






"""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   a    (     __UpperCAmelCase      :    Any      ,   __UpperCAmelCase      :    Tuple  )    ->							Any:
						__magic_name__:	Union[str, Any]							=       old_name

						if "patch_embed" in old_name:
												__magic_name__,			__magic_name__,			__magic_name__:	List[str]							=       old_name.split("""."""  )

												if layer == "0":
																		__magic_name__:	Optional[int]							=       old_name.replace("""0"""      ,   """convolution1"""  )
												elif layer == "1":
																		__magic_name__:	str							=       old_name.replace("""1"""      ,   """batchnorm_before"""  )
												elif layer == "3":
																		__magic_name__:	Dict							=       old_name.replace("""3"""      ,   """convolution2"""  )
												else:
																		__magic_name__:	Dict							=       old_name.replace("""4"""      ,   """batchnorm_after"""  )

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

																		__magic_name__:	List[Any]							=       """last_stage.""" + trimmed_name

						elif "network" in old_name and re.search(R""".\d."""      ,   __UpperCAmelCase  ):
												__magic_name__:	Optional[int]							=       old_name.replace("""network"""      ,   """intermediate_stages"""  )

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

						return new_name



def   a    (     __UpperCAmelCase      :    Tuple      ,   __UpperCAmelCase      :    Optional[int]  )    ->							Optional[Any]:
						for key in checkpoint.copy().keys():
												__magic_name__:	List[str]							=       checkpoint.pop(__UpperCAmelCase  )
												__magic_name__:	Union[str, Any]							=       val

						return checkpoint



def   a    (     )    ->							Dict:
						__magic_name__:	Optional[int]							=       """http://images.cocodataset.org/val2017/000000039769.jpg"""
						__magic_name__:	List[Any]							=       Image.open(requests.get(__UpperCAmelCase      ,   stream=__UpperCAmelCase  ).raw  )

						return image



def   a    (     __UpperCAmelCase      :    Path      ,   __UpperCAmelCase      :    Path      ,   __UpperCAmelCase      :    Path      ,   __UpperCAmelCase      :    bool  )    ->							Any:
						__magic_name__:	Union[str, Any]							=       torch.load(__UpperCAmelCase      ,   map_location="""cpu"""  )["""model"""]
						__magic_name__:	List[Any]							=       EfficientFormerConfig.from_json_file(__UpperCAmelCase  )
						__magic_name__:	Any							=       EfficientFormerForImageClassificationWithTeacher(__UpperCAmelCase  )
						__magic_name__:	Dict							=       """_""".join(checkpoint_path.split("""/"""  )[-1].split("""."""  )[0].split("""_"""  )[:-1]  )

						__magic_name__:	int							=       config.depths[-1] - config.num_metaad_blocks + 1
						__magic_name__:	int							=       convert_torch_checkpoint(__UpperCAmelCase      ,   __UpperCAmelCase  )

						model.load_state_dict(__UpperCAmelCase  )
						model.eval()

						__magic_name__:	Optional[Any]							=       {
						    """bilinear""": PILImageResampling.BILINEAR,
						    """bicubic""": PILImageResampling.BICUBIC,
						    """nearest""": PILImageResampling.NEAREST,
						}

						# prepare image
						__magic_name__:	Any							=       prepare_img()
						__magic_name__:	List[Any]							=       2_5_6
						__magic_name__:	Optional[int]							=       2_2_4
						__magic_name__:	List[str]							=       EfficientFormerImageProcessor(
						    size={"""shortest_edge""": image_size}      ,   crop_size={"""height""": crop_size, """width""": crop_size}      ,   resample=pillow_resamplings["""bicubic"""]      ,   )
						__magic_name__:	int							=       processor(images=__UpperCAmelCase      ,   return_tensors="""pt"""  ).pixel_values

						# original processing pipeline
						__magic_name__:	Optional[int]							=       Compose(
						    [
						        Resize(__UpperCAmelCase      ,   interpolation=pillow_resamplings["""bicubic"""]  ),
						        CenterCrop(__UpperCAmelCase  ),
						        ToTensor(),
						        Normalize(__UpperCAmelCase      ,   __UpperCAmelCase  ),
						    ]  )
						__magic_name__:	List[str]							=       image_transforms(__UpperCAmelCase  ).unsqueeze(0  )

						assert torch.allclose(__UpperCAmelCase      ,   __UpperCAmelCase  )

						__magic_name__:	Optional[Any]							=       model(__UpperCAmelCase  )
						__magic_name__:	Any							=       outputs.logits

