


import warnings

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


_SCREAMING_SNAKE_CASE      :			Tuple      				=   logging.get_logger(__name__)

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

class   A__ (   snake_case__     ):







  """simple docstring"""

  __magic_name__        =		'mvp'
  __magic_name__        =		['past_key_values']
  __magic_name__        =		{'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}
  def __init__(	self      , __snake_case=5_0_2_6_7      , __snake_case=1_0_2_4      , __snake_case=1_2      , __snake_case=4_0_9_6      , __snake_case=1_6      , __snake_case=1_2      , __snake_case=4_0_9_6      , __snake_case=1_6      , __snake_case=0.0      , __snake_case=0.0      , __snake_case="gelu"      , __snake_case=1_0_2_4      , __snake_case=0.1      , __snake_case=0.0      , __snake_case=0.0      , __snake_case=0.02      , __snake_case=0.0      , __snake_case=False      , __snake_case=True      , __snake_case=1      , __snake_case=0      , __snake_case=2      , __snake_case=True      , __snake_case=2      , __snake_case=2      , __snake_case=False      , __snake_case=1_0_0      , __snake_case=8_0_0      , **__snake_case      , ):
       snake_case					=    vocab_size
       snake_case					=    max_position_embeddings
       snake_case					=    d_model
       snake_case					=    encoder_ffn_dim
       snake_case					=    encoder_layers
       snake_case					=    encoder_attention_heads
       snake_case					=    decoder_ffn_dim
       snake_case					=    decoder_layers
       snake_case					=    decoder_attention_heads
       snake_case					=    dropout
       snake_case					=    attention_dropout
       snake_case					=    activation_dropout
       snake_case					=    activation_function
       snake_case					=    init_std
       snake_case					=    encoder_layerdrop
       snake_case					=    decoder_layerdrop
       snake_case					=    classifier_dropout
       snake_case					=    use_cache
       snake_case					=    encoder_layers
       snake_case					=    scale_embedding  # scale factor will be sqrt(d_model) if True
       snake_case					=    use_prompt
       snake_case					=    prompt_length
       snake_case					=    prompt_mid_dim

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

       if self.forced_bos_token_id is None and kwargs.get('''force_bos_token_to_be_generated'''      , __snake_case   ):
            snake_case					=    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 ...configuration_utils import PretrainedConfig
from ...utils import logging


_SCREAMING_SNAKE_CASE      :			Dict      				=   logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE      :			Optional[Any]      				=   {
    "alibaba-damo/mgp-str-base": "https://huggingface.co/alibaba-damo/mgp-str-base/resolve/main/config.json",
}

class   A__ (   snake_case__     ):







    """simple docstring"""

    __magic_name__        =		'mgp-str'
    def __init__(	self      , __snake_case=[3_2, 1_2_8]      , __snake_case=4      , __snake_case=3      , __snake_case=2_7      , __snake_case=3_8      , __snake_case=5_0_2_5_7      , __snake_case=3_0_5_2_2      , __snake_case=7_6_8      , __snake_case=1_2      , __snake_case=1_2      , __snake_case=4.0      , __snake_case=True      , __snake_case=False      , __snake_case=1E-5      , __snake_case=0.0      , __snake_case=0.0      , __snake_case=0.0      , __snake_case=False      , __snake_case=0.02      , **__snake_case      , ):
         super().__init__(**__snake_case   )

         snake_case					=    image_size
         snake_case					=    patch_size
         snake_case					=    num_channels
         snake_case					=    max_token_length
         snake_case					=    num_character_labels
         snake_case					=    num_bpe_labels
         snake_case					=    num_wordpiece_labels
         snake_case					=    hidden_size
         snake_case					=    num_hidden_layers
         snake_case					=    num_attention_heads
         snake_case					=    mlp_ratio
         snake_case					=    distilled
         snake_case					=    layer_norm_eps
         snake_case					=    drop_rate
         snake_case					=    qkv_bias
         snake_case					=    attn_drop_rate
         snake_case					=    drop_path_rate
         snake_case					=    output_aa_attentions
         snake_case					=    initializer_range




