



from typing import TYPE_CHECKING

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


a_       :int				  =				{
    "configuration_openai": ["OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP", "OpenAIGPTConfig"],
    "tokenization_openai": ["OpenAIGPTTokenizer"],
}

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a_       :str				  =				["OpenAIGPTTokenizerFast"]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a_       :Any				  =				[
     "OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST",
     "OpenAIGPTDoubleHeadsModel",
     "OpenAIGPTForSequenceClassification",
     "OpenAIGPTLMHeadModel",
     "OpenAIGPTModel",
     "OpenAIGPTPreTrainedModel",
     "load_tf_weights_in_openai_gpt",
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a_       :Any				  =				[
     "TF_OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST",
     "TFOpenAIGPTDoubleHeadsModel",
     "TFOpenAIGPTForSequenceClassification",
     "TFOpenAIGPTLMHeadModel",
     "TFOpenAIGPTMainLayer",
     "TFOpenAIGPTModel",
     "TFOpenAIGPTPreTrainedModel",
 ]


if TYPE_CHECKING:
 from .configuration_openai import OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP, OpenAIGPTConfig
 from .tokenization_openai import OpenAIGPTTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_openai_fast import OpenAIGPTTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_openai import (
      OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
      OpenAIGPTDoubleHeadsModel,
      OpenAIGPTForSequenceClassification,
      OpenAIGPTLMHeadModel,
      OpenAIGPTModel,
      OpenAIGPTPreTrainedModel,
      load_tf_weights_in_openai_gpt,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_openai import (
      TF_OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFOpenAIGPTDoubleHeadsModel,
      TFOpenAIGPTForSequenceClassification,
      TFOpenAIGPTLMHeadModel,
      TFOpenAIGPTMainLayer,
      TFOpenAIGPTModel,
      TFOpenAIGPTPreTrainedModel,
  )

else:
 import sys

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











def 	lowercase_							(A			:			Dict				):
       snake_case__							:     Optional[int]            =     len(A				)
       for i in range(length - 1				):
              snake_case__							:     List[str]            =     False
              for j in range(length - 1 - i				):
                     if collection[j] > collection[j + 1]:
                            snake_case__							:     Optional[Any]            =     True
                            snake_case__     ,    snake_case__							:     Dict            =     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()

 a_       :Any				  =				input("Enter numbers separated by a comma:").strip()
 a_       :Optional[Any]				  =				[int(item) for item in user_input.split(",")]
 a_       :Optional[int]				  =				time.process_time()
 print(*bubble_sort(unsorted), sep=",")
 print(F"""Processing time: {(time.process_time() - start)%1e9 + 7}""")






