





'''simple docstring'''


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


_SCREAMING_SNAKE_CASE	 =							logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE	 =							{
    "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_		(		__magic_name__		):
 __lowerCamelCase						:							Tuple         =					"gpt_neox"




 def __init__(	self			,     _lowerCAmelCase=50432			,     _lowerCAmelCase=6144			,     _lowerCAmelCase=44			,     _lowerCAmelCase=64			,     _lowerCAmelCase=24576			,     _lowerCAmelCase="gelu"			,     _lowerCAmelCase=0.25			,     _lowerCAmelCase=10000			,     _lowerCAmelCase=0.0			,     _lowerCAmelCase=0.0			,     _lowerCAmelCase=0.1			,     _lowerCAmelCase=2048			,     _lowerCAmelCase=0.02			,     _lowerCAmelCase=1E-5			,     _lowerCAmelCase=True			,     _lowerCAmelCase=0			,     _lowerCAmelCase=2			,     _lowerCAmelCase=False			,     _lowerCAmelCase=True			,     _lowerCAmelCase=None			,     **_lowerCAmelCase			,     )    ->							Optional[int]:
     super().__init__(bos_token_id=_lowerCAmelCase			,     eos_token_id=_lowerCAmelCase			,     **_lowerCAmelCase		)
     _lowerCAmelCase				=      vocab_size
     _lowerCAmelCase				=      max_position_embeddings
     _lowerCAmelCase				=      hidden_size
     _lowerCAmelCase				=      num_hidden_layers
     _lowerCAmelCase				=      num_attention_heads
     _lowerCAmelCase				=      intermediate_size
     _lowerCAmelCase				=      hidden_act
     _lowerCAmelCase				=      rotary_pct
     _lowerCAmelCase				=      rotary_emb_base
     _lowerCAmelCase				=      attention_dropout
     _lowerCAmelCase				=      hidden_dropout
     _lowerCAmelCase				=      classifier_dropout
     _lowerCAmelCase				=      initializer_range
     _lowerCAmelCase				=      layer_norm_eps
     _lowerCAmelCase				=      use_cache
     _lowerCAmelCase				=      tie_word_embeddings
     _lowerCAmelCase				=      use_parallel_residual
     _lowerCAmelCase				=      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 							_snake_case   (	self		)    ->							List[Any]:
     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}'''		)
     _lowerCAmelCase				=      self.rope_scaling.get("type"			,     _lowerCAmelCase		)
     _lowerCAmelCase				=      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}'''		)









'''simple docstring'''


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_SCREAMING_SNAKE_CASE	 =							{
    "configuration_bigbird_pegasus": [
        "BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "BigBirdPegasusConfig",
        "BigBirdPegasusOnnxConfig",
    ],
}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       _SCREAMING_SNAKE_CASE	 =							[
           "BIGBIRD_PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST",
           "BigBirdPegasusForCausalLM",
           "BigBirdPegasusForConditionalGeneration",
           "BigBirdPegasusForQuestionAnswering",
           "BigBirdPegasusForSequenceClassification",
           "BigBirdPegasusModel",
           "BigBirdPegasusPreTrainedModel",
       ]


if TYPE_CHECKING:
       from .configuration_bigbird_pegasus import (
           BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP,
           BigBirdPegasusConfig,
           BigBirdPegasusOnnxConfig,
       )

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_bigbird_pegasus import (
                  BIGBIRD_PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST,
                  BigBirdPegasusForCausalLM,
                  BigBirdPegasusForConditionalGeneration,
                  BigBirdPegasusForQuestionAnswering,
                  BigBirdPegasusForSequenceClassification,
                  BigBirdPegasusModel,
                  BigBirdPegasusPreTrainedModel,
              )


else:
       import sys

       _SCREAMING_SNAKE_CASE	 =							_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


