





import argparse
import pickle

import numpy as np
import torch
from torch import nn

from transformers import ReformerConfig, ReformerModelWithLMHead
from transformers.utils import logging


logging.set_verbosity_info()


def 							__snake_case							(    lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_=None	)				->						Union[str, Any]:
    # set parameter of one layer
    assert torch_layer.weight.shape == weight.shape, f'''{torch_layer} layer.weight does not match'''
    SCREAMING_SNAKE_CASE__											=			nn.Parameter(lowerCAmelCase_	)
    if bias is not None:
        assert torch_layer.bias.shape == bias.shape, f'''{torch_layer} layer.bias does not match'''
        SCREAMING_SNAKE_CASE__											=			nn.Parameter(lowerCAmelCase_	)


def 							__snake_case							(    lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_	)				->						Any:
    # set torch weights for 1-to-1 comparison
    SCREAMING_SNAKE_CASE__											=			np.asarray(weights[0]	)
    SCREAMING_SNAKE_CASE__											=			np.asarray(weights[1]	)
    SCREAMING_SNAKE_CASE__											=			np.asarray(weights[2]	)

    set_param(
        torch_layer.self_attention.query_key ,							torch.tensor(lowerCAmelCase_	).transpose(1 ,							2	).contiguous().view(-1 ,							lowerCAmelCase_	) ,							)
    set_param(
        torch_layer.self_attention.value ,							torch.tensor(lowerCAmelCase_	).transpose(1 ,							2	).contiguous().view(-1 ,							lowerCAmelCase_	) ,							)
    set_param(
        torch_layer.output.dense ,							torch.tensor(lowerCAmelCase_	).view(-1 ,							lowerCAmelCase_	).contiguous().transpose(0 ,							1	) ,							)


def 							__snake_case							(    lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_	)				->						Optional[Any]:
    # set torch weights for 1-to-1 comparison
    SCREAMING_SNAKE_CASE__											=			np.asarray(weights[0]	)
    SCREAMING_SNAKE_CASE__											=			np.asarray(weights[1]	)
    SCREAMING_SNAKE_CASE__											=			np.asarray(weights[2]	)
    SCREAMING_SNAKE_CASE__											=			np.asarray(weights[3]	)

    set_param(
        torch_layer.self_attention.query ,							torch.tensor(lowerCAmelCase_	).transpose(1 ,							2	).contiguous().view(-1 ,							lowerCAmelCase_	) ,							)
    set_param(
        torch_layer.self_attention.key ,							torch.tensor(lowerCAmelCase_	).transpose(1 ,							2	).contiguous().view(-1 ,							lowerCAmelCase_	) ,							)
    set_param(
        torch_layer.self_attention.value ,							torch.tensor(lowerCAmelCase_	).transpose(1 ,							2	).contiguous().view(-1 ,							lowerCAmelCase_	) ,							)
    set_param(
        torch_layer.output.dense ,							torch.tensor(lowerCAmelCase_	).view(-1 ,							lowerCAmelCase_	).contiguous().transpose(0 ,							1	) ,							)


def 							__snake_case							(    lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_	)				->						List[str]:
    # layernorm 1
    SCREAMING_SNAKE_CASE__											=			weights[0][0][0]
    SCREAMING_SNAKE_CASE__											=			np.asarray(layer_norm_a[0]	)
    SCREAMING_SNAKE_CASE__											=			np.asarray(layer_norm_a[1]	)
    set_param(
        torch_block.attention.layer_norm ,							torch.tensor(lowerCAmelCase_	) ,							torch.tensor(lowerCAmelCase_	) ,							)

    # lsh weights + output
    SCREAMING_SNAKE_CASE__											=			weights[0][1]
    if len(lowerCAmelCase_	) < 4:
        set_layer_weights_in_torch_lsh(lowerCAmelCase_ ,							torch_block.attention ,							lowerCAmelCase_	)
    else:
        set_layer_weights_in_torch_local(lowerCAmelCase_ ,							torch_block.attention ,							lowerCAmelCase_	)

    # intermediate weighs
    SCREAMING_SNAKE_CASE__											=			weights[2][0][1][2]

    # Chunked Feed Forward
    if len(lowerCAmelCase_	) == 4:
        SCREAMING_SNAKE_CASE__											=			intermediate_weights[2]

