
'''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







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

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


if is_sentencepiece_available():
						from .tokenization_barthez import BarthezTokenizer
else:
						_snake_case      :							Any     	=    None

_snake_case      :							Union[str, Any]     	=    logging.get_logger(__name__)

_snake_case      :							Optional[Any]     	=    {"vocab_file": "sentencepiece.bpe.model", "tokenizer_file": "tokenizer.json"}

_snake_case      :							int     	=    {
    "vocab_file": {
        "moussaKam/mbarthez": "https://huggingface.co/moussaKam/mbarthez/resolve/main/sentencepiece.bpe.model",
        "moussaKam/barthez": "https://huggingface.co/moussaKam/barthez/resolve/main/sentencepiece.bpe.model",
        "moussaKam/barthez-orangesum-title": (
            "https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/sentencepiece.bpe.model"
        ),
    },
    "tokenizer_file": {
        "moussaKam/mbarthez": "https://huggingface.co/moussaKam/mbarthez/resolve/main/tokenizer.json",
        "moussaKam/barthez": "https://huggingface.co/moussaKam/barthez/resolve/main/tokenizer.json",
        "moussaKam/barthez-orangesum-title": (
            "https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/tokenizer.json"
        ),
    },
}

_snake_case      :							str     	=    {
    "moussaKam/mbarthez": 10_24,
    "moussaKam/barthez": 10_24,
    "moussaKam/barthez-orangesum-title": 10_24,
}

_snake_case      :							Optional[int]     	=    "▁"





class   UpperCamelCase_      (      __a		):







						'''simple docstring'''



						UpperCamelCase      :  Any				   =					VOCAB_FILES_NAMES
						UpperCamelCase      :  Any				   =					PRETRAINED_VOCAB_FILES_MAP
						UpperCamelCase      :  Union[str, Any]				   =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						UpperCamelCase      :  Dict				   =					['''input_ids''', '''attention_mask''']
						UpperCamelCase      :  Optional[Any]				   =					BarthezTokenizer





						def __init__(			self							:str		,    lowerCAmelCase__							:str=None		,    lowerCAmelCase__							:List[str]=None		,    lowerCAmelCase__							:Union[str, Any]="<s>"		,    lowerCAmelCase__							:Optional[Any]="</s>"		,    lowerCAmelCase__							:str="</s>"		,    lowerCAmelCase__							:List[str]="<s>"		,    lowerCAmelCase__							:Dict="<unk>"		,    lowerCAmelCase__							:int="<pad>"		,    lowerCAmelCase__							:Optional[Any]="<mask>"		,    **lowerCAmelCase__							:Optional[int]		,    )  ->int:
										# Mask token behave like a normal word, i.e. include the space before it
										lowercase         =      AddedToken(lowerCAmelCase__		,    lstrip=lowerCAmelCase__		,    rstrip=lowerCAmelCase__     ) if isinstance(lowerCAmelCase__		,    lowerCAmelCase__     ) else mask_token

										super().__init__(
										    lowerCAmelCase__		,    tokenizer_file=lowerCAmelCase__		,    bos_token=lowerCAmelCase__		,    eos_token=lowerCAmelCase__		,    unk_token=lowerCAmelCase__		,    sep_token=lowerCAmelCase__		,    cls_token=lowerCAmelCase__		,    pad_token=lowerCAmelCase__		,    mask_token=lowerCAmelCase__		,    **lowerCAmelCase__		,    )

										lowercase         =      vocab_file
										lowercase         =      False if not self.vocab_file else True





						def        SCREAMING_SNAKE_CASE(			self							:Optional[int]		,    lowerCAmelCase__							:List[int]		,    lowerCAmelCase__							:Optional[List[int]] = None     )  ->List[int]:

										if token_ids_a is None:
														return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
										lowercase         =      [self.cls_token_id]
										lowercase         =      [self.sep_token_id]
										return cls + token_ids_a + sep + sep + token_ids_a + sep





						def        SCREAMING_SNAKE_CASE(			self							:Optional[int]		,    lowerCAmelCase__							:List[int]		,    lowerCAmelCase__							:Optional[List[int]] = None     )  ->List[int]:
										lowercase         =      [self.sep_token_id]
										lowercase         =      [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        SCREAMING_SNAKE_CASE(			self							:List[Any]		,    lowerCAmelCase__							:str		,    lowerCAmelCase__							:Optional[str] = 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
										lowercase         =      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,)
