



from typing import List, Optional, Union

from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType







class 		snake_case						(	__snake_case  ):



   '''simple docstring'''






   snake_case_					: Optional[int]          =		['image_processor', 'tokenizer']
   snake_case_					: Tuple          =		'BlipImageProcessor'
   snake_case_					: Optional[int]          =		'AutoTokenizer'

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

    """simple docstring"""







    _snake_case    :  str					=					False
    super().__init__(A_				,		A_)
    _snake_case    :  str					=					self.image_processor

   def __call__(					self							:			Tuple				,		lowerCAmelCase							:			Optional[Any] = None				,		lowerCAmelCase							:			str = None				,		lowerCAmelCase							:			Optional[int] = True				,		lowerCAmelCase							:			List[str] = False				,		lowerCAmelCase							:			Any = None				,		lowerCAmelCase							:			str = None				,		lowerCAmelCase							:			str = 0				,		lowerCAmelCase							:			int = None				,		lowerCAmelCase							:			Tuple = None				,		lowerCAmelCase							:			Any = False				,		lowerCAmelCase							:			Tuple = False				,		lowerCAmelCase							:			Union[str, Any] = False				,		lowerCAmelCase							:			Union[str, Any] = False				,		lowerCAmelCase							:			Union[str, Any] = False				,		lowerCAmelCase							:			str = True				,		lowerCAmelCase							:			Optional[Any] = None				,		**lowerCAmelCase							:			str				,		)				->				Any:

    """simple docstring"""







    if images is None and text is None:
     raise ValueError("""You have to specify either images or text.""")

    # Get only text
    if images is None:
     _snake_case    :  int					=					self.tokenizer
     _snake_case    :  Optional[int]					=					self.tokenizer(
         text=A_				,		add_special_tokens=A_				,		padding=A_				,		truncation=A_				,		max_length=A_				,		stride=A_				,		pad_to_multiple_of=A_				,		return_attention_mask=A_				,		return_overflowing_tokens=A_				,		return_special_tokens_mask=A_				,		return_offsets_mapping=A_				,		return_token_type_ids=A_				,		return_length=A_				,		verbose=A_				,		return_tensors=A_				,		**A_				,		)
     return text_encoding

    # add pixel_values
    _snake_case    :  int					=					self.image_processor(A_				,		return_tensors=A_)

    if text is not None:
     _snake_case    :  Dict					=					self.tokenizer(
         text=A_				,		add_special_tokens=A_				,		padding=A_				,		truncation=A_				,		max_length=A_				,		stride=A_				,		pad_to_multiple_of=A_				,		return_attention_mask=A_				,		return_overflowing_tokens=A_				,		return_special_tokens_mask=A_				,		return_offsets_mapping=A_				,		return_token_type_ids=A_				,		return_length=A_				,		verbose=A_				,		return_tensors=A_				,		**A_				,		)
    else:
     _snake_case    :  Dict					=					None

    if text_encoding is not None:
     encoding_image_processor.update(A_)

    return encoding_image_processor

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

    """simple docstring"""







    return self.tokenizer.batch_decode(*A_				,		**A_)

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

    """simple docstring"""







    return self.tokenizer.decode(*A_				,		**A_)


   @property
   # Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
   def      UpperCamelCase_  (					self							:			Optional[Any])				->				Any:

    """simple docstring"""







    _snake_case    :  List[str]					=					self.tokenizer.model_input_names
    _snake_case    :  int					=					self.image_processor.model_input_names
    return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))





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
