





import argparse
import json
from collections import OrderedDict
from pathlib import Path

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

from transformers import (
    SegformerConfig,
    SegformerForImageClassification,
    SegformerForSemanticSegmentation,
    SegformerImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
_UpperCAmelCase				:       Any	   =				logging.get_logger(__name__)


def        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase						,	_UpperCAmelCase=False )			->     List[str]:
    lowerCamelCase__    :      Any					 =		OrderedDict()
    for key, value in state_dict.items():
        if encoder_only and not key.startswith('head' ):
            lowerCamelCase__    :      Optional[int]					 =		'segformer.encoder.' + key
        if key.startswith('backbone' ):
            lowerCamelCase__    :      Tuple					 =		key.replace('backbone'						,	'segformer.encoder' )
        if "patch_embed" in key:
            # replace for example patch_embed1 by patch_embeddings.0
            lowerCamelCase__    :      str					 =		key[key.find('patch_embed' ) + len('patch_embed' )]
            lowerCamelCase__    :      Union[str, Any]					 =		key.replace(F"""patch_embed{idx}"""						,	F"""patch_embeddings.{int(_UpperCAmelCase )-1}""" )
        if "norm" in key:
            lowerCamelCase__    :      Any					 =		key.replace('norm'						,	'layer_norm' )
        if "segformer.encoder.layer_norm" in key:
            # replace for example layer_norm1 by layer_norm.0
            lowerCamelCase__    :      Dict					 =		key[key.find('segformer.encoder.layer_norm' ) + len('segformer.encoder.layer_norm' )]
            lowerCamelCase__    :      Tuple					 =		key.replace(F"""layer_norm{idx}"""						,	F"""layer_norm.{int(_UpperCAmelCase )-1}""" )
        if "layer_norm1" in key:
            lowerCamelCase__    :      Optional[Any]					 =		key.replace('layer_norm1'						,	'layer_norm_1' )
        if "layer_norm2" in key:
            lowerCamelCase__    :      List[str]					 =		key.replace('layer_norm2'						,	'layer_norm_2' )
        if "block" in key:
            # replace for example block1 by block.0
            lowerCamelCase__    :      List[Any]					 =		key[key.find('block' ) + len('block' )]
            lowerCamelCase__    :      Optional[int]					 =		key.replace(F"""block{idx}"""						,	F"""block.{int(_UpperCAmelCase )-1}""" )
        if "attn.q" in key:
            lowerCamelCase__    :      Dict					 =		key.replace('attn.q'						,	'attention.self.query' )
        if "attn.proj" in key:
            lowerCamelCase__    :      str					 =		key.replace('attn.proj'						,	'attention.output.dense' )
        if "attn" in key:
            lowerCamelCase__    :      Any					 =		key.replace('attn'						,	'attention.self' )
        if "fc1" in key:
            lowerCamelCase__    :      Tuple					 =		key.replace('fc1'						,	'dense1' )
        if "fc2" in key:
            lowerCamelCase__    :      Any					 =		key.replace('fc2'						,	'dense2' )
        if "linear_pred" in key:
            lowerCamelCase__    :      Optional[Any]					 =		key.replace('linear_pred'						,	'classifier' )
        if "linear_fuse" in key:
            lowerCamelCase__    :      Optional[Any]					 =		key.replace('linear_fuse.conv'						,	'linear_fuse' )
            lowerCamelCase__    :      Union[str, Any]					 =		key.replace('linear_fuse.bn'						,	'batch_norm' )
        if "linear_c" in key:
            # replace for example linear_c4 by linear_c.3
            lowerCamelCase__    :      Tuple					 =		key[key.find('linear_c' ) + len('linear_c' )]
            lowerCamelCase__    :      Tuple					 =		key.replace(F"""linear_c{idx}"""						,	F"""linear_c.{int(_UpperCAmelCase )-1}""" )
        if key.startswith('head' ):
            lowerCamelCase__    :      str					 =		key.replace('head'						,	'classifier' )
        lowerCamelCase__    :      Union[str, Any]					 =		value

