



# 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


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

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_snake_case		=  [
						    "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

						_snake_case		=  _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





"""simple docstring"""







def      __UpperCAmelCase      (    __lowerCamelCase						,     __lowerCamelCase	)							->      str:
		if not isinstance(__lowerCamelCase						,     __lowerCamelCase	):
				raise ValueError('''iterations must be defined as integers'''	)
		if not isinstance(__lowerCamelCase						,     __lowerCamelCase	) or not number >= 1:
				raise ValueError(
				    '''starting number must be
                         and integer and be more than 0'''	)
		if not iterations >= 1:
				raise ValueError('''Iterations must be done more than 0 times to play FizzBuzz'''	)

		lowercase__   :    Tuple				=			''''''
		while number <= iterations:
				if number % 3 == 0:
						out += "Fizz"
				if number % 5 == 0:
						out += "Buzz"
				if 0 not in (number % 3, number % 5):
						out += str(__lowerCamelCase	)

				# print(out)
				number += 1
				out += " "
		return out


if __name__ == "__main__":
							import doctest

							doctest.testmod()
