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      __lowercase				(      _UpperCAmelCase			, _UpperCAmelCase			)      ->     Dict:






  '''simple docstring'''

  __lowercase					=		old_name

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

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

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

      __lowercase					=		"last_stage." + trimmed_name

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

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

  return new_name



def      __lowercase				(      _UpperCAmelCase			, _UpperCAmelCase			)      ->     int:






  '''simple docstring'''

  for key in checkpoint.copy().keys():
    __lowercase					=		checkpoint.pop(__lowerCAmelCase			)
    __lowercase					=		val

  return checkpoint



def      __lowercase				(      )      ->     List[str]:






  '''simple docstring'''

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

  return image



def      __lowercase				(      _UpperCAmelCase			, _UpperCAmelCase			, _UpperCAmelCase			, _UpperCAmelCase			)      ->     Union[str, Any]:






  '''simple docstring'''

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

  __lowercase					=		config.depths[-1] - config.num_metaad_blocks + 1
  __lowercase					=		convert_torch_checkpoint(__lowerCAmelCase			, __lowerCAmelCase			)

  model.load_state_dict(__lowerCAmelCase			)
  model.eval()

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

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

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

  assert torch.allclose(__lowerCAmelCase			, __lowerCAmelCase			)

  __lowercase					=		model(__lowerCAmelCase			)
  __lowercase					=		outputs.logits

  __lowercase					=		(1, 1_000)

  if "l1" in model_name:
    __lowercase					=		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:
    __lowercase					=		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:
    __lowercase					=		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__	   =						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,
     )








'''simple docstring'''




import logging

from transformers.configuration_utils import PretrainedConfig


_SCREAMING_SNAKE_CASE							     =			logging.getLogger(__name__)
class 			_lowerCAmelCase		(			A__	):






     """simple docstring"""






     snake_case_						      =   "masked_bert"



     def __init__(   self							:			Any   ,       __snake_case							:			Optional[Any]=3_05_22   ,       __snake_case							:			List[Any]=7_68   ,       __snake_case							:			int=12   ,       __snake_case							:			List[str]=12   ,       __snake_case							:			int=30_72   ,       __snake_case							:			Optional[int]="gelu"   ,       __snake_case							:			Any=0.1   ,       __snake_case							:			Tuple=0.1   ,       __snake_case							:			List[str]=5_12   ,       __snake_case							:			Tuple=2   ,       __snake_case							:			Tuple=0.02   ,       __snake_case							:			Tuple=1e-12   ,       __snake_case							:			Union[str, Any]=0   ,       __snake_case							:			int="topK"   ,       __snake_case							:			int="constant"   ,       __snake_case							:			List[str]=0.0   ,       **__snake_case							:			Any   ,       )->	Union[str, Any]:
        super().__init__(pad_token_id=__snake_case   ,       **__snake_case						)

        snake_case 						=   vocab_size
        snake_case 						=   hidden_size
        snake_case 						=   num_hidden_layers
        snake_case 						=   num_attention_heads
        snake_case 						=   hidden_act
        snake_case 						=   intermediate_size
        snake_case 						=   hidden_dropout_prob
        snake_case 						=   attention_probs_dropout_prob
        snake_case 						=   max_position_embeddings
        snake_case 						=   type_vocab_size
        snake_case 						=   initializer_range
        snake_case 						=   layer_norm_eps
        snake_case 						=   pruning_method
        snake_case 						=   mask_init
        snake_case 						=   mask_scale

