





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_							(	_lowerCAmelCase				,  _lowerCAmelCase	)		->					Any:
 UpperCamelCase      :   Tuple			      =  old_name

 if "patch_embed" in old_name:
  UpperCamelCase      ,       UpperCamelCase      ,       UpperCamelCase      :   Optional[int]			      =  old_name.split("."	)

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

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

   UpperCamelCase      :   List[Any]			      =  "last_stage." + trimmed_name

 elif "network" in old_name and re.search(r".\d."				,  _lowerCAmelCase	):
  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      :   List[str]			      =  new_name.replace("norm1"				,  "batchnorm_before"	)
 elif ("norm2" in new_name) and ("layernorm2" not in new_name):
  UpperCamelCase      :   Union[str, Any]			      =  new_name.replace("norm2"				,  "batchnorm_after"	)
 if "proj" in new_name:
  UpperCamelCase      :   Dict			      =  new_name.replace("proj"				,  "projection"	)
 if "dist_head" in new_name:
  UpperCamelCase      :   List[str]			      =  new_name.replace("dist_head"				,  "distillation_classifier"	)
 elif "head" in new_name:
  UpperCamelCase      :   List[str]			      =  new_name.replace("head"				,  "classifier"	)
 elif "patch_embed" in new_name:
  UpperCamelCase      :   str			      =  "efficientformer." + new_name
 elif new_name == "norm.weight" or new_name == "norm.bias":
  UpperCamelCase      :   List[str]			      =  new_name.replace("norm"				,  "layernorm"	)
  UpperCamelCase      :   Optional[int]			      =  "efficientformer." + new_name
 else:
  UpperCamelCase      :   List[Any]			      =  "efficientformer.encoder." + new_name

 return new_name



def 						A_							(	_lowerCAmelCase				,  _lowerCAmelCase	)		->					str:
 for key in checkpoint.copy().keys():
  UpperCamelCase      :   Union[str, Any]			      =  checkpoint.pop(_lowerCAmelCase	)
  UpperCamelCase      :   Optional[int]			      =  val

 return checkpoint



def 						A_							(	)		->					Dict:
 UpperCamelCase      :   Dict			      =  "http://images.cocodataset.org/val2017/000000039769.jpg"
 UpperCamelCase      :   str			      =  Image.open(requests.get(_lowerCAmelCase				,  stream=_lowerCAmelCase	).raw	)

 return image



def 						A_							(	_lowerCAmelCase				,  _lowerCAmelCase				,  _lowerCAmelCase				,  _lowerCAmelCase	)		->					Union[str, Any]:
 UpperCamelCase      :   List[str]			      =  torch.load(_lowerCAmelCase				,  map_location="cpu"	)["model"]
 UpperCamelCase      :   int			      =  EfficientFormerConfig.from_json_file(_lowerCAmelCase	)
 UpperCamelCase      :   int			      =  EfficientFormerForImageClassificationWithTeacher(_lowerCAmelCase	)
 UpperCamelCase      :   List[Any]			      =  "_".join(checkpoint_path.split("/"	)[-1].split("."	)[0].split("_"	)[:-1]	)

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

 model.load_state_dict(_lowerCAmelCase	)
 model.eval()

 UpperCamelCase      :   Union[str, Any]			      =  {
     "bilinear": PILImageResampling.BILINEAR,
     "bicubic": PILImageResampling.BICUBIC,
     "nearest": PILImageResampling.NEAREST,
 }

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

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

 assert torch.allclose(_lowerCAmelCase				,  _lowerCAmelCase	)

 UpperCamelCase      :   Union[str, Any]			      =  model(_lowerCAmelCase	)
 UpperCamelCase      :   str			      =  outputs.logits

 UpperCamelCase      :   Union[str, Any]			      =  (1, 1000)

 if "l1" in model_name:
  UpperCamelCase      :   Optional[Any]			      =  torch.Tensor(
      [-0.1_312, 0.4_353, -1.0_499, -0.5_124, 0.4_183, -0.6_793, -1.3_777, -0.0_893, -0.7_358, -2.4_328]	)
  assert torch.allclose(logits[0, :10]				,  _lowerCAmelCase				,  atol=1e-3	)
  assert logits.shape == expected_shape
 elif "l3" in model_name:
  UpperCamelCase      :   Optional[int]			      =  torch.Tensor(
      [-1.3_150, -1.5_456, -1.2_556, -0.8_496, -0.7_127, -0.7_897, -0.9_728, -0.3_052, 0.3_751, -0.3_127]	)
  assert torch.allclose(logits[0, :10]				,  _lowerCAmelCase				,  atol=1e-3	)
  assert logits.shape == expected_shape
 elif "l7" in model_name:
  UpperCamelCase      :   str			      =  torch.Tensor(
      [-1.0_283, -1.4_131, -0.5_644, -1.3_115, -0.5_785, -1.2_049, -0.7_528, 0.1_992, -0.3_822, -0.0_878]	)
  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(_lowerCAmelCase	).mkdir(exist_ok=_lowerCAmelCase	)
 model.save_pretrained(_lowerCAmelCase	)
 print(F"""Checkpoint successfuly converted. Model saved at {pytorch_dump_path}"""	)
 processor.save_pretrained(_lowerCAmelCase	)
 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=_lowerCAmelCase				,  )
  processor.push_to_hub(
      repo_id=F"""Bearnardd/{pytorch_dump_path}"""				,  commit_message="Add image processor"				,  use_temp_dir=_lowerCAmelCase				,  )


if __name__ == "__main__":
    __lowerCamelCase			:	Union[str, Any]					    =	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			:	Union[str, Any]					    =	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_torch_available, is_vision_available


__lowerCamelCase			:	List[Any]					    =	{
    """configuration_chinese_clip""": [
        """CHINESE_CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """ChineseCLIPConfig""",
        """ChineseCLIPOnnxConfig""",
        """ChineseCLIPTextConfig""",
        """ChineseCLIPVisionConfig""",
    ],
    """processing_chinese_clip""": ["""ChineseCLIPProcessor"""],
}

try:
    if not is_vision_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __lowerCamelCase			:	Tuple					    =	["""ChineseCLIPFeatureExtractor"""]
    __lowerCamelCase			:	List[str]					    =	["""ChineseCLIPImageProcessor"""]

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __lowerCamelCase			:	str					    =	[
        """CHINESE_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST""",
        """ChineseCLIPModel""",
        """ChineseCLIPPreTrainedModel""",
        """ChineseCLIPTextModel""",
        """ChineseCLIPVisionModel""",
    ]

if TYPE_CHECKING:
    from .configuration_chinese_clip import (
        CHINESE_CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
        ChineseCLIPConfig,
        ChineseCLIPOnnxConfig,
        ChineseCLIPTextConfig,
        ChineseCLIPVisionConfig,
    )
    from .processing_chinese_clip import ChineseCLIPProcessor

    try:
        if not is_vision_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .feature_extraction_chinese_clip import ChineseCLIPFeatureExtractor, ChineseCLIPImageProcessor

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_chinese_clip import (
            CHINESE_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
            ChineseCLIPModel,
            ChineseCLIPPreTrainedModel,
            ChineseCLIPTextModel,
            ChineseCLIPVisionModel,
        )

else:
    import sys

    __lowerCamelCase			:	Any					    =	_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
