
'''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    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)     ->     Tuple:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE            =				old_name

   if "patch_embed" in old_name:
      _SCREAMING_SNAKE_CASE       ,							_SCREAMING_SNAKE_CASE       ,							_SCREAMING_SNAKE_CASE            =				old_name.split("""."""						)

      if layer == "0":
         _SCREAMING_SNAKE_CASE            =				old_name.replace("""0"""    ,      """convolution1"""						)
      elif layer == "1":
         _SCREAMING_SNAKE_CASE            =				old_name.replace("""1"""    ,      """batchnorm_before"""						)
      elif layer == "3":
         _SCREAMING_SNAKE_CASE            =				old_name.replace("""3"""    ,      """convolution2"""						)
      else:
         _SCREAMING_SNAKE_CASE            =				old_name.replace("""4"""    ,      """batchnorm_after"""						)

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

         _SCREAMING_SNAKE_CASE            =				"""last_stage.""" + trimmed_name

   elif "network" in old_name and re.search(r""".\d."""    ,      SCREAMING_SNAKE_CASE_						):
      _SCREAMING_SNAKE_CASE            =				old_name.replace("""network"""    ,      """intermediate_stages"""						)

   if "fc" in new_name:
      _SCREAMING_SNAKE_CASE            =				new_name.replace("""fc"""    ,      """convolution"""						)
   elif ("norm1" in new_name) and ("layernorm1" not in new_name):
      _SCREAMING_SNAKE_CASE            =				new_name.replace("""norm1"""    ,      """batchnorm_before"""						)
   elif ("norm2" in new_name) and ("layernorm2" not in new_name):
      _SCREAMING_SNAKE_CASE            =				new_name.replace("""norm2"""    ,      """batchnorm_after"""						)
   if "proj" in new_name:
      _SCREAMING_SNAKE_CASE            =				new_name.replace("""proj"""    ,      """projection"""						)
   if "dist_head" in new_name:
      _SCREAMING_SNAKE_CASE            =				new_name.replace("""dist_head"""    ,      """distillation_classifier"""						)
   elif "head" in new_name:
      _SCREAMING_SNAKE_CASE            =				new_name.replace("""head"""    ,      """classifier"""						)
   elif "patch_embed" in new_name:
      _SCREAMING_SNAKE_CASE            =				"""efficientformer.""" + new_name
   elif new_name == "norm.weight" or new_name == "norm.bias":
      _SCREAMING_SNAKE_CASE            =				new_name.replace("""norm"""    ,      """layernorm"""						)
      _SCREAMING_SNAKE_CASE            =				"""efficientformer.""" + new_name
   else:
      _SCREAMING_SNAKE_CASE            =				"""efficientformer.encoder.""" + new_name

   return new_name
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)     ->     Dict:




   """simple docstring"""
   for key in checkpoint.copy().keys():
      _SCREAMING_SNAKE_CASE            =				checkpoint.pop(SCREAMING_SNAKE_CASE_						)
      _SCREAMING_SNAKE_CASE            =				val

   return checkpoint
def    lowerCAmelCase_	(				)     ->     Union[str, Any]:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE            =				"""http://images.cocodataset.org/val2017/000000039769.jpg"""
   _SCREAMING_SNAKE_CASE            =				Image.open(requests.get(SCREAMING_SNAKE_CASE_    ,      stream=SCREAMING_SNAKE_CASE_						).raw						)

   return image
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)     ->     Optional[Any]:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE            =				torch.load(SCREAMING_SNAKE_CASE_    ,      map_location="""cpu"""						)["""model"""]
   _SCREAMING_SNAKE_CASE            =				EfficientFormerConfig.from_json_file(SCREAMING_SNAKE_CASE_						)
   _SCREAMING_SNAKE_CASE            =				EfficientFormerForImageClassificationWithTeacher(SCREAMING_SNAKE_CASE_						)
   _SCREAMING_SNAKE_CASE            =				"""_""".join(checkpoint_path.split("""/"""						)[-1].split("""."""						)[0].split("""_"""						)[:-1]						)

   _SCREAMING_SNAKE_CASE            =				config.depths[-1] - config.num_metaad_blocks + 1
   _SCREAMING_SNAKE_CASE            =				convert_torch_checkpoint(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)

   model.load_state_dict(SCREAMING_SNAKE_CASE_						)
   model.eval()

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

   # prepare image
   _SCREAMING_SNAKE_CASE            =				prepare_img()
   _SCREAMING_SNAKE_CASE            =				2_56
   _SCREAMING_SNAKE_CASE            =				2_24
   _SCREAMING_SNAKE_CASE            =				EfficientFormerImageProcessor(
       size={"""shortest_edge""": image_size}    ,      crop_size={"""height""": crop_size, """width""": crop_size}    ,      resample=pillow_resamplings["""bicubic"""]    ,      )
   _SCREAMING_SNAKE_CASE            =				processor(images=SCREAMING_SNAKE_CASE_    ,      return_tensors="""pt"""						).pixel_values

   # original processing pipeline
   _SCREAMING_SNAKE_CASE            =				Compose(
       [
           Resize(SCREAMING_SNAKE_CASE_    ,      interpolation=pillow_resamplings["""bicubic"""]						),
           CenterCrop(SCREAMING_SNAKE_CASE_						),
           ToTensor(),
           Normalize(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						),
       ]						)
   _SCREAMING_SNAKE_CASE            =				image_transforms(SCREAMING_SNAKE_CASE_						).unsqueeze(0						)

   assert torch.allclose(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)

   _SCREAMING_SNAKE_CASE            =				model(SCREAMING_SNAKE_CASE_						)
   _SCREAMING_SNAKE_CASE            =				outputs.logits

   _SCREAMING_SNAKE_CASE            =				(1, 10_00)

   if "l1" in model_name:
      _SCREAMING_SNAKE_CASE            =				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]    ,      SCREAMING_SNAKE_CASE_    ,      atol=1e-3						)
      assert logits.shape == expected_shape
   elif "l3" in model_name:
      _SCREAMING_SNAKE_CASE            =				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]    ,      SCREAMING_SNAKE_CASE_    ,      atol=1e-3						)
      assert logits.shape == expected_shape
   elif "l7" in model_name:
      _SCREAMING_SNAKE_CASE            =				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(SCREAMING_SNAKE_CASE_						).mkdir(exist_ok=SCREAMING_SNAKE_CASE_						)
   model.save_pretrained(SCREAMING_SNAKE_CASE_						)
   print(F"Checkpoint successfuly converted. Model saved at {pytorch_dump_path}"						)
   processor.save_pretrained(SCREAMING_SNAKE_CASE_						)
   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=SCREAMING_SNAKE_CASE_    ,      )
      processor.push_to_hub(
          repo_id=F"Bearnardd/{pytorch_dump_path}"    ,      commit_message="""Add image processor"""    ,      use_temp_dir=SCREAMING_SNAKE_CASE_    ,      )


if __name__ == "__main__":
     UpperCamelCase__      :		List[str]       							=  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)

     UpperCamelCase__      :		str       							=  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 typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available


UpperCamelCase__      :		int       							=  {"tokenization_herbert": ["HerbertTokenizer"]}

try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCamelCase__      :		Tuple       							=  ["HerbertTokenizerFast"]


if TYPE_CHECKING:
     from .tokenization_herbert import HerbertTokenizer

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_herbert_fast import HerbertTokenizerFast

else:
     import sys

     UpperCamelCase__      :		Tuple       							=  _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)






