


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


SCREAMING_SNAKE_CASE						:Any			=  logging.get_logger(__name__)

SCREAMING_SNAKE_CASE						:List[str]			=  {
    '''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      __lowerCAmelCase  (			a       ):






  """simple docstring"""





  _SCREAMING_SNAKE_CASE		     =							'gpt_neox'



  def __init__(  self							:							Any     ,					_lowerCAmelCase							:							Union[str, Any]=5_0_4_3_2     ,					_lowerCAmelCase							:							int=6_1_4_4     ,					_lowerCAmelCase							:							str=4_4     ,					_lowerCAmelCase							:							int=6_4     ,					_lowerCAmelCase							:							str=2_4_5_7_6     ,					_lowerCAmelCase							:							Tuple="gelu"     ,					_lowerCAmelCase							:							int=0.25     ,					_lowerCAmelCase							:							Tuple=1_0_0_0_0     ,					_lowerCAmelCase							:							Optional[Any]=0.0     ,					_lowerCAmelCase							:							Tuple=0.0     ,					_lowerCAmelCase							:							str=0.1     ,					_lowerCAmelCase							:							Tuple=2_0_4_8     ,					_lowerCAmelCase							:							List[Any]=0.02     ,					_lowerCAmelCase							:							Dict=1e-5     ,					_lowerCAmelCase							:							Dict=True     ,					_lowerCAmelCase							:							Any=0     ,					_lowerCAmelCase							:							Union[str, Any]=2     ,					_lowerCAmelCase							:							List[Any]=False     ,					_lowerCAmelCase							:							Union[str, Any]=True     ,					_lowerCAmelCase							:							Any=None     ,					**_lowerCAmelCase							:							List[Any]     ,					) ->			int:




     """simple docstring"""
     super().__init__(bos_token_id=_lowerCAmelCase     ,					eos_token_id=_lowerCAmelCase     ,					**_lowerCAmelCase	)
     snake_case_            =       vocab_size
     snake_case_            =       max_position_embeddings
     snake_case_            =       hidden_size
     snake_case_            =       num_hidden_layers
     snake_case_            =       num_attention_heads
     snake_case_            =       intermediate_size
     snake_case_            =       hidden_act
     snake_case_            =       rotary_pct
     snake_case_            =       rotary_emb_base
     snake_case_            =       attention_dropout
     snake_case_            =       hidden_dropout
     snake_case_            =       classifier_dropout
     snake_case_            =       initializer_range
     snake_case_            =       layer_norm_eps
     snake_case_            =       use_cache
     snake_case_            =       tie_word_embeddings
     snake_case_            =       use_parallel_residual
     snake_case_            =       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							:							List[Any]	) ->			List[str]:




     """simple docstring"""
     if self.rope_scaling is None:
        return

     if not isinstance(self.rope_scaling     ,					_lowerCAmelCase	) 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}'''	)
     snake_case_            =       self.rope_scaling.get("type"     ,					_lowerCAmelCase	)
     snake_case_            =       self.rope_scaling.get("factor"     ,					_lowerCAmelCase	)
     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(_lowerCAmelCase     ,					_lowerCAmelCase	) or rope_scaling_factor <= 1.0:
        raise ValueError(F'''`rope_scaling`\'s factor field must be an float > 1, got {rope_scaling_factor}'''	)








from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


SCREAMING_SNAKE_CASE						:str			=  {'''configuration_unispeech''': ['''UNISPEECH_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''UniSpeechConfig''']}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   SCREAMING_SNAKE_CASE						:List[str]			=  [
       '''UNISPEECH_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''UniSpeechForCTC''',
       '''UniSpeechForPreTraining''',
       '''UniSpeechForSequenceClassification''',
       '''UniSpeechModel''',
       '''UniSpeechPreTrainedModel''',
   ]

if TYPE_CHECKING:
   from .configuration_unispeech import UNISPEECH_PRETRAINED_CONFIG_ARCHIVE_MAP, UniSpeechConfig

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_unispeech import (
          UNISPEECH_PRETRAINED_MODEL_ARCHIVE_LIST,
          UniSpeechForCTC,
          UniSpeechForPreTraining,
          UniSpeechForSequenceClassification,
          UniSpeechModel,
          UniSpeechPreTrainedModel,
      )

else:
   import sys

   SCREAMING_SNAKE_CASE						:List[str]			=  _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




