




"""simple docstring"""




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


lowerCAmelCase							:    int  							=							{
    """configuration_data2vec_audio""": ["""DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP""", """Data2VecAudioConfig"""],
    """configuration_data2vec_text""": [
        """DATA2VEC_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """Data2VecTextConfig""",
        """Data2VecTextOnnxConfig""",
    ],
    """configuration_data2vec_vision""": [
        """DATA2VEC_VISION_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """Data2VecVisionConfig""",
        """Data2VecVisionOnnxConfig""",
    ],
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCAmelCase							:    Union[str, Any]  							=							[
					    """DATA2VEC_AUDIO_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """Data2VecAudioForAudioFrameClassification""",
					    """Data2VecAudioForCTC""",
					    """Data2VecAudioForSequenceClassification""",
					    """Data2VecAudioForXVector""",
					    """Data2VecAudioModel""",
					    """Data2VecAudioPreTrainedModel""",
					]
					lowerCAmelCase							:    str  							=							[
					    """DATA2VEC_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """Data2VecTextForCausalLM""",
					    """Data2VecTextForMaskedLM""",
					    """Data2VecTextForMultipleChoice""",
					    """Data2VecTextForQuestionAnswering""",
					    """Data2VecTextForSequenceClassification""",
					    """Data2VecTextForTokenClassification""",
					    """Data2VecTextModel""",
					    """Data2VecTextPreTrainedModel""",
					]
					lowerCAmelCase							:    Tuple  							=							[
					    """DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """Data2VecVisionForImageClassification""",
					    """Data2VecVisionForMaskedImageModeling""",
					    """Data2VecVisionForSemanticSegmentation""",
					    """Data2VecVisionModel""",
					    """Data2VecVisionPreTrainedModel""",
					]

if is_tf_available():
					lowerCAmelCase							:    Any  							=							[
					    """TFData2VecVisionForImageClassification""",
					    """TFData2VecVisionForSemanticSegmentation""",
					    """TFData2VecVisionModel""",
					    """TFData2VecVisionPreTrainedModel""",
					]

if TYPE_CHECKING:
					from .configuration_dataavec_audio import DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP, DataaVecAudioConfig
					from .configuration_dataavec_text import (
					    DATA2VEC_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP,
					    DataaVecTextConfig,
					    DataaVecTextOnnxConfig,
					)
					from .configuration_dataavec_vision import (
					    DATA2VEC_VISION_PRETRAINED_CONFIG_ARCHIVE_MAP,
					    DataaVecVisionConfig,
					    DataaVecVisionOnnxConfig,
					)

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_dataavec_audio import (
										    DATA2VEC_AUDIO_PRETRAINED_MODEL_ARCHIVE_LIST,
										    DataaVecAudioForAudioFrameClassification,
										    DataaVecAudioForCTC,
										    DataaVecAudioForSequenceClassification,
										    DataaVecAudioForXVector,
										    DataaVecAudioModel,
										    DataaVecAudioPreTrainedModel,
										)
										from .modeling_dataavec_text import (
										    DATA2VEC_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
										    DataaVecTextForCausalLM,
										    DataaVecTextForMaskedLM,
										    DataaVecTextForMultipleChoice,
										    DataaVecTextForQuestionAnswering,
										    DataaVecTextForSequenceClassification,
										    DataaVecTextForTokenClassification,
										    DataaVecTextModel,
										    DataaVecTextPreTrainedModel,
										)
										from .modeling_dataavec_vision import (
										    DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST,
										    DataaVecVisionForImageClassification,
										    DataaVecVisionForMaskedImageModeling,
										    DataaVecVisionForSemanticSegmentation,
										    DataaVecVisionModel,
										    DataaVecVisionPreTrainedModel,
										)
					if is_tf_available():
										from .modeling_tf_dataavec_vision import (
										    TFDataaVecVisionForImageClassification,
										    TFDataaVecVisionForSemanticSegmentation,
										    TFDataaVecVisionModel,
										    TFDataaVecVisionPreTrainedModel,
										)

else:
					import sys

					lowerCAmelCase							:    List[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),
					)



