
"""simple docstring"""

from typing import TYPE_CHECKING

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


SCREAMING_SNAKE_CASE_         =  {
    'configuration_gpt2': ['GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GPT2Config', 'GPT2OnnxConfig'],
    'tokenization_gpt2': ['GPT2Tokenizer'],
}

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   SCREAMING_SNAKE_CASE_         =  ['GPT2TokenizerFast']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   SCREAMING_SNAKE_CASE_         =  [
       'GPT2_PRETRAINED_MODEL_ARCHIVE_LIST',
       'GPT2DoubleHeadsModel',
       'GPT2ForQuestionAnswering',
       'GPT2ForSequenceClassification',
       'GPT2ForTokenClassification',
       'GPT2LMHeadModel',
       'GPT2Model',
       'GPT2PreTrainedModel',
       'load_tf_weights_in_gpt2',
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   SCREAMING_SNAKE_CASE_         =  [
       'TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST',
       'TFGPT2DoubleHeadsModel',
       'TFGPT2ForSequenceClassification',
       'TFGPT2LMHeadModel',
       'TFGPT2MainLayer',
       'TFGPT2Model',
       'TFGPT2PreTrainedModel',
   ]

try:
   if not is_keras_nlp_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   SCREAMING_SNAKE_CASE_         =  ['TFGPT2Tokenizer']

try:
   if not is_flax_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   SCREAMING_SNAKE_CASE_         =  ['FlaxGPT2LMHeadModel', 'FlaxGPT2Model', 'FlaxGPT2PreTrainedModel']

if TYPE_CHECKING:
   from .configuration_gpta import GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTaConfig, GPTaOnnxConfig
   from .tokenization_gpta import GPTaTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_gpta_fast import GPTaTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_gpta import (
          GPT2_PRETRAINED_MODEL_ARCHIVE_LIST,
          GPTaDoubleHeadsModel,
          GPTaForQuestionAnswering,
          GPTaForSequenceClassification,
          GPTaForTokenClassification,
          GPTaLMHeadModel,
          GPTaModel,
          GPTaPreTrainedModel,
          load_tf_weights_in_gpta,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_gpta import (
          TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFGPTaDoubleHeadsModel,
          TFGPTaForSequenceClassification,
          TFGPTaLMHeadModel,
          TFGPTaMainLayer,
          TFGPTaModel,
          TFGPTaPreTrainedModel,
      )

   try:
      if not is_keras_nlp_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_gpta_tf import TFGPTaTokenizer

   try:
      if not is_flax_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_flax_gpta import FlaxGPTaLMHeadModel, FlaxGPTaModel, FlaxGPTaPreTrainedModel

else:
   import sys

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





"""simple docstring"""

from __future__ import annotations

SCREAMING_SNAKE_CASE_         =  '''Alexander Joslin'''




def       lowercase__  (   lowerCAmelCase			:   int	)	-> int:






    """simple docstring"""




    if number <= 0:
        UpperCAmelCase       =   F"n must be greater than 0. Got n = {number}"
        raise ValueError(lowerCAmelCase	)

    UpperCAmelCase       =   [number + 1] * (number + 1)

    # starting position
    UpperCAmelCase       =   0
    for i in range(1	,			lowerCAmelCase	):
        UpperCAmelCase       =   min(table[i + 1]	,			table[i] + 1	)
        # check if out of bounds
        if i * 2 <= number:
            UpperCAmelCase       =   min(table[i * 2]	,			table[i] + 1	)
        # check if out of bounds
        if i * 3 <= number:
            UpperCAmelCase       =   min(table[i * 3]	,			table[i] + 1	)
    return table[number]


if __name__ == "__main__":
   import doctest

   doctest.testmod()



