

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




  '''simple docstring'''



  lowercase									=old_name

  if "patch_embed" in old_name:
    lowercase									=old_name.split('''.'''	)

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

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

      lowercase									="last_stage." + trimmed_name

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

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

  return new_name




def 	UpperCamelCase   (       lowercase_						:    Optional[int]     ,					lowercase_						:    Optional[Any]	)		->	Dict:




  '''simple docstring'''



  for key in checkpoint.copy().keys():
    lowercase									=checkpoint.pop(lowercase_	)
    lowercase									=val

  return checkpoint




def 	UpperCamelCase   (       )		->	Tuple:




  '''simple docstring'''



  lowercase									="http://images.cocodataset.org/val2017/000000039769.jpg"
  lowercase									=Image.open(requests.get(lowercase_     ,					stream=lowercase_	).raw	)

  return image




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




  '''simple docstring'''



  lowercase									=torch.load(lowercase_     ,					map_location='''cpu'''	)["model"]
  lowercase									=EfficientFormerConfig.from_json_file(lowercase_	)
  lowercase									=EfficientFormerForImageClassificationWithTeacher(lowercase_	)
  lowercase									="_".join(checkpoint_path.split('''/'''	)[-1].split('''.'''	)[0].split('''_'''	)[:-1]	)

  lowercase									=config.depths[-1] - config.num_metaad_blocks + 1
  lowercase									=convert_torch_checkpoint(lowercase_     ,					lowercase_	)

  model.load_state_dict(lowercase_	)
  model.eval()

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

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

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

  assert torch.allclose(lowercase_     ,					lowercase_	)

  lowercase									=model(lowercase_	)
  lowercase									=outputs.logits

  lowercase									=(1, 1_0_0_0)

  if "l1" in model_name:
    lowercase									=torch.Tensor(
        [-0.1_3_1_2, 0.4_3_5_3, -1.0_4_9_9, -0.5_1_2_4, 0.4_1_8_3, -0.6_7_9_3, -1.3_7_7_7, -0.0_8_9_3, -0.7_3_5_8, -2.4_3_2_8]	)
    assert torch.allclose(logits[0, :1_0]     ,					lowercase_     ,					atol=1E-3	)
    assert logits.shape == expected_shape
  elif "l3" in model_name:
    lowercase									=torch.Tensor(
        [-1.3_1_5_0, -1.5_4_5_6, -1.2_5_5_6, -0.8_4_9_6, -0.7_1_2_7, -0.7_8_9_7, -0.9_7_2_8, -0.3_0_5_2, 0.3_7_5_1, -0.3_1_2_7]	)
    assert torch.allclose(logits[0, :1_0]     ,					lowercase_     ,					atol=1E-3	)
    assert logits.shape == expected_shape
  elif "l7" in model_name:
    lowercase									=torch.Tensor(
        [-1.0_2_8_3, -1.4_1_3_1, -0.5_6_4_4, -1.3_1_1_5, -0.5_7_8_5, -1.2_0_4_9, -0.7_5_2_8, 0.1_9_9_2, -0.3_8_2_2, -0.0_8_7_8]	)
    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(lowercase_	).mkdir(exist_ok=lowercase_	)
  model.save_pretrained(lowercase_	)
  print(f'Checkpoint successfuly converted. Model saved at {pytorch_dump_path}'	)
  processor.save_pretrained(lowercase_	)
  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=lowercase_     ,					)
    processor.push_to_hub(
        repo_id=f'Bearnardd/{pytorch_dump_path}'     ,					commit_message='''Add image processor'''     ,					use_temp_dir=lowercase_     ,					)


if __name__ == "__main__":
     _UpperCAmelCase  :		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  :		List[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,
     )







import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


SCREAMING_SNAKE_CASE__							:      Tuple         =    logging.get_logger(__name__)


SCREAMING_SNAKE_CASE__							:      int         =    """▁"""

SCREAMING_SNAKE_CASE__							:      Union[str, Any]         =    {"""vocab_file""": """spiece.model"""}

SCREAMING_SNAKE_CASE__							:      Optional[int]         =    {
    """vocab_file""": {
        """google/reformer-crime-and-punishment""": (
            """https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/spiece.model"""
        )
    }
}