    # layernorm 2
    SCREAMING_SNAKE_CASE__											=			np.asarray(intermediate_weights[0][0]	)
    SCREAMING_SNAKE_CASE__											=			np.asarray(intermediate_weights[0][1]	)
    set_param(
        torch_block.feed_forward.layer_norm ,							torch.tensor(lowerCAmelCase_	) ,							torch.tensor(lowerCAmelCase_	) ,							)

    # intermediate dense
    SCREAMING_SNAKE_CASE__											=			np.asarray(intermediate_weights[1][0]	)
    SCREAMING_SNAKE_CASE__											=			np.asarray(intermediate_weights[1][1]	)
    set_param(
        torch_block.feed_forward.dense.dense ,							torch.tensor(lowerCAmelCase_	).transpose(0 ,							1	).contiguous() ,							torch.tensor(lowerCAmelCase_	) ,							)

    # intermediate out
    SCREAMING_SNAKE_CASE__											=			np.asarray(intermediate_weights[4][0]	)
    SCREAMING_SNAKE_CASE__											=			np.asarray(intermediate_weights[4][1]	)
    set_param(
        torch_block.feed_forward.output.dense ,							torch.tensor(lowerCAmelCase_	).transpose(0 ,							1	).contiguous() ,							torch.tensor(lowerCAmelCase_	) ,							)


def 							__snake_case							(    lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_	)				->						Dict:
    # reformer model
    SCREAMING_SNAKE_CASE__											=			torch_model.reformer

    # word embeds
    SCREAMING_SNAKE_CASE__											=			np.asarray(weights[1]	)
    set_param(
        torch_model_reformer.embeddings.word_embeddings ,							torch.tensor(lowerCAmelCase_	) ,							)

    if isinstance(weights[3] ,							lowerCAmelCase_	):
        SCREAMING_SNAKE_CASE__											=			torch_model_reformer.embeddings.position_embeddings
        for emb_idx in range(len(position_embeddings.weights	)	):
            SCREAMING_SNAKE_CASE__											=			np.asarray(weights[3][emb_idx][0]	)
            assert (
                position_embeddings.weights[emb_idx].shape == emb_weights.shape
            ), f'''{position_embeddings[emb_idx]} emb does not match'''
            SCREAMING_SNAKE_CASE__											=			nn.Parameter(torch.tensor(lowerCAmelCase_	)	)

    SCREAMING_SNAKE_CASE__											=			weights[5]
    assert len(torch_model_reformer.encoder.layers	) * 4 == len(
        lowerCAmelCase_	), "HF and trax model do not have the same number of layers"
    for layer_idx, layer in enumerate(torch_model_reformer.encoder.layers	):
        SCREAMING_SNAKE_CASE__											=			trax_layer_weights[4 * layer_idx : 4 * (layer_idx + 1)]
        set_block_weights_in_torch(lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_	)

    # output layer norm
    SCREAMING_SNAKE_CASE__											=			np.asarray(weights[7][0]	)
    SCREAMING_SNAKE_CASE__											=			np.asarray(weights[7][1]	)
    set_param(
        torch_model_reformer.encoder.layer_norm ,							torch.tensor(lowerCAmelCase_	) ,							torch.tensor(lowerCAmelCase_	) ,							)

    # output embeddings
    SCREAMING_SNAKE_CASE__											=			np.asarray(weights[9][0]	)
    SCREAMING_SNAKE_CASE__											=			np.asarray(weights[9][1]	)
    set_param(
        torch_model.lm_head.decoder ,							torch.tensor(lowerCAmelCase_	).transpose(0 ,							1	).contiguous() ,							torch.tensor(lowerCAmelCase_	) ,							)


def 							__snake_case							(    lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_	)				->						str:
    # Initialise PyTorch model
    SCREAMING_SNAKE_CASE__											=			ReformerConfig.from_json_file(lowerCAmelCase_	)
    print(f'''Building PyTorch model from configuration: {config}'''	)
    SCREAMING_SNAKE_CASE__											=			ReformerModelWithLMHead(lowerCAmelCase_	)

    with open(lowerCAmelCase_ ,							'''rb'''	) as f:
        SCREAMING_SNAKE_CASE__											=			pickle.load(lowerCAmelCase_	)['''weights''']

    set_model_weights_in_torch(lowerCAmelCase_ ,							lowerCAmelCase_ ,							config.hidden_size	)

