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


A		: Any								=     logging.get_logger(__name__)

A		: 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    a_ (			_a    ):
			a	:			List[str]					   =							'''gpt_neox'''






			def __init__(      self				,			__UpperCamelCase=50_432				,			__UpperCamelCase=6_144				,			__UpperCamelCase=44				,			__UpperCamelCase=64				,			__UpperCamelCase=24_576				,			__UpperCamelCase="gelu"				,			__UpperCamelCase=0.25				,			__UpperCamelCase=10_000				,			__UpperCamelCase=0.0				,			__UpperCamelCase=0.0				,			__UpperCamelCase=0.1				,			__UpperCamelCase=2_048				,			__UpperCamelCase=0.02				,			__UpperCamelCase=1E-5				,			__UpperCamelCase=True				,			__UpperCamelCase=0				,			__UpperCamelCase=2				,			__UpperCamelCase=False				,			__UpperCamelCase=True				,			__UpperCamelCase=None				,			**__UpperCamelCase				,			):
									super().__init__(bos_token_id=snake_case_				,			eos_token_id=snake_case_				,			**snake_case_  )
									_lowercase					       =					vocab_size
									_lowercase					       =					max_position_embeddings
									_lowercase					       =					hidden_size
									_lowercase					       =					num_hidden_layers
									_lowercase					       =					num_attention_heads
									_lowercase					       =					intermediate_size
									_lowercase					       =					hidden_act
									_lowercase					       =					rotary_pct
									_lowercase					       =					rotary_emb_base
									_lowercase					       =					attention_dropout
									_lowercase					       =					hidden_dropout
									_lowercase					       =					classifier_dropout
									_lowercase					       =					initializer_range
									_lowercase					       =					layer_norm_eps
									_lowercase					       =					use_cache
									_lowercase					       =					tie_word_embeddings
									_lowercase					       =					use_parallel_residual
									_lowercase					       =					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    UpperCamelCase_				(      self  ):
									if self.rope_scaling is None:
															return

									if not isinstance(self.rope_scaling				,			snake_case_  ) 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}"""  )
									_lowercase					       =					self.rope_scaling.get("""type"""				,			snake_case_  )
									_lowercase					       =					self.rope_scaling.get("""factor"""				,			snake_case_  )
									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(snake_case_				,			snake_case_  ) 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_bigbird_pegasus": [
        "BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "BigBirdPegasusConfig",
        "BigBirdPegasusOnnxConfig",
    ],
}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __a  			=						[
           "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

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