



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 	_lowerCAmelCase						(				__lowerCamelCase   :     str		,	__lowerCamelCase   :     List[str]     ):



 """simple docstring"""





 __SCREAMING_SNAKE_CASE       :    Dict       =	old_name

 if "patch_embed" in old_name:
  __SCREAMING_SNAKE_CASE       :    Dict       =	old_name.split("."     )

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

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

   __SCREAMING_SNAKE_CASE       :    Dict       =	"last_stage." + trimmed_name

 elif "network" in old_name and re.search(r".\d."		,	_lowerCamelCase     ):
  __SCREAMING_SNAKE_CASE       :    Tuple       =	old_name.replace("network"		,	"intermediate_stages"     )

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

 return new_name






def 	_lowerCAmelCase						(				__lowerCamelCase   :     List[Any]		,	__lowerCamelCase   :     Any     ):



 """simple docstring"""





 for key in checkpoint.copy().keys():
  __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	checkpoint.pop(_lowerCamelCase     )
  __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	val

 return checkpoint






def 	_lowerCAmelCase						(				):



 """simple docstring"""





 __SCREAMING_SNAKE_CASE       :    Tuple       =	"http://images.cocodataset.org/val2017/000000039769.jpg"
 __SCREAMING_SNAKE_CASE       :    Dict       =	Image.open(requests.get(_lowerCamelCase		,	stream=_lowerCamelCase     ).raw     )

 return image






def 	_lowerCAmelCase						(				__lowerCamelCase   :     Path		,	__lowerCamelCase   :     Path		,	__lowerCamelCase   :     Path		,	__lowerCamelCase   :     bool     ):



 """simple docstring"""





 __SCREAMING_SNAKE_CASE       :    Optional[int]       =	torch.load(_lowerCamelCase		,	map_location="cpu"     )["model"]
 __SCREAMING_SNAKE_CASE       :    int       =	EfficientFormerConfig.from_json_file(_lowerCamelCase     )
 __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	EfficientFormerForImageClassificationWithTeacher(_lowerCamelCase     )
 __SCREAMING_SNAKE_CASE       :    int       =	"_".join(checkpoint_path.split("/"     )[-1].split("."     )[0].split("_"     )[:-1]     )

 __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	config.depths[-1] - config.num_metaad_blocks + 1
 __SCREAMING_SNAKE_CASE       :    List[Any]       =	convert_torch_checkpoint(_lowerCamelCase		,	_lowerCamelCase     )

 model.load_state_dict(_lowerCamelCase     )
 model.eval()

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

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

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

 assert torch.allclose(_lowerCamelCase		,	_lowerCamelCase     )

 __SCREAMING_SNAKE_CASE       :    Tuple       =	model(_lowerCamelCase     )
 __SCREAMING_SNAKE_CASE       :    int       =	outputs.logits

 __SCREAMING_SNAKE_CASE       :    str       =	(1, 1000)

 if "l1" in model_name:
  __SCREAMING_SNAKE_CASE       :    Optional[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:
  __SCREAMING_SNAKE_CASE       :    Dict       =	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:
  __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	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__":
  _lowerCamelCase						    =  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						    =  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,
  )











import inspect
import os
import unittest

import torch

import accelerate
from accelerate import debug_launcher
from accelerate.test_utils import (
    execute_subprocess_async,
    require_cpu,
    require_huggingface_suite,
    require_multi_gpu,
    require_single_gpu,
)
from accelerate.utils import patch_environment

@require_huggingface_suite
class       _SCREAMING_SNAKE_CASE	(unittest.TestCase ):





     def 	__snake_case    (       self :							Optional[Any]				)->Union[str, Any]:
      __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	inspect.getfile(accelerate.test_utils				)
      __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	os.path.sep.join(
          mod_file.split(os.path.sep				)[:-1] + ["scripts", "external_deps", "test_metrics.py"]				)

      from accelerate.test_utils.scripts.external_deps import test_metrics  # noqa: F401

      __SCREAMING_SNAKE_CASE       :    Tuple       =	test_metrics





     @require_cpu
     def 	__snake_case    (       self :							Union[str, Any]				)->List[Any]:
      debug_launcher(self.test_metrics.main     ,  num_processes=1				)





     @require_cpu
     def 	__snake_case    (       self :							str				)->List[str]:
      debug_launcher(self.test_metrics.main				)





     @require_single_gpu
     def 	__snake_case    (       self :							Union[str, Any]				)->str:
      self.test_metrics.main()






     @require_multi_gpu
     def 	__snake_case    (       self :							List[str]				)->int:
      print(F"""Found {torch.cuda.device_count()} devices."""				)
      __SCREAMING_SNAKE_CASE       :    Any       =	["torchrun", F"""--nproc_per_node={torch.cuda.device_count()}""", self.test_file_path]
      with patch_environment(omp_num_threads=1				):
       execute_subprocess_async(UpperCamelCase     ,  env=os.environ.copy()				)






