





"""simple docstring"""





import argparse
import json
import os

import tensorstore as ts
import torch
from flax import serialization
from flax.traverse_util import flatten_dict, unflatten_dict
from tensorflow.io import gfile

from transformers.modeling_utils import dtype_byte_size
from transformers.models.switch_transformers.convert_switch_transformers_original_flax_checkpoint_to_pytorch import (
    rename_keys,
)
from transformers.utils import WEIGHTS_INDEX_NAME, WEIGHTS_NAME
from transformers.utils.hub import convert_file_size_to_int
def  __A   (_SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE			)					->Optional[Any]:





  """simple docstring"""


  if flax_key_tuple[-1] == "kernel" and flax_tensor.ndim == 3:
    # expert layer
    lowerCAmelCase__			:Dict											=							flax_key_tuple[:-1] + ('weight',)
    lowerCAmelCase__			:Optional[Any]											=							torch.permute(_SCREAMING_SNAKE_CASE     ,   (0, 2, 1)			)
  elif flax_key_tuple[-1] == "kernel" and ".".join(_SCREAMING_SNAKE_CASE			):
    # linear layer
    lowerCAmelCase__			:Any											=							flax_key_tuple[:-1] + ('weight',)
    lowerCAmelCase__			:int											=							flax_tensor.T
  elif flax_key_tuple[-1] in ["scale", "embedding"]:
    lowerCAmelCase__			:int											=							flax_key_tuple[:-1] + ('weight',)

  return flax_key_tuple, flax_tensor
def  __A   (_SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE			)					->Any:





  """simple docstring"""


  if "metadata" in layer:
    lowerCAmelCase__			:Optional[Any]											=							layer.split('metadata'			)
    lowerCAmelCase__			:Tuple											=							''.join(split_layer[0]			)[:-1]
    lowerCAmelCase__			:Optional[int]											=							[tuple(('metadata' + split_layer[1]).split('/'			)			)]
  elif "kvstore" in layer:
    lowerCAmelCase__			:List[Any]											=							layer.split('kvstore'			)
    lowerCAmelCase__			:Union[str, Any]											=							''.join(split_layer[0]			)[:-1]
    lowerCAmelCase__			:Any											=							[tuple(('kvstore' + split_layer[1]).split('/'			)			)]

  else:
    lowerCAmelCase__			:Optional[Any]											=							layer.split('/'			)
    lowerCAmelCase__			:Tuple											=							'/'.join(split_layer[:-1]			)
    lowerCAmelCase__			:int											=							(split_layer[-1],)

  if "kvstore/path" in layer:
    lowerCAmelCase__			:str											=							F"{switch_checkpoint_path}/{checkpoint_info[layer]}"
  elif "kvstore/driver" in layer:
    lowerCAmelCase__			:List[Any]											=							'file'
  else:
    lowerCAmelCase__			:Optional[Any]											=							checkpoint_info[layer]

  return curr_real_layer_name, split_layer, content
def  __A   (_SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE			)					->Dict:





  """simple docstring"""


  lowerCAmelCase__			:Optional[int]											=							rename_keys(_SCREAMING_SNAKE_CASE			)
  lowerCAmelCase__			:List[str]											=							{}
  for k, v in current_block.items():
    lowerCAmelCase__			:Union[str, Any]											=							v
  lowerCAmelCase__			:Optional[Any]											=							new_current_block
  torch.save(_SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE			)
def  __A   (_SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE = WEIGHTS_NAME			)					->int:





  """simple docstring"""


  lowerCAmelCase__			:Optional[Any]											=							convert_file_size_to_int(_SCREAMING_SNAKE_CASE			)
  lowerCAmelCase__			:Dict											=							[]
  lowerCAmelCase__			:Tuple											=							{}
  lowerCAmelCase__			:Optional[Any]											=							0
  lowerCAmelCase__			:int											=							0

  os.makedirs(_SCREAMING_SNAKE_CASE     ,   exist_ok=_SCREAMING_SNAKE_CASE			)
  with gfile.GFile(switch_checkpoint_path + '/checkpoint'     ,   'rb'			) as fp:
    lowerCAmelCase__			:Optional[int]											=							serialization.msgpack_restore(fp.read()			)['optimizer']['target']
    lowerCAmelCase__			:Tuple											=							flatten_dict(_SCREAMING_SNAKE_CASE     ,   sep='/'			)

