import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    MobileViTConfig,
    MobileViTForImageClassification,
    MobileViTForSemanticSegmentation,
    MobileViTImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
lowerCAmelCase			:							List[str]     =logging.get_logger(__name__)







def 						A__  (					__A	):







    '''simple docstring'''
    _lowerCamelCase     :		Optional[Any] 						=						MobileViTConfig()

    # size of the architecture
    if "mobilevit_s" in mobilevit_name:
        _lowerCamelCase     :		Tuple 						=						[144, 192, 240]
        _lowerCamelCase     :		str 						=						[16, 32, 64, 96, 128, 160, 640]
    elif "mobilevit_xs" in mobilevit_name:
        _lowerCamelCase     :		List[Any] 						=						[96, 120, 144]
        _lowerCamelCase     :		Optional[int] 						=						[16, 32, 48, 64, 80, 96, 384]
    elif "mobilevit_xxs" in mobilevit_name:
        _lowerCamelCase     :		Union[str, Any] 						=						[64, 80, 96]
        _lowerCamelCase     :		Tuple 						=						[16, 16, 24, 48, 64, 80, 320]
        _lowerCamelCase     :		Any 						=						0.05
        _lowerCamelCase     :		Tuple 						=						2.0

    if mobilevit_name.startswith("""deeplabv3_"""	):
        _lowerCamelCase     :		Union[str, Any] 						=						512
        _lowerCamelCase     :		Optional[int] 						=						16
        _lowerCamelCase     :		Optional[Any] 						=						21
        _lowerCamelCase     :		Dict 						=						"""pascal-voc-id2label.json"""
    else:
        _lowerCamelCase     :		Optional[Any] 						=						1_000
        _lowerCamelCase     :		Tuple 						=						"""imagenet-1k-id2label.json"""

    _lowerCamelCase     :		Tuple 						=						"""huggingface/label-files"""
    _lowerCamelCase     :		Optional[Any] 						=						json.load(open(hf_hub_download(__A   , __A   , repo_type="""dataset"""	)   , """r"""	)	)
    _lowerCamelCase     :		Dict 						=						{int(__A	): v for k, v in idalabel.items()}
    _lowerCamelCase     :		Tuple 						=						idalabel
    _lowerCamelCase     :		Union[str, Any] 						=						{v: k for k, v in idalabel.items()}

    return config







def 						A__  (					__A   , __A=False	):







    '''simple docstring'''
    for i in range(1   , 6	):
        if F"""layer_{i}.""" in name:
            _lowerCamelCase     :		Tuple 						=						name.replace(F"""layer_{i}."""   , F"""encoder.layer.{i - 1}."""	)

    if "conv_1." in name:
        _lowerCamelCase     :		Any 						=						name.replace("""conv_1."""   , """conv_stem."""	)
    if ".block." in name:
        _lowerCamelCase     :		Tuple 						=						name.replace(""".block."""   , """."""	)

    if "exp_1x1" in name:
        _lowerCamelCase     :		Optional[int] 						=						name.replace("""exp_1x1"""   , """expand_1x1"""	)
    if "red_1x1" in name:
        _lowerCamelCase     :		Tuple 						=						name.replace("""red_1x1"""   , """reduce_1x1"""	)
    if ".local_rep.conv_3x3." in name:
        _lowerCamelCase     :		List[Any] 						=						name.replace(""".local_rep.conv_3x3."""   , """.conv_kxk."""	)
    if ".local_rep.conv_1x1." in name:
        _lowerCamelCase     :		Union[str, Any] 						=						name.replace(""".local_rep.conv_1x1."""   , """.conv_1x1."""	)
    if ".norm." in name:
        _lowerCamelCase     :		Optional[int] 						=						name.replace(""".norm."""   , """.normalization."""	)
    if ".conv." in name:
        _lowerCamelCase     :		List[Any] 						=						name.replace(""".conv."""   , """.convolution."""	)
    if ".conv_proj." in name:
        _lowerCamelCase     :		Optional[int] 						=						name.replace(""".conv_proj."""   , """.conv_projection."""	)

