


'''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





'''simple docstring'''







import argparse
from collections import defaultdict

import yaml


lowerCAmelCase     :  Dict											=    """docs/source/en/_toctree.yml"""






def       lowercase					(_A       ):







       """simple docstring"""







       _lowerCAmelCase     :				Optional[int]       =							defaultdict(_A       )
       _lowerCAmelCase     :				Tuple       =							[]
       _lowerCAmelCase     :				Dict       =							[]
       for doc in doc_list:
              if "local" in doc:
                     counts[doc["local"]] += 1

              if doc["title"].lower() == "overview":
                     overview_doc.append({'local': doc['local'], 'title': doc['title']}       )
              else:
                     new_doc_list.append(_A       )

       _lowerCAmelCase     :				str       =							new_doc_list
       _lowerCAmelCase     :				List[Any]       =							[key for key, value in counts.items() if value > 1]

       _lowerCAmelCase     :				List[str]       =							[]
       for duplicate_key in duplicates:
              _lowerCAmelCase     :				Any       =							list({doc['title'] for doc in doc_list if doc['local'] == duplicate_key}       )
              if len(_A       ) > 1:
                     raise ValueError(
                         f'{duplicate_key} is present several times in the documentation table of content at '
                         '`docs/source/en/_toctree.yml` with different *Title* values. Choose one of those and remove the '
                         'others.'       )
              # Only add this once
              new_doc.append({'local': duplicate_key, 'title': titles[0]}       )

       # Add none duplicate-keys
       new_doc.extend([doc for doc in doc_list if 'local' not in counts or counts[doc['local']] == 1]       )
       _lowerCAmelCase     :				Any       =							sorted(_A ,    key=lambda _A       : s["title"].lower()       )

       # "overview" gets special treatment and is always first
       if len(_A       ) > 1:
              raise ValueError('{doc_list} has two \'overview\' docs which is not allowed.'       )

       overview_doc.extend(_A       )

       # Sort
       return overview_doc






def       lowercase					(_A=False       ):







       """simple docstring"""







       with open(_A ,    encoding='utf-8'       ) as f:
              _lowerCAmelCase     :				Union[str, Any]       =							yaml.safe_load(f.read()       )

       # Get to the API doc
       _lowerCAmelCase     :				Tuple       =							0
       while content[api_idx]["title"] != "API":
              api_idx += 1
       _lowerCAmelCase     :				str       =							content[api_idx]['sections']

       # Then to the model doc
       _lowerCAmelCase     :				Dict       =							0
       while api_doc[scheduler_idx]["title"] != "Schedulers":
              scheduler_idx += 1

       _lowerCAmelCase     :				Optional[int]       =							api_doc[scheduler_idx]['sections']
       _lowerCAmelCase     :				Any       =							clean_doc_toc(_A       )

       _lowerCAmelCase     :				int       =							False
       if new_scheduler_doc != scheduler_doc:
              _lowerCAmelCase     :				str       =							True
              if overwrite:
                     _lowerCAmelCase     :				int       =							new_scheduler_doc

       if diff:
              if overwrite:
                     _lowerCAmelCase     :				Dict       =							api_doc
                     with open(_A ,    'w' ,    encoding='utf-8'       ) as f:
                            f.write(yaml.dump(_A ,    allow_unicode=_A       )       )
              else:
                     raise ValueError(
                         'The model doc part of the table of content is not properly sorted, run `make style` to fix this.'       )






def       lowercase					(_A=False       ):







       """simple docstring"""







       with open(_A ,    encoding='utf-8'       ) as f:
              _lowerCAmelCase     :				Optional[Any]       =							yaml.safe_load(f.read()       )

       # Get to the API doc
       _lowerCAmelCase     :				Optional[Any]       =							0
       while content[api_idx]["title"] != "API":
              api_idx += 1
       _lowerCAmelCase     :				Union[str, Any]       =							content[api_idx]['sections']

       # Then to the model doc
       _lowerCAmelCase     :				Optional[int]       =							0
       while api_doc[pipeline_idx]["title"] != "Pipelines":
              pipeline_idx += 1

       _lowerCAmelCase     :				List[Any]       =							False
       _lowerCAmelCase     :				List[Any]       =							api_doc[pipeline_idx]['sections']
       _lowerCAmelCase     :				Union[str, Any]       =							[]

       # sort sub pipeline docs
       for pipeline_doc in pipeline_docs:
              if "section" in pipeline_doc:
                     _lowerCAmelCase     :				Tuple       =							pipeline_doc['section']
                     _lowerCAmelCase     :				Optional[int]       =							clean_doc_toc(_A       )
                     if overwrite:
                            _lowerCAmelCase     :				str       =							new_sub_pipeline_doc
              new_pipeline_docs.append(_A       )

       # sort overall pipeline doc
       _lowerCAmelCase     :				str       =							clean_doc_toc(_A       )

       if new_pipeline_docs != pipeline_docs:
              _lowerCAmelCase     :				Any       =							True
              if overwrite:
                     _lowerCAmelCase     :				Optional[int]       =							new_pipeline_docs

       if diff:
              if overwrite:
                     _lowerCAmelCase     :				str       =							api_doc
                     with open(_A ,    'w' ,    encoding='utf-8'       ) as f:
                            f.write(yaml.dump(_A ,    allow_unicode=_A       )       )
              else:
                     raise ValueError(
                         'The model doc part of the table of content is not properly sorted, run `make style` to fix this.'       )


if __name__ == "__main__":
       lowerCAmelCase     :  Tuple											=    argparse.ArgumentParser()
       parser.add_argument("""--fix_and_overwrite""", action="""store_true""", help="""Whether to fix inconsistencies.""")
       lowerCAmelCase     :  List[Any]											=    parser.parse_args()

       check_scheduler_doc(args.fix_and_overwrite)
       check_pipeline_doc(args.fix_and_overwrite)

