

from collections import OrderedDict
from typing import Mapping

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


A_						:     int			  =				logging.get_logger(__name__)

A_						:     str			  =				{
    'flaubert/flaubert_small_cased': 'https://huggingface.co/flaubert/flaubert_small_cased/resolve/main/config.json',
    'flaubert/flaubert_base_uncased': 'https://huggingface.co/flaubert/flaubert_base_uncased/resolve/main/config.json',
    'flaubert/flaubert_base_cased': 'https://huggingface.co/flaubert/flaubert_base_cased/resolve/main/config.json',
    'flaubert/flaubert_large_cased': 'https://huggingface.co/flaubert/flaubert_large_cased/resolve/main/config.json',
}
class    A_				( _a  ):


    '''simple docstring'''






    a__       							=    "flaubert"
    a__       							=    {
        "hidden_size": "emb_dim",
        "num_attention_heads": "n_heads",
        "num_hidden_layers": "n_layers",
        "n_words": "vocab_size",  # For backward compatibility
    }


    def __init__(self       , lowercase__=False       , lowercase__=0.0       , lowercase__=30_145       , lowercase__=2_048       , lowercase__=12       , lowercase__=16       , lowercase__=0.1       , lowercase__=0.1       , lowercase__=True       , lowercase__=False       , lowercase__=False       , lowercase__=False       , lowercase__=1       , lowercase__=True       , lowercase__=512       , lowercase__=2_048**-0.5       , lowercase__=1E-12       , lowercase__=0.02       , lowercase__=0       , lowercase__=1       , lowercase__=2       , lowercase__=3       , lowercase__=5       , lowercase__=True       , lowercase__="first"       , lowercase__=True       , lowercase__=None       , lowercase__=True       , lowercase__=0.1       , lowercase__=5       , lowercase__=5       , lowercase__=0       , lowercase__=0       , lowercase__=2       , lowercase__=0       , **lowercase__       , )       -> Optional[Any]:
        __UpperCAmelCase			   =     pre_norm
        __UpperCAmelCase			   =     layerdrop
        __UpperCAmelCase			   =     vocab_size
        __UpperCAmelCase			   =     emb_dim
        __UpperCAmelCase			   =     n_layers
        __UpperCAmelCase			   =     n_heads
        __UpperCAmelCase			   =     dropout
        __UpperCAmelCase			   =     attention_dropout
        __UpperCAmelCase			   =     gelu_activation
        __UpperCAmelCase			   =     sinusoidal_embeddings
        __UpperCAmelCase			   =     causal
        __UpperCAmelCase			   =     asm
        __UpperCAmelCase			   =     n_langs
        __UpperCAmelCase			   =     use_lang_emb
        __UpperCAmelCase			   =     layer_norm_eps
        __UpperCAmelCase			   =     bos_index
        __UpperCAmelCase			   =     eos_index
        __UpperCAmelCase			   =     pad_index
        __UpperCAmelCase			   =     unk_index
        __UpperCAmelCase			   =     mask_index
        __UpperCAmelCase			   =     is_encoder
        __UpperCAmelCase			   =     max_position_embeddings
        __UpperCAmelCase			   =     embed_init_std
        __UpperCAmelCase			   =     init_std
        __UpperCAmelCase			   =     summary_type
        __UpperCAmelCase			   =     summary_use_proj
        __UpperCAmelCase			   =     summary_activation
        __UpperCAmelCase			   =     summary_proj_to_labels
        __UpperCAmelCase			   =     summary_first_dropout
        __UpperCAmelCase			   =     start_n_top
        __UpperCAmelCase			   =     end_n_top
        __UpperCAmelCase			   =     mask_token_id
        __UpperCAmelCase			   =     lang_id

        if "n_words" in kwargs:
            __UpperCAmelCase			   =     kwargs['''n_words''']

        super().__init__(pad_token_id=lowercase__       , bos_token_id=lowercase__       , **lowercase__					)






class    A_				( _a  ):


    '''simple docstring'''






    @property
    def        lowerCAmelCase_  (self					)       -> Mapping[str, Mapping[int, str]]:
        if self.task == "multiple-choice":
            __UpperCAmelCase			   =     {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
        else:
            __UpperCAmelCase			   =     {0: '''batch''', 1: '''sequence'''}
        return OrderedDict(
            [
                ('''input_ids''', dynamic_axis),
                ('''attention_mask''', dynamic_axis),
            ]					)







# 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/




