




from typing import TYPE_CHECKING

from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable


snake_case_ : Union[str, Any]    		=       {"configuration_gpt_neox": ["GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTNeoXConfig"]}

try:
    if not is_tokenizers_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    snake_case_ : Dict    		=       ["GPTNeoXTokenizerFast"]

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    snake_case_ : str    		=       [
        "GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST",
        "GPTNeoXForCausalLM",
        "GPTNeoXForQuestionAnswering",
        "GPTNeoXForSequenceClassification",
        "GPTNeoXForTokenClassification",
        "GPTNeoXLayer",
        "GPTNeoXModel",
        "GPTNeoXPreTrainedModel",
    ]


if TYPE_CHECKING:
    from .configuration_gpt_neox import GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoXConfig

    try:
        if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_gpt_neox_fast import GPTNeoXTokenizerFast

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_gpt_neox import (
            GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST,
            GPTNeoXForCausalLM,
            GPTNeoXForQuestionAnswering,
            GPTNeoXForSequenceClassification,
            GPTNeoXForTokenClassification,
            GPTNeoXLayer,
            GPTNeoXModel,
            GPTNeoXPreTrainedModel,
        )


else:
    import sys

    snake_case_ : Optional[int]    		=       _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)












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


snake_case_ : int    		=       logging.get_logger(__name__)

snake_case_ : str    		=       {}







class __snake_case			(			a		):
   UpperCAmelCase__  :    str          =  '''llama'''
   UpperCAmelCase__  :    Dict          =  ['''past_key_values''']







   def __init__(      self		:						str					,						_snake_case		:						List[str]=32000					,						_snake_case		:						int=4096					,						_snake_case		:						List[str]=11008					,						_snake_case		:						Optional[int]=32					,						_snake_case		:						List[Any]=32					,						_snake_case		:						Tuple=None					,						_snake_case		:						int="silu"					,						_snake_case		:						List[Any]=2048					,						_snake_case		:						List[str]=0.0_2					,						_snake_case		:						Any=1e-6					,						_snake_case		:						List[str]=True					,						_snake_case		:						Optional[Any]=0					,						_snake_case		:						Dict=1					,						_snake_case		:						List[Any]=2					,						_snake_case		:						str=1					,						_snake_case		:						Union[str, Any]=False					,						_snake_case		:						str=None					,						**_snake_case		:						List[Any]					,						):

        """simple docstring"""


        UpperCAmelCase_          =							vocab_size
        UpperCAmelCase_          =							max_position_embeddings
        UpperCAmelCase_          =							hidden_size
        UpperCAmelCase_          =							intermediate_size
        UpperCAmelCase_          =							num_hidden_layers
        UpperCAmelCase_          =							num_attention_heads

        # for backward compatibility
        if num_key_value_heads is None:
             UpperCAmelCase_          =							num_attention_heads

        UpperCAmelCase_          =							num_key_value_heads
        UpperCAmelCase_          =							hidden_act
        UpperCAmelCase_          =							initializer_range
        UpperCAmelCase_          =							rms_norm_eps
        UpperCAmelCase_          =							pretraining_tp
        UpperCAmelCase_          =							use_cache
        UpperCAmelCase_          =							rope_scaling
        self._rope_scaling_validation()

        super().__init__(
            pad_token_id=_snake_case					,						bos_token_id=_snake_case					,						eos_token_id=_snake_case					,						tie_word_embeddings=_snake_case					,						**_snake_case					,						)







   def lowerCamelCase						(      self		:						Optional[int]):

        """simple docstring"""


        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}""")
        UpperCAmelCase_          =							self.rope_scaling.get('''type'''					,						_snake_case)
        UpperCAmelCase_          =							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}""")






