

# 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

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


a		={"""configuration_mra""": ["""MRA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MraConfig"""]}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					a		=[
					    """MRA_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """MraForMaskedLM""",
					    """MraForMultipleChoice""",
					    """MraForQuestionAnswering""",
					    """MraForSequenceClassification""",
					    """MraForTokenClassification""",
					    """MraLayer""",
					    """MraModel""",
					    """MraPreTrainedModel""",
					]


if TYPE_CHECKING:
					from .configuration_mra import MRA_PRETRAINED_CONFIG_ARCHIVE_MAP, MraConfig

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_mra import (
										    MRA_PRETRAINED_MODEL_ARCHIVE_LIST,
										    MraForMaskedLM,
										    MraForMultipleChoice,
										    MraForQuestionAnswering,
										    MraForSequenceClassification,
										    MraForTokenClassification,
										    MraLayer,
										    MraModel,
										    MraPreTrainedModel,
										)
else:
					import sys

					a		=_LazyModule(__name__, globals()["""__file__"""], _import_structure)












"""simple docstring"""



def 						lowercase    (				__snake_case				: int = 1_0_0	):
		lowercase_ : str							       =   0
		lowercase_ : List[Any]							       =   0
		for i in range(1		,					n + 1	):
				sum_of_squares += i**2
				sum_of_ints += i
		return sum_of_ints**2 - sum_of_squares


if __name__ == "__main__":
						print(F"""{solution() = }""")



