


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)

lowerCAmelCase__      		= 'bert-base-cased'
lowerCAmelCase__      		= 'fp16'
lowerCAmelCase__      		= 'bf16'
lowerCAmelCase__      		= [FPaa, BFaa]







@require_fsdp
@require_cuda
class      lowerCAmelCase__       (				a):




     '''simple docstring'''


     def  _lowerCamelCase (						self)			->				Optional[int]:
            super().setUp()

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


     def  _lowerCamelCase (						self)			->				List[Any]:
            from torch.distributed.fsdp.fully_sharded_data_parallel import ShardingStrategy

            for i, strategy in enumerate(__lowerCamelCase):
                   _A      :       List[Any]				    = self.dist_env.copy()
                   _A      :       Tuple				    = F"{i + 1}"
                   _A      :       List[str]				    = strategy
                   with mockenv_context(**__lowerCamelCase):
                          _A      :       List[Any]				    = FullyShardedDataParallelPlugin()
                          self.assertEqual(fsdp_plugin.sharding_strategy ,						ShardingStrategy(i + 1))


     def  _lowerCamelCase (						self)			->				str:
            from torch.distributed.fsdp.fully_sharded_data_parallel import BackwardPrefetch

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


     def  _lowerCamelCase (						self)			->				List[str]:
            from torch.distributed.fsdp.fully_sharded_data_parallel import StateDictType

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

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

            _A      :       Tuple				    = self.dist_env.copy()
            _A      :       Optional[Any]				    = "SIZE_BASED_WRAP"
            _A      :       int				    = "0"
            with mockenv_context(**__lowerCamelCase):
                   _A      :       Any				    = FullyShardedDataParallelPlugin()
                   fsdp_plugin.set_auto_wrap_policy(__lowerCamelCase)
                   self.assertIsNone(fsdp_plugin.auto_wrap_policy)


     def  _lowerCamelCase (						self)			->				List[Any]:
            from torch.distributed.fsdp.fully_sharded_data_parallel import MixedPrecision
            from torch.distributed.fsdp.sharded_grad_scaler import ShardedGradScaler

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


     def  _lowerCamelCase (						self)			->				Any:
            from torch.distributed.fsdp.fully_sharded_data_parallel import CPUOffload

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







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




     '''simple docstring'''


     def  _lowerCamelCase (						self)			->				int:
            super().setUp()
            _A      :       List[str]				    = 0.8_2
            _A      :       List[Any]				    = [
                "fsdp_shard_grad_op_transformer_based_wrap",
                "fsdp_full_shard_transformer_based_wrap",
            ]
            _A      :       Tuple				    = {
                "multi_gpu_fp16": 3_2_0_0,
                "fsdp_shard_grad_op_transformer_based_wrap_fp16": 2_0_0_0,
                "fsdp_full_shard_transformer_based_wrap_fp16": 1_9_0_0,
                # 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
            }
            _A      :       int				    = 1_6_0
            _A      :       Optional[Any]				    = 1_6_0

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


     def  _lowerCamelCase (						self)			->				Optional[int]:
            _A      :       Tuple				    = os.path.join(self.test_scripts_folder ,						"test_performance.py")
            _A      :       Optional[int]				    = ["accelerate", "launch", "--num_processes=2", "--num_machines=1", "--machine_rank=0", "--use_fsdp"]
            for config in self.performance_configs:
                   _A      :       Optional[Any]				    = cmd.copy()
                   for i, strategy in enumerate(__lowerCamelCase):
                          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(__lowerCamelCase ,						env=os.environ.copy())


     def  _lowerCamelCase (						self)			->				Dict:
            _A      :       List[Any]				    = os.path.join(self.test_scripts_folder ,						"test_checkpointing.py")
            _A      :       Any				    = [
                "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(__lowerCamelCase):
                   _A      :       List[Any]				    = cmd.copy()
                   cmd_config.append(F"--fsdp_sharding_strategy={i+1}")
                   if strategy != "FULL_SHARD":
                          continue
                   _A      :       List[Any]				    = len(__lowerCamelCase)
                   for state_dict_type in FSDP_STATE_DICT_TYPE:
                          _A      :       int				    = 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(__lowerCamelCase ,						env=os.environ.copy())

