





import argparse
import json

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import ViTImageProcessor, ViTMSNConfig, ViTMSNModel
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD


torch.set_grad_enabled(False)






def 	_SCREAMING_SNAKE_CASE		(      SCREAMING_SNAKE_CASE		:Optional[int]   ,   SCREAMING_SNAKE_CASE		:Optional[int]=False   )      ->   Union[str, Any]:
 __lowerCAmelCase					:	int      =       []
 for i in range(config.num_hidden_layers   ):
  # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
  rename_keys.append((F'''module.blocks.{i}.norm1.weight''', F'''vit.encoder.layer.{i}.layernorm_before.weight''')   )
  rename_keys.append((F'''module.blocks.{i}.norm1.bias''', F'''vit.encoder.layer.{i}.layernorm_before.bias''')   )
  rename_keys.append(
      (F'''module.blocks.{i}.attn.proj.weight''', F'''vit.encoder.layer.{i}.attention.output.dense.weight''')   )
  rename_keys.append((F'''module.blocks.{i}.attn.proj.bias''', F'''vit.encoder.layer.{i}.attention.output.dense.bias''')   )
  rename_keys.append((F'''module.blocks.{i}.norm2.weight''', F'''vit.encoder.layer.{i}.layernorm_after.weight''')   )
  rename_keys.append((F'''module.blocks.{i}.norm2.bias''', F'''vit.encoder.layer.{i}.layernorm_after.bias''')   )
  rename_keys.append((F'''module.blocks.{i}.mlp.fc1.weight''', F'''vit.encoder.layer.{i}.intermediate.dense.weight''')   )
  rename_keys.append((F'''module.blocks.{i}.mlp.fc1.bias''', F'''vit.encoder.layer.{i}.intermediate.dense.bias''')   )
  rename_keys.append((F'''module.blocks.{i}.mlp.fc2.weight''', F'''vit.encoder.layer.{i}.output.dense.weight''')   )
  rename_keys.append((F'''module.blocks.{i}.mlp.fc2.bias''', F'''vit.encoder.layer.{i}.output.dense.bias''')   )

 # projection layer + position embeddings
 rename_keys.extend(
     [
         ("""module.cls_token""", """vit.embeddings.cls_token"""),
         ("""module.patch_embed.proj.weight""", """vit.embeddings.patch_embeddings.projection.weight"""),
         ("""module.patch_embed.proj.bias""", """vit.embeddings.patch_embeddings.projection.bias"""),
         ("""module.pos_embed""", """vit.embeddings.position_embeddings"""),
     ]   )

 if base_model:
  # layernorm + pooler
  rename_keys.extend(
      [
          ("""module.norm.weight""", """layernorm.weight"""),
          ("""module.norm.bias""", """layernorm.bias"""),
      ]   )

  # if just the base model, we should remove "vit" from all keys that start with "vit"
  __lowerCAmelCase					:	Tuple      =       [(pair[0], pair[1][4:]) if pair[1].startswith("""vit"""   ) else pair for pair in rename_keys]
 else:
  # layernorm + classification head
  rename_keys.extend(
      [
          ("""norm.weight""", """vit.layernorm.weight"""),
          ("""norm.bias""", """vit.layernorm.bias"""),
          ("""head.weight""", """classifier.weight"""),
          ("""head.bias""", """classifier.bias"""),
      ]   )

