




import inspect
import os

import torch
from transformers import AutoModel
from transformers.testing_utils import mockenv_context
from transformers.trainer_utils import set_seed

import accelerate
from accelerate.accelerator import Accelerator
from accelerate.state import AcceleratorState
from accelerate.test_utils.testing import (
    AccelerateTestCase,
    TempDirTestCase,
    execute_subprocess_async,
    require_cuda,
    require_fsdp,
    require_multi_gpu,
    slow,
)
from accelerate.utils.constants import (
    FSDP_AUTO_WRAP_POLICY,
    FSDP_BACKWARD_PREFETCH,
    FSDP_SHARDING_STRATEGY,
    FSDP_STATE_DICT_TYPE,
)
from accelerate.utils.dataclasses import FullyShardedDataParallelPlugin
from accelerate.utils.other import patch_environment


set_seed(42)

SCREAMING_SNAKE_CASE__       :      List[str]  =   'bert-base-cased'
SCREAMING_SNAKE_CASE__       :      List[Any]  =   'fp16'
SCREAMING_SNAKE_CASE__       :      Optional[Any]  =   'bf16'
SCREAMING_SNAKE_CASE__       :      Union[str, Any]  =   [FPaa, BFaa]




@require_fsdp
@require_cuda
class 		UpperCamelCase__	(lowerCAmelCase__     ):

    '''simple docstring'''



    def     _lowercase     (    self     )     ->      Union[str, Any]:
           super().setUp()

           lowerCamelCase   :      int			 =					dict(
               ACCELERATE_USE_FSDP="true"     ,  MASTER_ADDR="localhost"     ,  MASTER_PORT="10999"     ,  RANK="0"     ,  LOCAL_RANK="0"     ,  WORLD_SIZE="1"     ,  )







    def     _lowercase     (    self     )     ->      Optional[int]:
           from torch.distributed.fsdp.fully_sharded_data_parallel import ShardingStrategy

           for i, strategy in enumerate(UpperCamelCase__     ):
                  lowerCamelCase   :      List[Any]			 =					self.dist_env.copy()
                  lowerCamelCase   :      Optional[Any]			 =					F'''{i + 1}'''
                  lowerCamelCase   :      str			 =					strategy
                  with mockenv_context(**UpperCamelCase__     ):
                         lowerCamelCase   :      Union[str, Any]			 =					FullyShardedDataParallelPlugin()
                         self.assertEqual(fsdp_plugin.sharding_strategy     ,  ShardingStrategy(i + 1     )     )







    def     _lowercase     (    self     )     ->      int:
           from torch.distributed.fsdp.fully_sharded_data_parallel import BackwardPrefetch

           for i, prefetch_policy in enumerate(UpperCamelCase__     ):
                  lowerCamelCase   :      Optional[Any]			 =					self.dist_env.copy()
                  lowerCamelCase   :      List[str]			 =					prefetch_policy
                  with mockenv_context(**UpperCamelCase__     ):
                         lowerCamelCase   :      Optional[Any]			 =					FullyShardedDataParallelPlugin()
                         if prefetch_policy == "NO_PREFETCH":
                                self.assertIsNone(fsdp_plugin.backward_prefetch     )
                         else:
                                self.assertEqual(fsdp_plugin.backward_prefetch     ,  BackwardPrefetch(i + 1     )     )







    def     _lowercase     (    self     )     ->      Tuple:
           from torch.distributed.fsdp.fully_sharded_data_parallel import StateDictType

           for i, state_dict_type in enumerate(UpperCamelCase__     ):
                  lowerCamelCase   :      List[str]			 =					self.dist_env.copy()
                  lowerCamelCase   :      Optional[Any]			 =					state_dict_type
                  with mockenv_context(**UpperCamelCase__     ):
                         lowerCamelCase   :      List[Any]			 =					FullyShardedDataParallelPlugin()
                         self.assertEqual(fsdp_plugin.state_dict_type     ,  StateDictType(i + 1     )     )
                         if state_dict_type == "FULL_STATE_DICT":
                                self.assertTrue(fsdp_plugin.state_dict_config.offload_to_cpu     )
                                self.assertTrue(fsdp_plugin.state_dict_config.ranka_only     )







