





import os
import unittest

from transformers import MobileBertTokenizer, MobileBertTokenizerFast
from transformers.models.bert.tokenization_bert import (
    VOCAB_FILES_NAMES,
    BasicTokenizer,
    WordpieceTokenizer,
    _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    _SCREAMING_SNAKE_CASE						(	__SCREAMING_SNAKE_CASE    ,							unittest.TestCase    ):







 '''simple docstring'''
 lowercase_		=			MobileBertTokenizer
 lowercase_		=			MobileBertTokenizerFast
 lowercase_		=			True
 lowercase_		=			True
 lowercase_		=			filter_non_english
 lowercase_		=			"google/mobilebert-uncased"

 def 							SCREAMING_SNAKE_CASE_					(self				:   Any)       ->Any:



   '''simple docstring'''



   super().setUp()

   lowerCamelCase__:      Any			  =[
       "[UNK]",
       "[CLS]",
       "[SEP]",
       "[PAD]",
       "[MASK]",
       "want",
       "##want",
       "##ed",
       "wa",
       "un",
       "runn",
       "##ing",
       ",",
       "low",
       "lowest",
   ]
   lowerCamelCase__:      Optional[int]			  =os.path.join(self.tmpdirname       , VOCAB_FILES_NAMES["vocab_file"])
   with open(self.vocab_file       , "w"       , encoding="utf-8") as vocab_writer:
     vocab_writer.write("".join([x + "\n" for x in vocab_tokens]))

   lowerCamelCase__:      str			  =[
       (tokenizer_def[0], self.pre_trained_model_path, tokenizer_def[2])  # else the 'google/' prefix is stripped
       for tokenizer_def in self.tokenizers_list
   ]

 def 							SCREAMING_SNAKE_CASE_					(self				:   List[str]       , UpperCAmelCase_				:   int)       ->List[str]:



   '''simple docstring'''



   lowerCamelCase__:      List[str]			  ="UNwant\u00E9d,running"
   lowerCamelCase__:      Dict			  ="unwanted, running"
   return input_text, output_text

 def 							SCREAMING_SNAKE_CASE_					(self				:   int)       ->str:



   '''simple docstring'''



   lowerCamelCase__:      Optional[int]			  =self.tokenizer_class(self.vocab_file)

   lowerCamelCase__:      Union[str, Any]			  =tokenizer.tokenize("UNwant\u00E9d,running")
   self.assertListEqual(UpperCAmelCase_       , ["un", "##want", "##ed", ",", "runn", "##ing"])
   self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCAmelCase_)       , [9, 6, 7, 12, 10, 11])

 def 							SCREAMING_SNAKE_CASE_					(self				:   Dict)       ->Optional[Any]:



   '''simple docstring'''



   if not self.test_rust_tokenizer:
     return

   lowerCamelCase__:      Tuple			  =self.get_tokenizer()
   lowerCamelCase__:      Union[str, Any]			  =self.get_rust_tokenizer()

   lowerCamelCase__:      List[Any]			  ="UNwant\u00E9d,running"

   lowerCamelCase__:      Optional[Any]			  =tokenizer.tokenize(UpperCAmelCase_)
   lowerCamelCase__:      List[str]			  =rust_tokenizer.tokenize(UpperCAmelCase_)
   self.assertListEqual(UpperCAmelCase_       , UpperCAmelCase_)

   lowerCamelCase__:      Optional[int]			  =tokenizer.encode(UpperCAmelCase_       , add_special_tokens=UpperCAmelCase_)
   lowerCamelCase__:      int			  =rust_tokenizer.encode(UpperCAmelCase_       , add_special_tokens=UpperCAmelCase_)
   self.assertListEqual(UpperCAmelCase_       , UpperCAmelCase_)

   lowerCamelCase__:      List[str]			  =self.get_rust_tokenizer()
   lowerCamelCase__:      Any			  =tokenizer.encode(UpperCAmelCase_)
   lowerCamelCase__:      int			  =rust_tokenizer.encode(UpperCAmelCase_)
   self.assertListEqual(UpperCAmelCase_       , UpperCAmelCase_)

