





'''simple docstring'''

# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.


import warnings
from typing import List
from unittest.mock import Mock

import torch
from torch.utils.data import DataLoader, IterableDataset, TensorDataset

from accelerate.accelerator import Accelerator
from accelerate.utils.dataclasses import DistributedType







class __a			( __SCREAMING_SNAKE_CASE      ):





   def __init__(       self					:      int					,lowerCamelCase					:      Any     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							data





   def __iter__(       self					:      Dict     ):

      '''simple docstring'''

      for element in self.data:
         yield element





def     __magic_name__							(				__UpperCAmelCase=True   )    ->       int:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE   				=							Accelerator(even_batches=lowerCAmelCase_   )
   assert accelerator.num_processes == 2, "this script expects that two GPUs are available"
   return accelerator

def     __magic_name__							(				__UpperCAmelCase				,						__UpperCAmelCase				,						__UpperCAmelCase				,						__UpperCAmelCase = False   )    ->       Union[str, Any]:

   '''simple docstring'''







   if iterable:
      __SCREAMING_SNAKE_CASE   				=							DummyIterableDataset(torch.as_tensor(range(lowerCAmelCase_   )   )   )
   else:
      __SCREAMING_SNAKE_CASE   				=							TensorDataset(torch.as_tensor(range(lowerCAmelCase_   )   )   )

   __SCREAMING_SNAKE_CASE   				=							DataLoader(lowerCAmelCase_				,						batch_size=lowerCAmelCase_   )
   __SCREAMING_SNAKE_CASE   				=							accelerator.prepare(lowerCAmelCase_   )

   return dl

def     __magic_name__							(				__UpperCAmelCase				,						__UpperCAmelCase				,						__UpperCAmelCase				,						__UpperCAmelCase				,						__UpperCAmelCase				,						)    ->       List[str]:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE   				=							create_dataloader(accelerator=lowerCAmelCase_				,						dataset_size=lowerCAmelCase_				,						batch_size=lowerCAmelCase_   )

   __SCREAMING_SNAKE_CASE   				=							[len(batch[0]   ) for batch in dl]

   if accelerator.process_index == 0:
      assert batch_sizes == process_0_expected_batch_sizes
   elif accelerator.process_index == 1:
      assert batch_sizes == process_1_expected_batch_sizes

def     __magic_name__							(				)    ->       Optional[Any]:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE   				=							create_accelerator()

   # without padding, we would expect a different number of batches
   verify_dataloader_batch_sizes(
       lowerCAmelCase_				,						dataset_size=3				,						batch_size=1				,						process_0_expected_batch_sizes=[1, 1]				,						process_1_expected_batch_sizes=[1, 1]				,						)

   # without padding, we would expect the same number of batches, but different sizes
   verify_dataloader_batch_sizes(
       lowerCAmelCase_				,						dataset_size=7				,						batch_size=2				,						process_0_expected_batch_sizes=[2, 2]				,						process_1_expected_batch_sizes=[2, 2]				,						)

def     __magic_name__							(				)    ->       str:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE   				=							create_accelerator(even_batches=lowerCAmelCase_   )

   verify_dataloader_batch_sizes(
       lowerCAmelCase_				,						dataset_size=3				,						batch_size=1				,						process_0_expected_batch_sizes=[1, 1]				,						process_1_expected_batch_sizes=[1]				,						)

   verify_dataloader_batch_sizes(
       lowerCAmelCase_				,						dataset_size=7				,						batch_size=2				,						process_0_expected_batch_sizes=[2, 2]				,						process_1_expected_batch_sizes=[2, 1]				,						)

def     __magic_name__							(				)    ->       List[str]:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE   				=							create_accelerator(even_batches=lowerCAmelCase_   )

   __SCREAMING_SNAKE_CASE   				=							torch.nn.Linear(1				,						1   )
   __SCREAMING_SNAKE_CASE   				=							accelerator.prepare(lowerCAmelCase_   )

   __SCREAMING_SNAKE_CASE   				=							create_dataloader(lowerCAmelCase_				,						dataset_size=3				,						batch_size=1   )

   __SCREAMING_SNAKE_CASE   				=							[]
   with accelerator.join_uneven_inputs([ddp_model]   ):
      for batch_idx, batch in enumerate(lowerCAmelCase_   ):
         __SCREAMING_SNAKE_CASE   				=							ddp_model(batch[0].float()   )
         __SCREAMING_SNAKE_CASE   				=							output.sum()
         loss.backward()
         batch_idxs.append(lowerCAmelCase_   )

   accelerator.wait_for_everyone()

   if accelerator.process_index == 0:
      assert batch_idxs == [0, 1]
   elif accelerator.process_index == 1:
      assert batch_idxs == [0]

def     __magic_name__							(				__UpperCAmelCase   )    ->       Any:

