






import json
import os
import unittest

from transformers import AutoTokenizer, GPTaTokenizer, GPTaTokenizerFast
from transformers.models.gpta.tokenization_gpta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin

@require_tokenizers
class 		lowerCAmelCase  (			a_   ,							unittest.TestCase      ):

					'''simple docstring'''

					_A    :      str					       =   GPTaTokenizer
					_A    :      Optional[Any]					       =   GPTaTokenizerFast
					_A    :      Optional[int]					       =   True
					_A    :      str					       =   {'''add_prefix_space''': True}
					_A    :      Union[str, Any]					       =   False



					def    lowerCAmelCase					(	self					:						str				)      ->	str:


												"""simple docstring"""

												super().setUp()

												# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
												__lowercase   :				Any						      =  [
												    "l",
												    "o",
												    "w",
												    "e",
												    "r",
												    "s",
												    "t",
												    "i",
												    "d",
												    "n",
												    "\u0120",
												    "\u0120l",
												    "\u0120n",
												    "\u0120lo",
												    "\u0120low",
												    "er",
												    "\u0120lowest",
												    "\u0120newer",
												    "\u0120wider",
												    "<unk>",
												    "<|endoftext|>",
												]
												__lowercase   :				Any						      =  dict(zip(_a		,  range(len(_a				)				)				)				)
												__lowercase   :				Dict						      =  ["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
												__lowercase   :				int						      =  {"unk_token": "<unk>"}

												__lowercase   :				Tuple						      =  os.path.join(self.tmpdirname		,  VOCAB_FILES_NAMES["""vocab_file"""]				)
												__lowercase   :				Optional[Any]						      =  os.path.join(self.tmpdirname		,  VOCAB_FILES_NAMES["""merges_file"""]				)
												with open(self.vocab_file		,  """w"""		,  encoding="""utf-8"""				) as fp:
																			fp.write(json.dumps(_a				) + """\n"""				)
												with open(self.merges_file		,  """w"""		,  encoding="""utf-8"""				) as fp:
																			fp.write("""\n""".join(_a				)				)



					def    lowerCAmelCase					(	self					:						str		,  **__a					:						str				)      ->	List[Any]:


												"""simple docstring"""

												kwargs.update(self.special_tokens_map				)
												return GPTaTokenizer.from_pretrained(self.tmpdirname		,  **_a				)



					def    lowerCAmelCase					(	self					:						Optional[int]		,  **__a					:						Dict				)      ->	Optional[int]:


												"""simple docstring"""

												kwargs.update(self.special_tokens_map				)
												return GPTaTokenizerFast.from_pretrained(self.tmpdirname		,  **_a				)



					def    lowerCAmelCase					(	self					:						Optional[Any]		,  __a					:						Tuple				)      ->	List[str]:


												"""simple docstring"""

												__lowercase   :				Tuple						      =  "lower newer"
												__lowercase   :				Dict						      =  "lower newer"
												return input_text, output_text



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


												"""simple docstring"""

												__lowercase   :				str						      =  GPTaTokenizer(self.vocab_file		,  self.merges_file		,  **self.special_tokens_map				)
												__lowercase   :				Union[str, Any]						      =  "lower newer"
												__lowercase   :				Any						      =  ["\u0120low", "er", "\u0120", "n", "e", "w", "er"]
												__lowercase   :				int						      =  tokenizer.tokenize(_a		,  add_prefix_space=_a				)
												self.assertListEqual(_a		,  _a				)

												__lowercase   :				int						      =  tokens + [tokenizer.unk_token]
												__lowercase   :				List[Any]						      =  [14, 15, 10, 9, 3, 2, 15, 19]
												self.assertListEqual(tokenizer.convert_tokens_to_ids(_a				)		,  _a				)



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


												"""simple docstring"""

												if not self.test_rust_tokenizer:
																			return

												__lowercase   :				Union[str, Any]						      =  self.get_tokenizer()
												__lowercase   :				int						      =  self.get_rust_tokenizer(add_prefix_space=_a				)

