




"""simple docstring"""



import warnings

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


SCREAMING_SNAKE_CASE				=       logging.get_logger(__name__)

SCREAMING_SNAKE_CASE				=       {
    "RUCAIBox/mvp": "https://huggingface.co/RUCAIBox/mvp/resolve/main/config.json",
}




class 	UpperCAmelCase_  (						snake_case_							):
    lowercase__											=      '''mvp'''
    lowercase__											=      ['''past_key_values''']
    lowercase__											=      {'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}




    def __init__(    self						:       List[str]	, snake_case_						:       List[str]=50_267	, snake_case_						:       Optional[Any]=1_024	, snake_case_						:       Tuple=12	, snake_case_						:       Optional[Any]=4_096	, snake_case_						:       int=16	, snake_case_						:       Tuple=12	, snake_case_						:       int=4_096	, snake_case_						:       List[Any]=16	, snake_case_						:       Tuple=0.0	, snake_case_						:       Tuple=0.0	, snake_case_						:       Tuple="gelu"	, snake_case_						:       Union[str, Any]=1_024	, snake_case_						:       List[str]=0.1	, snake_case_						:       Any=0.0	, snake_case_						:       Dict=0.0	, snake_case_						:       Tuple=0.02	, snake_case_						:       Tuple=0.0	, snake_case_						:       Optional[Any]=False	, snake_case_						:       int=True	, snake_case_						:       Tuple=1	, snake_case_						:       Dict=0	, snake_case_						:       Union[str, Any]=2	, snake_case_						:       Optional[int]=True	, snake_case_						:       Tuple=2	, snake_case_						:       Any=2	, snake_case_						:       Optional[Any]=False	, snake_case_						:       Dict=100	, snake_case_						:       Union[str, Any]=800	, **snake_case_						:       Dict	, )      ->							List[Any]:




     '''simple docstring'''

     A__       =				vocab_size
     A__       =				max_position_embeddings
     A__       =				d_model
     A__       =				encoder_ffn_dim
     A__       =				encoder_layers
     A__       =				encoder_attention_heads
     A__       =				decoder_ffn_dim
     A__       =				decoder_layers
     A__       =				decoder_attention_heads
     A__       =				dropout
     A__       =				attention_dropout
     A__       =				activation_dropout
     A__       =				activation_function
     A__       =				init_std
     A__       =				encoder_layerdrop
     A__       =				decoder_layerdrop
     A__       =				classifier_dropout
     A__       =				use_cache
     A__       =				encoder_layers
     A__       =				scale_embedding  # scale factor will be sqrt(d_model) if True
     A__       =				use_prompt
     A__       =				prompt_length
     A__       =				prompt_mid_dim

     super().__init__(
         pad_token_id=UpperCAmelCase__	, bos_token_id=UpperCAmelCase__	, eos_token_id=UpperCAmelCase__	, is_encoder_decoder=UpperCAmelCase__	, decoder_start_token_id=UpperCAmelCase__	, forced_eos_token_id=UpperCAmelCase__	, **UpperCAmelCase__	, )

     if self.forced_bos_token_id is None and kwargs.get("force_bos_token_to_be_generated"	, UpperCAmelCase__      ):
      A__       =				self.bos_token_id
      warnings.warn(
          F"""Please make sure the config includes `forced_bos_token_id={self.bos_token_id}` in future versions. """
          "The config can simply be saved and uploaded again to be fixed."      )













"""simple docstring"""




_snake_case          =   '\n# Transformers installation\n! pip install transformers datasets\n# To install from source instead of the last release, comment the command above and uncomment the following one.\n# ! pip install git+https://github.com/huggingface/transformers.git\n'

_snake_case          =   [{'type': 'code', 'content': INSTALL_CONTENT}]
_snake_case          =   {
    '{processor_class}': 'FakeProcessorClass',
    '{model_class}': 'FakeModelClass',
    '{object_class}': 'FakeObjectClass',
}

