'''simple docstring'''






def  UpperCamelCase__					(					_lowercase    :				List[str]   ,    _lowercase    :				List[str]     )							->	str:
		# "extended trapezoidal rule"
		# int(f) = dx/2 * (f1 + 2f2 + ... + fn)
		__UpperCAmelCase:					int		= (boundary[1] - boundary[0]) / steps
		__UpperCAmelCase:					str		= boundary[0]
		__UpperCAmelCase:					Optional[int]		= boundary[1]
		__UpperCAmelCase:					List[str]		= make_points(_lowercase   ,    _lowercase   ,    _lowercase     )
		__UpperCAmelCase:					Tuple		= 0.0
		y += (h / 2.0) * f(_lowercase     )
		for i in x_i:
				# print(i)
				y += h * f(_lowercase     )
		y += (h / 2.0) * f(_lowercase     )
		return y






def  UpperCamelCase__					(					_lowercase    :				str   ,    _lowercase    :				int   ,    _lowercase    :				Dict     )							->	List[str]:
		__UpperCAmelCase:					Any		= a + h
		while x < (b - h):
				yield x
				__UpperCAmelCase:					Dict		= x + h






def  UpperCamelCase__					(					_lowercase    :				Optional[Any]     )							->	Optional[Any]:  # enter your function here
		__UpperCAmelCase:					List[Any]		= (x - 0) * (x - 0)
		return y






def  UpperCamelCase__					(					)							->	Tuple:
		__UpperCAmelCase:					Any		= 0.0  # Lower bound of integration
		__UpperCAmelCase:					List[str]		= 1.0  # Upper bound of integration
		__UpperCAmelCase:					int		= 10.0  # define number of steps or resolution
		__UpperCAmelCase:					Dict		= [a, b]  # define boundary of integration
		__UpperCAmelCase:					List[str]		= method_a(_lowercase   ,    _lowercase     )
		print(F'''y = {y}'''     )


if __name__ == "__main__":
				main()
'''simple docstring'''






from typing import TYPE_CHECKING

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


SCREAMING_SNAKE_CASE_	   =			{
    'configuration_rembert': ['REMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'RemBertConfig', 'RemBertOnnxConfig']
}

try:
    if not is_sentencepiece_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    SCREAMING_SNAKE_CASE_	   =			['RemBertTokenizer']

try:
    if not is_tokenizers_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    SCREAMING_SNAKE_CASE_	   =			['RemBertTokenizerFast']

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    SCREAMING_SNAKE_CASE_	   =			[
        'REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
        'RemBertForCausalLM',
        'RemBertForMaskedLM',
        'RemBertForMultipleChoice',
        'RemBertForQuestionAnswering',
        'RemBertForSequenceClassification',
        'RemBertForTokenClassification',
        'RemBertLayer',
        'RemBertModel',
        'RemBertPreTrainedModel',
        'load_tf_weights_in_rembert',
    ]


try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    SCREAMING_SNAKE_CASE_	   =			[
        'TF_REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
        'TFRemBertForCausalLM',
        'TFRemBertForMaskedLM',
        'TFRemBertForMultipleChoice',
        'TFRemBertForQuestionAnswering',
        'TFRemBertForSequenceClassification',
        'TFRemBertForTokenClassification',
        'TFRemBertLayer',
        'TFRemBertModel',
        'TFRemBertPreTrainedModel',
    ]


if TYPE_CHECKING:
    from .configuration_rembert import REMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RemBertConfig, RemBertOnnxConfig

    try:
        if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_rembert import RemBertTokenizer

    try:
        if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_rembert_fast import RemBertTokenizerFast

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_rembert import (
            REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
            RemBertForCausalLM,
            RemBertForMaskedLM,
            RemBertForMultipleChoice,
            RemBertForQuestionAnswering,
            RemBertForSequenceClassification,
            RemBertForTokenClassification,
            RemBertLayer,
            RemBertModel,
            RemBertPreTrainedModel,
            load_tf_weights_in_rembert,
        )

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_rembert import (
            TF_REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
            TFRemBertForCausalLM,
            TFRemBertForMaskedLM,
            TFRemBertForMultipleChoice,
            TFRemBertForQuestionAnswering,
            TFRemBertForSequenceClassification,
            TFRemBertForTokenClassification,
            TFRemBertLayer,
            TFRemBertModel,
            TFRemBertPreTrainedModel,
        )


else:
    import sys

    SCREAMING_SNAKE_CASE_	   =			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)