 return rename_keys






def 	_SCREAMING_SNAKE_CASE		(      SCREAMING_SNAKE_CASE		:List[str]   ,   SCREAMING_SNAKE_CASE		:Union[str, Any]   ,   SCREAMING_SNAKE_CASE		:Optional[int]=False   )      ->   Union[str, Any]:
 for i in range(config.num_hidden_layers   ):
  if base_model:
   __lowerCAmelCase					:	Any      =       """"""
  else:
   __lowerCAmelCase					:	str      =       """vit."""
  # read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
  __lowerCAmelCase					:	Dict      =       state_dict.pop(F'''module.blocks.{i}.attn.qkv.weight'''   )
  __lowerCAmelCase					:	List[Any]      =       state_dict.pop(F'''module.blocks.{i}.attn.qkv.bias'''   )
  # next, add query, keys and values (in that order) to the state dict
  __lowerCAmelCase					:	str      =       in_proj_weight[
      : config.hidden_size, :
  ]
  __lowerCAmelCase					:	Optional[int]      =       in_proj_bias[: config.hidden_size]
  __lowerCAmelCase					:	Optional[Any]      =       in_proj_weight[
      config.hidden_size : config.hidden_size * 2, :
  ]
  __lowerCAmelCase					:	str      =       in_proj_bias[
      config.hidden_size : config.hidden_size * 2
  ]
  __lowerCAmelCase					:	List[str]      =       in_proj_weight[
      -config.hidden_size :, :
  ]
  __lowerCAmelCase					:	List[str]      =       in_proj_bias[-config.hidden_size :]






def 	_SCREAMING_SNAKE_CASE		(      SCREAMING_SNAKE_CASE		:Any   )      ->   Any:
 __lowerCAmelCase					:	List[Any]      =       ["""head.weight""", """head.bias"""]
 for k in ignore_keys:
  state_dict.pop(SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   )






def 	_SCREAMING_SNAKE_CASE		(      SCREAMING_SNAKE_CASE		:Optional[Any]   )      ->   Any:
 # projection head is used in the self-supervised pre-training in MSN,
 # for downstream task it's not needed.
 __lowerCAmelCase					:	List[Any]      =       [
     """module.fc.fc1.weight""",
     """module.fc.fc1.bias""",
     """module.fc.bn1.weight""",
     """module.fc.bn1.bias""",
     """module.fc.bn1.running_mean""",
     """module.fc.bn1.running_var""",
     """module.fc.bn1.num_batches_tracked""",
     """module.fc.fc2.weight""",
     """module.fc.fc2.bias""",
     """module.fc.bn2.weight""",
     """module.fc.bn2.bias""",
     """module.fc.bn2.running_mean""",
     """module.fc.bn2.running_var""",
     """module.fc.bn2.num_batches_tracked""",
     """module.fc.fc3.weight""",
     """module.fc.fc3.bias""",
 ]
 for k in ignore_keys:
  state_dict.pop(SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   )






def 	_SCREAMING_SNAKE_CASE		(      SCREAMING_SNAKE_CASE		:Tuple   ,   SCREAMING_SNAKE_CASE		:int   ,   SCREAMING_SNAKE_CASE		:List[Any]   )      ->   Dict:
 __lowerCAmelCase					:	Tuple      =       dct.pop(SCREAMING_SNAKE_CASE   )
 __lowerCAmelCase					:	Any      =       val






def 	_SCREAMING_SNAKE_CASE		(      SCREAMING_SNAKE_CASE		:Optional[Any]   ,   SCREAMING_SNAKE_CASE		:Dict   )      ->   str:
 __lowerCAmelCase					:	Dict      =       ViTMSNConfig()
 __lowerCAmelCase					:	str      =       1_000

 __lowerCAmelCase					:	str      =       """datasets/huggingface/label-files"""
 __lowerCAmelCase					:	str      =       """imagenet-1k-id2label.json"""
 __lowerCAmelCase					:	Optional[int]      =       json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   )   ,   """r"""   )   )
 __lowerCAmelCase					:	Tuple      =       {int(SCREAMING_SNAKE_CASE   ): v for k, v in idalabel.items()}
 __lowerCAmelCase					:	List[str]      =       idalabel
 __lowerCAmelCase					:	Union[str, Any]      =       {v: k for k, v in idalabel.items()}