                          _A      :       Dict				    = cmd_config[:-1]
                          _A      :       Any				    = 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(__lowerCamelCase ,						env=os.environ.copy())


     def  _lowerCamelCase (						self)			->				List[Any]:
            _A      :       Optional[int]				    = os.path.join(self.test_scripts_folder ,						"test_peak_memory_usage.py")
            _A      :       List[Any]				    = [
                "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():
                   _A      :       List[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(__lowerCamelCase):
                                 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(__lowerCamelCase ,						env=os.environ.copy())




import os
from shutil import copyfile
from typing import List, Optional, Tuple

from tokenizers import processors

from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
    from .tokenization_nllb import NllbTokenizer
else:
    lowerCAmelCase__      		= None


lowerCAmelCase__      		= logging.get_logger(__name__)


lowerCAmelCase__      		= {'vocab_file': 'sentencepiece.bpe.model', 'tokenizer_file': 'tokenizer.json'}

lowerCAmelCase__      		= {
    'vocab_file': {
        'facebook/nllb-200-distilled-600M': (
            'https://huggingface.co/facebook/nllb-200-distilled-600M/resolve/main/sentencepiece.bpe.model'
        ),
    },
    'tokenizer_file': {
        'facebook/nllb-200-distilled-600M': (
            'https://huggingface.co/facebook/nllb-200-distilled-600M/resolve/main/tokenizer.json'
        ),
    },
}

lowerCAmelCase__      		= {
    'facebook/nllb-large-en-ro': 10_24,
    'facebook/nllb-200-distilled-600M': 10_24,
}

# fmt: off
lowerCAmelCase__      		= ['ace_Arab', 'ace_Latn', 'acm_Arab', 'acq_Arab', 'aeb_Arab', 'afr_Latn', 'ajp_Arab', 'aka_Latn', 'amh_Ethi', 'apc_Arab', 'arb_Arab', 'ars_Arab', 'ary_Arab', 'arz_Arab', 'asm_Beng', 'ast_Latn', 'awa_Deva', 'ayr_Latn', 'azb_Arab', 'azj_Latn', 'bak_Cyrl', 'bam_Latn', 'ban_Latn', 'bel_Cyrl', 'bem_Latn', 'ben_Beng', 'bho_Deva', 'bjn_Arab', 'bjn_Latn', 'bod_Tibt', 'bos_Latn', 'bug_Latn', 'bul_Cyrl', 'cat_Latn', 'ceb_Latn', 'ces_Latn', 'cjk_Latn', 'ckb_Arab', 'crh_Latn', 'cym_Latn', 'dan_Latn', 'deu_Latn', 'dik_Latn', 'dyu_Latn', 'dzo_Tibt', 'ell_Grek', 'eng_Latn', 'epo_Latn', 'est_Latn', 'eus_Latn', 'ewe_Latn', 'fao_Latn', 'pes_Arab', 'fij_Latn', 'fin_Latn', 'fon_Latn', 'fra_Latn', 'fur_Latn', 'fuv_Latn', 'gla_Latn', 'gle_Latn', 'glg_Latn', 'grn_Latn', 'guj_Gujr', 'hat_Latn', 'hau_Latn', 'heb_Hebr', 'hin_Deva', 'hne_Deva', 'hrv_Latn', 'hun_Latn', 'hye_Armn', 'ibo_Latn', 'ilo_Latn', 'ind_Latn', 'isl_Latn', 'ita_Latn', 'jav_Latn', 'jpn_Jpan', 'kab_Latn', 'kac_Latn', 'kam_Latn', 'kan_Knda', 'kas_Arab', 'kas_Deva', 'kat_Geor', 'knc_Arab', 'knc_Latn', 'kaz_Cyrl', 'kbp_Latn', 'kea_Latn', 'khm_Khmr', 'kik_Latn', 'kin_Latn', 'kir_Cyrl', 'kmb_Latn', 'kon_Latn', 'kor_Hang', 'kmr_Latn', 'lao_Laoo', 'lvs_Latn', 'lij_Latn', 'lim_Latn', 'lin_Latn', 'lit_Latn', 'lmo_Latn', 'ltg_Latn', 'ltz_Latn', 'lua_Latn', 'lug_Latn', 'luo_Latn', 'lus_Latn', 'mag_Deva', 'mai_Deva', 'mal_Mlym', 'mar_Deva', 'min_Latn', 'mkd_Cyrl', 'plt_Latn', 'mlt_Latn', 'mni_Beng', 'khk_Cyrl', 'mos_Latn', 'mri_Latn', 'zsm_Latn', 'mya_Mymr', 'nld_Latn', 'nno_Latn', 'nob_Latn', 'npi_Deva', 'nso_Latn', 'nus_Latn', 'nya_Latn', 'oci_Latn', 'gaz_Latn', 'ory_Orya', 'pag_Latn', 'pan_Guru', 'pap_Latn', 'pol_Latn', 'por_Latn', 'prs_Arab', 'pbt_Arab', 'quy_Latn', 'ron_Latn', 'run_Latn', 'rus_Cyrl', 'sag_Latn', 'san_Deva', 'sat_Beng', 'scn_Latn', 'shn_Mymr', 'sin_Sinh', 'slk_Latn', 'slv_Latn', 'smo_Latn', 'sna_Latn', 'snd_Arab', 'som_Latn', 'sot_Latn', 'spa_Latn', 'als_Latn', 'srd_Latn', 'srp_Cyrl', 'ssw_Latn', 'sun_Latn', 'swe_Latn', 'swh_Latn', 'szl_Latn', 'tam_Taml', 'tat_Cyrl', 'tel_Telu', 'tgk_Cyrl', 'tgl_Latn', 'tha_Thai', 'tir_Ethi', 'taq_Latn', 'taq_Tfng', 'tpi_Latn', 'tsn_Latn', 'tso_Latn', 'tuk_Latn', 'tum_Latn', 'tur_Latn', 'twi_Latn', 'tzm_Tfng', 'uig_Arab', 'ukr_Cyrl', 'umb_Latn', 'urd_Arab', 'uzn_Latn', 'vec_Latn', 'vie_Latn', 'war_Latn', 'wol_Latn', 'xho_Latn', 'ydd_Hebr', 'yor_Latn', 'yue_Hant', 'zho_Hans', 'zho_Hant', 'zul_Latn']