  lowerCAmelCase__			:List[str]											=							{}
  for layer in checkpoint_info.keys():
    lowerCAmelCase__							,				lowerCAmelCase__							,				lowerCAmelCase__			:Optional[Any]											=							get_key_and_tensorstore_dict(
        _SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE			)
    if curr_real_layer_name in all_layers:
      lowerCAmelCase__			:Dict											=							content
    else:
      lowerCAmelCase__			:Tuple											=							{split_layer[-1]: content}

  for key in all_layers.keys():
    # open tensorstore file
    lowerCAmelCase__			:Dict											=							ts.open(unflatten_dict(all_layers[key]			)			).result().read().result()
    lowerCAmelCase__			:Optional[int]											=							torch.tensor(_SCREAMING_SNAKE_CASE			)
    lowerCAmelCase__			:str											=							raw_weights.numel() * dtype_byte_size(raw_weights.dtype			)

    # use the renaming pattern from the small conversion scripts
    lowerCAmelCase__							,				lowerCAmelCase__			:List[str]											=							rename_base_flax_keys(tuple(key.split('/'			)			)     ,   _SCREAMING_SNAKE_CASE			)
    lowerCAmelCase__			:Optional[int]											=							'/'.join(_SCREAMING_SNAKE_CASE			)

    # If this weight is going to tip up over the maximal size, we split.
    if current_block_size + weight_size > max_shard_size:
      lowerCAmelCase__			:Optional[int]											=							os.path.join(
          _SCREAMING_SNAKE_CASE     ,   weights_name.replace('.bin'     ,   F"-{len(_SCREAMING_SNAKE_CASE			)+1:05d}-of-???.bin"			)			)
      rename_and_save_block(_SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE			)
      sharded_state_dicts.append(current_block.keys()			)
      del current_block
      lowerCAmelCase__			:Dict											=							{}
      lowerCAmelCase__			:List[Any]											=							0

    lowerCAmelCase__			:Optional[Any]											=							raw_weights.to(getattr(_SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE			)			)
    current_block_size += weight_size
    total_size += weight_size

  # Add the last block
  lowerCAmelCase__			:Optional[Any]											=							os.path.join(_SCREAMING_SNAKE_CASE     ,   weights_name.replace('.bin'     ,   F"-{len(_SCREAMING_SNAKE_CASE			)+1:05d}-of-???.bin"			)			)
  rename_and_save_block(_SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE			)
  sharded_state_dicts.append(current_block.keys()			)

  # If we only have one shard, we return it
  if len(_SCREAMING_SNAKE_CASE			) == 1:
    return {weights_name: sharded_state_dicts[0]}, None

  # Otherwise, let's build the index
  lowerCAmelCase__			:Union[str, Any]											=							{}
  lowerCAmelCase__			:Optional[Any]											=							{}
  for idx, shard in enumerate(_SCREAMING_SNAKE_CASE			):
    lowerCAmelCase__			:List[str]											=							weights_name.replace(
        '.bin'     ,   F"-{idx+1:05d}-of-{len(_SCREAMING_SNAKE_CASE			):05d}.bin"			)  # len(sharded_state_dicts):05d}
    lowerCAmelCase__			:Tuple											=							os.path.join(_SCREAMING_SNAKE_CASE     ,   weights_name.replace('.bin'     ,   F"-{idx+1:05d}-of-???.bin"			)			)
    os.rename(_SCREAMING_SNAKE_CASE     ,   os.path.join(_SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE			)			)
    lowerCAmelCase__			:Dict											=							shard
    for key in shard:
      lowerCAmelCase__			:Optional[Any]											=							shard_file

    # Add the metadata
  lowerCAmelCase__			:Optional[int]											=							{'total_size': total_size}
  lowerCAmelCase__			:Any											=							{'metadata': metadata, 'weight_map': weight_map}

  with open(os.path.join(_SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE			)     ,   'w'     ,   encoding='utf-8'			) as f:
    lowerCAmelCase__			:Optional[int]											=							json.dumps(_SCREAMING_SNAKE_CASE     ,   indent=2     ,   sort_keys=_SCREAMING_SNAKE_CASE			) + '\n'
    f.write(_SCREAMING_SNAKE_CASE			)

  return metadata, index


if __name__ == "__main__":
  __A											=		argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      """--switch_t5x_checkpoint_path""",
      default="""/mnt/disks/disk_switch/original_checkpoints/switch-xxl-128/checkpoint_634600""",
      type=str,
      required=False,
      help="""Path to a directory containing a folder per layer. Follows the original Google format.""",
  )
  parser.add_argument("""--max_shard_size""", default="""10GB""", required=False, help="""Max shard size""")
  parser.add_argument("""--dtype""", default="""bfloat16""", type=str, required=False, help="""dtype of the saved model""")
  parser.add_argument(
      """--pytorch_dump_folder_path""",
      default="""/mnt/disks/disk_switch/original_checkpoints/switch-xxl-128-converted""",
      type=str,
      required=False,
      help="""Path to the output pytorch model.""",
  )
  __A											=		parser.parse_args()
  shard_on_the_fly(
      args.switch_tax_checkpoint_path,
      args.pytorch_dump_folder_path,
      args.max_shard_size,
      args.dtype,
  )




def  __A   ()					->List[str]:





  """simple docstring"""


  from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration, TaTokenizer

  lowerCAmelCase__			:Optional[Any]											=							SwitchTransformersConfig.from_pretrained('google/switch-base-8'			)
  config.save_pretrained('/home/arthur_huggingface_co/transformers/switch_converted'			)
  lowerCAmelCase__			:Optional[Any]											=							SwitchTransformersForConditionalGeneration.from_pretrained(
      '/home/arthur_huggingface_co/transformers/switch_converted'     ,   device_map='auto'			)

  lowerCAmelCase__			:List[str]											=							TaTokenizer.from_pretrained('t5-small'			)
  lowerCAmelCase__			:List[str]											=							'A <extra_id_0> walks into a bar a orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>.'

  lowerCAmelCase__			:Any											=							tokenizer(_SCREAMING_SNAKE_CASE     ,   return_tensors='pt'			).input_ids
  lowerCAmelCase__			:str											=							model.generate(_SCREAMING_SNAKE_CASE     ,   decoder_start_token_id=0			)
  print(tokenizer.decode(out[0]			)			)







"""simple docstring"""





import pickle
import shutil
import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, XGLMTokenizer, XGLMTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


__A											=		get_tests_dir("""fixtures/test_sentencepiece.model""")






@require_sentencepiece
@require_tokenizers
class 					_lowerCAmelCase   (     a  ,   unittest.TestCase       ):
       """simple docstring"""




       __magic_name__     :List[str]		  =					XGLMTokenizer
       __magic_name__     :Any		  =					XGLMTokenizerFast
       __magic_name__     :Dict		  =					True
       __magic_name__     :Union[str, Any]		  =					True






       def      snake_case						(    self      ):



         '''simple docstring'''

         super().setUp()

         # We have a SentencePiece fixture for testing
         lowerCAmelCase__			:int											=							XGLMTokenizer(__UpperCAmelCase    , keep_accents=__UpperCAmelCase      )
         tokenizer.save_pretrained(self.tmpdirname      )






       def      snake_case						(    self      ):



         '''simple docstring'''

         lowerCAmelCase__			:List[Any]											=							'<pad>'
         lowerCAmelCase__			:int											=							1

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






       def      snake_case						(    self      ):



         '''simple docstring'''

         lowerCAmelCase__			:Optional[int]											=							list(self.get_tokenizer().get_vocab().keys()      )

         self.assertEqual(vocab_keys[0]    , '<s>'      )
         self.assertEqual(vocab_keys[1]    , '<pad>'      )
         self.assertEqual(len(__UpperCAmelCase      )    , 1_0_0_8      )






       def      snake_case						(    self      ):



         '''simple docstring'''

         self.assertEqual(self.get_tokenizer().vocab_size    , 1_0_0_8      )






       def      snake_case						(    self      ):



         '''simple docstring'''

         lowerCAmelCase__			:List[Any]											=							XGLMTokenizer(__UpperCAmelCase    , keep_accents=__UpperCAmelCase      )

         lowerCAmelCase__			:Optional[Any]											=							tokenizer.tokenize('This is a test'      )
         self.assertListEqual(__UpperCAmelCase    , ['▁This', '▁is', '▁a', '▁t', 'est']      )

         self.assertListEqual(
             tokenizer.convert_tokens_to_ids(__UpperCAmelCase      )    , [value + tokenizer.fairseq_offset for value in [2_8_5, 4_6, 1_0, 1_7_0, 3_8_2]]    , )

