





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


lowercase				:	List[str]      						=  logging.get_logger(__name__)

lowercase				:	int      						=  {}





class 			A__   (							__UpperCAmelCase     ):




   """simple docstring"""



   __A       :    Optional[int]	  =  '''llama'''
   __A       :    List[Any]	  =  ['''past_key_values''']

   def __init__( self ,  lowercase=3_2000 ,  lowercase=4096 ,  lowercase=1_1008 ,  lowercase=32 ,  lowercase=32 ,  lowercase=None ,  lowercase="silu" ,  lowercase=2048 ,  lowercase=0.02 ,  lowercase=1e-6 ,  lowercase=True ,  lowercase=0 ,  lowercase=1 ,  lowercase=2 ,  lowercase=1 ,  lowercase=False ,  lowercase=None ,  **lowercase ,  )      ->						Optional[int]:







          '''simple docstring'''

          a__						:     str			=    vocab_size
          a__						:     int			=    max_position_embeddings
          a__						:     Union[str, Any]			=    hidden_size
          a__						:     List[str]			=    intermediate_size
          a__						:     Union[str, Any]			=    num_hidden_layers
          a__						:     Tuple			=    num_attention_heads

          # for backward compatibility
          if num_key_value_heads is None:
                 a__						:     Dict			=    num_attention_heads

          a__						:     Union[str, Any]			=    num_key_value_heads
          a__						:     Dict			=    hidden_act
          a__						:     Union[str, Any]			=    initializer_range
          a__						:     Any			=    rms_norm_eps
          a__						:     Optional[Any]			=    pretraining_tp
          a__						:     Any			=    use_cache
          a__						:     Optional[int]			=    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 							__lowercase		( self)      ->						Union[str, Any]:







          '''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__						:     Any			=    self.rope_scaling.get('type' ,  lowercase)
          a__						:     List[Any]			=    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_flax_available,
    is_tf_available,
    is_torch_available,
)


lowercase				:	str      						=  {
    """configuration_vision_encoder_decoder""": ["""VisionEncoderDecoderConfig""", """VisionEncoderDecoderOnnxConfig"""]
}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowercase				:	Optional[Any]      						=  ["""VisionEncoderDecoderModel"""]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowercase				:	Optional[int]      						=  ["""TFVisionEncoderDecoderModel"""]

try:
    if not is_flax_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowercase				:	List[str]      						=  ["""FlaxVisionEncoderDecoderModel"""]

if TYPE_CHECKING:
    from .configuration_vision_encoder_decoder import VisionEncoderDecoderConfig, VisionEncoderDecoderOnnxConfig

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_vision_encoder_decoder import VisionEncoderDecoderModel

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_vision_encoder_decoder import TFVisionEncoderDecoderModel

    try:
        if not is_flax_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_flax_vision_encoder_decoder import FlaxVisionEncoderDecoderModel

else:
    import sys

    lowercase				:	Optional[int]      						=  _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


