



'''simple docstring'''
from .number_theory.prime_numbers import next_prime



class _A  :





		def __init__(     self ,       __UpperCAmelCase ,       __UpperCAmelCase = None ,       __UpperCAmelCase = None ,       )       ->     None:

					'''simple docstring'''
					__UpperCAmelCase   :      List[str]										=     size_table
					__UpperCAmelCase   :      str										=     [None] * self.size_table
					__UpperCAmelCase   :      Optional[Any]										=     0.75 if lim_charge is None else lim_charge
					__UpperCAmelCase   :      Tuple										=     1 if charge_factor is None else charge_factor
					__UpperCAmelCase   :      list										=     []
					__UpperCAmelCase   :      dict										=     {}





		def 							__A       (     self				)       ->     Any:

					'''simple docstring'''
					return self._keys





		def 							__A       (     self				)       ->     List[str]:

					'''simple docstring'''
					return sum(1 for slot in self.values if slot is not None				) / (
					    self.size_table * self.charge_factor
					)





		def 							__A       (     self ,       __UpperCAmelCase				)       ->     Any:

					'''simple docstring'''
					return key % self.size_table





		def 							__A       (     self ,       __UpperCAmelCase				)       ->     str:

					'''simple docstring'''
					print(f'step {step_ord}'				)
					print(list(range(len(self.values				)				)				)				)
					print(self.values				)





		def 							__A       (     self ,       __UpperCAmelCase				)       ->     Dict:

					'''simple docstring'''
					__UpperCAmelCase   :      Union[str, Any]										=     1
					__UpperCAmelCase   :      Any										=     values
					for value in values:
								self.insert_data(__UpperCAmelCase				)
								self._step_by_step(__UpperCAmelCase				)
								i += 1





		def 							__A       (     self ,       __UpperCAmelCase ,       __UpperCAmelCase				)       ->     Optional[int]:

					'''simple docstring'''
					__UpperCAmelCase   :      Optional[int]										=     data
					__UpperCAmelCase   :      Tuple										=     data





		def 							__A       (     self ,       __UpperCAmelCase ,       __UpperCAmelCase=None				)       ->     Dict:

					'''simple docstring'''
					__UpperCAmelCase   :      List[str]										=     self.hash_function(key + 1				)

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

					return new_key





		def 							__A       (     self				)       ->     List[Any]:

					'''simple docstring'''
					__UpperCAmelCase   :      int										=     [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   :      List[str]										=     [None] * self.size_table  # hell's pointers D: don't DRY ;/
					for value in survivor_values:
								self.insert_data(__UpperCAmelCase				)





		def 							__A       (     self ,       __UpperCAmelCase				)       ->     List[str]:

					'''simple docstring'''
					__UpperCAmelCase   :      Dict										=     self.hash_function(__UpperCAmelCase				)

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

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

					else:
								__UpperCAmelCase   :      Union[str, Any]										=     self._collision_resolution(__UpperCAmelCase ,       __UpperCAmelCase				)
								if collision_resolution is not None:
											self._set_value(__UpperCAmelCase ,       __UpperCAmelCase				)
								else:
											self.rehashing()
											self.insert_data(__UpperCAmelCase				)









'''simple docstring'''
from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_UpperCamelCase											=			logging.get_logger(__name__)

