






'''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 				lowercase_					(     __A			:							List[str]       ,   __A			:							List[Any]      )							->   int:



 """simple docstring"""




 lowercase       : Tuple						=old_name

 if "patch_embed" in old_name:
  lowercase	,		lowercase	,		lowercase       : List[Any]						=old_name.split('''.'''      )

  if layer == "0":
   lowercase       : int						=old_name.replace('''0'''       ,   '''convolution1'''      )
  elif layer == "1":
   lowercase       : List[Any]						=old_name.replace('''1'''       ,   '''batchnorm_before'''      )
  elif layer == "3":
   lowercase       : Union[str, Any]						=old_name.replace('''3'''       ,   '''convolution2'''      )
  else:
   lowercase       : Tuple						=old_name.replace('''4'''       ,   '''batchnorm_after'''      )

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

   lowercase       : List[Any]						='''last_stage.''' + trimmed_name

 elif "network" in old_name and re.search(R'''.\d.'''       ,   __A      ):
  lowercase       : Dict						=old_name.replace('''network'''       ,   '''intermediate_stages'''      )

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

 return new_name


def 				lowercase_					(     __A			:							Union[str, Any]       ,   __A			:							int      )							->   Dict:



 """simple docstring"""




 for key in checkpoint.copy().keys():
  lowercase       : List[Any]						=checkpoint.pop(__A      )
  lowercase       : str						=val

 return checkpoint


def 				lowercase_					(     )							->   int:



 """simple docstring"""




 lowercase       : Dict						='''http://images.cocodataset.org/val2017/000000039769.jpg'''
 lowercase       : Union[str, Any]						=Image.open(requests.get(__A       ,   stream=__A      ).raw      )

 return image


def 				lowercase_					(     __A			:							Path       ,   __A			:							Path       ,   __A			:							Path       ,   __A			:							bool      )							->   int:



 """simple docstring"""




 lowercase       : Optional[int]						=torch.load(__A       ,   map_location='''cpu'''      )['''model''']
 lowercase       : List[Any]						=EfficientFormerConfig.from_json_file(__A      )
 lowercase       : List[str]						=EfficientFormerForImageClassificationWithTeacher(__A      )
 lowercase       : List[str]						='''_'''.join(checkpoint_path.split('''/'''      )[-1].split('''.'''      )[0].split('''_'''      )[:-1]      )

 lowercase       : Optional[int]						=config.depths[-1] - config.num_metaad_blocks + 1
 lowercase       : List[Any]						=convert_torch_checkpoint(__A       ,   __A      )

 model.load_state_dict(__A      )
 model.eval()

 lowercase       : List[str]						={
     '''bilinear''': PILImageResampling.BILINEAR,
     '''bicubic''': PILImageResampling.BICUBIC,
     '''nearest''': PILImageResampling.NEAREST,
 }

 # prepare image
 lowercase       : int						=prepare_img()
 lowercase       : List[str]						=2_5_6
 lowercase       : int						=2_2_4
 lowercase       : Union[str, Any]						=EfficientFormerImageProcessor(
     size={'''shortest_edge''': image_size}       ,   crop_size={'''height''': crop_size, '''width''': crop_size}       ,   resample=pillow_resamplings['''bicubic''']       ,   )
 lowercase       : List[str]						=processor(images=__A       ,   return_tensors='''pt'''      ).pixel_values

 # original processing pipeline
 lowercase       : int						=Compose(
     [
         Resize(__A       ,   interpolation=pillow_resamplings['''bicubic''']      ),
         CenterCrop(__A      ),
         ToTensor(),
         Normalize(__A       ,   __A      ),
     ]      )
 lowercase       : Tuple						=image_transforms(__A      ).unsqueeze(0      )

 assert torch.allclose(__A       ,   __A      )

 lowercase       : Any						=model(__A      )
 lowercase       : Union[str, Any]						=outputs.logits

 lowercase       : List[Any]						=(1, 1_0_0_0)

 if "l1" in model_name:
  lowercase       : Optional[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, :1_0]       ,   __A       ,   atol=1E-3      )
  assert logits.shape == expected_shape
 elif "l3" in model_name:
  lowercase       : List[Any]						=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, :1_0]       ,   __A       ,   atol=1E-3      )
  assert logits.shape == expected_shape
 elif "l7" in model_name:
  lowercase       : Any						=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(__A      ).mkdir(exist_ok=__A      )
 model.save_pretrained(__A      )
 print(F'Checkpoint successfuly converted. Model saved at {pytorch_dump_path}'      )
 processor.save_pretrained(__A      )
 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=__A       ,   )
  processor.push_to_hub(
      repo_id=F'Bearnardd/{pytorch_dump_path}'       ,   commit_message='''Add image processor'''       ,   use_temp_dir=__A       ,   )


if __name__ == "__main__":
      SCREAMING_SNAKE_CASE							=						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)

      SCREAMING_SNAKE_CASE							=						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'''



import importlib.util
import os
import platform
from argparse import ArgumentParser

import huggingface_hub

from .. import __version__ as version
from ..utils import (
    is_accelerate_available,
    is_flax_available,
    is_safetensors_available,
    is_tf_available,
    is_torch_available,
)
from . import BaseTransformersCLICommand


def 				lowercase_					(     __A			:							List[Any]      )							->   Union[str, Any]:



