


'''simple docstring'''

import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class   __lowerCAmelCase						(lowercase_     ):





      '''simple docstring'''






      lowerCAmelCase__					:     List[Any]  					=							["""image_processor""", """tokenizer"""]
      lowerCAmelCase__					:     Optional[int]  					=							"""ViTImageProcessor"""
      lowerCAmelCase__					:     Optional[Any]  					=							("""CLIPTokenizer""", """CLIPTokenizerFast""")



      def __init__(self    :    List[Any]   ,						UpperCamelCase    :    List[str]=None   ,						UpperCamelCase    :    List[Any]=None   ,						**UpperCamelCase    :    Union[str, Any] ):

             '''simple docstring'''







             lowercase__      			= None
             if "feature_extractor" in kwargs:
                    warnings.warn(
                        '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
                        ''' instead.'''   ,						UpperCamelCase   ,						)
                    lowercase__      			= kwargs.pop('''feature_extractor''' )

             lowercase__      			= image_processor if image_processor is not None else feature_extractor
             if image_processor is None:
                    raise ValueError('''You need to specify an `image_processor`.''' )
             if tokenizer is None:
                    raise ValueError('''You need to specify a `tokenizer`.''' )

             super().__init__(UpperCamelCase   ,						UpperCamelCase )



      def __call__(self    :    Optional[Any]   ,						UpperCamelCase    :    Optional[Any]=None   ,						UpperCamelCase    :    Dict=None   ,						UpperCamelCase    :    Union[str, Any]=None   ,						UpperCamelCase    :    List[str]=None   ,						**UpperCamelCase    :    List[Any] ):

             '''simple docstring'''







             if text is None and visual_prompt is None and images is None:
                    raise ValueError('''You have to specify either text, visual prompt or images.''' )

             if text is not None and visual_prompt is not None:
                    raise ValueError('''You have to specify exactly one type of prompt. Either text or visual prompt.''' )

             if text is not None:
                    lowercase__      			= self.tokenizer(UpperCamelCase   ,						return_tensors=UpperCamelCase   ,						**UpperCamelCase )

             if visual_prompt is not None:
                    lowercase__      			= self.image_processor(UpperCamelCase   ,						return_tensors=UpperCamelCase   ,						**UpperCamelCase )

             if images is not None:
                    lowercase__      			= self.image_processor(UpperCamelCase   ,						return_tensors=UpperCamelCase   ,						**UpperCamelCase )

             if visual_prompt is not None and images is not None:
                    lowercase__      			= {
                        '''pixel_values''': image_features.pixel_values,
                        '''conditional_pixel_values''': prompt_features.pixel_values,
                    }
                    return encoding
             elif text is not None and images is not None:
                    lowercase__      			= image_features.pixel_values
                    return encoding
             elif text is not None:
                    return encoding
             elif visual_prompt is not None:
                    lowercase__      			= {
                        '''conditional_pixel_values''': prompt_features.pixel_values,
                    }
                    return encoding
             else:
                    return BatchEncoding(data=dict(**UpperCamelCase )   ,						tensor_type=UpperCamelCase )



      def       UpperCamelCase__		(self    :    Tuple   ,						*UpperCamelCase    :    List[str]   ,						**UpperCamelCase    :    Union[str, Any] ):

             '''simple docstring'''







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



      def       UpperCamelCase__		(self    :    List[str]   ,						*UpperCamelCase    :    Any   ,						**UpperCamelCase    :    int ):

             '''simple docstring'''







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



      @property
      def       UpperCamelCase__		(self    :    List[Any] ):

             '''simple docstring'''







             warnings.warn(
                 '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''   ,						UpperCamelCase   ,						)
             return self.image_processor_class






      @property
      def       UpperCamelCase__		(self    :    Dict ):

             '''simple docstring'''







             warnings.warn(
                 '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'''   ,						UpperCamelCase   ,						)
             return self.image_processor








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


