





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       __UpperCamelCase  (_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		)    ->       List[str]:
  lowercase__           =  old_name

  if "patch_embed" in old_name:
    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'		,						_SCREAMING_SNAKE_CASE		):
    lowercase__           =  R"""\b\d{2}\b"""
    if bool(re.search(_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		)		):
      lowercase__           =  re.search(R'\d\.\d\d.'		,						_SCREAMING_SNAKE_CASE		).group()
    else:
      lowercase__           =  re.search(R'\d\.\d.'		,						_SCREAMING_SNAKE_CASE		).group()
    if int(match[0]		) < 6:
      lowercase__           =  old_name.replace(_SCREAMING_SNAKE_CASE		,						''		)
      lowercase__           =  trimmed_name.replace('network'		,						match[0] + '.meta4D_layers.blocks.' + match[2:-1]		)
      lowercase__           =  """intermediate_stages.""" + trimmed_name
    else:
      lowercase__           =  old_name.replace(_SCREAMING_SNAKE_CASE		,						''		)
      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.'		,						_SCREAMING_SNAKE_CASE		):
    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       __UpperCamelCase  (_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		)    ->       List[Any]:
  for key in checkpoint.copy().keys():
    lowercase__           =  checkpoint.pop(_SCREAMING_SNAKE_CASE		)
    lowercase__           =  val

  return checkpoint




def       __UpperCamelCase  ()    ->       Union[str, Any]:
  lowercase__           =  """http://images.cocodataset.org/val2017/000000039769.jpg"""
  lowercase__           =  Image.open(requests.get(_SCREAMING_SNAKE_CASE		,						stream=_SCREAMING_SNAKE_CASE		).raw		)

  return image




def       __UpperCamelCase  (_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		)    ->       str:
  lowercase__           =  torch.load(_SCREAMING_SNAKE_CASE		,						map_location='cpu'		)["""model"""]
  lowercase__           =  EfficientFormerConfig.from_json_file(_SCREAMING_SNAKE_CASE		)
  lowercase__           =  EfficientFormerForImageClassificationWithTeacher(_SCREAMING_SNAKE_CASE		)
  lowercase__           =  """_""".join(checkpoint_path.split('/'		)[-1].split('.'		)[0].split('_'		)[:-1]		)

  lowercase__           =  config.depths[-1] - config.num_metaad_blocks + 1
  lowercase__           =  convert_torch_checkpoint(_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		)

  model.load_state_dict(_SCREAMING_SNAKE_CASE		)
  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=_SCREAMING_SNAKE_CASE		,						return_tensors='pt'		).pixel_values

  # original processing pipeline
  lowercase__           =  Compose(
      [
          Resize(_SCREAMING_SNAKE_CASE		,						interpolation=pillow_resamplings['bicubic']		),
          CenterCrop(_SCREAMING_SNAKE_CASE		),
          ToTensor(),
          Normalize(_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		),
      ]		)
  lowercase__           =  image_transforms(_SCREAMING_SNAKE_CASE		).unsqueeze(0		)

  assert torch.allclose(_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		)

  lowercase__           =  model(_SCREAMING_SNAKE_CASE		)
  lowercase__           =  outputs.logits

  lowercase__           =  (1, 1000)

  if "l1" in model_name:
    lowercase__           =  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]		,						_SCREAMING_SNAKE_CASE		,						atol=1E-3		)
    assert logits.shape == expected_shape
  elif "l3" in model_name:
    lowercase__           =  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]		,						_SCREAMING_SNAKE_CASE		,						atol=1E-3		)
    assert logits.shape == expected_shape
  elif "l7" in model_name:
    lowercase__           =  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(_SCREAMING_SNAKE_CASE		).mkdir(exist_ok=_SCREAMING_SNAKE_CASE		)
  model.save_pretrained(_SCREAMING_SNAKE_CASE		)
  print(F"""Checkpoint successfuly converted. Model saved at {pytorch_dump_path}"""		)
  processor.save_pretrained(_SCREAMING_SNAKE_CASE		)
  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=_SCREAMING_SNAKE_CASE		,						)
    processor.push_to_hub(
        repo_id=F"""Bearnardd/{pytorch_dump_path}"""		,						commit_message='Add image processor'		,						use_temp_dir=_SCREAMING_SNAKE_CASE		,						)


if __name__ == "__main__":
      lowercase_          =		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)

      lowercase_          =		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 datasets.utils.patching import _PatchedModuleObj, patch_submodule

from . import _test_patching




def       __UpperCamelCase  ()    ->       str:
  import os as original_os
  from os import path as original_path
  from os import rename as original_rename
  from os.path import dirname as original_dirname
  from os.path import join as original_join

  assert _test_patching.os is original_os
  assert _test_patching.path is original_path
  assert _test_patching.join is original_join

  assert _test_patching.renamed_os is original_os
  assert _test_patching.renamed_path is original_path
  assert _test_patching.renamed_join is original_join

  lowercase__           =  '__test_patch_submodule_mock__'
  with patch_submodule(_test_patching		,						'os.path.join'		,						_SCREAMING_SNAKE_CASE		):
    # Every way to access os.path.join must be patched, and the rest must stay untouched

    # check os.path.join
    assert isinstance(_test_patching.os		,						_PatchedModuleObj		)
    assert isinstance(_test_patching.os.path		,						_PatchedModuleObj		)
    assert _test_patching.os.path.join is mock