 """simple docstring"""




 return EnvironmentCommand()


def 				lowercase_					(     __A			:							int      )							->   Dict:



 """simple docstring"""




 return EnvironmentCommand(args.accelerate_config_file      )





class UpperCAmelCase_       (     __A					):




      """simple docstring"""




      @staticmethod
      def 					A__   (			UpperCAmelCase      :		ArgumentParser		)	->	List[str]:


       '''simple docstring'''

       lowercase       : Optional[int]						=parser.add_parser('''env'''		)
       download_parser.set_defaults(func=UpperCAmelCase		)
       download_parser.add_argument(
           '''--accelerate-config_file'''  ,   default=UpperCAmelCase  ,   help='''The accelerate config file to use for the default values in the launching script.'''  ,   )
       download_parser.set_defaults(func=UpperCAmelCase		)






      def __init__(			self      :		Dict  ,   UpperCAmelCase      :		Tuple  ,   *UpperCAmelCase      :		Tuple		)	->	None:


       '''simple docstring'''

       lowercase       : List[str]						=accelerate_config_file






      def 					A__   (			self      :		Tuple		)	->	int:


       '''simple docstring'''

       lowercase       : Optional[Any]						='''not installed'''
       if is_safetensors_available():
        import safetensors

        lowercase       : List[Any]						=safetensors.__version__
       elif importlib.util.find_spec('''safetensors'''		) is not None:
        import safetensors

        lowercase       : List[str]						=f'{safetensors.__version__} but is ignored because of PyTorch version too old.'

       lowercase       : List[str]						='''not installed'''
       lowercase       : str						='''not found'''
       if is_accelerate_available():
        import accelerate
        from accelerate.commands.config import default_config_file, load_config_from_file

        lowercase       : List[str]						=accelerate.__version__
        # Get the default from the config file.
        if self._accelerate_config_file is not None or os.path.isfile(UpperCAmelCase		):
         lowercase       : Dict						=load_config_from_file(self._accelerate_config_file		).to_dict()

        lowercase       : Optional[int]						=(
            '''\n'''.join([f'\t- {prop}: {val}' for prop, val in accelerate_config.items()]		)
            if isinstance(UpperCAmelCase  ,   UpperCAmelCase		)
            else f'\t{accelerate_config}'
        )

       lowercase       : Optional[int]						='''not installed'''
       lowercase       : Optional[Any]						='''NA'''
       if is_torch_available():
        import torch

        lowercase       : List[Any]						=torch.__version__
        lowercase       : Dict						=torch.cuda.is_available()

       lowercase       : Optional[Any]						='''not installed'''
       lowercase       : List[str]						='''NA'''
       if is_tf_available():
        import tensorflow as tf

        lowercase       : List[Any]						=tf.__version__
        try:
         # deprecated in v2.1
         lowercase       : Optional[Any]						=tf.test.is_gpu_available()
        except AttributeError:
         # returns list of devices, convert to bool
         lowercase       : Optional[int]						=bool(tf.config.list_physical_devices('''GPU'''		)		)

       lowercase       : Optional[Any]						='''not installed'''
       lowercase       : Optional[Any]						='''not installed'''
       lowercase       : Optional[Any]						='''not installed'''
       lowercase       : List[str]						='''NA'''
       if is_flax_available():
        import flax
        import jax
        import jaxlib

        lowercase       : Dict						=flax.__version__
        lowercase       : List[Any]						=jax.__version__
        lowercase       : Any						=jaxlib.__version__
        lowercase       : Tuple						=jax.lib.xla_bridge.get_backend().platform

       lowercase       : str						={
           '''`transformers` version''': version,
           '''Platform''': platform.platform(),
           '''Python version''': platform.python_version(),
           '''Huggingface_hub version''': huggingface_hub.__version__,
           '''Safetensors version''': f'{safetensors_version}',
           '''Accelerate version''': f'{accelerate_version}',
           '''Accelerate config''': f'{accelerate_config_str}',
           '''PyTorch version (GPU?)''': f'{pt_version} ({pt_cuda_available})',
           '''Tensorflow version (GPU?)''': f'{tf_version} ({tf_cuda_available})',
           '''Flax version (CPU?/GPU?/TPU?)''': f'{flax_version} ({jax_backend})',
           '''Jax version''': f'{jax_version}',
           '''JaxLib version''': f'{jaxlib_version}',
           '''Using GPU in script?''': '''<fill in>''',
           '''Using distributed or parallel set-up in script?''': '''<fill in>''',
       }

       print('''\nCopy-and-paste the text below in your GitHub issue and FILL OUT the two last points.\n'''		)
       print(self.format_dict(UpperCAmelCase		)		)

       return info






      @staticmethod
      def 					A__   (			UpperCAmelCase      :		Any		)	->	Any:


       '''simple docstring'''

       return "\n".join([f'- {prop}: {val}' for prop, val in d.items()]		) + "\n"