   # With lower casing
   lowerCamelCase__:      Dict			  =self.get_tokenizer(do_lower_case=UpperCAmelCase_)
   lowerCamelCase__:      List[str]			  =self.get_rust_tokenizer(do_lower_case=UpperCAmelCase_)

   lowerCamelCase__:      Optional[int]			  ="UNwant\u00E9d,running"

   lowerCamelCase__:      Tuple			  =tokenizer.tokenize(UpperCAmelCase_)
   lowerCamelCase__:      List[str]			  =rust_tokenizer.tokenize(UpperCAmelCase_)
   self.assertListEqual(UpperCAmelCase_       , UpperCAmelCase_)

   lowerCamelCase__:      Union[str, Any]			  =tokenizer.encode(UpperCAmelCase_       , add_special_tokens=UpperCAmelCase_)
   lowerCamelCase__:      Tuple			  =rust_tokenizer.encode(UpperCAmelCase_       , add_special_tokens=UpperCAmelCase_)
   self.assertListEqual(UpperCAmelCase_       , UpperCAmelCase_)

   lowerCamelCase__:      Union[str, Any]			  =self.get_rust_tokenizer()
   lowerCamelCase__:      Any			  =tokenizer.encode(UpperCAmelCase_)
   lowerCamelCase__:      Union[str, Any]			  =rust_tokenizer.encode(UpperCAmelCase_)
   self.assertListEqual(UpperCAmelCase_       , UpperCAmelCase_)

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



   '''simple docstring'''



   lowerCamelCase__:      str			  =BasicTokenizer()

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

 def 							SCREAMING_SNAKE_CASE_					(self				:   Dict)       ->Optional[int]:



   '''simple docstring'''



   lowerCamelCase__:      Union[str, Any]			  =BasicTokenizer(do_lower_case=UpperCAmelCase_)

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

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



   '''simple docstring'''



   lowerCamelCase__:      Dict			  =BasicTokenizer(do_lower_case=UpperCAmelCase_       , strip_accents=UpperCAmelCase_)

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

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



   '''simple docstring'''



   lowerCamelCase__:      Optional[int]			  =BasicTokenizer(do_lower_case=UpperCAmelCase_       , strip_accents=UpperCAmelCase_)

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

 def 							SCREAMING_SNAKE_CASE_					(self				:   Optional[int])       ->Dict:



   '''simple docstring'''



   lowerCamelCase__:      Any			  =BasicTokenizer(do_lower_case=UpperCAmelCase_)

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

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



   '''simple docstring'''



   lowerCamelCase__:      Optional[Any]			  =BasicTokenizer(do_lower_case=UpperCAmelCase_)

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

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



   '''simple docstring'''



   lowerCamelCase__:      str			  =BasicTokenizer(do_lower_case=UpperCAmelCase_       , strip_accents=UpperCAmelCase_)

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

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



   '''simple docstring'''



   lowerCamelCase__:      Optional[int]			  =BasicTokenizer(do_lower_case=UpperCAmelCase_       , strip_accents=UpperCAmelCase_)

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

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



   '''simple docstring'''



   lowerCamelCase__:      List[Any]			  =BasicTokenizer(do_lower_case=UpperCAmelCase_       , never_split=["[UNK]"])

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

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



   '''simple docstring'''



   lowerCamelCase__:      Dict			  =["[UNK]", "[CLS]", "[SEP]", "want", "##want", "##ed", "wa", "un", "runn", "##ing"]

   lowerCamelCase__:      Optional[int]			  ={}
   for i, token in enumerate(UpperCAmelCase_):
     lowerCamelCase__:      List[str]			  =i
   lowerCamelCase__:      Optional[int]			  =WordpieceTokenizer(vocab=UpperCAmelCase_       , 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 							SCREAMING_SNAKE_CASE_					(self				:   Any)       ->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 							SCREAMING_SNAKE_CASE_					(self				:   Any)       ->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 							SCREAMING_SNAKE_CASE_					(self				:   Optional[Any])       ->List[str]:



