



"""simple docstring"""







def   lowerCAmelCase__			(     lowerCamelCase__			) ->							List[str]:
							A         =					[0 for i in range(len(_A			)			)]

							# initialize interval's left pointer and right pointer
							A         =					0, 0

							for i in range(1				,      len(_A			)			):
														# case when current index is inside the interval
														if i <= right_pointer:
																					A         =					min(right_pointer - i + 1				,      z_result[i - left_pointer]			)
																					A         =					min_edge

														while go_next(_A				,      _A				,      _A			):
																					z_result[i] += 1

														# if new index's result gives us more right interval,
														# we've to update left_pointer and right_pointer
														if i + z_result[i] - 1 > right_pointer:
																					A         =					i, i + z_result[i] - 1

							return z_result

def   lowerCAmelCase__			(     lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__			) ->							Tuple:
							return i + z_result[i] < len(_A			) and s[z_result[i]] == s[i + z_result[i]]

def   lowerCAmelCase__			(     lowerCamelCase__				,      lowerCamelCase__			) ->							List[Any]:
							A         =					0
							# concatenate 'pattern' and 'input_str' and call z_function
							# with concatenated string
							A         =					z_function(pattern + input_str			)

							for val in z_result:
														# if value is greater then length of the pattern string
														# that means this index is starting position of substring
														# which is equal to pattern string
														if val >= len(_A			):
																					answer += 1

							return answer


if __name__ == "__main__":
							import doctest

							doctest.testmod()











"""simple docstring"""







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available


A    =			{
    'configuration_gpt_neo': ['GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GPTNeoConfig', 'GPTNeoOnnxConfig'],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							A    =			[
							    'GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'GPTNeoForCausalLM',
							    'GPTNeoForQuestionAnswering',
							    'GPTNeoForSequenceClassification',
							    'GPTNeoForTokenClassification',
							    'GPTNeoModel',
							    'GPTNeoPreTrainedModel',
							    'load_tf_weights_in_gpt_neo',
							]

try:
							if not is_flax_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							A    =			[
							    'FlaxGPTNeoForCausalLM',
							    'FlaxGPTNeoModel',
							    'FlaxGPTNeoPreTrainedModel',
							]


if TYPE_CHECKING:
							from .configuration_gpt_neo import GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoConfig, GPTNeoOnnxConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_gpt_neo import (
														    GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST,
														    GPTNeoForCausalLM,
														    GPTNeoForQuestionAnswering,
														    GPTNeoForSequenceClassification,
														    GPTNeoForTokenClassification,
														    GPTNeoModel,
														    GPTNeoPreTrainedModel,
														    load_tf_weights_in_gpt_neo,
														)

							try:
														if not is_flax_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_flax_gpt_neo import FlaxGPTNeoForCausalLM, FlaxGPTNeoModel, FlaxGPTNeoPreTrainedModel


else:
							import sys

							A    =			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