    def     _lowercase     (    self     )     ->      List[Any]:
           lowerCamelCase   :      Optional[int]			 =					AutoModel.from_pretrained(UpperCamelCase__     )
           for policy in FSDP_AUTO_WRAP_POLICY:
                  lowerCamelCase   :      Union[str, Any]			 =					self.dist_env.copy()
                  lowerCamelCase   :      Tuple			 =					policy
                  if policy == "TRANSFORMER_BASED_WRAP":
                         lowerCamelCase   :      Dict			 =					"BertLayer"
                  elif policy == "SIZE_BASED_WRAP":
                         lowerCamelCase   :      Optional[int]			 =					"2000"
                  with mockenv_context(**UpperCamelCase__     ):
                         lowerCamelCase   :      Union[str, Any]			 =					FullyShardedDataParallelPlugin()
                         fsdp_plugin.set_auto_wrap_policy(UpperCamelCase__     )
                         if policy == "NO_WRAP":
                                self.assertIsNone(fsdp_plugin.auto_wrap_policy     )
                         else:
                                self.assertIsNotNone(fsdp_plugin.auto_wrap_policy     )

           lowerCamelCase   :      Optional[Any]			 =					self.dist_env.copy()
           lowerCamelCase   :      List[Any]			 =					"TRANSFORMER_BASED_WRAP"
           lowerCamelCase   :      List[Any]			 =					"T5Layer"
           with mockenv_context(**UpperCamelCase__     ):
                  lowerCamelCase   :      Optional[int]			 =					FullyShardedDataParallelPlugin()
                  with self.assertRaises(UpperCamelCase__     ) as cm:
                         fsdp_plugin.set_auto_wrap_policy(UpperCamelCase__     )
                  self.assertTrue("Could not find the transformer layer class to wrap in the model." in str(cm.exception     )     )

           lowerCamelCase   :      List[Any]			 =					self.dist_env.copy()
           lowerCamelCase   :      Any			 =					"SIZE_BASED_WRAP"
           lowerCamelCase   :      Tuple			 =					"0"
           with mockenv_context(**UpperCamelCase__     ):
                  lowerCamelCase   :      List[str]			 =					FullyShardedDataParallelPlugin()
                  fsdp_plugin.set_auto_wrap_policy(UpperCamelCase__     )
                  self.assertIsNone(fsdp_plugin.auto_wrap_policy     )







    def     _lowercase     (    self     )     ->      Tuple:
           from torch.distributed.fsdp.fully_sharded_data_parallel import MixedPrecision
           from torch.distributed.fsdp.sharded_grad_scaler import ShardedGradScaler

           for mp_dtype in dtypes:
                  lowerCamelCase   :      str			 =					self.dist_env.copy()
                  lowerCamelCase   :      List[Any]			 =					mp_dtype
                  with mockenv_context(**UpperCamelCase__     ):
                         lowerCamelCase   :      List[Any]			 =					Accelerator()
                         if mp_dtype == "fp16":
                                lowerCamelCase   :      Any			 =					torch.floataa
                         elif mp_dtype == "bf16":
                                lowerCamelCase   :      List[Any]			 =					torch.bfloataa
                         lowerCamelCase   :      str			 =					MixedPrecision(param_dtype=UpperCamelCase__     ,  reduce_dtype=UpperCamelCase__     ,  buffer_dtype=UpperCamelCase__     )
                         self.assertEqual(accelerator.state.fsdp_plugin.mixed_precision_policy     ,  UpperCamelCase__     )
                         if mp_dtype == FPaa:
                                self.assertTrue(isinstance(accelerator.scaler     ,  UpperCamelCase__     )     )
                         elif mp_dtype == BFaa:
                                self.assertIsNone(accelerator.scaler     )
                         AcceleratorState._reset_state(UpperCamelCase__     )







