"""simple docstring"""




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

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
     from .tokenization_pegasus import PegasusTokenizer
else:
     _a				:		str	   =       None


_a				:		Optional[int]	   =       logging.get_logger(__name__)


_a				:		Any	   =       '▁'

_a				:		int	   =       {'vocab_file': 'spiece.model', 'tokenizer_file': 'tokenizer.json'}

_a				:		str	   =       {
    'vocab_file': {'google/pegasus-xsum': 'https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model'},
    'tokenizer_file': {
        'google/pegasus-xsum': 'https://huggingface.co/google/pegasus-xsum/resolve/main/tokenizer.json'
    },
}

_a				:		Union[str, Any]	   =       {
    'google/pegasus-xsum': 512,
}
class 						__A							(			SCREAMING_SNAKE_CASE_		):
     _UpperCamelCase							:      Tuple		 =		VOCAB_FILES_NAMES
     _UpperCamelCase							:      str		 =		PRETRAINED_VOCAB_FILES_MAP
     _UpperCamelCase							:      str		 =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     _UpperCamelCase							:      List[str]		 =		PegasusTokenizer
     _UpperCamelCase							:      List[Any]		 =		["input_ids", "attention_mask"]






     def __init__(		self    ,					a__=None    ,					a__=None    ,					a__="<pad>"    ,					a__="</s>"    ,					a__="<unk>"    ,					a__="<mask_2>"    ,					a__="<mask_1>"    ,					a__=None    ,					a__=103    ,					**a__    ,					):
       _lowerCAmelCase						:	Optional[int]        =					offset

       if additional_special_tokens is not None:
         if not isinstance(a__    ,					a__  ):
           raise TypeError(
               F"additional_special_tokens should be of type {type(a__  )}, but is"
               F" {type(a__  )}"  )

         _lowerCAmelCase						:	Any        =					(
             ([mask_token_sent] + additional_special_tokens)
             if mask_token_sent not in additional_special_tokens and mask_token_sent is not None
             else additional_special_tokens
         )
         # fill additional tokens with ..., <unk_token_102> in case not all additional tokens are already taken
         additional_special_tokens_extended += [
             F"<unk_{i}>" for i in range(len(a__  )    ,					self.offset - 1  )
         ]

         if len(set(a__  )  ) != len(a__  ):
           raise ValueError(
               """Please make sure that the provided additional_special_tokens do not contain an incorrectly"""
               F" shifted list of <unk_x> tokens. Found {additional_special_tokens_extended}."  )
         _lowerCAmelCase						:	Dict        =					additional_special_tokens_extended
       else:
         _lowerCAmelCase						:	Any        =					[mask_token_sent] if mask_token_sent is not None else []
         additional_special_tokens += [F"<unk_{i}>" for i in range(2    ,					self.offset  )]

       super().__init__(
           a__    ,					tokenizer_file=a__    ,					pad_token=a__    ,					eos_token=a__    ,					unk_token=a__    ,					mask_token=a__    ,					mask_token_sent=a__    ,					offset=a__    ,					additional_special_tokens=a__    ,					**a__    ,					)
       _lowerCAmelCase						:	Optional[int]        =					vocab_file
       _lowerCAmelCase						:	List[Any]        =					False if not self.vocab_file else True






     def    __A  (		self    ,					a__  ):
       _lowerCAmelCase						:	List[str]        =					set(self.all_special_ids  )  # call it once instead of inside list comp
       all_special_ids.remove(self.unk_token_id  )  # <unk> is only sometimes special

       if all_special_ids != set(range(len(self.additional_special_tokens  ) + 3  )  ):
         raise ValueError(
             """There should be 3 special tokens: mask_token, pad_token, and eos_token +"""
             F" {len(self.additional_special_tokens  )} additional_special_tokens, but got {all_special_ids}"  )

       return [1 if x in all_special_ids else 0 for x in seq]






     def    __A  (		self    ,					a__    ,					a__ = None    ,					a__ = False  ):
       if already_has_special_tokens:
         return self._special_token_mask(a__  )
       elif token_ids_a is None:
         return self._special_token_mask(a__  ) + [1]
       else:
         return self._special_token_mask(token_ids_a + token_ids_a  ) + [1]






     def    __A  (		self    ,					a__    ,					a__=None  ):
       if token_ids_a is None:
         return token_ids_a + [self.eos_token_id]
       # We don't expect to process pairs, but leave the pair logic for API consistency
       return token_ids_a + token_ids_a + [self.eos_token_id]






     def    __A  (		self    ,					a__    ,					a__ = None  ):
       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(a__  ):
         logger.error(F"Vocabulary path ({save_directory}) should be a directory"  )
         return
       _lowerCAmelCase						:	List[Any]        =					os.path.join(
           a__    ,					(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]  )

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

       return (out_vocab_file,)





