
import inspect
import math
import tempfile
import unittest

import numpy as np

from transformers import ViTMAEConfig
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_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 ViTMAEForPreTraining, ViTMAEModel
			from transformers.models.vit.modeling_vit import VIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
			from PIL import Image

			from transformers import ViTImageProcessor




class     _SCREAMING_SNAKE_CASE     :



	def __init__(    self						,  lowercase						,  lowercase=13						,  lowercase=30						,  lowercase=2						,  lowercase=3						,  lowercase=True						,  lowercase=True						,  lowercase=32						,  lowercase=5						,  lowercase=4						,  lowercase=37						,  lowercase="gelu"						,  lowercase=0.1						,  lowercase=0.1						,  lowercase=10						,  lowercase=0.0_2						,  lowercase=3						,  lowercase=0.6						,  lowercase=None						,  )				->       Tuple:
					lowerCamelCase_						=			parent
					lowerCamelCase_						=			batch_size
					lowerCamelCase_						=			image_size
					lowerCamelCase_						=			patch_size
					lowerCamelCase_						=			num_channels
					lowerCamelCase_						=			is_training
					lowerCamelCase_						=			use_labels
					lowerCamelCase_						=			hidden_size
					lowerCamelCase_						=			num_hidden_layers
					lowerCamelCase_						=			num_attention_heads
					lowerCamelCase_						=			intermediate_size
					lowerCamelCase_						=			hidden_act
					lowerCamelCase_						=			hidden_dropout_prob
					lowerCamelCase_						=			attention_probs_dropout_prob
					lowerCamelCase_						=			type_sequence_label_size
					lowerCamelCase_						=			initializer_range
					lowerCamelCase_						=			mask_ratio
					lowerCamelCase_						=			scope

					# in ViTMAE, the expected sequence length = (num_patches + 1) * (1 - config.mask_ratio), rounded above
					# (we add 1 for the [CLS] token)
					lowerCamelCase_						=			(image_size // patch_size) ** 2
					lowerCamelCase_						=			int(math.ceil((1 - mask_ratio) * (num_patches + 1) ) )



	def     SCREAMING_SNAKE_CASE_(    self )				->       int:
					lowerCamelCase_						=			floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )

					lowerCamelCase_						=			None
					if self.use_labels:
									lowerCamelCase_						=			ids_tensor([self.batch_size]						,  self.type_sequence_label_size )

					lowerCamelCase_						=			self.get_config()

					return config, pixel_values, labels



	def     SCREAMING_SNAKE_CASE_(    self )				->       List[str]:
					return ViTMAEConfig(
					    image_size=self.image_size						,  patch_size=self.patch_size						,  num_channels=self.num_channels						,  hidden_size=self.hidden_size						,  num_hidden_layers=self.num_hidden_layers						,  num_attention_heads=self.num_attention_heads						,  intermediate_size=self.intermediate_size						,  hidden_act=self.hidden_act						,  hidden_dropout_prob=self.hidden_dropout_prob						,  attention_probs_dropout_prob=self.attention_probs_dropout_prob						,  is_decoder=lowercase						,  initializer_range=self.initializer_range						,  mask_ratio=self.mask_ratio						,  )



	def     SCREAMING_SNAKE_CASE_(    self						,  lowercase						,  lowercase						,  lowercase )				->       str:
					lowerCamelCase_						=			ViTMAEModel(config=lowercase )
					model.to(lowercase )
					model.eval()
					lowerCamelCase_						=			model(lowercase )
					self.parent.assertEqual(result.last_hidden_state.shape						,  (self.batch_size, self.seq_length, self.hidden_size) )



	def     SCREAMING_SNAKE_CASE_(    self						,  lowercase						,  lowercase						,  lowercase )				->       int:
					lowerCamelCase_						=			ViTMAEForPreTraining(lowercase )
					model.to(lowercase )
					model.eval()
					lowerCamelCase_						=			model(lowercase )
					lowerCamelCase_						=			(self.image_size // self.patch_size) ** 2
					lowerCamelCase_						=			self.patch_size**2 * self.num_channels
					self.parent.assertEqual(result.logits.shape						,  (self.batch_size, num_patches, expected_num_channels) )

					# test greyscale images
					lowerCamelCase_						=			1
					lowerCamelCase_						=			ViTMAEForPreTraining(lowercase )
					model.to(lowercase )
					model.eval()
					lowerCamelCase_						=			floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
					lowerCamelCase_						=			model(lowercase )
					lowerCamelCase_						=			self.patch_size**2
					self.parent.assertEqual(result.logits.shape						,  (self.batch_size, num_patches, expected_num_channels) )



	def     SCREAMING_SNAKE_CASE_(    self )				->       Optional[int]:
					lowerCamelCase_						=			self.prepare_config_and_inputs()
					lowerCamelCase_	,    lowerCamelCase_	,    lowerCamelCase_						=			config_and_inputs
					lowerCamelCase_						=			{"pixel_values": pixel_values}
					return config, inputs_dict




