





'''simple docstring'''


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


_SCREAMING_SNAKE_CASE		       =							logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE		       =							{
    'google/switch-base-8': 'https://huggingface.co/google/switch-base-8/blob/main/config.json',
}

class       lowerCAmelCase_						(						UpperCAmelCase__						):
 __lowerCamelCase  :							Dict				     =    """switch_transformers"""
 __lowerCamelCase  :							str				     =    ["""past_key_values"""]
 __lowerCamelCase  :							Dict				     =    {"""hidden_size""": """d_model""", """num_attention_heads""": """num_heads""", """num_hidden_layers""": """num_layers"""}




 def __init__(		self	,   _lowerCAmelCase=32128	,   _lowerCAmelCase=768	,   _lowerCAmelCase=64	,   _lowerCAmelCase=2048	,   _lowerCAmelCase=64	,   _lowerCAmelCase=12	,   _lowerCAmelCase=3	,   _lowerCAmelCase=12	,   _lowerCAmelCase=3	,   _lowerCAmelCase=12	,   _lowerCAmelCase=8	,   _lowerCAmelCase=False	,   _lowerCAmelCase=0.01	,   _lowerCAmelCase="float32"	,   _lowerCAmelCase=False	,   _lowerCAmelCase=32	,   _lowerCAmelCase=128	,   _lowerCAmelCase=0.1	,   _lowerCAmelCase=1E-6	,   _lowerCAmelCase=0.001	,   _lowerCAmelCase=0.001	,   _lowerCAmelCase=1.0	,   _lowerCAmelCase="relu"	,   _lowerCAmelCase=True	,   _lowerCAmelCase=False	,   _lowerCAmelCase=True	,   _lowerCAmelCase=0	,   _lowerCAmelCase=1	,   **_lowerCAmelCase	,   )			->							List[str]:
     _lowerCAmelCase			=					vocab_size
     _lowerCAmelCase			=					d_model
     _lowerCAmelCase			=					d_kv
     _lowerCAmelCase			=					d_ff

     _lowerCAmelCase			=					num_sparse_encoder_layers

     _lowerCAmelCase			=					num_layers
     _lowerCAmelCase			=					(
         num_decoder_layers if num_decoder_layers is not None else self.num_layers
     )  # default = symmetry
     _lowerCAmelCase			=					num_sparse_decoder_layers

     # This tells us, each how many encoder layer we'll have to set a sparse layer.
     if self.num_sparse_encoder_layers > 0:
         _lowerCAmelCase			=					self.num_layers // self.num_sparse_encoder_layers
     else:
         _lowerCAmelCase			=					self.num_layers  # HACK: this will create 0 sparse layers

     # This tells us, each how many encoder layer we'll have to set a sparse layer.
     if self.num_sparse_decoder_layers > 0:
         _lowerCAmelCase			=					self.num_decoder_layers // self.num_sparse_decoder_layers
     else:
         _lowerCAmelCase			=					self.num_decoder_layers  # HACK: this will create 0 sparse layers

     _lowerCAmelCase			=					num_heads
     _lowerCAmelCase			=					num_experts
     _lowerCAmelCase			=					expert_capacity
     _lowerCAmelCase			=					router_bias
     _lowerCAmelCase			=					router_jitter_noise
     if router_dtype not in ["float32", "float16", "bfloat16"]:
         raise ValueError(f'''`router_dtype` must be one of \'float32\', \'float16\' or \'bfloat16\', got {router_dtype}'''						)
     _lowerCAmelCase			=					router_dtype

     _lowerCAmelCase			=					router_ignore_padding_tokens
     _lowerCAmelCase			=					relative_attention_num_buckets
     _lowerCAmelCase			=					relative_attention_max_distance

     _lowerCAmelCase			=					dropout_rate
     _lowerCAmelCase			=					layer_norm_epsilon
     _lowerCAmelCase			=					initializer_factor
     _lowerCAmelCase			=					feed_forward_proj
     _lowerCAmelCase			=					use_cache
     _lowerCAmelCase			=					add_router_probs

