






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_       (					_A					,      _A     )	->	List[Any]:







				"""simple docstring"""
				snake_case__													=   old_name

				if "patch_embed" in old_name:
								snake_case__													=   old_name.split('.'     )

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

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

												snake_case__													=   'last_stage.' + trimmed_name

				elif "network" in old_name and re.search(R'.\d.'					,      _UpperCAmelCase     ):
								snake_case__													=   old_name.replace('network'					,      'intermediate_stages'     )

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

				return new_name
def    a_       (					_A					,      _A     )	->	Any:







				"""simple docstring"""





				for key in checkpoint.copy().keys():
								snake_case__													=   checkpoint.pop(_UpperCAmelCase     )
								snake_case__													=   val

				return checkpoint
def    a_       (					)	->	List[Any]:







				"""simple docstring"""
				snake_case__													=   'http://images.cocodataset.org/val2017/000000039769.jpg'
				snake_case__													=   Image.open(requests.get(_UpperCAmelCase					,      stream=_UpperCAmelCase     ).raw     )

				return image
def    a_       (					_A					,      _A					,      _A					,      _A     )	->	Union[str, Any]:







				"""simple docstring"""
				snake_case__													=   torch.load(_UpperCAmelCase					,      map_location='cpu'     )['model']
				snake_case__													=   EfficientFormerConfig.from_json_file(_UpperCAmelCase     )
				snake_case__													=   EfficientFormerForImageClassificationWithTeacher(_UpperCAmelCase     )
				snake_case__													=   '_'.join(checkpoint_path.split('/'     )[-1].split('.'     )[0].split('_'     )[:-1]     )

				snake_case__													=   config.depths[-1] - config.num_metaad_blocks + 1
				snake_case__													=   convert_torch_checkpoint(_UpperCAmelCase					,      _UpperCAmelCase     )

				model.load_state_dict(_UpperCAmelCase     )
				model.eval()

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

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

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

				assert torch.allclose(_UpperCAmelCase					,      _UpperCAmelCase     )

				snake_case__													=   model(_UpperCAmelCase     )
				snake_case__													=   outputs.logits

				snake_case__													=   (1, 1000)

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


if __name__ == "__main__":
							__UpperCamelCase    :						Tuple		     =  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)

							__UpperCamelCase    :						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,
							)











"""simple docstring"""




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available


lowerCamelCase_     :						int						=					{}


try:
   if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCamelCase_     :						Tuple						=					['MLukeTokenizer']

if TYPE_CHECKING:
   try:
      if not is_sentencepiece_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_mluke import MLukeTokenizer


else:
   import sys

   lowerCamelCase_     :						List[str]						=					_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)