





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available


SCREAMING_SNAKE_CASE_		     = {}


try:
      if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE_		     = ["MLukeTokenizer"]

if TYPE_CHECKING:
      try:
            if not is_sentencepiece_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_mluke import MLukeTokenizer


else:
      import sys

      SCREAMING_SNAKE_CASE_		     = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)









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


SCREAMING_SNAKE_CASE_		     = logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_		     = {
    """google/pegasus-large""": """https://huggingface.co/google/pegasus-large/resolve/main/config.json""",
    # See all PEGASUS models at https://huggingface.co/models?filter=pegasus
}

class 	UpperCamelCase__				(   lowerCAmelCase_     ):



     '''simple docstring'''

     __snake_case					:			str				=    "pegasus"
     __snake_case					:			List[Any]				=    ["past_key_values"]
     __snake_case					:			Any				=    {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}






     def __init__(							self		:					Dict		,lowerCamelCase__		:					List[str]=50265		,lowerCamelCase__		:					int=1024		,lowerCamelCase__		:					Optional[Any]=12		,lowerCamelCase__		:					Tuple=4096		,lowerCamelCase__		:					Dict=16		,lowerCamelCase__		:					Optional[Any]=12		,lowerCamelCase__		:					str=4096		,lowerCamelCase__		:					Dict=16		,lowerCamelCase__		:					Tuple=0.0		,lowerCamelCase__		:					Dict=0.0		,lowerCamelCase__		:					str=True		,lowerCamelCase__		:					Any=True		,lowerCamelCase__		:					List[Any]="gelu"		,lowerCamelCase__		:					Tuple=1024		,lowerCamelCase__		:					Optional[int]=0.1		,lowerCamelCase__		:					Tuple=0.0		,lowerCamelCase__		:					Dict=0.0		,lowerCamelCase__		:					Optional[int]=0.02		,lowerCamelCase__		:					Optional[int]=0		,lowerCamelCase__		:					List[Any]=False		,lowerCamelCase__		:					Union[str, Any]=0		,lowerCamelCase__		:					Optional[int]=1		,lowerCamelCase__		:					Any=1		,**lowerCamelCase__		:					Dict		,)  ->	str:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		vocab_size
           SCREAMING_SNAKE_CASE	=		max_position_embeddings
           SCREAMING_SNAKE_CASE	=		d_model
           SCREAMING_SNAKE_CASE	=		encoder_ffn_dim
           SCREAMING_SNAKE_CASE	=		encoder_layers
           SCREAMING_SNAKE_CASE	=		encoder_attention_heads
           SCREAMING_SNAKE_CASE	=		decoder_ffn_dim
           SCREAMING_SNAKE_CASE	=		decoder_layers
           SCREAMING_SNAKE_CASE	=		decoder_attention_heads
           SCREAMING_SNAKE_CASE	=		dropout
           SCREAMING_SNAKE_CASE	=		attention_dropout
           SCREAMING_SNAKE_CASE	=		activation_dropout
           SCREAMING_SNAKE_CASE	=		activation_function
           SCREAMING_SNAKE_CASE	=		init_std
           SCREAMING_SNAKE_CASE	=		encoder_layerdrop
           SCREAMING_SNAKE_CASE	=		decoder_layerdrop
           SCREAMING_SNAKE_CASE	=		use_cache
           SCREAMING_SNAKE_CASE	=		encoder_layers
           SCREAMING_SNAKE_CASE	=		scale_embedding  # scale factor will be sqrt(d_model) if True
           super().__init__(
               pad_token_id=lowerCamelCase__		,eos_token_id=lowerCamelCase__		,is_encoder_decoder=lowerCamelCase__		,decoder_start_token_id=lowerCamelCase__		,forced_eos_token_id=lowerCamelCase__		,**lowerCamelCase__		,)






     @property
     def        SCREAMING_SNAKE_CASE__						(							self		:					Tuple )  ->	int:




           '''simple docstring'''


           return self.encoder_attention_heads






     @property
     def        SCREAMING_SNAKE_CASE__						(							self		:					List[str] )  ->	int:




           '''simple docstring'''


           return self.d_model


