
"""simple docstring"""




from typing import TYPE_CHECKING

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


UpperCAmelCase__				:   Optional[int]   							=			{'configuration_gptj': ['GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GPTJConfig', 'GPTJOnnxConfig']}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCAmelCase__				:   List[Any]   							=			[
					    'GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'GPTJForCausalLM',
					    'GPTJForQuestionAnswering',
					    'GPTJForSequenceClassification',
					    'GPTJModel',
					    'GPTJPreTrainedModel',
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCAmelCase__				:   Dict   							=			[
					    'TFGPTJForCausalLM',
					    'TFGPTJForQuestionAnswering',
					    'TFGPTJForSequenceClassification',
					    'TFGPTJModel',
					    'TFGPTJPreTrainedModel',
					]

try:
					if not is_flax_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCAmelCase__				:   Optional[int]   							=			[
					    'FlaxGPTJForCausalLM',
					    'FlaxGPTJModel',
					    'FlaxGPTJPreTrainedModel',
					]


if TYPE_CHECKING:
					from .configuration_gptj import GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTJConfig, GPTJOnnxConfig

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_gptj import (
										    GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST,
										    GPTJForCausalLM,
										    GPTJForQuestionAnswering,
										    GPTJForSequenceClassification,
										    GPTJModel,
										    GPTJPreTrainedModel,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_gptj import (
										    TFGPTJForCausalLM,
										    TFGPTJForQuestionAnswering,
										    TFGPTJForSequenceClassification,
										    TFGPTJModel,
										    TFGPTJPreTrainedModel,
										)

					try:
										if not is_flax_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_flax_gptj import FlaxGPTJForCausalLM, FlaxGPTJModel, FlaxGPTJPreTrainedModel

else:
					import sys

					UpperCAmelCase__				:   Any   							=			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)






"""simple docstring"""




import re

def lowercase_  (       _snake_case						):
							def docstring_decorator(_snake_case						):
														SCREAMING_SNAKE_CASE__						:	Any									=       fn.__doc__
														SCREAMING_SNAKE_CASE__						:	Dict									=       func_doc.split("""\n"""						)
														SCREAMING_SNAKE_CASE__						:	Any									=       0
														while i < len(_snake_case						) and re.search(R"""^\s*Examples?:\s*$"""       ,lines[i]						) is None:
																					i += 1
														if i < len(_snake_case						):
																					SCREAMING_SNAKE_CASE__						:	List[str]									=       example_docstring
																					SCREAMING_SNAKE_CASE__						:	List[Any]									=       """\n""".join(_snake_case						)
														else:
																					raise ValueError(
																					    f'''The function {fn} should have an empty \'Examples:\' in its docstring as placeholder, '''
																					    f'''current docstring is:\n{func_doc}'''						)
														SCREAMING_SNAKE_CASE__						:	int									=       func_doc
														return fn

							return docstring_decorator




