






# 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


__UpperCamelCase   :   Any      =    {
    """configuration_xmod""": [
        """XMOD_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """XmodConfig""",
        """XmodOnnxConfig""",
    ],
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__UpperCamelCase   :   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

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






'''simple docstring'''





import random
from typing import Any

def 			a_	(				_lowerCAmelCase			) -> list[Any]:
  for _ in range(len(_lowerCAmelCase			)			):
    __lowerCamelCase			:			Optional[Any]       			=					random.randint(0			,len(_lowerCAmelCase			) - 1			)
    __lowerCamelCase			:			str       			=					random.randint(0			,len(_lowerCAmelCase			) - 1			)
    __lowerCamelCase			,__lowerCamelCase			:			List[Any]       			=					data[b], data[a]
  return data


if __name__ == "__main__":
   _UpperCamelCase											=		[0, 1, 2, 3, 4, 5, 6, 7]
   _UpperCamelCase											=		['python', 'says', 'hello', '!']
   print('Fisher-Yates Shuffle:')
   print('List', integers, strings)
   print('FY Shuffle', fisher_yates_shuffle(integers), fisher_yates_shuffle(strings))