class      lowerCAmelCase__       (				a):




     '''simple docstring'''
     __SCREAMING_SNAKE_CASE  			= VOCAB_FILES_NAMES
     __SCREAMING_SNAKE_CASE  			= PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     __SCREAMING_SNAKE_CASE  			= PRETRAINED_VOCAB_FILES_MAP
     __SCREAMING_SNAKE_CASE  			= ["input_ids", "attention_mask"]
     __SCREAMING_SNAKE_CASE  			= NllbTokenizer

     __SCREAMING_SNAKE_CASE  			= []
     __SCREAMING_SNAKE_CASE  			= []


     def __init__(						self ,						__lowerCamelCase=None ,						__lowerCamelCase=None ,						__lowerCamelCase="<s>" ,						__lowerCamelCase="</s>" ,						__lowerCamelCase="</s>" ,						__lowerCamelCase="<s>" ,						__lowerCamelCase="<unk>" ,						__lowerCamelCase="<pad>" ,						__lowerCamelCase="<mask>" ,						__lowerCamelCase=None ,						__lowerCamelCase=None ,						__lowerCamelCase=None ,						__lowerCamelCase=False ,						**__lowerCamelCase ,						)			->				Tuple:
            # Mask token behave like a normal word, i.e. include the space before it
            _A      :       Any				    = AddedToken(__lowerCamelCase ,						lstrip=__lowerCamelCase ,						rstrip=__lowerCamelCase) if isinstance(__lowerCamelCase ,						__lowerCamelCase) else mask_token
            _A      :       Optional[int]				    = legacy_behaviour
            super().__init__(
                vocab_file=__lowerCamelCase ,						tokenizer_file=__lowerCamelCase ,						bos_token=__lowerCamelCase ,						eos_token=__lowerCamelCase ,						sep_token=__lowerCamelCase ,						cls_token=__lowerCamelCase ,						unk_token=__lowerCamelCase ,						pad_token=__lowerCamelCase ,						mask_token=__lowerCamelCase ,						src_lang=__lowerCamelCase ,						tgt_lang=__lowerCamelCase ,						additional_special_tokens=__lowerCamelCase ,						legacy_behaviour=__lowerCamelCase ,						**__lowerCamelCase ,						)

            _A      :       int				    = vocab_file
            _A      :       Optional[Any]				    = False if not self.vocab_file else True

            _A      :       Tuple				    = FAIRSEQ_LANGUAGE_CODES.copy()

            if additional_special_tokens is not None:
                   # Only add those special tokens if they are not already there.
                   _additional_special_tokens.extend(
                       [t for t in additional_special_tokens if t not in _additional_special_tokens])

            self.add_special_tokens({"additional_special_tokens": _additional_special_tokens})
            _A      :       Union[str, Any]				    = {
                lang_code: self.convert_tokens_to_ids(__lowerCamelCase) for lang_code in FAIRSEQ_LANGUAGE_CODES
            }

            _A      :       Optional[int]				    = src_lang if src_lang is not None else "eng_Latn"
            _A      :       Union[str, Any]				    = self.convert_tokens_to_ids(self._src_lang)
            _A      :       List[str]				    = tgt_lang
            self.set_src_lang_special_tokens(self._src_lang)


     @property
     def  _lowerCamelCase (						self)			->				str:
            return self._src_lang


