




from .hash_table import HashTable
from .number_theory.prime_numbers import is_prime, next_prime


class 							__SCREAMING_SNAKE_CASE				(						lowerCamelCase    ):

					def __init__(							self					,			*__lowercase					,			**__lowercase   ):







						'''simple docstring'''




						super().__init__(*__lowercase					,			**__lowercase   )

					def       lowercase_				(							self					,			__lowercase					,			__lowercase   ):







						'''simple docstring'''




						lowerCamelCase_				:						Dict				  =				(
						    next_prime(value % self.size_table   )
						    if not is_prime(value % self.size_table   )
						    else value % self.size_table
						)  # gt = bigger than
						return next_prime_gt - (data % next_prime_gt)

					def       lowercase_				(							self					,			__lowercase					,			__lowercase					,			__lowercase   ):







						'''simple docstring'''




						return (increment * self.__hash_function_a(__lowercase					,			__lowercase   )) % self.size_table





					def       lowercase_				(							self					,			__lowercase					,			__lowercase=None   ):







						'''simple docstring'''




						lowerCamelCase_				:						Optional[Any]				  =				1
						lowerCamelCase_				:						Optional[Any]				  =				self.hash_function(__lowercase   )

						while self.values[new_key] is not None and self.values[new_key] != key:
							lowerCamelCase_				:						Optional[Any]				  =				(
							    self.__hash_double_function(__lowercase					,			__lowercase					,			__lowercase   )
							    if self.balanced_factor() >= self.lim_charge
							    else None
							)
							if new_key is None:
								break
							else:
								i += 1

						return new_key








from typing import TYPE_CHECKING

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


snake_case_   :	Union[str, Any]							      ={'''configuration_gptj''': ['''GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPTJConfig''', '''GPTJOnnxConfig''']}

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

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

try:
					if not is_flax_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					snake_case_   :	List[Any]							      =[
					    '''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

					snake_case_   :	List[str]							      =_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)

