

"""simple docstring"""






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 				__lowercase			(			_UpperCamelCase							):







					'''simple docstring'''




					__lowerCAmelCase  =  '''gpt_neox'''







					def __init__(						self	,			_UpperCAmelCase=50432	,			_UpperCAmelCase=6144	,			_UpperCAmelCase=44	,			_UpperCAmelCase=64	,			_UpperCAmelCase=24576	,			_UpperCAmelCase="gelu"	,			_UpperCAmelCase=0.2_5	,			_UpperCAmelCase=10000	,			_UpperCAmelCase=0.0	,			_UpperCAmelCase=0.0	,			_UpperCAmelCase=0.1	,			_UpperCAmelCase=2048	,			_UpperCAmelCase=0.0_2	,			_UpperCAmelCase=1e-5	,			_UpperCAmelCase=True	,			_UpperCAmelCase=0	,			_UpperCAmelCase=2	,			_UpperCAmelCase=False	,			_UpperCAmelCase=True	,			_UpperCAmelCase=None	,			**_UpperCAmelCase	,			):
									super().__init__(bos_token_id=_UpperCAmelCase	,			eos_token_id=_UpperCAmelCase	,			**_UpperCAmelCase     )
									__a :						Tuple			       =  vocab_size
									__a :						List[Any]			       =  max_position_embeddings
									__a :						Union[str, Any]			       =  hidden_size
									__a :						Optional[Any]			       =  num_hidden_layers
									__a :						Union[str, Any]			       =  num_attention_heads
									__a :						List[str]			       =  intermediate_size
									__a :						int			       =  hidden_act
									__a :						Any			       =  rotary_pct
									__a :						int			       =  rotary_emb_base
									__a :						Any			       =  attention_dropout
									__a :						List[str]			       =  hidden_dropout
									__a :						Any			       =  classifier_dropout
									__a :						Dict			       =  initializer_range
									__a :						int			       =  layer_norm_eps
									__a :						List[Any]			       =  use_cache
									__a :						Tuple			       =  tie_word_embeddings
									__a :						Optional[Any]			       =  use_parallel_residual
									__a :						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  _lowerCamelCase		(						self     ):
									if self.rope_scaling is None:
													return

									if not isinstance(self.rope_scaling	,			_UpperCAmelCase     ) 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 :						List[Any]			       =  self.rope_scaling.get('''type'''	,			_UpperCAmelCase     )
									__a :						Optional[Any]			       =  self.rope_scaling.get('''factor'''	,			_UpperCAmelCase     )
									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(_UpperCAmelCase	,			_UpperCAmelCase     ) 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"""






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


A 			=  {
    '''configuration_jukebox''': [
        '''JUKEBOX_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''JukeboxConfig''',
        '''JukeboxPriorConfig''',
        '''JukeboxVQVAEConfig''',
    ],
    '''tokenization_jukebox''': ['''JukeboxTokenizer'''],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		A 			=  [
		    '''JUKEBOX_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''JukeboxModel''',
		    '''JukeboxPreTrainedModel''',
		    '''JukeboxVQVAE''',
		    '''JukeboxPrior''',
		]

if TYPE_CHECKING:
		from .configuration_jukebox import (
		    JUKEBOX_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    JukeboxConfig,
		    JukeboxPriorConfig,
		    JukeboxVQVAEConfig,
		)
		from .tokenization_jukebox import JukeboxTokenizer

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_jukebox import (
				    JUKEBOX_PRETRAINED_MODEL_ARCHIVE_LIST,
				    JukeboxModel,
				    JukeboxPreTrainedModel,
				    JukeboxPrior,
				    JukeboxVQVAE,
				)

else:
		import sys

		A 			=  _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)