code: 
  
  from typing import Mapping
  
  from ...configuration_utils import PretrainedConfig
  from ...onnx import OnnxSeqaSeqConfigWithPast
  from ...utils import logging
  
  
  __A   :     int      =			logging.get_logger(__name__)
  
  __A   :     int      =			{
      '''google/long-t5-local-base''': '''https://huggingface.co/google/long-t5-local-base/blob/main/config.json''',
      '''google/long-t5-local-large''': '''https://huggingface.co/google/long-t5-local-large/blob/main/config.json''',
      '''google/long-t5-tglobal-base''': '''https://huggingface.co/google/long-t5-tglobal-base/blob/main/config.json''',
      '''google/long-t5-tglobal-large''': '''https://huggingface.co/google/long-t5-tglobal-large/blob/main/config.json''',
  }
  
  
  
  
  
  
  class    __A				(							lowerCAmelCase ):
      lowerCAmelCase_							:		List[Any]						=							"longt5"
      lowerCAmelCase_							:		int						=							["past_key_values"]
      lowerCAmelCase_							:		Optional[int]						=							{"hidden_size": "d_model", "num_attention_heads": "num_heads", "num_hidden_layers": "num_layers"}
  
  
  
  
  
  
      def __init__(	self			:    str    ,				UpperCAmelCase_			:    Dict=32128    ,				UpperCAmelCase_			:    List[Any]=512    ,				UpperCAmelCase_			:    List[str]=64    ,				UpperCAmelCase_			:    Any=2048    ,				UpperCAmelCase_			:    Union[str, Any]=6    ,				UpperCAmelCase_			:    str=None    ,				UpperCAmelCase_			:    List[Any]=8    ,				UpperCAmelCase_			:    str=127    ,				UpperCAmelCase_			:    Union[str, Any]=16    ,				UpperCAmelCase_			:    int=32    ,				UpperCAmelCase_			:    Tuple=128    ,				UpperCAmelCase_			:    List[Any]=0.1    ,				UpperCAmelCase_			:    Dict=1E-6    ,				UpperCAmelCase_			:    Any=1.0    ,				UpperCAmelCase_			:    Union[str, Any]="relu"    ,				UpperCAmelCase_			:    Optional[Any]=True    ,				UpperCAmelCase_			:    Union[str, Any]="local"    ,				UpperCAmelCase_			:    Optional[int]=True    ,				UpperCAmelCase_			:    Dict=0    ,				UpperCAmelCase_			:    List[Any]=1    ,				**UpperCAmelCase_			:    Any    ,				):
          lowerCAmelCase				:			Union[str, Any]    			=      vocab_size
          lowerCAmelCase				:			List[str]    			=      d_model
          lowerCAmelCase				:			Tuple    			=      d_kv
          lowerCAmelCase				:			List[Any]    			=      d_ff
          lowerCAmelCase				:			List[Any]    			=      num_layers
          # default = symmetry
          lowerCAmelCase				:			Dict    			=      num_decoder_layers if num_decoder_layers is not None else self.num_layers
          lowerCAmelCase				:			str    			=      num_heads
          lowerCAmelCase				:			Union[str, Any]    			=      local_radius
          lowerCAmelCase				:			Optional[int]    			=      global_block_size
          lowerCAmelCase				:			Any    			=      relative_attention_num_buckets
          lowerCAmelCase				:			Optional[int]    			=      relative_attention_max_distance
          lowerCAmelCase				:			int    			=      dropout_rate
          lowerCAmelCase				:			List[Any]    			=      layer_norm_epsilon
          lowerCAmelCase				:			List[Any]    			=      initializer_factor
          lowerCAmelCase				:			List[str]    			=      feed_forward_proj
          lowerCAmelCase				:			List[Any]    			=      encoder_attention_type
          lowerCAmelCase				:			int    			=      use_cache
  
          lowerCAmelCase				:			Any    			=      self.feed_forward_proj.split('-'    )
          lowerCAmelCase				:			Union[str, Any]    			=      act_info[-1]
          lowerCAmelCase				:			Optional[int]    			=      act_info[0] == 'gated'
  
          if len(UpperCAmelCase_    ) > 1 and act_info[0] != "gated" or len(UpperCAmelCase_    ) > 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				:			Optional[Any]    			=      'gelu_new'
  
          super().__init__(
              pad_token_id=UpperCAmelCase_    ,				eos_token_id=UpperCAmelCase_    ,				is_encoder_decoder=UpperCAmelCase_    ,				**UpperCAmelCase_    ,				)
  
  
  
  
  
  
  class    __A				(							lowerCAmelCase ):
  
  
  
  
  
  
      @property
      def 	lowercase__ (	self			:    Any    ):
          lowerCAmelCase				:			int    			=      {
              'input_ids': {0: 'batch', 1: 'encoder_sequence'},
              'attention_mask': {0: 'batch', 1: 'encoder_sequence'},
          }
          if self.use_past:
              lowerCAmelCase				:			Dict    			=      'past_encoder_sequence + sequence'
              lowerCAmelCase				:			Any    			=      {0: 'batch'}
              lowerCAmelCase				:			List[str]    			=      {0: 'batch', 1: 'past_decoder_sequence + sequence'}
          else:
              lowerCAmelCase				:			Optional[Any]    			=      {0: 'batch', 1: 'decoder_sequence'}
              lowerCAmelCase				:			Optional[Any]    			=      {0: 'batch', 1: 'decoder_sequence'}
  
          if self.use_past:
              self.fill_with_past_key_values_(UpperCAmelCase_    ,				direction='inputs'    )
  
          return common_inputs
  
  
  
  
  
  
      @property
      def 	lowercase__ (	self			:    Optional[int]    ):
          return 13
  
  
  
  
  
  
style_context: 
  
  from typing import TYPE_CHECKING
  
  from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available, is_tokenizers_available
  
  
  __A   :     Dict      =			{}
  
  try:
   if not is_sentencepiece_available():
    raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
   pass
  else:
   __A   :     List[Any]      =			['''BarthezTokenizer''']
  
  try:
   if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
   pass
  else:
   __A   :     List[str]      =			['''BarthezTokenizerFast''']
  
  
  if TYPE_CHECKING:
   try:
    if not is_sentencepiece_available():
     raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
    pass
   else:
    from .tokenization_barthez import BarthezTokenizer
  
   try:
    if not is_tokenizers_available():
     raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
    pass
   else:
    from .tokenization_barthez_fast import BarthezTokenizerFast
  
  else:
   import sys
  
   __A   :     Tuple      =			_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
  
  
  
  
  
  