


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       a_ (       SCREAMING_SNAKE_CASE__   : Union[str, Any]						,     SCREAMING_SNAKE_CASE__   : Tuple	):
       '''simple docstring'''





       _lowerCamelCase			:						List[str]            =old_name

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

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

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

                     _lowerCamelCase			:						Any            ='last_stage.' + trimmed_name

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

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

       return new_name




def       a_ (       SCREAMING_SNAKE_CASE__   : Union[str, Any]						,     SCREAMING_SNAKE_CASE__   : Tuple	):
       '''simple docstring'''





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

       return checkpoint




def       a_ (       ):
       '''simple docstring'''





       _lowerCamelCase			:						Optional[int]            ='http://images.cocodataset.org/val2017/000000039769.jpg'
       _lowerCamelCase			:						int            =Image.open(requests.get(SCREAMING_SNAKE_CASE__						,     stream=SCREAMING_SNAKE_CASE__	).raw	)

       return image




def       a_ (       SCREAMING_SNAKE_CASE__   : Path						,     SCREAMING_SNAKE_CASE__   : Path						,     SCREAMING_SNAKE_CASE__   : Path						,     SCREAMING_SNAKE_CASE__   : bool	):
       '''simple docstring'''





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

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

       model.load_state_dict(SCREAMING_SNAKE_CASE__	)
       model.eval()

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

       # prepare image
       _lowerCamelCase			:						Optional[int]            =prepare_img()
       _lowerCamelCase			:						Optional[int]            =256
       _lowerCamelCase			:						Dict            =224
       _lowerCamelCase			:						int            =EfficientFormerImageProcessor(
           size={'shortest_edge': image_size}						,     crop_size={'height': crop_size, 'width': crop_size}						,     resample=pillow_resamplings['bicubic']						,     )
       _lowerCamelCase			:						Optional[int]            =processor(images=SCREAMING_SNAKE_CASE__						,     return_tensors='pt'	).pixel_values

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

       assert torch.allclose(SCREAMING_SNAKE_CASE__						,     SCREAMING_SNAKE_CASE__	)

       _lowerCamelCase			:						List[str]            =model(SCREAMING_SNAKE_CASE__	)
       _lowerCamelCase			:						str            =outputs.logits

       _lowerCamelCase			:						Optional[Any]            =(1, 1_000)

       if "l1" in model_name:
              _lowerCamelCase			:						Dict            =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, :10]						,     SCREAMING_SNAKE_CASE__						,     atol=1e-3	)
              assert logits.shape == expected_shape
       elif "l3" in model_name:
              _lowerCamelCase			:						Dict            =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, :10]						,     SCREAMING_SNAKE_CASE__						,     atol=1e-3	)
              assert logits.shape == expected_shape
       elif "l7" in model_name:
              _lowerCamelCase			:						Union[str, 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(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__":
 lowerCamelCase		   =			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)

 lowerCamelCase		   =			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,
 )






from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


lowerCamelCase		   =			logging.get_logger(__name__)


if is_vision_available():
 import PIL




class A    (  UpperCamelCase_	):
      UpperCamelCase__				:    List[str] =['pixel_values']






      def __init__(   self		:	Any     ,  lowercase_		:	bool = True     ,  lowercase_		:	Dict[str, int] = None     ,  lowercase_		:	PILImageResampling = PILImageResampling.BICUBIC     ,  lowercase_		:	bool = True     ,  lowercase_		:	Dict[str, int] = None     ,  lowercase_		:	bool = True     ,  lowercase_		:	Union[int, float] = 1 / 255     ,  lowercase_		:	bool = True     ,  lowercase_		:	Optional[Union[float, List[float]]] = None     ,  lowercase_		:	Optional[Union[float, List[float]]] = None     ,  lowercase_		:	bool = True     ,  **lowercase_		:	str     ,  )   ->		None:
             """simple docstring"""




             super().__init__(**lowercase_					)
             _lowerCamelCase			:						Optional[int]            =size if size is not None else {'shortest_edge': 224}
             _lowerCamelCase			:						List[Any]            =get_size_dict(lowercase_     ,  default_to_square=lowercase_					)
             _lowerCamelCase			:						str            =crop_size if crop_size is not None else {'height': 224, 'width': 224}
             _lowerCamelCase			:						str            =get_size_dict(lowercase_     ,  default_to_square=lowercase_     ,  param_name='crop_size'					)

