





"""simple docstring"""






from collections.abc import Iterator, MutableMapping
from dataclasses import dataclass
from typing import Generic, TypeVar

A			      =	TypeVar('''KEY''')
A			      =	TypeVar('''VAL''')




@dataclass(frozen=_UpperCamelCase ,   slots=_UpperCamelCase				)
class 					__lowercase      (      Generic[KEY, VAL]				):







					'''simple docstring'''




					__lowerCAmelCase	=	42
					__lowerCAmelCase	=	42






class 					__lowercase      (      _Item				):







					'''simple docstring'''




					def __init__(    self  ):
									super().__init__(_UpperCAmelCase						,      _UpperCAmelCase  )







					def __bool__(    self  ):
									return False






A			      =	_DeletedItem()




class 					__lowercase      (      MutableMapping[KEY, VAL]				):







					'''simple docstring'''




					def __init__(    self						,      _UpperCAmelCase = 8						,      _UpperCAmelCase = 0.7_5  ):
									__a			:    Union[str, Any]      							=	initial_block_size
									__a			:    list[_Item | None]      							=	[None] * initial_block_size
									assert 0.0 < capacity_factor < 1.0
									__a			:    Optional[Any]      							=	capacity_factor
									__a			:    Union[str, Any]      							=	0







					def 			_lowerCamelCase		(    self						,      _UpperCAmelCase  ):
									return hash(_UpperCAmelCase  ) % len(self._buckets  )







					def 			_lowerCamelCase		(    self						,      _UpperCAmelCase  ):
									return (ind + 1) % len(self._buckets  )







					def 			_lowerCamelCase		(    self						,      _UpperCAmelCase						,      _UpperCAmelCase						,      _UpperCAmelCase  ):
									__a			:    List[str]      							=	self._buckets[ind]
									if not stored:
													__a			:    int      							=	_Item(_UpperCAmelCase						,      _UpperCAmelCase  )
													self._len += 1
													return True
									elif stored.key == key:
													__a			:    Dict      							=	_Item(_UpperCAmelCase						,      _UpperCAmelCase  )
													return True
									else:
													return False







					def 			_lowerCamelCase		(    self  ):
									__a			:    Optional[int]      							=	len(self._buckets  ) * self._capacity_factor
									return len(self  ) >= int(_UpperCAmelCase  )







					def 			_lowerCamelCase		(    self  ):
									if len(self._buckets  ) <= self._initial_block_size:
													return False
									__a			:    Optional[Any]      							=	len(self._buckets  ) * self._capacity_factor / 2
									return len(self  ) < limit







					def 			_lowerCamelCase		(    self						,      _UpperCAmelCase  ):
									__a			:    List[str]      							=	self._buckets
									__a			:    List[Any]      							=	[None] * new_size
									__a			:    int      							=	0
									for item in old_buckets:
													if item:
																	self._add_item(item.key						,      item.val  )







					def 			_lowerCamelCase		(    self  ):
									self._resize(len(self._buckets  ) * 2  )







					def 			_lowerCamelCase		(    self  ):
									self._resize(len(self._buckets  ) // 2  )







					def 			_lowerCamelCase		(    self						,      _UpperCAmelCase  ):
									__a			:    int      							=	self._get_bucket_index(_UpperCAmelCase  )
									for _ in range(len(self._buckets  )  ):
													yield ind
													__a			:    Dict      							=	self._get_next_ind(_UpperCAmelCase  )







					def 			_lowerCamelCase		(    self						,      _UpperCAmelCase						,      _UpperCAmelCase  ):
									for ind in self._iterate_buckets(_UpperCAmelCase  ):
													if self._try_set(_UpperCAmelCase						,      _UpperCAmelCase						,      _UpperCAmelCase  ):
																	break







					def __setitem__(    self						,      _UpperCAmelCase						,      _UpperCAmelCase  ):
									if self._is_full():
													self._size_up()

									self._add_item(_UpperCAmelCase						,      _UpperCAmelCase  )







					def __delitem__(    self						,      _UpperCAmelCase  ):
									for ind in self._iterate_buckets(_UpperCAmelCase  ):
													__a			:    List[str]      							=	self._buckets[ind]
													if item is None:
																	raise KeyError(_UpperCAmelCase  )
													if item is _deleted:
																	continue
													if item.key == key:
																	__a			:    Dict      							=	_deleted
																	self._len -= 1
																	break
									if self._is_sparse():
													self._size_down()







