


def 							__lowerCamelCase		(					UpperCAmelCase_		:						list[int]		):




      """simple docstring"""





      if not numbers:
            return 0

      if not isinstance(UpperCAmelCase_					,    (list, tuple)		) or not all(
          isinstance(UpperCAmelCase_					,    UpperCAmelCase_		) for number in numbers		):
            raise ValueError('''numbers must be an iterable of integers'''		)

      a		:Optional[Any]									=			numbers[0]

      for i in range(1					,    len(UpperCAmelCase_		)		):
            # update the maximum and minimum subarray products
            a		:int									=			numbers[i]
            if number < 0:
                  a				,							a		:Optional[Any]									=			min_till_now, max_till_now
            a		:Tuple									=			max(UpperCAmelCase_					,    max_till_now * number		)
            a		:str									=			min(UpperCAmelCase_					,    min_till_now * number		)

            # update the maximum product found till now
            a		:str									=			max(UpperCAmelCase_					,    UpperCAmelCase_		)

      return max_prod









from typing import TYPE_CHECKING

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


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

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     snake_case							:       List[Any]										=	[
         '''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:
     snake_case							:       Any										=	[
         '''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

     snake_case							:       Optional[int]										=	_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)