 if "s16" in checkpoint_url:
  __lowerCAmelCase					:	Dict      =       384
  __lowerCAmelCase					:	str      =       1_536
  __lowerCAmelCase					:	Tuple      =       6
 elif "l16" in checkpoint_url:
  __lowerCAmelCase					:	str      =       1_024
  __lowerCAmelCase					:	Any      =       4_096
  __lowerCAmelCase					:	Optional[int]      =       24
  __lowerCAmelCase					:	Tuple      =       16
  __lowerCAmelCase					:	str      =       0.1
 elif "b4" in checkpoint_url:
  __lowerCAmelCase					:	int      =       4
 elif "l7" in checkpoint_url:
  __lowerCAmelCase					:	Optional[Any]      =       7
  __lowerCAmelCase					:	Any      =       1_024
  __lowerCAmelCase					:	int      =       4_096
  __lowerCAmelCase					:	Optional[Any]      =       24
  __lowerCAmelCase					:	str      =       16
  __lowerCAmelCase					:	int      =       0.1

 __lowerCAmelCase					:	List[str]      =       ViTMSNModel(SCREAMING_SNAKE_CASE   )

 __lowerCAmelCase					:	List[Any]      =       torch.hub.load_state_dict_from_url(SCREAMING_SNAKE_CASE   ,   map_location="""cpu"""   )["""target_encoder"""]

 __lowerCAmelCase					:	Dict      =       ViTImageProcessor(size=config.image_size   )

 remove_projection_head(SCREAMING_SNAKE_CASE   )
 __lowerCAmelCase					:	Optional[int]      =       create_rename_keys(SCREAMING_SNAKE_CASE   ,   base_model=SCREAMING_SNAKE_CASE   )

 for src, dest in rename_keys:
  rename_key(SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   )
 read_in_q_k_v(SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   ,   base_model=SCREAMING_SNAKE_CASE   )

 model.load_state_dict(SCREAMING_SNAKE_CASE   )
 model.eval()

 __lowerCAmelCase					:	Union[str, Any]      =       """http://images.cocodataset.org/val2017/000000039769.jpg"""

 __lowerCAmelCase					:	Optional[int]      =       Image.open(requests.get(SCREAMING_SNAKE_CASE   ,   stream=SCREAMING_SNAKE_CASE   ).raw   )
 __lowerCAmelCase					:	List[str]      =       ViTImageProcessor(
     size=config.image_size   ,   image_mean=SCREAMING_SNAKE_CASE   ,   image_std=SCREAMING_SNAKE_CASE   )
 __lowerCAmelCase					:	int      =       image_processor(images=SCREAMING_SNAKE_CASE   ,   return_tensors="""pt"""   )

 # forward pass
 torch.manual_seed(2   )
 __lowerCAmelCase					:	List[str]      =       model(**SCREAMING_SNAKE_CASE   )
 __lowerCAmelCase					:	Union[str, Any]      =       outputs.last_hidden_state

 # The following Colab Notebook was used to generate these outputs:
 # https://colab.research.google.com/gist/sayakpaul/3672419a04f5997827503fd84079bdd1/scratchpad.ipynb
 if "s16" in checkpoint_url:
  __lowerCAmelCase					:	Tuple      =       torch.tensor([[-1.09_15, -1.48_76, -1.18_09]]   )
 elif "b16" in checkpoint_url:
  __lowerCAmelCase					:	int      =       torch.tensor([[14.28_89, -18.90_45, 11.72_81]]   )
 elif "l16" in checkpoint_url:
  __lowerCAmelCase					:	Optional[int]      =       torch.tensor([[41.50_28, -22.86_81, 45.64_75]]   )
 elif "b4" in checkpoint_url:
  __lowerCAmelCase					:	List[str]      =       torch.tensor([[-4.38_68, 5.29_32, -0.41_37]]   )
 else:
  __lowerCAmelCase					:	Optional[int]      =       torch.tensor([[-0.17_92, -0.64_65, 2.42_63]]   )

 # verify logits
 assert torch.allclose(last_hidden_state[:, 0, :3]   ,   SCREAMING_SNAKE_CASE   ,   atol=1e-4   )

 print(F'''Saving model to {pytorch_dump_folder_path}'''   )
 model.save_pretrained(SCREAMING_SNAKE_CASE   )

 print(F'''Saving image processor to {pytorch_dump_folder_path}'''   )
 image_processor.save_pretrained(SCREAMING_SNAKE_CASE   )


if __name__ == "__main__":
      _UpperCAmelCase             =							argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          '--checkpoint_url',
          default='https://dl.fbaipublicfiles.com/msn/vits16_800ep.pth.tar',
          type=str,
          help='URL of the checkpoint you\'d like to convert.',
      )
      parser.add_argument(
          '--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
      )

