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


UpperCAmelCase							   =      logging.get_logger(__name__)

UpperCAmelCase							   =      {
    """microsoft/mpnet-base""": """https://huggingface.co/microsoft/mpnet-base/resolve/main/config.json""",
}






class 							_SCREAMING_SNAKE_CASE(   snake_case__	):
   UpperCamelCase_				:						Any 			=   'mpnet'
   def __init__(    self							,     lowerCAmelCase_=3_05_27							,     lowerCAmelCase_=7_68							,     lowerCAmelCase_=12							,     lowerCAmelCase_=12							,     lowerCAmelCase_=30_72							,     lowerCAmelCase_="gelu"							,     lowerCAmelCase_=0.1							,     lowerCAmelCase_=0.1							,     lowerCAmelCase_=5_12							,     lowerCAmelCase_=0.02							,     lowerCAmelCase_=1e-12							,     lowerCAmelCase_=32							,     lowerCAmelCase_=1							,     lowerCAmelCase_=0							,     lowerCAmelCase_=2							,     **lowerCAmelCase_							,     )	->Optional[Any]:
        super().__init__(pad_token_id=lowerCAmelCase_							,     bos_token_id=lowerCAmelCase_							,     eos_token_id=lowerCAmelCase_							,     **lowerCAmelCase_    )

        a  :    str				=						vocab_size
        a  :    Optional[int]				=						hidden_size
        a  :    Tuple				=						num_hidden_layers
        a  :    Optional[Any]				=						num_attention_heads
        a  :    Union[str, Any]				=						hidden_act
        a  :    Dict				=						intermediate_size
        a  :    Dict				=						hidden_dropout_prob
        a  :    Optional[int]				=						attention_probs_dropout_prob
        a  :    Tuple				=						max_position_embeddings
        a  :    str				=						initializer_range
        a  :    List[Any]				=						layer_norm_eps
        a  :    List[str]				=						relative_attention_num_buckets
# this script builds a small sample spm file tests/fixtures/test_sentencepiece_no_bos.model, with features needed by pegasus

# 1. pip install sentencepiece
#
# 2. wget https://raw.githubusercontent.com/google/sentencepiece/master/data/botchan.txt

# 3. build
import sentencepiece as spm

# pegasus:
# 1. no bos
# 2. eos_id is 1
# 3. unk_id is 2
# build a sample spm file accordingly
spm.SentencePieceTrainer.train(
    """--input=botchan.txt --model_prefix=test_sentencepiece_no_bos --bos_id=-1 --unk_id=2  --eos_id=1  --vocab_size=1000"""
)

# 4. now update the fixture
# mv test_sentencepiece_no_bos.model ../../tests/fixtures/