         lowerCAmelCase__			:int											=							tokenizer.tokenize('I was born in 92000, and this is falsé.'      )
         self.assertListEqual(
             __UpperCAmelCase    , [
                 SPIECE_UNDERLINE + 'I',
                 SPIECE_UNDERLINE + 'was',
                 SPIECE_UNDERLINE + 'b',
                 'or',
                 'n',
                 SPIECE_UNDERLINE + 'in',
                 SPIECE_UNDERLINE + '',
                 '9',
                 '2',
                 '0',
                 '0',
                 '0',
                 ',',
                 SPIECE_UNDERLINE + 'and',
                 SPIECE_UNDERLINE + 'this',
                 SPIECE_UNDERLINE + 'is',
                 SPIECE_UNDERLINE + 'f',
                 'al',
                 's',
                 'é',
                 '.',
             ]    , )
         lowerCAmelCase__			:Tuple											=							tokenizer.convert_tokens_to_ids(__UpperCAmelCase      )
         self.assertListEqual(
             __UpperCAmelCase    , [
                 value + tokenizer.fairseq_offset
                 for value in [8, 2_1, 8_4, 5_5, 2_4, 1_9, 7, 2, 6_0_2, 3_4_7, 3_4_7, 3_4_7, 3, 1_2, 6_6, 4_6, 7_2, 8_0, 6, 2, 4]
             ]    , )

         lowerCAmelCase__			:Optional[int]											=							tokenizer.convert_ids_to_tokens(__UpperCAmelCase      )
         self.assertListEqual(
             __UpperCAmelCase    , [
                 SPIECE_UNDERLINE + 'I',
                 SPIECE_UNDERLINE + 'was',
                 SPIECE_UNDERLINE + 'b',
                 'or',
                 'n',
                 SPIECE_UNDERLINE + 'in',
                 SPIECE_UNDERLINE + '',
                 '<unk>',
                 '2',
                 '0',
                 '0',
                 '0',
                 ',',
                 SPIECE_UNDERLINE + 'and',
                 SPIECE_UNDERLINE + 'this',
                 SPIECE_UNDERLINE + 'is',
                 SPIECE_UNDERLINE + 'f',
                 'al',
                 's',
                 '<unk>',
                 '.',
             ]    , )






       @cached_property
       def      snake_case						(    self      ):



         '''simple docstring'''

         return XGLMTokenizer.from_pretrained('facebook/xglm-564M'      )






       def      snake_case						(    self      ):



         '''simple docstring'''

         with tempfile.NamedTemporaryFile() as f:
           shutil.copyfile(__UpperCAmelCase    , f.name      )
           lowerCAmelCase__			:Dict											=							XGLMTokenizer(f.name    , keep_accents=__UpperCAmelCase      )
           lowerCAmelCase__			:List[Any]											=							pickle.dumps(__UpperCAmelCase      )
         pickle.loads(__UpperCAmelCase      )






       def      snake_case						(    self      ):



         '''simple docstring'''

         if not self.test_rust_tokenizer:
           return

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

         lowerCAmelCase__			:Optional[Any]											=							'I was born in 92000, and this is falsé.'

         lowerCAmelCase__			:Dict											=							tokenizer.tokenize(__UpperCAmelCase      )
         lowerCAmelCase__			:Union[str, Any]											=							rust_tokenizer.tokenize(__UpperCAmelCase      )
         self.assertListEqual(__UpperCAmelCase    , __UpperCAmelCase      )

         lowerCAmelCase__			:Optional[Any]											=							tokenizer.encode(__UpperCAmelCase    , add_special_tokens=__UpperCAmelCase      )
         lowerCAmelCase__			:List[Any]											=							rust_tokenizer.encode(__UpperCAmelCase    , add_special_tokens=__UpperCAmelCase      )
         self.assertListEqual(__UpperCAmelCase    , __UpperCAmelCase      )

         lowerCAmelCase__			:int											=							self.get_rust_tokenizer()
         lowerCAmelCase__			:Dict											=							tokenizer.encode(__UpperCAmelCase      )
         lowerCAmelCase__			:Tuple											=							rust_tokenizer.encode(__UpperCAmelCase      )
         self.assertListEqual(__UpperCAmelCase    , __UpperCAmelCase      )






       @slow
       def      snake_case						(    self      ):



         '''simple docstring'''

         lowerCAmelCase__			:str											=							'Hello World!'
         lowerCAmelCase__			:Tuple											=							[2, 3_1_2_2_7, 4_4_4_7, 3_5]

         self.assertListEqual(__UpperCAmelCase    , self.big_tokenizer.encode(__UpperCAmelCase      )      )






       @slow
       def      snake_case						(    self      ):