   '''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 							SCREAMING_SNAKE_CASE_					(self				:   Dict)       ->Optional[Any]:



   '''simple docstring'''



   lowerCamelCase__:      List[str]			  =self.get_tokenizer()
   lowerCamelCase__:      Optional[Any]			  =self.get_rust_tokenizer()

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

   self.assertListEqual(
       [rust_tokenizer.tokenize(UpperCAmelCase_) for t in ["Test", "\xad", "test"]]       , [["[UNK]"], [], ["[UNK]"]])

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



   '''simple docstring'''



   lowerCamelCase__:      Dict			  =self.tokenizer_class.from_pretrained("google/mobilebert-uncased")

   lowerCamelCase__:      Dict			  =tokenizer.encode("sequence builders"       , add_special_tokens=UpperCAmelCase_)
   lowerCamelCase__:      Union[str, Any]			  =tokenizer.encode("multi-sequence build"       , add_special_tokens=UpperCAmelCase_)

   lowerCamelCase__:      Union[str, Any]			  =tokenizer.build_inputs_with_special_tokens(UpperCAmelCase_)
   lowerCamelCase__:      Union[str, Any]			  =tokenizer.build_inputs_with_special_tokens(UpperCAmelCase_       , UpperCAmelCase_)

   assert encoded_sentence == [101] + text + [102]
   assert encoded_pair == [101] + text + [102] + text_a + [102]

 def 							SCREAMING_SNAKE_CASE_					(self				:   Any)       ->Tuple:



   '''simple docstring'''



   for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
     with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""):
       lowerCamelCase__:      List[str]			  =self.rust_tokenizer_class.from_pretrained(UpperCAmelCase_       , **UpperCAmelCase_)

       lowerCamelCase__:      Optional[Any]			  =F"""A, naïve {tokenizer_r.mask_token} AllenNLP sentence."""
       lowerCamelCase__:      Any			  =tokenizer_r.encode_plus(
           UpperCAmelCase_       , return_attention_mask=UpperCAmelCase_       , return_token_type_ids=UpperCAmelCase_       , return_offsets_mapping=UpperCAmelCase_       , add_special_tokens=UpperCAmelCase_       , )

       lowerCamelCase__:      Optional[int]			  =tokenizer_r.do_lower_case if hasattr(UpperCAmelCase_       , "do_lower_case") else False
       lowerCamelCase__:      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 							SCREAMING_SNAKE_CASE_					(self				:   List[str])       ->Dict:



   '''simple docstring'''



   lowerCamelCase__:      str			  =["的", "人", "有"]
   lowerCamelCase__:      Optional[Any]			  ="".join(UpperCAmelCase_)
   for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
     with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""):
       lowerCamelCase__:      str			  =True
       lowerCamelCase__:      List[str]			  =self.tokenizer_class.from_pretrained(UpperCAmelCase_       , **UpperCAmelCase_)
       lowerCamelCase__:      Tuple			  =self.rust_tokenizer_class.from_pretrained(UpperCAmelCase_       , **UpperCAmelCase_)

       lowerCamelCase__:      List[Any]			  =tokenizer_p.encode(UpperCAmelCase_       , add_special_tokens=UpperCAmelCase_)
       lowerCamelCase__:      Any			  =tokenizer_r.encode(UpperCAmelCase_       , add_special_tokens=UpperCAmelCase_)

       lowerCamelCase__:      List[str]			  =tokenizer_r.convert_ids_to_tokens(UpperCAmelCase_)
       lowerCamelCase__:      Dict			  =tokenizer_p.convert_ids_to_tokens(UpperCAmelCase_)

