




"""simple docstring"""


import json
import os
import unittest

from transformers.models.roc_bert.tokenization_roc_bert import (
    VOCAB_FILES_NAMES,
    RoCBertBasicTokenizer,
    RoCBertTokenizer,
    RoCBertWordpieceTokenizer,
    _is_control,
    _is_punctuation,
    _is_whitespace,
)
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin, filter_non_english






@require_tokenizers
class 					lowercase(     __a       ,			unittest.TestCase ):







 '''simple docstring'''





 lowercase__ 					=					RoCBertTokenizer
 lowercase__ 					=					None
 lowercase__ 					=					False
 lowercase__ 					=					True
 lowercase__ 					=					filter_non_english


 def  UpperCamelCase_   (				self:		Optional[int]		):




   '''simple docstring'''



   super().setUp()

   _snake_case							:		List[str]								=  ["""[UNK]""", """[CLS]""", """[SEP]""", """[PAD]""", """[MASK]""", """你""", """好""", """是""", """谁""", """a""", """b""", """c""", """d"""]
   _snake_case							:		Tuple								=  {}
   _snake_case							:		Any								=  {}
   for i, value in enumerate(a_		):
     _snake_case							:		List[str]								=  i
     _snake_case							:		Optional[int]								=  i
   _snake_case							:		List[Any]								=  os.path.join(self.tmpdirname,      VOCAB_FILES_NAMES["""vocab_file"""]		)
   _snake_case							:		Dict								=  os.path.join(self.tmpdirname,      VOCAB_FILES_NAMES["""word_shape_file"""]		)
   _snake_case							:		Optional[Any]								=  os.path.join(self.tmpdirname,      VOCAB_FILES_NAMES["""word_pronunciation_file"""]		)
   with open(self.vocab_file,      """w""",      encoding="""utf-8"""		) as vocab_writer:
     vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens]		)		)
   with open(self.word_shape_file,      """w""",      encoding="""utf-8"""		) as word_shape_writer:
     json.dump(a_,      a_,      ensure_ascii=a_		)
   with open(self.word_pronunciation_file,      """w""",      encoding="""utf-8"""		) as word_pronunciation_writer:
     json.dump(a_,      a_,      ensure_ascii=a_		)


 def  UpperCamelCase_   (				self:		Union[str, Any]		):




   '''simple docstring'''



   _snake_case							:		Optional[int]								=  self.tokenizer_class(self.vocab_file,      self.word_shape_file,      self.word_pronunciation_file		)

   _snake_case							:		str								=  tokenizer.tokenize("""你好[SEP]你是谁"""		)
   self.assertListEqual(a_,      ["""你""", """好""", """[SEP]""", """你""", """是""", """谁"""]		)
   self.assertListEqual(tokenizer.convert_tokens_to_ids(a_		),      [5, 6, 2, 5, 7, 8]		)
   self.assertListEqual(tokenizer.convert_tokens_to_shape_ids(a_		),      [5, 6, 2, 5, 7, 8]		)
   self.assertListEqual(tokenizer.convert_tokens_to_pronunciation_ids(a_		),      [5, 6, 2, 5, 7, 8]		)


 def  UpperCamelCase_   (				self:		str		):




   '''simple docstring'''



   _snake_case							:		Any								=  RoCBertBasicTokenizer()

   self.assertListEqual(tokenizer.tokenize("""ah\u535A\u63A8zz"""		),      ["""ah""", """\u535A""", """\u63A8""", """zz"""]		)


 def  UpperCamelCase_   (				self:		Any		):




   '''simple docstring'''



   _snake_case							:		Tuple								=  RoCBertBasicTokenizer(do_lower_case=a_		)

   self.assertListEqual(
       tokenizer.tokenize(""" \tHeLLo!how  \n Are yoU?  """		),      ["""hello""", """!""", """how""", """are""", """you""", """?"""]		)
   self.assertListEqual(tokenizer.tokenize("""H\u00E9llo"""		),      ["""hello"""]		)


 def  UpperCamelCase_   (				self:		Optional[Any]		):




   '''simple docstring'''



