

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


lowerCAmelCase__		      =       logging.get_logger(__name__)

lowerCAmelCase__		      =       {
    """s-JoL/Open-Llama-V1""": """https://huggingface.co/s-JoL/Open-Llama-V1/blob/main/config.json""",
}

class     a__  (    snake_case	):
			"""simple docstring"""







			__lowerCamelCase       				=				'open-llama'


			def __init__(			self					,					lowercase=100000					,					lowercase=4096					,					lowercase=11008					,					lowercase=32					,					lowercase=32					,					lowercase="silu"					,					lowercase=2048					,					lowercase=0.02					,					lowercase=1e-6					,					lowercase=True					,					lowercase=0					,					lowercase=1					,					lowercase=2					,					lowercase=False					,					lowercase=True					,					lowercase=0.1					,					lowercase=0.1					,					lowercase=True					,					lowercase=True					,					lowercase=None					,					**lowercase					,					)     ->  Dict:
							'''simple docstring'''






							A__						=	vocab_size
							A__						=	max_position_embeddings
							A__						=	hidden_size
							A__						=	intermediate_size
							A__						=	num_hidden_layers
							A__						=	num_attention_heads
							A__						=	hidden_act
							A__						=	initializer_range
							A__						=	rms_norm_eps
							A__						=	use_cache
							A__						=	kwargs.pop(
							    "use_memorry_efficient_attention"					,					lowercase				)
							A__						=	hidden_dropout_prob
							A__						=	attention_dropout_prob
							A__						=	use_stable_embedding
							A__						=	shared_input_output_embedding
							A__						=	rope_scaling
							self._rope_scaling_validation()

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


			def    UpperCamelCase						(			self				)     ->  Dict:
							'''simple docstring'''






							if self.rope_scaling is None:
											return

							if not isinstance(self.rope_scaling					,					lowercase				) 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}'				)
							A__						=	self.rope_scaling.get("type"					,					lowercase				)
							A__						=	self.rope_scaling.get("factor"					,					lowercase				)
							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(lowercase					,					lowercase				) 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_tokenizers_available, is_torch_available


__lowerCAmelCase		       =							{
    '''configuration_canine''': ['''CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''CanineConfig'''],
    '''tokenization_canine''': ['''CanineTokenizer'''],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __lowerCAmelCase		       =							[
     '''CANINE_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''CanineForMultipleChoice''',
     '''CanineForQuestionAnswering''',
     '''CanineForSequenceClassification''',
     '''CanineForTokenClassification''',
     '''CanineLayer''',
     '''CanineModel''',
     '''CaninePreTrainedModel''',
     '''load_tf_weights_in_canine''',
 ]


if TYPE_CHECKING:
 from .configuration_canine import CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP, CanineConfig
 from .tokenization_canine import CanineTokenizer

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_canine import (
      CANINE_PRETRAINED_MODEL_ARCHIVE_LIST,
      CanineForMultipleChoice,
      CanineForQuestionAnswering,
      CanineForSequenceClassification,
      CanineForTokenClassification,
      CanineLayer,
      CanineModel,
      CaninePreTrainedModel,
      load_tf_weights_in_canine,
  )


else:
 import sys

 __lowerCAmelCase		       =							_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


