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


lowerCamelCase      =logging.get_logger(__name__)

lowerCamelCase      ={
    "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 (      UpperCamelCase_ ):





     """simple docstring"""







     SCREAMING_SNAKE_CASE_		   =  '''gpt_neox'''






     def __init__(							self						,						__SCREAMING_SNAKE_CASE=5_0_4_3_2						,						__SCREAMING_SNAKE_CASE=6_1_4_4						,						__SCREAMING_SNAKE_CASE=4_4						,						__SCREAMING_SNAKE_CASE=6_4						,						__SCREAMING_SNAKE_CASE=2_4_5_7_6						,						__SCREAMING_SNAKE_CASE="gelu"						,						__SCREAMING_SNAKE_CASE=0.25						,						__SCREAMING_SNAKE_CASE=1_0_0_0_0						,						__SCREAMING_SNAKE_CASE=0.0						,						__SCREAMING_SNAKE_CASE=0.0						,						__SCREAMING_SNAKE_CASE=0.1						,						__SCREAMING_SNAKE_CASE=2_0_4_8						,						__SCREAMING_SNAKE_CASE=0.02						,						__SCREAMING_SNAKE_CASE=1e-5						,						__SCREAMING_SNAKE_CASE=True						,						__SCREAMING_SNAKE_CASE=0						,						__SCREAMING_SNAKE_CASE=2						,						__SCREAMING_SNAKE_CASE=False						,						__SCREAMING_SNAKE_CASE=True						,						__SCREAMING_SNAKE_CASE=None						,						**__SCREAMING_SNAKE_CASE						,						)  ->   Tuple:




           """simple docstring"""







           super().__init__(bos_token_id=__SCREAMING_SNAKE_CASE						,						eos_token_id=__SCREAMING_SNAKE_CASE						,						**__SCREAMING_SNAKE_CASE  )
           UpperCamelCase__    :   int					=				vocab_size
           UpperCamelCase__    :   Optional[int]					=				max_position_embeddings
           UpperCamelCase__    :   str					=				hidden_size
           UpperCamelCase__    :   List[str]					=				num_hidden_layers
           UpperCamelCase__    :   Union[str, Any]					=				num_attention_heads
           UpperCamelCase__    :   int					=				intermediate_size
           UpperCamelCase__    :   Optional[int]					=				hidden_act
           UpperCamelCase__    :   Dict					=				rotary_pct
           UpperCamelCase__    :   Dict					=				rotary_emb_base
           UpperCamelCase__    :   Any					=				attention_dropout
           UpperCamelCase__    :   List[str]					=				hidden_dropout
           UpperCamelCase__    :   List[str]					=				classifier_dropout
           UpperCamelCase__    :   int					=				initializer_range
           UpperCamelCase__    :   Optional[Any]					=				layer_norm_eps
           UpperCamelCase__    :   Optional[int]					=				use_cache
           UpperCamelCase__    :   int					=				tie_word_embeddings
           UpperCamelCase__    :   Tuple					=				use_parallel_residual
           UpperCamelCase__    :   Tuple					=				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    __SCREAMING_SNAKE_CASE			(							self  )  ->   str:




           """simple docstring"""







           if self.rope_scaling is None:
                 return

           if not isinstance(self.rope_scaling						,						__SCREAMING_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__    :   Optional[Any]					=				self.rope_scaling.get('''type'''						,						__SCREAMING_SNAKE_CASE  )
           UpperCamelCase__    :   Optional[int]					=				self.rope_scaling.get('''factor'''						,						__SCREAMING_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(__SCREAMING_SNAKE_CASE						,						__SCREAMING_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}'''  )




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


lowerCamelCase      ={"configuration_swin": ["SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP", "SwinConfig", "SwinOnnxConfig"]}


try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCamelCase      =[
        "SWIN_PRETRAINED_MODEL_ARCHIVE_LIST",
        "SwinForImageClassification",
        "SwinForMaskedImageModeling",
        "SwinModel",
        "SwinPreTrainedModel",
        "SwinBackbone",
    ]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCamelCase      =[
        "TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST",
        "TFSwinForImageClassification",
        "TFSwinForMaskedImageModeling",
        "TFSwinModel",
        "TFSwinPreTrainedModel",
    ]

if TYPE_CHECKING:
    from .configuration_swin import SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP, SwinConfig, SwinOnnxConfig

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_swin import (
            SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
            SwinBackbone,
            SwinForImageClassification,
            SwinForMaskedImageModeling,
            SwinModel,
            SwinPreTrainedModel,
        )

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_swin import (
            TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
            TFSwinForImageClassification,
            TFSwinForMaskedImageModeling,
            TFSwinModel,
            TFSwinPreTrainedModel,
        )

else:
    import sys

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


