






from typing import TYPE_CHECKING

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


snake_case    				=		{
    """configuration_albert""": ["""ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """AlbertConfig""", """AlbertOnnxConfig"""],
}

try:
   if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   snake_case    				=		["""AlbertTokenizer"""]

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   snake_case    				=		["""AlbertTokenizerFast"""]

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   snake_case    				=		[
       """ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """AlbertForMaskedLM""",
       """AlbertForMultipleChoice""",
       """AlbertForPreTraining""",
       """AlbertForQuestionAnswering""",
       """AlbertForSequenceClassification""",
       """AlbertForTokenClassification""",
       """AlbertModel""",
       """AlbertPreTrainedModel""",
       """load_tf_weights_in_albert""",
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   snake_case    				=		[
       """TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """TFAlbertForMaskedLM""",
       """TFAlbertForMultipleChoice""",
       """TFAlbertForPreTraining""",
       """TFAlbertForQuestionAnswering""",
       """TFAlbertForSequenceClassification""",
       """TFAlbertForTokenClassification""",
       """TFAlbertMainLayer""",
       """TFAlbertModel""",
       """TFAlbertPreTrainedModel""",
   ]

try:
   if not is_flax_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   snake_case    				=		[
       """FlaxAlbertForMaskedLM""",
       """FlaxAlbertForMultipleChoice""",
       """FlaxAlbertForPreTraining""",
       """FlaxAlbertForQuestionAnswering""",
       """FlaxAlbertForSequenceClassification""",
       """FlaxAlbertForTokenClassification""",
       """FlaxAlbertModel""",
       """FlaxAlbertPreTrainedModel""",
   ]

if TYPE_CHECKING:
   from .configuration_albert import ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, AlbertConfig, AlbertOnnxConfig

   try:
      if not is_sentencepiece_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_albert import AlbertTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_albert_fast import AlbertTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_albert import (
          ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
          AlbertForMaskedLM,
          AlbertForMultipleChoice,
          AlbertForPreTraining,
          AlbertForQuestionAnswering,
          AlbertForSequenceClassification,
          AlbertForTokenClassification,
          AlbertModel,
          AlbertPreTrainedModel,
          load_tf_weights_in_albert,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_albert import (
          TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFAlbertForMaskedLM,
          TFAlbertForMultipleChoice,
          TFAlbertForPreTraining,
          TFAlbertForQuestionAnswering,
          TFAlbertForSequenceClassification,
          TFAlbertForTokenClassification,
          TFAlbertMainLayer,
          TFAlbertModel,
          TFAlbertPreTrainedModel,
      )

   try:
      if not is_flax_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_flax_albert import (
          FlaxAlbertForMaskedLM,
          FlaxAlbertForMultipleChoice,
          FlaxAlbertForPreTraining,
          FlaxAlbertForQuestionAnswering,
          FlaxAlbertForSequenceClassification,
          FlaxAlbertForTokenClassification,
          FlaxAlbertModel,
          FlaxAlbertPreTrainedModel,
      )
else:
   import sys

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














snake_case    				=		"""Tobias Carryer"""

from time import time


class  SCREAMING_SNAKE_CASE			:
     '''simple docstring'''


     def __init__(				self       :	List[Any]					, UpperCAmelCase_       :	List[str]					, UpperCAmelCase_       :	Optional[int]					, UpperCAmelCase_       :	Any					, UpperCAmelCase_       :	int=int(time()      )      ):  # noqa: B008
       SCREAMING_SNAKE_CASE       :      Optional[Any]							=    multiplier
       SCREAMING_SNAKE_CASE       :      Tuple							=    increment
       SCREAMING_SNAKE_CASE       :      int							=    modulo
       SCREAMING_SNAKE_CASE       :      Optional[int]							=    seed






     def 				_A       (				self       :	Tuple      ):
       SCREAMING_SNAKE_CASE       :      Optional[Any]							=    (self.multiplier * self.seed + self.increment) % self.modulo
       return self.seed


if __name__ == "__main__":
   # Show the LCG in action.
   snake_case    				=		LinearCongruentialGenerator(1_664_525, 1_013_904_223, 2 << 31)
   while True:
      print(lcg.next_number())