             _lowerCamelCase			:						Dict            =do_resize
             _lowerCamelCase			:						int            =size
             _lowerCamelCase			:						Optional[Any]            =resample
             _lowerCamelCase			:						Optional[int]            =do_center_crop
             _lowerCamelCase			:						Tuple            =crop_size
             _lowerCamelCase			:						Optional[int]            =do_rescale
             _lowerCamelCase			:						Optional[Any]            =rescale_factor
             _lowerCamelCase			:						Union[str, Any]            =do_normalize
             _lowerCamelCase			:						Optional[int]            =image_mean if image_mean is not None else OPENAI_CLIP_MEAN
             _lowerCamelCase			:						Any            =image_std if image_std is not None else OPENAI_CLIP_STD
             _lowerCamelCase			:						Any            =do_convert_rgb






      def 			lowerCamelCase			(   self		:	int     ,  lowercase_		:	np.ndarray     ,  lowercase_		:	Dict[str, int]     ,  lowercase_		:	PILImageResampling = PILImageResampling.BICUBIC     ,  lowercase_		:	Optional[Union[str, ChannelDimension]] = None     ,  **lowercase_		:	Any     ,  )   ->		np.ndarray:
             """simple docstring"""




             _lowerCamelCase			:						int            =get_size_dict(lowercase_     ,  default_to_square=lowercase_					)
             if "shortest_edge" not in size:
                    raise ValueError(F'''The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}'''					)
             _lowerCamelCase			:						Union[str, Any]            =get_resize_output_image_size(lowercase_     ,  size=size['shortest_edge']     ,  default_to_square=lowercase_					)
             return resize(lowercase_     ,  size=lowercase_     ,  resample=lowercase_     ,  data_format=lowercase_     ,  **lowercase_					)






      def 			lowerCamelCase			(   self		:	Union[str, Any]     ,  lowercase_		:	np.ndarray     ,  lowercase_		:	Dict[str, int]     ,  lowercase_		:	Optional[Union[str, ChannelDimension]] = None     ,  **lowercase_		:	Tuple     ,  )   ->		np.ndarray:
             """simple docstring"""




             _lowerCamelCase			:						Union[str, Any]            =get_size_dict(lowercase_					)
             if "height" not in size or "width" not in size:
                    raise ValueError(F'''The `size` parameter must contain the keys (height, width). Got {size.keys()}'''					)
             return center_crop(lowercase_     ,  size=(size['height'], size['width'])     ,  data_format=lowercase_     ,  **lowercase_					)






      def 			lowerCamelCase			(   self		:	str     ,  lowercase_		:	np.ndarray     ,  lowercase_		:	Union[int, float]     ,  lowercase_		:	Optional[Union[str, ChannelDimension]] = None     ,  **lowercase_		:	int     ,  )   ->		str:
             """simple docstring"""




             return rescale(lowercase_     ,  scale=lowercase_     ,  data_format=lowercase_     ,  **lowercase_					)






      def 			lowerCamelCase			(   self		:	Optional[int]     ,  lowercase_		:	np.ndarray     ,  lowercase_		:	Union[float, List[float]]     ,  lowercase_		:	Union[float, List[float]]     ,  lowercase_		:	Optional[Union[str, ChannelDimension]] = None     ,  **lowercase_		:	Tuple     ,  )   ->		np.ndarray:
             """simple docstring"""




             return normalize(lowercase_     ,  mean=lowercase_     ,  std=lowercase_     ,  data_format=lowercase_     ,  **lowercase_					)