    return new_state_dict


def        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase						,	_UpperCAmelCase )			->     Union[str, Any]:
    # for each of the encoder blocks:
    for i in range(config.num_encoder_blocks ):
        for j in range(config.depths[i] ):
            # read in weights + bias of keys and values (which is a single matrix in the original implementation)
            lowerCamelCase__    :      List[Any]					 =		state_dict.pop(F"""segformer.encoder.block.{i}.{j}.attention.self.kv.weight""" )
            lowerCamelCase__    :      List[Any]					 =		state_dict.pop(F"""segformer.encoder.block.{i}.{j}.attention.self.kv.bias""" )
            # next, add keys and values (in that order) to the state dict
            lowerCamelCase__    :      int					 =		kv_weight[
                : config.hidden_sizes[i], :
            ]
            lowerCamelCase__    :      List[str]					 =		kv_bias[: config.hidden_sizes[i]]
            lowerCamelCase__    :      Union[str, Any]					 =		kv_weight[
                config.hidden_sizes[i] :, :
            ]
            lowerCamelCase__    :      str					 =		kv_bias[
                config.hidden_sizes[i] :
            ]


def        SCREAMING_SNAKE_CASE      (       )			->     Union[str, Any]:
    lowerCamelCase__    :      Dict					 =		'http://images.cocodataset.org/val2017/000000039769.jpg'
    lowerCamelCase__    :      Any					 =		Image.open(requests.get(_UpperCAmelCase						,	stream=_UpperCAmelCase ).raw )

    return image


@torch.no_grad()
def        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase )			->     List[str]:
    lowerCamelCase__    :      List[str]					 =		SegformerConfig()
    lowerCamelCase__    :      Optional[int]					 =		False

    # set attributes based on model_name
    lowerCamelCase__    :      Optional[int]					 =		'huggingface/label-files'
    if "segformer" in model_name:
        lowerCamelCase__    :      Optional[Any]					 =		model_name[len('segformer.' ) : len('segformer.' ) + 2]
        if "ade" in model_name:
            lowerCamelCase__    :      Optional[Any]					 =		150
            lowerCamelCase__    :      List[str]					 =		'ade20k-id2label.json'
            lowerCamelCase__    :      int					 =		(1, 150, 128, 128)
        elif "city" in model_name:
            lowerCamelCase__    :      str					 =		19
            lowerCamelCase__    :      Tuple					 =		'cityscapes-id2label.json'
            lowerCamelCase__    :      Optional[int]					 =		(1, 19, 128, 128)
        else:
            raise ValueError(F"""Model {model_name} not supported""" )
    elif "mit" in model_name:
        lowerCamelCase__    :      int					 =		True
        lowerCamelCase__    :      Tuple					 =		model_name[4:6]
        lowerCamelCase__    :      Tuple					 =		1000
        lowerCamelCase__    :      Union[str, Any]					 =		'imagenet-1k-id2label.json'
        lowerCamelCase__    :      int					 =		(1, 1000)
    else:
        raise ValueError(F"""Model {model_name} not supported""" )

    # set config attributes
    lowerCamelCase__    :      Dict					 =		json.load(open(hf_hub_download(_UpperCAmelCase						,	_UpperCAmelCase						,	repo_type='dataset' )						,	'r' ) )
    lowerCamelCase__    :      Optional[Any]					 =		{int(_UpperCAmelCase ): v for k, v in idalabel.items()}
    lowerCamelCase__    :      Union[str, Any]					 =		idalabel
    lowerCamelCase__    :      List[Any]					 =		{v: k for k, v in idalabel.items()}
    if size == "b0":
        pass
    elif size == "b1":
        lowerCamelCase__    :      int					 =		[64, 128, 320, 512]
        lowerCamelCase__    :      Any					 =		256
    elif size == "b2":
        lowerCamelCase__    :      str					 =		[64, 128, 320, 512]
        lowerCamelCase__    :      Any					 =		768
        lowerCamelCase__    :      int					 =		[3, 4, 6, 3]
    elif size == "b3":
        lowerCamelCase__    :      List[str]					 =		[64, 128, 320, 512]
        lowerCamelCase__    :      Optional[Any]					 =		768
        lowerCamelCase__    :      List[Any]					 =		[3, 4, 18, 3]
    elif size == "b4":
        lowerCamelCase__    :      int					 =		[64, 128, 320, 512]
        lowerCamelCase__    :      Dict					 =		768
        lowerCamelCase__    :      List[str]					 =		[3, 8, 27, 3]
    elif size == "b5":
        lowerCamelCase__    :      Optional[Any]					 =		[64, 128, 320, 512]
        lowerCamelCase__    :      List[Any]					 =		768
        lowerCamelCase__    :      List[str]					 =		[3, 6, 40, 3]
    else:
        raise ValueError(F"""Size {size} not supported""" )