    def     _lowercase     (    self     )     ->      Tuple:
           from torch.distributed.fsdp.fully_sharded_data_parallel import CPUOffload

           for flag in [True, False]:
                  lowerCamelCase   :      Optional[Any]			 =					self.dist_env.copy()
                  lowerCamelCase   :      Optional[int]			 =					str(UpperCamelCase__     ).lower()
                  with mockenv_context(**UpperCamelCase__     ):
                         lowerCamelCase   :      Tuple			 =					FullyShardedDataParallelPlugin()
                         self.assertEqual(fsdp_plugin.cpu_offload     ,  CPUOffload(offload_params=UpperCamelCase__     )     )




@require_fsdp
@require_multi_gpu
@slow
class 		UpperCamelCase__	(lowerCAmelCase__     ):

    '''simple docstring'''



    def     _lowercase     (    self     )     ->      Optional[int]:
           super().setUp()
           lowerCamelCase   :      int			 =					0.82
           lowerCamelCase   :      Any			 =					[
               "fsdp_shard_grad_op_transformer_based_wrap",
               "fsdp_full_shard_transformer_based_wrap",
           ]
           lowerCamelCase   :      Dict			 =					{
               "multi_gpu_fp16": 3200,
               "fsdp_shard_grad_op_transformer_based_wrap_fp16": 2000,
               "fsdp_full_shard_transformer_based_wrap_fp16": 1900,
               # Disabling below test as it overwhelms the RAM memory usage
               # on CI self-hosted runner leading to tests getting killed.
               # "fsdp_full_shard_cpu_offload_transformer_based_wrap_fp32": 1500,  # fp16 was leading to indefinite hang
           }
           lowerCamelCase   :      int			 =					160
           lowerCamelCase   :      Optional[int]			 =					160

           lowerCamelCase   :      List[Any]			 =					inspect.getfile(accelerate.test_utils     )
           lowerCamelCase   :      Optional[Any]			 =					os.path.sep.join(mod_file.split(os.path.sep     )[:-1] + ["scripts", "external_deps"]     )







    def     _lowercase     (    self     )     ->      Optional[int]:
           lowerCamelCase   :      int			 =					os.path.join(self.test_scripts_folder     ,  "test_performance.py"     )
           lowerCamelCase   :      Union[str, Any]			 =					["accelerate", "launch", "--num_processes=2", "--num_machines=1", "--machine_rank=0", "--use_fsdp"]
           for config in self.performance_configs:
                  lowerCamelCase   :      int			 =					cmd.copy()
                  for i, strategy in enumerate(UpperCamelCase__     ):
                         if strategy.lower() in config:
                                cmd_config.append(F'''--fsdp_sharding_strategy={i+1}'''     )
                                break

                  if "fp32" in config:
                         cmd_config.append("--mixed_precision=no"     )
                  else:
                         cmd_config.append("--mixed_precision=fp16"     )

                  if "cpu_offload" in config:
                         cmd_config.append("--fsdp_offload_params=True"     )

                  for policy in FSDP_AUTO_WRAP_POLICY:
                         if policy.lower() in config:
                                cmd_config.append(F'''--fsdp_auto_wrap_policy={policy}'''     )
                                break

                  if policy == "TRANSFORMER_BASED_WRAP":
                         cmd_config.append("--fsdp_transformer_layer_cls_to_wrap=BertLayer"     )
                  elif policy == "SIZE_BASED_WRAP":
                         cmd_config.append("--fsdp_min_num_params=2000"     )

                  cmd_config.extend(
                      [
                          self.test_file_path,
                          F'''--output_dir={self.tmpdir}''',
                          F'''--performance_lower_bound={self.performance_lower_bound}''',
                      ]     )
                  with patch_environment(omp_num_threads=1     ):
                         execute_subprocess_async(UpperCamelCase__     ,  env=os.environ.copy()     )