      _UpperCAmelCase             =							parser.parse_args()
      convert_vit_msn_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)






import unittest

from transformers import PegasusTokenizer, PegasusTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


_UpperCAmelCase             =							get_tests_dir('fixtures/test_sentencepiece_no_bos.model')






@require_sentencepiece
@require_tokenizers
class        snake_case_    (		__lowercase					,unittest.TestCase						):
  A_      							=   PegasusTokenizer
  A_      							=   PegasusTokenizerFast
  A_      							=   True
  A_      							=   True






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






   '''simple docstring'''

   super().setUp()

   # We have a SentencePiece fixture for testing
   __lowerCAmelCase					:	Optional[int]      =       PegasusTokenizer(_snake_case      )
   tokenizer.save_pretrained(self.tmpdirname      )






  @cached_property
  def 				UpperCAmelCase__						(							self			:					str      )->Dict:






   '''simple docstring'''

   return PegasusTokenizer.from_pretrained("""google/pegasus-large"""      )






  def 				UpperCAmelCase__						(							self			:					Optional[Any]       ,  **_snake_case			:					Tuple      )->PegasusTokenizer:






   '''simple docstring'''

   return PegasusTokenizer.from_pretrained(self.tmpdirname       ,  **_snake_case      )






  def 				UpperCAmelCase__						(							self			:					Dict       ,  _snake_case			:					List[Any]      )->Tuple:






   '''simple docstring'''

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






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






   '''simple docstring'''

   __lowerCAmelCase					:	Dict      =       """</s>"""
   __lowerCAmelCase					:	int      =       1

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






  def 				UpperCAmelCase__						(							self			:					int      )->Tuple:






   '''simple docstring'''

   __lowerCAmelCase					:	List[Any]      =       list(self.get_tokenizer().get_vocab().keys()      )

   self.assertEqual(vocab_keys[0]       ,  """<pad>"""      )
   self.assertEqual(vocab_keys[1]       ,  """</s>"""      )
   self.assertEqual(vocab_keys[-1]       ,  """v"""      )
   self.assertEqual(len(_snake_case      )       ,  1103      )






  def 				UpperCAmelCase__						(							self			:					Optional[int]      )->Optional[int]:






   '''simple docstring'''

   self.assertEqual(self.get_tokenizer().vocab_size       ,  1103      )






  def 				UpperCAmelCase__						(							self			:					Dict      )->str:






   '''simple docstring'''

   __lowerCAmelCase					:	Any      =       self.rust_tokenizer_class.from_pretrained(self.tmpdirname      )
   __lowerCAmelCase					:	Dict      =       self.tokenizer_class.from_pretrained(self.tmpdirname      )
   __lowerCAmelCase					:	str      =       (
       """Let's see which <unk> is the better <unk_token_11> one <mask_1> It seems like this <mask_2> was important"""
       """ </s> <pad> <pad> <pad>"""
   )
   __lowerCAmelCase					:	str      =       rust_tokenizer([raw_input_str]       ,  return_tensors=_snake_case       ,  add_special_tokens=_snake_case      ).input_ids[0]
   __lowerCAmelCase					:	Tuple      =       py_tokenizer([raw_input_str]       ,  return_tensors=_snake_case       ,  add_special_tokens=_snake_case      ).input_ids[0]
   self.assertListEqual(_snake_case       ,  _snake_case      )






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






   '''simple docstring'''

