

from __future__ import annotations


def     __SCREAMING_SNAKE_CASE   (SCREAMING_SNAKE_CASE__			):
						snake_case_  =      len(SCREAMING_SNAKE_CASE__			) // 2

						# choose the middle 3 elements
						snake_case_  =      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()

from typing import TYPE_CHECKING

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


__A							={
    '''configuration_gpt_neo''': ['''GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPTNeoConfig''', '''GPTNeoOnnxConfig'''],
}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__A							=[
			    '''GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''GPTNeoForCausalLM''',
			    '''GPTNeoForQuestionAnswering''',
			    '''GPTNeoForSequenceClassification''',
			    '''GPTNeoForTokenClassification''',
			    '''GPTNeoModel''',
			    '''GPTNeoPreTrainedModel''',
			    '''load_tf_weights_in_gpt_neo''',
			]

try:
			if not is_flax_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__A							=[
			    '''FlaxGPTNeoForCausalLM''',
			    '''FlaxGPTNeoModel''',
			    '''FlaxGPTNeoPreTrainedModel''',
			]


if TYPE_CHECKING:
			from .configuration_gpt_neo import GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoConfig, GPTNeoOnnxConfig

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_gpt_neo import (
						    GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST,
						    GPTNeoForCausalLM,
						    GPTNeoForQuestionAnswering,
						    GPTNeoForSequenceClassification,
						    GPTNeoForTokenClassification,
						    GPTNeoModel,
						    GPTNeoPreTrainedModel,
						    load_tf_weights_in_gpt_neo,
						)

			try:
						if not is_flax_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_flax_gpt_neo import FlaxGPTNeoForCausalLM, FlaxGPTNeoModel, FlaxGPTNeoPreTrainedModel


else:
			import sys

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


