


'''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    UpperCAmelCase_  (       A		,	A      ):
   '''simple docstring'''





   _a					:  Any						=  old_name

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

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

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

         _a					:  Dict						=  'last_stage.' + trimmed_name

   elif "network" in old_name and re.search(r'.\d.'		,	_lowerCamelCase      ):
      _a					:  List[Any]						=  old_name.replace('network'		,	'intermediate_stages'      )

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

   return new_name


def    UpperCAmelCase_  (       A		,	A      ):
   '''simple docstring'''





   for key in checkpoint.copy().keys():
      _a					:  Optional[int]						=  checkpoint.pop(_lowerCamelCase      )
      _a					:  Optional[Any]						=  val

   return checkpoint


def    UpperCAmelCase_  (       ):
   '''simple docstring'''





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

   return image


def    UpperCAmelCase_  (       A		,	A		,	A		,	A      ):
   '''simple docstring'''





   _a					:  List[str]						=  torch.load(_lowerCamelCase		,	map_location='cpu'      )['model']
   _a					:  Dict						=  EfficientFormerConfig.from_json_file(_lowerCamelCase      )
   _a					:  Union[str, Any]						=  EfficientFormerForImageClassificationWithTeacher(_lowerCamelCase      )
   _a					:  List[Any]						=  '_'.join(checkpoint_path.split('/'      )[-1].split('.'      )[0].split('_'      )[:-1]      )

   _a					:  List[Any]						=  config.depths[-1] - config.num_metaad_blocks + 1
   _a					:  Tuple						=  convert_torch_checkpoint(_lowerCamelCase		,	_lowerCamelCase      )

   model.load_state_dict(_lowerCamelCase      )
   model.eval()

   _a					:  Tuple						=  {
       'bilinear': PILImageResampling.BILINEAR,
       'bicubic': PILImageResampling.BICUBIC,
       'nearest': PILImageResampling.NEAREST,
   }

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

   # original processing pipeline
   _a					:  List[Any]						=  Compose(
       [
           Resize(_lowerCamelCase		,	interpolation=pillow_resamplings['bicubic']      ),
           CenterCrop(_lowerCamelCase      ),
           ToTensor(),
           Normalize(_lowerCamelCase		,	_lowerCamelCase      ),
       ]      )
   _a					:  Tuple						=  image_transforms(_lowerCamelCase      ).unsqueeze(0      )

   assert torch.allclose(_lowerCamelCase		,	_lowerCamelCase      )

   _a					:  List[Any]						=  model(_lowerCamelCase      )
   _a					:  Optional[Any]						=  outputs.logits

   _a					:  Optional[int]						=  (1, 1_0_0_0)

   if "l1" in model_name:
      _a					:  str						=  torch.Tensor(
          [-0.13_12, 0.43_53, -1.04_99, -0.51_24, 0.41_83, -0.67_93, -1.37_77, -0.08_93, -0.73_58, -2.43_28]      )
      assert torch.allclose(logits[0, :1_0]		,	_lowerCamelCase		,	atol=1E-3      )
      assert logits.shape == expected_shape
   elif "l3" in model_name:
      _a					:  Any						=  torch.Tensor(
          [-1.31_50, -1.54_56, -1.25_56, -0.84_96, -0.71_27, -0.78_97, -0.97_28, -0.30_52, 0.37_51, -0.31_27]      )
      assert torch.allclose(logits[0, :1_0]		,	_lowerCamelCase		,	atol=1E-3      )
      assert logits.shape == expected_shape
   elif "l7" in model_name:
      _a					:  Optional[Any]						=  torch.Tensor(
          [-1.02_83, -1.41_31, -0.56_44, -1.31_15, -0.57_85, -1.20_49, -0.75_28, 0.19_92, -0.38_22, -0.08_78]      )
      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__":
 UpperCAmelCase_      :		Optional[int]          =		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_      :		Union[str, Any]          =		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 Optional

import pyspark

from .. import Features, NamedSplit
from ..download import DownloadMode
from ..packaged_modules.spark.spark import Spark
from .abc import AbstractDatasetReader





class      lowerCAmelCase (			__lowerCAmelCase):


       def __init__( self     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE = None     ,	__SCREAMING_SNAKE_CASE = None     ,	__SCREAMING_SNAKE_CASE = True     ,	__SCREAMING_SNAKE_CASE = None     ,	__SCREAMING_SNAKE_CASE = False     ,	__SCREAMING_SNAKE_CASE = None     ,	__SCREAMING_SNAKE_CASE = True     ,	__SCREAMING_SNAKE_CASE = "arrow"     ,	**__SCREAMING_SNAKE_CASE     ,	)   ->	Union[str, Any]:



            '''simple docstring'''




            super().__init__(
                split=__SCREAMING_SNAKE_CASE     ,	features=__SCREAMING_SNAKE_CASE     ,	cache_dir=__SCREAMING_SNAKE_CASE     ,	keep_in_memory=__SCREAMING_SNAKE_CASE     ,	streaming=__SCREAMING_SNAKE_CASE     ,	**__SCREAMING_SNAKE_CASE     ,	)
            __snake_case            =	load_from_cache_file
            __snake_case            =	file_format
            __snake_case            =	Spark(
                df=__SCREAMING_SNAKE_CASE     ,	features=__SCREAMING_SNAKE_CASE     ,	cache_dir=__SCREAMING_SNAKE_CASE     ,	working_dir=__SCREAMING_SNAKE_CASE     ,	**__SCREAMING_SNAKE_CASE     ,	)




       def    lowerCAmelCase   ( self	)   ->	str:



            '''simple docstring'''




            if self.streaming:
                 return self.builder.as_streaming_dataset(split=self.split	)
            __snake_case            =	None if self._load_from_cache_file else DownloadMode.FORCE_REDOWNLOAD
            self.builder.download_and_prepare(
                download_mode=__SCREAMING_SNAKE_CASE     ,	file_format=self._file_format     ,	)
            return self.builder.as_dataset(split=self.split	)