   __lowerCAmelCase					:	List[str]      =       self._large_tokenizer
   # <mask_1> masks whole sentence while <mask_2> masks single word
   __lowerCAmelCase					:	Tuple      =       """<mask_1> To ensure a <mask_2> flow of bank resolutions."""
   __lowerCAmelCase					:	List[str]      =       [2, 413, 615, 114, 3, 1971, 113, 1679, 10710, 107, 1]
   __lowerCAmelCase					:	str      =       tokenizer([raw_input_str]       ,  return_tensors=_snake_case      ).input_ids[0]
   self.assertListEqual(_snake_case       ,  _snake_case      )






  def 				UpperCAmelCase__						(							self			:					List[str]      )->List[str]:






   '''simple docstring'''

   __lowerCAmelCase					:	Optional[Any]      =       self._large_tokenizer
   # The tracebacks for the following asserts are **better** without messages or self.assertEqual
   assert tokenizer.vocab_size == 96103
   assert tokenizer.pad_token_id == 0
   assert tokenizer.eos_token_id == 1
   assert tokenizer.offset == 103
   assert tokenizer.unk_token_id == tokenizer.offset + 2 == 105
   assert tokenizer.unk_token == "<unk>"
   assert tokenizer.model_max_length == 1024
   __lowerCAmelCase					:	Tuple      =       """To ensure a smooth flow of bank resolutions."""
   __lowerCAmelCase					:	Optional[Any]      =       [413, 615, 114, 2291, 1971, 113, 1679, 10710, 107, 1]
   __lowerCAmelCase					:	int      =       tokenizer([raw_input_str]       ,  return_tensors=_snake_case      ).input_ids[0]
   self.assertListEqual(_snake_case       ,  _snake_case      )
   assert tokenizer.convert_ids_to_tokens([0, 1, 2, 3]      ) == ["<pad>", "</s>", "<mask_1>", "<mask_2>"]






  @require_torch
  def 				UpperCAmelCase__						(							self			:					Any      )->Any:






   '''simple docstring'''

   __lowerCAmelCase					:	List[Any]      =       ["""This is going to be way too long.""" * 150, """short example"""]
   __lowerCAmelCase					:	Union[str, Any]      =       ["""not super long but more than 5 tokens""", """tiny"""]
   __lowerCAmelCase					:	Dict      =       self._large_tokenizer(_snake_case       ,  padding=_snake_case       ,  truncation=_snake_case       ,  return_tensors="""pt"""      )
   __lowerCAmelCase					:	Tuple      =       self._large_tokenizer(
       text_target=_snake_case       ,  max_length=5       ,  padding=_snake_case       ,  truncation=_snake_case       ,  return_tensors="""pt"""      )

   assert batch.input_ids.shape == (2, 1024)
   assert batch.attention_mask.shape == (2, 1024)
   assert targets["input_ids"].shape == (2, 5)
   assert len(_snake_case      ) == 2  # input_ids, attention_mask.






  @slow
  def 				UpperCAmelCase__						(							self			:					Optional[Any]      )->Any:






   '''simple docstring'''

   __lowerCAmelCase					:	Optional[Any]      =       {"""input_ids""": [[38979, 143, 18485, 606, 130, 26669, 87686, 121, 54189, 1129, 111, 26669, 87686, 121, 9114, 14787, 121, 13249, 158, 592, 956, 121, 14621, 31576, 143, 62613, 108, 9688, 930, 43430, 11562, 62613, 304, 108, 11443, 897, 108, 9314, 17415, 63399, 108, 11443, 7614, 18316, 118, 4284, 7148, 12430, 143, 1400, 25703, 158, 111, 4284, 7148, 11772, 143, 21297, 1064, 158, 122, 204, 3506, 1754, 1133, 14787, 1581, 115, 33224, 4482, 111, 1355, 110, 29173, 317, 50833, 108, 20147, 94665, 111, 77198, 107, 1], [110, 62613, 117, 638, 112, 1133, 121, 20098, 1355, 79050, 13872, 135, 1596, 53541, 1352, 141, 13039, 5542, 124, 302, 518, 111, 268, 2956, 115, 149, 4427, 107, 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], [139, 1235, 2799, 18289, 17780, 204, 109, 9474, 1296, 107, 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]], """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, 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, 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=_snake_case       ,  model_name="""google/bigbird-pegasus-large-arxiv"""       ,  revision="""ba85d0851d708441f91440d509690f1ab6353415"""       ,  )






