





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     SCREAMING_SNAKE_CASE			(      lowerCAmelCase    ,       lowerCAmelCase					):
       _UpperCamelCase              =						old_name

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

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

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

                     _UpperCamelCase              =						'''last_stage.''' + trimmed_name

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

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

       return new_name


def     SCREAMING_SNAKE_CASE			(      lowerCAmelCase    ,       lowerCAmelCase					):
       for key in checkpoint.copy().keys():
              _UpperCamelCase              =						checkpoint.pop(lowerCAmelCase					)
              _UpperCamelCase              =						val

       return checkpoint


def     SCREAMING_SNAKE_CASE			(      ):
       _UpperCamelCase              =						'''http://images.cocodataset.org/val2017/000000039769.jpg'''
       _UpperCamelCase              =						Image.open(requests.get(lowerCAmelCase    ,       stream=lowerCAmelCase					).raw					)

       return image


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

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

       model.load_state_dict(lowerCAmelCase					)
       model.eval()

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

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

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

       assert torch.allclose(lowerCAmelCase    ,       lowerCAmelCase					)

       _UpperCamelCase              =						model(lowerCAmelCase					)
       _UpperCamelCase              =						outputs.logits

       _UpperCamelCase              =						(1, 1_000)

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


if __name__ == "__main__":
       lowercase    : List[str]    	=  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    : int    	=  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 math import factorial


def     SCREAMING_SNAKE_CASE			(      lowerCAmelCase = 20					):
       _UpperCamelCase              =						2 * n  # middle entry of odd rows starting at row 3 is the solution for n = 1,
       # 2, 3,...
       _UpperCamelCase              =						n // 2

       return int(factorial(lowerCAmelCase					) / (factorial(lowerCAmelCase					) * factorial(n - k					))					)


if __name__ == "__main__":
       import sys

       if len(sys.argv) == 1:
              print(solution(20))
       else:
              try:
                     lowercase    : Dict    	=  int(sys.argv[1])
                     print(solution(n))
              except ValueError:
                     print("""Invalid entry - please enter a number.""")






