"""simple docstring"""


import warnings
from typing import List, Optional, Union

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


class 				a (						lowerCAmelCase_      ):
    _snake_case   :     str   							=							['image_processor', 'tokenizer']
    _snake_case   :     Union[str, Any]   							=							'LayoutLMv2ImageProcessor'
    _snake_case   :     Optional[int]   							=							('LayoutXLMTokenizer', 'LayoutXLMTokenizerFast')


    def __init__(    self		:     Tuple      , __lowerCAmelCase		:     Tuple=None      , __lowerCAmelCase		:     Optional[int]=None      , **__lowerCAmelCase		:     List[Any]    ):
      if "feature_extractor" in kwargs:
        warnings.warn(
            """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
            """ instead."""      , __lowerCAmelCase      , )
        _UpperCAmelCase         =					kwargs.pop("""feature_extractor"""    )

      _UpperCAmelCase         =					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__(__lowerCAmelCase      , __lowerCAmelCase    )


    def __call__(    self		:     Tuple      , __lowerCAmelCase		:     str      , __lowerCAmelCase		:     Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None      , __lowerCAmelCase		:     Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None      , __lowerCAmelCase		:     Union[List[List[int]], List[List[List[int]]]] = None      , __lowerCAmelCase		:     Optional[Union[List[int], List[List[int]]]] = None      , __lowerCAmelCase		:     bool = True      , __lowerCAmelCase		:     Union[bool, str, PaddingStrategy] = False      , __lowerCAmelCase		:     Union[bool, str, TruncationStrategy] = None      , __lowerCAmelCase		:     Optional[int] = None      , __lowerCAmelCase		:     int = 0      , __lowerCAmelCase		:     Optional[int] = None      , __lowerCAmelCase		:     Optional[bool] = None      , __lowerCAmelCase		:     Optional[bool] = None      , __lowerCAmelCase		:     bool = False      , __lowerCAmelCase		:     bool = False      , __lowerCAmelCase		:     bool = False      , __lowerCAmelCase		:     bool = False      , __lowerCAmelCase		:     bool = True      , __lowerCAmelCase		:     Optional[Union[str, TensorType]] = None      , **__lowerCAmelCase		:     Union[str, Any]      , ):
      # verify input
      if self.image_processor.apply_ocr and (boxes is not None):
        raise ValueError(
            """You cannot provide bounding boxes """
            """if you initialized the image processor with apply_ocr set to True."""    )

      if self.image_processor.apply_ocr and (word_labels is not None):
        raise ValueError(
            """You cannot provide word labels if you initialized the image processor with apply_ocr set to True."""    )

      if return_overflowing_tokens is True and return_offsets_mapping is False:
        raise ValueError("""You cannot return overflowing tokens without returning the offsets mapping."""    )

      # first, apply the image processor
      _UpperCAmelCase         =					self.image_processor(images=__lowerCAmelCase      , return_tensors=__lowerCAmelCase    )

      # second, apply the tokenizer
      if text is not None and self.image_processor.apply_ocr and text_pair is None:
        if isinstance(__lowerCAmelCase      , __lowerCAmelCase    ):
          _UpperCAmelCase         =					[text]  # add batch dimension (as the image processor always adds a batch dimension)
        _UpperCAmelCase         =					features["""words"""]

      _UpperCAmelCase         =					self.tokenizer(
          text=text if text is not None else features["""words"""]      , text_pair=text_pair if text_pair is not None else None      , boxes=boxes if boxes is not None else features["""boxes"""]      , word_labels=__lowerCAmelCase      , add_special_tokens=__lowerCAmelCase      , padding=__lowerCAmelCase      , truncation=__lowerCAmelCase      , max_length=__lowerCAmelCase      , stride=__lowerCAmelCase      , pad_to_multiple_of=__lowerCAmelCase      , return_token_type_ids=__lowerCAmelCase      , return_attention_mask=__lowerCAmelCase      , return_overflowing_tokens=__lowerCAmelCase      , return_special_tokens_mask=__lowerCAmelCase      , return_offsets_mapping=__lowerCAmelCase      , return_length=__lowerCAmelCase      , verbose=__lowerCAmelCase      , return_tensors=__lowerCAmelCase      , **__lowerCAmelCase      , )

      # add pixel values
      _UpperCAmelCase         =					features.pop("""pixel_values"""    )
      if return_overflowing_tokens is True:
        _UpperCAmelCase         =					self.get_overflowing_images(__lowerCAmelCase      , encoded_inputs["""overflow_to_sample_mapping"""]    )
      _UpperCAmelCase         =					images

      return encoded_inputs


    def    lowerCAmelCase_     (    self		:     List[str]      , __lowerCAmelCase		:     List[str]      , __lowerCAmelCase		:     Dict    ):
      # in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
      _UpperCAmelCase         =					[]
      for sample_idx in overflow_to_sample_mapping:
        images_with_overflow.append(images[sample_idx]    )

      if len(__lowerCAmelCase    ) != len(__lowerCAmelCase    ):
        raise ValueError(
            """Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got"""
            f''' {len(__lowerCAmelCase    )} and {len(__lowerCAmelCase    )}'''    )

      return images_with_overflow


    def    lowerCAmelCase_     (    self		:     Union[str, Any]      , *__lowerCAmelCase		:     List[Any]      , **__lowerCAmelCase		:     int    ):
      return self.tokenizer.batch_decode(*__lowerCAmelCase      , **__lowerCAmelCase    )


    def    lowerCAmelCase_     (    self		:     int      , *__lowerCAmelCase		:     List[str]      , **__lowerCAmelCase		:     Optional[int]    ):
      return self.tokenizer.decode(*__lowerCAmelCase      , **__lowerCAmelCase    )


    @property
    def    lowerCAmelCase_     (    self		:     Tuple    ):
      return ["input_ids", "bbox", "attention_mask", "image"]


    @property
    def    lowerCAmelCase_     (    self		:     Any    ):
      warnings.warn(
          """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""      , __lowerCAmelCase      , )
      return self.image_processor_class


    @property
    def    lowerCAmelCase_     (    self		:     Optional[int]    ):
      warnings.warn(
          """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."""      , __lowerCAmelCase      , )
      return self.image_processor

"""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