												__lowercase   :				Union[str, Any]						      =  "lower newer"

												# Testing tokenization
												__lowercase   :				Union[str, Any]						      =  tokenizer.tokenize(_a		,  add_prefix_space=_a				)
												__lowercase   :				List[Any]						      =  rust_tokenizer.tokenize(_a				)
												self.assertListEqual(_a		,  _a				)

												# Testing conversion to ids without special tokens
												__lowercase   :				Dict						      =  tokenizer.encode(_a		,  add_special_tokens=_a		,  add_prefix_space=_a				)
												__lowercase   :				Dict						      =  rust_tokenizer.encode(_a		,  add_special_tokens=_a				)
												self.assertListEqual(_a		,  _a				)

												# Testing conversion to ids with special tokens
												__lowercase   :				Any						      =  self.get_rust_tokenizer(add_prefix_space=_a				)
												__lowercase   :				Any						      =  tokenizer.encode(_a		,  add_prefix_space=_a				)
												__lowercase   :				Any						      =  rust_tokenizer.encode(_a				)
												self.assertListEqual(_a		,  _a				)

												# Testing the unknown token
												__lowercase   :				Union[str, Any]						      =  tokens + [rust_tokenizer.unk_token]
												__lowercase   :				str						      =  [14, 15, 10, 9, 3, 2, 15, 19]
												self.assertListEqual(rust_tokenizer.convert_tokens_to_ids(_a				)		,  _a				)



					def    lowerCAmelCase					(	self					:						int		,  *__a					:						Optional[int]		,  **__a					:						Optional[Any]				)      ->	Optional[int]:


												"""simple docstring"""

												pass



					def    lowerCAmelCase					(	self					:						str		,  __a					:						Dict=15				)      ->	List[str]:


												"""simple docstring"""

												for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
																			with self.subTest(F"{tokenizer.__class__.__name__} ({pretrained_name})"				):
																										__lowercase   :				int						      =  self.rust_tokenizer_class.from_pretrained(_a		,  **_a				)

																										# Simple input
																										__lowercase   :				int						      =  "This is a simple input"
																										__lowercase   :				Optional[int]						      =  ["This is a simple input 1", "This is a simple input 2"]
																										__lowercase   :				Union[str, Any]						      =  ("This is a simple input", "This is a pair")
																										__lowercase   :				Any						      =  [
																										    ("This is a simple input 1", "This is a simple input 2"),
																										    ("This is a simple pair 1", "This is a simple pair 2"),
																										]

																										# Simple input tests
																										self.assertRaises(_a		,  tokenizer_r.encode		,  _a		,  max_length=_a		,  padding="""max_length"""				)

																										# Simple input
																										self.assertRaises(_a		,  tokenizer_r.encode_plus		,  _a		,  max_length=_a		,  padding="""max_length"""				)

																										# Simple input
																										self.assertRaises(
																										    _a		,  tokenizer_r.batch_encode_plus		,  _a		,  max_length=_a		,  padding="""max_length"""		,  )

																										# Pair input
																										self.assertRaises(_a		,  tokenizer_r.encode		,  _a		,  max_length=_a		,  padding="""max_length"""				)

																										# Pair input
																										self.assertRaises(_a		,  tokenizer_r.encode_plus		,  _a		,  max_length=_a		,  padding="""max_length"""				)

																										# Pair input
																										self.assertRaises(
																										    _a		,  tokenizer_r.batch_encode_plus		,  _a		,  max_length=_a		,  padding="""max_length"""		,  )



					def    lowerCAmelCase					(	self					:						Optional[Any]				)      ->	Optional[int]:


												"""simple docstring"""

