

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


__A      :   Any          =	logging.get_logger(__name__)

__A      :   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       __A (							__a       ):
    lowerCAmelCase_							:						Optional[Any]						 =							"gpt_neox"






    def __init__(		self	:			Dict			, UpperCAmelCase_	:			Any=50432			, UpperCAmelCase_	:			Any=6144			, UpperCAmelCase_	:			Any=44			, UpperCAmelCase_	:			Tuple=64			, UpperCAmelCase_	:			Optional[int]=24576			, UpperCAmelCase_	:			Union[str, Any]="gelu"			, UpperCAmelCase_	:			int=0.25			, UpperCAmelCase_	:			str=10000			, UpperCAmelCase_	:			Dict=0.0			, UpperCAmelCase_	:			Any=0.0			, UpperCAmelCase_	:			Optional[int]=0.1			, UpperCAmelCase_	:			str=2048			, UpperCAmelCase_	:			Union[str, Any]=0.02			, UpperCAmelCase_	:			Dict=1E-5			, UpperCAmelCase_	:			Any=True			, UpperCAmelCase_	:			Optional[Any]=0			, UpperCAmelCase_	:			Optional[int]=2			, UpperCAmelCase_	:			Dict=False			, UpperCAmelCase_	:			Tuple=True			, UpperCAmelCase_	:			str=None			, **UpperCAmelCase_	:			List[Any]			, ):
        super().__init__(bos_token_id=_UpperCamelCase			, eos_token_id=_UpperCamelCase			, **_UpperCamelCase			)
        lowerCAmelCase :	Dict				=					vocab_size
        lowerCAmelCase :	List[Any]				=					max_position_embeddings
        lowerCAmelCase :	Optional[int]				=					hidden_size
        lowerCAmelCase :	Dict				=					num_hidden_layers
        lowerCAmelCase :	List[Any]				=					num_attention_heads
        lowerCAmelCase :	List[str]				=					intermediate_size
        lowerCAmelCase :	List[str]				=					hidden_act
        lowerCAmelCase :	Union[str, Any]				=					rotary_pct
        lowerCAmelCase :	Tuple				=					rotary_emb_base
        lowerCAmelCase :	Any				=					attention_dropout
        lowerCAmelCase :	Dict				=					hidden_dropout
        lowerCAmelCase :	Union[str, Any]				=					classifier_dropout
        lowerCAmelCase :	Union[str, Any]				=					initializer_range
        lowerCAmelCase :	Union[str, Any]				=					layer_norm_eps
        lowerCAmelCase :	str				=					use_cache
        lowerCAmelCase :	Any				=					tie_word_embeddings
        lowerCAmelCase :	Optional[int]				=					use_parallel_residual
        lowerCAmelCase :	Optional[int]				=					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 		lowercase__       (		self	:			Union[str, Any]			):
        if self.rope_scaling is None:
            return

        if not isinstance(self.rope_scaling			, _UpperCamelCase			) 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 :	List[Any]				=					self.rope_scaling.get('type'			, _UpperCamelCase			)
        lowerCAmelCase :	Any				=					self.rope_scaling.get('factor'			, _UpperCamelCase			)
        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(_UpperCamelCase			, _UpperCamelCase			) 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_tokenizers_available,
    is_torch_available,
)


_A   =     {
    "configuration_distilbert": [
        "DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "DistilBertConfig",
        "DistilBertOnnxConfig",
    ],
    "tokenization_distilbert": ["DistilBertTokenizer"],
}

try:
			if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_A   =     ["DistilBertTokenizerFast"]

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_A   =     [
			    "DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
			    "DistilBertForMaskedLM",
			    "DistilBertForMultipleChoice",
			    "DistilBertForQuestionAnswering",
			    "DistilBertForSequenceClassification",
			    "DistilBertForTokenClassification",
			    "DistilBertModel",
			    "DistilBertPreTrainedModel",
			]

try:
			if not is_tf_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_A   =     [
			    "TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
			    "TFDistilBertForMaskedLM",
			    "TFDistilBertForMultipleChoice",
			    "TFDistilBertForQuestionAnswering",
			    "TFDistilBertForSequenceClassification",
			    "TFDistilBertForTokenClassification",
			    "TFDistilBertMainLayer",
			    "TFDistilBertModel",
			    "TFDistilBertPreTrainedModel",
			]

try:
			if not is_flax_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_A   =     [
			    "FlaxDistilBertForMaskedLM",
			    "FlaxDistilBertForMultipleChoice",
			    "FlaxDistilBertForQuestionAnswering",
			    "FlaxDistilBertForSequenceClassification",
			    "FlaxDistilBertForTokenClassification",
			    "FlaxDistilBertModel",
			    "FlaxDistilBertPreTrainedModel",
			]


if TYPE_CHECKING:
			from .configuration_distilbert import (
			    DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
			    DistilBertConfig,
			    DistilBertOnnxConfig,
			)
			from .tokenization_distilbert import DistilBertTokenizer

			try:
						if not is_tokenizers_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_distilbert_fast import DistilBertTokenizerFast

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_distilbert import (
						    DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
						    DistilBertForMaskedLM,
						    DistilBertForMultipleChoice,
						    DistilBertForQuestionAnswering,
						    DistilBertForSequenceClassification,
						    DistilBertForTokenClassification,
						    DistilBertModel,
						    DistilBertPreTrainedModel,
						)

			try:
						if not is_tf_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tf_distilbert import (
						    TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
						    TFDistilBertForMaskedLM,
						    TFDistilBertForMultipleChoice,
						    TFDistilBertForQuestionAnswering,
						    TFDistilBertForSequenceClassification,
						    TFDistilBertForTokenClassification,
						    TFDistilBertMainLayer,
						    TFDistilBertModel,
						    TFDistilBertPreTrainedModel,
						)

			try:
						if not is_flax_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_flax_distilbert import (
						    FlaxDistilBertForMaskedLM,
						    FlaxDistilBertForMultipleChoice,
						    FlaxDistilBertForQuestionAnswering,
						    FlaxDistilBertForSequenceClassification,
						    FlaxDistilBertForTokenClassification,
						    FlaxDistilBertModel,
						    FlaxDistilBertPreTrainedModel,
						)

else:
			import sys

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



