"""simple docstring"""







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()
__SCREAMING_SNAKE_CASE								=logging.get_logger(__name__)



def     lowercase__( __SCREAMING_SNAKE_CASE							:  List[Any]   ):
       lowercase_			:       Optional[Any]									=			MobileViTConfig()

       # size of the architecture
       if "mobilevit_s" in mobilevit_name:
              lowercase_			:       Dict									=			[1_44, 1_92, 2_40]
              lowercase_			:       Union[str, Any]									=			[16, 32, 64, 96, 1_28, 1_60, 6_40]
       elif "mobilevit_xs" in mobilevit_name:
              lowercase_			:       Dict									=			[96, 1_20, 1_44]
              lowercase_			:       Optional[Any]									=			[16, 32, 48, 64, 80, 96, 3_84]
       elif "mobilevit_xxs" in mobilevit_name:
              lowercase_			:       str									=			[64, 80, 96]
              lowercase_			:       List[str]									=			[16, 16, 24, 48, 64, 80, 3_20]
              lowercase_			:       List[str]									=			0.05
              lowercase_			:       Dict									=			2.0

       if mobilevit_name.startswith('deeplabv3_'   ):
              lowercase_			:       Dict									=			5_12
              lowercase_			:       Tuple									=			16
              lowercase_			:       Any									=			21
              lowercase_			:       int									=			'pascal-voc-id2label.json'
       else:
              lowercase_			:       Any									=			10_00
              lowercase_			:       Dict									=			'imagenet-1k-id2label.json'

       lowercase_			:       Optional[int]									=			'huggingface/label-files'
       lowercase_			:       List[str]									=			json.load(open(hf_hub_download(UpperCamelCase__  ,    UpperCamelCase__  ,    repo_type='dataset'   )  ,    'r'   )   )
       lowercase_			:       Union[str, Any]									=			{int(UpperCamelCase__   ): v for k, v in idalabel.items()}
       lowercase_			:       List[Any]									=			idalabel
       lowercase_			:       Union[str, Any]									=			{v: k for k, v in idalabel.items()}

       return config



def     lowercase__( __SCREAMING_SNAKE_CASE							:  List[Any]  ,    __SCREAMING_SNAKE_CASE							:  int=False   ):






       for i in range(1  ,    6   ):
              if F'''layer_{i}.''' in name:
                     lowercase_			:       List[Any]									=			name.replace(F'''layer_{i}.'''  ,    F'''encoder.layer.{i - 1}.'''   )

       if "conv_1." in name:
              lowercase_			:       Union[str, Any]									=			name.replace('conv_1.'  ,    'conv_stem.'   )
       if ".block." in name:
              lowercase_			:       str									=			name.replace('.block.'  ,    '.'   )

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

       for i in range(0  ,    2   ):
              for j in range(0  ,    4   ):
                     if F'''.{i}.{j}.''' in name:
                            lowercase_			:       int									=			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:
                            lowercase_			:       List[str]									=			name.replace(F'''.{i}.{j}.'''  ,    F'''.{i}.'''   )
                            if "expand_1x1" in name:
                                   lowercase_			:       Tuple									=			name.replace('expand_1x1'  ,    'downsampling_layer.expand_1x1'   )
                            if "conv_3x3" in name:
                                   lowercase_			:       List[str]									=			name.replace('conv_3x3'  ,    'downsampling_layer.conv_3x3'   )
                            if "reduce_1x1" in name:
                                   lowercase_			:       Optional[int]									=			name.replace('reduce_1x1'  ,    'downsampling_layer.reduce_1x1'   )

       for i in range(2  ,    5   ):
              if F'''.global_rep.{i}.weight''' in name:
                     lowercase_			:       List[Any]									=			name.replace(F'''.global_rep.{i}.weight'''  ,    '.layernorm.weight'   )
              if F'''.global_rep.{i}.bias''' in name:
                     lowercase_			:       Optional[Any]									=			name.replace(F'''.global_rep.{i}.bias'''  ,    '.layernorm.bias'   )

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

       if ".aspp_layer." in name:
              lowercase_			:       str									=			name.replace('.aspp_layer.'  ,    '.'   )
       if ".aspp_pool." in name:
              lowercase_			:       Any									=			name.replace('.aspp_pool.'  ,    '.'   )
       if "seg_head." in name:
              lowercase_			:       List[Any]									=			name.replace('seg_head.'  ,    'segmentation_head.'   )
       if "segmentation_head.classifier.classifier." in name:
              lowercase_			:       Dict									=			name.replace('segmentation_head.classifier.classifier.'  ,    'segmentation_head.classifier.'   )