												__lowercase   :				List[Any]						      =  GPTaTokenizer.from_pretrained(self.tmpdirname		,  pad_token="""<pad>"""				)

												# Simple input
												__lowercase   :				int						      =  "This is a simple input"
												__lowercase   :				Tuple						      =  ["This is a simple input looooooooong", "This is a simple input"]
												__lowercase   :				Any						      =  ("This is a simple input", "This is a pair")
												__lowercase   :				str						      =  [
												    ("This is a simple input loooooong", "This is a simple input"),
												    ("This is a simple pair loooooong", "This is a simple pair"),
												]

												__lowercase   :				List[Any]						      =  tokenizer.pad_token_id

												__lowercase   :				List[Any]						      =  tokenizer(_a		,  padding="""max_length"""		,  max_length=30		,  return_tensors="""np"""				)
												__lowercase   :				List[str]						      =  tokenizer(_a		,  padding=_a		,  truncate=_a		,  return_tensors="""np"""				)
												__lowercase   :				List[Any]						      =  tokenizer(*_a		,  padding="""max_length"""		,  max_length=60		,  return_tensors="""np"""				)
												__lowercase   :				Any						      =  tokenizer(_a		,  padding=_a		,  truncate=_a		,  return_tensors="""np"""				)

												# s
												# test single string max_length padding
												self.assertEqual(out_s["""input_ids"""].shape[-1]		,  30				)
												self.assertTrue(pad_token_id in out_s["""input_ids"""]				)
												self.assertTrue(0 in out_s["""attention_mask"""]				)

												# s2
												# test automatic padding
												self.assertEqual(out_sa["""input_ids"""].shape[-1]		,  33				)
												# long slice doesn't have padding
												self.assertFalse(pad_token_id in out_sa["""input_ids"""][0]				)
												self.assertFalse(0 in out_sa["""attention_mask"""][0]				)
												# short slice does have padding
												self.assertTrue(pad_token_id in out_sa["""input_ids"""][1]				)
												self.assertTrue(0 in out_sa["""attention_mask"""][1]				)

												# p
												# test single pair max_length padding
												self.assertEqual(out_p["""input_ids"""].shape[-1]		,  60				)
												self.assertTrue(pad_token_id in out_p["""input_ids"""]				)
												self.assertTrue(0 in out_p["""attention_mask"""]				)

												# p2
												# test automatic padding pair
												self.assertEqual(out_pa["""input_ids"""].shape[-1]		,  52				)
												# long slice pair doesn't have padding
												self.assertFalse(pad_token_id in out_pa["""input_ids"""][0]				)
												self.assertFalse(0 in out_pa["""attention_mask"""][0]				)
												# short slice pair does have padding
												self.assertTrue(pad_token_id in out_pa["""input_ids"""][1]				)
												self.assertTrue(0 in out_pa["""attention_mask"""][1]				)



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


												"""simple docstring"""

												__lowercase   :				List[Any]						      =  "$$$"
												__lowercase   :				List[str]						      =  GPTaTokenizer.from_pretrained(self.tmpdirname		,  bos_token=_a		,  add_bos_token=_a				)

												__lowercase   :				List[Any]						      =  "This is a simple input"
												__lowercase   :				str						      =  ["This is a simple input 1", "This is a simple input 2"]

												__lowercase   :				Union[str, Any]						      =  tokenizer.bos_token_id

												__lowercase   :				List[str]						      =  tokenizer(_a				)
												__lowercase   :				Any						      =  tokenizer(_a				)

												self.assertEqual(out_s.input_ids[0]		,  _a				)
												self.assertTrue(all(o[0] == bos_token_id for o in out_sa.input_ids				)				)

												__lowercase   :				List[Any]						      =  tokenizer.decode(out_s.input_ids				)
												__lowercase   :				Tuple						      =  tokenizer.batch_decode(out_sa.input_ids				)

												self.assertEqual(decode_s.split()[0]		,  _a				)
												self.assertTrue(all(d.split()[0] == bos_token for d in decode_sa				)				)



					def    lowerCAmelCase					(	self					:						Dict				)      ->	List[Any]:


												"""simple docstring"""

												pass



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


												"""simple docstring"""

												__lowercase   :				Dict						      =  [self.get_tokenizer(do_lower_case=_a		,  add_bos_token=_a				)]
												for tokenizer in tokenizers:
																			with self.subTest(F"{tokenizer.__class__.__name__}"				):
																										__lowercase   :				Optional[Any]						      =  "Encode this."
																										__lowercase   :				Union[str, Any]						      =  "This one too please."
																										__lowercase   :				Dict						      =  tokenizer.encode(_a		,  add_special_tokens=_a				)
																										encoded_sequence += tokenizer.encode(_a		,  add_special_tokens=_a				)
																										__lowercase   :				Optional[Any]						      =  tokenizer.encode_plus(
																										    _a		,  _a		,  add_special_tokens=_a		,  return_special_tokens_mask=_a		,  )
																										__lowercase   :				Tuple						      =  encoded_sequence_dict["input_ids"]
																										__lowercase   :				int						      =  encoded_sequence_dict["special_tokens_mask"]
																										self.assertEqual(len(_a				)		,  len(_a				)				)

																										__lowercase   :				List[str]						      =  [
																										    (x if not special_tokens_mask[i] else None) for i, x in enumerate(_a				)
																										]
																										__lowercase   :				List[str]						      =  [x for x in filtered_sequence if x is not None]
																										self.assertEqual(_a		,  _a				)







@require_tokenizers
class 		lowerCAmelCase  (			unittest.TestCase      ):

					'''simple docstring'''

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


												"""simple docstring"""

												__lowercase   :				Tuple						      =  AutoTokenizer.from_pretrained("""facebook/opt-350m"""		,  from_slow=_a				)
												__lowercase   :				Any						      =  "A photo of a cat"

												__lowercase   :				List[str]						      =  tokenizer.encode(
												    _a		,  )
												self.assertEqual(_a		,  [2, 250, 1345, 9, 10, 4758]				)
												tokenizer.save_pretrained("""test_opt"""				)

												__lowercase   :				Union[str, Any]						      =  AutoTokenizer.from_pretrained("""./test_opt"""				)
												__lowercase   :				Tuple						      =  tokenizer.encode(
												    _a		,  )
												self.assertEqual(_a		,  [2, 250, 1345, 9, 10, 4758]				)



					def    lowerCAmelCase					(	self					:						str				)      ->	Dict:


												"""simple docstring"""

												__lowercase   :				Union[str, Any]						      =  AutoTokenizer.from_pretrained("""facebook/opt-350m"""		,  use_slow=_a				)
												__lowercase   :				Union[str, Any]						      =  "A photo of a cat"

												__lowercase   :				Union[str, Any]						      =  tokenizer.encode(
												    _a		,  )
												# Same as above
												self.assertEqual(_a		,  [2, 250, 1345, 9, 10, 4758]				)



					@unittest.skip("""This test is failing because of a bug in the fast tokenizer"""				)
					def    lowerCAmelCase					(	self					:						List[str]				)      ->	int:


												"""simple docstring"""

												__lowercase   :				int						      =  AutoTokenizer.from_pretrained("""facebook/opt-350m"""		,  from_slow=_a				)

												__lowercase   :				List[Any]						      =  "bos"
												__lowercase   :				Optional[int]						      =  tokenizer.get_vocab()["bos"]

												__lowercase   :				List[str]						      =  "A photo of a cat"
												__lowercase   :				Dict						      =  tokenizer.encode(
												    _a		,  )
												# We changed the bos token
												self.assertEqual(_a		,  [31957, 250, 1345, 9, 10, 4758]				)
												tokenizer.save_pretrained("""./tok"""				)
												__lowercase   :				Tuple						      =  AutoTokenizer.from_pretrained("""./tok"""				)
												self.assertTrue(tokenizer.is_fast				)
												__lowercase   :				str						      =  tokenizer.encode(
												    _a		,  )
												self.assertEqual(_a		,  [31957, 250, 1345, 9, 10, 4758]				)