    # load image processor (only resize + normalize)
    lowerCamelCase__    :      Tuple					 =		SegformerImageProcessor(
        image_scale=(512, 512)						,	keep_ratio=_UpperCAmelCase						,	align=_UpperCAmelCase						,	do_random_crop=_UpperCAmelCase )

    # prepare image
    lowerCamelCase__    :      List[str]					 =		prepare_img()
    lowerCamelCase__    :      int					 =		image_processor(images=_UpperCAmelCase						,	return_tensors='pt' ).pixel_values

    logger.info(F"""Converting model {model_name}...""" )

    # load original state dict
    if encoder_only:
        lowerCamelCase__    :      int					 =		torch.load(_UpperCAmelCase						,	map_location=torch.device('cpu' ) )
    else:
        lowerCamelCase__    :      Tuple					 =		torch.load(_UpperCAmelCase						,	map_location=torch.device('cpu' ) )['state_dict']

    # rename keys
    lowerCamelCase__    :      Optional[Any]					 =		rename_keys(_UpperCAmelCase						,	encoder_only=_UpperCAmelCase )
    if not encoder_only:
        del state_dict["decode_head.conv_seg.weight"]
        del state_dict["decode_head.conv_seg.bias"]

    # key and value matrices need special treatment
    read_in_k_v(_UpperCAmelCase						,	_UpperCAmelCase )

    # create HuggingFace model and load state dict
    if encoder_only:
        lowerCamelCase__    :      List[Any]					 =		False
        lowerCamelCase__    :      Optional[Any]					 =		SegformerForImageClassification(_UpperCAmelCase )
    else:
        lowerCamelCase__    :      str					 =		SegformerForSemanticSegmentation(_UpperCAmelCase )
    model.load_state_dict(_UpperCAmelCase )
    model.eval()

    # forward pass
    lowerCamelCase__    :      Union[str, Any]					 =		model(_UpperCAmelCase )
    lowerCamelCase__    :      Optional[Any]					 =		outputs.logits