    for i in range(0   , 2	):
        for j in range(0   , 4	):
            if F""".{i}.{j}.""" in name:
                _lowerCamelCase     :		str 						=						name.replace(F""".{i}.{j}."""   , F""".{i}.layer.{j}."""	)

    for i in range(2   , 6	):
        for j in range(0   , 4	):
            if F""".{i}.{j}.""" in name:
                _lowerCamelCase     :		Dict 						=						name.replace(F""".{i}.{j}."""   , F""".{i}."""	)
                if "expand_1x1" in name:
                    _lowerCamelCase     :		List[Any] 						=						name.replace("""expand_1x1"""   , """downsampling_layer.expand_1x1"""	)
                if "conv_3x3" in name:
                    _lowerCamelCase     :		Union[str, Any] 						=						name.replace("""conv_3x3"""   , """downsampling_layer.conv_3x3"""	)
                if "reduce_1x1" in name:
                    _lowerCamelCase     :		int 						=						name.replace("""reduce_1x1"""   , """downsampling_layer.reduce_1x1"""	)

    for i in range(2   , 5	):
        if F""".global_rep.{i}.weight""" in name:
            _lowerCamelCase     :		int 						=						name.replace(F""".global_rep.{i}.weight"""   , """.layernorm.weight"""	)
        if F""".global_rep.{i}.bias""" in name:
            _lowerCamelCase     :		Optional[int] 						=						name.replace(F""".global_rep.{i}.bias"""   , """.layernorm.bias"""	)

    if ".global_rep." in name:
        _lowerCamelCase     :		Optional[int] 						=						name.replace(""".global_rep."""   , """.transformer."""	)
    if ".pre_norm_mha.0." in name:
        _lowerCamelCase     :		Tuple 						=						name.replace(""".pre_norm_mha.0."""   , """.layernorm_before."""	)
    if ".pre_norm_mha.1.out_proj." in name:
        _lowerCamelCase     :		Dict 						=						name.replace(""".pre_norm_mha.1.out_proj."""   , """.attention.output.dense."""	)
    if ".pre_norm_ffn.0." in name:
        _lowerCamelCase     :		int 						=						name.replace(""".pre_norm_ffn.0."""   , """.layernorm_after."""	)
    if ".pre_norm_ffn.1." in name:
        _lowerCamelCase     :		Optional[Any] 						=						name.replace(""".pre_norm_ffn.1."""   , """.intermediate.dense."""	)
    if ".pre_norm_ffn.4." in name:
        _lowerCamelCase     :		Union[str, Any] 						=						name.replace(""".pre_norm_ffn.4."""   , """.output.dense."""	)
    if ".transformer." in name:
        _lowerCamelCase     :		str 						=						name.replace(""".transformer."""   , """.transformer.layer."""	)

    if ".aspp_layer." in name:
        _lowerCamelCase     :		Optional[int] 						=						name.replace(""".aspp_layer."""   , """."""	)
    if ".aspp_pool." in name:
        _lowerCamelCase     :		Optional[int] 						=						name.replace(""".aspp_pool."""   , """."""	)
    if "seg_head." in name:
        _lowerCamelCase     :		int 						=						name.replace("""seg_head."""   , """segmentation_head."""	)
    if "segmentation_head.classifier.classifier." in name:
        _lowerCamelCase     :		List[str] 						=						name.replace("""segmentation_head.classifier.classifier."""   , """segmentation_head.classifier."""	)

    if "classifier.fc." in name:
        _lowerCamelCase     :		Any 						=						name.replace("""classifier.fc."""   , """classifier."""	)
    elif (not base_model) and ("segmentation_head." not in name):
        _lowerCamelCase     :		Optional[Any] 						=						"""mobilevit.""" + name

    return name







def 						A__  (					__A   , __A   , __A=False	):







    '''simple docstring'''
    if base_model:
        _lowerCamelCase     :		Union[str, Any] 						=						""""""
    else:
        _lowerCamelCase     :		Dict 						=						"""mobilevit."""

