


'''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        __lowercase						(      __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE		)				->					Union[str, Any]:
     """simple docstring"""




     __a       =   old_name

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

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

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

               __a       =   """last_stage.""" + trimmed_name

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

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

     return new_name




def        __lowercase						(      __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE		)				->					Tuple:
     """simple docstring"""




     for key in checkpoint.copy().keys():
          __a       =   checkpoint.pop(__SCREAMING_SNAKE_CASE		)
          __a       =   val

     return checkpoint




def        __lowercase						(      )				->					Any:
     """simple docstring"""




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

     return image




def        __lowercase						(      __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE		)				->					str:
     """simple docstring"""




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

     __a       =   config.depths[-1] - config.num_metaad_blocks + 1
     __a       =   convert_torch_checkpoint(__SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE		)

     model.load_state_dict(__SCREAMING_SNAKE_CASE		)
     model.eval()

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

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

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

     assert torch.allclose(__SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE		)

     __a       =   model(__SCREAMING_SNAKE_CASE		)
     __a       =   outputs.logits

     __a       =   (1, 1000)

     if "l1" in model_name:
          __a       =   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]				,  __SCREAMING_SNAKE_CASE				,  atol=1E-3		)
          assert logits.shape == expected_shape
     elif "l3" in model_name:
          __a       =   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]				,  __SCREAMING_SNAKE_CASE				,  atol=1E-3		)
          assert logits.shape == expected_shape
     elif "l7" in model_name:
          __a       =   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(__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__":
 SCREAMING_SNAKE_CASE_      =			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)

 SCREAMING_SNAKE_CASE_      =			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 __future__ import annotations

import os
from typing import Any

import requests

SCREAMING_SNAKE_CASE_      =			'https://api.github.com'

# https://docs.github.com/en/free-pro-team@latest/rest/reference/users#get-the-authenticated-user
SCREAMING_SNAKE_CASE_      =			BASE_URL + '/user'

# https://github.com/settings/tokens
SCREAMING_SNAKE_CASE_      =			os.environ.get('USER_TOKEN', '')




def        __lowercase						(      __SCREAMING_SNAKE_CASE		)				->					dict[Any, Any]:
     """simple docstring"""




     __a       =   {
         """Authorization""": F'''token {auth_token}''',
         """Accept""": """application/vnd.github.v3+json""",
     }
     return requests.get(__SCREAMING_SNAKE_CASE				,  headers=__SCREAMING_SNAKE_CASE		).json()


if __name__ == "__main__":  # pragma: no cover
 if USER_TOKEN:
  for key, value in fetch_github_info(USER_TOKEN).items():
   print(f"""{key}: {value}""")
 else:
  raise ValueError('\'USER_TOKEN\' field cannot be empty.')