    def     _lowercase     (    self     )     ->      Any:
           lowerCamelCase   :      List[str]			 =					os.path.join(self.test_scripts_folder     ,  "test_checkpointing.py"     )
           lowerCamelCase   :      List[str]			 =					[
               "accelerate",
               "launch",
               "--num_processes=2",
               "--num_machines=1",
               "--machine_rank=0",
               "--use_fsdp",
               "--mixed_precision=fp16",
               "--fsdp_transformer_layer_cls_to_wrap=BertLayer",
           ]

           for i, strategy in enumerate(UpperCamelCase__     ):
                  lowerCamelCase   :      str			 =					cmd.copy()
                  cmd_config.append(F'''--fsdp_sharding_strategy={i+1}'''     )
                  if strategy != "FULL_SHARD":
                         continue
                  lowerCamelCase   :      Dict			 =					len(UpperCamelCase__     )
                  for state_dict_type in FSDP_STATE_DICT_TYPE:
                         lowerCamelCase   :      List[str]			 =					cmd_config[:state_dict_config_index]
                         cmd_config.append(F'''--fsdp_state_dict_type={state_dict_type}'''     )
                         cmd_config.extend(
                             [
                                 self.test_file_path,
                                 F'''--output_dir={self.tmpdir}''',
                                 "--partial_train_epoch=1",
                             ]     )
                         with patch_environment(omp_num_threads=1     ):
                                execute_subprocess_async(UpperCamelCase__     ,  env=os.environ.copy()     )

                         lowerCamelCase   :      Dict			 =					cmd_config[:-1]
                         lowerCamelCase   :      Dict			 =					os.path.join(self.tmpdir     ,  "epoch_0"     )
                         cmd_config.extend(
                             [
                                 F'''--resume_from_checkpoint={resume_from_checkpoint}''',
                             ]     )
                         with patch_environment(omp_num_threads=1     ):
                                execute_subprocess_async(UpperCamelCase__     ,  env=os.environ.copy()     )







    def     _lowercase     (    self     )     ->      Tuple:
           lowerCamelCase   :      int			 =					os.path.join(self.test_scripts_folder     ,  "test_peak_memory_usage.py"     )
           lowerCamelCase   :      Tuple			 =					[
               "accelerate",
               "launch",
               "--num_processes=2",
               "--num_machines=1",
               "--machine_rank=0",
           ]
           for spec, peak_mem_upper_bound in self.peak_memory_usage_upper_bound.items():
                  lowerCamelCase   :      str			 =					cmd.copy()
                  if "fp16" in spec:
                         cmd_config.extend(["--mixed_precision=fp16"]     )
                  else:
                         cmd_config.extend(["--mixed_precision=no"]     )

                  if "multi_gpu" in spec:
                         continue
                  else:
                         cmd_config.extend(["--use_fsdp"]     )
                         for i, strategy in enumerate(UpperCamelCase__     ):
                                if strategy.lower() in spec:
                                       cmd_config.append(F'''--fsdp_sharding_strategy={i+1}'''     )
                                       break

                         if "cpu_offload" in spec:
                                cmd_config.append("--fsdp_offload_params=True"     )

                         for policy in FSDP_AUTO_WRAP_POLICY:
                                if policy.lower() in spec:
                                       cmd_config.append(F'''--fsdp_auto_wrap_policy={policy}'''     )
                                       break

                         if policy == "TRANSFORMER_BASED_WRAP":
                                cmd_config.append("--fsdp_transformer_layer_cls_to_wrap=BertLayer"     )
                         elif policy == "SIZE_BASED_WRAP":
                                cmd_config.append("--fsdp_min_num_params=2000"     )

                  cmd_config.extend(
                      [
                          self.test_file_path,
                          F'''--output_dir={self.tmpdir}''',
                          F'''--peak_memory_upper_bound={peak_mem_upper_bound}''',
                          F'''--n_train={self.n_train}''',
                          F'''--n_val={self.n_val}''',
                      ]     )
                  with patch_environment(omp_num_threads=1     ):
                         execute_subprocess_async(UpperCamelCase__     ,  env=os.environ.copy()     )












import os
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import SPIECE_UNDERLINE, logging