    for key in orig_state_dict.copy().keys():
        _lowerCamelCase     :		Optional[Any] 						=						orig_state_dict.pop(__A	)

        if key[:8] == "encoder.":
            _lowerCamelCase     :		str 						=						key[8:]

        if "qkv" in key:
            _lowerCamelCase     :		List[str] 						=						key.split("""."""	)
            _lowerCamelCase     :		List[Any] 						=						int(key_split[0][6:]	) - 1
            _lowerCamelCase     :		str 						=						int(key_split[3]	)
            _lowerCamelCase     :		Any 						=						model.get_submodule(F"""{model_prefix}encoder.layer.{layer_num}"""	)
            _lowerCamelCase     :		Tuple 						=						layer.transformer.layer[transformer_num].attention.attention.all_head_size
            _lowerCamelCase     :		int 						=						(
                F"""{model_prefix}encoder.layer.{layer_num}.transformer.layer.{transformer_num}.attention.attention."""
            )
            if "weight" in key:
                _lowerCamelCase     :		Union[str, Any] 						=						val[:dim, :]
                _lowerCamelCase     :		Tuple 						=						val[dim : dim * 2, :]
                _lowerCamelCase     :		Any 						=						val[-dim:, :]
            else:
                _lowerCamelCase     :		Any 						=						val[:dim]
                _lowerCamelCase     :		str 						=						val[dim : dim * 2]
                _lowerCamelCase     :		str 						=						val[-dim:]
        else:
            _lowerCamelCase     :		List[str] 						=						val

    return orig_state_dict







def 						A__  (					):







    '''simple docstring'''
    _lowerCamelCase     :		Dict 						=						"""http://images.cocodataset.org/val2017/000000039769.jpg"""
    _lowerCamelCase     :		Any 						=						Image.open(requests.get(__A   , stream=__A	).raw	)
    return im







@torch.no_grad()
def 						A__  (					__A   , __A   , __A   , __A=False	):







    '''simple docstring'''
    _lowerCamelCase     :		List[str] 						=						get_mobilevit_config(__A	)

    # load original state_dict
    _lowerCamelCase     :		int 						=						torch.load(__A   , map_location="""cpu"""	)

    # load 🤗 model
    if mobilevit_name.startswith("""deeplabv3_"""	):
        _lowerCamelCase     :		List[str] 						=						MobileViTForSemanticSegmentation(__A	).eval()
    else:
        _lowerCamelCase     :		List[str] 						=						MobileViTForImageClassification(__A	).eval()

    _lowerCamelCase     :		List[str] 						=						convert_state_dict(__A   , __A	)
    model.load_state_dict(__A	)

    # Check outputs on an image, prepared by MobileViTImageProcessor
    _lowerCamelCase     :		int 						=						MobileViTImageProcessor(crop_size=config.image_size   , size=config.image_size + 32	)
    _lowerCamelCase     :		Any 						=						image_processor(images=prepare_img()   , return_tensors="""pt"""	)
    _lowerCamelCase     :		Any 						=						model(**__A	)
    _lowerCamelCase     :		Any 						=						outputs.logits

    if mobilevit_name.startswith("""deeplabv3_"""	):
        assert logits.shape == (1, 21, 32, 32)

        if mobilevit_name == "deeplabv3_mobilevit_s":
            _lowerCamelCase     :		List[str] 						=						torch.tensor(
                [
                    [[6.2_065, 6.1_292, 6.2_070], [6.1_079, 6.1_254, 6.1_747], [6.0_042, 6.1_071, 6.1_034]],
                    [[-6.9_253, -6.8_653, -7.0_398], [-7.3_218, -7.3_983, -7.3_670], [-7.1_961, -7.2_482, -7.1_569]],
                    [[-4.4_723, -4.4_348, -4.3_769], [-5.3_629, -5.4_632, -5.4_598], [-5.1_587, -5.3_402, -5.5_059]],
                ]	)
        elif mobilevit_name == "deeplabv3_mobilevit_xs":
            _lowerCamelCase     :		Union[str, Any] 						=						torch.tensor(
                [
                    [[5.4_449, 5.5_733, 5.6_314], [5.1_815, 5.3_930, 5.5_963], [5.1_656, 5.4_333, 5.4_853]],
                    [[-9.4_423, -9.7_766, -9.6_714], [-9.1_581, -9.5_720, -9.5_519], [-9.1_006, -9.6_458, -9.5_703]],
                    [[-7.7_721, -7.3_716, -7.1_583], [-8.4_599, -8.0_624, -7.7_944], [-8.4_172, -7.8_366, -7.5_025]],
                ]	)
        elif mobilevit_name == "deeplabv3_mobilevit_xxs":
            _lowerCamelCase     :		Optional[Any] 						=						torch.tensor(
                [
                    [[6.9_811, 6.9_743, 7.3_123], [7.1_777, 7.1_931, 7.3_938], [7.5_633, 7.8_050, 7.8_901]],
                    [[-10.5_536, -10.2_332, -10.2_924], [-10.2_336, -9.8_624, -9.5_964], [-10.8_840, -10.8_158, -10.6_659]],
                    [[-3.4_938, -3.0_631, -2.8_620], [-3.4_205, -2.8_135, -2.6_875], [-3.4_179, -2.7_945, -2.8_750]],
                ]	)
        else:
            raise ValueError(F"""Unknown mobilevit_name: {mobilevit_name}"""	)