   _snake_case							:		List[str]								=  RoCBertBasicTokenizer(do_lower_case=a_,      strip_accents=a_		)

   self.assertListEqual(
       tokenizer.tokenize(""" \tHäLLo!how  \n Are yoU?  """		),      ["""hällo""", """!""", """how""", """are""", """you""", """?"""]		)
   self.assertListEqual(tokenizer.tokenize("""H\u00E9llo"""		),      ["""h\u00E9llo"""]		)


 def  UpperCamelCase_   (				self:		Union[str, Any]		):




   '''simple docstring'''



   _snake_case							:		Any								=  RoCBertBasicTokenizer(do_lower_case=a_,      strip_accents=a_		)

   self.assertListEqual(
       tokenizer.tokenize(""" \tHäLLo!how  \n Are yoU?  """		),      ["""hallo""", """!""", """how""", """are""", """you""", """?"""]		)
   self.assertListEqual(tokenizer.tokenize("""H\u00E9llo"""		),      ["""hello"""]		)


 def  UpperCamelCase_   (				self:		str		):




   '''simple docstring'''



   _snake_case							:		Optional[Any]								=  RoCBertBasicTokenizer(do_lower_case=a_		)

   self.assertListEqual(
       tokenizer.tokenize(""" \tHäLLo!how  \n Are yoU?  """		),      ["""hallo""", """!""", """how""", """are""", """you""", """?"""]		)
   self.assertListEqual(tokenizer.tokenize("""H\u00E9llo"""		),      ["""hello"""]		)


 def  UpperCamelCase_   (				self:		Dict		):




   '''simple docstring'''



   _snake_case							:		Optional[int]								=  RoCBertBasicTokenizer(do_lower_case=a_		)

   self.assertListEqual(
       tokenizer.tokenize(""" \tHeLLo!how  \n Are yoU?  """		),      ["""HeLLo""", """!""", """how""", """Are""", """yoU""", """?"""]		)


 def  UpperCamelCase_   (				self:		List[Any]		):




   '''simple docstring'''



   _snake_case							:		str								=  RoCBertBasicTokenizer(do_lower_case=a_,      strip_accents=a_		)

   self.assertListEqual(
       tokenizer.tokenize(""" \tHäLLo!how  \n Are yoU?  """		),      ["""HäLLo""", """!""", """how""", """Are""", """yoU""", """?"""]		)


 def  UpperCamelCase_   (				self:		Any		):




   '''simple docstring'''



   _snake_case							:		Union[str, Any]								=  RoCBertBasicTokenizer(do_lower_case=a_,      strip_accents=a_		)

   self.assertListEqual(
       tokenizer.tokenize(""" \tHäLLo!how  \n Are yoU?  """		),      ["""HaLLo""", """!""", """how""", """Are""", """yoU""", """?"""]		)


 def  UpperCamelCase_   (				self:		str		):




   '''simple docstring'''



   _snake_case							:		str								=  RoCBertBasicTokenizer(do_lower_case=a_,      never_split=["""[UNK]"""]		)

   self.assertListEqual(
       tokenizer.tokenize(""" \tHeLLo!how  \n Are yoU? [UNK]"""		),      ["""HeLLo""", """!""", """how""", """Are""", """yoU""", """?""", """[UNK]"""]		)


 def  UpperCamelCase_   (				self:		int		):




   '''simple docstring'''



   _snake_case							:		Optional[Any]								=  ["""[UNK]""", """[CLS]""", """[SEP]""", """want""", """##want""", """##ed""", """wa""", """un""", """runn""", """##ing"""]

   _snake_case							:		Optional[Any]								=  {}
   for i, token in enumerate(a_		):
     _snake_case							:		Dict								=  i
   _snake_case							:		Optional[Any]								=  RoCBertWordpieceTokenizer(vocab=a_,      unk_token="""[UNK]"""		)

   self.assertListEqual(tokenizer.tokenize(""""""		),      []		)

   self.assertListEqual(tokenizer.tokenize("""unwanted running"""		),      ["""un""", """##want""", """##ed""", """runn""", """##ing"""]		)

   self.assertListEqual(tokenizer.tokenize("""unwantedX running"""		),      ["""[UNK]""", """runn""", """##ing"""]		)


 def  UpperCamelCase_   (				self:		List[str]		):