@require_sentencepiece
@require_tokenizers
class        snake_case_    (		__lowercase					,unittest.TestCase						):
  A_      							=   PegasusTokenizer
  A_      							=   PegasusTokenizerFast
  A_      							=   True
  A_      							=   True






  def 				UpperCAmelCase__						(							self			:					Tuple      )->Tuple:






   '''simple docstring'''

   super().setUp()

   # We have a SentencePiece fixture for testing
   __lowerCAmelCase					:	Any      =       PegasusTokenizer(_snake_case       ,  offset=0       ,  mask_token_sent=_snake_case       ,  mask_token="""[MASK]"""      )
   tokenizer.save_pretrained(self.tmpdirname      )






  @cached_property
  def 				UpperCAmelCase__						(							self			:					Any      )->str:






   '''simple docstring'''

   return PegasusTokenizer.from_pretrained("""google/bigbird-pegasus-large-arxiv"""      )






  def 				UpperCAmelCase__						(							self			:					Union[str, Any]       ,  **_snake_case			:					Optional[Any]      )->PegasusTokenizer:






   '''simple docstring'''

   return PegasusTokenizer.from_pretrained(self.tmpdirname       ,  **_snake_case      )






  def 				UpperCAmelCase__						(							self			:					List[str]       ,  _snake_case			:					Optional[int]      )->Union[str, Any]:






   '''simple docstring'''

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






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






   '''simple docstring'''

   __lowerCAmelCase					:	Tuple      =       self.rust_tokenizer_class.from_pretrained(self.tmpdirname      )
   __lowerCAmelCase					:	List[str]      =       self.tokenizer_class.from_pretrained(self.tmpdirname      )
   __lowerCAmelCase					:	int      =       (
       """Let's see which <unk> is the better <unk_token> one [MASK] It seems like this [MASK] was important </s>"""
       """ <pad> <pad> <pad>"""
   )
   __lowerCAmelCase					:	str      =       rust_tokenizer([raw_input_str]       ,  return_tensors=_snake_case       ,  add_special_tokens=_snake_case      ).input_ids[0]
   __lowerCAmelCase					:	Tuple      =       py_tokenizer([raw_input_str]       ,  return_tensors=_snake_case       ,  add_special_tokens=_snake_case      ).input_ids[0]
   self.assertListEqual(_snake_case       ,  _snake_case      )






  @require_torch
  def 				UpperCAmelCase__						(							self			:					str      )->Optional[Any]:






   '''simple docstring'''

   __lowerCAmelCase					:	int      =       ["""This is going to be way too long.""" * 1000, """short example"""]
   __lowerCAmelCase					:	Optional[int]      =       ["""not super long but more than 5 tokens""", """tiny"""]
   __lowerCAmelCase					:	str      =       self._large_tokenizer(_snake_case       ,  padding=_snake_case       ,  truncation=_snake_case       ,  return_tensors="""pt"""      )
   __lowerCAmelCase					:	List[Any]      =       self._large_tokenizer(
       text_target=_snake_case       ,  max_length=5       ,  padding=_snake_case       ,  truncation=_snake_case       ,  return_tensors="""pt"""      )

   assert batch.input_ids.shape == (2, 4096)
   assert batch.attention_mask.shape == (2, 4096)
   assert targets["input_ids"].shape == (2, 5)
   assert len(_snake_case      ) == 2  # input_ids, attention_mask.






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






   '''simple docstring'''

   __lowerCAmelCase					:	Tuple      =       (
       """This is an example string that is used to test the original TF implementation against the HF"""
       """ implementation"""
   )

   __lowerCAmelCase					:	Optional[Any]      =       self._large_tokenizer(_snake_case      ).input_ids

   self.assertListEqual(
       _snake_case       ,  [182, 117, 142, 587, 4211, 120, 117, 263, 112, 804, 109, 856, 25016, 3137, 464, 109, 26955, 3137, 1]       ,  )