any    						=      EfficientFormerImageProcessor(
          size={"""shortest_edge""": image_size}     ,   crop_size={"""height""": crop_size, """width""": crop_size}     ,   resample=pillow_resamplings["""bicubic"""]     ,   )
      snake_case__  :    Tuple    						=      processor(images=_lowerCAmelCase     ,   return_tensors="""pt"""		).pixel_values

      # original processing pipeline
      snake_case__  :    Union[str, Any]    						=      Compose(
          [
              Resize(_lowerCAmelCase     ,   interpolation=pillow_resamplings["""bicubic"""]		),
              CenterCrop(_lowerCAmelCase		),
              ToTensor(),
              Normalize(_lowerCAmelCase     ,   _lowerCAmelCase		),
          ]		)
      snake_case__  :    Optional[int]    						=      image_transforms(_lowerCAmelCase		).unsqueeze(0		)

      assert torch.allclose(_lowerCAmelCase     ,   _lowerCAmelCase		)

      snake_case__  :    Any    						=      model(_lowerCAmelCase		)
      snake_case__  :    Union[str, Any]    						=      outputs.logits

      snake_case__  :    int    						=      (1, 1_000)

      if "l1" in model_name:
            snake_case__  :    List[Any]    						=      torch.Tensor(
                [-0.1312, 0.4353, -1.0499, -0.5124, 0.4183, -0.6793, -1.3777, -0.0893, -0.7358, -2.4328]		)
            assert torch.allclose(logits[0, :10]     ,   _lowerCAmelCase     ,   atol=1e-3		)
            assert logits.shape == expected_shape
      elif "l3" in model_name:
            snake_case__  :    str    						=      torch.Tensor(
                [-1.3150, -1.5456, -1.2556, -0.8496, -0.7127, -0.7897, -0.9728, -0.3052, 0.3751, -0.3127]		)
            assert torch.allclose(logits[0, :10]     ,   _lowerCAmelCase     ,   atol=1e-3		)
            assert logits.shape == expected_shape
      elif "l7" in model_name:
            snake_case__  :    List[str]    						=      torch.Tensor(
                [-1.0283, -1.4131, -0.5644, -1.3115, -0.5785, -1.2049, -0.7528, 0.1992, -0.3822, -0.0878]		)
            assert logits.shape == expected_shape
      else:
            raise ValueError(
                f"Unknown model checkpoint: {checkpoint_path}. Supported version of efficientformer are l1, l3 and l7"		)

      # Save Checkpoints
      Path(_lowerCAmelCase		).mkdir(exist_ok=_lowerCAmelCase		)
      model.save_pretrained(_lowerCAmelCase		)
      print(f"Checkpoint successfuly converted. Model saved at {pytorch_dump_path}"		)
      processor.save_pretrained(_lowerCAmelCase		)
      print(f"Processor successfuly saved at {pytorch_dump_path}"		)

      if push_to_hub:
            print("""Pushing model to the hub..."""		)

            model.push_to_hub(
                repo_id=f"Bearnardd/{pytorch_dump_path}"     ,   commit_message="""Add model"""     ,   use_temp_dir=_lowerCAmelCase     ,   )
            processor.push_to_hub(
                repo_id=f"Bearnardd/{pytorch_dump_path}"     ,   commit_message="""Add image processor"""     ,   use_temp_dir=_lowerCAmelCase     ,   )


if __name__ == "__main__":
       __a          =		argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           "--pytorch_model_path",
           default=None,
           type=str,
           required=True,
           help="Path to EfficientFormer pytorch checkpoint.",
       )
       parser.add_argument(
           "--config_file",
           default=None,
           type=str,
           required=True,
           help="The json file for EfficientFormer model config.",
       )
       parser.add_argument(
           "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
       )

       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",
       )
       parser.set_defaults(push_to_hub=True)

       __a          =		parser.parse_args()
       convert_efficientformer_checkpoint(
           checkpoint_path=args.pytorch_model_path,
           efficientformer_config_file=args.config_file,
           pytorch_dump_path=args.pytorch_dump_path,
           push_to_hub=args.push_to_hub,
       )





'''simple docstring'''






from manim import *

class 				SCREAMING_SNAKE_CASE(							A__			):







