



import warnings

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


SCREAMING_SNAKE_CASE_:Tuple             =						logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_:List[str]             =						{
    """RUCAIBox/mvp""": """https://huggingface.co/RUCAIBox/mvp/resolve/main/config.json""",
}






class SCREAMING_SNAKE_CASE__     (		SCREAMING_SNAKE_CASE__    ):




    '''simple docstring'''






    __lowerCamelCase			: List[Any]					=      "mvp"
    __lowerCamelCase			: Optional[int]					=      ["past_key_values"]
    __lowerCamelCase			: List[Any]					=      {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}






    def __init__(       self,		lowerCamelCase__=5_0267,		lowerCamelCase__=1024,		lowerCamelCase__=12,		lowerCamelCase__=4096,		lowerCamelCase__=16,		lowerCamelCase__=12,		lowerCamelCase__=4096,		lowerCamelCase__=16,		lowerCamelCase__=0.0,		lowerCamelCase__=0.0,		lowerCamelCase__="gelu",		lowerCamelCase__=1024,		lowerCamelCase__=0.1,		lowerCamelCase__=0.0,		lowerCamelCase__=0.0,		lowerCamelCase__=0.02,		lowerCamelCase__=0.0,		lowerCamelCase__=False,		lowerCamelCase__=True,		lowerCamelCase__=1,		lowerCamelCase__=0,		lowerCamelCase__=2,		lowerCamelCase__=True,		lowerCamelCase__=2,		lowerCamelCase__=2,		lowerCamelCase__=False,		lowerCamelCase__=100,		lowerCamelCase__=800,		**lowerCamelCase__,		):
        A :       Any          =   vocab_size
        A :       Union[str, Any]          =   max_position_embeddings
        A :       Union[str, Any]          =   d_model
        A :       List[str]          =   encoder_ffn_dim
        A :       Union[str, Any]          =   encoder_layers
        A :       Dict          =   encoder_attention_heads
        A :       Tuple          =   decoder_ffn_dim
        A :       str          =   decoder_layers
        A :       List[str]          =   decoder_attention_heads
        A :       Any          =   dropout
        A :       Dict          =   attention_dropout
        A :       str          =   activation_dropout
        A :       List[str]          =   activation_function
        A :       Optional[Any]          =   init_std
        A :       Union[str, Any]          =   encoder_layerdrop
        A :       str          =   decoder_layerdrop
        A :       Any          =   classifier_dropout
        A :       Tuple          =   use_cache
        A :       List[Any]          =   encoder_layers
        A :       Union[str, Any]          =   scale_embedding  # scale factor will be sqrt(d_model) if True
        A :       List[Any]          =   use_prompt
        A :       Tuple          =   prompt_length
        A :       Dict          =   prompt_mid_dim

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

        if self.forced_bos_token_id is None and kwargs.get("""force_bos_token_to_be_generated""",		lowerCamelCase__		):
            A :       Optional[int]          =   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."""		)







from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


SCREAMING_SNAKE_CASE_:Optional[Any]             =						logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_:Dict             =						{
    """camembert-base""": """https://huggingface.co/camembert-base/resolve/main/config.json""",
    """umberto-commoncrawl-cased-v1""": (
        """https://huggingface.co/Musixmatch/umberto-commoncrawl-cased-v1/resolve/main/config.json"""
    ),
    """umberto-wikipedia-uncased-v1""": (
        """https://huggingface.co/Musixmatch/umberto-wikipedia-uncased-v1/resolve/main/config.json"""
    ),
}






class SCREAMING_SNAKE_CASE__     (		SCREAMING_SNAKE_CASE__    ):




    '''simple docstring'''






    __lowerCamelCase			: Optional[int]					=      "camembert"






    def __init__(       self,		lowerCamelCase__=3_0522,		lowerCamelCase__=768,		lowerCamelCase__=12,		lowerCamelCase__=12,		lowerCamelCase__=3072,		lowerCamelCase__="gelu",		lowerCamelCase__=0.1,		lowerCamelCase__=0.1,		lowerCamelCase__=512,		lowerCamelCase__=2,		lowerCamelCase__=0.02,		lowerCamelCase__=1e-12,		lowerCamelCase__=1,		lowerCamelCase__=0,		lowerCamelCase__=2,		lowerCamelCase__="absolute",		lowerCamelCase__=True,		lowerCamelCase__=None,		**lowerCamelCase__,		):
        super().__init__(pad_token_id=lowerCamelCase__,		bos_token_id=lowerCamelCase__,		eos_token_id=lowerCamelCase__,		**lowerCamelCase__		)

        A :       List[Any]          =   vocab_size
        A :       Dict          =   hidden_size
        A :       str          =   num_hidden_layers
        A :       List[Any]          =   num_attention_heads
        A :       List[str]          =   hidden_act
        A :       Tuple          =   intermediate_size
        A :       Tuple          =   hidden_dropout_prob
        A :       List[Any]          =   attention_probs_dropout_prob
        A :       Optional[int]          =   max_position_embeddings
        A :       Tuple          =   type_vocab_size
        A :       List[Any]          =   initializer_range
        A :       str          =   layer_norm_eps
        A :       Tuple          =   position_embedding_type
        A :       str          =   use_cache
        A :       Any          =   classifier_dropout






class SCREAMING_SNAKE_CASE__     (		SCREAMING_SNAKE_CASE__    ):




    '''simple docstring'''






    @property
    def 			_lowerCAmelCase					(       self		):
        if self.task == "multiple-choice":
            A :       int          =   {0: """batch""", 1: """choice""", 2: """sequence"""}
        else:
            A :       Tuple          =   {0: """batch""", 1: """sequence"""}
        return OrderedDict(
            [
                ("""input_ids""", dynamic_axis),
                ("""attention_mask""", dynamic_axis),
            ]		)


