





'''simple docstring'''

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


lowercase__		:     List[str]			=							logging.get_logger(__name__)

lowercase__		:     int			=							{
    '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__					):






       """simple docstring"""
       _snake_case      :	List[Any]									=       'gpt_neox'





       def __init__(				self  :						str			,					lowerCAmelCase__  :						Optional[int]=50432			,					lowerCAmelCase__  :						Optional[int]=6144			,					lowerCAmelCase__  :						Optional[Any]=44			,					lowerCAmelCase__  :						Optional[Any]=64			,					lowerCAmelCase__  :						List[str]=24576			,					lowerCAmelCase__  :						Any="gelu"			,					lowerCAmelCase__  :						Any=0.25			,					lowerCAmelCase__  :						str=10000			,					lowerCAmelCase__  :						Any=0.0			,					lowerCAmelCase__  :						Optional[Any]=0.0			,					lowerCAmelCase__  :						Tuple=0.1			,					lowerCAmelCase__  :						Dict=2048			,					lowerCAmelCase__  :						Optional[Any]=0.02			,					lowerCAmelCase__  :						Tuple=1e-5			,					lowerCAmelCase__  :						Tuple=True			,					lowerCAmelCase__  :						Dict=0			,					lowerCAmelCase__  :						Union[str, Any]=2			,					lowerCAmelCase__  :						int=False			,					lowerCAmelCase__  :						Union[str, Any]=True			,					lowerCAmelCase__  :						int=None			,					**lowerCAmelCase__  :						int			,					)       ->	Tuple:

            '''simple docstring'''




            super().__init__(bos_token_id=lowerCAmelCase__			,					eos_token_id=lowerCAmelCase__			,					**lowerCAmelCase__	)
            _UpperCamelCase							       =			vocab_size
            _UpperCamelCase							       =			max_position_embeddings
            _UpperCamelCase							       =			hidden_size
            _UpperCamelCase							       =			num_hidden_layers
            _UpperCamelCase							       =			num_attention_heads
            _UpperCamelCase							       =			intermediate_size
            _UpperCamelCase							       =			hidden_act
            _UpperCamelCase							       =			rotary_pct
            _UpperCamelCase							       =			rotary_emb_base
            _UpperCamelCase							       =			attention_dropout
            _UpperCamelCase							       =			hidden_dropout
            _UpperCamelCase							       =			classifier_dropout
            _UpperCamelCase							       =			initializer_range
            _UpperCamelCase							       =			layer_norm_eps
            _UpperCamelCase							       =			use_cache
            _UpperCamelCase							       =			tie_word_embeddings
            _UpperCamelCase							       =			use_parallel_residual
            _UpperCamelCase							       =			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  :						str	)       ->	Tuple:

            '''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}"""	)
            _UpperCamelCase							       =			self.rope_scaling.get('''type'''			,					lowerCAmelCase__	)
            _UpperCamelCase							       =			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_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCAmelCase   : List[Any]             =				{
    """configuration_albert""": ["""ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """AlbertConfig""", """AlbertOnnxConfig"""],
}

try:
					if not is_sentencepiece_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCAmelCase   : Any             =				["""AlbertTokenizer"""]

try:
					if not is_tokenizers_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCAmelCase   : Optional[Any]             =				["""AlbertTokenizerFast"""]

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCAmelCase   : List[str]             =				[
					    """ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """AlbertForMaskedLM""",
					    """AlbertForMultipleChoice""",
					    """AlbertForPreTraining""",
					    """AlbertForQuestionAnswering""",
					    """AlbertForSequenceClassification""",
					    """AlbertForTokenClassification""",
					    """AlbertModel""",
					    """AlbertPreTrainedModel""",
					    """load_tf_weights_in_albert""",
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCAmelCase   : Tuple             =				[
					    """TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """TFAlbertForMaskedLM""",
					    """TFAlbertForMultipleChoice""",
					    """TFAlbertForPreTraining""",
					    """TFAlbertForQuestionAnswering""",
					    """TFAlbertForSequenceClassification""",
					    """TFAlbertForTokenClassification""",
					    """TFAlbertMainLayer""",
					    """TFAlbertModel""",
					    """TFAlbertPreTrainedModel""",
					]

try:
					if not is_flax_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCAmelCase   : Optional[int]             =				[
					    """FlaxAlbertForMaskedLM""",
					    """FlaxAlbertForMultipleChoice""",
					    """FlaxAlbertForPreTraining""",
					    """FlaxAlbertForQuestionAnswering""",
					    """FlaxAlbertForSequenceClassification""",
					    """FlaxAlbertForTokenClassification""",
					    """FlaxAlbertModel""",
					    """FlaxAlbertPreTrainedModel""",
					]

if TYPE_CHECKING:
					from .configuration_albert import ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, AlbertConfig, AlbertOnnxConfig

					try:
										if not is_sentencepiece_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_albert import AlbertTokenizer

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_albert_fast import AlbertTokenizerFast

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_albert import (
										    ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
										    AlbertForMaskedLM,
										    AlbertForMultipleChoice,
										    AlbertForPreTraining,
										    AlbertForQuestionAnswering,
										    AlbertForSequenceClassification,
										    AlbertForTokenClassification,
										    AlbertModel,
										    AlbertPreTrainedModel,
										    load_tf_weights_in_albert,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_albert import (
										    TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFAlbertForMaskedLM,
										    TFAlbertForMultipleChoice,
										    TFAlbertForPreTraining,
										    TFAlbertForQuestionAnswering,
										    TFAlbertForSequenceClassification,
										    TFAlbertForTokenClassification,
										    TFAlbertMainLayer,
										    TFAlbertModel,
										    TFAlbertPreTrainedModel,
										)

					try:
										if not is_flax_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_flax_albert import (
										    FlaxAlbertForMaskedLM,
										    FlaxAlbertForMultipleChoice,
										    FlaxAlbertForPreTraining,
										    FlaxAlbertForQuestionAnswering,
										    FlaxAlbertForSequenceClassification,
										    FlaxAlbertForTokenClassification,
										    FlaxAlbertModel,
										    FlaxAlbertPreTrainedModel,
										)
else:
					import sys

					lowerCAmelCase   : Union[str, Any]             =				_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