_UpperCamelCase											=			{
    '''nghuyong/ernie-1.0-base-zh''': '''https://huggingface.co/nghuyong/ernie-1.0-base-zh/resolve/main/config.json''',
    '''nghuyong/ernie-2.0-base-en''': '''https://huggingface.co/nghuyong/ernie-2.0-base-en/resolve/main/config.json''',
    '''nghuyong/ernie-2.0-large-en''': '''https://huggingface.co/nghuyong/ernie-2.0-large-en/resolve/main/config.json''',
    '''nghuyong/ernie-3.0-base-zh''': '''https://huggingface.co/nghuyong/ernie-3.0-base-zh/resolve/main/config.json''',
    '''nghuyong/ernie-3.0-medium-zh''': '''https://huggingface.co/nghuyong/ernie-3.0-medium-zh/resolve/main/config.json''',
    '''nghuyong/ernie-3.0-mini-zh''': '''https://huggingface.co/nghuyong/ernie-3.0-mini-zh/resolve/main/config.json''',
    '''nghuyong/ernie-3.0-micro-zh''': '''https://huggingface.co/nghuyong/ernie-3.0-micro-zh/resolve/main/config.json''',
    '''nghuyong/ernie-3.0-nano-zh''': '''https://huggingface.co/nghuyong/ernie-3.0-nano-zh/resolve/main/config.json''',
    '''nghuyong/ernie-gram-zh''': '''https://huggingface.co/nghuyong/ernie-gram-zh/resolve/main/config.json''',
    '''nghuyong/ernie-health-zh''': '''https://huggingface.co/nghuyong/ernie-health-zh/resolve/main/config.json''',
}



class _A  (    __SCREAMING_SNAKE_CASE    ):
		_SCREAMING_SNAKE_CASE					:    Optional[int]						      =			"ernie"





		def __init__(     self ,       __UpperCAmelCase=30_522 ,       __UpperCAmelCase=768 ,       __UpperCAmelCase=12 ,       __UpperCAmelCase=12 ,       __UpperCAmelCase=3_072 ,       __UpperCAmelCase="gelu" ,       __UpperCAmelCase=0.1 ,       __UpperCAmelCase=0.1 ,       __UpperCAmelCase=512 ,       __UpperCAmelCase=2 ,       __UpperCAmelCase=3 ,       __UpperCAmelCase=False ,       __UpperCAmelCase=0.02 ,       __UpperCAmelCase=1E-12 ,       __UpperCAmelCase=0 ,       __UpperCAmelCase="absolute" ,       __UpperCAmelCase=True ,       __UpperCAmelCase=None ,       **__UpperCAmelCase ,       )       ->     List[Any]:

					'''simple docstring'''
					super().__init__(pad_token_id=__UpperCAmelCase ,       **__UpperCAmelCase				)

					__UpperCAmelCase   :      List[str]										=     vocab_size
					__UpperCAmelCase   :      Optional[int]										=     hidden_size
					__UpperCAmelCase   :      Optional[Any]										=     num_hidden_layers
					__UpperCAmelCase   :      int										=     num_attention_heads
					__UpperCAmelCase   :      int										=     hidden_act
					__UpperCAmelCase   :      List[Any]										=     intermediate_size
					__UpperCAmelCase   :      Optional[Any]										=     hidden_dropout_prob
					__UpperCAmelCase   :      Dict										=     attention_probs_dropout_prob
					__UpperCAmelCase   :      int										=     max_position_embeddings
					__UpperCAmelCase   :      str										=     type_vocab_size
					__UpperCAmelCase   :      int										=     task_type_vocab_size
					__UpperCAmelCase   :      List[str]										=     use_task_id
					__UpperCAmelCase   :      Union[str, Any]										=     initializer_range
					__UpperCAmelCase   :      Tuple										=     layer_norm_eps
					__UpperCAmelCase   :      List[str]										=     position_embedding_type
					__UpperCAmelCase   :      Union[str, Any]										=     use_cache
					__UpperCAmelCase   :      Optional[int]										=     classifier_dropout



class _A  (    __SCREAMING_SNAKE_CASE    ):





		@property
		def 							__A       (     self				)       ->     Mapping[str, Mapping[int, str]]:

					'''simple docstring'''
					if self.task == "multiple-choice":
								__UpperCAmelCase   :      Union[str, Any]										=     {0: """batch""", 1: """choice""", 2: """sequence"""}
					else:
								__UpperCAmelCase   :      Tuple										=     {0: """batch""", 1: """sequence"""}
					return OrderedDict(
					    [
					        ("""input_ids""", dynamic_axis),
					        ("""attention_mask""", dynamic_axis),
					        ("""token_type_ids""", dynamic_axis),
					        ("""task_type_ids""", dynamic_axis),
					    ]				)




