





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


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

__UpperCAmelCase	:       str    	=							{}







class    __snake_case	(							__lowerCamelCase      ):





       '''simple docstring'''







       lowerCAmelCase__						=					"""llama"""
       lowerCAmelCase__						=					["""past_key_values"""]




       def __init__(  self			:   int							,    A			:   str=32_000							,    A			:   Tuple=4_096							,    A			:   Dict=11_008							,    A			:   Optional[Any]=32							,    A			:   List[Any]=32							,    A			:   Optional[int]=None							,    A			:   Dict="silu"							,    A			:   List[Any]=2_048							,    A			:   int=0.02							,    A			:   List[str]=1E-6							,    A			:   Optional[Any]=True							,    A			:   List[str]=0							,    A			:   Any=1							,    A			:   Dict=2							,    A			:   List[str]=1							,    A			:   Dict=False							,    A			:   str=None							,    **A			:   Union[str, Any]							,    ):
         __snake_case:						Any			  =						vocab_size
         __snake_case:						Any			  =						max_position_embeddings
         __snake_case:						str			  =						hidden_size
         __snake_case:						List[Any]			  =						intermediate_size
         __snake_case:						int			  =						num_hidden_layers
         __snake_case:						List[str]			  =						num_attention_heads

         # for backward compatibility
         if num_key_value_heads is None:
           __snake_case:						int			  =						num_attention_heads

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













from ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
      if not (is_transformers_available() and is_torch_available()):
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      from ...utils.dummy_torch_and_transformers_objects import (
          ImageTextPipelineOutput,
          UniDiffuserPipeline,
      )
else:
      from .modeling_text_decoder import UniDiffuserTextDecoder
      from .modeling_uvit import UniDiffuserModel, UTransformeraDModel
      from .pipeline_unidiffuser import ImageTextPipelineOutput, UniDiffuserPipeline