    # set expected_slice based on model name
    # ADE20k checkpoints
    if model_name == "segformer.b0.512x512.ade.160k":
        lowerCamelCase__    :      List[Any]					 =		torch.tensor(
            [
                [[-4.6_310, -5.5_232, -6.2_356], [-5.1_921, -6.1_444, -6.5_996], [-5.4_424, -6.2_790, -6.7_574]],
                [[-12.1_391, -13.3_122, -13.9_554], [-12.8_732, -13.9_352, -14.3_563], [-12.9_438, -13.8_226, -14.2_513]],
                [[-12.5_134, -13.4_686, -14.4_915], [-12.8_669, -14.4_343, -14.7_758], [-13.2_523, -14.5_819, -15.0_694]],
            ] )
    elif model_name == "segformer.b1.512x512.ade.160k":
        lowerCamelCase__    :      int					 =		torch.tensor(
            [
                [[-7.5_820, -8.7_231, -8.3_215], [-8.0_600, -10.3_529, -10.0_304], [-7.5_208, -9.4_103, -9.6_239]],
                [[-12.6_918, -13.8_994, -13.7_137], [-13.3_196, -15.7_523, -15.4_789], [-12.9_343, -14.8_757, -14.9_689]],
                [[-11.1_911, -11.9_421, -11.3_243], [-11.3_342, -13.6_839, -13.3_581], [-10.3_909, -12.1_832, -12.4_858]],
            ] )
    elif model_name == "segformer.b2.512x512.ade.160k":
        lowerCamelCase__    :      Tuple					 =		torch.tensor(
            [
                [[-11.8_173, -14.3_850, -16.3_128], [-14.5_648, -16.5_804, -18.6_568], [-14.7_223, -15.7_387, -18.4_218]],
                [[-15.7_290, -17.9_171, -19.4_423], [-18.3_105, -19.9_448, -21.4_661], [-17.9_296, -18.6_497, -20.7_910]],
                [[-15.0_783, -17.0_336, -18.2_789], [-16.8_771, -18.6_870, -20.1_612], [-16.2_454, -17.1_426, -19.5_055]],
            ] )
    elif model_name == "segformer.b3.512x512.ade.160k":
        lowerCamelCase__    :      str					 =		torch.tensor(
            [
                [[-9.0_878, -10.2_081, -10.1_891], [-9.3_144, -10.7_941, -10.9_843], [-9.2_294, -10.3_855, -10.5_704]],
                [[-12.2_316, -13.9_068, -13.6_102], [-12.9_161, -14.3_702, -14.3_235], [-12.5_233, -13.7_174, -13.7_932]],
                [[-14.6_275, -15.2_490, -14.9_727], [-14.3_400, -15.9_687, -16.2_827], [-14.1_484, -15.4_033, -15.8_937]],
            ] )
    elif model_name == "segformer.b4.512x512.ade.160k":
        lowerCamelCase__    :      int					 =		torch.tensor(
            [
                [[-12.3_144, -13.2_447, -14.0_802], [-13.3_614, -14.5_816, -15.6_117], [-13.3_340, -14.4_433, -16.2_219]],
                [[-19.2_781, -20.4_128, -20.7_506], [-20.6_153, -21.6_566, -22.0_998], [-19.9_800, -21.0_430, -22.1_494]],
                [[-18.8_739, -19.7_804, -21.1_834], [-20.1_233, -21.6_765, -23.2_944], [-20.0_315, -21.2_641, -23.6_944]],
            ] )
    elif model_name == "segformer.b5.640x640.ade.160k":
        lowerCamelCase__    :      Dict					 =		torch.tensor(
            [
                [[-9.5_524, -12.0_835, -11.7_348], [-10.5_229, -13.6_446, -14.5_662], [-9.5_842, -12.8_851, -13.9_414]],
                [[-15.3_432, -17.5_323, -17.0_818], [-16.3_330, -18.9_255, -19.2_101], [-15.1_340, -17.7_848, -18.3_971]],
                [[-12.6_072, -14.9_486, -14.6_631], [-13.7_629, -17.0_907, -17.7_745], [-12.7_899, -16.1_695, -17.1_671]],
            ] )
    # Cityscapes checkpoints
    elif model_name == "segformer.b0.1024x1024.city.160k":
        lowerCamelCase__    :      int					 =		torch.tensor(
            [
                [[-11.9_295, -13.4_057, -14.8_106], [-13.3_431, -14.8_179, -15.3_781], [-14.2_836, -15.5_942, -16.1_588]],
                [[-11.4_906, -12.8_067, -13.6_564], [-13.1_189, -14.0_500, -14.1_543], [-13.8_748, -14.5_136, -14.8_789]],
                [[0.5_374, 0.1_067, -0.4_742], [0.1_141, -0.2_255, -0.7_099], [-0.3_000, -0.5_924, -1.3_105]],
            ] )
    elif model_name == "segformer.b0.512x1024.city.160k":
        lowerCamelCase__    :      int					 =		torch.tensor(
            [
                [[-7.8_217, -9.8_767, -10.1_717], [-9.4_438, -10.9_058, -11.4_047], [-9.7_939, -12.3_495, -12.1_079]],
                [[-7.1_514, -9.5_336, -10.0_860], [-9.7_776, -11.6_822, -11.8_439], [-10.1_411, -12.7_655, -12.8_972]],
                [[0.3_021, 0.0_805, -0.2_310], [-0.0_328, -0.1_605, -0.2_714], [-0.1_408, -0.5_477, -0.6_976]],