    # Save pytorch-model
    print(f'''Save PyTorch model to {pytorch_dump_path}'''	)
    torch.save(model.state_dict() ,							lowerCAmelCase_	)


if __name__ == "__main__":
  _A					:				Union[str, Any]				=			argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      """--trax_model_pkl_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
  )
  parser.add_argument(
      """--config_file""",
      default=None,
      type=str,
      required=True,
      help=(
          """The config json file corresponding to the pre-trained Reformer model. \n"""
          """This specifies the model architecture."""
      ),
  )
  parser.add_argument(
      """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
  )
  _A					:				List[Any]				=			parser.parse_args()
  convert_trax_checkpoint_to_pytorch(args.trax_model_pkl_path, args.config_file, args.pytorch_dump_path)







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()
_A					:				str				=			logging.get_logger(__name__)


def 							__snake_case							(    lowerCAmelCase_	)				->						Optional[Any]:
    SCREAMING_SNAKE_CASE__											=			MobileViTConfig()

    # size of the architecture
    if "mobilevit_s" in mobilevit_name:
        SCREAMING_SNAKE_CASE__											=			[1_4_4, 1_9_2, 2_4_0]
        SCREAMING_SNAKE_CASE__											=			[1_6, 3_2, 6_4, 9_6, 1_2_8, 1_6_0, 6_4_0]
    elif "mobilevit_xs" in mobilevit_name:
        SCREAMING_SNAKE_CASE__											=			[9_6, 1_2_0, 1_4_4]
        SCREAMING_SNAKE_CASE__											=			[1_6, 3_2, 4_8, 6_4, 8_0, 9_6, 3_8_4]
    elif "mobilevit_xxs" in mobilevit_name:
        SCREAMING_SNAKE_CASE__											=			[6_4, 8_0, 9_6]
        SCREAMING_SNAKE_CASE__											=			[1_6, 1_6, 2_4, 4_8, 6_4, 8_0, 3_2_0]
        SCREAMING_SNAKE_CASE__											=			0.05
        SCREAMING_SNAKE_CASE__											=			2.0

    if mobilevit_name.startswith('''deeplabv3_'''	):
        SCREAMING_SNAKE_CASE__											=			5_1_2
        SCREAMING_SNAKE_CASE__											=			1_6
        SCREAMING_SNAKE_CASE__											=			2_1
        SCREAMING_SNAKE_CASE__											=			'''pascal-voc-id2label.json'''
    else:
        SCREAMING_SNAKE_CASE__											=			1_0_0_0
        SCREAMING_SNAKE_CASE__											=			'''imagenet-1k-id2label.json'''

    SCREAMING_SNAKE_CASE__											=			'''huggingface/label-files'''
    SCREAMING_SNAKE_CASE__											=			json.load(open(hf_hub_download(lowerCAmelCase_ ,							lowerCAmelCase_ ,							repo_type='''dataset'''	) ,							'''r'''	)	)
    SCREAMING_SNAKE_CASE__											=			{int(lowerCAmelCase_	): v for k, v in idalabel.items()}
    SCREAMING_SNAKE_CASE__											=			idalabel
    SCREAMING_SNAKE_CASE__											=			{v: k for k, v in idalabel.items()}

    return config


def 							__snake_case							(    lowerCAmelCase_ ,							lowerCAmelCase_=False	)				->						Union[str, Any]:
    for i in range(1 ,							6	):
        if f'''layer_{i}.''' in name:
            SCREAMING_SNAKE_CASE__											=			name.replace(f'''layer_{i}.''' ,							f'''encoder.layer.{i - 1}.'''	)

    if "conv_1." in name:
        SCREAMING_SNAKE_CASE__											=			name.replace('''conv_1.''' ,							'''conv_stem.'''	)
    if ".block." in name:
        SCREAMING_SNAKE_CASE__											=			name.replace('''.block.''' ,							'''.'''	)