					def __getitem__(    self						,      _UpperCAmelCase  ):
									for ind in self._iterate_buckets(_UpperCAmelCase  ):
													__a			:    List[str]      							=	self._buckets[ind]
													if item is None:
																	break
													if item is _deleted:
																	continue
													if item.key == key:
																	return item.val
									raise KeyError(_UpperCAmelCase  )







					def __len__(    self  ):
									return self._len







					def __iter__(    self  ):
									yield from (item.key for item in self._buckets if item)







					def __repr__(    self  ):
									__a			:    List[str]      							=	''' ,'''.join(
									    f"""{item.key}: {item.val}""" for item in self._buckets if item  )
									return f"""HashMap({val_string})"""




"""simple docstring"""






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


__snake_case				:  Tuple =       logging.get_logger(__name__)

__snake_case				:  Any =       {
    'facebook/xglm-564M': 'https://huggingface.co/facebook/xglm-564M/resolve/main/config.json',
    # See all XGLM models at https://huggingface.co/models?filter=xglm
}



class      A__  (    __SCREAMING_SNAKE_CASE				):




	'''simple docstring'''





	SCREAMING_SNAKE_CASE        =      'xglm'
	SCREAMING_SNAKE_CASE        =      ['past_key_values']

	SCREAMING_SNAKE_CASE        =      {
	    'num_attention_heads': 'attention_heads',
	    'hidden_size': 'd_model',
	    'num_hidden_layers': 'num_layers',
	}

	def __init__(	self:						Optional[Any]			,     _SCREAMING_SNAKE_CASE:						List[Any]=25_6008			,     _SCREAMING_SNAKE_CASE:						Dict=2048			,     _SCREAMING_SNAKE_CASE:						int=1024			,     _SCREAMING_SNAKE_CASE:						Dict=4096			,     _SCREAMING_SNAKE_CASE:						Optional[Any]=24			,     _SCREAMING_SNAKE_CASE:						int=16			,     _SCREAMING_SNAKE_CASE:						List[str]="gelu"			,     _SCREAMING_SNAKE_CASE:						Optional[Any]=0.1			,     _SCREAMING_SNAKE_CASE:						int=0.1			,     _SCREAMING_SNAKE_CASE:						List[Any]=0.0			,     _SCREAMING_SNAKE_CASE:						Union[str, Any]=0.0			,     _SCREAMING_SNAKE_CASE:						Any=0.02			,     _SCREAMING_SNAKE_CASE:						Any=True			,     _SCREAMING_SNAKE_CASE:						Tuple=True			,     _SCREAMING_SNAKE_CASE:						Any=2			,     _SCREAMING_SNAKE_CASE:						str=1			,     _SCREAMING_SNAKE_CASE:						Dict=0			,     _SCREAMING_SNAKE_CASE:						Dict=2			,     **_SCREAMING_SNAKE_CASE:						List[Any]			,     )							->			List[str]:

				"""simple docstring"""

				__lowerCAmelCase :    Optional[int]				=			vocab_size
				__lowerCAmelCase :    int				=			max_position_embeddings
				__lowerCAmelCase :    Optional[Any]				=			d_model
				__lowerCAmelCase :    List[Any]				=			ffn_dim
				__lowerCAmelCase :    int				=			num_layers
				__lowerCAmelCase :    Any				=			attention_heads
				__lowerCAmelCase :    int				=			activation_function
				__lowerCAmelCase :    List[Any]				=			dropout
				__lowerCAmelCase :    Optional[int]				=			attention_dropout
				__lowerCAmelCase :    Optional[int]				=			activation_dropout
				__lowerCAmelCase :    Optional[int]				=			layerdrop
				__lowerCAmelCase :    Optional[int]				=			init_std
				__lowerCAmelCase :    Tuple				=			scale_embedding  # scale factor will be sqrt(d_model) if True
				__lowerCAmelCase :    Dict				=			use_cache

				super().__init__(
				    pad_token_id=_SCREAMING_SNAKE_CASE			,     bos_token_id=_SCREAMING_SNAKE_CASE			,     eos_token_id=_SCREAMING_SNAKE_CASE			,     decoder_start_token_id=_SCREAMING_SNAKE_CASE			,     **_SCREAMING_SNAKE_CASE			,     )