






"""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    (__a					,       __a							)				-> Optional[Any]:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_   :						Any							=	old_name

    if "patch_embed" in old_name:
        SCREAMING_SNAKE_CASE_   :						Optional[int]							=	old_name.split('''.'''							)

        if layer == "0":
            SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	old_name.replace('''0'''					,       '''convolution1'''							)
        elif layer == "1":
            SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	old_name.replace('''1'''					,       '''batchnorm_before'''							)
        elif layer == "3":
            SCREAMING_SNAKE_CASE_   :						int							=	old_name.replace('''3'''					,       '''convolution2'''							)
        else:
            SCREAMING_SNAKE_CASE_   :						Tuple							=	old_name.replace('''4'''					,       '''batchnorm_after'''							)

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

            SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	'''last_stage.''' + trimmed_name

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

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

    return new_name



def       _A    (__a					,       __a							)				-> str:
    """simple docstring"""






    for key in checkpoint.copy().keys():
        SCREAMING_SNAKE_CASE_   :						List[Any]							=	checkpoint.pop(__a							)
        SCREAMING_SNAKE_CASE_   :						Any							=	val

    return checkpoint



def       _A    ()				-> Optional[int]:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_   :						int							=	'''http://images.cocodataset.org/val2017/000000039769.jpg'''
    SCREAMING_SNAKE_CASE_   :						Dict							=	Image.open(requests.get(__a					,       stream=__a							).raw							)

    return image



def       _A    (__a					,       __a					,       __a					,       __a							)				-> Optional[Any]:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	torch.load(__a					,       map_location='''cpu'''							)['''model''']
    SCREAMING_SNAKE_CASE_   :						Any							=	EfficientFormerConfig.from_json_file(__a							)
    SCREAMING_SNAKE_CASE_   :						List[str]							=	EfficientFormerForImageClassificationWithTeacher(__a							)
    SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	'''_'''.join(checkpoint_path.split('''/'''							)[-1].split('''.'''							)[0].split('''_'''							)[:-1]							)

    SCREAMING_SNAKE_CASE_   :						Dict							=	config.depths[-1] - config.num_metaad_blocks + 1
    SCREAMING_SNAKE_CASE_   :						Optional[int]							=	convert_torch_checkpoint(__a					,       __a							)

    model.load_state_dict(__a							)
    model.eval()

    SCREAMING_SNAKE_CASE_   :						Optional[int]							=	{
        '''bilinear''': PILImageResampling.BILINEAR,
        '''bicubic''': PILImageResampling.BICUBIC,
        '''nearest''': PILImageResampling.NEAREST,
    }

    # prepare image
    SCREAMING_SNAKE_CASE_   :						str							=	prepare_img()
    SCREAMING_SNAKE_CASE_   :						List[Any]							=	2_56
    SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	2_24
    SCREAMING_SNAKE_CASE_   :						Any							=	EfficientFormerImageProcessor(
        size={'''shortest_edge''': image_size}					,       crop_size={'''height''': crop_size, '''width''': crop_size}					,       resample=pillow_resamplings['''bicubic''']					,       )
    SCREAMING_SNAKE_CASE_   :						str							=	processor(images=__a					,       return_tensors='''pt'''							).pixel_values

    # original processing pipeline
    SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	Compose(
        [
            Resize(__a					,       interpolation=pillow_resamplings['''bicubic''']							),
            CenterCrop(__a							),
            ToTensor(),
            Normalize(__a					,       __a							),
        ]							)
    SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	image_transforms(__a							).unsqueeze(0							)

    assert torch.allclose(__a					,       __a							)

    SCREAMING_SNAKE_CASE_   :						List[str]							=	model(__a							)
    SCREAMING_SNAKE_CASE_   :						List[Any]							=	outputs.logits

    SCREAMING_SNAKE_CASE_   :						Tuple							=	(1, 10_00)

    if "l1" in model_name:
        SCREAMING_SNAKE_CASE_   :						Optional[int]							=	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, :10]					,       __a					,       atol=1e-3							)
        assert logits.shape == expected_shape
    elif "l3" in model_name:
        SCREAMING_SNAKE_CASE_   :						Optional[int]							=	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, :10]					,       __a					,       atol=1e-3							)
        assert logits.shape == expected_shape
    elif "l7" in model_name:
        SCREAMING_SNAKE_CASE_   :						Optional[int]							=	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(__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__":
     UpperCAmelCase_     :				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)

     UpperCAmelCase_     :				Tuple         =				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 multiprocessing
import time

from arguments import PretokenizationArguments
from datasets import load_dataset

from transformers import AutoTokenizer, HfArgumentParser



def       _A    (__a							)				-> Optional[int]:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	{}
    SCREAMING_SNAKE_CASE_   :						List[Any]							=	tokenizer(example['''content''']					,       truncation=__a							)['''input_ids''']
    SCREAMING_SNAKE_CASE_   :						List[Any]							=	len(example['''content''']							) / len(output['''input_ids''']							)
    return output


UpperCAmelCase_     :				Tuple         =				HfArgumentParser(PretokenizationArguments)
UpperCAmelCase_     :				Tuple         =				parser.parse_args()
if args.num_workers is None:
  UpperCAmelCase_     :				Tuple         =				multiprocessing.cpu_count()
UpperCAmelCase_     :				str         =				AutoTokenizer.from_pretrained(args.tokenizer_dir)

UpperCAmelCase_     :				Tuple         =				time.time()
UpperCAmelCase_     :				List[Any]         =				load_dataset(args.dataset_name, split="""train""")
print(f'''Dataset loaded in {time.time()-t_start:.2f}s''')

UpperCAmelCase_     :				Dict         =				time.time()
UpperCAmelCase_     :				Dict         =				ds.map(
    tokenize,
    num_proc=args.num_workers,
    remove_columns=[
        """repo_name""",
        """path""",
        """copies""",
        """size""",
        """content""",
        """license""",
        """hash""",
        """line_mean""",
        """line_max""",
        """alpha_frac""",
        """autogenerated""",
    ],
)
print(f'''Dataset tokenized in {time.time()-t_start:.2f}s''')

UpperCAmelCase_     :				str         =				time.time()
ds.push_to_hub(args.tokenized_data_repo)





print(f'''Data pushed to the hub in {time.time()-t_start:.2f}s''')