    if "exp_1x1" in name:
        SCREAMING_SNAKE_CASE__											=			name.replace('''exp_1x1''' ,							'''expand_1x1'''	)
    if "red_1x1" in name:
        SCREAMING_SNAKE_CASE__											=			name.replace('''red_1x1''' ,							'''reduce_1x1'''	)
    if ".local_rep.conv_3x3." in name:
        SCREAMING_SNAKE_CASE__											=			name.replace('''.local_rep.conv_3x3.''' ,							'''.conv_kxk.'''	)
    if ".local_rep.conv_1x1." in name:
        SCREAMING_SNAKE_CASE__											=			name.replace('''.local_rep.conv_1x1.''' ,							'''.conv_1x1.'''	)
    if ".norm." in name:
        SCREAMING_SNAKE_CASE__											=			name.replace('''.norm.''' ,							'''.normalization.'''	)
    if ".conv." in name:
        SCREAMING_SNAKE_CASE__											=			name.replace('''.conv.''' ,							'''.convolution.'''	)
    if ".conv_proj." in name:
        SCREAMING_SNAKE_CASE__											=			name.replace('''.conv_proj.''' ,							'''.conv_projection.'''	)

    for i in range(0 ,							2	):
        for j in range(0 ,							4	):
            if f'''.{i}.{j}.''' in name:
                SCREAMING_SNAKE_CASE__											=			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:
                SCREAMING_SNAKE_CASE__											=			name.replace(f'''.{i}.{j}.''' ,							f'''.{i}.'''	)
                if "expand_1x1" in name:
                    SCREAMING_SNAKE_CASE__											=			name.replace('''expand_1x1''' ,							'''downsampling_layer.expand_1x1'''	)
                if "conv_3x3" in name:
                    SCREAMING_SNAKE_CASE__											=			name.replace('''conv_3x3''' ,							'''downsampling_layer.conv_3x3'''	)
                if "reduce_1x1" in name:
                    SCREAMING_SNAKE_CASE__											=			name.replace('''reduce_1x1''' ,							'''downsampling_layer.reduce_1x1'''	)

    for i in range(2 ,							5	):
        if f'''.global_rep.{i}.weight''' in name:
            SCREAMING_SNAKE_CASE__											=			name.replace(f'''.global_rep.{i}.weight''' ,							'''.layernorm.weight'''	)
        if f'''.global_rep.{i}.bias''' in name:
            SCREAMING_SNAKE_CASE__											=			name.replace(f'''.global_rep.{i}.bias''' ,							'''.layernorm.bias'''	)

    if ".global_rep." in name:
        SCREAMING_SNAKE_CASE__											=			name.replace('''.global_rep.''' ,							'''.transformer.'''	)
    if ".pre_norm_mha.0." in name:
        SCREAMING_SNAKE_CASE__											=			name.replace('''.pre_norm_mha.0.''' ,							'''.layernorm_before.'''	)
    if ".pre_norm_mha.1.out_proj." in name:
        SCREAMING_SNAKE_CASE__											=			name.replace('''.pre_norm_mha.1.out_proj.''' ,							'''.attention.output.dense.'''	)
    if ".pre_norm_ffn.0." in name:
        SCREAMING_SNAKE_CASE__											=			name.replace('''.pre_norm_ffn.0.''' ,							'''.layernorm_after.'''	)
    if ".pre_norm_ffn.1." in name:
        SCREAMING_SNAKE_CASE__											=			name.replace('''.pre_norm_ffn.1.''' ,							'''.intermediate.dense.'''	)
    if ".pre_norm_ffn.4." in name:
        SCREAMING_SNAKE_CASE__											=			name.replace('''.pre_norm_ffn.4.''' ,							'''.output.dense.'''	)
    if ".transformer." in name:
        SCREAMING_SNAKE_CASE__											=			name.replace('''.transformer.''' ,							'''.transformer.layer.'''	)

    if ".aspp_layer." in name:
        SCREAMING_SNAKE_CASE__											=			name.replace('''.aspp_layer.''' ,							'''.'''	)
    if ".aspp_pool." in name:
        SCREAMING_SNAKE_CASE__											=			name.replace('''.aspp_pool.''' ,							'''.'''	)
    if "seg_head." in name:
        SCREAMING_SNAKE_CASE__											=			name.replace('''seg_head.''' ,							'''segmentation_head.'''	)
    if "segmentation_head.classifier.classifier." in name:
        SCREAMING_SNAKE_CASE__											=			name.replace('''segmentation_head.classifier.classifier.''' ,							'''segmentation_head.classifier.'''	)