     _lowerCAmelCase			=					router_z_loss_coef
     _lowerCAmelCase			=					router_aux_loss_coef

     _lowerCAmelCase			=					self.feed_forward_proj.split("-"						)
     _lowerCAmelCase			=					act_info[-1]
     _lowerCAmelCase			=					act_info[0] == 'gated'

     if len(snake_case_						) > 1 and act_info[0] != "gated" or len(snake_case_						) > 2:
         raise ValueError(
             f'''`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer.'''
             "Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. "
             "\'gated-gelu\' or \'relu\'"						)

     # for backwards compatibility
     if feed_forward_proj == "gated-gelu":
         _lowerCAmelCase			=					'gelu_new'

     super().__init__(
         pad_token_id=snake_case_	,   eos_token_id=snake_case_	,   is_encoder_decoder=snake_case_	,   **snake_case_	,   )









"""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  _UpperCAmelCase  (   UpperCAmelCase__		):



      '''simple docstring'''
      lowercase_      : Dict	    =   ["""image_processor""", """tokenizer"""]
      lowercase_      : Union[str, Any]	    =   """ViltImageProcessor"""
      lowercase_      : Any	    =   ("""BertTokenizer""", """BertTokenizerFast""")

      def __init__(     self					,      snake_case_=None					,      snake_case_=None					,      **snake_case_  ):

        """simple docstring"""

        A_  :       Union[str, Any]										=  None
        if "feature_extractor" in kwargs:
          warnings.warn(
              'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
              ' instead.'					,      snake_case_					,      )
          A_  :       Dict										=  kwargs.pop('feature_extractor'  )

        A_  :       Dict										=  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__(snake_case_					,      snake_case_  )
        A_  :       List[str]										=  self.image_processor

      def __call__(     self					,      snake_case_					,      snake_case_ = None					,      snake_case_ = True					,      snake_case_ = False					,      snake_case_ = None					,      snake_case_ = None					,      snake_case_ = 0					,      snake_case_ = None					,      snake_case_ = None					,      snake_case_ = None					,      snake_case_ = False					,      snake_case_ = False					,      snake_case_ = False					,      snake_case_ = False					,      snake_case_ = True					,      snake_case_ = None					,      **snake_case_					,      ):

        """simple docstring"""

        A_  :       str										=  self.tokenizer(
            text=snake_case_					,      add_special_tokens=snake_case_					,      padding=snake_case_					,      truncation=snake_case_					,      max_length=snake_case_					,      stride=snake_case_					,      pad_to_multiple_of=snake_case_					,      return_token_type_ids=snake_case_					,      return_attention_mask=snake_case_					,      return_overflowing_tokens=snake_case_					,      return_special_tokens_mask=snake_case_					,      return_offsets_mapping=snake_case_					,      return_length=snake_case_					,      verbose=snake_case_					,      return_tensors=snake_case_					,      **snake_case_					,      )
        # add pixel_values + pixel_mask
        A_  :       Optional[int]										=  self.image_processor(snake_case_					,      return_tensors=snake_case_  )
        encoding.update(snake_case_  )

        return encoding

      def 					lowerCamelCase_			(     self					,      *snake_case_					,      **snake_case_  ):

        """simple docstring"""

        return self.tokenizer.batch_decode(*snake_case_					,      **snake_case_  )

      def 					lowerCamelCase_			(     self					,      *snake_case_					,      **snake_case_  ):

        """simple docstring"""

        return self.tokenizer.decode(*snake_case_					,      **snake_case_  )

      @property
      def 					lowerCamelCase_			(     self  ):

        """simple docstring"""

        A_  :       Any										=  self.tokenizer.model_input_names
        A_  :       Any										=  self.image_processor.model_input_names
        return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names  )  )

      @property
      def 					lowerCamelCase_			(     self  ):

        """simple docstring"""

        warnings.warn(
            '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'					,      snake_case_					,      )
        return self.image_processor_class

      @property
      def 					lowerCamelCase_			(     self  ):

        """simple docstring"""

        warnings.warn(
            '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'					,      snake_case_					,      )
        return self.image_processor