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,
)


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

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

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

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

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




from .number_theory.prime_numbers import next_prime




class  __lowerCAmelCase	:



 '''simple docstring'''

 def __init__(      self							:		List[Any]							,      lowerCamelCase							:		int							,      lowerCamelCase							:		int | None = None							,      lowerCamelCase							:		float | None = None							,      )			->				None:
        _UpperCamelCase  :							Tuple	    =	size_table
        _UpperCamelCase  :							Tuple	    =	[None] * self.size_table
        _UpperCamelCase  :							int	    =	0.7_5 if lim_charge is None else lim_charge
        _UpperCamelCase  :							Tuple	    =	1 if charge_factor is None else charge_factor
        _UpperCamelCase  :							list	    =	[]
        _UpperCamelCase  :							dict	    =	{}

 def        __snake_case       (      self							:		List[Any]	)			->				Union[str, Any]:
        return self._keys

 def        __snake_case       (      self							:		List[Any]	)			->				Optional[int]:
        return sum(1 for slot in self.values if slot is not None	) / (
            self.size_table * self.charge_factor
        )

 def        __snake_case       (      self							:		Union[str, Any]							,      lowerCamelCase							:		int	)			->				Any:
        return key % self.size_table

 def        __snake_case       (      self							:		Any							,      lowerCamelCase							:		Any	)			->				Optional[Any]:
        print(f"step {step_ord}"	)
        print(list(range(len(self.values	)	)	)	)
        print(self.values	)

 def        __snake_case       (      self							:		Tuple							,      lowerCamelCase							:		Optional[int]	)			->				Any:
        _UpperCamelCase  :							int	    =	1
        _UpperCamelCase  :							Union[str, Any]	    =	values
        for value in values:
               self.insert_data(lowerCamelCase	)
               self._step_by_step(lowerCamelCase	)
               i += 1

 def        __snake_case       (      self							:		Optional[Any]							,      lowerCamelCase							:		Any							,      lowerCamelCase							:		Optional[int]	)			->				Any:
        _UpperCamelCase  :							int	    =	data
        _UpperCamelCase  :							Optional[int]	    =	data

 def        __snake_case       (      self							:		Optional[int]							,      lowerCamelCase							:		Optional[int]							,      lowerCamelCase							:		str=None	)			->				List[Any]:
        _UpperCamelCase  :							Union[str, Any]	    =	self.hash_function(key + 1	)

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

        return new_key

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

 def        __snake_case       (      self							:		Tuple							,      lowerCamelCase							:		str	)			->				Union[str, Any]:
        _UpperCamelCase  :							Dict	    =	self.hash_function(lowerCamelCase	)

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

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

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



