



import multiprocessing
from typing import TYPE_CHECKING, Optional, Union

from .. import Dataset, Features, config
from ..formatting import query_table
from ..packaged_modules.sql.sql import Sql
from ..utils import logging
from .abc import AbstractDatasetInputStream


if TYPE_CHECKING:
  import sqlitea

  import sqlalchemy







class 		snake_case						(	SCREAMING_SNAKE_CASE_  ):



   '''simple docstring'''






   def __init__(					self							:			str				,		lowerCAmelCase							:			Union[str, "sqlalchemy.sql.Selectable"]				,		lowerCAmelCase							:			Union[str, "sqlalchemy.engine.Connection", "sqlalchemy.engine.Engine", "sqlite3.Connection"]				,		lowerCAmelCase							:			Optional[Features] = None				,		lowerCAmelCase							:			str = None				,		lowerCAmelCase							:			bool = False				,		**lowerCAmelCase							:			Optional[Any]				,		)				->				List[str]:

    """simple docstring"""







    super().__init__(features=lowerCAmelCase				,		cache_dir=lowerCAmelCase				,		keep_in_memory=lowerCAmelCase				,		**lowerCAmelCase)
    _snake_case    :  Union[str, Any]					=					Sql(
        cache_dir=lowerCAmelCase				,		features=lowerCAmelCase				,		sql=lowerCAmelCase				,		con=lowerCAmelCase				,		**lowerCAmelCase				,		)


   def      UpperCamelCase_  (					self							:			Any)				->				Dict:

    """simple docstring"""







    _snake_case    :  Tuple					=					None
    _snake_case    :  str					=					None
    _snake_case    :  List[str]					=					None
    _snake_case    :  Union[str, Any]					=					None

    self.builder.download_and_prepare(
        download_config=lowerCAmelCase				,		download_mode=lowerCAmelCase				,		verification_mode=lowerCAmelCase				,		base_path=lowerCAmelCase				,		)

    # Build dataset for splits
    _snake_case    :  Tuple					=					self.builder.as_dataset(
        split="""train"""				,		verification_mode=lowerCAmelCase				,		in_memory=self.keep_in_memory)
    return dataset







class 		snake_case						:



   '''simple docstring'''






   def __init__(					self							:			Union[str, Any]				,		lowerCAmelCase							:			Dataset				,		lowerCAmelCase							:			str				,		lowerCAmelCase							:			Union[str, "sqlalchemy.engine.Connection", "sqlalchemy.engine.Engine", "sqlite3.Connection"]				,		lowerCAmelCase							:			Optional[int] = None				,		lowerCAmelCase							:			Optional[int] = None				,		**lowerCAmelCase							:			Any				,		)				->				str:

    """simple docstring"""







    if num_proc is not None and num_proc <= 0:
     raise ValueError(F'''num_proc {num_proc} must be an integer > 0.''')

    _snake_case    :  Union[str, Any]					=					dataset
    _snake_case    :  Optional[Any]					=					name
    _snake_case    :  List[Any]					=					con
    _snake_case    :  Optional[int]					=					batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
    _snake_case    :  List[Any]					=					num_proc
    _snake_case    :  int					=					to_sql_kwargs

   def      UpperCamelCase_  (					self							:			List[str])				->				int:

    """simple docstring"""







    _snake_case    :  Optional[int]					=					self.to_sql_kwargs.pop("""sql"""				,		lowerCAmelCase)
    _snake_case    :  Any					=					self.to_sql_kwargs.pop("""con"""				,		lowerCAmelCase)
    _snake_case    :  Tuple					=					self.to_sql_kwargs.pop("""index"""				,		lowerCAmelCase)

    _snake_case    :  Union[str, Any]					=					self._write(index=lowerCAmelCase				,		**self.to_sql_kwargs)
    return written

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

    """simple docstring"""







    _snake_case	,    _snake_case	,    _snake_case    :  Optional[Any]					=					args
    _snake_case    :  List[str]					=					{**to_sql_kwargs, """if_exists""": """append"""} if offset > 0 else to_sql_kwargs
    _snake_case    :  Tuple					=					query_table(
        table=self.dataset.data				,		key=slice(lowerCAmelCase				,		offset + self.batch_size)				,		indices=self.dataset._indices				,		)
    _snake_case    :  List[Any]					=					batch.to_pandas()
    _snake_case    :  Union[str, Any]					=					df.to_sql(self.name				,		self.con				,		index=lowerCAmelCase				,		**lowerCAmelCase)
    return num_rows or len(lowerCAmelCase)


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

    """simple docstring"""







    _snake_case    :  List[Any]					=					0

    if self.num_proc is None or self.num_proc == 1:
     for offset in logging.tqdm(
         range(0				,		len(self.dataset)				,		self.batch_size)				,		unit="""ba"""				,		disable=not logging.is_progress_bar_enabled()				,		desc="""Creating SQL from Arrow format"""				,		):
      written += self._batch_sql((offset, index, to_sql_kwargs))
    else:
     _snake_case	,    _snake_case    :  List[str]					=					len(self.dataset), self.batch_size
     with multiprocessing.Pool(self.num_proc) as pool:
      for num_rows in logging.tqdm(
          pool.imap(
              self._batch_sql				,		[(offset, index, to_sql_kwargs) for offset in range(0				,		lowerCAmelCase				,		lowerCAmelCase)]				,		)				,		total=(num_rows // batch_size) + 1 if num_rows % batch_size else num_rows // batch_size				,		unit="""ba"""				,		disable=not logging.is_progress_bar_enabled()				,		desc="""Creating SQL from Arrow format"""				,		):
       written += num_rows

    return written





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