	"""simple docstring"""






	def      A	(						self			:     Union[str, Any]		)			->		List[str]:
								UpperCAmelCase :       Optional[Any] 							=		Rectangle(height=0.5		,			width=0.5		)
								UpperCAmelCase :       str 							=		Rectangle(height=0.46		,			width=0.46		).set_stroke(width=0		)

								UpperCAmelCase :       Tuple 							=		[mem.copy() for i in range(6		)]
								UpperCAmelCase :       Optional[int] 							=		[mem.copy() for i in range(6		)]
								UpperCAmelCase :       Dict 							=		VGroup(*__snake_case		).arrange(__snake_case		,			buff=0		)
								UpperCAmelCase :       Any 							=		VGroup(*__snake_case		).arrange(__snake_case		,			buff=0		)
								UpperCAmelCase :       Union[str, Any] 							=		VGroup(__snake_case		,			__snake_case		).arrange(__snake_case		,			buff=0		)
								UpperCAmelCase :       Optional[Any] 							=		Text('''CPU'''		,			font_size=24		)
								UpperCAmelCase :       Union[str, Any] 							=		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 :       Optional[Any] 							=		[mem.copy() for i in range(4		)]
								UpperCAmelCase :       Union[str, Any] 							=		VGroup(*__snake_case		).arrange(__snake_case		,			buff=0		)
								UpperCAmelCase :       List[str] 							=		Text('''GPU'''		,			font_size=24		)
								UpperCAmelCase :       Dict 							=		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 :       int 							=		[mem.copy() for i in range(6		)]
								UpperCAmelCase :       Union[str, Any] 							=		VGroup(*__snake_case		).arrange(__snake_case		,			buff=0		)

								UpperCAmelCase :       List[str] 							=		Text('''Model'''		,			font_size=24		)
								UpperCAmelCase :       Tuple 							=		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 							=		[]
								for i, rect in enumerate(__snake_case		):
															rect.set_stroke(__snake_case		)
															# target = fill.copy().set_fill(YELLOW, opacity=0.7)
															# target.move_to(rect)
															# self.add(target)

															UpperCAmelCase :       Dict 							=		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(cpu_targs[0]		,			direction=__snake_case		,			buff=0.0		)
															else:
																						cpu_target.next_to(cpu_targs[i - 1]		,			direction=__snake_case		,			buff=0.0		)
															self.add(__snake_case		)
															cpu_targs.append(__snake_case		)

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

								UpperCAmelCase :       Any 							=		Text('''Loaded Checkpoint'''		,			font_size=24		)
								UpperCAmelCase :       Union[str, Any] 							=		Group(__snake_case		,			__snake_case		).arrange(__snake_case		,			aligned_edge=__snake_case		,			buff=0.4		)
								checkpoint.move_to([3, 0.5, 0]		)

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

								UpperCAmelCase :       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(__snake_case		,			__snake_case		)

								UpperCAmelCase :       Tuple 							=		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()		)

								UpperCAmelCase :       List[Any] 							=		MarkupText(
								    F"""Next, a <i><span fgcolor=\"{BLUE}\">second</span></i> model is loaded into memory,\nwith the weights of a <span fgcolor=\"{BLUE}\">single shard</span>."""		,			font_size=24		,			)
								step_a.move_to([2, 2, 0]		)
								self.play(Write(__snake_case		)		,			Write(__snake_case		)		)

								self.play(Write(__snake_case		,			run_time=1		)		,			Create(__snake_case		,			run_time=1		)		)

								UpperCAmelCase :       Tuple 							=		[]
								UpperCAmelCase :       int 							=		[]
								for i, rect in enumerate(__snake_case		):
															UpperCAmelCase :       Any 							=		fill.copy().set_fill(__snake_case		,			opacity=0.7		)
															target.move_to(__snake_case		)
															first_animations.append(GrowFromCenter(__snake_case		,			run_time=1		)		)

															UpperCAmelCase :       List[str] 							=		target.copy()
															cpu_target.generate_target()
															if i < 5:
																						cpu_target.target.move_to(cpu_left_col_base[i + 1]		)
															else:
																						cpu_target.target.move_to(cpu_right_col_base[i - 5]		)
															second_animations.append(MoveToTarget(__snake_case		,			run_time=1.5		)		)

								self.play(*__snake_case		)
								self.play(*__snake_case		)
								self.wait()





