
"""simple docstring"""







from typing import TYPE_CHECKING

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


_SCREAMING_SNAKE_CASE	       =      {"""configuration_mbart""": ["""MBART_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MBartConfig""", """MBartOnnxConfig"""]}

try:
		if not is_sentencepiece_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_SCREAMING_SNAKE_CASE	       =      ["""MBartTokenizer"""]

try:
		if not is_tokenizers_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_SCREAMING_SNAKE_CASE	       =      ["""MBartTokenizerFast"""]

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_SCREAMING_SNAKE_CASE	       =      [
		    """MBART_PRETRAINED_MODEL_ARCHIVE_LIST""",
		    """MBartForCausalLM""",
		    """MBartForConditionalGeneration""",
		    """MBartForQuestionAnswering""",
		    """MBartForSequenceClassification""",
		    """MBartModel""",
		    """MBartPreTrainedModel""",
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_SCREAMING_SNAKE_CASE	       =      [
		    """TFMBartForConditionalGeneration""",
		    """TFMBartModel""",
		    """TFMBartPreTrainedModel""",
		]

try:
		if not is_flax_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_SCREAMING_SNAKE_CASE	       =      [
		    """FlaxMBartForConditionalGeneration""",
		    """FlaxMBartForQuestionAnswering""",
		    """FlaxMBartForSequenceClassification""",
		    """FlaxMBartModel""",
		    """FlaxMBartPreTrainedModel""",
		]


if TYPE_CHECKING:
		from .configuration_mbart import MBART_PRETRAINED_CONFIG_ARCHIVE_MAP, MBartConfig, MBartOnnxConfig

		try:
				if not is_sentencepiece_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_mbart import MBartTokenizer

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_mbart_fast import MBartTokenizerFast

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_mbart import (
				    MBART_PRETRAINED_MODEL_ARCHIVE_LIST,
				    MBartForCausalLM,
				    MBartForConditionalGeneration,
				    MBartForQuestionAnswering,
				    MBartForSequenceClassification,
				    MBartModel,
				    MBartPreTrainedModel,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_mbart import TFMBartForConditionalGeneration, TFMBartModel, TFMBartPreTrainedModel

		try:
				if not is_flax_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_flax_mbart import (
				    FlaxMBartForConditionalGeneration,
				    FlaxMBartForQuestionAnswering,
				    FlaxMBartForSequenceClassification,
				    FlaxMBartModel,
				    FlaxMBartPreTrainedModel,
				)

else:
		import sys

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


"""simple docstring"""







def   __UpperCamelCase   (					SCREAMING_SNAKE_CASE )  ->     list:







							"""simple docstring"""


							__snake_case     			=       len(SCREAMING_SNAKE_CASE )
							for i in range(1							,    SCREAMING_SNAKE_CASE ):
														__snake_case     			=       collection[i]
														__snake_case     			=       0
														__snake_case     			=       i - 1

														while low <= high:
																					__snake_case     			=       (low + high) // 2
																					if val < collection[mid]:
																												__snake_case     			=       mid - 1
																					else:
																												__snake_case     			=       mid + 1
														for j in range(SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    -1 ):
																					__snake_case     			=       collection[j - 1]
														__snake_case     			=       val
							return collection


if __name__ == "__main__":
		_SCREAMING_SNAKE_CASE	       =      input("""Enter numbers separated by a comma:\n""").strip()
		_SCREAMING_SNAKE_CASE	       =      [int(item) for item in user_input.split(""",""")]
		print(binary_insertion_sort(unsorted))