      def 			lowerCamelCase			(   self		:	List[str]     ,  lowercase_		:	ImageInput     ,  lowercase_		:	bool = None     ,  lowercase_		:	Dict[str, int] = None     ,  lowercase_		:	PILImageResampling = None     ,  lowercase_		:	bool = None     ,  lowercase_		:	int = None     ,  lowercase_		:	bool = None     ,  lowercase_		:	float = None     ,  lowercase_		:	bool = None     ,  lowercase_		:	Optional[Union[float, List[float]]] = None     ,  lowercase_		:	Optional[Union[float, List[float]]] = None     ,  lowercase_		:	bool = None     ,  lowercase_		:	Optional[Union[str, TensorType]] = None     ,  lowercase_		:	Optional[ChannelDimension] = ChannelDimension.FIRST     ,  **lowercase_		:	Union[str, Any]     ,  )   ->		PIL.Image.Image:
             """simple docstring"""




             _lowerCamelCase			:						Union[str, Any]            =do_resize if do_resize is not None else self.do_resize
             _lowerCamelCase			:						List[str]            =size if size is not None else self.size
             _lowerCamelCase			:						Any            =get_size_dict(lowercase_     ,  param_name='size'     ,  default_to_square=lowercase_					)
             _lowerCamelCase			:						str            =resample if resample is not None else self.resample
             _lowerCamelCase			:						List[Any]            =do_center_crop if do_center_crop is not None else self.do_center_crop
             _lowerCamelCase			:						Optional[Any]            =crop_size if crop_size is not None else self.crop_size
             _lowerCamelCase			:						Dict            =get_size_dict(lowercase_     ,  param_name='crop_size'     ,  default_to_square=lowercase_					)
             _lowerCamelCase			:						str            =do_rescale if do_rescale is not None else self.do_rescale
             _lowerCamelCase			:						Tuple            =rescale_factor if rescale_factor is not None else self.rescale_factor
             _lowerCamelCase			:						List[str]            =do_normalize if do_normalize is not None else self.do_normalize
             _lowerCamelCase			:						Tuple            =image_mean if image_mean is not None else self.image_mean
             _lowerCamelCase			:						int            =image_std if image_std is not None else self.image_std
             _lowerCamelCase			:						Tuple            =do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

             _lowerCamelCase			:						Any            =make_list_of_images(lowercase_					)

             if not valid_images(lowercase_					):
                    raise ValueError(
                        'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
                        'torch.Tensor, tf.Tensor or jax.ndarray.'					)

             if do_resize and size is None:
                    raise ValueError('Size must be specified if do_resize is True.'					)

             if do_center_crop and crop_size is None:
                    raise ValueError('Crop size must be specified if do_center_crop is True.'					)

             if do_rescale and rescale_factor is None:
                    raise ValueError('Rescale factor must be specified if do_rescale is True.'					)

             if do_normalize and (image_mean is None or image_std is None):
                    raise ValueError('Image mean and std must be specified if do_normalize is True.'					)

             # PIL RGBA images are converted to RGB
             if do_convert_rgb:
                    _lowerCamelCase			:						Tuple            =[convert_to_rgb(lowercase_					) for image in images]

             # All transformations expect numpy arrays.
             _lowerCamelCase			:						Optional[Any]            =[to_numpy_array(lowercase_					) for image in images]

             if do_resize:
                    _lowerCamelCase			:						Optional[Any]            =[self.resize(image=lowercase_     ,  size=lowercase_     ,  resample=lowercase_					) for image in images]

             if do_center_crop:
                    _lowerCamelCase			:						Optional[int]            =[self.center_crop(image=lowercase_     ,  size=lowercase_					) for image in images]

             if do_rescale:
                    _lowerCamelCase			:						Optional[Any]            =[self.rescale(image=lowercase_     ,  scale=lowercase_					) for image in images]

             if do_normalize:
                    _lowerCamelCase			:						List[Any]            =[self.normalize(image=lowercase_     ,  mean=lowercase_     ,  std=lowercase_					) for image in images]

             _lowerCamelCase			:						List[str]            =[to_channel_dimension_format(lowercase_     ,  lowercase_					) for image in images]

             _lowerCamelCase			:						Tuple            ={'pixel_values': images}
             return BatchFeature(data=lowercase_     ,  tensor_type=lowercase_					)


