
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__    (       A_,     A_					):






  '''simple docstring'''





  __magic_name__				     =       old_name

  if "patch_embed" in old_name:
    __magic_name__				     =       old_name.split("""."""					)

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

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

      __magic_name__				     =       'last_stage.' + trimmed_name

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

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

  return new_name

def a__    (       A_,     A_					):






  '''simple docstring'''





  for key in checkpoint.copy().keys():
    __magic_name__				     =       checkpoint.pop(snake_case__					)
    __magic_name__				     =       val

  return checkpoint

def a__    (       ):






  '''simple docstring'''





  __magic_name__				     =       'http://images.cocodataset.org/val2017/000000039769.jpg'
  __magic_name__				     =       Image.open(requests.get(snake_case__,     stream=snake_case__					).raw					)

  return image

def a__    (       A_,     A_,     A_,     A_					):






  '''simple docstring'''





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

  __magic_name__				     =       config.depths[-1] - config.num_metaad_blocks + 1
  __magic_name__				     =       convert_torch_checkpoint(snake_case__,     snake_case__					)

  model.load_state_dict(snake_case__					)
  model.eval()

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

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

  # original processing pipeline
  __magic_name__				     =       Compose(
      [
          Resize(snake_case__,     interpolation=pillow_resamplings["""bicubic"""]					),
          CenterCrop(snake_case__					),
          ToTensor(),
          Normalize(snake_case__,     snake_case__					),
      ]					)
  __magic_name__				     =       image_transforms(snake_case__					).unsqueeze(0					)

  assert torch.allclose(snake_case__,     snake_case__					)

  __magic_name__				     =       model(snake_case__					)
  __magic_name__				     =       outputs.logits

  __magic_name__				     =       (1, 1000)

  if "l1" in model_name:
    __magic_name__				     =       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:
    __magic_name__				     =       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:
    __magic_name__				     =       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__":
    __lowerCAmelCase			:     List[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)

    __lowerCAmelCase			:     Optional[int]           =   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)