   '''simple docstring'''



   self.assertTrue(_is_whitespace(""" """		)		)
   self.assertTrue(_is_whitespace("""\t"""		)		)
   self.assertTrue(_is_whitespace("""\r"""		)		)
   self.assertTrue(_is_whitespace("""\n"""		)		)
   self.assertTrue(_is_whitespace("""\u00A0"""		)		)

   self.assertFalse(_is_whitespace("""A"""		)		)
   self.assertFalse(_is_whitespace("""-"""		)		)


 def  UpperCamelCase_   (				self:		Optional[int]		):




   '''simple docstring'''



   self.assertTrue(_is_control("""\u0005"""		)		)

   self.assertFalse(_is_control("""A"""		)		)
   self.assertFalse(_is_control(""" """		)		)
   self.assertFalse(_is_control("""\t"""		)		)
   self.assertFalse(_is_control("""\r"""		)		)


 def  UpperCamelCase_   (				self:		Optional[Any]		):




   '''simple docstring'''



   self.assertTrue(_is_punctuation("""-"""		)		)
   self.assertTrue(_is_punctuation("""$"""		)		)
   self.assertTrue(_is_punctuation("""`"""		)		)
   self.assertTrue(_is_punctuation("""."""		)		)

   self.assertFalse(_is_punctuation("""A"""		)		)
   self.assertFalse(_is_punctuation(""" """		)		)


 def  UpperCamelCase_   (				self:		Dict		):




   '''simple docstring'''



   _snake_case							:		Optional[int]								=  self.get_tokenizer()

   # Example taken from the issue https://github.com/huggingface/tokenizers/issues/340
   self.assertListEqual([tokenizer.tokenize(a_		) for t in ["""Test""", """\xad""", """test"""]],      [["""[UNK]"""], [], ["""[UNK]"""]]		)

   if self.test_rust_tokenizer:
     _snake_case							:		Optional[int]								=  self.get_rust_tokenizer()
     self.assertListEqual(
         [rust_tokenizer.tokenize(a_		) for t in ["""Test""", """\xad""", """test"""]],      [["""[UNK]"""], [], ["""[UNK]"""]]		)


 def  UpperCamelCase_   (				self:		Any		):




   '''simple docstring'''



   for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
     with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"		):
       _snake_case							:		Optional[int]								=  self.rust_tokenizer_class.from_pretrained(a_,      **a_		)

       _snake_case							:		List[Any]								=  f"A, naïve {tokenizer_r.mask_token} AllenNLP sentence."
       _snake_case							:		List[Any]								=  tokenizer_r.encode_plus(
           a_,      return_attention_mask=a_,      return_token_type_ids=a_,      return_offsets_mapping=a_,      add_special_tokens=a_,      )

       _snake_case							:		Optional[Any]								=  tokenizer_r.do_lower_case if hasattr(a_,      """do_lower_case"""		) else False
       _snake_case							:		Optional[Any]								=  (
           [
               ((0, 0), tokenizer_r.cls_token),
               ((0, 1), """A"""),
               ((1, 2), ""","""),
               ((3, 5), """na"""),
               ((5, 6), """##ï"""),
               ((6, 8), """##ve"""),
               ((9, 15), tokenizer_r.mask_token),
               ((16, 21), """Allen"""),
               ((21, 23), """##NL"""),
               ((23, 24), """##P"""),
               ((25, 33), """sentence"""),
               ((33, 34), """."""),
               ((0, 0), tokenizer_r.sep_token),
           ]
           if not do_lower_case
           else [
               ((0, 0), tokenizer_r.cls_token),
               ((0, 1), """a"""),
               ((1, 2), ""","""),
               ((3, 8), """naive"""),
               ((9, 15), tokenizer_r.mask_token),
               ((16, 21), """allen"""),
               ((21, 23), """##nl"""),
               ((23, 24), """##p"""),
               ((25, 33), """sentence"""),
               ((33, 34), """."""),
               ((0, 0), tokenizer_r.sep_token),
           ]
       )

       self.assertEqual(
           [e[1] for e in expected_results],      tokenizer_r.convert_ids_to_tokens(tokens["""input_ids"""]		)		)
       self.assertEqual([e[0] for e in expected_results],      tokens["""offset_mapping"""]		)


 def  UpperCamelCase_   (				self:		int		):




