



'''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 						__snake_case(				_lowerCAmelCase     ,   _lowerCAmelCase		)		-> str:
      snake_case__  :    Optional[int]    						=      old_name

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

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

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

                  snake_case__  :    Any    						=      """last_stage.""" + trimmed_name

      elif "network" in old_name and re.search(r""".\d."""     ,   _lowerCAmelCase		):
            snake_case__  :    Dict    						=      old_name.replace("""network"""     ,   """intermediate_stages"""		)

      if "fc" in new_name:
            snake_case__  :    Optional[Any]    						=      new_name.replace("""fc"""     ,   """convolution"""		)
      elif ("norm1" in new_name) and ("layernorm1" not in new_name):
            snake_case__  :    Optional[Any]    						=      new_name.replace("""norm1"""     ,   """batchnorm_before"""		)
      elif ("norm2" in new_name) and ("layernorm2" not in new_name):
            snake_case__  :    Optional[int]    						=      new_name.replace("""norm2"""     ,   """batchnorm_after"""		)
      if "proj" in new_name:
            snake_case__  :    str    						=      new_name.replace("""proj"""     ,   """projection"""		)
      if "dist_head" in new_name:
            snake_case__  :    Any    						=      new_name.replace("""dist_head"""     ,   """distillation_classifier"""		)
      elif "head" in new_name:
            snake_case__  :    Any    						=      new_name.replace("""head"""     ,   """classifier"""		)
      elif "patch_embed" in new_name:
            snake_case__  :    List[Any]    						=      """efficientformer.""" + new_name
      elif new_name == "norm.weight" or new_name == "norm.bias":
            snake_case__  :    Union[str, Any]    						=      new_name.replace("""norm"""     ,   """layernorm"""		)
            snake_case__  :    int    						=      """efficientformer.""" + new_name
      else:
            snake_case__  :    Optional[int]    						=      """efficientformer.encoder.""" + new_name

      return new_name





def 						__snake_case(				_lowerCAmelCase     ,   _lowerCAmelCase		)		-> Optional[Any]:
      for key in checkpoint.copy().keys():
            snake_case__  :    Dict    						=      checkpoint.pop(_lowerCAmelCase		)
            snake_case__  :    int    						=      val

      return checkpoint





def 						__snake_case(				)		-> Any:
      snake_case__  :    int    						=      """http://images.cocodataset.org/val2017/000000039769.jpg"""
      snake_case__  :    List[str]    						=      Image.open(requests.get(_lowerCAmelCase     ,   stream=_lowerCAmelCase		).raw		)

      return image





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

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

      model.load_state_dict(_lowerCAmelCase		)
      model.eval()

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

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

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

      assert torch.allclose(_lowerCAmelCase     ,   _lowerCAmelCase		)

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

      snake_case__  :    int    						=      (1, 1_000)

      if "l1" in model_name:
            snake_case__  :    List[Any]    						=      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]     ,   _lowerCAmelCase     ,   atol=1e-3		)
            assert logits.shape == expected_shape
      elif "l3" in model_name:
            snake_case__  :    str    						=      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]     ,   _lowerCAmelCase     ,   atol=1e-3		)
            assert logits.shape == expected_shape
      elif "l7" in model_name:
            snake_case__  :    List[str]    						=      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(_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__":
       __a          =		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)

       __a          =		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'''





import logging
from dataclasses import dataclass, field
from pathlib import Path
from typing import Optional, Union

from .generation.configuration_utils import GenerationConfig
from .training_args import TrainingArguments
from .utils import add_start_docstrings


__a          =		logging.getLogger(__name__)







@dataclass
@add_start_docstrings(TrainingArguments.__doc__	)
class     UpperCAmelCase_       (       _a	):
    """simple docstring"""







    lowercase					=		field(default=_a		,		metadata={"help": "Whether to use SortishSampler or not."}	)
    lowercase					=		field(
        default=_a		,		metadata={"help": "Whether to use generate to calculate generative metrics (ROUGE, BLEU)."}	)
    lowercase					=		field(
        default=_a		,		metadata={
            "help": (
                "The `max_length` to use on each evaluation loop when `predict_with_generate=True`. Will default "
                "to the `max_length` value of the model configuration."
            )
        }		,		)
    lowercase					=		field(
        default=_a		,		metadata={
            "help": (
                "The `num_beams` to use on each evaluation loop when `predict_with_generate=True`. Will default "
                "to the `num_beams` value of the model configuration."
            )
        }		,		)
    lowercase					=		field(
        default=_a		,		metadata={
            "help": "Model id, file path or url pointing to a GenerationConfig json file, to use during prediction."
        }		,		)




    def lowerCamelCase       (    self					:     List[str]						):
          snake_case__  :    int    						=      super().to_dict()
          for k, v in d.items():
                if isinstance(snake_case_ ,     snake_case_						):
                      snake_case__  :    Optional[int]    						=      v.to_dict()
          return d
