

import inspect
import unittest

from transformers import BitConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
     import torch
     from torch import nn

     from transformers import BitBackbone, BitForImageClassification, BitImageProcessor, BitModel
     from transformers.models.bit.modeling_bit import BIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
     from PIL import Image


class  A						:







    """simple docstring"""





    def __init__(      self :       Union[str, Any],lowercase_ :       int,lowercase_ :       List[Any]=3,lowercase_ :       Dict=3_2,lowercase_ :       List[str]=3,lowercase_ :       List[str]=1_0,lowercase_ :       List[Any]=[8, 1_6, 3_2, 6_4],lowercase_ :       Any=[1, 1, 2, 1],lowercase_ :       Any=True,lowercase_ :       List[Any]=True,lowercase_ :       Union[str, Any]="relu",lowercase_ :       int=3,lowercase_ :       Tuple=None,lowercase_ :       Union[str, Any]=["stage2", "stage3", "stage4"],lowercase_ :       Tuple=[2, 3, 4],lowercase_ :       List[Any]=1,)->    int:





        '''simple docstring'''







        A__          =   parent
        A__          =   batch_size
        A__          =   image_size
        A__          =   num_channels
        A__          =   embeddings_size
        A__          =   hidden_sizes
        A__          =   depths
        A__          =   is_training
        A__          =   use_labels
        A__          =   hidden_act
        A__          =   num_labels
        A__          =   scope
        A__          =   len(lowercase_		)
        A__          =   out_features
        A__          =   out_indices
        A__          =   num_groups


    def 						snake_case__     (      self :       Any		)->    Optional[Any]:





        '''simple docstring'''







        A__          =   floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

        A__          =   None
        if self.use_labels:
            A__          =   ids_tensor([self.batch_size],self.num_labels		)

        A__          =   self.get_config()

        return config, pixel_values, labels


    def 						snake_case__     (      self :       List[str]		)->    Tuple:





        '''simple docstring'''







        return BitConfig(
            num_channels=self.num_channels,embeddings_size=self.embeddings_size,hidden_sizes=self.hidden_sizes,depths=self.depths,hidden_act=self.hidden_act,num_labels=self.num_labels,out_features=self.out_features,out_indices=self.out_indices,num_groups=self.num_groups,)


    def 						snake_case__     (      self :       Dict,lowercase_ :       int,lowercase_ :       Optional[int],lowercase_ :       Optional[Any]		)->    Dict:





        '''simple docstring'''







        A__          =   BitModel(config=lowercase_		)
        model.to(lowercase_		)
        model.eval()
        A__          =   model(lowercase_		)
        self.parent.assertEqual(
            result.last_hidden_state.shape,(self.batch_size, self.hidden_sizes[-1], self.image_size // 3_2, self.image_size // 3_2),)


    def 						snake_case__     (      self :       Union[str, Any],lowercase_ :       List[Any],lowercase_ :       Tuple,lowercase_ :       Tuple		)->    Optional[int]:





        '''simple docstring'''







        A__          =   self.num_labels
        A__          =   BitForImageClassification(lowercase_		)
        model.to(lowercase_		)
        model.eval()
        A__          =   model(lowercase_,labels=lowercase_		)
        self.parent.assertEqual(result.logits.shape,(self.batch_size, self.num_labels)		)


    def 						snake_case__     (      self :       int,lowercase_ :       str,lowercase_ :       Dict,lowercase_ :       List[str]		)->    Union[str, Any]:





        '''simple docstring'''







        A__          =   BitBackbone(config=lowercase_		)
        model.to(lowercase_		)
        model.eval()
        A__          =   model(lowercase_		)

        # verify feature maps
        self.parent.assertEqual(len(result.feature_maps		),len(config.out_features		)		)
        self.parent.assertListEqual(list(result.feature_maps[0].shape		),[self.batch_size, self.hidden_sizes[1], 4, 4]		)

        # verify channels
        self.parent.assertEqual(len(model.channels		),len(config.out_features		)		)
        self.parent.assertListEqual(model.channels,config.hidden_sizes[1:]		)

