




"""simple docstring"""




from typing import TYPE_CHECKING

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


lowerCAmelCase							:    List[str]  							=							{
    """configuration_mobilebert""": [
        """MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """MobileBertConfig""",
        """MobileBertOnnxConfig""",
    ],
    """tokenization_mobilebert""": ["""MobileBertTokenizer"""],
}

try:
					if not is_tokenizers_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCAmelCase							:    Optional[int]  							=							["""MobileBertTokenizerFast"""]

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCAmelCase							:    int  							=							[
					    """MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """MobileBertForMaskedLM""",
					    """MobileBertForMultipleChoice""",
					    """MobileBertForNextSentencePrediction""",
					    """MobileBertForPreTraining""",
					    """MobileBertForQuestionAnswering""",
					    """MobileBertForSequenceClassification""",
					    """MobileBertForTokenClassification""",
					    """MobileBertLayer""",
					    """MobileBertModel""",
					    """MobileBertPreTrainedModel""",
					    """load_tf_weights_in_mobilebert""",
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCAmelCase							:    Dict  							=							[
					    """TF_MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """TFMobileBertForMaskedLM""",
					    """TFMobileBertForMultipleChoice""",
					    """TFMobileBertForNextSentencePrediction""",
					    """TFMobileBertForPreTraining""",
					    """TFMobileBertForQuestionAnswering""",
					    """TFMobileBertForSequenceClassification""",
					    """TFMobileBertForTokenClassification""",
					    """TFMobileBertMainLayer""",
					    """TFMobileBertModel""",
					    """TFMobileBertPreTrainedModel""",
					]


if TYPE_CHECKING:
					from .configuration_mobilebert import (
					    MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
					    MobileBertConfig,
					    MobileBertOnnxConfig,
					)
					from .tokenization_mobilebert import MobileBertTokenizer

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_mobilebert_fast import MobileBertTokenizerFast

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_mobilebert import (
										    MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
										    MobileBertForMaskedLM,
										    MobileBertForMultipleChoice,
										    MobileBertForNextSentencePrediction,
										    MobileBertForPreTraining,
										    MobileBertForQuestionAnswering,
										    MobileBertForSequenceClassification,
										    MobileBertForTokenClassification,
										    MobileBertLayer,
										    MobileBertModel,
										    MobileBertPreTrainedModel,
										    load_tf_weights_in_mobilebert,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_mobilebert import (
										    TF_MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFMobileBertForMaskedLM,
										    TFMobileBertForMultipleChoice,
										    TFMobileBertForNextSentencePrediction,
										    TFMobileBertForPreTraining,
										    TFMobileBertForQuestionAnswering,
										    TFMobileBertForSequenceClassification,
										    TFMobileBertForTokenClassification,
										    TFMobileBertMainLayer,
										    TFMobileBertModel,
										    TFMobileBertPreTrainedModel,
										)

else:
					import sys

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









"""simple docstring"""




from __future__ import annotations

lowerCAmelCase							:    str  							=							[-10, -5, 0, 5, 5.1, 11, 13, 21, 3, 4, -21, -10, -5, -1, 0]
lowerCAmelCase							:    Dict  							=							[-5, 0, 5, 5.1, 11, 13, 21, -1, 4, -1, -10, -5, -1, 0, -1]



def 					a__				(       snake_case__       )		->      list[float]:
			lowerCamelCase       =      []
			lowerCamelCase       =      len(snake_case__       )

			for i in range(snake_case__       ):
						lowerCamelCase       =      -1
						for j in range(i + 1 ,				snake_case__       ):
									if arr[i] < arr[j]:
												lowerCamelCase       =      arr[j]
												break
						result.append(snake_case__       )
			return result



def 					a__				(       snake_case__       )		->      list[float]:
			lowerCamelCase       =      []
			for i, outer in enumerate(snake_case__       ):
						lowerCamelCase       =      -1
						for inner in arr[i + 1 :]:
									if outer < inner:
												lowerCamelCase       =      inner
												break
						result.append(snake_case__       )
			return result



def 					a__				(       snake_case__       )		->      list[float]:
			lowerCamelCase       =      len(snake_case__       )
			lowerCamelCase       =      []
			lowerCamelCase       =      [-1] * arr_size

			for index in reversed(range(snake_case__       )       ):
						if stack:
									while stack[-1] <= arr[index]:
												stack.pop()
												if not stack:
															break
						if stack:
									lowerCamelCase       =      stack[-1]
						stack.append(arr[index]       )
			return result


if __name__ == "__main__":
					from doctest import testmod
					from timeit import timeit

					testmod()
					print(next_greatest_element_slow(arr))
					print(next_greatest_element_fast(arr))
					print(next_greatest_element(arr))

					lowerCAmelCase							:    Dict  							=							(
					    """from __main__ import arr, next_greatest_element_slow, """
					    """next_greatest_element_fast, next_greatest_element"""
					)
					print(
					    """next_greatest_element_slow():""",
					    timeit("""next_greatest_element_slow(arr)""", setup=setup),
					)
					print(
					    """next_greatest_element_fast():""",
					    timeit("""next_greatest_element_fast(arr)""", setup=setup),
					)
					print(
					    """     next_greatest_element():""",
					    timeit("""next_greatest_element(arr)""", setup=setup),
					)