    if "classifier.fc." in name:
        SCREAMING_SNAKE_CASE__											=			name.replace('''classifier.fc.''' ,							'''classifier.'''	)
    elif (not base_model) and ("segmentation_head." not in name):
        SCREAMING_SNAKE_CASE__											=			'''mobilevit.''' + name

    return name


def 							__snake_case							(    lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_=False	)				->						Optional[Any]:
    if base_model:
        SCREAMING_SNAKE_CASE__											=			''''''
    else:
        SCREAMING_SNAKE_CASE__											=			'''mobilevit.'''

    for key in orig_state_dict.copy().keys():
        SCREAMING_SNAKE_CASE__											=			orig_state_dict.pop(lowerCAmelCase_	)

        if key[:8] == "encoder.":
            SCREAMING_SNAKE_CASE__											=			key[8:]

        if "qkv" in key:
            SCREAMING_SNAKE_CASE__											=			key.split('''.'''	)
            SCREAMING_SNAKE_CASE__											=			int(key_split[0][6:]	) - 1
            SCREAMING_SNAKE_CASE__											=			int(key_split[3]	)
            SCREAMING_SNAKE_CASE__											=			model.get_submodule(f'''{model_prefix}encoder.layer.{layer_num}'''	)
            SCREAMING_SNAKE_CASE__											=			layer.transformer.layer[transformer_num].attention.attention.all_head_size
            SCREAMING_SNAKE_CASE__											=			(
                f'''{model_prefix}encoder.layer.{layer_num}.transformer.layer.{transformer_num}.attention.attention.'''
            )
            if "weight" in key:
                SCREAMING_SNAKE_CASE__											=			val[:dim, :]
                SCREAMING_SNAKE_CASE__											=			val[dim : dim * 2, :]
                SCREAMING_SNAKE_CASE__											=			val[-dim:, :]
            else:
                SCREAMING_SNAKE_CASE__											=			val[:dim]
                SCREAMING_SNAKE_CASE__											=			val[dim : dim * 2]
                SCREAMING_SNAKE_CASE__											=			val[-dim:]
        else:
            SCREAMING_SNAKE_CASE__											=			val

    return orig_state_dict


def 							__snake_case							(    )				->						Optional[Any]:
    SCREAMING_SNAKE_CASE__											=			'''http://images.cocodataset.org/val2017/000000039769.jpg'''
    SCREAMING_SNAKE_CASE__											=			Image.open(requests.get(lowerCAmelCase_ ,							stream=lowerCAmelCase_	).raw	)
    return im


@torch.no_grad()
def 							__snake_case							(    lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_=False	)				->						Any:
    SCREAMING_SNAKE_CASE__											=			get_mobilevit_config(lowerCAmelCase_	)

    # load original state_dict
    SCREAMING_SNAKE_CASE__											=			torch.load(lowerCAmelCase_ ,							map_location='''cpu'''	)

    # load 🤗 model
    if mobilevit_name.startswith('''deeplabv3_'''	):
        SCREAMING_SNAKE_CASE__											=			MobileViTForSemanticSegmentation(lowerCAmelCase_	).eval()
    else:
        SCREAMING_SNAKE_CASE__											=			MobileViTForImageClassification(lowerCAmelCase_	).eval()

    SCREAMING_SNAKE_CASE__											=			convert_state_dict(lowerCAmelCase_ ,							lowerCAmelCase_	)
    model.load_state_dict(lowerCAmelCase_	)

