





from typing import TYPE_CHECKING

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


snake_case__   :				Optional[Any]						=	{
    'configuration_openai': ['OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'OpenAIGPTConfig'],
    'tokenization_openai': ['OpenAIGPTTokenizer'],
}

try:
       if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       snake_case__   :				Optional[Any]						=	['OpenAIGPTTokenizerFast']

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       snake_case__   :				List[str]						=	[
           '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:
       snake_case__   :				Optional[int]						=	[
           '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

       snake_case__   :				Dict						=	_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)












def _a							(      lowerCamelCase:  list					)      ->		list:







      '''simple docstring'''






      __A			  =							len(lowerCamelCase					)
      for cycle_start in range(0   ,							array_len - 1					):
            __A			  =							array[cycle_start]

            __A			  =							cycle_start
            for i in range(cycle_start + 1   ,							lowerCamelCase					):
                  if array[i] < item:
                        pos += 1

            if pos == cycle_start:
                  continue

            while item == array[pos]:
                  pos += 1

            __A  ,		__A			  =							item, array[pos]
            while pos != cycle_start:
                  __A			  =							cycle_start
                  for i in range(cycle_start + 1   ,							lowerCamelCase					):
                        if array[i] < item:
                              pos += 1

                  while item == array[pos]:
                        pos += 1

                  __A  ,		__A			  =							item, array[pos]

      return array


if __name__ == "__main__":
       assert cycle_sort([4, 5, 3, 2, 1]) == [1, 2, 3, 4, 5]
       assert cycle_sort([0, 1, -10, 15, 2, -2]) == [-10, -2, 0, 1, 2, 15]





