





from typing import TYPE_CHECKING

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


_a									=				{"configuration_gptj": ["GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTJConfig", "GPTJOnnxConfig"]}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_a									=				[
			    "GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST",
			    "GPTJForCausalLM",
			    "GPTJForQuestionAnswering",
			    "GPTJForSequenceClassification",
			    "GPTJModel",
			    "GPTJPreTrainedModel",
			]

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

try:
			if not is_flax_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_a									=				[
			    "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

			_a									=				_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)










def lowerCAmelCase__(__snake_case							)    ->				int:







	'''simple docstring'''







	lowerCamelCase__        =		str(__snake_case							).strip()
	if not bin_string:
		raise ValueError('''Empty string was passed to the function'''							)
	lowerCamelCase__        =		bin_string[0] == '''-'''
	if is_negative:
		lowerCamelCase__        =		bin_string[1:]
	if not all(char in '''01''' for char in bin_string							):
		raise ValueError('''Non-binary value was passed to the function'''							)
	lowerCamelCase__        =		0
	for char in bin_string:
		lowerCamelCase__        =		2 * decimal_number + int(__snake_case							)
	return -decimal_number if is_negative else decimal_number


if __name__ == "__main__":
			from doctest import testmod

			testmod()