            ] )
    elif model_name == "segformer.b0.640x1280.city.160k":
        lowerCamelCase__    :      Optional[int]					 =		torch.tensor(
            [
                [
                    [-1.1_372e01, -1.2_787e01, -1.3_477e01],
                    [-1.2_536e01, -1.4_194e01, -1.4_409e01],
                    [-1.3_217e01, -1.4_888e01, -1.5_327e01],
                ],
                [
                    [-1.4_791e01, -1.7_122e01, -1.8_277e01],
                    [-1.7_163e01, -1.9_192e01, -1.9_533e01],
                    [-1.7_897e01, -1.9_991e01, -2.0_315e01],
                ],
                [
                    [7.6_723e-01, 4.1_921e-01, -7.7_878e-02],
                    [4.7_772e-01, 9.5_557e-03, -2.8_082e-01],
                    [3.6_032e-01, -2.4_826e-01, -5.1_168e-01],
                ],
            ] )
    elif model_name == "segformer.b0.768x768.city.160k":
        lowerCamelCase__    :      Any					 =		torch.tensor(
            [
                [[-9.4_959, -11.3_087, -11.7_479], [-11.0_025, -12.6_540, -12.3_319], [-11.4_064, -13.0_487, -12.9_905]],
                [[-9.8_905, -11.3_084, -12.0_854], [-11.1_726, -12.7_698, -12.9_583], [-11.5_985, -13.3_278, -14.1_774]],
                [[0.2_213, 0.0_192, -0.2_466], [-0.1_731, -0.4_213, -0.4_874], [-0.3_126, -0.6_541, -1.1_389]],
            ] )
    elif model_name == "segformer.b1.1024x1024.city.160k":
        lowerCamelCase__    :      Optional[int]					 =		torch.tensor(
            [
                [[-13.5_748, -13.9_111, -12.6_500], [-14.3_500, -15.3_683, -14.2_328], [-14.7_532, -16.0_424, -15.6_087]],
                [[-17.1_651, -15.8_725, -12.9_653], [-17.2_580, -17.3_718, -14.8_223], [-16.6_058, -16.8_783, -16.7_452]],
                [[-3.6_456, -3.0_209, -1.4_203], [-3.0_797, -3.1_959, -2.0_000], [-1.8_757, -1.9_217, -1.6_997]],
            ] )
    elif model_name == "segformer.b2.1024x1024.city.160k":
        lowerCamelCase__    :      Union[str, Any]					 =		torch.tensor(
            [
                [[-16.0_976, -16.4_856, -17.3_962], [-16.6_234, -19.0_342, -19.7_685], [-16.0_900, -18.0_661, -19.1_180]],
                [[-18.4_750, -18.8_488, -19.5_074], [-19.4_030, -22.1_570, -22.5_977], [-19.1_191, -20.8_486, -22.3_783]],
                [[-4.5_178, -5.5_037, -6.5_109], [-5.0_884, -7.2_174, -8.0_334], [-4.4_156, -5.8_117, -7.2_970]],
            ] )
    elif model_name == "segformer.b3.1024x1024.city.160k":
        lowerCamelCase__    :      Union[str, Any]					 =		torch.tensor(
            [
                [[-14.2_081, -14.4_732, -14.1_977], [-14.5_867, -16.4_423, -16.6_356], [-13.4_441, -14.9_685, -16.8_696]],
                [[-14.4_576, -14.7_073, -15.0_451], [-15.0_816, -17.6_237, -17.9_873], [-14.4_213, -16.0_199, -18.5_992]],
                [[-4.7_349, -4.9_588, -5.0_966], [-4.3_210, -6.9_325, -7.2_591], [-3.4_312, -4.7_484, -7.1_917]],
            ] )
    elif model_name == "segformer.b4.1024x1024.city.160k":
        lowerCamelCase__    :      Optional[Any]					 =		torch.tensor(
            [
                [[-11.7_737, -11.9_526, -11.3_273], [-13.6_692, -14.4_574, -13.8_878], [-13.8_937, -14.6_924, -15.9_345]],
                [[-14.6_706, -14.5_330, -14.1_306], [-16.1_502, -16.8_180, -16.4_269], [-16.8_338, -17.8_939, -20.1_746]],
                [[1.0_491, 0.8_289, 1.0_310], [1.1_044, 0.5_219, 0.8_055], [1.0_899, 0.6_926, 0.5_590]],
            ] )
    elif model_name == "segformer.b5.1024x1024.city.160k":
        lowerCamelCase__    :      Any					 =		torch.tensor(
            [
                [[-12.5_641, -13.4_777, -13.0_684], [-13.9_587, -15.8_983, -16.6_557], [-13.3_109, -15.7_350, -16.3_141]],
                [[-14.7_074, -15.4_352, -14.5_944], [-16.6_353, -18.1_663, -18.6_120], [-15.1_702, -18.0_329, -18.1_547]],
                [[-1.7_990, -2.0_951, -1.7_784], [-2.6_397, -3.8_245, -3.9_686], [-1.5_264, -2.8_126, -2.9_316]],
            ] )
    else:
        lowerCamelCase__    :      Union[str, Any]					 =		logits.argmax(-1 ).item()
        print('Predicted class:'						,	model.config.idalabel[predicted_class_idx] )