@require_torch
class     _SCREAMING_SNAKE_CASE     (      snake_case_			,       snake_case_			,       unittest.TestCase						):
	lowerCAmelCase__         = (ViTMAEModel, ViTMAEForPreTraining) if is_torch_available() else ()
	lowerCAmelCase__         = {'feature-extraction': ViTMAEModel} if is_torch_available() else {}

	lowerCAmelCase__         = False
	lowerCAmelCase__         = False
	lowerCAmelCase__         = False
	lowerCAmelCase__         = False



	def     SCREAMING_SNAKE_CASE_(    self )				->       Optional[Any]:
					lowerCamelCase_						=			ViTMAEModelTester(self )
					lowerCamelCase_						=			ConfigTester(self						,  config_class=lowercase						,  has_text_modality=lowercase						,  hidden_size=37 )



	def     SCREAMING_SNAKE_CASE_(    self )				->       Union[str, Any]:
					self.config_tester.run_common_tests()



	@unittest.skip(reason="ViTMAE does not use inputs_embeds" )
	def     SCREAMING_SNAKE_CASE_(    self )				->       str:
					pass



	def     SCREAMING_SNAKE_CASE_(    self )				->       Optional[Any]:
					lowerCamelCase_	,    lowerCamelCase_						=			self.model_tester.prepare_config_and_inputs_for_common()

					for model_class in self.all_model_classes:
									lowerCamelCase_						=			model_class(lowercase )
									self.assertIsInstance(model.get_input_embeddings()						,  (nn.Module) )
									lowerCamelCase_						=			model.get_output_embeddings()
									self.assertTrue(x is None or isinstance(lowercase						,  nn.Linear ) )



	def     SCREAMING_SNAKE_CASE_(    self )				->       Union[str, Any]:
					lowerCamelCase_	,    lowerCamelCase_						=			self.model_tester.prepare_config_and_inputs_for_common()

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

									lowerCamelCase_						=			["pixel_values"]
									self.assertListEqual(arg_names[:1]						,  lowercase )



	def     SCREAMING_SNAKE_CASE_(    self )				->       Tuple:
					lowerCamelCase_						=			self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_model(*lowercase )



	def     SCREAMING_SNAKE_CASE_(    self )				->       Tuple:
					lowerCamelCase_						=			self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_pretraining(*lowercase )



	def     SCREAMING_SNAKE_CASE_(    self						,  lowercase						,  lowercase						,  lowercase )				->       Optional[Any]:
					# make masks reproducible
					np.random.seed(2 )

					lowerCamelCase_						=			int((pt_model.config.image_size // pt_model.config.patch_size) ** 2 )
					lowerCamelCase_						=			np.random.uniform(size=(self.model_tester.batch_size, num_patches) )
					lowerCamelCase_						=			torch.from_numpy(lowercase )

					# Add `noise` argument.
					# PT inputs will be prepared in `super().check_pt_tf_models()` with this added `noise` argument
					lowerCamelCase_						=			pt_noise

					super().check_pt_tf_models(lowercase						,  lowercase						,  lowercase )



	def     SCREAMING_SNAKE_CASE_(    self )				->       Optional[Any]:
					lowerCamelCase_	,    lowerCamelCase_						=			self.model_tester.prepare_config_and_inputs_for_common()

					for model_class in self.all_model_classes:
									lowerCamelCase_						=			model_class(lowercase )
									model.to(lowercase )
									model.eval()
									# make random mask reproducible
									torch.manual_seed(2 )
									with torch.no_grad():
													lowerCamelCase_						=			model(**self._prepare_for_class(lowercase						,  lowercase ) )

									lowerCamelCase_						=			outputs[0].cpu().numpy()
									lowerCamelCase_						=			0

									with tempfile.TemporaryDirectory() as tmpdirname:
													model.save_pretrained(lowercase )
													lowerCamelCase_						=			model_class.from_pretrained(lowercase )
													model.to(lowercase )
													# make random mask reproducible
													torch.manual_seed(2 )
													with torch.no_grad():
																	lowerCamelCase_						=			model(**self._prepare_for_class(lowercase						,  lowercase ) )

													# Make sure we don't have nans
													lowerCamelCase_						=			after_outputs[0].cpu().numpy()
													lowerCamelCase_						=			0
													lowerCamelCase_						=			np.amax(np.abs(out_a - out_a ) )
													self.assertLessEqual(lowercase						,  1e-5 )



	@unittest.skip(
	    reason="ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load\n    to get deterministic results." )
	def     SCREAMING_SNAKE_CASE_(    self )				->       Union[str, Any]:
					pass



