'''simple docstring'''





import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType




class        __A      ( UpperCamelCase__	):
    a__       :						Union[str, Any]          =  ["""image_processor""", """tokenizer"""]
    a__       :						List[str]          =  """ViltImageProcessor"""
    a__       :						List[str]          =  ("""BertTokenizer""", """BertTokenizerFast""")



    def __init__(self    :    Tuple   ,			__a    :    int=None   ,			__a    :    Union[str, Any]=None   ,			**__a    :    Dict ):
         UpperCAmelCase_           =   None
         if "feature_extractor" in kwargs:
              warnings.warn(
                  "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
                  " instead."   ,			__a   ,			)
              UpperCAmelCase_           =   kwargs.pop("feature_extractor" )

         UpperCAmelCase_           =   image_processor if image_processor is not None else feature_extractor
         if image_processor is None:
              raise ValueError("You need to specify an `image_processor`." )
         if tokenizer is None:
              raise ValueError("You need to specify a `tokenizer`." )

         super().__init__(__a   ,			__a )
         UpperCAmelCase_           =   self.image_processor



    def __call__(self    :    List[str]   ,			__a    :    List[Any]   ,			__a    :    Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None   ,			__a    :    bool = True   ,			__a    :    Union[bool, str, PaddingStrategy] = False   ,			__a    :    Union[bool, str, TruncationStrategy] = None   ,			__a    :    Optional[int] = None   ,			__a    :    int = 0   ,			__a    :    Optional[int] = None   ,			__a    :    Optional[bool] = None   ,			__a    :    Optional[bool] = None   ,			__a    :    bool = False   ,			__a    :    bool = False   ,			__a    :    bool = False   ,			__a    :    bool = False   ,			__a    :    bool = True   ,			__a    :    Optional[Union[str, TensorType]] = None   ,			**__a    :    List[str]   ,			):
         UpperCAmelCase_           =   self.tokenizer(
             text=__a   ,			add_special_tokens=__a   ,			padding=__a   ,			truncation=__a   ,			max_length=__a   ,			stride=__a   ,			pad_to_multiple_of=__a   ,			return_token_type_ids=__a   ,			return_attention_mask=__a   ,			return_overflowing_tokens=__a   ,			return_special_tokens_mask=__a   ,			return_offsets_mapping=__a   ,			return_length=__a   ,			verbose=__a   ,			return_tensors=__a   ,			**__a   ,			)
         # add pixel_values + pixel_mask
         UpperCAmelCase_           =   self.image_processor(__a   ,			return_tensors=__a )
         encoding.update(__a )

         return encoding



    def 					_lowercase						(self    :    Optional[Any]   ,			*__a    :    Union[str, Any]   ,			**__a    :    List[Any] ):
         return self.tokenizer.batch_decode(*__a   ,			**__a )



    def 					_lowercase						(self    :    Any   ,			*__a    :    Tuple   ,			**__a    :    str ):
         return self.tokenizer.decode(*__a   ,			**__a )



    @property
    def 					_lowercase						(self    :    Union[str, Any] ):
         UpperCAmelCase_           =   self.tokenizer.model_input_names
         UpperCAmelCase_           =   self.image_processor.model_input_names
         return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )



    @property
    def 					_lowercase						(self    :    List[Any] ):
         warnings.warn(
             "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."   ,			__a   ,			)
         return self.image_processor_class






    @property
    def 					_lowercase						(self    :    Any ):
         warnings.warn(
             "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."   ,			__a   ,			)
         return self.image_processor





'''simple docstring'''





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


SCREAMING_SNAKE_CASE_:     Dict  =logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_:     Tuple  ={}




class        __A      ( UpperCamelCase__	):
    a__       :						int          =  """llama"""
    a__       :						Any          =  ["""past_key_values"""]



    def __init__(self    :    List[str]   ,			__a    :    List[str]=32000   ,			__a    :    Tuple=4096   ,			__a    :    List[Any]=11008   ,			__a    :    Dict=32   ,			__a    :    Tuple=32   ,			__a    :    Any=None   ,			__a    :    Any="silu"   ,			__a    :    List[Any]=2048   ,			__a    :    List[Any]=0.02   ,			__a    :    str=1E-6   ,			__a    :    Optional[Any]=True   ,			__a    :    Union[str, Any]=0   ,			__a    :    Any=1   ,			__a    :    Dict=2   ,			__a    :    Dict=1   ,			__a    :    str=False   ,			__a    :    str=None   ,			**__a    :    Optional[Any]   ,			):
         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 					_lowercase						(self    :    List[str] ):
         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}""" )




