





import tempfile
import unittest
from pathlib import Path
from shutil import copyfile

from transformers import MaMaaaTokenizer, is_torch_available
from transformers.testing_utils import (
    get_tests_dir,
    nested_simplify,
    require_sentencepiece,
    require_tokenizers,
    require_torch,
    slow,
)
from transformers.utils import is_sentencepiece_available


if is_sentencepiece_available():
     from transformers.models.mam_aaa.tokenization_mam_aaa import VOCAB_FILES_NAMES, save_json

from ...test_tokenization_common import TokenizerTesterMixin


if is_sentencepiece_available():
     __A									=				get_tests_dir("fixtures/test_sentencepiece.model")


if is_torch_available():
     from transformers.models.mam_aaa.modeling_mam_aaa import shift_tokens_right

__A									=				12_8022
__A									=				12_8028



@require_sentencepiece
class    _SCREAMING_SNAKE_CASE						(	__SCREAMING_SNAKE_CASE    ,							unittest.TestCase    ):







 '''simple docstring'''
 lowercase_		=			MaMaaaTokenizer
 lowercase_		=			False
 lowercase_		=			False
 lowercase_		=			True

 def 							SCREAMING_SNAKE_CASE_					(self				:   Dict)       ->Tuple:



   '''simple docstring'''



   super().setUp()

   lowerCamelCase__:      List[Any]			  =["</s>", "<unk>", "▁This", "▁is", "▁a", "▁t", "est", "\u0120", "<pad>"]
   lowerCamelCase__:      Optional[Any]			  =dict(zip(UpperCAmelCase_       , range(len(UpperCAmelCase_))))
   lowerCamelCase__:      int			  =Path(self.tmpdirname)
   save_json(UpperCAmelCase_       , save_dir / VOCAB_FILES_NAMES["vocab_file"])
   if not (save_dir / VOCAB_FILES_NAMES["spm_file"]).exists():
     copyfile(UpperCAmelCase_       , save_dir / VOCAB_FILES_NAMES["spm_file"])

   lowerCamelCase__:      List[Any]			  =MaMaaaTokenizer.from_pretrained(self.tmpdirname)
   tokenizer.save_pretrained(self.tmpdirname)

 def 							SCREAMING_SNAKE_CASE_					(self				:   Optional[int]       , **UpperCAmelCase_				:   Any)       ->str:



   '''simple docstring'''



   return MaMaaaTokenizer.from_pretrained(self.tmpdirname       , **UpperCAmelCase_)

 def 							SCREAMING_SNAKE_CASE_					(self				:   int       , UpperCAmelCase_				:   Any)       ->Optional[Any]:



   '''simple docstring'''



   return (
       "This is a test",
       "This is a test",
   )

 def 							SCREAMING_SNAKE_CASE_					(self				:   str)       ->Dict:



   '''simple docstring'''



   lowerCamelCase__:      Dict			  ="</s>"
   lowerCamelCase__:      int			  =0

   self.assertEqual(self.get_tokenizer()._convert_token_to_id(UpperCAmelCase_)       , UpperCAmelCase_)
   self.assertEqual(self.get_tokenizer()._convert_id_to_token(UpperCAmelCase_)       , UpperCAmelCase_)

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



   '''simple docstring'''



   lowerCamelCase__:      Any			  =self.get_tokenizer()
   lowerCamelCase__:      Dict			  =list(tokenizer.get_vocab().keys())

   self.assertEqual(vocab_keys[0]       , "</s>")
   self.assertEqual(vocab_keys[1]       , "<unk>")
   self.assertEqual(vocab_keys[-1]       , "<s>")
   self.assertEqual(len(UpperCAmelCase_)       , tokenizer.vocab_size + len(tokenizer.get_added_vocab()))

 @unittest.skip("Skip this test while all models are still to be uploaded.")
 def 							SCREAMING_SNAKE_CASE_					(self				:   List[Any])       ->List[str]:



   '''simple docstring'''



   pass

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



   '''simple docstring'''



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

   lowerCamelCase__:      Any			  =tokenizer.tokenize("This is a test")
   self.assertListEqual(UpperCAmelCase_       , ["▁This", "▁is", "▁a", "▁t", "est"])

   self.assertListEqual(
       tokenizer.convert_tokens_to_ids(UpperCAmelCase_)       , [2, 3, 4, 5, 6]       , )

   lowerCamelCase__:      List[Any]			  =tokenizer.convert_ids_to_tokens([2, 3, 4, 5, 6])
   self.assertListEqual(UpperCAmelCase_       , ["▁This", "▁is", "▁a", "▁t", "est"])

   lowerCamelCase__:      int			  =tokenizer.convert_tokens_to_string(UpperCAmelCase_)
   self.assertEqual(UpperCAmelCase_       , "This is a test")







