




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 SCREAMING_SNAKE_CASE    (	lowercase_			,    lowercase_						)       ->	Dict:



       """simple docstring"""

       A__							=   old_name

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

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

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

                     A__							=   '''last_stage.''' + trimmed_name

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

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

       return new_name







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



       """simple docstring"""

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

       return checkpoint







def SCREAMING_SNAKE_CASE    (	)       ->	Dict:



       """simple docstring"""

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

       return image







def SCREAMING_SNAKE_CASE    (	lowercase_			,    lowercase_			,    lowercase_			,    lowercase_						)       ->	Dict:



       """simple docstring"""

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

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

       model.load_state_dict(lowercase_						)
       model.eval()

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

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

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

       assert torch.allclose(lowercase_			,    lowercase_						)

       A__							=   model(lowercase_						)
       A__							=   outputs.logits

       A__							=   (1, 1_000)

       if "l1" in model_name:
              A__							=   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]			,    lowercase_			,    atol=1E-3						)
              assert logits.shape == expected_shape
       elif "l3" in model_name:
              A__							=   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]			,    lowercase_			,    atol=1E-3						)
              assert logits.shape == expected_shape
       elif "l7" in model_name:
              A__							=   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(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__":
  _lowerCamelCase     :		Tuple								=       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,
  )








from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_speech_available,
    is_torch_available,
)


_lowerCamelCase     :		Optional[Any]								=       {
    """configuration_trocr""": ["""TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP""", """TrOCRConfig"""],
    """processing_trocr""": ["""TrOCRProcessor"""],
}


try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _lowerCamelCase     :		Any								=       [
      """TROCR_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """TrOCRForCausalLM""",
      """TrOCRPreTrainedModel""",
  ]


if TYPE_CHECKING:
  from .configuration_trocr import TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP, TrOCRConfig
  from .processing_trocr import TrOCRProcessor

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_trocr import TROCR_PRETRAINED_MODEL_ARCHIVE_LIST, TrOCRForCausalLM, TrOCRPreTrainedModel

else:
  import sys

  _lowerCamelCase     :		List[str]								=       _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