   '''simple docstring'''







   with warnings.catch_warnings(record=lowerCAmelCase_   ) as w:
      with accelerator.join_uneven_inputs([Mock()]   ):
         pass

      assert issubclass(w[-1].category				,						lowerCAmelCase_   )
      assert "only supported for multi-GPU" in str(w[-1].message   )

def     __magic_name__							(				)    ->       Union[str, Any]:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE   				=							True
   __SCREAMING_SNAKE_CASE   				=							False
   __SCREAMING_SNAKE_CASE   				=							create_accelerator(even_batches=lowerCAmelCase_   )
   __SCREAMING_SNAKE_CASE   				=							torch.nn.Linear(1				,						1   )
   __SCREAMING_SNAKE_CASE   				=							accelerator.prepare(lowerCAmelCase_   )
   __SCREAMING_SNAKE_CASE   				=							create_dataloader(lowerCAmelCase_				,						dataset_size=3				,						batch_size=1   )
   __SCREAMING_SNAKE_CASE   				=							create_dataloader(lowerCAmelCase_				,						dataset_size=3				,						batch_size=1   )

   with accelerator.join_uneven_inputs([ddp_model]				,						even_batches=lowerCAmelCase_   ):
      __SCREAMING_SNAKE_CASE   				=							train_dl.batch_sampler.even_batches
      __SCREAMING_SNAKE_CASE   				=							valid_dl.batch_sampler.even_batches

   assert train_dl_overridden_value == overridden_even_batches
   assert valid_dl_overridden_value == overridden_even_batches
   assert train_dl.batch_sampler.even_batches == default_even_batches
   assert valid_dl.batch_sampler.even_batches == default_even_batches

def     __magic_name__							(				)    ->       str:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE   				=							True
   __SCREAMING_SNAKE_CASE   				=							False
   __SCREAMING_SNAKE_CASE   				=							create_accelerator(even_batches=lowerCAmelCase_   )
   __SCREAMING_SNAKE_CASE   				=							torch.nn.Linear(1				,						1   )
   __SCREAMING_SNAKE_CASE   				=							accelerator.prepare(lowerCAmelCase_   )
   create_dataloader(lowerCAmelCase_				,						dataset_size=3				,						batch_size=1				,						iterable=lowerCAmelCase_   )
   __SCREAMING_SNAKE_CASE   				=							create_dataloader(lowerCAmelCase_				,						dataset_size=3				,						batch_size=1   )

   with warnings.catch_warnings():
      warnings.filterwarnings("""ignore"""   )
      try:
         with accelerator.join_uneven_inputs([ddp_model]				,						even_batches=lowerCAmelCase_   ):
            __SCREAMING_SNAKE_CASE   				=							batch_dl.batch_sampler.even_batches
      except AttributeError:
         # ensure attribute error is not raised when processing iterable dl
         raise AssertionError

   assert batch_dl_overridden_value == overridden_even_batches
   assert batch_dl.batch_sampler.even_batches == default_even_batches

def     __magic_name__							(				)    ->       Optional[int]:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE   				=							create_accelerator()
   __SCREAMING_SNAKE_CASE   				=							torch.nn.Linear(1				,						1   )
   __SCREAMING_SNAKE_CASE   				=							accelerator.prepare(lowerCAmelCase_   )
   create_dataloader(lowerCAmelCase_				,						dataset_size=3				,						batch_size=1				,						iterable=lowerCAmelCase_   )

   with warnings.catch_warnings(record=lowerCAmelCase_   ) as w:
      with accelerator.join_uneven_inputs([ddp_model]				,						even_batches=lowerCAmelCase_   ):
         pass

      assert issubclass(w[-1].category				,						lowerCAmelCase_   )
      assert "only supported for map-style datasets" in str(w[-1].message   )

def     __magic_name__							(				)    ->       Optional[int]:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE   				=							create_accelerator()

   accelerator.print("""Test that even_batches variable ensures uniform batches across processes"""   )
   test_default_ensures_even_batch_sizes()

   accelerator.print("""Run tests with even_batches disabled"""   )
   test_can_disable_even_batches()

   accelerator.print("""Test joining uneven inputs"""   )
   test_can_join_uneven_inputs()

   accelerator.print("""Test overriding even_batches when joining uneven inputs"""   )
   test_join_can_override_even_batches()

   accelerator.print("""Test overriding even_batches for mixed dataloader types"""   )
   test_join_can_override_for_mixed_type_dataloaders()

   accelerator.print("""Test overriding even_batches raises a warning for iterable dataloaders"""   )
   test_join_raises_warning_for_iterable_when_overriding_even_batches()

   accelerator.print("""Test join with non DDP distributed raises warning"""   )
   __SCREAMING_SNAKE_CASE   				=							accelerator.state.distributed_type
   __SCREAMING_SNAKE_CASE   				=							DistributedType.FSDP
   test_join_raises_warning_for_non_ddp_distributed(lowerCAmelCase_   )
   __SCREAMING_SNAKE_CASE   				=							original_state


if __name__ == "__main__":
    main()












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
  )