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



   '''simple docstring'''



   lowerCamelCase__:      Optional[Any]			  ={"input_ids": [[128_022, 110_108, 397, 11, 38_272, 2_247, 124_811, 285, 18_105, 1_586, 207, 7, 39_534, 4_428, 397, 1_019, 18_105, 1_586, 207, 7, 41_337, 16_786, 241, 7, 20_214, 17, 125_690, 10_398, 7, 44_378, 58_069, 68_342, 7_798, 7_343, 11, 299, 33_310, 4, 158, 37_350, 94_077, 4_569, 299, 33_310, 90, 4, 52_840, 290, 4, 31_270, 112, 299, 682, 4, 52_840, 39_953, 14_079, 193, 52_519, 90_894, 17_894, 120_697, 11, 40_445, 551, 17, 1_019, 52_519, 90_894, 17_756, 963, 11, 40_445, 480, 17, 9_792, 1_120, 5_173, 1_393, 6_240, 16_786, 241, 120_996, 28, 1_245, 1_393, 118_240, 11_123, 1_019, 93_612, 2_691, 10_618, 98_058, 120_409, 1_928, 279, 4, 40_683, 367, 178, 207, 1_019, 103, 103_121, 506, 65_296, 5, 2], [128_022, 21_217, 367, 117, 125_450, 128, 719, 7, 7_308, 40, 93_612, 12_669, 1_116, 16_704, 71, 17_785, 3_699, 15_592, 35, 144, 9_584, 241, 11_943, 713, 950, 799, 2_247, 88_427, 150, 149, 118_813, 120_706, 1_019, 106_906, 81_518, 28, 1_224, 22_799, 397, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [128_022, 1_658, 123_311, 5_155, 5_578, 4_722, 279, 14_947, 2_366, 1_120, 1_197, 14, 1_348, 9_232, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], "attention_mask": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
   # fmt: on

   self.tokenizer_integration_test_util(
       expected_encoding=UpperCAmelCase_       , model_name="facebook/m2m100_418M"       , revision="c168bae485c864188cf9aa0e4108b0b6934dc91e"       , )



@require_torch
@require_sentencepiece
@require_tokenizers
class    _SCREAMING_SNAKE_CASE						(	unittest.TestCase    ):







 '''simple docstring'''
 lowercase_		=			"facebook/m2m100_418M"
 lowercase_		=			[
     "In my opinion, there are two levels of response from the French government.",
     "NSA Affair Emphasizes Complete Lack of Debate on Intelligence",
 ]
 lowercase_		=			[
     "Selon moi, il y a deux niveaux de réponse de la part du gouvernement français.",
     "L'affaire NSA souligne l'absence totale de débat sur le renseignement",
 ]

 # fmt: off
 lowercase_		=			[EN_CODE, 593, 1949, 11_5781, 4, 7_1586, 4234, 6_0633, 12_6233, 432, 12_3808, 1_5592, 1197, 11_7132, 12_0618, 5, 2]

 @classmethod
 def 							SCREAMING_SNAKE_CASE_					(cls				:   Optional[Any])       ->Dict:



   '''simple docstring'''



   lowerCamelCase__:      MaMaaaTokenizer			  =MaMaaaTokenizer.from_pretrained(
       cls.checkpoint_name       , src_lang="en"       , tgt_lang="fr")
   lowerCamelCase__:      List[Any]			  =1
   return cls

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



   '''simple docstring'''



   self.assertEqual(self.tokenizer.get_lang_id("ar")       , 128_006)
   self.assertEqual(self.tokenizer.get_lang_id("en")       , 128_022)
   self.assertEqual(self.tokenizer.get_lang_id("ro")       , 128_076)
   self.assertEqual(self.tokenizer.get_lang_id("mr")       , 128_063)

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



   '''simple docstring'''



   lowerCamelCase__:      List[str]			  =self.tokenizer.get_vocab()
   self.assertEqual(len(UpperCAmelCase_)       , self.tokenizer.vocab_size)
   self.assertEqual(vocab["<unk>"]       , 3)
   self.assertIn(self.tokenizer.get_lang_token("en")       , UpperCAmelCase_)

 def 							SCREAMING_SNAKE_CASE_					(self				:   int)       ->Dict:



   '''simple docstring'''



   lowerCamelCase__:      str			  ="en"
   lowerCamelCase__:      int			  =self.tokenizer.batch_encode_plus(self.src_text).input_ids[0]
   self.assertListEqual(self.expected_src_tokens       , UpperCAmelCase_)

 def 							SCREAMING_SNAKE_CASE_					(self				:   Dict)       ->Dict:



   '''simple docstring'''



   self.assertIn(UpperCAmelCase_       , self.tokenizer.all_special_ids)
   # fmt: off
   lowerCamelCase__:      Dict			  =[FR_CODE, 5_364, 82, 8_642, 4, 294, 47, 8, 14_028, 136, 3_286, 9_706, 6, 90_797, 6, 144_012, 162, 88_128, 30_061, 5, 2]
   # fmt: on
   lowerCamelCase__:      int			  =self.tokenizer.decode(UpperCAmelCase_       , skip_special_tokens=UpperCAmelCase_)
   lowerCamelCase__:      Dict			  =self.tokenizer.decode(generated_ids[1:]       , skip_special_tokens=UpperCAmelCase_)
   self.assertEqual(UpperCAmelCase_       , UpperCAmelCase_)
   self.assertNotIn(self.tokenizer.eos_token       , UpperCAmelCase_)

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



   '''simple docstring'''



   lowerCamelCase__:      int			  =tempfile.mkdtemp()
   lowerCamelCase__:      Any			  =self.tokenizer.lang_token_to_id
   self.tokenizer.save_pretrained(UpperCAmelCase_)
   lowerCamelCase__:      Tuple			  =MaMaaaTokenizer.from_pretrained(UpperCAmelCase_)
   self.assertDictEqual(new_tok.lang_token_to_id       , UpperCAmelCase_)

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



   '''simple docstring'''



   lowerCamelCase__:      Union[str, Any]			  ="en"
   lowerCamelCase__:      Any			  ="fr"

   lowerCamelCase__:      Optional[Any]			  =self.tokenizer(self.src_text       , text_target=self.tgt_text       , padding=UpperCAmelCase_       , return_tensors="pt")

   lowerCamelCase__:      List[str]			  =shift_tokens_right(
       batch["labels"]       , self.tokenizer.pad_token_id       , self.tokenizer.eos_token_id)

   for k in batch:
     lowerCamelCase__:      Any			  =batch[k].tolist()
   # batch = {k: v.tolist() for k,v in batch.items()}
   # fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4
   # batch.decoder_inputs_ids[0][0] ==
   assert batch.input_ids[1][0] == EN_CODE
   assert batch.input_ids[1][-1] == 2
   assert batch.labels[1][0] == FR_CODE
   assert batch.labels[1][-1] == 2
   assert batch.decoder_input_ids[1][:2] == [2, FR_CODE]

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



   '''simple docstring'''



   lowerCamelCase__:      Tuple			  ="mr"
   self.assertListEqual(self.tokenizer.prefix_tokens       , [self.tokenizer.get_lang_id("mr")])
   self.assertListEqual(self.tokenizer.suffix_tokens       , [self.tokenizer.eos_token_id])

   lowerCamelCase__:      List[Any]			  ="zh"
   self.assertListEqual(self.tokenizer.prefix_tokens       , [self.tokenizer.get_lang_id("zh")])
   self.assertListEqual(self.tokenizer.suffix_tokens       , [self.tokenizer.eos_token_id])

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



   '''simple docstring'''



   lowerCamelCase__:      Any			  ="mr"
   self.tokenizer._switch_to_target_mode()
   self.assertListEqual(self.tokenizer.prefix_tokens       , [self.tokenizer.get_lang_id("mr")])
   self.assertListEqual(self.tokenizer.suffix_tokens       , [self.tokenizer.eos_token_id])
   self.tokenizer._switch_to_input_mode()
   self.assertListEqual(self.tokenizer.prefix_tokens       , [self.tokenizer.get_lang_id(self.tokenizer.src_lang)])

   lowerCamelCase__:      Tuple			  ="zh"
   self.tokenizer._switch_to_target_mode()
   self.assertListEqual(self.tokenizer.prefix_tokens       , [self.tokenizer.get_lang_id("zh")])
   self.assertListEqual(self.tokenizer.suffix_tokens       , [self.tokenizer.eos_token_id])
   self.tokenizer._switch_to_input_mode()
   self.assertListEqual(self.tokenizer.prefix_tokens       , [self.tokenizer.get_lang_id(self.tokenizer.src_lang)])







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



   '''simple docstring'''



   lowerCamelCase__:      List[str]			  =self.tokenizer._build_translation_inputs("A test"       , return_tensors="pt"       , src_lang="en"       , tgt_lang="ar")

   self.assertEqual(
       nested_simplify(UpperCAmelCase_)       , {
           # en_XX, A, test, EOS
           "input_ids": [[128_022, 58, 4_183, 2]],
           "attention_mask": [[1, 1, 1, 1]],
           # ar_AR
           "forced_bos_token_id": 128_006,
       }       , )








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)

