






"""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 _a			(		_snake_case				,	_snake_case							):







      """simple docstring"""






      UpperCAmelCase          =	old_name

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

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

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

                  UpperCAmelCase          =	"""last_stage.""" + trimmed_name

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

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

      return new_name

def _a			(		_snake_case				,	_snake_case							):







      """simple docstring"""






      for key in checkpoint.copy().keys():
            UpperCAmelCase          =	checkpoint.pop(_snake_case							)
            UpperCAmelCase          =	val

      return checkpoint

def _a			(		):







      """simple docstring"""






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

      return image

def _a			(		_snake_case				,	_snake_case				,	_snake_case				,	_snake_case							):







      """simple docstring"""






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

      UpperCAmelCase          =	config.depths[-1] - config.num_metaad_blocks + 1
      UpperCAmelCase          =	convert_torch_checkpoint(_snake_case				,	_snake_case							)

      model.load_state_dict(_snake_case							)
      model.eval()

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

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

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

      assert torch.allclose(_snake_case				,	_snake_case							)

      UpperCAmelCase          =	model(_snake_case							)
      UpperCAmelCase          =	outputs.logits

      UpperCAmelCase          =	(1, 1000)

      if "l1" in model_name:
            UpperCAmelCase          =	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          =	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          =	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,
     )











"""simple docstring"""







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


_UpperCamelCase					      =  {"""configuration_glpn""": ["""GLPN_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GLPNConfig"""]}

try:
  if not is_vision_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _UpperCamelCase					      =  ["""GLPNFeatureExtractor"""]
  _UpperCamelCase					      =  ["""GLPNImageProcessor"""]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _UpperCamelCase					      =  [
      """GLPN_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """GLPNForDepthEstimation""",
      """GLPNLayer""",
      """GLPNModel""",
      """GLPNPreTrainedModel""",
  ]


if TYPE_CHECKING:
  from .configuration_glpn import GLPN_PRETRAINED_CONFIG_ARCHIVE_MAP, GLPNConfig

  try:
    if not is_vision_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .feature_extraction_glpn import GLPNFeatureExtractor
    from .image_processing_glpn import GLPNImageProcessor

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_glpn import (
        GLPN_PRETRAINED_MODEL_ARCHIVE_LIST,
        GLPNForDepthEstimation,
        GLPNLayer,
        GLPNModel,
        GLPNPreTrainedModel,
    )


else:
  import sys

  _UpperCamelCase					      =  _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



