'''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__				(     _A:   List[str] ,  _A:   Dict					)       ->      List[str]:

		'''simple docstring'''


		lowerCAmelCase     =    old_name

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

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

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

						lowerCAmelCase     =    """last_stage.""" + trimmed_name

		elif "network" in old_name and re.search(r""".\d.""" ,  _A					):
				lowerCAmelCase     =    old_name.replace("""network""" ,  """intermediate_stages"""					)

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

		return new_name






def 							snake_case__				(     _A:   Tuple ,  _A:   Any					)       ->      str:

		'''simple docstring'''


		for key in checkpoint.copy().keys():
				lowerCAmelCase     =    checkpoint.pop(_A					)
				lowerCAmelCase     =    val

		return checkpoint






def 							snake_case__				(     )       ->      Any:

		'''simple docstring'''


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

		return image






def 							snake_case__				(     _A:   Path ,  _A:   Path ,  _A:   Path ,  _A:   bool					)       ->      Dict:

		'''simple docstring'''


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

		lowerCAmelCase     =    config.depths[-1] - config.num_metaad_blocks + 1
		lowerCAmelCase     =    convert_torch_checkpoint(_A ,  _A					)

		model.load_state_dict(_A					)
		model.eval()

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

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

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

		assert torch.allclose(_A ,  _A					)

		lowerCAmelCase     =    model(_A					)
		lowerCAmelCase     =    outputs.logits

		lowerCAmelCase     =    (1, 1000)

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


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,
						)





'''simple docstring'''




import os
from distutils.util import strtobool






def 							snake_case__				(     _A:   int ,  _A:   List[Any]					)       ->      Union[str, Any]:

		'''simple docstring'''


		for e in env_keys:
				lowerCAmelCase     =    int(os.environ.get(_A ,  -1					)					)
				if val >= 0:
						return val
		return default






def 							snake_case__				(     _A:   str ,  _A:   List[Any]=False					)       ->      int:

		'''simple docstring'''


		lowerCAmelCase     =    os.environ.get(_A ,  str(_A					)					)
		return strtobool(_A					) == 1  # As its name indicates `strtobool` actually returns an int...






def 							snake_case__				(     _A:   Dict ,  _A:   List[Any]="no"					)       ->      str:

		'''simple docstring'''


		lowerCAmelCase     =    os.environ.get(_A ,  str(_A					)					)
		return value




