

"""simple docstring"""






from __future__ import annotations



def   lowercase_	(    _lowercase       :		list[int]	):




					'''simple docstring'''






					UpperCAmelCase			:   str      =				len(_lowercase	) // 2

					# choose the middle 3 elements
					UpperCAmelCase			:   str      =				lst[m - 1 : m + 2]

					# if middle element is peak
					if three[1] > three[0] and three[1] > three[2]:
										return three[1]

					# if increasing, recurse on right
					elif three[0] < three[2]:
										if len(lst[:m]	) == 2:
															m -= 1
										return peak(lst[m:]	)

					# decreasing
					else:
										if len(lst[:m]	) == 2:
															m += 1
										return peak(lst[:m]	)


if __name__ == "__main__":
	import doctest

	doctest.testmod()





"""simple docstring"""






from typing import TYPE_CHECKING

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


snake_case_   :   Dict        =       {
    """configuration_mvp""": ["""MVP_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MvpConfig""", """MvpOnnxConfig"""],
    """tokenization_mvp""": ["""MvpTokenizer"""],
}

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	snake_case_   :   List[Any]        =       ["""MvpTokenizerFast"""]

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	snake_case_   :   Any        =       [
	    """MVP_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """MvpForCausalLM""",
	    """MvpForConditionalGeneration""",
	    """MvpForQuestionAnswering""",
	    """MvpForSequenceClassification""",
	    """MvpModel""",
	    """MvpPreTrainedModel""",
	]

if TYPE_CHECKING:
	from .configuration_mvp import MVP_PRETRAINED_CONFIG_ARCHIVE_MAP, MvpConfig, MvpOnnxConfig
	from .tokenization_mvp import MvpTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_mvp_fast import MvpTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_mvp import (
		    MVP_PRETRAINED_MODEL_ARCHIVE_LIST,
		    MvpForCausalLM,
		    MvpForConditionalGeneration,
		    MvpForQuestionAnswering,
		    MvpForSequenceClassification,
		    MvpModel,
		    MvpPreTrainedModel,
		)

else:
	import sys

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


