





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


lowercase    : Optional[Any]    	=  logging.get_logger(__name__)

lowercase    : int    	=  {}

class  __A(						__UpperCAmelCase ):
					__A	  =       "llama"
					__A	  =       ["past_key_values"]

					def __init__(      self,       A=3_2000,       A=4096,       A=1_1008,       A=32,       A=32,       A=None,       A="silu",       A=2048,       A=0.02,       A=1E-6,       A=True,       A=0,       A=1,       A=2,       A=1,       A=False,       A=None,       **A,       ):





												"""simple docstring"""

												_UpperCamelCase              =						vocab_size
												_UpperCamelCase              =						max_position_embeddings
												_UpperCamelCase              =						hidden_size
												_UpperCamelCase              =						intermediate_size
												_UpperCamelCase              =						num_hidden_layers
												_UpperCamelCase              =						num_attention_heads

												# for backward compatibility
												if num_key_value_heads is None:
																			_UpperCamelCase              =						num_attention_heads

												_UpperCamelCase              =						num_key_value_heads
												_UpperCamelCase              =						hidden_act
												_UpperCamelCase              =						initializer_range
												_UpperCamelCase              =						rms_norm_eps
												_UpperCamelCase              =						pretraining_tp
												_UpperCamelCase              =						use_cache
												_UpperCamelCase              =						rope_scaling
												self._rope_scaling_validation()

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



					def 	_UpperCamelCase     (      self      ):





												"""simple docstring"""

												if self.rope_scaling is None:
																			return

												if not isinstance(self.rope_scaling,       A      ) 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''',       A      )
												_UpperCamelCase              =						self.rope_scaling.get('''factor''',       A      )
												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(A,       A      ) or rope_scaling_factor <= 1.0:
																			raise ValueError(F'''`rope_scaling`\'s factor field must be an float > 1, got {rope_scaling_factor}'''      )













import warnings

from ...utils import logging
from .image_processing_clip import CLIPImageProcessor


lowercase    : str    	=  logging.get_logger(__name__)

class  __A(						__UpperCAmelCase ):

     def __init__(      self,       *A,       **A      ):





            """simple docstring"""

            warnings.warn(
                '''The class CLIPFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'''
                ''' use CLIPImageProcessor instead.''',       A,       )
            super().__init__(*A,       **A      )