    # check path.join
    assert isinstance(_test_patching.path		,						_PatchedModuleObj		)
    assert _test_patching.path.join is mock

    # check join
    assert _test_patching.join is mock

    # check that the other attributes are untouched
    assert _test_patching.os.rename is original_rename
    assert _test_patching.path.dirname is original_dirname
    assert _test_patching.os.path.dirname is original_dirname

    # Even renamed modules or objects must be patched

    # check renamed_os.path.join
    assert isinstance(_test_patching.renamed_os		,						_PatchedModuleObj		)
    assert isinstance(_test_patching.renamed_os.path		,						_PatchedModuleObj		)
    assert _test_patching.renamed_os.path.join is mock

    # check renamed_path.join
    assert isinstance(_test_patching.renamed_path		,						_PatchedModuleObj		)
    assert _test_patching.renamed_path.join is mock

    # check renamed_join
    assert _test_patching.renamed_join is mock

    # check that the other attributes are untouched
    assert _test_patching.renamed_os.rename is original_rename
    assert _test_patching.renamed_path.dirname is original_dirname
    assert _test_patching.renamed_os.path.dirname is original_dirname

  # check that everthing is back to normal when the patch is over

  assert _test_patching.os is original_os
  assert _test_patching.path is original_path
  assert _test_patching.join is original_join

  assert _test_patching.renamed_os is original_os
  assert _test_patching.renamed_path is original_path
  assert _test_patching.renamed_join is original_join




def       __UpperCamelCase  ()    ->       Any:
  assert _test_patching.open is open

  lowercase__           =  '__test_patch_submodule_builtin_mock__'
  # _test_patching has "open" in its globals
  assert _test_patching.open is open
  with patch_submodule(_test_patching		,						'open'		,						_SCREAMING_SNAKE_CASE		):
    assert _test_patching.open is mock

  # check that everthing is back to normal when the patch is over

  assert _test_patching.open is open




def       __UpperCamelCase  ()    ->       List[str]:
  # pandas.read_csv is not present in _test_patching
  lowercase__           =  '__test_patch_submodule_missing_mock__'
  with patch_submodule(_test_patching		,						'pandas.read_csv'		,						_SCREAMING_SNAKE_CASE		):
    pass




def       __UpperCamelCase  ()    ->       List[str]:
  # builtin should always be mocked even if they're not in the globals
  # in case they're loaded at one point
  lowercase__           =  '__test_patch_submodule_missing_builtin_mock__'
  # _test_patching doesn't have "len" in its globals
  assert getattr(_test_patching		,						'len'		,						_SCREAMING_SNAKE_CASE		) is None
  with patch_submodule(_test_patching		,						'len'		,						_SCREAMING_SNAKE_CASE		):
    assert _test_patching.len is mock
  assert _test_patching.len is len




def       __UpperCamelCase  ()    ->       List[str]:
  lowercase__           =  '__test_patch_submodule_start_and_stop_mock__'
  lowercase__           =  patch_submodule(_test_patching		,						'open'		,						_SCREAMING_SNAKE_CASE		)
  assert _test_patching.open is open
  patch.start()
  assert _test_patching.open is mock
  patch.stop()
  assert _test_patching.open is open




def       __UpperCamelCase  ()    ->       Optional[int]:
  from os import rename as original_rename
  from os.path import dirname as original_dirname
  from os.path import join as original_join

  lowercase__           =  '__test_patch_submodule_successive_join__'
  lowercase__           =  '__test_patch_submodule_successive_dirname__'
  lowercase__           =  '__test_patch_submodule_successive_rename__'
  assert _test_patching.os.path.join is original_join
  assert _test_patching.os.path.dirname is original_dirname
  assert _test_patching.os.rename is original_rename

  with patch_submodule(_test_patching		,						'os.path.join'		,						_SCREAMING_SNAKE_CASE		):
    with patch_submodule(_test_patching		,						'os.rename'		,						_SCREAMING_SNAKE_CASE		):
      with patch_submodule(_test_patching		,						'os.path.dirname'		,						_SCREAMING_SNAKE_CASE		):
        assert _test_patching.os.path.join is mock_join
        assert _test_patching.os.path.dirname is mock_dirname
        assert _test_patching.os.rename is mock_rename

    # try another order
  with patch_submodule(_test_patching		,						'os.rename'		,						_SCREAMING_SNAKE_CASE		):
    with patch_submodule(_test_patching		,						'os.path.join'		,						_SCREAMING_SNAKE_CASE		):
      with patch_submodule(_test_patching		,						'os.path.dirname'		,						_SCREAMING_SNAKE_CASE		):
        assert _test_patching.os.path.join is mock_join
        assert _test_patching.os.path.dirname is mock_dirname
        assert _test_patching.os.rename is mock_rename

  assert _test_patching.os.path.join is original_join
  assert _test_patching.os.path.dirname is original_dirname
  assert _test_patching.os.rename is original_rename




def       __UpperCamelCase  ()    ->       Optional[Any]:
  lowercase__           =  '__test_patch_submodule_doesnt_exist_mock__'
  with patch_submodule(_test_patching		,						'__module_that_doesn_exist__.__attribute_that_doesn_exist__'		,						_SCREAMING_SNAKE_CASE		):
    pass
  with patch_submodule(_test_patching		,						'os.__attribute_that_doesn_exist__'		,						_SCREAMING_SNAKE_CASE		):
    pass