	@unittest.skip(
	    reason="ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load\n    to get deterministic results." )
	def     SCREAMING_SNAKE_CASE_(    self )				->       str:
					pass



	@unittest.skip(
	    reason="ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load\n    to get deterministic results." )
	def     SCREAMING_SNAKE_CASE_(    self )				->       Union[str, Any]:
					pass



	@unittest.skip(reason="ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load" )
	def     SCREAMING_SNAKE_CASE_(    self )				->       Union[str, Any]:
					pass



	@unittest.skip("Will be fixed soon by reducing the size of the model used for common tests." )
	def     SCREAMING_SNAKE_CASE_(    self )				->       int:
					pass



	@slow
	def     SCREAMING_SNAKE_CASE_(    self )				->       List[Any]:
					for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									lowerCamelCase_						=			ViTMAEModel.from_pretrained(lowercase )
									self.assertIsNotNone(lowercase )





def 			lowerCamelCase_ (				):
				lowerCamelCase_						=			Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"    )
				return image





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



	@cached_property
	def     SCREAMING_SNAKE_CASE_(    self )				->       Optional[int]:
					return ViTImageProcessor.from_pretrained("facebook/vit-mae-base" ) if is_vision_available() else None



	@slow
	def     SCREAMING_SNAKE_CASE_(    self )				->       Tuple:
					# make random mask reproducible across the PT and TF model
					np.random.seed(2 )

					lowerCamelCase_						=			ViTMAEForPreTraining.from_pretrained("facebook/vit-mae-base" ).to(lowercase )

					lowerCamelCase_						=			self.default_image_processor
					lowerCamelCase_						=			prepare_img()
					lowerCamelCase_						=			image_processor(images=lowercase						,  return_tensors="pt" ).to(lowercase )

					# prepare a noise vector that will be also used for testing the TF model
					# (this way we can ensure that the PT and TF models operate on the same inputs)
					lowerCamelCase_						=			ViTMAEConfig()
					lowerCamelCase_						=			int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2 )
					lowerCamelCase_						=			np.random.uniform(size=(1, num_patches) )

					# forward pass
					with torch.no_grad():
									lowerCamelCase_						=			model(**lowercase						,  noise=torch.from_numpy(lowercase ).to(device=lowercase ) )

					# verify the logits
					lowerCamelCase_						=			torch.Size((1, 196, 768) )
					self.assertEqual(outputs.logits.shape						,  lowercase )

					lowerCamelCase_						=			torch.tensor(
					    [[-0.0_5_4_8, -1.7_0_2_3, -0.9_3_2_5], [0.3_7_2_1, -0.5_6_7_0, -0.2_2_3_3], [0.8_2_3_5, -1.3_8_7_8, -0.3_5_2_4]] )

					self.assertTrue(torch.allclose(outputs.logits[0, :3, :3]						,  expected_slice.to(lowercase )						,  atol=1e-4 ) )




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 			lowerCamelCase_ (				lowerCamelCase__    ):
				lowerCamelCase_						=			YolosConfig()

				# size of the architecture
				if "yolos_ti" in yolos_name:
								lowerCamelCase_						=			1_9_2
								lowerCamelCase_						=			7_6_8
								lowerCamelCase_						=			1_2
								lowerCamelCase_						=			3
								lowerCamelCase_						=			[8_0_0, 1_3_3_3]
								lowerCamelCase_						=			False
				elif yolos_name == "yolos_s_dWr":
								lowerCamelCase_						=			3_3_0
								lowerCamelCase_						=			1_4
								lowerCamelCase_						=			6
								lowerCamelCase_						=			1_3_2_0
				elif "yolos_s" in yolos_name:
								lowerCamelCase_						=			3_8_4
								lowerCamelCase_						=			1_5_3_6
								lowerCamelCase_						=			1_2
								lowerCamelCase_						=			6
				elif "yolos_b" in yolos_name:
								lowerCamelCase_						=			[8_0_0, 1_3_4_4]

				lowerCamelCase_						=			9_1
				lowerCamelCase_						=			"huggingface/label-files"
				lowerCamelCase_						=			"coco-detection-id2label.json"
				lowerCamelCase_						=			json.load(open(hf_hub_download(lowerCamelCase__	, lowerCamelCase__	, repo_type="dataset"    )	, "r"    )    )
				lowerCamelCase_						=			{int(lowerCamelCase__    ): v for k, v in idalabel.items()}
				lowerCamelCase_						=			idalabel
				lowerCamelCase_						=			{v: k for k, v in idalabel.items()}