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

       return name



def     lowercase__( __SCREAMING_SNAKE_CASE							:  int  ,    __SCREAMING_SNAKE_CASE							:  int  ,    __SCREAMING_SNAKE_CASE							:  List[str]=False   ):






       if base_model:
              lowercase_			:       List[str]									=			''
       else:
              lowercase_			:       List[Any]									=			'mobilevit.'

       for key in orig_state_dict.copy().keys():
              lowercase_			:       str									=			orig_state_dict.pop(UpperCamelCase__   )

              if key[:8] == "encoder.":
                     lowercase_			:       List[str]									=			key[8:]

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

       return orig_state_dict



def     lowercase__( ):
       lowercase_			:       Dict									=			'http://images.cocodataset.org/val2017/000000039769.jpg'
       lowercase_			:       Optional[Any]									=			Image.open(requests.get(UpperCamelCase__  ,    stream=UpperCamelCase__   ).raw   )
       return im



@torch.no_grad()
def     lowercase__( __SCREAMING_SNAKE_CASE							:  Optional[Any]  ,    __SCREAMING_SNAKE_CASE							:  Optional[Any]  ,    __SCREAMING_SNAKE_CASE							:  List[Any]  ,    __SCREAMING_SNAKE_CASE							:  Dict=False   ):
       lowercase_			:       int									=			get_mobilevit_config(UpperCamelCase__   )

       # load original state_dict
       lowercase_			:       str									=			torch.load(UpperCamelCase__  ,    map_location='cpu'   )

       # load 🤗 model
       if mobilevit_name.startswith('deeplabv3_'   ):
              lowercase_			:       str									=			MobileViTForSemanticSegmentation(UpperCamelCase__   ).eval()
       else:
              lowercase_			:       Union[str, Any]									=			MobileViTForImageClassification(UpperCamelCase__   ).eval()

       lowercase_			:       Any									=			convert_state_dict(UpperCamelCase__  ,    UpperCamelCase__   )
       model.load_state_dict(UpperCamelCase__   )

       # Check outputs on an image, prepared by MobileViTImageProcessor
       lowercase_			:       int									=			MobileViTImageProcessor(crop_size=config.image_size  ,    size=config.image_size + 32   )
       lowercase_			:       Dict									=			image_processor(images=prepare_img()  ,    return_tensors='pt'   )
       lowercase_			:       Union[str, Any]									=			model(**UpperCamelCase__   )
       lowercase_			:       Union[str, Any]									=			outputs.logits

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

              if mobilevit_name == "deeplabv3_mobilevit_s":
                     lowercase_			:       Union[str, Any]									=			torch.tensor(
                         [
                             [[6.2065, 6.1292, 6.2070], [6.1079, 6.1254, 6.1747], [6.0042, 6.1071, 6.1034]],
                             [[-6.9253, -6.8653, -7.0398], [-7.3218, -7.3983, -7.3670], [-7.1961, -7.2482, -7.1569]],
                             [[-4.4723, -4.4348, -4.3769], [-5.3629, -5.4632, -5.4598], [-5.1587, -5.3402, -5.5059]],
                         ]   )
              elif mobilevit_name == "deeplabv3_mobilevit_xs":
                     lowercase_			:       str									=			torch.tensor(
                         [
                             [[5.4449, 5.5733, 5.6314], [5.1815, 5.3930, 5.5963], [5.1656, 5.4333, 5.4853]],
                             [[-9.4423, -9.7766, -9.6714], [-9.1581, -9.5720, -9.5519], [-9.1006, -9.6458, -9.5703]],
                             [[-7.7721, -7.3716, -7.1583], [-8.4599, -8.0624, -7.7944], [-8.4172, -7.8366, -7.5025]],
                         ]   )
              elif mobilevit_name == "deeplabv3_mobilevit_xxs":
                     lowercase_			:       str									=			torch.tensor(
                         [
                             [[6.9811, 6.9743, 7.3123], [7.1777, 7.1931, 7.3938], [7.5633, 7.8050, 7.8901]],
                             [[-10.5536, -10.2332, -10.2924], [-10.2336, -9.8624, -9.5964], [-10.8840, -10.8158, -10.6659]],
                             [[-3.4938, -3.0631, -2.8620], [-3.4205, -2.8135, -2.6875], [-3.4179, -2.7945, -2.8750]],
                         ]   )
              else:
                     raise ValueError(F'''Unknown mobilevit_name: {mobilevit_name}'''   )