						__magic_name__:	Any							=       (1, 1_0_0_0)

						if "l1" in model_name:
												__magic_name__:	List[str]							=       torch.Tensor(
												    [-0.13_12, 0.43_53, -1.04_99, -0.51_24, 0.41_83, -0.67_93, -1.37_77, -0.08_93, -0.73_58, -2.43_28]  )
												assert torch.allclose(logits[0, :1_0]      ,   __UpperCAmelCase      ,   atol=1E-3  )
												assert logits.shape == expected_shape
						elif "l3" in model_name:
												__magic_name__:	List[Any]							=       torch.Tensor(
												    [-1.31_50, -1.54_56, -1.25_56, -0.84_96, -0.71_27, -0.78_97, -0.97_28, -0.30_52, 0.37_51, -0.31_27]  )
												assert torch.allclose(logits[0, :1_0]      ,   __UpperCAmelCase      ,   atol=1E-3  )
												assert logits.shape == expected_shape
						elif "l7" in model_name:
												__magic_name__:	Any							=       torch.Tensor(
												    [-1.02_83, -1.41_31, -0.56_44, -1.31_15, -0.57_85, -1.20_49, -0.75_28, 0.19_92, -0.38_22, -0.08_78]  )
												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__":
							__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"""


from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


lowerCamelCase__						= logging.get_logger(__name__)


if is_vision_available():
  import PIL



class   __SCREAMING_SNAKE_CASE     (    _UpperCamelCase ):







  '''simple docstring'''


  SCREAMING_SNAKE_CASE__				:Optional[int]						  =       ["pixel_values"]

  def __init__(		self :							Tuple       , __a :							bool = True       , __a :							Dict[str, int] = None       , __a :							PILImageResampling = PILImageResampling.BICUBIC       , __a :							bool = True       , __a :							Dict[str, int] = None       , __a :							bool = True       , __a :							Union[int, float] = 1 / 255       , __a :							bool = True       , __a :							Optional[Union[float, List[float]]] = None       , __a :							Optional[Union[float, List[float]]] = None       , __a :							bool = True       , **__a :							List[Any]       , )		->					None:
        super().__init__(**__a						)
        _UpperCamelCase   :						Optional[int]    =							size if size is not None else {"shortest_edge": 224}
        _UpperCamelCase   :						Optional[int]    =							get_size_dict(__a       , default_to_square=__a						)
        _UpperCamelCase   :						Union[str, Any]    =							crop_size if crop_size is not None else {"height": 224, "width": 224}
        _UpperCamelCase   :						Tuple    =							get_size_dict(__a       , default_to_square=__a       , param_name="crop_size"						)

        _UpperCamelCase   :						Optional[Any]    =							do_resize
        _UpperCamelCase   :						Dict    =							size
        _UpperCamelCase   :						Any    =							resample
        _UpperCamelCase   :						Tuple    =							do_center_crop
        _UpperCamelCase   :						str    =							crop_size
        _UpperCamelCase   :						Any    =							do_rescale
        _UpperCamelCase   :						Dict    =							rescale_factor
        _UpperCamelCase   :						int    =							do_normalize
        _UpperCamelCase   :						Dict    =							image_mean if image_mean is not None else OPENAI_CLIP_MEAN
        _UpperCamelCase   :						Tuple    =							image_std if image_std is not None else OPENAI_CLIP_STD
        _UpperCamelCase   :						List[Any]    =							do_convert_rgb

  def  __SCREAMING_SNAKE_CASE					(		self :							Optional[int]       , __a :							np.ndarray       , __a :							Dict[str, int]       , __a :							PILImageResampling = PILImageResampling.BICUBIC       , __a :							Optional[Union[str, ChannelDimension]] = None       , **__a :							str       , )		->					np.ndarray:
        _UpperCamelCase   :						Any    =							get_size_dict(__a       , default_to_square=__a						)
        if "shortest_edge" not in size:
              raise ValueError(F'''The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}'''						)
        _UpperCamelCase   :						Dict    =							get_resize_output_image_size(__a       , size=size["shortest_edge"]       , default_to_square=__a						)
        return resize(__a       , size=__a       , resample=__a       , data_format=__a       , **__a						)

  def  __SCREAMING_SNAKE_CASE					(		self :							List[Any]       , __a :							np.ndarray       , __a :							Dict[str, int]       , __a :							Optional[Union[str, ChannelDimension]] = None       , **__a :							Tuple       , )		->					np.ndarray:
        _UpperCamelCase   :						List[str]    =							get_size_dict(__a						)
        if "height" not in size or "width" not in size:
              raise ValueError(F'''The `size` parameter must contain the keys (height, width). Got {size.keys()}'''						)
        return center_crop(__a       , size=(size["height"], size["width"])       , data_format=__a       , **__a						)

