
from typing import TYPE_CHECKING

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


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

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

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowercase__          =[
						    '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:
						lowercase__          =[
						    '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:
						lowercase__          =[
						    '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

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


def        __UpperCamelCase					(    lowerCAmelCase__							:    list  ):

 def merge(lowerCAmelCase__							:    list  ,       lowerCAmelCase__							:    list  ) -> list:

  def _merge():
   while left and right:
    yield (left if left[0] <= right[0] else right).pop(0  )
   yield from left
   yield from right

  return list(_merge()  )

 if len(lowerCAmelCase__  ) <= 1:
  return collection
 __a			:  str		    =  len(lowerCAmelCase__  ) // 2
 return merge(merge_sort(collection[:mid]  )  ,       merge_sort(collection[mid:]  )  )


if __name__ == "__main__":
      import doctest

      doctest.testmod()
      lowercase__          =input('Enter numbers separated by a comma:\n').strip()
      lowercase__          =[int(item) for item in user_input.split(',')]
      print(*merge_sort(unsorted), sep=',')