SCREAMING_SNAKE_CASE__       :      Optional[Any]  =   logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__       :      Tuple  =   {'vocab_file': 'spiece.model'}

SCREAMING_SNAKE_CASE__       :      int  =   {
    'vocab_file': {
        'xlnet-base-cased': 'https://huggingface.co/xlnet-base-cased/resolve/main/spiece.model',
        'xlnet-large-cased': 'https://huggingface.co/xlnet-large-cased/resolve/main/spiece.model',
    }
}

SCREAMING_SNAKE_CASE__       :      str  =   {
    'xlnet-base-cased': None,
    'xlnet-large-cased': None,
}

# Segments (not really needed)
SCREAMING_SNAKE_CASE__       :      Dict  =   0
SCREAMING_SNAKE_CASE__       :      Tuple  =   1
SCREAMING_SNAKE_CASE__       :      Optional[int]  =   2
SCREAMING_SNAKE_CASE__       :      List[str]  =   3
SCREAMING_SNAKE_CASE__       :      Optional[int]  =   4




class 		UpperCamelCase__	(lowerCAmelCase__     ):

    '''simple docstring'''



    lowerCamelCase_      :	Dict	     =      VOCAB_FILES_NAMES
    lowerCamelCase_      :	Optional[Any]	     =      PRETRAINED_VOCAB_FILES_MAP
    lowerCamelCase_      :	Optional[int]	     =      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    lowerCamelCase_      :	List[str]	     =      """left"""







    def __init__(    self     ,  UpperCamelCase__     ,  UpperCamelCase__=False     ,  UpperCamelCase__=True     ,  UpperCamelCase__=False     ,  UpperCamelCase__="<s>"     ,  UpperCamelCase__="</s>"     ,  UpperCamelCase__="<unk>"     ,  UpperCamelCase__="<sep>"     ,  UpperCamelCase__="<pad>"     ,  UpperCamelCase__="<cls>"     ,  UpperCamelCase__="<mask>"     ,  UpperCamelCase__=["<eop>", "<eod>"]     ,  UpperCamelCase__ = None     ,  **UpperCamelCase__     ,  )     ->      None:
           # Mask token behave like a normal word, i.e. include the space before it
           lowerCamelCase   :      str			 =					AddedToken(UpperCamelCase__     ,  lstrip=UpperCamelCase__     ,  rstrip=UpperCamelCase__     ) if isinstance(UpperCamelCase__     ,  UpperCamelCase__     ) else mask_token

           lowerCamelCase   :      Dict			 =					{} if sp_model_kwargs is None else sp_model_kwargs

           super().__init__(
               do_lower_case=UpperCamelCase__     ,  remove_space=UpperCamelCase__     ,  keep_accents=UpperCamelCase__     ,  bos_token=UpperCamelCase__     ,  eos_token=UpperCamelCase__     ,  unk_token=UpperCamelCase__     ,  sep_token=UpperCamelCase__     ,  pad_token=UpperCamelCase__     ,  cls_token=UpperCamelCase__     ,  mask_token=UpperCamelCase__     ,  additional_special_tokens=UpperCamelCase__     ,  sp_model_kwargs=self.sp_model_kwargs     ,  **UpperCamelCase__     ,  )

           lowerCamelCase   :      Any			 =					3

           lowerCamelCase   :      Optional[Any]			 =					do_lower_case
           lowerCamelCase   :      List[Any]			 =					remove_space
           lowerCamelCase   :      str			 =					keep_accents
           lowerCamelCase   :      List[Any]			 =					vocab_file

           lowerCamelCase   :      int			 =					spm.SentencePieceProcessor(**self.sp_model_kwargs     )
           self.sp_model.Load(UpperCamelCase__     )