        # verify backbone works with out_features=None
        A__          =   None
        A__          =   BitBackbone(config=lowercase_		)
        model.to(lowercase_		)
        model.eval()
        A__          =   model(lowercase_		)

        # verify feature maps
        self.parent.assertEqual(len(result.feature_maps		),1		)
        self.parent.assertListEqual(list(result.feature_maps[0].shape		),[self.batch_size, self.hidden_sizes[-1], 1, 1]		)

        # verify channels
        self.parent.assertEqual(len(model.channels		),1		)
        self.parent.assertListEqual(model.channels,[config.hidden_sizes[-1]]		)


    def 						snake_case__     (      self :       Tuple		)->    Optional[int]:





        '''simple docstring'''







        A__          =   self.prepare_config_and_inputs()
        A__      ,						A__      ,						A__          =   config_and_inputs
        A__          =   {'pixel_values': pixel_values}
        return config, inputs_dict




@require_torch
class  A						(							_UpperCAmelCase    ,							_UpperCAmelCase    ,							unittest.TestCase ):







    """simple docstring"""





    lowerCamelCase			=							(BitModel, BitForImageClassification, BitBackbone) if is_torch_available() else ()
    lowerCamelCase			=							(
        {'feature-extraction': BitModel, 'image-classification': BitForImageClassification}
        if is_torch_available()
        else {}
    )

    lowerCamelCase			=							False
    lowerCamelCase			=							False
    lowerCamelCase			=							False
    lowerCamelCase			=							False
    lowerCamelCase			=							False


    def 						snake_case__     (      self :       Union[str, Any]		)->    int:





        '''simple docstring'''







        A__          =   BitModelTester(self		)
        A__          =   ConfigTester(self,config_class=lowercase_,has_text_modality=lowercase_		)


    def 						snake_case__     (      self :       Dict		)->    Tuple:





        '''simple docstring'''







        self.create_and_test_config_common_properties()
        self.config_tester.create_and_test_config_to_json_string()
        self.config_tester.create_and_test_config_to_json_file()
        self.config_tester.create_and_test_config_from_and_save_pretrained()
        self.config_tester.create_and_test_config_with_num_labels()
        self.config_tester.check_config_can_be_init_without_params()
        self.config_tester.check_config_arguments_init()


    def 						snake_case__     (      self :       Union[str, Any]		)->    Tuple:





        '''simple docstring'''







        return


    @unittest.skip(reason='Bit does not output attentions'		)
    def 						snake_case__     (      self :       Tuple		)->    Tuple:





        '''simple docstring'''







        pass


    @unittest.skip(reason='Bit does not use inputs_embeds'		)
    def 						snake_case__     (      self :       Dict		)->    Optional[Any]:





        '''simple docstring'''







        pass


    @unittest.skip(reason='Bit does not support input and output embeddings'		)
    def 						snake_case__     (      self :       Optional[Any]		)->    Optional[Any]:





        '''simple docstring'''







        pass


    def 						snake_case__     (      self :       List[str]		)->    int:





        '''simple docstring'''







        A__      ,						A__          =   self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
            A__          =   model_class(lowercase_		)
            A__          =   inspect.signature(model.forward		)
            # signature.parameters is an OrderedDict => so arg_names order is deterministic
            A__          =   [*signature.parameters.keys()]

            A__          =   ['pixel_values']
            self.assertListEqual(arg_names[:1],lowercase_		)


    def 						snake_case__     (      self :       List[Any]		)->    int:





        '''simple docstring'''







        A__          =   self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*lowercase_		)


    def 						snake_case__     (      self :       List[str]		)->    Optional[Any]:





        '''simple docstring'''







        A__          =   self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_backbone(*lowercase_		)


    def 						snake_case__     (      self :       Any		)->    Any:





        '''simple docstring'''







