

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


a__												=						logging.get_logger(__name__)

a__												=						{
    '''EleutherAI/gpt-neox-20b''': '''https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/config.json''',
    # See all GPTNeoX models at https://huggingface.co/models?filter=gpt_neox
}

class 							UpperCAmelCase_						(     __lowercase  ):
							"""simple docstring"""







							UpperCAmelCase__			:		Tuple             =       "gpt_neox"


							def __init__(     self			,				_a=5_0_4_3_2			,				_a=6_1_4_4			,				_a=4_4			,				_a=6_4			,				_a=2_4_5_7_6			,				_a="gelu"			,				_a=0.25			,				_a=1_0_0_0_0			,				_a=0.0			,				_a=0.0			,				_a=0.1			,				_a=2_0_4_8			,				_a=0.02			,				_a=1e-5			,				_a=True			,				_a=0			,				_a=2			,				_a=False			,				_a=True			,				_a=None			,				**_a			,				)       ->       str:
												super().__init__(bos_token_id=_a			,				eos_token_id=_a			,				**_a			)
												_a      :       Union[str, Any]   =						vocab_size
												_a      :       Optional[Any]   =						max_position_embeddings
												_a      :       str   =						hidden_size
												_a      :       Tuple   =						num_hidden_layers
												_a      :       Optional[int]   =						num_attention_heads
												_a      :       int   =						intermediate_size
												_a      :       Dict   =						hidden_act
												_a      :       Union[str, Any]   =						rotary_pct
												_a      :       List[Any]   =						rotary_emb_base
												_a      :       List[Any]   =						attention_dropout
												_a      :       Tuple   =						hidden_dropout
												_a      :       Union[str, Any]   =						classifier_dropout
												_a      :       List[Any]   =						initializer_range
												_a      :       Optional[int]   =						layer_norm_eps
												_a      :       str   =						use_cache
												_a      :       int   =						tie_word_embeddings
												_a      :       Tuple   =						use_parallel_residual
												_a      :       Union[str, Any]   =						rope_scaling
												self._rope_scaling_validation()

												if self.hidden_size % self.num_attention_heads != 0:
																	raise ValueError(
																	    '''The hidden size is not divisble by the number of attention heads! Make sure to update them!'''			)




							def 							__lowercase							(     self			)       ->       List[str]:
												if self.rope_scaling is None:
																	return

												if not isinstance(self.rope_scaling			,				_a			) or len(self.rope_scaling			) != 2:
																	raise ValueError(
																	    '''`rope_scaling` must be a dictionary with with two fields, `name` and `factor`, '''
																	    F"""got {self.rope_scaling}"""			)
												_a      :       Optional[int]   =						self.rope_scaling.get('''type'''			,				_a			)
												_a      :       List[str]   =						self.rope_scaling.get('''factor'''			,				_a			)
												if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
																	raise ValueError(
																	    F"""`rope_scaling`'s name field must be one of ['linear', 'dynamic'], got {rope_scaling_type}"""			)
												if rope_scaling_factor is None or not isinstance(_a			,				_a			) or rope_scaling_factor <= 1.0:
																	raise ValueError(F"""`rope_scaling`'s factor field must be an float > 1, got {rope_scaling_factor}"""			)






"""simple docstring"""


import re

from filelock import FileLock


try:
  import nltk

  UpperCAmelCase													=True
except (ImportError, ModuleNotFoundError):
  UpperCAmelCase													=False

if NLTK_AVAILABLE:
  with FileLock(".lock") as lock:
    nltk.download("punkt", quiet=True)



def   _A					(	_a	:						str				):



      """simple docstring"""






      re.sub("""<n>"""     ,   """"""     ,   _a				)  # remove pegasus newline char
      assert NLTK_AVAILABLE, "nltk must be installed to separate newlines between sentences. (pip install nltk)"
      return "\n".join(nltk.sent_tokenize(_a				)				)