    @property
    def     _lowercase     (    self     )     ->      Optional[Any]:
           return len(self.sp_model     )







    def     _lowercase     (    self     )     ->      Optional[int]:
           lowerCamelCase   :      int			 =					{self.convert_ids_to_tokens(UpperCamelCase__     ): i for i in range(self.vocab_size     )}
           vocab.update(self.added_tokens_encoder     )
           return vocab







    def __getstate__(    self     )     ->      Optional[Any]:
           lowerCamelCase   :      Optional[int]			 =					self.__dict__.copy()
           lowerCamelCase   :      Union[str, Any]			 =					None
           return state







    def __setstate__(    self     ,  UpperCamelCase__     )     ->      int:
           lowerCamelCase   :      int			 =					d

           # for backward compatibility
           if not hasattr(self     ,  "sp_model_kwargs"     ):
                  lowerCamelCase   :      Any			 =					{}

           lowerCamelCase   :      Optional[int]			 =					spm.SentencePieceProcessor(**self.sp_model_kwargs     )
           self.sp_model.Load(self.vocab_file     )







    def     _lowercase     (    self     ,  UpperCamelCase__     )     ->      Any:
           if self.remove_space:
                  lowerCamelCase   :      Dict			 =					" ".join(inputs.strip().split()     )
           else:
                  lowerCamelCase   :      Union[str, Any]			 =					inputs
           lowerCamelCase   :      Optional[Any]			 =					outputs.replace("``"     ,  "\""     ).replace("''"     ,  "\""     )

           if not self.keep_accents:
                  lowerCamelCase   :      Optional[int]			 =					unicodedata.normalize("NFKD"     ,  UpperCamelCase__     )
                  lowerCamelCase   :      List[Any]			 =					"".join([c for c in outputs if not unicodedata.combining(UpperCamelCase__     )]     )
           if self.do_lower_case:
                  lowerCamelCase   :      List[str]			 =					outputs.lower()

           return outputs







    def     _lowercase     (    self     ,  UpperCamelCase__     )     ->      List[str]:
           lowerCamelCase   :      Optional[Any]			 =					self.preprocess_text(UpperCamelCase__     )
           lowerCamelCase   :      Dict			 =					self.sp_model.encode(UpperCamelCase__     ,  out_type=UpperCamelCase__     )
           lowerCamelCase   :      Dict			 =					[]
           for piece in pieces:
                  if len(UpperCamelCase__     ) > 1 and piece[-1] == str(","     ) and piece[-2].isdigit():
                         lowerCamelCase   :      List[Any]			 =					self.sp_model.EncodeAsPieces(piece[:-1].replace(UpperCamelCase__     ,  ""     )     )
                         if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
                                if len(cur_pieces[0]     ) == 1:
                                       lowerCamelCase   :      Union[str, Any]			 =					cur_pieces[1:]
                                else:
                                       lowerCamelCase   :      Optional[int]			 =					cur_pieces[0][1:]
                         cur_pieces.append(piece[-1]     )
                         new_pieces.extend(UpperCamelCase__     )
                  else:
                         new_pieces.append(UpperCamelCase__     )

           return new_pieces







    def     _lowercase     (    self     ,  UpperCamelCase__     )     ->      int:
           return self.sp_model.PieceToId(UpperCamelCase__     )







    def     _lowercase     (    self     ,  UpperCamelCase__     )     ->      Tuple:
           return self.sp_model.IdToPiece(UpperCamelCase__     )







    def     _lowercase     (    self     ,  UpperCamelCase__     )     ->      List[str]:
           lowerCamelCase   :      Union[str, Any]			 =					"".join(UpperCamelCase__     ).replace(UpperCamelCase__     ,  " "     ).strip()
           return out_string







    def     _lowercase     (    self     ,  UpperCamelCase__     ,  UpperCamelCase__ = False     ,  UpperCamelCase__ = None     ,  UpperCamelCase__ = True     ,  **UpperCamelCase__     ,  )     ->      str:
           lowerCamelCase   :      Optional[int]			 =					kwargs.pop("use_source_tokenizer"     ,  UpperCamelCase__     )

           lowerCamelCase   :      Optional[int]			 =					self.convert_ids_to_tokens(UpperCamelCase__     ,  skip_special_tokens=UpperCamelCase__     )

