




# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


SCREAMING_SNAKE_CASE__     :     Tuple		    =       {
    "configuration_xmod": [
        "XMOD_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "XmodConfig",
        "XmodOnnxConfig",
    ],
}

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				SCREAMING_SNAKE_CASE__     :     Dict		    =       [
				    "XMOD_PRETRAINED_MODEL_ARCHIVE_LIST",
				    "XmodForCausalLM",
				    "XmodForMaskedLM",
				    "XmodForMultipleChoice",
				    "XmodForQuestionAnswering",
				    "XmodForSequenceClassification",
				    "XmodForTokenClassification",
				    "XmodModel",
				    "XmodPreTrainedModel",
				]

if TYPE_CHECKING:
				from .configuration_xmod import XMOD_PRETRAINED_CONFIG_ARCHIVE_MAP, XmodConfig, XmodOnnxConfig

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_xmod import (
								    XMOD_PRETRAINED_MODEL_ARCHIVE_LIST,
								    XmodForCausalLM,
								    XmodForMaskedLM,
								    XmodForMultipleChoice,
								    XmodForQuestionAnswering,
								    XmodForSequenceClassification,
								    XmodForTokenClassification,
								    XmodModel,
								    XmodPreTrainedModel,
								)

else:
				import sys

				SCREAMING_SNAKE_CASE__     :     Tuple		    =       _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)






def lowercase    (  SCREAMING_SNAKE_CASE )			->							int:


			'''simple docstring'''







			if not numbers:
						return 0

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

			SCREAMING_SNAKE_CASE_								=						SCREAMING_SNAKE_CASE_								=						SCREAMING_SNAKE_CASE_								=						numbers[0]

			for i in range(1       ,    len(SCREAMING_SNAKE_CASE ) ):
						# update the maximum and minimum subarray products
						SCREAMING_SNAKE_CASE_								=						numbers[i]
						if number < 0:
									SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_								=						min_till_now, max_till_now
						SCREAMING_SNAKE_CASE_								=						max(SCREAMING_SNAKE_CASE       ,    max_till_now * number )
						SCREAMING_SNAKE_CASE_								=						min(SCREAMING_SNAKE_CASE       ,    min_till_now * number )

						# update the maximum product found till now
						SCREAMING_SNAKE_CASE_								=						max(SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE )

			return max_prod