        assert torch.allclose(logits[0, :3, :3, :3]   , __A   , atol=1E-4	)
    else:
        assert logits.shape == (1, 1_000)

        if mobilevit_name == "mobilevit_s":
            _lowerCamelCase     :		Optional[int] 						=						torch.tensor([-0.9_866, 0.2_392, -1.1_241]	)
        elif mobilevit_name == "mobilevit_xs":
            _lowerCamelCase     :		Dict 						=						torch.tensor([-2.4_761, -0.9_399, -1.9_587]	)
        elif mobilevit_name == "mobilevit_xxs":
            _lowerCamelCase     :		List[str] 						=						torch.tensor([-1.9_364, -1.2_327, -0.4_653]	)
        else:
            raise ValueError(F"""Unknown mobilevit_name: {mobilevit_name}"""	)

        assert torch.allclose(logits[0, :3]   , __A   , atol=1E-4	)

    Path(__A	).mkdir(exist_ok=__A	)
    print(F"""Saving model {mobilevit_name} to {pytorch_dump_folder_path}"""	)
    model.save_pretrained(__A	)
    print(F"""Saving image processor to {pytorch_dump_folder_path}"""	)
    image_processor.save_pretrained(__A	)

    if push_to_hub:
        _lowerCamelCase     :		Optional[Any] 						=						{
            """mobilevit_s""": """mobilevit-small""",
            """mobilevit_xs""": """mobilevit-x-small""",
            """mobilevit_xxs""": """mobilevit-xx-small""",
            """deeplabv3_mobilevit_s""": """deeplabv3-mobilevit-small""",
            """deeplabv3_mobilevit_xs""": """deeplabv3-mobilevit-x-small""",
            """deeplabv3_mobilevit_xxs""": """deeplabv3-mobilevit-xx-small""",
        }

        print("""Pushing to the hub..."""	)
        _lowerCamelCase     :		Dict 						=						model_mapping[mobilevit_name]
        image_processor.push_to_hub(__A   , organization="""apple"""	)
        model.push_to_hub(__A   , organization="""apple"""	)


if __name__ == "__main__":
    lowerCAmelCase			:							List[str]     =argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        "--mobilevit_name",
        default="mobilevit_s",
        type=str,
        help=(
            "Name of the MobileViT model you'd like to convert. Should be one of 'mobilevit_s', 'mobilevit_xs',"
            " 'mobilevit_xxs', 'deeplabv3_mobilevit_s', 'deeplabv3_mobilevit_xs', 'deeplabv3_mobilevit_xxs'."
        ),
    )
    parser.add_argument(
        "--checkpoint_path", required=True, type=str, help="Path to the original state dict (.pt file)."
    )
    parser.add_argument(
        "--pytorch_dump_folder_path", required=True, type=str, help="Path to the output PyTorch model directory."
    )
    parser.add_argument(
        "--push_to_hub", action="store_true", help="Whether or not to push the converted model to the 🤗 hub."
    )

    lowerCAmelCase			:							Tuple     =parser.parse_args()
    convert_movilevit_checkpoint(
        args.mobilevit_name, args.checkpoint_path, args.pytorch_dump_folder_path, 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()