           # To avoid mixing byte-level and unicode for byte-level BPT
           # we need to build string separately for added tokens and byte-level tokens
           # cf. https://github.com/huggingface/transformers/issues/1133
           lowerCamelCase   :      Any			 =					[]
           lowerCamelCase   :      Any			 =					[]
           for token in filtered_tokens:
                  if skip_special_tokens and token in self.all_special_ids:
                         continue
                  if token in self.added_tokens_encoder:
                         if current_sub_text:
                                sub_texts.append(self.convert_tokens_to_string(UpperCamelCase__     )     )
                                lowerCamelCase   :      int			 =					[]
                         sub_texts.append(UpperCamelCase__     )
                  else:
                         current_sub_text.append(UpperCamelCase__     )
           if current_sub_text:
                  sub_texts.append(self.convert_tokens_to_string(UpperCamelCase__     )     )

           # Mimic the behavior of the Rust tokenizer:
           # By default, there are no spaces between special tokens
           lowerCamelCase   :      Union[str, Any]			 =					"".join(UpperCamelCase__     )

           lowerCamelCase   :      Tuple			 =					(
               clean_up_tokenization_spaces
               if clean_up_tokenization_spaces is not None
               else self.clean_up_tokenization_spaces
           )
           if clean_up_tokenization_spaces:
                  lowerCamelCase   :      int			 =					self.clean_up_tokenization(UpperCamelCase__     )
                  return clean_text
           else:
                  return text







    def     _lowercase     (    self     ,  UpperCamelCase__     ,  UpperCamelCase__ = None     )     ->      List[int]:
           lowerCamelCase   :      str			 =					[self.sep_token_id]
           lowerCamelCase   :      Optional[int]			 =					[self.cls_token_id]
           if token_ids_a is None:
                  return token_ids_a + sep + cls
           return token_ids_a + sep + token_ids_a + sep + cls







    def     _lowercase     (    self     ,  UpperCamelCase__     ,  UpperCamelCase__ = None     ,  UpperCamelCase__ = False     )     ->      List[int]:

           if already_has_special_tokens:
                  return super().get_special_tokens_mask(
                      token_ids_a=UpperCamelCase__     ,  token_ids_a=UpperCamelCase__     ,  already_has_special_tokens=UpperCamelCase__     )

           if token_ids_a is not None:
                  return ([0] * len(UpperCamelCase__     )) + [1] + ([0] * len(UpperCamelCase__     )) + [1, 1]
           return ([0] * len(UpperCamelCase__     )) + [1, 1]







    def     _lowercase     (    self     ,  UpperCamelCase__     ,  UpperCamelCase__ = None     )     ->      List[int]:
           lowerCamelCase   :      Any			 =					[self.sep_token_id]
           lowerCamelCase   :      List[str]			 =					[2]

           if token_ids_a is None:
                  return len(token_ids_a + sep     ) * [0] + cls_segment_id
           return len(token_ids_a + sep     ) * [0] + len(token_ids_a + sep     ) * [1] + cls_segment_id







    def     _lowercase     (    self     ,  UpperCamelCase__     ,  UpperCamelCase__ = None     )     ->      Tuple[str]:
           if not os.path.isdir(UpperCamelCase__     ):
                  logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''     )
                  return
           lowerCamelCase   :      Union[str, Any]			 =					os.path.join(
               UpperCamelCase__     ,  (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]     )

           if os.path.abspath(self.vocab_file     ) != os.path.abspath(UpperCamelCase__     ) and os.path.isfile(self.vocab_file     ):
                  copyfile(self.vocab_file     ,  UpperCamelCase__     )
           elif not os.path.isfile(self.vocab_file     ):
                  with open(UpperCamelCase__     ,  "wb"     ) as fi:
                         lowerCamelCase   :      str			 =					self.sp_model.serialized_model_proto()
                         fi.write(UpperCamelCase__     )

           return (out_vocab_file,)