    # Check outputs on an image, prepared by MobileViTImageProcessor
    SCREAMING_SNAKE_CASE__											=			MobileViTImageProcessor(crop_size=config.image_size ,							size=config.image_size + 3_2	)
    SCREAMING_SNAKE_CASE__											=			image_processor(images=prepare_img() ,							return_tensors='''pt'''	)
    SCREAMING_SNAKE_CASE__											=			model(**lowerCAmelCase_	)
    SCREAMING_SNAKE_CASE__											=			outputs.logits

    if mobilevit_name.startswith('''deeplabv3_'''	):
        assert logits.shape == (1, 2_1, 3_2, 3_2)

        if mobilevit_name == "deeplabv3_mobilevit_s":
            SCREAMING_SNAKE_CASE__											=			torch.tensor(
                [
                    [[6.20_65, 6.12_92, 6.20_70], [6.10_79, 6.12_54, 6.17_47], [6.00_42, 6.10_71, 6.10_34]],
                    [[-6.92_53, -6.86_53, -7.03_98], [-7.32_18, -7.39_83, -7.36_70], [-7.19_61, -7.24_82, -7.15_69]],
                    [[-4.47_23, -4.43_48, -4.37_69], [-5.36_29, -5.46_32, -5.45_98], [-5.15_87, -5.34_02, -5.50_59]],
                ]	)
        elif mobilevit_name == "deeplabv3_mobilevit_xs":
            SCREAMING_SNAKE_CASE__											=			torch.tensor(
                [
                    [[5.44_49, 5.57_33, 5.63_14], [5.18_15, 5.39_30, 5.59_63], [5.16_56, 5.43_33, 5.48_53]],
                    [[-9.44_23, -9.77_66, -9.67_14], [-9.15_81, -9.57_20, -9.55_19], [-9.10_06, -9.64_58, -9.57_03]],
                    [[-7.77_21, -7.37_16, -7.15_83], [-8.45_99, -8.06_24, -7.79_44], [-8.41_72, -7.83_66, -7.50_25]],
                ]	)
        elif mobilevit_name == "deeplabv3_mobilevit_xxs":
            SCREAMING_SNAKE_CASE__											=			torch.tensor(
                [
                    [[6.98_11, 6.97_43, 7.31_23], [7.17_77, 7.19_31, 7.39_38], [7.56_33, 7.80_50, 7.89_01]],
                    [[-10.55_36, -10.23_32, -10.29_24], [-10.23_36, -9.86_24, -9.59_64], [-10.88_40, -10.81_58, -10.66_59]],
                    [[-3.49_38, -3.06_31, -2.86_20], [-3.42_05, -2.81_35, -2.68_75], [-3.41_79, -2.79_45, -2.87_50]],
                ]	)
        else:
            raise ValueError(f'''Unknown mobilevit_name: {mobilevit_name}'''	)

        assert torch.allclose(logits[0, :3, :3, :3] ,							lowerCAmelCase_ ,							atol=1e-4	)
    else:
        assert logits.shape == (1, 1_0_0_0)

        if mobilevit_name == "mobilevit_s":
            SCREAMING_SNAKE_CASE__											=			torch.tensor([-0.98_66, 0.23_92, -1.12_41]	)
        elif mobilevit_name == "mobilevit_xs":
            SCREAMING_SNAKE_CASE__											=			torch.tensor([-2.47_61, -0.93_99, -1.95_87]	)
        elif mobilevit_name == "mobilevit_xxs":
            SCREAMING_SNAKE_CASE__											=			torch.tensor([-1.93_64, -1.23_27, -0.46_53]	)
        else:
            raise ValueError(f'''Unknown mobilevit_name: {mobilevit_name}'''	)

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

    Path(lowerCAmelCase_	).mkdir(exist_ok=lowerCAmelCase_	)
    print(f'''Saving model {mobilevit_name} to {pytorch_dump_folder_path}'''	)
    model.save_pretrained(lowerCAmelCase_	)
    print(f'''Saving image processor to {pytorch_dump_folder_path}'''	)
    image_processor.save_pretrained(lowerCAmelCase_	)

    if push_to_hub:
        SCREAMING_SNAKE_CASE__											=			{
            '''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...'''	)
        SCREAMING_SNAKE_CASE__											=			model_mapping[mobilevit_name]
        image_processor.push_to_hub(lowerCAmelCase_ ,							organization='''apple'''	)
        model.push_to_hub(lowerCAmelCase_ ,							organization='''apple'''	)


if __name__ == "__main__":
  _A					:				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."""
  )

  _A					:				str				=			parser.parse_args()
  convert_movilevit_checkpoint(
      args.mobilevit_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
  )
