




'''simple docstring'''





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


UpperCAmelCase_		:				Union[str, Any]									=     logging.get_logger(__name__)

UpperCAmelCase_		:				Any									=     {}




class 		__A      (			UpperCamelCase__			):
						UpperCamelCase     						=					"""llama"""
						UpperCamelCase     						=					["""past_key_values"""]

						def __init__(		self   :List[Any]    , __snake_case   :Optional[int]=3_20_00    , __snake_case   :Optional[int]=40_96    , __snake_case   :Optional[Any]=1_10_08    , __snake_case   :int=32    , __snake_case   :Any=32    , __snake_case   :str=None    , __snake_case   :int="silu"    , __snake_case   :Any=20_48    , __snake_case   :Optional[Any]=0.02    , __snake_case   :Optional[int]=1E-6    , __snake_case   :Dict=True    , __snake_case   :List[Any]=0    , __snake_case   :List[Any]=1    , __snake_case   :Tuple=2    , __snake_case   :Union[str, Any]=1    , __snake_case   :Dict=False    , __snake_case   :int=None    , **__snake_case   :Dict    , ):
										'''simple docstring'''

										__magic_name__	:	Optional[Any]								=vocab_size
										__magic_name__	:	Optional[int]								=max_position_embeddings
										__magic_name__	:	int								=hidden_size
										__magic_name__	:	Tuple								=intermediate_size
										__magic_name__	:	Tuple								=num_hidden_layers
										__magic_name__	:	List[str]								=num_attention_heads

										# for backward compatibility
										if num_key_value_heads is None:
														__magic_name__	:	Optional[int]								=num_attention_heads

										__magic_name__	:	List[str]								=num_key_value_heads
										__magic_name__	:	List[str]								=hidden_act
										__magic_name__	:	List[str]								=initializer_range
										__magic_name__	:	List[Any]								=rms_norm_eps
										__magic_name__	:	Any								=pretraining_tp
										__magic_name__	:	List[Any]								=use_cache
										__magic_name__	:	Any								=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     A__						(		self   :Tuple ):
										'''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}" )
										__magic_name__	:	Tuple								=self.rope_scaling.get("""type"""    , __snake_case )
										__magic_name__	:	List[str]								=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}" )










from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCAmelCase_		:				Dict									=     {"configuration_wavlm": ["WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP", "WavLMConfig"]}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase_		:				Optional[int]									=     [
							    "WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST",
							    "WavLMForAudioFrameClassification",
							    "WavLMForCTC",
							    "WavLMForSequenceClassification",
							    "WavLMForXVector",
							    "WavLMModel",
							    "WavLMPreTrainedModel",
							]

if TYPE_CHECKING:
							from .configuration_wavlm import WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP, WavLMConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_wavlm import (
														    WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST,
														    WavLMForAudioFrameClassification,
														    WavLMForCTC,
														    WavLMForSequenceClassification,
														    WavLMForXVector,
														    WavLMModel,
														    WavLMPreTrainedModel,
														)

else:
							import sys

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




