





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__lowercase       :List[str]       =  {
    "configuration_roberta": ["ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP", "RobertaConfig", "RobertaOnnxConfig"],
    "tokenization_roberta": ["RobertaTokenizer"],
}

try:
    if not is_tokenizers_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __lowercase       :str       =  ["RobertaTokenizerFast"]

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __lowercase       :Union[str, Any]       =  [
        "ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST",
        "RobertaForCausalLM",
        "RobertaForMaskedLM",
        "RobertaForMultipleChoice",
        "RobertaForQuestionAnswering",
        "RobertaForSequenceClassification",
        "RobertaForTokenClassification",
        "RobertaModel",
        "RobertaPreTrainedModel",
    ]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __lowercase       :Any       =  [
        "TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST",
        "TFRobertaForCausalLM",
        "TFRobertaForMaskedLM",
        "TFRobertaForMultipleChoice",
        "TFRobertaForQuestionAnswering",
        "TFRobertaForSequenceClassification",
        "TFRobertaForTokenClassification",
        "TFRobertaMainLayer",
        "TFRobertaModel",
        "TFRobertaPreTrainedModel",
    ]

try:
    if not is_flax_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __lowercase       :Any       =  [
        "FlaxRobertaForCausalLM",
        "FlaxRobertaForMaskedLM",
        "FlaxRobertaForMultipleChoice",
        "FlaxRobertaForQuestionAnswering",
        "FlaxRobertaForSequenceClassification",
        "FlaxRobertaForTokenClassification",
        "FlaxRobertaModel",
        "FlaxRobertaPreTrainedModel",
    ]


if TYPE_CHECKING:
    from .configuration_roberta import ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, RobertaConfig, RobertaOnnxConfig
    from .tokenization_roberta import RobertaTokenizer

    try:
        if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_roberta_fast import RobertaTokenizerFast

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_roberta import (
            ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
            RobertaForCausalLM,
            RobertaForMaskedLM,
            RobertaForMultipleChoice,
            RobertaForQuestionAnswering,
            RobertaForSequenceClassification,
            RobertaForTokenClassification,
            RobertaModel,
            RobertaPreTrainedModel,
        )

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_roberta import (
            TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
            TFRobertaForCausalLM,
            TFRobertaForMaskedLM,
            TFRobertaForMultipleChoice,
            TFRobertaForQuestionAnswering,
            TFRobertaForSequenceClassification,
            TFRobertaForTokenClassification,
            TFRobertaMainLayer,
            TFRobertaModel,
            TFRobertaPreTrainedModel,
        )

    try:
        if not is_flax_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_flax_roberta import (
            FlaxRobertaForCausalLM,
            FlaxRobertaForMaskedLM,
            FlaxRobertaForMultipleChoice,
            FlaxRobertaForQuestionAnswering,
            FlaxRobertaForSequenceClassification,
            FlaxRobertaForTokenClassification,
            FlaxRobertaModel,
            FlaxRobertaPreTrainedModel,
        )

else:
    import sys

    __lowercase       :str       =  _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)






from __future__ import annotations







def       UpperCAmelCase							(     _lowerCamelCase      :  list[int | float]			,   _lowerCamelCase      :  int			,   _lowerCamelCase      :  int     ):





						'''simple docstring'''


						if len(_lowerCamelCase     ) == 0:
												raise ValueError("find_max() arg is an empty sequence"     )
						if (
						    left >= len(_lowerCamelCase     )
						    or left < -len(_lowerCamelCase     )
						    or right >= len(_lowerCamelCase     )
						    or right < -len(_lowerCamelCase     )
						):
												raise IndexError("list index out of range"     )
						if left == right:
												return nums[left]
						SCREAMING_SNAKE_CASE__     :				Optional[int]   =					(left + right) >> 1  # the middle
						SCREAMING_SNAKE_CASE__     :				List[Any]   =					find_max(_lowerCamelCase			,   _lowerCamelCase			,   _lowerCamelCase     )  # find max in range[left, mid]
						SCREAMING_SNAKE_CASE__     :				Optional[int]   =					find_max(_lowerCamelCase			,   mid + 1			,   _lowerCamelCase     )  # find max in range[mid + 1, right]

						return left_max if left_max >= right_max else right_max


if __name__ == "__main__":
				import doctest

				doctest.testmod(verbose=True)