



import os
import warnings
from typing import List, Optional

from ...tokenization_utils_base import BatchEncoding
from ...utils import logging
from .configuration_rag import RagConfig


a__    =  logging.get_logger(__name__)







class 		snake_case						:



   '''simple docstring'''






   def __init__(					self							:			Tuple				,		lowerCAmelCase							:			Optional[int]				,		lowerCAmelCase							:			List[str])				->				Union[str, Any]:

    """simple docstring"""







    _snake_case    :  List[str]					=					question_encoder
    _snake_case    :  List[str]					=					generator
    _snake_case    :  Dict					=					self.question_encoder

   def      UpperCamelCase_  (					self							:			str				,		lowerCAmelCase							:			List[str])				->				List[Any]:

    """simple docstring"""







    if os.path.isfile(lowerCAmelCase):
     raise ValueError(F'''Provided path ({save_directory}) should be a directory, not a file''')
    os.makedirs(lowerCAmelCase				,		exist_ok=lowerCAmelCase)
    _snake_case    :  Optional[Any]					=					os.path.join(lowerCAmelCase				,		"""question_encoder_tokenizer""")
    _snake_case    :  List[Any]					=					os.path.join(lowerCAmelCase				,		"""generator_tokenizer""")
    self.question_encoder.save_pretrained(lowerCAmelCase)
    self.generator.save_pretrained(lowerCAmelCase)

   @classmethod
   def      UpperCamelCase_  (					cls							:			Union[str, Any]				,		lowerCAmelCase							:			Optional[Any]				,		**lowerCAmelCase							:			List[str])				->				Dict:

    """simple docstring"""







    from ..auto.tokenization_auto import AutoTokenizer

    _snake_case    :  Optional[int]					=					kwargs.pop("""config"""				,		lowerCAmelCase)

    if config is None:
     _snake_case    :  List[Any]					=					RagConfig.from_pretrained(lowerCAmelCase)

    _snake_case    :  Tuple					=					AutoTokenizer.from_pretrained(
        lowerCAmelCase				,		config=config.question_encoder				,		subfolder="""question_encoder_tokenizer""")
    _snake_case    :  Tuple					=					AutoTokenizer.from_pretrained(
        lowerCAmelCase				,		config=config.generator				,		subfolder="""generator_tokenizer""")
    return cls(question_encoder=lowerCAmelCase				,		generator=lowerCAmelCase)

   def __call__(					self							:			Any				,		*lowerCAmelCase							:			List[str]				,		**lowerCAmelCase							:			int)				->				Any:

    """simple docstring"""







    return self.current_tokenizer(*lowerCAmelCase				,		**lowerCAmelCase)

   def      UpperCamelCase_  (					self							:			Dict				,		*lowerCAmelCase							:			Optional[int]				,		**lowerCAmelCase							:			str)				->				List[Any]:

    """simple docstring"""







    return self.generator.batch_decode(*lowerCAmelCase				,		**lowerCAmelCase)

   def      UpperCamelCase_  (					self							:			Any				,		*lowerCAmelCase							:			List[str]				,		**lowerCAmelCase							:			int)				->				List[str]:

    """simple docstring"""







    return self.generator.decode(*lowerCAmelCase				,		**lowerCAmelCase)

   def      UpperCamelCase_  (					self							:			Union[str, Any])				->				List[str]:

    """simple docstring"""







    _snake_case    :  Tuple					=					self.question_encoder

   def      UpperCamelCase_  (					self							:			Union[str, Any])				->				Union[str, Any]:

    """simple docstring"""







    _snake_case    :  Dict					=					self.generator


   def      UpperCamelCase_  (					self							:			Tuple				,		lowerCAmelCase							:			List[str]				,		lowerCAmelCase							:			Optional[List[str]] = None				,		lowerCAmelCase							:			Optional[int] = None				,		lowerCAmelCase							:			Optional[int] = None				,		lowerCAmelCase							:			str = "longest"				,		lowerCAmelCase							:			str = None				,		lowerCAmelCase							:			bool = True				,		**lowerCAmelCase							:			List[str]				,		)				->				BatchEncoding:

    """simple docstring"""







    warnings.warn(
        """`prepare_seq2seq_batch` is deprecated and will be removed in version 5 of 🤗 Transformers. Use the """
        """regular `__call__` method to prepare your inputs and the tokenizer under the `with_target_tokenizer` """
        """context manager to prepare your targets. See the documentation of your specific tokenizer for more """
        """details"""				,		lowerCAmelCase				,		)
    if max_length is None:
     _snake_case    :  int					=					self.current_tokenizer.model_max_length
    _snake_case    :  List[str]					=					self(
        lowerCAmelCase				,		add_special_tokens=lowerCAmelCase				,		return_tensors=lowerCAmelCase				,		max_length=lowerCAmelCase				,		padding=lowerCAmelCase				,		truncation=lowerCAmelCase				,		**lowerCAmelCase				,		)
    if tgt_texts is None:
     return model_inputs
    # Process tgt_texts
    if max_target_length is None:
     _snake_case    :  int					=					self.current_tokenizer.model_max_length
    _snake_case    :  Optional[Any]					=					self(
        text_target=lowerCAmelCase				,		add_special_tokens=lowerCAmelCase				,		return_tensors=lowerCAmelCase				,		padding=lowerCAmelCase				,		max_length=lowerCAmelCase				,		truncation=lowerCAmelCase				,		**lowerCAmelCase				,		)
    _snake_case    :  Dict					=					labels["""input_ids"""]
    return model_inputs





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
