"""simple docstring"""


from typing import TYPE_CHECKING

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


__UpperCamelCase       :    Any         =		{
    "configuration_openai": ["OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP", "OpenAIGPTConfig"],
    "tokenization_openai": ["OpenAIGPTTokenizer"],
}

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __UpperCamelCase       :    Optional[int]         =		["OpenAIGPTTokenizerFast"]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __UpperCamelCase       :    List[Any]         =		[
      "OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST",
      "OpenAIGPTDoubleHeadsModel",
      "OpenAIGPTForSequenceClassification",
      "OpenAIGPTLMHeadModel",
      "OpenAIGPTModel",
      "OpenAIGPTPreTrainedModel",
      "load_tf_weights_in_openai_gpt",
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __UpperCamelCase       :    Tuple         =		[
      "TF_OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST",
      "TFOpenAIGPTDoubleHeadsModel",
      "TFOpenAIGPTForSequenceClassification",
      "TFOpenAIGPTLMHeadModel",
      "TFOpenAIGPTMainLayer",
      "TFOpenAIGPTModel",
      "TFOpenAIGPTPreTrainedModel",
  ]


if TYPE_CHECKING:
  from .configuration_openai import OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP, OpenAIGPTConfig
  from .tokenization_openai import OpenAIGPTTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_openai_fast import OpenAIGPTTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_openai import (
        OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
        OpenAIGPTDoubleHeadsModel,
        OpenAIGPTForSequenceClassification,
        OpenAIGPTLMHeadModel,
        OpenAIGPTModel,
        OpenAIGPTPreTrainedModel,
        load_tf_weights_in_openai_gpt,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_openai import (
        TF_OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFOpenAIGPTDoubleHeadsModel,
        TFOpenAIGPTForSequenceClassification,
        TFOpenAIGPTLMHeadModel,
        TFOpenAIGPTMainLayer,
        TFOpenAIGPTModel,
        TFOpenAIGPTPreTrainedModel,
    )

else:
  import sys

  __UpperCamelCase       :    str         =		_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
"""simple docstring"""


from .number_theory.prime_numbers import next_prime
class   UpperCAmelCase_	:
    def __init__(	self			:       str   ,						_lowercase			:       int   ,						_lowercase			:       int | None = None   ,						_lowercase			:       float | None = None   ,						)						->    None:
       _lowercase							     =       size_table
       _lowercase							     =       [None] * self.size_table
       _lowercase							     =       0.75 if lim_charge is None else lim_charge
       _lowercase							     =       1 if charge_factor is None else charge_factor
       _lowercase							     =       []
       _lowercase							     =       {}
    def 		_lowerCamelCase      (	self			:       Any		)						->    Optional[Any]:
       return self._keys
    def 		_lowerCamelCase      (	self			:       Dict		)						->    Dict:
       return sum(1 for slot in self.values if slot is not None		) / (
           self.size_table * self.charge_factor
       )
    def 		_lowerCamelCase      (	self			:       Union[str, Any]   ,						_lowercase			:       List[str]		)						->    str:
       return key % self.size_table
    def 		_lowerCamelCase      (	self			:       Any   ,						_lowercase			:       Union[str, Any]		)						->    Optional[Any]:
       print(f"""step {step_ord}"""		)
       print(list(range(len(self.values		)		)		)		)
       print(self.values		)
    def 		_lowerCamelCase      (	self			:       str   ,						_lowercase			:       List[Any]		)						->    List[Any]:
       _lowercase							     =       1
       _lowercase							     =       values
       for value in values:
          self.insert_data(_lowercase		)
          self._step_by_step(_lowercase		)
          i += 1
    def 		_lowerCamelCase      (	self			:       Union[str, Any]   ,						_lowercase			:       Optional[int]   ,						_lowercase			:       Union[str, Any]		)						->    List[Any]:
       _lowercase							     =       data
       _lowercase							     =       data
    def 		_lowerCamelCase      (	self			:       int   ,						_lowercase			:       Optional[Any]   ,						_lowercase			:       Tuple=None		)						->    Any:
       _lowercase							     =       self.hash_function(key + 1		)

       while self.values[new_key] is not None and self.values[new_key] != key:
          if self.values.count(_lowercase		) > 0:
             _lowercase							     =       self.hash_function(new_key + 1		)
          else:
             _lowercase							     =       None
             break

       return new_key
    def 		_lowerCamelCase      (	self			:       str		)						->    Any:
       _lowercase							     =       [value for value in self.values if value is not None]
       _lowercase							     =       next_prime(self.size_table   ,						factor=2		)
       self._keys.clear()
       _lowercase							     =       [None] * self.size_table  # hell's pointers D: don't DRY ;/
       for value in survivor_values:
          self.insert_data(_lowercase		)




    def 		_lowerCamelCase      (	self			:       Union[str, Any]   ,						_lowercase			:       Tuple		)						->    Optional[int]:
       _lowercase							     =       self.hash_function(_lowercase		)

       if self.values[key] is None:
          self._set_value(_lowercase   ,						_lowercase		)

       elif self.values[key] == data:
          pass

       else:
          _lowercase							     =       self._collision_resolution(_lowercase   ,						_lowercase		)
          if collision_resolution is not None:
             self._set_value(_lowercase   ,						_lowercase		)
          else:
             self.rehashing()
             self.insert_data(_lowercase		)