



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	(   snake_case__       :    Optional[int]       ,     snake_case__       :    Union[str, Any]	)	->     Any:
   UpperCamelCase    :  Optional[int]							=	old_name

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

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

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

         UpperCamelCase    :  Union[str, Any]							=	'last_stage.' + trimmed_name

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

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

   return new_name
def 		UpperCamelCase	(   snake_case__       :    int       ,     snake_case__       :    Optional[int]	)	->     Optional[int]:
   for key in checkpoint.copy().keys():
      UpperCamelCase    :  int							=	checkpoint.pop(snake_case__	)
      UpperCamelCase    :  List[str]							=	val

   return checkpoint
def 		UpperCamelCase	(   )	->     Dict:
   UpperCamelCase    :  str							=	'http://images.cocodataset.org/val2017/000000039769.jpg'
   UpperCamelCase    :  str							=	Image.open(requests.get(snake_case__       ,     stream=snake_case__	).raw	)

   return image
def 		UpperCamelCase	(   snake_case__       :    Path       ,     snake_case__       :    Path       ,     snake_case__       :    Path       ,     snake_case__       :    bool	)	->     Union[str, Any]:
   UpperCamelCase    :  List[str]							=	torch.load(snake_case__       ,     map_location='cpu'	)['model']
   UpperCamelCase    :  str							=	EfficientFormerConfig.from_json_file(snake_case__	)
   UpperCamelCase    :  Union[str, Any]							=	EfficientFormerForImageClassificationWithTeacher(snake_case__	)
   UpperCamelCase    :  Optional[Any]							=	'_'.join(checkpoint_path.split('/'	)[-1].split('.'	)[0].split('_'	)[:-1]	)

   UpperCamelCase    :  Optional[Any]							=	config.depths[-1] - config.num_metaad_blocks + 1
   UpperCamelCase    :  str							=	convert_torch_checkpoint(snake_case__       ,     snake_case__	)

   model.load_state_dict(snake_case__	)
   model.eval()

   UpperCamelCase    :  Any							=	{
       'bilinear': PILImageResampling.BILINEAR,
       'bicubic': PILImageResampling.BICUBIC,
       'nearest': PILImageResampling.NEAREST,
   }

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

   # original processing pipeline
   UpperCamelCase    :  int							=	Compose(
       [
           Resize(snake_case__       ,     interpolation=pillow_resamplings['bicubic']	),
           CenterCrop(snake_case__	),
           ToTensor(),
           Normalize(snake_case__       ,     snake_case__	),
       ]	)
   UpperCamelCase    :  Union[str, Any]							=	image_transforms(snake_case__	).unsqueeze(0	)

   assert torch.allclose(snake_case__       ,     snake_case__	)

   UpperCamelCase    :  str							=	model(snake_case__	)
   UpperCamelCase    :  List[str]							=	outputs.logits

   UpperCamelCase    :  List[Any]							=	(1, 1000)

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


if __name__ == "__main__":
    __UpperCAmelCase          =       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          =       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 argparse

import torch

from transformers import MobileBertConfig, MobileBertForPreTraining, load_tf_weights_in_mobilebert
from transformers.utils import logging


logging.set_verbosity_info()
def 		UpperCamelCase	(   snake_case__       :    List[str]       ,     snake_case__       :    Union[str, Any]       ,     snake_case__       :    Union[str, Any]	)	->     int:
   # Initialise PyTorch model
   UpperCamelCase    :  Any							=	MobileBertConfig.from_json_file(snake_case__	)
   print(F"""Building PyTorch model from configuration: {config}"""	)
   UpperCamelCase    :  Tuple							=	MobileBertForPreTraining(snake_case__	)
   # Load weights from tf checkpoint
   UpperCamelCase    :  int							=	load_tf_weights_in_mobilebert(snake_case__       ,     snake_case__       ,     snake_case__	)
   # Save pytorch-model
   print(F"""Save PyTorch model to {pytorch_dump_path}"""	)
   torch.save(model.state_dict()       ,     snake_case__	)


if __name__ == "__main__":
    __UpperCAmelCase          =       argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        '''--tf_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
    )
    parser.add_argument(
        '''--mobilebert_config_file''',
        default=None,
        type=str,
        required=True,
        help=(
            '''The config json file corresponding to the pre-trained MobileBERT model. \n'''
            '''This specifies the model architecture.'''
        ),
    )
    parser.add_argument(
        '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
    )
    __UpperCAmelCase          =       parser.parse_args()
    convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.mobilebert_config_file, args.pytorch_dump_path)