  def  __SCREAMING_SNAKE_CASE					(		self :							Union[str, Any]       , __a :							np.ndarray       , __a :							Union[int, float]       , __a :							Optional[Union[str, ChannelDimension]] = None       , **__a :							Optional[Any]       , )		->					List[str]:
        return rescale(__a       , scale=__a       , data_format=__a       , **__a						)

  def  __SCREAMING_SNAKE_CASE					(		self :							Tuple       , __a :							np.ndarray       , __a :							Union[float, List[float]]       , __a :							Union[float, List[float]]       , __a :							Optional[Union[str, ChannelDimension]] = None       , **__a :							Tuple       , )		->					np.ndarray:
        return normalize(__a       , mean=__a       , std=__a       , data_format=__a       , **__a						)


  def  __SCREAMING_SNAKE_CASE					(		self :							Optional[int]       , __a :							ImageInput       , __a :							bool = None       , __a :							Dict[str, int] = None       , __a :							PILImageResampling = None       , __a :							bool = None       , __a :							int = None       , __a :							bool = None       , __a :							float = None       , __a :							bool = None       , __a :							Optional[Union[float, List[float]]] = None       , __a :							Optional[Union[float, List[float]]] = None       , __a :							bool = None       , __a :							Optional[Union[str, TensorType]] = None       , __a :							Optional[ChannelDimension] = ChannelDimension.FIRST       , **__a :							Union[str, Any]       , )		->					PIL.Image.Image:
        _UpperCamelCase   :						Tuple    =							do_resize if do_resize is not None else self.do_resize
        _UpperCamelCase   :						int    =							size if size is not None else self.size
        _UpperCamelCase   :						int    =							get_size_dict(__a       , param_name="size"       , default_to_square=__a						)
        _UpperCamelCase   :						List[Any]    =							resample if resample is not None else self.resample
        _UpperCamelCase   :						List[Any]    =							do_center_crop if do_center_crop is not None else self.do_center_crop
        _UpperCamelCase   :						Union[str, Any]    =							crop_size if crop_size is not None else self.crop_size
        _UpperCamelCase   :						List[Any]    =							get_size_dict(__a       , param_name="crop_size"       , default_to_square=__a						)
        _UpperCamelCase   :						Dict    =							do_rescale if do_rescale is not None else self.do_rescale
        _UpperCamelCase   :						Union[str, Any]    =							rescale_factor if rescale_factor is not None else self.rescale_factor
        _UpperCamelCase   :						Union[str, Any]    =							do_normalize if do_normalize is not None else self.do_normalize
        _UpperCamelCase   :						List[Any]    =							image_mean if image_mean is not None else self.image_mean
        _UpperCamelCase   :						List[Any]    =							image_std if image_std is not None else self.image_std
        _UpperCamelCase   :						List[str]    =							do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

        _UpperCamelCase   :						Any    =							make_list_of_images(__a						)

        if not valid_images(__a						):
              raise ValueError(
                  "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
                  "torch.Tensor, tf.Tensor or jax.ndarray."						)

        if do_resize and size is None:
              raise ValueError("Size must be specified if do_resize is True."						)

        if do_center_crop and crop_size is None:
              raise ValueError("Crop size must be specified if do_center_crop is True."						)

        if do_rescale and rescale_factor is None:
              raise ValueError("Rescale factor must be specified if do_rescale is True."						)

        if do_normalize and (image_mean is None or image_std is None):
              raise ValueError("Image mean and std must be specified if do_normalize is True."						)

        # PIL RGBA images are converted to RGB
        if do_convert_rgb:
              _UpperCamelCase   :						int    =							[convert_to_rgb(__a						) for image in images]

        # All transformations expect numpy arrays.
        _UpperCamelCase   :						List[Any]    =							[to_numpy_array(__a						) for image in images]

        if do_resize:
              _UpperCamelCase   :						Union[str, Any]    =							[self.resize(image=__a       , size=__a       , resample=__a						) for image in images]

        if do_center_crop:
              _UpperCamelCase   :						Optional[Any]    =							[self.center_crop(image=__a       , size=__a						) for image in images]

        if do_rescale:
              _UpperCamelCase   :						Dict    =							[self.rescale(image=__a       , scale=__a						) for image in images]

        if do_normalize:
              _UpperCamelCase   :						List[Any]    =							[self.normalize(image=__a       , mean=__a       , std=__a						) for image in images]

        _UpperCamelCase   :						List[Any]    =							[to_channel_dimension_format(__a       , __a						) for image in images]

        _UpperCamelCase   :						str    =							{"pixel_values": images}
        return BatchFeature(data=__a       , tensor_type=__a						)