				return config





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





def 			lowerCamelCase_ (				lowerCamelCase__    ):
				if "backbone" in name:
								lowerCamelCase_						=			name.replace("backbone"	, "vit"    )
				if "cls_token" in name:
								lowerCamelCase_						=			name.replace("cls_token"	, "embeddings.cls_token"    )
				if "det_token" in name:
								lowerCamelCase_						=			name.replace("det_token"	, "embeddings.detection_tokens"    )
				if "mid_pos_embed" in name:
								lowerCamelCase_						=			name.replace("mid_pos_embed"	, "encoder.mid_position_embeddings"    )
				if "pos_embed" in name:
								lowerCamelCase_						=			name.replace("pos_embed"	, "embeddings.position_embeddings"    )
				if "patch_embed.proj" in name:
								lowerCamelCase_						=			name.replace("patch_embed.proj"	, "embeddings.patch_embeddings.projection"    )
				if "blocks" in name:
								lowerCamelCase_						=			name.replace("blocks"	, "encoder.layer"    )
				if "attn.proj" in name:
								lowerCamelCase_						=			name.replace("attn.proj"	, "attention.output.dense"    )
				if "attn" in name:
								lowerCamelCase_						=			name.replace("attn"	, "attention.self"    )
				if "norm1" in name:
								lowerCamelCase_						=			name.replace("norm1"	, "layernorm_before"    )
				if "norm2" in name:
								lowerCamelCase_						=			name.replace("norm2"	, "layernorm_after"    )
				if "mlp.fc1" in name:
								lowerCamelCase_						=			name.replace("mlp.fc1"	, "intermediate.dense"    )
				if "mlp.fc2" in name:
								lowerCamelCase_						=			name.replace("mlp.fc2"	, "output.dense"    )
				if "class_embed" in name:
								lowerCamelCase_						=			name.replace("class_embed"	, "class_labels_classifier"    )
				if "bbox_embed" in name:
								lowerCamelCase_						=			name.replace("bbox_embed"	, "bbox_predictor"    )
				if "vit.norm" in name:
								lowerCamelCase_						=			name.replace("vit.norm"	, "vit.layernorm"    )

				return name





def 			lowerCamelCase_ (				lowerCamelCase__	, lowerCamelCase__    ):
				for key in orig_state_dict.copy().keys():
								lowerCamelCase_						=			orig_state_dict.pop(lowerCamelCase__    )

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

				return orig_state_dict





def 			lowerCamelCase_ (				):
				lowerCamelCase_						=			"http://images.cocodataset.org/val2017/000000039769.jpg"
				lowerCamelCase_						=			Image.open(requests.get(lowerCamelCase__	, stream=lowerCamelCase__    ).raw    )
				return im





@torch.no_grad()
def 			lowerCamelCase_ (				lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__ = False    ):
				lowerCamelCase_						=			get_yolos_config(lowerCamelCase__    )

				# load original state_dict
				lowerCamelCase_						=			torch.load(lowerCamelCase__	, map_location="cpu"    )["model"]

				# load 🤗 model
				lowerCamelCase_						=			YolosForObjectDetection(lowerCamelCase__    )
				model.eval()
				lowerCamelCase_						=			convert_state_dict(lowerCamelCase__	, lowerCamelCase__    )
				model.load_state_dict(lowerCamelCase__    )

				# Check outputs on an image, prepared by YolosImageProcessor
				lowerCamelCase_						=			8_0_0 if yolos_name != "yolos_ti" else 5_1_2
				lowerCamelCase_						=			YolosImageProcessor(format="coco_detection"	, size=lowerCamelCase__    )
				lowerCamelCase_						=			image_processor(images=prepare_img()	, return_tensors="pt"    )
				lowerCamelCase_						=			model(**lowerCamelCase__    )
				lowerCamelCase_	,    lowerCamelCase_						=			outputs.logits, outputs.pred_boxes

				lowerCamelCase_	,    lowerCamelCase_						=			None, None
				if yolos_name == "yolos_ti":
								lowerCamelCase_						=			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]]    )
								lowerCamelCase_						=			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":
								lowerCamelCase_						=			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]]    )
								lowerCamelCase_						=			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":
								lowerCamelCase_						=			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]]    )
								lowerCamelCase_						=			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":
								lowerCamelCase_						=			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]]    )
								lowerCamelCase_						=			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":
								lowerCamelCase_						=			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]]    )
								lowerCamelCase_						=			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]	, lowerCamelCase__	, atol=1e-4    )
				assert torch.allclose(pred_boxes[0, :3, :3]	, lowerCamelCase__	, atol=1e-4    )

				Path(lowerCamelCase__    ).mkdir(exist_ok=lowerCamelCase__    )
				print(F'Saving model {yolos_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:
								lowerCamelCase_						=			{
								    "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..."    )
								lowerCamelCase_						=			model_mapping[yolos_name]
								image_processor.push_to_hub(lowerCamelCase__	, organization="hustvl"    )
								model.push_to_hub(lowerCamelCase__	, 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)


