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	):







    '''simple docstring'''
    _lowerCamelCase     :		Dict 						=						old_name

    if "patch_embed" in old_name:
        _lowerCamelCase						,							_lowerCamelCase						,							_lowerCamelCase     :		Optional[int] 						=						old_name.split("""."""	)

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

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

            _lowerCamelCase     :		Dict 						=						"""last_stage.""" + trimmed_name

    elif "network" in old_name and re.search(r""".\d."""   , __A	):
        _lowerCamelCase     :		str 						=						old_name.replace("""network"""   , """intermediate_stages"""	)

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

    return new_name







def 						A__  (					__A   , __A	):







    '''simple docstring'''


    for key in checkpoint.copy().keys():
        _lowerCamelCase     :		List[str] 						=						checkpoint.pop(__A	)
        _lowerCamelCase     :		List[Any] 						=						val

    return checkpoint







def 						A__  (					):







    '''simple docstring'''
    _lowerCamelCase     :		List[Any] 						=						"""http://images.cocodataset.org/val2017/000000039769.jpg"""
    _lowerCamelCase     :		Optional[int] 						=						Image.open(requests.get(__A   , stream=__A	).raw	)

    return image







def 						A__  (					__A   , __A   , __A   , __A	):







    '''simple docstring'''
    _lowerCamelCase     :		List[Any] 						=						torch.load(__A   , map_location="""cpu"""	)["""model"""]
    _lowerCamelCase     :		str 						=						EfficientFormerConfig.from_json_file(__A	)
    _lowerCamelCase     :		int 						=						EfficientFormerForImageClassificationWithTeacher(__A	)
    _lowerCamelCase     :		Optional[Any] 						=						"""_""".join(checkpoint_path.split("""/"""	)[-1].split("""."""	)[0].split("""_"""	)[:-1]	)

    _lowerCamelCase     :		Union[str, Any] 						=						config.depths[-1] - config.num_metaad_blocks + 1
    _lowerCamelCase     :		int 						=						convert_torch_checkpoint(__A   , __A	)

    model.load_state_dict(__A	)
    model.eval()

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

    # prepare image
    _lowerCamelCase     :		str 						=						prepare_img()
    _lowerCamelCase     :		Optional[Any] 						=						256
    _lowerCamelCase     :		Tuple 						=						224
    _lowerCamelCase     :		Optional[Any] 						=						EfficientFormerImageProcessor(
        size={"""shortest_edge""": image_size}   , crop_size={"""height""": crop_size, """width""": crop_size}   , resample=pillow_resamplings["""bicubic"""]   , )
    _lowerCamelCase     :		Union[str, Any] 						=						processor(images=__A   , return_tensors="""pt"""	).pixel_values

    # original processing pipeline
    _lowerCamelCase     :		Optional[Any] 						=						Compose(
        [
            Resize(__A   , interpolation=pillow_resamplings["""bicubic"""]	),
            CenterCrop(__A	),
            ToTensor(),
            Normalize(__A   , __A	),
        ]	)
    _lowerCamelCase     :		int 						=						image_transforms(__A	).unsqueeze(0	)

    assert torch.allclose(__A   , __A	)

    _lowerCamelCase     :		Dict 						=						model(__A	)
    _lowerCamelCase     :		Any 						=						outputs.logits

    _lowerCamelCase     :		List[Any] 						=						(1, 1_000)

    if "l1" in model_name:
        _lowerCamelCase     :		Optional[int] 						=						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     :		Tuple 						=						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     :		Dict 						=						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__":
    lowerCAmelCase			:							Union[str, Any]     =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			:							Dict     =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,
    )





def 						A__  (					__A   , __A	):







    '''simple docstring'''
    _enforce_args(__A   , __A	)
    if n == 0:
        return 0
    _lowerCamelCase     :		Tuple 						=						float("""-inf"""	)
    for i in range(1   , n + 1	):
        _lowerCamelCase     :		Any 						=						max(
            __A   , prices[i - 1] + naive_cut_rod_recursive(n - i   , __A	)	)

    return max_revue







def 						A__  (					__A   , __A	):







    '''simple docstring'''
    _enforce_args(__A   , __A	)
    _lowerCamelCase     :		Optional[Any] 						=						[float("""-inf"""	) for _ in range(n + 1	)]
    return _top_down_cut_rod_recursive(__A   , __A   , __A	)







def 						A__  (					__A   , __A   , __A	):







    '''simple docstring'''
    if max_rev[n] >= 0:
        return max_rev[n]
    elif n == 0:
        return 0
    else:
        _lowerCamelCase     :		int 						=						float("""-inf"""	)
        for i in range(1   , n + 1	):
            _lowerCamelCase     :		Optional[Any] 						=						max(
                __A   , prices[i - 1] + _top_down_cut_rod_recursive(n - i   , __A   , __A	)   , )

        _lowerCamelCase     :		Optional[Any] 						=						max_revenue

    return max_rev[n]







def 						A__  (					__A   , __A	):







    '''simple docstring'''
    _enforce_args(__A   , __A	)

    # length(max_rev) = n + 1, to accommodate for the revenue obtainable from a rod of
    # length 0.
    _lowerCamelCase     :		List[Any] 						=						[float("""-inf"""	) for _ in range(n + 1	)]
    _lowerCamelCase     :		Any 						=						0

    for i in range(1   , n + 1	):
        _lowerCamelCase     :		Any 						=						max_rev[i]
        for j in range(1   , i + 1	):
            _lowerCamelCase     :		List[Any] 						=						max(__A   , prices[j - 1] + max_rev[i - j]	)

        _lowerCamelCase     :		int 						=						max_revenue_i

    return max_rev[n]







def 						A__  (					__A   , __A	):







    '''simple docstring'''
    if n < 0:
        _lowerCamelCase     :		Any 						=						F"""n must be greater than or equal to 0. Got n = {n}"""
        raise ValueError(__A	)

    if n > len(__A	):
        _lowerCamelCase     :		List[Any] 						=						(
            """Each integral piece of rod must have a corresponding price. """
            F"""Got n = {n} but length of prices = {len(__A	)}"""
        )
        raise ValueError(__A	)







def 						A__  (					):







    '''simple docstring'''
    _lowerCamelCase     :		str 						=						[6, 10, 12, 15, 20, 23]
    _lowerCamelCase     :		List[str] 						=						len(__A	)

    # the best revenue comes from cutting the rod into 6 pieces, each
    # of length 1 resulting in a revenue of 6 * 6 = 36.
    _lowerCamelCase     :		Tuple 						=						36

    _lowerCamelCase     :		Any 						=						top_down_cut_rod(__A   , __A	)
    _lowerCamelCase     :		Dict 						=						bottom_up_cut_rod(__A   , __A	)
    _lowerCamelCase     :		List[str] 						=						naive_cut_rod_recursive(__A   , __A	)

    assert expected_max_revenue == max_rev_top_down
    assert max_rev_top_down == max_rev_bottom_up
    assert max_rev_bottom_up == max_rev_naive


if __name__ == "__main__":
    main()