        A__      ,						A__          =   self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
            A__          =   model_class(config=lowercase_		)
            for name, module in model.named_modules():
                if isinstance(lowercase_,(nn.BatchNormad, nn.GroupNorm)		):
                    self.assertTrue(
                        torch.all(module.weight == 1		),msg=F'Parameter {name} of model {model_class} seems not properly initialized',)
                    self.assertTrue(
                        torch.all(module.bias == 0		),msg=F'Parameter {name} of model {model_class} seems not properly initialized',)


    def 						snake_case__     (      self :       Dict		)->    Tuple:





        '''simple docstring'''







        def check_hidden_states_output(lowercase_ :       Any,lowercase_ :       Union[str, Any],lowercase_ :       Dict		):
            A__          =   model_class(lowercase_		)
            model.to(lowercase_		)
            model.eval()

            with torch.no_grad():
                A__          =   model(**self._prepare_for_class(lowercase_,lowercase_		)		)

            A__          =   outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

            A__          =   self.model_tester.num_stages
            self.assertEqual(len(lowercase_		),expected_num_stages + 1		)

            # Bit's feature maps are of shape (batch_size, num_channels, height, width)
            self.assertListEqual(
                list(hidden_states[0].shape[-2:]		),[self.model_tester.image_size // 4, self.model_tester.image_size // 4],)

        A__      ,						A__          =   self.model_tester.prepare_config_and_inputs_for_common()
        A__          =   ['preactivation', 'bottleneck']
        for model_class in self.all_model_classes:
            for layer_type in layers_type:
                A__          =   layer_type
                A__          =   True
                check_hidden_states_output(lowercase_,lowercase_,lowercase_		)

                # check that output_hidden_states also work using config
                del inputs_dict["output_hidden_states"]
                A__          =   True

                check_hidden_states_output(lowercase_,lowercase_,lowercase_		)


    @unittest.skip(reason='Bit does not use feedforward chunking'		)
    def 						snake_case__     (      self :       int		)->    str:





        '''simple docstring'''







        pass


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





        '''simple docstring'''







        A__          =   self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_image_classification(*lowercase_		)


    @slow
    def 						snake_case__     (      self :       str		)->    str:





        '''simple docstring'''







        for model_name in BIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
            A__          =   BitModel.from_pretrained(lowercase_		)
            self.assertIsNotNone(lowercase_		)




def 						_snake_case(    )							->      str:





    '''simple docstring'''






    A__          =   Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'      )
    return image


@require_torch
@require_vision
class  A						(							unittest.TestCase ):







    """simple docstring"""





    @cached_property
    def 						snake_case__     (      self :       Tuple		)->    Optional[int]:





        '''simple docstring'''







        return (
            BitImageProcessor.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]		) if is_vision_available() else None
        )


    @slow
    def 						snake_case__     (      self :       Optional[int]		)->    Dict:





        '''simple docstring'''







        A__          =   BitForImageClassification.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]		).to(lowercase_		)

        A__          =   self.default_image_processor
        A__          =   prepare_img()
        A__          =   image_processor(images=lowercase_,return_tensors='pt'		).to(lowercase_		)

        # forward pass
        with torch.no_grad():
            A__          =   model(**lowercase_		)

        # verify the logits
        A__          =   torch.Size((1, 1_0_0_0)		)
        self.assertEqual(outputs.logits.shape,lowercase_		)

        A__          =   torch.tensor([[-0.6_526, -0.5_263, -1.4_398]]		).to(lowercase_		)

        self.assertTrue(torch.allclose(outputs.logits[0, :3],lowercase_,atol=1E-4		)		)




@require_torch
class  A						(							_UpperCAmelCase    ,							unittest.TestCase ):







    """simple docstring"""





    lowerCamelCase			=							(BitBackbone,) if is_torch_available() else ()
    lowerCamelCase			=							BitConfig

    lowerCamelCase			=							False


    def 						snake_case__     (      self :       Tuple		)->    Optional[int]:





        '''simple docstring'''







        A__          =   BitModelTester(self		)







"""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 YolosConfig, YolosForObjectDetection, YolosImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
A_						=				logging.get_logger(__name__)


def 					UpperCAmelCase__       (snake_case__							:					str    ):
  """simple docstring"""