    # verify logits
    if not encoder_only:
        assert logits.shape == expected_shape
        assert torch.allclose(logits[0, :3, :3, :3]						,	_UpperCAmelCase						,	atol=1e-2 )

    # finally, save model and image processor
    logger.info(F"""Saving PyTorch model and image processor to {pytorch_dump_folder_path}...""" )
    Path(_UpperCAmelCase ).mkdir(exist_ok=_UpperCAmelCase )
    model.save_pretrained(_UpperCAmelCase )
    image_processor.save_pretrained(_UpperCAmelCase )


if __name__ == "__main__":
    _UpperCAmelCase				:       Optional[int]	   =				argparse.ArgumentParser()

    parser.add_argument(
        """--model_name""",
        default="""segformer.b0.512x512.ade.160k""",
        type=str,
        help="""Name of the model you'd like to convert.""",
    )
    parser.add_argument(
        """--checkpoint_path""", default=None, type=str, help="""Path to the original PyTorch checkpoint (.pth file)."""
    )
    parser.add_argument(
        """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the folder to output PyTorch model."""
    )
    _UpperCAmelCase				:       str	   =				parser.parse_args()
    convert_segformer_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path)












# 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 argparse
import os

from accelerate.test_utils import execute_subprocess_async


def        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase=None )			->     Tuple:
    if subparsers is not None:
        lowerCamelCase__    :      Any					 =		subparsers.add_parser('test' )
    else:
        lowerCamelCase__    :      int					 =		argparse.ArgumentParser('Accelerate test command' )

    parser.add_argument(
        '--config_file'						,	default=_UpperCAmelCase						,	help=(
            'The path to use to store the config file. Will default to a file named default_config.yaml in the cache '
            'location, which is the content of the environment `HF_HOME` suffixed with \'accelerate\', or if you don\'t have '
            'such an environment variable, your cache directory (\'~/.cache\' or the content of `XDG_CACHE_HOME`) suffixed '
            'with \'huggingface\'.'
        )						,	)

    if subparsers is not None:
        parser.set_defaults(func=_UpperCAmelCase )
    return parser


def        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase )			->     Union[str, Any]:
    lowerCamelCase__    :      Tuple					 =		os.path.sep.join(__file__.split(os.path.sep )[:-2] + ['test_utils', 'scripts', 'test_script.py'] )

    if args.config_file is None:
        lowerCamelCase__    :      List[str]					 =		script_name
    else:
        lowerCamelCase__    :      List[Any]					 =		F"""--config_file={args.config_file} {script_name}"""

    lowerCamelCase__    :      str					 =		['accelerate-launch'] + test_args.split()
    lowerCamelCase__    :      Dict					 =		execute_subprocess_async(_UpperCAmelCase						,	env=os.environ.copy() )
    if result.returncode == 0:
        print('Test is a success! You are ready for your distributed training!' )


def        SCREAMING_SNAKE_CASE      (       )			->     Any:
    lowerCamelCase__    :      Any					 =		test_command_parser()
    lowerCamelCase__    :      List[Any]					 =		parser.parse_args()
    test_command(_UpperCAmelCase )


if __name__ == "__main__":
    main()





