



import tempfile
import unittest

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from transformers.testing_utils import (
    is_torch_available,
    require_optimum,
    require_torch,
    slow,
)


if is_torch_available():
      import torch

@require_torch
@require_optimum
@slow
class       _SCREAMING_SNAKE_CASE	(unittest.TestCase ):





     def 	__snake_case    (       self :							Tuple				)->List[str]:
      __SCREAMING_SNAKE_CASE       :    Dict       =	'hf-internal-testing/tiny-random-t5'
      __SCREAMING_SNAKE_CASE       :    List[Any]       =	AutoTokenizer.from_pretrained(_A				)
      __SCREAMING_SNAKE_CASE       :    int       =	AutoModelForSeqaSeqLM.from_pretrained(_A				)

      __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	tokenizer("This is me"     ,  return_tensors="pt"				)

      __SCREAMING_SNAKE_CASE       :    int       =	model.to_bettertransformer()

      self.assertTrue(any("BetterTransformer" in mod.__class__.__name__ for _, mod in model.named_modules()				)				)

      __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	model.generate(**_A				)

      __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	model.reverse_bettertransformer()

      self.assertFalse(any("BetterTransformer" in mod.__class__.__name__ for _, mod in model.named_modules()				)				)

      with tempfile.TemporaryDirectory() as tmpdirname:
       model.save_pretrained(_A				)

       __SCREAMING_SNAKE_CASE       :    List[str]       =	AutoModelForSeqaSeqLM.from_pretrained(_A				)

       self.assertFalse(
           any("BetterTransformer" in mod.__class__.__name__ for _, mod in model_reloaded.named_modules()				)				)

       __SCREAMING_SNAKE_CASE       :    Optional[int]       =	model_reloaded.generate(**_A				)
       self.assertTrue(torch.allclose(_A     ,  _A				)				)






     def 	__snake_case    (       self :							List[Any]				)->str:
      __SCREAMING_SNAKE_CASE       :    List[Any]       =	'hf-internal-testing/tiny-random-t5'
      __SCREAMING_SNAKE_CASE       :    Optional[int]       =	AutoModelForSeqaSeqLM.from_pretrained(_A				)

      __SCREAMING_SNAKE_CASE       :    str       =	model.to_bettertransformer()

      with tempfile.TemporaryDirectory() as tmpdirname:
       with self.assertRaises(_A				):
        model.save_pretrained(_A				)

       __SCREAMING_SNAKE_CASE       :    str       =	model.reverse_bettertransformer()
       model.save_pretrained(_A				)





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