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

       lowerCamelCase__:      Any			  =False
       lowerCamelCase__:      List[Any]			  =self.rust_tokenizer_class.from_pretrained(UpperCAmelCase_       , **UpperCAmelCase_)
       lowerCamelCase__:      str			  =self.tokenizer_class.from_pretrained(UpperCAmelCase_       , **UpperCAmelCase_)

       lowerCamelCase__:      Dict			  =tokenizer_r.encode(UpperCAmelCase_       , add_special_tokens=UpperCAmelCase_)
       lowerCamelCase__:      List[Any]			  =tokenizer_p.encode(UpperCAmelCase_       , add_special_tokens=UpperCAmelCase_)

       lowerCamelCase__:      Dict			  =tokenizer_r.convert_ids_to_tokens(UpperCAmelCase_)
       lowerCamelCase__:      Dict			  =tokenizer_p.convert_ids_to_tokens(UpperCAmelCase_)

       # it is expected that only the first Chinese character is not preceded by "##".
       lowerCamelCase__:      str			  =[
           F"""##{token}""" if idx != 0 else token for idx, token in enumerate(UpperCAmelCase_)
       ]
       self.assertListEqual(UpperCAmelCase_       , UpperCAmelCase_)
       self.assertListEqual(UpperCAmelCase_       , UpperCAmelCase_)








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_ (       __a		)     ->		YolosConfig:







  """simple docstring"""





  lowerCamelCase__:      str			  =YolosConfig()

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

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

  return config





def      lowerCAmelCase_ (       __a	,    __a	,    __a = False		)     ->		Dict:







  """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)
    lowerCamelCase__:      Optional[int]			  =state_dict.pop(F"""blocks.{i}.attn.qkv.weight"""		)
    lowerCamelCase__:      Dict			  =state_dict.pop(F"""blocks.{i}.attn.qkv.bias"""		)
    # next, add query, keys and values (in that order) to the state dict
    lowerCamelCase__:      Union[str, Any]			  =in_proj_weight[: config.hidden_size, :]
    lowerCamelCase__:      str			  =in_proj_bias[: config.hidden_size]
    lowerCamelCase__:      str			  =in_proj_weight[
        config.hidden_size : config.hidden_size * 2, :
    ]
    lowerCamelCase__:      str			  =in_proj_bias[
        config.hidden_size : config.hidden_size * 2
    ]
    lowerCamelCase__:      Optional[int]			  =in_proj_weight[-config.hidden_size :, :]
    lowerCamelCase__:      List[Any]			  =in_proj_bias[-config.hidden_size :]





def      lowerCAmelCase_ (       __a		)     ->		str:







  """simple docstring"""





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

  return name





def      lowerCAmelCase_ (       __a	,    __a		)     ->		dict:







  """simple docstring"""





  for key in orig_state_dict.copy().keys():
    lowerCamelCase__:      Any			  =orig_state_dict.pop(__a		)

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

  return orig_state_dict





def      lowerCAmelCase_ (       )     ->		torch.Tensor:







  """simple docstring"""





  lowerCamelCase__:      Any			  ="http://images.cocodataset.org/val2017/000000039769.jpg"
  lowerCamelCase__:      Optional[Any]			  =Image.open(requests.get(__a	,    stream=__a		).raw		)
  return im





@torch.no_grad()
def      lowerCAmelCase_ (       __a	,    __a	,    __a	,    __a = False		)     ->		List[str]:







  """simple docstring"""





  lowerCamelCase__:      int			  =get_yolos_config(__a		)

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

  # load 🤗 model
  lowerCamelCase__:      int			  =YolosForObjectDetection(__a		)
  model.eval()
  lowerCamelCase__:      Union[str, Any]			  =convert_state_dict(__a	,    __a		)
  model.load_state_dict(__a		)

