


from typing import TYPE_CHECKING

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


UpperCamelCase			:					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:
					UpperCamelCase			:					Optional[Any]         =							['OpenAIGPTTokenizerFast']

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCamelCase			:					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:
					UpperCamelCase			:					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

					UpperCamelCase			:					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]