  _snake_case							:		List[Any]								=  YolosConfig()

  # size of the architecture
  if "yolos_ti" in yolos_name:
    _snake_case							:		Tuple								=  1_92
    _snake_case							:		Any								=  7_68
    _snake_case							:		Any								=  12
    _snake_case							:		List[Any]								=  3
    _snake_case							:		int								=  [8_00, 13_33]
    _snake_case							:		Tuple								=  False
  elif yolos_name == "yolos_s_dWr":
    _snake_case							:		Tuple								=  3_30
    _snake_case							:		List[str]								=  14
    _snake_case							:		List[str]								=  6
    _snake_case							:		Union[str, Any]								=  13_20
  elif "yolos_s" in yolos_name:
    _snake_case							:		Union[str, Any]								=  3_84
    _snake_case							:		List[str]								=  15_36
    _snake_case							:		Any								=  12
    _snake_case							:		Optional[int]								=  6
  elif "yolos_b" in yolos_name:
    _snake_case							:		Dict								=  [8_00, 13_44]

  _snake_case							:		str								=  91
  _snake_case							:		Optional[Any]								=  """huggingface/label-files"""
  _snake_case							:		str								=  """coco-detection-id2label.json"""
  _snake_case							:		str								=  json.load(open(hf_hub_download(snake_case__		,   snake_case__		,   repo_type="""dataset"""    )		,   """r"""    )    )
  _snake_case							:		Union[str, Any]								=  {int(snake_case__    ): v for k, v in idalabel.items()}
  _snake_case							:		List[str]								=  idalabel
  _snake_case							:		List[str]								=  {v: k for k, v in idalabel.items()}

  return config


def 					UpperCAmelCase__       (snake_case__							:					dict		,   snake_case__							:					YolosConfig		,   snake_case__							:					bool = False    ):
  """simple docstring"""

  for i in range(config.num_hidden_layers    ):
    # read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
    _snake_case							:		int								=  state_dict.pop(F"blocks.{i}.attn.qkv.weight"    )
    _snake_case							:		Union[str, Any]								=  state_dict.pop(F"blocks.{i}.attn.qkv.bias"    )
    # next, add query, keys and values (in that order) to the state dict
    _snake_case							:		Any								=  in_proj_weight[: config.hidden_size, :]
    _snake_case							:		Optional[Any]								=  in_proj_bias[: config.hidden_size]
    _snake_case							:		Optional[int]								=  in_proj_weight[
        config.hidden_size : config.hidden_size * 2, :
    ]
    _snake_case							:		int								=  in_proj_bias[
        config.hidden_size : config.hidden_size * 2
    ]
    _snake_case							:		Tuple								=  in_proj_weight[-config.hidden_size :, :]
    _snake_case							:		List[Any]								=  in_proj_bias[-config.hidden_size :]


def 					UpperCAmelCase__       (snake_case__							:					str    ):
  """simple docstring"""

  if "backbone" in name:
    _snake_case							:		str								=  name.replace("""backbone"""		,   """vit"""    )
  if "cls_token" in name:
    _snake_case							:		Union[str, Any]								=  name.replace("""cls_token"""		,   """embeddings.cls_token"""    )
  if "det_token" in name:
    _snake_case							:		str								=  name.replace("""det_token"""		,   """embeddings.detection_tokens"""    )
  if "mid_pos_embed" in name:
    _snake_case							:		str								=  name.replace("""mid_pos_embed"""		,   """encoder.mid_position_embeddings"""    )
  if "pos_embed" in name:
    _snake_case							:		Tuple								=  name.replace("""pos_embed"""		,   """embeddings.position_embeddings"""    )
  if "patch_embed.proj" in name:
    _snake_case							:		str								=  name.replace("""patch_embed.proj"""		,   """embeddings.patch_embeddings.projection"""    )
  if "blocks" in name:
    _snake_case							:		str								=  name.replace("""blocks"""		,   """encoder.layer"""    )
  if "attn.proj" in name:
    _snake_case							:		Any								=  name.replace("""attn.proj"""		,   """attention.output.dense"""    )
  if "attn" in name:
    _snake_case							:		str								=  name.replace("""attn"""		,   """attention.self"""    )
  if "norm1" in name:
    _snake_case							:		List[str]								=  name.replace("""norm1"""		,   """layernorm_before"""    )
  if "norm2" in name:
    _snake_case							:		str								=  name.replace("""norm2"""		,   """layernorm_after"""    )
  if "mlp.fc1" in name:
    _snake_case							:		List[str]								=  name.replace("""mlp.fc1"""		,   """intermediate.dense"""    )
  if "mlp.fc2" in name:
    _snake_case							:		int								=  name.replace("""mlp.fc2"""		,   """output.dense"""    )
  if "class_embed" in name:
    _snake_case							:		Union[str, Any]								=  name.replace("""class_embed"""		,   """class_labels_classifier"""    )
  if "bbox_embed" in name:
    _snake_case							:		str								=  name.replace("""bbox_embed"""		,   """bbox_predictor"""    )
  if "vit.norm" in name:
    _snake_case							:		Union[str, Any]								=  name.replace("""vit.norm"""		,   """vit.layernorm"""    )

