






from .hash_table import HashTable
from .number_theory.prime_numbers import is_prime, next_prime




class 							__A	(     lowercase_					):





				"""simple docstring"""







				def __init__(					self				,    *lowerCamelCase__				,    **lowerCamelCase__       ):




									"""simple docstring"""

									super().__init__(*__UpperCamelCase				,    **__UpperCamelCase       )


				def  __lowercase				(					self				,    lowerCamelCase__				,    lowerCamelCase__       ):




									"""simple docstring"""

									__UpperCamelCase :   Optional[int]									=(
									    next_prime(value % self.size_table       )
									    if not is_prime(value % self.size_table       )
									    else value % self.size_table
									)  # gt = bigger than
									return next_prime_gt - (data % next_prime_gt)


				def  __lowercase				(					self				,    lowerCamelCase__				,    lowerCamelCase__				,    lowerCamelCase__       ):




									"""simple docstring"""

									return (increment * self.__hash_function_a(__UpperCamelCase				,    __UpperCamelCase       )) % self.size_table







				def  __lowercase				(					self				,    lowerCamelCase__				,    lowerCamelCase__=None       ):




									"""simple docstring"""

									__UpperCamelCase :   Optional[Any]									=1
									__UpperCamelCase :   str									=self.hash_function(__UpperCamelCase       )

									while self.values[new_key] is not None and self.values[new_key] != key:
														__UpperCamelCase :   Tuple									=(
														    self.__hash_double_function(__UpperCamelCase				,    __UpperCamelCase				,    __UpperCamelCase       )
														    if self.balanced_factor() >= self.lim_charge
														    else None
														)
														if new_key is None:
																			break
														else:
																			i += 1

									return new_key








"""simple docstring"""
import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging


_snake_case	:						int					=					logging.get_logger(__name__)

_snake_case	:						Optional[int]					=					{}






class    _UpperCAmelCase  (      lowercase_					):
					UpperCamelCase						=				'''fsmt_decoder'''


					def __init__(   self     :Optional[int]	,			__UpperCamelCase     :Tuple=0	,			__UpperCamelCase     :List[str]=0						):
												super().__init__()
												A						=    vocab_size
												A						=    bos_token_id







class    _UpperCAmelCase  (      lowercase_					):
					UpperCamelCase						=				'''fsmt'''
					UpperCamelCase						=				{'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}


					def __init__(   self     :Dict	,			__UpperCamelCase     :List[str]=["en", "de"]	,			__UpperCamelCase     :List[str]=4_20_24	,			__UpperCamelCase     :Dict=4_20_24	,			__UpperCamelCase     :Tuple="relu"	,			__UpperCamelCase     :Optional[Any]=10_24	,			__UpperCamelCase     :Dict=2_00	,			__UpperCamelCase     :Optional[Any]=10_24	,			__UpperCamelCase     :Optional[Any]=40_96	,			__UpperCamelCase     :Union[str, Any]=12	,			__UpperCamelCase     :Dict=16	,			__UpperCamelCase     :Optional[Any]=0.0	,			__UpperCamelCase     :Dict=40_96	,			__UpperCamelCase     :Dict=12	,			__UpperCamelCase     :str=16	,			__UpperCamelCase     :Any=0.0	,			__UpperCamelCase     :str=0.0	,			__UpperCamelCase     :List[Any]=0.1	,			__UpperCamelCase     :List[Any]=0.0	,			__UpperCamelCase     :str=0.02	,			__UpperCamelCase     :List[str]=2	,			__UpperCamelCase     :Union[str, Any]=True	,			__UpperCamelCase     :Optional[Any]=True	,			__UpperCamelCase     :int=False	,			__UpperCamelCase     :List[str]=5	,			__UpperCamelCase     :Optional[int]=1.0	,			__UpperCamelCase     :List[str]=False	,			__UpperCamelCase     :Any=True	,			__UpperCamelCase     :List[str]=1	,			__UpperCamelCase     :int=0	,			__UpperCamelCase     :str=2	,			__UpperCamelCase     :Dict=2	,			**__UpperCamelCase     :Optional[int]	,			):
												A						=    langs
												A						=    src_vocab_size
												A						=    tgt_vocab_size
												A						=    d_model  # encoder_embed_dim and decoder_embed_dim

												A						=    encoder_ffn_dim
												A						=    A						=    encoder_layers
												A						=    encoder_attention_heads
												A						=    encoder_layerdrop
												A						=    decoder_layerdrop
												A						=    decoder_ffn_dim
												A						=    decoder_layers
												A						=    decoder_attention_heads
												A						=    max_position_embeddings
												A						=    init_std  # Normal(0, this parameter)
												A						=    activation_function

												A						=    DecoderConfig(vocab_size=__UpperCamelCase	,			bos_token_id=__UpperCamelCase						)
												if "decoder" in common_kwargs:
																			del common_kwargs["decoder"]

												A						=    scale_embedding  # scale factor will be sqrt(d_model) if True

												# 3 Types of Dropout
												A						=    attention_dropout
												A						=    activation_dropout
												A						=    dropout

												A						=    use_cache
												super().__init__(
												    pad_token_id=__UpperCamelCase	,			bos_token_id=__UpperCamelCase	,			eos_token_id=__UpperCamelCase	,			decoder_start_token_id=__UpperCamelCase	,			is_encoder_decoder=__UpperCamelCase	,			tie_word_embeddings=__UpperCamelCase	,			forced_eos_token_id=__UpperCamelCase	,			max_length=__UpperCamelCase	,			num_beams=__UpperCamelCase	,			length_penalty=__UpperCamelCase	,			early_stopping=__UpperCamelCase	,			**__UpperCamelCase	,			)


					def       lowerCamelCase			(   self     :List[str]						):
												A						=    copy.deepcopy(self.__dict__						)
												A						=    self.decoder.to_dict()
												A						=    self.__class__.model_type
												return output