         '''simple docstring'''

         lowerCAmelCase__			:Tuple											=							(
             'This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) " [ ] ! : - . Also we will'
             ' add words that should not exsist and be tokenized to unk, such as saoneuhaoesuth'
         )
         # fmt: off
         lowerCAmelCase__			:List[str]											=							[2, 1_0_1_8, 6_7, 1_1, 1_9_8_8, 2_6_1_7, 5_6_3_1, 2_7_8, 1_1, 3_4_0_7, 4_8, 7_1_6_3_0, 2_8_0_8_5, 4, 3_2_3_4, 1_5_7, 1_3, 6, 5, 6, 4, 3_5_2_6, 7_6_8, 1_5, 6_5_9, 5_7, 2_9_8, 3_9_8_3, 8_6_4, 1_2_9, 2_1, 6, 5, 1_3_6_7_5, 3_7_7, 6_5_2, 7_5_8_0, 1_0_3_4_1, 1_5_5, 2_8_1_7, 4_2_2, 1_6_6_6, 7, 1_6_7_4, 5_3, 1_1_3, 2_0_2_2_7_7, 1_7_8_9_2, 3_3, 6_0, 8_7, 4, 3_2_3_4, 1_5_7, 6_1, 2_6_6_7, 5_2_3_7_6, 1_9, 8_8, 2_3, 7_3_5]
         # fmt: on

         self.assertListEqual(__UpperCAmelCase    , self.big_tokenizer.encode(__UpperCAmelCase      )      )






       @slow
       def      snake_case						(    self      ):



         '''simple docstring'''

         lowerCAmelCase__			:Union[str, Any]											=							{
             'input_ids': [[2, 1_0_8_8_2_5, 1_1_6_3, 1_5, 8_8_0_1_0, 4_7_3, 1_5_8_9_8, 1_5_7, 1_3_6_7_2, 1_8_5_7, 3_1_2, 8, 2_3_8_0_2_1, 1_1_6_3, 5_3, 1_3_6_7_2, 1_8_5_7, 3_1_2, 8, 5_3_2_8_3, 1_8_2_3_9_6, 8, 1_8_5_6_6, 1_6, 3_6_7_3_3, 4_1_0_1, 8, 2_3_0, 2_4_4_0_1_7, 1_2_2_5_5_3, 7, 1_5, 1_3_2_5_9_7, 4, 2_9_3, 1_2_5_1_1, 7_6_1_0, 4, 3_4_1_4, 1_3_2_5_9_7, 9, 4, 3_2_3_6_1, 3_6_2, 4, 7_3_4, 2_8_5_1_2, 3_2_5_6_9, 1_8, 4, 3_2_3_6_1, 2_6_0_9_6, 1_4_9_8_2, 7_3, 1_8_7_1_5, 2_1_4_3_3, 2_3_5_2_6_1, 1_5, 4_9_2, 1_2_4_2_7, 1_6, 5_3, 1_8_7_1_5, 2_1_4_3_3, 6_5_4_5_4, 1_5, 2_3_6_5_9, 5_6_3, 1_6, 2_7_8, 5_9_7, 2_8_4_3, 5_9_5, 7_9_3_1, 1_8_2_3_9_6, 6_4_1_8_6, 2_2, 8_8_6, 5_9_5, 1_3_2_9_8_1, 5_3, 2_5_5_4_0, 3_4_4_9, 4_3_9_8_2, 3_9_9_0_1, 5_9_5_1, 8_7_8, 3_3_0, 4, 2_7_6_9_4, 8_0_2_6_9, 3_1_2, 5_3, 6_5_1_7, 1_1_7_8_0, 6_1_1, 2_0_4_0_8, 5], [2, 6, 1_3_2_5_9_7, 6_7, 4_2_8_9_7, 3_3, 5_9_2, 8, 1_6_3_7_2_9, 2_5_5_4_0, 3_6_1, 1_3_6_9_9_7, 1_0_9_5_1_4, 1_7_3_2_3_0, 7, 5_0_1, 6_0, 1_0_2_9_1_3, 1_9_6, 5_6_3_1, 2_3_5, 6_3_2_4_3, 4_7_3, 6, 2_3_1_7_5_7, 7_4, 5_2_7_7, 7_9_0_5, 5_3, 3_0_9_5, 3_7_3_1_7, 2_2, 4_5_4, 1_8_3_8_7_4, 5], [2, 2_6_8, 3_1_2_9_8, 4_6_5_3_0, 6, 1_3_2_9_3_5, 4_3_8_3_1, 7, 5_9_7, 3_2, 2_4, 3_6_8_8, 9_8_6_5, 5]],
             '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]]
         }  # noqa: E501
         # fmt: on

         self.tokenizer_integration_test_util(
             expected_encoding=__UpperCAmelCase    , model_name='facebook/xglm-564M'    , padding=__UpperCAmelCase    , )
