
'''simple docstring'''







from typing import TYPE_CHECKING

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


_snake_case					: Union[str, Any]     			=					{
    """configuration_gpt2""": ["""GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPT2Config""", """GPT2OnnxConfig"""],
    """tokenization_gpt2""": ["""GPT2Tokenizer"""],
}

try:
						if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_snake_case					: Optional[Any]     			=					["""GPT2TokenizerFast"""]

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_snake_case					: Union[str, Any]     			=					[
						    """GPT2_PRETRAINED_MODEL_ARCHIVE_LIST""",
						    """GPT2DoubleHeadsModel""",
						    """GPT2ForQuestionAnswering""",
						    """GPT2ForSequenceClassification""",
						    """GPT2ForTokenClassification""",
						    """GPT2LMHeadModel""",
						    """GPT2Model""",
						    """GPT2PreTrainedModel""",
						    """load_tf_weights_in_gpt2""",
						]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_snake_case					: Any     			=					[
						    """TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST""",
						    """TFGPT2DoubleHeadsModel""",
						    """TFGPT2ForSequenceClassification""",
						    """TFGPT2LMHeadModel""",
						    """TFGPT2MainLayer""",
						    """TFGPT2Model""",
						    """TFGPT2PreTrainedModel""",
						]

try:
						if not is_keras_nlp_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_snake_case					: Union[str, Any]     			=					["""TFGPT2Tokenizer"""]

try:
						if not is_flax_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_snake_case					: Any     			=					["""FlaxGPT2LMHeadModel""", """FlaxGPT2Model""", """FlaxGPT2PreTrainedModel"""]

if TYPE_CHECKING:
						from .configuration_gpta import GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTaConfig, GPTaOnnxConfig
						from .tokenization_gpta import GPTaTokenizer

						try:
												if not is_tokenizers_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_gpta_fast import GPTaTokenizerFast

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_gpta import (
												    GPT2_PRETRAINED_MODEL_ARCHIVE_LIST,
												    GPTaDoubleHeadsModel,
												    GPTaForQuestionAnswering,
												    GPTaForSequenceClassification,
												    GPTaForTokenClassification,
												    GPTaLMHeadModel,
												    GPTaModel,
												    GPTaPreTrainedModel,
												    load_tf_weights_in_gpta,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_gpta import (
												    TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TFGPTaDoubleHeadsModel,
												    TFGPTaForSequenceClassification,
												    TFGPTaLMHeadModel,
												    TFGPTaMainLayer,
												    TFGPTaModel,
												    TFGPTaPreTrainedModel,
												)

						try:
												if not is_keras_nlp_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_gpta_tf import TFGPTaTokenizer

						try:
												if not is_flax_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_flax_gpta import FlaxGPTaLMHeadModel, FlaxGPTaModel, FlaxGPTaPreTrainedModel

else:
						import sys

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

'''simple docstring'''







def       _a		(						_SCREAMING_SNAKE_CASE		: str	):
						_SCREAMING_SNAKE_CASE					=			len(_SCREAMING_SNAKE_CASE	)
						for i in range(length - 1	):
												_SCREAMING_SNAKE_CASE					=			False
												for j in range(length - 1 - i	):
																		if collection[j] > collection[j + 1]:
																								_SCREAMING_SNAKE_CASE					=			True
																								_SCREAMING_SNAKE_CASE,				_SCREAMING_SNAKE_CASE					=			collection[j + 1], collection[j]
												if not swapped:
																		break  # Stop iteration if the collection is sorted.
						return collection


if __name__ == "__main__":
						import doctest
						import time

						doctest.testmod()

						_snake_case					: Union[str, Any]     			=					input("""Enter numbers separated by a comma:""").strip()
						_snake_case					: List[Any]     			=					[int(item) for item in user_input.split(""",""")]
						_snake_case					: Optional[int]     			=					time.process_time()
						print(*bubble_sort(unsorted), sep=""",""")
						print(F"Processing time: {(time.process_time() - start)%1E9 + 7}")