"""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     :			Optional[int]  						=			logging.get_logger(__name__)


def     __magic_name__      ( __snake_case							:			str    )					->							YolosConfig:
       lowercase       :							int									=				YolosConfig()

       # size of the architecture
       if "yolos_ti" in yolos_name:
              lowercase       :							Union[str, Any]									=				192
              lowercase       :							Tuple									=				768
              lowercase       :							Optional[Any]									=				12
              lowercase       :							List[str]									=				3
              lowercase       :							int									=				[800, 1333]
              lowercase       :							Union[str, Any]									=				False
       elif yolos_name == "yolos_s_dWr":
              lowercase       :							int									=				330
              lowercase       :							List[str]									=				14
              lowercase       :							Dict									=				6
              lowercase       :							Any									=				1320
       elif "yolos_s" in yolos_name:
              lowercase       :							str									=				384
              lowercase       :							int									=				1536
              lowercase       :							Any									=				12
              lowercase       :							int									=				6
       elif "yolos_b" in yolos_name:
              lowercase       :							str									=				[800, 1344]

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

       return config


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


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

       return name


def     __magic_name__      ( __snake_case							:			dict	,					__snake_case							:			YolosForObjectDetection    )					->							dict:
       for key in orig_state_dict.copy().keys():
              lowercase       :							List[Any]									=				orig_state_dict.pop(__snake_case    )

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

       return orig_state_dict


def     __magic_name__      ( )					->							torch.Tensor:
       lowercase       :							Optional[int]									=				"http://images.cocodataset.org/val2017/000000039769.jpg"
       lowercase       :							Tuple									=				Image.open(requests.get(__snake_case	,					stream=__snake_case    ).raw    )
       return im


@torch.no_grad()
def     __magic_name__      ( __snake_case							:			str	,					__snake_case							:			str	,					__snake_case							:			str	,					__snake_case							:			bool = False    )					->							Dict:
       lowercase       :							Optional[int]									=				get_yolos_config(__snake_case    )

       # load original state_dict
       lowercase       :							str									=				torch.load(__snake_case	,					map_location="cpu"    )["model"]

       # load 🤗 model
       lowercase       :							str									=				YolosForObjectDetection(__snake_case    )
       model.eval()
       lowercase       :							Dict									=				convert_state_dict(__snake_case	,					__snake_case    )
       model.load_state_dict(__snake_case    )

       # Check outputs on an image, prepared by YolosImageProcessor
       lowercase       :							Any									=				800 if yolos_name != "yolos_ti" else 512
       lowercase       :							List[Any]									=				YolosImageProcessor(format="coco_detection"	,					size=__snake_case    )
       lowercase       :							Any									=				image_processor(images=prepare_img()	,					return_tensors="pt"    )
       lowercase       :							Any									=				model(**__snake_case    )
       lowercase						,       lowercase       :							Tuple									=				outputs.logits, outputs.pred_boxes

       lowercase						,       lowercase       :							int									=				None, None
       if yolos_name == "yolos_ti":
              lowercase       :							int									=				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]]    )
              lowercase       :							List[Any]									=				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":
              lowercase       :							List[Any]									=				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]]    )
              lowercase       :							List[Any]									=				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":
              lowercase       :							Optional[int]									=				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]]    )
              lowercase       :							Tuple									=				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":
              lowercase       :							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]]    )
              lowercase       :							List[str]									=				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":
              lowercase       :							Optional[Any]									=				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]]    )
              lowercase       :							List[str]									=				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:
              lowercase       :							Optional[int]									=				{
                  "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..."    )
              lowercase       :							Optional[Any]									=				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     :			List[Any]  						=			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     :			Any  						=			parser.parse_args()
   convert_yolos_checkpoint(args.yolos_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)