     @src_lang.setter
     def  _lowerCamelCase (						self ,						__lowerCamelCase)			->				None:
            _A      :       Tuple				    = new_src_lang
            self.set_src_lang_special_tokens(self._src_lang)


     def  _lowerCamelCase (						self ,						__lowerCamelCase ,						__lowerCamelCase = None)			->				List[int]:
            if token_ids_a is None:
                   return self.prefix_tokens + token_ids_a + self.suffix_tokens
            # We don't expect to process pairs, but leave the pair logic for API consistency
            return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens


     def  _lowerCamelCase (						self ,						__lowerCamelCase ,						__lowerCamelCase = None)			->				List[int]:
            _A      :       Tuple				    = [self.sep_token_id]
            _A      :       List[Any]				    = [self.cls_token_id]

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


     def  _lowerCamelCase (						self ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase ,						**__lowerCamelCase)			->				Optional[int]:
            if src_lang is None or tgt_lang is None:
                   raise ValueError("Translation requires a `src_lang` and a `tgt_lang` for this model")
            _A      :       List[Any]				    = src_lang
            _A      :       Optional[int]				    = self(__lowerCamelCase ,						add_special_tokens=__lowerCamelCase ,						return_tensors=__lowerCamelCase ,						**__lowerCamelCase)
            _A      :       Tuple				    = self.convert_tokens_to_ids(__lowerCamelCase)
            _A      :       Tuple				    = tgt_lang_id
            return inputs


     def  _lowerCamelCase (						self ,						__lowerCamelCase ,						__lowerCamelCase = "eng_Latn" ,						__lowerCamelCase = None ,						__lowerCamelCase = "fra_Latn" ,						**__lowerCamelCase ,						)			->				BatchEncoding:
            _A      :       Tuple				    = src_lang
            _A      :       int				    = tgt_lang
            return super().prepare_seqaseq_batch(__lowerCamelCase ,						__lowerCamelCase ,						**__lowerCamelCase)


     def  _lowerCamelCase (						self)			->				str:
            return self.set_src_lang_special_tokens(self.src_lang)


     def  _lowerCamelCase (						self)			->				List[str]:
            return self.set_tgt_lang_special_tokens(self.tgt_lang)


     def  _lowerCamelCase (						self ,						__lowerCamelCase)			->				None:
            _A      :       Dict				    = self.convert_tokens_to_ids(__lowerCamelCase)

            if self.legacy_behaviour:
                   _A      :       List[str]				    = []
                   _A      :       Dict				    = [self.eos_token_id, self.cur_lang_code]
            else:
                   _A      :       Tuple				    = [self.cur_lang_code]
                   _A      :       Optional[Any]				    = [self.eos_token_id]

            _A      :       Optional[int]				    = self.convert_ids_to_tokens(self.prefix_tokens)
            _A      :       int				    = self.convert_ids_to_tokens(self.suffix_tokens)

            _A      :       List[Any]				    = processors.TemplateProcessing(
                single=prefix_tokens_str + ["$A"] + suffix_tokens_str ,						pair=prefix_tokens_str + ["$A", "$B"] + suffix_tokens_str ,						special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str ,						self.prefix_tokens + self.suffix_tokens)) ,						)


     def  _lowerCamelCase (						self ,						__lowerCamelCase)			->				None:
            _A      :       Optional[Any]				    = self.convert_tokens_to_ids(__lowerCamelCase)
            if self.legacy_behaviour:
                   _A      :       Tuple				    = []
                   _A      :       Any				    = [self.eos_token_id, self.cur_lang_code]
            else:
                   _A      :       Union[str, Any]				    = [self.cur_lang_code]
                   _A      :       str				    = [self.eos_token_id]

            _A      :       Optional[Any]				    = self.convert_ids_to_tokens(self.prefix_tokens)
            _A      :       Dict				    = self.convert_ids_to_tokens(self.suffix_tokens)

            _A      :       Union[str, Any]				    = processors.TemplateProcessing(
                single=prefix_tokens_str + ["$A"] + suffix_tokens_str ,						pair=prefix_tokens_str + ["$A", "$B"] + suffix_tokens_str ,						special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str ,						self.prefix_tokens + self.suffix_tokens)) ,						)


     def  _lowerCamelCase (						self ,						__lowerCamelCase ,						__lowerCamelCase = None)			->				Tuple[str]:
            if not self.can_save_slow_tokenizer:
                   raise ValueError(
                       "Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
                       "tokenizer.")

            if not os.path.isdir(__lowerCamelCase):
                   logger.error(F"Vocabulary path ({save_directory}) should be a directory.")
                   return
            _A      :       Dict				    = os.path.join(
                __lowerCamelCase ,						(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"])

            if os.path.abspath(self.vocab_file) != os.path.abspath(__lowerCamelCase):
                   copyfile(self.vocab_file ,						__lowerCamelCase)

            return (out_vocab_file,)
