

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 			=		{
    "camembert-base": "https://huggingface.co/camembert-base/resolve/main/config.json",
    "umberto-commoncrawl-cased-v1": (
        "https://huggingface.co/Musixmatch/umberto-commoncrawl-cased-v1/resolve/main/config.json"
    ),
    "umberto-wikipedia-uncased-v1": (
        "https://huggingface.co/Musixmatch/umberto-wikipedia-uncased-v1/resolve/main/config.json"
    ),
}







class   __lowercase      (_UpperCAmelCase ):
 _UpperCamelCase			  =  """camembert"""
 def __init__(							self      ,							A_=3_0522      ,							A_=768      ,							A_=12      ,							A_=12      ,							A_=3072      ,							A_="gelu"      ,							A_=0.1      ,							A_=0.1      ,							A_=512      ,							A_=2      ,							A_=0.02      ,							A_=1e-12      ,							A_=1      ,							A_=0      ,							A_=2      ,							A_="absolute"      ,							A_=True      ,							A_=None      ,							**A_      ,							)					->List[Any]:



       '''simple docstring'''





       super().__init__(pad_token_id=A_      ,							bos_token_id=A_      ,							eos_token_id=A_      ,							**A_					)

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







class   __lowercase      (_UpperCAmelCase ):
 @property
 def 	UpperCamelCase__  (							self					)					->Mapping[str, Mapping[int, str]]:



       '''simple docstring'''





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





from collections import deque

from .hash_table import HashTable







class   __lowercase      (_UpperCAmelCase ):
 def __init__(							self      ,							*A_      ,							**A_					)					->int:



       '''simple docstring'''





       super().__init__(*A_      ,							**A_					)
 def 	UpperCamelCase__  (							self      ,							A_      ,							A_					)					->Optional[int]:



       '''simple docstring'''





       __lowerCAmelCase					:						Dict					=					deque([]					) if self.values[key] is None else self.values[key]
       self.values[key].appendleft(A_					)
       __lowerCAmelCase					:						int					=					self.values[key]
 def 	UpperCamelCase__  (							self					)					->Tuple:



       '''simple docstring'''





       return (
           sum(self.charge_factor - len(A_					) for slot in self.values					)
           / self.size_table
           * self.charge_factor
       )





 def 	UpperCamelCase__  (							self      ,							A_      ,							A_=None					)					->str:



       '''simple docstring'''





       if not (
           len(self.values[key]					) == self.charge_factor and self.values.count(A_					) == 0
       ):
             return key
       return super()._collision_resolution(A_      ,							A_					)