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

  lowerCamelCase__  ,					lowerCamelCase__:      Any			  =None, None
  if yolos_name == "yolos_ti":
    lowerCamelCase__:      Optional[Any]			  =torch.tensor(
        [[-3_9.5_0_2_2, -1_1.9_8_2_0, -1_7.6_8_8_8], [-2_9.9_5_7_4, -9.9_7_6_9, -1_7.7_6_9_1], [-4_2.3_2_8_1, -2_0.7_2_0_0, -3_0.6_2_9_4]]		)
    lowerCamelCase__:      List[Any]			  =torch.tensor(
        [[0.4_0_2_1, 0.0_8_3_6, 0.7_9_7_9], [0.0_1_8_4, 0.2_6_0_9, 0.0_3_6_4], [0.1_7_8_1, 0.2_0_0_4, 0.2_0_9_5]]		)
  elif yolos_name == "yolos_s_200_pre":
    lowerCamelCase__:      Optional[int]			  =torch.tensor(
        [[-2_4.0_2_4_8, -1_0.3_0_2_4, -1_4.8_2_9_0], [-4_2.0_3_9_2, -1_6.8_2_0_0, -2_7.4_3_3_4], [-2_7.2_7_4_3, -1_1.8_1_5_4, -1_8.7_1_4_8]]		)
    lowerCamelCase__:      Any			  =torch.tensor(
        [[0.2_5_5_9, 0.5_4_5_5, 0.4_7_0_6], [0.2_9_8_9, 0.7_2_7_9, 0.1_8_7_5], [0.7_7_3_2, 0.4_0_1_7, 0.4_4_6_2]]		)
  elif yolos_name == "yolos_s_300_pre":
    lowerCamelCase__:      str			  =torch.tensor(
        [[-3_6.2_2_2_0, -1_4.4_3_8_5, -2_3.5_4_5_7], [-3_5.6_9_7_0, -1_4.7_5_8_3, -2_1.3_9_3_5], [-3_1.5_9_3_9, -1_3.6_0_4_2, -1_6.8_0_4_9]]		)
    lowerCamelCase__:      Optional[Any]			  =torch.tensor(
        [[0.7_6_1_4, 0.2_3_1_6, 0.4_7_2_8], [0.7_1_6_8, 0.4_4_9_5, 0.3_8_5_5], [0.4_9_9_6, 0.1_4_6_6, 0.9_9_9_6]]		)
  elif yolos_name == "yolos_s_dWr":
    lowerCamelCase__:      str			  =torch.tensor(
        [[-4_2.8_6_6_8, -2_4.1_0_4_9, -4_1.1_6_9_0], [-3_4.7_4_5_6, -1_4.1_2_7_4, -2_4.9_1_9_4], [-3_3.7_8_9_8, -1_2.1_9_4_6, -2_5.6_4_9_5]]		)
    lowerCamelCase__:      Union[str, Any]			  =torch.tensor(
        [[0.5_5_8_7, 0.2_7_7_3, 0.0_6_0_5], [0.5_0_0_4, 0.3_0_1_4, 0.9_9_9_4], [0.4_9_9_9, 0.1_5_4_8, 0.9_9_9_4]]		)
  elif yolos_name == "yolos_base":
    lowerCamelCase__:      Tuple			  =torch.tensor(
        [[-4_0.6_0_6_4, -2_4.3_0_8_4, -3_2.6_4_4_7], [-5_5.1_9_9_0, -3_0.7_7_1_9, -3_5.5_8_7_7], [-5_1.4_3_1_1, -3_3.3_5_0_7, -3_5.6_4_6_2]]		)
    lowerCamelCase__:      Optional[int]			  =torch.tensor(
        [[0.5_5_5_5, 0.2_7_9_4, 0.0_6_5_5], [0.9_0_4_9, 0.2_6_6_4, 0.1_8_9_4], [0.9_1_8_3, 0.1_9_8_4, 0.1_6_3_5]]		)
  else:
    raise ValueError(F"""Unknown yolos_name: {yolos_name}"""		)

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

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

  if push_to_hub:
    lowerCamelCase__:      Any			  ={
        "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__:      Optional[int]			  =model_mapping[yolos_name]
    image_processor.push_to_hub(__a	,    organization="hustvl"		)
    model.push_to_hub(__a	,    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)