   '''simple docstring'''



   _snake_case							:		Union[str, Any]								=  ["""的""", """人""", """有"""]
   _snake_case							:		Any								=  """""".join(a_		)
   for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
     with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"		):
       _snake_case							:		int								=  True
       _snake_case							:		Tuple								=  self.tokenizer_class.from_pretrained(a_,      **a_		)
       _snake_case							:		List[Any]								=  self.rust_tokenizer_class.from_pretrained(a_,      **a_		)

       _snake_case							:		Optional[Any]								=  tokenizer_p.encode(a_,      add_special_tokens=a_		)
       _snake_case							:		int								=  tokenizer_r.encode(a_,      add_special_tokens=a_		)

       _snake_case							:		Optional[Any]								=  tokenizer_r.convert_ids_to_tokens(a_		)
       _snake_case							:		Optional[int]								=  tokenizer_p.convert_ids_to_tokens(a_		)

       # it is expected that each Chinese character is not preceded by "##"
       self.assertListEqual(a_,      a_		)
       self.assertListEqual(a_,      a_		)

       _snake_case							:		List[str]								=  False
       _snake_case							:		Union[str, Any]								=  self.rust_tokenizer_class.from_pretrained(a_,      **a_		)
       _snake_case							:		str								=  self.tokenizer_class.from_pretrained(a_,      **a_		)

       _snake_case							:		Optional[Any]								=  tokenizer_r.encode(a_,      add_special_tokens=a_		)
       _snake_case							:		Any								=  tokenizer_p.encode(a_,      add_special_tokens=a_		)

       _snake_case							:		Optional[int]								=  tokenizer_r.convert_ids_to_tokens(a_		)
       _snake_case							:		Tuple								=  tokenizer_p.convert_ids_to_tokens(a_		)

       # it is expected that only the first Chinese character is not preceded by "##".
       _snake_case							:		List[Any]								=  [
           f"##{token}" if idx != 0 else token for idx, token in enumerate(a_		)
       ]
       self.assertListEqual(a_,      a_		)
       self.assertListEqual(a_,      a_		)


 @slow
 def  UpperCamelCase_   (				self:		Tuple		):




   '''simple docstring'''



   _snake_case							:		Union[str, Any]								=  self.tokenizer_class(self.vocab_file,      self.word_shape_file,      self.word_pronunciation_file		)

   _snake_case							:		Tuple								=  tokenizer.encode("""你好""",      add_special_tokens=a_		)
   _snake_case							:		Optional[int]								=  tokenizer.encode("""你是谁""",      add_special_tokens=a_		)

   _snake_case							:		str								=  tokenizer.build_inputs_with_special_tokens(a_		)
   _snake_case							:		int								=  tokenizer.build_inputs_with_special_tokens(a_,      a_		)

   assert encoded_sentence == [1] + text + [2]
   assert encoded_pair == [1] + text + [2] + text_a + [2]


 def  UpperCamelCase_   (				self:		str		):




   '''simple docstring'''



   _snake_case							:		List[Any]								=  self.get_tokenizers(do_lower_case=a_		)
   for tokenizer in tokenizers:
     with self.subTest(f"{tokenizer.__class__.__name__}"		):
       _snake_case							:		int								=  """你好，你是谁"""
       _snake_case							:		List[str]								=  tokenizer.tokenize(a_		)
       _snake_case							:		List[str]								=  tokenizer.convert_tokens_to_ids(a_		)
       _snake_case							:		List[Any]								=  tokenizer.convert_tokens_to_shape_ids(a_		)
       _snake_case							:		Optional[Any]								=  tokenizer.convert_tokens_to_pronunciation_ids(a_		)
       _snake_case							:		Dict								=  tokenizer.prepare_for_model(
           a_,      a_,      a_,      add_special_tokens=a_		)

       _snake_case							:		Dict								=  tokenizer.encode_plus(a_,      add_special_tokens=a_		)

       self.assertEqual(a_,      a_		)








"""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)


