



from hashlib import mda, shaaaa

UpperCAmelCase__			:   List[Any]      			=    (shaaaa, mda)





class 							lowerCAmelCase_		:







		def __init__(    self  :   Tuple       ,		UpperCAmelCase_  :   Any = 8					)       ->		None:


						'''simple docstring'''







						_UpperCAmelCase						:  str     					=	0b0
						_UpperCAmelCase						:  str     					=	size







		def 					a_  (    self  :   Union[str, Any]       ,		UpperCAmelCase_  :   Any					)       ->		None:


						'''simple docstring'''







						_UpperCAmelCase						:  Union[str, Any]     					=	self.hash_(snake_case__					)
						self.bitarray |= h







		def 					a_  (    self  :   Any       ,		UpperCAmelCase_  :   List[str]					)       ->		bool:


						'''simple docstring'''







						_UpperCAmelCase						:  List[Any]     					=	self.hash_(snake_case__					)
						return (h & self.bitarray) == h







		def __contains__(    self  :   Any       ,		UpperCAmelCase_  :   str					)       ->		bool:


						'''simple docstring'''







						return self.exists(snake_case__					)







		def 					a_  (    self  :   str       ,		UpperCAmelCase_  :   Any					)       ->		str:


						'''simple docstring'''







						_UpperCAmelCase						:  Dict     					=	bin(snake_case__					)[2:]
						return res.zfill(self.size					)







		@property
		def 					a_  (    self  :   List[Any]					)       ->		str:


						'''simple docstring'''







						return self.format_bin(self.bitarray					)







		def 					a_  (    self  :   str       ,		UpperCAmelCase_  :   Dict					)       ->		int:


						'''simple docstring'''







						_UpperCAmelCase						:  Union[str, Any]     					=	0b0
						for func in HASH_FUNCTIONS:
										_UpperCAmelCase						:  Optional[Any]     					=	(
										    int.from_bytes(func(value.encode()					).digest()       ,		'''little'''					) % self.size
										)
										res |= 2**position
						return res







		def 					a_  (    self  :   Any       ,		UpperCAmelCase_  :   Any					)       ->		str:


						'''simple docstring'''







						return self.format_bin(self.hash_(snake_case__					)					)







		@property
		def 					a_  (    self  :   Tuple					)       ->		float:


						'''simple docstring'''







						_UpperCAmelCase						:  int     					=	bin(self.bitarray					).count('''1'''					)
						return (n_ones / self.size) ** len(snake_case__					)

from typing import TYPE_CHECKING

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


__snake_case         =     {'''configuration_gptj''': ['''GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPTJConfig''', '''GPTJOnnxConfig''']}

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

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

try:
     if not is_flax_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __snake_case         =     [
         '''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         =     _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
