




"""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   _SCREAMING_SNAKE_CASE       (							lowercase_      ,			lowercase_						):
 A__	=    old_name

 if "patch_embed" in old_name:
  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_						):
 for key in checkpoint.copy().keys():
  A__	=    checkpoint.pop(lowercase_						)
  A__	=    val

 return checkpoint







def   _SCREAMING_SNAKE_CASE       (							):
 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_						):
 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__	=    2_56
 A__	=    2_24
 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, 10_00)

 if "l1" in model_name:
  A__	=    torch.Tensor(
      [-0.1_3_1_2, 0.4_3_5_3, -1.0_4_9_9, -0.5_1_2_4, 0.4_1_8_3, -0.6_7_9_3, -1.3_7_7_7, -0.0_8_9_3, -0.7_3_5_8, -2.4_3_2_8]						)
  assert torch.allclose(logits[0, :10]      ,			lowercase_      ,			atol=1E-3						)
  assert logits.shape == expected_shape
 elif "l3" in model_name:
  A__	=    torch.Tensor(
      [-1.3_1_5_0, -1.5_4_5_6, -1.2_5_5_6, -0.8_4_9_6, -0.7_1_2_7, -0.7_8_9_7, -0.9_7_2_8, -0.3_0_5_2, 0.3_7_5_1, -0.3_1_2_7]						)
  assert torch.allclose(logits[0, :10]      ,			lowercase_      ,			atol=1E-3						)
  assert logits.shape == expected_shape
 elif "l7" in model_name:
  A__	=    torch.Tensor(
      [-1.0_2_8_3, -1.4_1_3_1, -0.5_6_4_4, -1.3_1_1_5, -0.5_7_8_5, -1.2_0_4_9, -0.7_5_2_8, 0.1_9_9_2, -0.3_8_2_2, -0.0_8_7_8]						)
  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__":
      SCREAMING_SNAKE_CASE    =	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)

      SCREAMING_SNAKE_CASE    =	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_tf_available,
    is_torch_available,
    is_vision_available,
)


SCREAMING_SNAKE_CASE    =	{
    "configuration_mobilevit": ["MOBILEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "MobileViTConfig", "MobileViTOnnxConfig"],
}

try:
      if not is_vision_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE    =	["MobileViTFeatureExtractor"]
      SCREAMING_SNAKE_CASE    =	["MobileViTImageProcessor"]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE    =	[
          "MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST",
          "MobileViTForImageClassification",
          "MobileViTForSemanticSegmentation",
          "MobileViTModel",
          "MobileViTPreTrainedModel",
      ]

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE    =	[
          "TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST",
          "TFMobileViTForImageClassification",
          "TFMobileViTForSemanticSegmentation",
          "TFMobileViTModel",
          "TFMobileViTPreTrainedModel",
      ]

if TYPE_CHECKING:
      from .configuration_mobilevit import MOBILEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP, MobileViTConfig, MobileViTOnnxConfig

      try:
            if not is_vision_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .feature_extraction_mobilevit import MobileViTFeatureExtractor
            from .image_processing_mobilevit import MobileViTImageProcessor

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_mobilevit import (
                MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
                MobileViTForImageClassification,
                MobileViTForSemanticSegmentation,
                MobileViTModel,
                MobileViTPreTrainedModel,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_mobilevit import (
                TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFMobileViTForImageClassification,
                TFMobileViTForSemanticSegmentation,
                TFMobileViTModel,
                TFMobileViTPreTrainedModel,
            )


else:
      import sys

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





