
"""simple docstring"""




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 						a__		( a_,     unittest.TestCase		):
   __lowerCAmelCase				=							GPTaTokenizer
   __lowerCAmelCase				=							GPTaTokenizerFast
   __lowerCAmelCase				=							True
   __lowerCAmelCase				=							{"""add_prefix_space""": True}
   __lowerCAmelCase				=							False
   def      __magic_name__     (     self							):
          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      __magic_name__     (     self      ,       **_a							):
          kwargs.update(self.special_tokens_map							)
          return GPTaTokenizer.from_pretrained(self.tmpdirname      ,       **_a							)
   def      __magic_name__     (     self      ,       **_a							):
          kwargs.update(self.special_tokens_map							)
          return GPTaTokenizerFast.from_pretrained(self.tmpdirname      ,       **_a							)
   def      __magic_name__     (     self      ,       _a							):
          lowercase       :							Tuple									=				"lower newer"
          lowercase       :							Dict									=				"lower newer"
          return input_text, output_text
   def      __magic_name__     (     self							):
          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      __magic_name__     (     self							):
          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      __magic_name__     (     self      ,       *_a      ,       **_a							):
          # It's very difficult to mix/test pretokenization with byte-level
          # And get both GPT2 and Roberta to work at the same time (mostly an issue of adding a space before the string)
          pass
   def      __magic_name__     (     self      ,       _a=15							):
          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      __magic_name__     (     self							):
          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      __magic_name__     (     self							):
          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      __magic_name__     (     self							):
          pass

   def      __magic_name__     (     self							):
          # TODO: change to self.get_tokenizers() when the fast version is implemented
          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 						a__		( unittest.TestCase		):
   def      __magic_name__     (     self							):
          # More context:
          # https://huggingface.co/wjmcat/opt-350m-paddle/discussions/1
          # https://huggingface.slack.com/archives/C01N44FJDHT/p1653511495183519
          # https://github.com/huggingface/transformers/pull/17088#discussion_r871246439

          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, 1_345, 9, 10, 4_758]							)
          tokenizer.save_pretrained("test_opt"							)

          lowercase       :							Union[str, Any]									=				AutoTokenizer.from_pretrained("./test_opt"							)
          lowercase       :							Tuple									=				tokenizer.encode(
              _a      ,       )
          self.assertEqual(_a      ,       [2, 250, 1_345, 9, 10, 4_758]							)
   def      __magic_name__     (     self							):
          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, 1_345, 9, 10, 4_758]							)

   @unittest.skip("This test is failing because of a bug in the fast tokenizer"							)
   def      __magic_name__     (     self							):
          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      ,       [31_957, 250, 1_345, 9, 10, 4_758]							)
          tokenizer.save_pretrained("./tok"							)
          lowercase       :							Tuple									=				AutoTokenizer.from_pretrained("./tok"							)
          self.assertTrue(tokenizer.is_fast							)
          lowercase       :							str									=				tokenizer.encode(
              _a      ,       )
          self.assertEqual(_a      ,       [31_957, 250, 1_345, 9, 10, 4_758]							)




"""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)


