

'''simple docstring'''





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


A											=     {
    'configuration_roformer': ['ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'RoFormerConfig', 'RoFormerOnnxConfig'],
    'tokenization_roformer': ['RoFormerTokenizer'],
}

try:
		if not is_tokenizers_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		A											=     ['RoFormerTokenizerFast']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		A											=     [
		    'ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'RoFormerForCausalLM',
		    'RoFormerForMaskedLM',
		    'RoFormerForMultipleChoice',
		    'RoFormerForQuestionAnswering',
		    'RoFormerForSequenceClassification',
		    'RoFormerForTokenClassification',
		    'RoFormerLayer',
		    'RoFormerModel',
		    'RoFormerPreTrainedModel',
		    'load_tf_weights_in_roformer',
		]


try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		A											=     [
		    'TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'TFRoFormerForCausalLM',
		    'TFRoFormerForMaskedLM',
		    'TFRoFormerForMultipleChoice',
		    'TFRoFormerForQuestionAnswering',
		    'TFRoFormerForSequenceClassification',
		    'TFRoFormerForTokenClassification',
		    'TFRoFormerLayer',
		    'TFRoFormerModel',
		    'TFRoFormerPreTrainedModel',
		]


try:
		if not is_flax_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		A											=     [
		    'FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'FlaxRoFormerForMaskedLM',
		    'FlaxRoFormerForMultipleChoice',
		    'FlaxRoFormerForQuestionAnswering',
		    'FlaxRoFormerForSequenceClassification',
		    'FlaxRoFormerForTokenClassification',
		    'FlaxRoFormerModel',
		    'FlaxRoFormerPreTrainedModel',
		]


if TYPE_CHECKING:
		from .configuration_roformer import ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, RoFormerConfig, RoFormerOnnxConfig
		from .tokenization_roformer import RoFormerTokenizer

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_roformer_fast import RoFormerTokenizerFast

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_roformer import (
				    ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
				    RoFormerForCausalLM,
				    RoFormerForMaskedLM,
				    RoFormerForMultipleChoice,
				    RoFormerForQuestionAnswering,
				    RoFormerForSequenceClassification,
				    RoFormerForTokenClassification,
				    RoFormerLayer,
				    RoFormerModel,
				    RoFormerPreTrainedModel,
				    load_tf_weights_in_roformer,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_roformer import (
				    TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFRoFormerForCausalLM,
				    TFRoFormerForMaskedLM,
				    TFRoFormerForMultipleChoice,
				    TFRoFormerForQuestionAnswering,
				    TFRoFormerForSequenceClassification,
				    TFRoFormerForTokenClassification,
				    TFRoFormerLayer,
				    TFRoFormerModel,
				    TFRoFormerPreTrainedModel,
				)

		try:
				if not is_flax_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_flax_roformer import (
				    FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
				    FlaxRoFormerForMaskedLM,
				    FlaxRoFormerForMultipleChoice,
				    FlaxRoFormerForQuestionAnswering,
				    FlaxRoFormerForSequenceClassification,
				    FlaxRoFormerForTokenClassification,
				    FlaxRoFormerModel,
				    FlaxRoFormerPreTrainedModel,
				)


else:
		import sys

		A											=     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



def a(lowercase__			, lowercase__	):





	'''simple docstring'''
	if not isinstance(lowercase__			, lowercase__	):
		raise ValueError('iterations must be defined as integers'	)
	if not isinstance(lowercase__			, lowercase__	) or not number >= 1:
		raise ValueError(
		    'starting number must be\n                         and integer and be more than 0'	)
	if not iterations >= 1:
		raise ValueError('Iterations must be done more than 0 times to play FizzBuzz'	)

	snake_case_      		= ''
	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(lowercase__	)

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


if __name__ == "__main__":
		import doctest

		doctest.testmod()