  return name


def 					UpperCAmelCase__       (snake_case__							:					dict		,   snake_case__							:					YolosForObjectDetection    ):
  """simple docstring"""

  for key in orig_state_dict.copy().keys():
    _snake_case							:		List[str]								=  orig_state_dict.pop(snake_case__    )

    if "qkv" in key:
      _snake_case							:		Optional[Any]								=  key.split("""."""    )
      _snake_case							:		Optional[Any]								=  int(key_split[2]    )
      _snake_case							:		Optional[int]								=  model.vit.encoder.layer[layer_num].attention.attention.all_head_size
      if "weight" in key:
        _snake_case							:		str								=  val[:dim, :]
        _snake_case							:		Optional[Any]								=  val[
            dim : dim * 2, :
        ]
        _snake_case							:		Optional[Any]								=  val[-dim:, :]
      else:
        _snake_case							:		Dict								=  val[:dim]
        _snake_case							:		Any								=  val[dim : dim * 2]
        _snake_case							:		Dict								=  val[-dim:]
    else:
      _snake_case							:		Tuple								=  val

  return orig_state_dict


def 					UpperCAmelCase__       ():
  """simple docstring"""

  _snake_case							:		str								=  """http://images.cocodataset.org/val2017/000000039769.jpg"""
  _snake_case							:		Union[str, Any]								=  Image.open(requests.get(snake_case__		,   stream=snake_case__    ).raw    )
  return im


@torch.no_grad()
def 					UpperCAmelCase__       (snake_case__							:					str		,   snake_case__							:					str		,   snake_case__							:					str		,   snake_case__							:					bool = False    ):
  """simple docstring"""

  _snake_case							:		Optional[Any]								=  get_yolos_config(snake_case__    )

  # load original state_dict
  _snake_case							:		Optional[int]								=  torch.load(snake_case__		,   map_location="""cpu"""    )["""model"""]

  # load 🤗 model
  _snake_case							:		Optional[Any]								=  YolosForObjectDetection(snake_case__    )
  model.eval()
  _snake_case							:		Optional[Any]								=  convert_state_dict(snake_case__		,   snake_case__    )
  model.load_state_dict(snake_case__    )

  # Check outputs on an image, prepared by YolosImageProcessor
  _snake_case							:		List[str]								=  8_00 if yolos_name != """yolos_ti""" else 5_12
  _snake_case							:		Optional[int]								=  YolosImageProcessor(format="""coco_detection"""		,   size=snake_case__    )
  _snake_case							:		Optional[Any]								=  image_processor(images=prepare_img()		,   return_tensors="""pt"""    )
  _snake_case							:		Optional[Any]								=  model(**snake_case__    )
  _snake_case		,					_snake_case							:		Optional[int]								=  outputs.logits, outputs.pred_boxes

