

class 							A_				:


     def __init__(							self    :	List[str]      )							->			Dict:
      # numbermap keys are the number and its values are lists of indexes sorted
      # in ascending order
      __lowerCAmelCase:     Tuple     			=   {}
      # indexmap keys are an index and it's values are the number at that index
      __lowerCAmelCase:     int     			=   {}


     def 						UpperCAmelCase						(							self    :	Union[str, Any]    ,						UpperCAmelCase    :	list | str | range    ,						UpperCAmelCase    :	int      )							->			List[str]:
      if isinstance(_lowerCAmelCase    ,						(range, str)      ):
       __lowerCAmelCase:     Tuple     			=   list(_lowerCAmelCase      )
      elif not isinstance(_lowerCAmelCase    ,						_lowerCAmelCase      ):
       raise TypeError(
           'binary_search_delete() only accepts either a list, range or str'      )

      __lowerCAmelCase:     Union[str, Any]     			=   0
      __lowerCAmelCase:     str     			=   len(_lowerCAmelCase      ) - 1

      while low <= high:
       __lowerCAmelCase:     Any     			=   (low + high) // 2
       if array[mid] == item:
        array.pop(_lowerCAmelCase      )
        return array
       elif array[mid] < item:
        __lowerCAmelCase:     Any     			=   mid + 1
       else:
        __lowerCAmelCase:     Tuple     			=   mid - 1
      raise ValueError(
          'Either the item is not in the array or the array was unsorted'      )


     def 						UpperCAmelCase						(							self    :	Dict    ,						UpperCAmelCase    :	list | str | range    ,						UpperCAmelCase    :	int      )							->			Optional[int]:
      if isinstance(_lowerCAmelCase    ,						(range, str)      ):
       __lowerCAmelCase:     Union[str, Any]     			=   list(_lowerCAmelCase      )
      elif not isinstance(_lowerCAmelCase    ,						_lowerCAmelCase      ):
       raise TypeError(
           'binary_search_insert() only accepts either a list, range or str'      )

      __lowerCAmelCase:     List[Any]     			=   0
      __lowerCAmelCase:     Dict     			=   len(_lowerCAmelCase      ) - 1

      while low <= high:
       __lowerCAmelCase:     Optional[int]     			=   (low + high) // 2
       if array[mid] == index:
        # If the item already exists in the array,
        # insert it after the existing item
        array.insert(mid + 1    ,						_lowerCAmelCase      )
        return array
       elif array[mid] < index:
        __lowerCAmelCase:     Optional[int]     			=   mid + 1
       else:
        __lowerCAmelCase:     Any     			=   mid - 1

        # If the item doesn't exist in the array, insert it at the appropriate position
      array.insert(_lowerCAmelCase    ,						_lowerCAmelCase      )
      return array


     def 						UpperCAmelCase						(							self    :	List[str]    ,						UpperCAmelCase    :	int    ,						UpperCAmelCase    :	int      )							->			str:
      # Remove previous index
      if index in self.indexmap:
       __lowerCAmelCase:     Optional[int]     			=   self.indexmap[index]
       if len(self.numbermap[n]      ) == 1:
        del self.numbermap[n]
       else:
        __lowerCAmelCase:     Optional[int]     			=   self.binary_search_delete(self.numbermap[n]    ,						_lowerCAmelCase      )

        # Set new index
      __lowerCAmelCase:     Union[str, Any]     			=   number

      # Number not seen before or empty so insert number value
      if number not in self.numbermap:
       __lowerCAmelCase:     List[str]     			=   [index]

      # Here we need to perform a binary search insertion in order to insert
      # The item in the correct place
      else:
       __lowerCAmelCase:     Tuple     			=   self.binary_search_insert(
           self.numbermap[number]    ,						_lowerCAmelCase      )


     def 						UpperCAmelCase						(							self    :	int    ,						UpperCAmelCase    :	int      )							->			Tuple:
      return self.numbermap.get(_lowerCAmelCase    ,						[-1]      )[0]


if __name__ == "__main__":
  import doctest

  doctest.testmod()








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,
)


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

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

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

try:
  if not is_flax_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _a	 =     ['''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

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