SCREAMING_SNAKE_CASE__							:      Optional[Any]         =    {
    """google/reformer-crime-and-punishment""": 5_2_4_2_8_8,
}







class 	__lowerCAmelCase      (       _UpperCamelCase	):
   _UpperCamelCase			:    List[Any]  		=					VOCAB_FILES_NAMES
   _UpperCamelCase			:    Any  		=					PRETRAINED_VOCAB_FILES_MAP
   _UpperCamelCase			:    Any  		=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   _UpperCamelCase			:    Tuple  		=					["""input_ids""", """attention_mask"""]





   def __init__(			self       ,      snake_case       ,      snake_case="</s>"       ,      snake_case="<unk>"       ,      snake_case=[]       ,      snake_case = None       ,      **snake_case       ,      )					->	None:
      """simple docstring"""





      a__						:    Optional[Any]   		=	{} if sp_model_kwargs is None else sp_model_kwargs

      super().__init__(
          eos_token=snake_case       ,      unk_token=snake_case       ,      additional_special_tokens=snake_case       ,      sp_model_kwargs=self.sp_model_kwargs       ,      **snake_case       ,      )

      a__						:    Union[str, Any]   		=	vocab_file
      a__						:    Optional[Any]   		=	spm.SentencePieceProcessor(**self.sp_model_kwargs   )
      self.sp_model.Load(snake_case   )





   @property
   def 						_snake_case			(			self   )					->	str:
      """simple docstring"""





      return self.sp_model.get_piece_size()





   def 						_snake_case			(			self   )					->	Dict[str, int]:
      """simple docstring"""





      a__						:    Dict   		=	{self.convert_ids_to_tokens(snake_case   ): i for i in range(self.vocab_size   )}
      vocab.update(self.added_tokens_encoder   )
      return vocab





   def __getstate__(			self   )					->	Any:
      """simple docstring"""





      a__						:    Tuple   		=	self.__dict__.copy()
      a__						:    Optional[int]   		=	None
      return state





   def __setstate__(			self       ,      snake_case   )					->	Union[str, Any]:
      """simple docstring"""





      a__						:    List[Any]   		=	d

      # for backward compatibility
      if not hasattr(self       ,      "sp_model_kwargs"   ):
         a__						:    List[Any]   		=	{}

      a__						:    int   		=	spm.SentencePieceProcessor(**self.sp_model_kwargs   )
      self.sp_model.Load(self.vocab_file   )





   def 						_snake_case			(			self       ,      snake_case   )					->	List[str]:
      """simple docstring"""





      return self.sp_model.encode(snake_case       ,      out_type=snake_case   )





   def 						_snake_case			(			self       ,      snake_case   )					->	Union[str, Any]:
      """simple docstring"""





      return self.sp_model.piece_to_id(snake_case   )





   def 						_snake_case			(			self       ,      snake_case   )					->	Any:
      """simple docstring"""





      if index < self.sp_model.get_piece_size():
         a__						:    Optional[Any]   		=	self.sp_model.IdToPiece(snake_case   )
      return token





   def 						_snake_case			(			self       ,      snake_case   )					->	Any:
      """simple docstring"""





      a__						:    int   		=	[]
      a__						:    Dict   		=	""
      for token in tokens:
         # make sure that special tokens are not decoded using sentencepiece model
         if token in self.all_special_tokens:
            out_string += self.sp_model.decode(snake_case   ) + token
            a__						:    Dict   		=	[]
         else:
            current_sub_tokens.append(snake_case   )
      out_string += self.sp_model.decode(snake_case   )
      return out_string.strip()





   def 						_snake_case			(			self       ,      snake_case       ,      snake_case = None   )					->	Tuple[str]:
      """simple docstring"""





      if not os.path.isdir(snake_case   ):
         logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""   )
         return
      a__						:    Tuple   		=	os.path.join(
          snake_case       ,      (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]   )

      if os.path.abspath(self.vocab_file   ) != os.path.abspath(snake_case   ) and os.path.isfile(self.vocab_file   ):
         copyfile(self.vocab_file       ,      snake_case   )
      elif not os.path.isfile(self.vocab_file   ):
         with open(snake_case       ,      "wb"   ) as fi:
            a__						:    str   		=	self.sp_model.serialized_model_proto()
            fi.write(snake_case   )

      return (out_vocab_file,)