  _snake_case		,					_snake_case							:		Dict								=  None, None
  if yolos_name == "yolos_ti":
    _snake_case							:		Optional[Any]								=  torch.tensor(
        [[-39.50_22, -11.98_20, -17.68_88], [-29.95_74, -9.97_69, -17.76_91], [-42.32_81, -20.72_00, -30.62_94]]    )
    _snake_case							:		Tuple								=  torch.tensor(
        [[0.40_21, 0.08_36, 0.79_79], [0.01_84, 0.26_09, 0.03_64], [0.17_81, 0.20_04, 0.20_95]]    )
  elif yolos_name == "yolos_s_200_pre":
    _snake_case							:		List[str]								=  torch.tensor(
        [[-24.02_48, -10.30_24, -14.82_90], [-42.03_92, -16.82_00, -27.43_34], [-27.27_43, -11.81_54, -18.71_48]]    )
    _snake_case							:		List[str]								=  torch.tensor(
        [[0.25_59, 0.54_55, 0.47_06], [0.29_89, 0.72_79, 0.18_75], [0.77_32, 0.40_17, 0.44_62]]    )
  elif yolos_name == "yolos_s_300_pre":
    _snake_case							:		Dict								=  torch.tensor(
        [[-36.22_20, -14.43_85, -23.54_57], [-35.69_70, -14.75_83, -21.39_35], [-31.59_39, -13.60_42, -16.80_49]]    )
    _snake_case							:		Union[str, Any]								=  torch.tensor(
        [[0.76_14, 0.23_16, 0.47_28], [0.71_68, 0.44_95, 0.38_55], [0.49_96, 0.14_66, 0.99_96]]    )
  elif yolos_name == "yolos_s_dWr":
    _snake_case							:		Tuple								=  torch.tensor(
        [[-42.86_68, -24.10_49, -41.16_90], [-34.74_56, -14.12_74, -24.91_94], [-33.78_98, -12.19_46, -25.64_95]]    )
    _snake_case							:		Optional[Any]								=  torch.tensor(
        [[0.55_87, 0.27_73, 0.06_05], [0.50_04, 0.30_14, 0.99_94], [0.49_99, 0.15_48, 0.99_94]]    )
  elif yolos_name == "yolos_base":
    _snake_case							:		int								=  torch.tensor(
        [[-40.60_64, -24.30_84, -32.64_47], [-55.19_90, -30.77_19, -35.58_77], [-51.43_11, -33.35_07, -35.64_62]]    )
    _snake_case							:		Optional[int]								=  torch.tensor(
        [[0.55_55, 0.27_94, 0.06_55], [0.90_49, 0.26_64, 0.18_94], [0.91_83, 0.19_84, 0.16_35]]    )
  else:
    raise ValueError(F"Unknown yolos_name: {yolos_name}"    )

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

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

  if push_to_hub:
    _snake_case							:		Dict								=  {
        """yolos_ti""": """yolos-tiny""",
        """yolos_s_200_pre""": """yolos-small""",
        """yolos_s_300_pre""": """yolos-small-300""",
        """yolos_s_dWr""": """yolos-small-dwr""",
        """yolos_base""": """yolos-base""",
    }

    print("""Pushing to the hub..."""    )
    _snake_case							:		str								=  model_mapping[yolos_name]
    image_processor.push_to_hub(snake_case__		,   organization="""hustvl"""    )
    model.push_to_hub(snake_case__		,   organization="""hustvl"""    )


if __name__ == "__main__":
     A_						=				argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         '''--yolos_name''',
         default='''yolos_s_200_pre''',
         type=str,
         help=(
             '''Name of the YOLOS model you\'d like to convert. Should be one of \'yolos_ti\', \'yolos_s_200_pre\','''
             ''' \'yolos_s_300_pre\', \'yolos_s_dWr\', \'yolos_base\'.'''
         ),
     )
     parser.add_argument(
         '''--checkpoint_path''', default=None, type=str, help='''Path to the original state dict (.pth file).'''
     )
     parser.add_argument(
         '''--pytorch_dump_folder_path''', default=None, 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_						=				parser.parse_args()
     convert_yolos_checkpoint(args.yolos_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)