              assert torch.allclose(logits[0, :3, :3, :3]  ,    UpperCamelCase__  ,    atol=1E-4   )
       else:
              assert logits.shape == (1, 10_00)

              if mobilevit_name == "mobilevit_s":
                     lowercase_			:       Dict									=			torch.tensor([-0.9866, 0.2392, -1.1241]   )
              elif mobilevit_name == "mobilevit_xs":
                     lowercase_			:       Optional[Any]									=			torch.tensor([-2.4761, -0.9399, -1.9587]   )
              elif mobilevit_name == "mobilevit_xxs":
                     lowercase_			:       Any									=			torch.tensor([-1.9364, -1.2327, -0.4653]   )
              else:
                     raise ValueError(F'''Unknown mobilevit_name: {mobilevit_name}'''   )

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

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

       if push_to_hub:
              lowercase_			:       Tuple									=			{
                  '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...'   )
              lowercase_			:       List[str]									=			model_mapping[mobilevit_name]
              image_processor.push_to_hub(UpperCamelCase__  ,    organization='apple'   )
              model.push_to_hub(UpperCamelCase__  ,    organization='apple'   )


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

       __SCREAMING_SNAKE_CASE								=parser.parse_args()
       convert_movilevit_checkpoint(
           args.mobilevit_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
       )


"""simple docstring"""







# Lint as: python3
# pylint: enable=line-too-long
# pylint: disable=g-import-not-at-top,g-bad-import-order,wrong-import-position

__SCREAMING_SNAKE_CASE								="2.13.1"

import platform

import pyarrow
from packaging import version


if version.parse(platform.python_version()) < version.parse("3.7"):
       raise ImportWarning(
           "To use `datasets`, Python>=3.7 is required, and the current version of Python doesn't match this condition."
       )

if version.parse(pyarrow.__version__).major < 8:
       raise ImportWarning(
           "To use `datasets`, the module `pyarrow>=8.0.0` is required, and the current version of `pyarrow` doesn't match this condition.\n"
           "If you are running this in a Google Colab, you should probably just restart the runtime to use the right version of `pyarrow`."
       )

del platform
del pyarrow
del version

from .arrow_dataset import Dataset
from .arrow_reader import ReadInstruction
from .builder import ArrowBasedBuilder, BeamBasedBuilder, BuilderConfig, DatasetBuilder, GeneratorBasedBuilder
from .combine import concatenate_datasets, interleave_datasets
from .dataset_dict import DatasetDict, IterableDatasetDict
from .download import *
from .features import *
from .fingerprint import disable_caching, enable_caching, is_caching_enabled, set_caching_enabled
from .info import DatasetInfo, MetricInfo
from .inspect import (
    get_dataset_config_info,
    get_dataset_config_names,
    get_dataset_infos,
    get_dataset_split_names,
    inspect_dataset,
    inspect_metric,
    list_datasets,
    list_metrics,
)
from .iterable_dataset import IterableDataset
from .load import load_dataset, load_dataset_builder, load_from_disk, load_metric
from .metric import Metric
from .splits import (
    NamedSplit,
    NamedSplitAll,
    Split,
    SplitBase,
    SplitDict,
    SplitGenerator,
    SplitInfo,
    SubSplitInfo,
    percent,
)
from .tasks import *
from .utils import *
from .utils import logging


# deprecated modules
from datasets import arrow_dataset as _arrow_dataset  # isort:skip
from datasets import utils as _utils  # isort:skip
from datasets.utils import download_manager as _deprecated_download_manager  # isort:skip

__SCREAMING_SNAKE_CASE								=concatenate_datasets
__SCREAMING_SNAKE_CASE								=DownloadConfig
__SCREAMING_SNAKE_CASE								=DownloadManager
__SCREAMING_SNAKE_CASE								=DownloadMode
__SCREAMING_SNAKE_CASE								=DownloadConfig
__SCREAMING_SNAKE_CASE								=DownloadMode
__SCREAMING_SNAKE_CASE								=DownloadManager

del _arrow_dataset, _utils, _deprecated_download_manager