"""simple docstring"""




from .constants import (
    MODEL_NAME,
    OPTIMIZER_NAME,
    RNG_STATE_NAME,
    SAFE_WEIGHTS_INDEX_NAME,
    SAFE_WEIGHTS_NAME,
    SCALER_NAME,
    SCHEDULER_NAME,
    TORCH_LAUNCH_PARAMS,
    WEIGHTS_INDEX_NAME,
    WEIGHTS_NAME,
)
from .dataclasses import (
    BnbQuantizationConfig,
    ComputeEnvironment,
    CustomDtype,
    DeepSpeedPlugin,
    DistributedDataParallelKwargs,
    DistributedType,
    DynamoBackend,
    FPaRecipeKwargs,
    FullyShardedDataParallelPlugin,
    GradientAccumulationPlugin,
    GradScalerKwargs,
    InitProcessGroupKwargs,
    KwargsHandler,
    LoggerType,
    MegatronLMPlugin,
    PrecisionType,
    ProjectConfiguration,
    RNGType,
    SageMakerDistributedType,
    TensorInformation,
    TorchDynamoPlugin,
)
from .environment import get_int_from_env, parse_choice_from_env, parse_flag_from_env
from .imports import (
    get_ccl_version,
    is_abit_bnb_available,
    is_abit_bnb_available,
    is_aim_available,
    is_bfaa_available,
    is_bnb_available,
    is_botoa_available,
    is_ccl_available,
    is_comet_ml_available,
    is_datasets_available,
    is_deepspeed_available,
    is_fpa_available,
    is_ipex_available,
    is_megatron_lm_available,
    is_mlflow_available,
    is_mps_available,
    is_npu_available,
    is_rich_available,
    is_safetensors_available,
    is_sagemaker_available,
    is_tensorboard_available,
    is_tpu_available,
    is_transformers_available,
    is_wandb_available,
    is_xpu_available,
)
from .modeling import (
    check_device_map,
    check_tied_parameters_in_config,
    check_tied_parameters_on_same_device,
    compute_module_sizes,
    convert_file_size_to_int,
    dtype_byte_size,
    find_tied_parameters,
    get_balanced_memory,
    get_max_layer_size,
    get_max_memory,
    get_mixed_precision_context_manager,
    id_tensor_storage,
    infer_auto_device_map,
    load_checkpoint_in_model,
    load_offloaded_weights,
    load_state_dict,
    named_module_tensors,
    retie_parameters,
    set_module_tensor_to_device,
    shard_checkpoint,
)
from .offload import (
    OffloadedWeightsLoader,
    PrefixedDataset,
    extract_submodules_state_dict,
    load_offloaded_weight,
    offload_state_dict,
    offload_weight,
    save_offload_index,
)
from .operations import (
    broadcast,
    broadcast_object_list,
    concatenate,
    convert_outputs_to_fpaa,
    convert_to_fpaa,
    find_batch_size,
    find_device,
    gather,
    gather_object,
    get_data_structure,
    honor_type,
    initialize_tensors,
    is_namedtuple,
    is_tensor_information,
    is_torch_tensor,
    listify,
    pad_across_processes,
    recursively_apply,
    reduce,
    send_to_device,
    slice_tensors,
)
from .versions import compare_versions, is_torch_version


if is_deepspeed_available():
     from .deepspeed import (
         DeepSpeedEngineWrapper,
         DeepSpeedOptimizerWrapper,
         DeepSpeedSchedulerWrapper,
         DummyOptim,
         DummyScheduler,
         HfDeepSpeedConfig,
     )

from .bnb import has_abit_bnb_layers, load_and_quantize_model
from .fsdp_utils import load_fsdp_model, load_fsdp_optimizer, save_fsdp_model, save_fsdp_optimizer
from .launch import (
    PrepareForLaunch,
    _filter_args,
    prepare_deepspeed_cmd_env,
    prepare_multi_gpu_env,
    prepare_sagemager_args_inputs,
    prepare_simple_launcher_cmd_env,
    prepare_tpu,
)
from .megatron_lm import (
    AbstractTrainStep,
    BertTrainStep,
    GPTTrainStep,
    MegatronEngine,
    MegatronLMDummyDataLoader,
    MegatronLMDummyScheduler,
    MegatronLMOptimizerWrapper,
    MegatronLMSchedulerWrapper,
    TaTrainStep,
    avg_losses_across_data_parallel_group,
    gather_across_data_parallel_groups,
)
from .megatron_lm import initialize as megatron_lm_initialize
from .megatron_lm import prepare_data_loader as megatron_lm_prepare_data_loader
from .megatron_lm import prepare_model as megatron_lm_prepare_model
from .megatron_lm import prepare_optimizer as megatron_lm_prepare_optimizer
from .megatron_lm import prepare_scheduler as megatron_lm_prepare_scheduler
from .memory import find_executable_batch_size, release_memory
from .other import (
    extract_model_from_parallel,
    get_pretty_name,
    is_port_in_use,
    merge_dicts,
    patch_environment,
    save,
    wait_for_everyone,
    write_basic_config,
)
from .random import set_seed, synchronize_rng_state, synchronize_rng_states
from .torch_xla import install_xla
from .tqdm import tqdm
from .transformer_engine import convert_model, has_transformer_engine_layers




