

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


A_						:     Tuple			  =				logging.get_logger(__name__)

A_						:     List[Any]			  =				{
    'google/reformer-crime-and-punishment': (
        'https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/config.json'
    ),
    'google/reformer-enwik8': 'https://huggingface.co/google/reformer-enwik8/resolve/main/config.json',
}
class    A_				( _a  ):


    '''simple docstring'''






    a__       							=    "reformer"
    a__       							=    ["past_buckets_states"]
    a__       							=    {}


    def __init__(self       , lowercase__=64       , lowercase__=["local", "lsh", "local", "lsh", "local", "lsh"]       , lowercase__=1.0       , lowercase__=True       , lowercase__=[64, 64]       , lowercase__=[64, 192]       , lowercase__=0       , lowercase__=2       , lowercase__=512       , lowercase__=None       , lowercase__="relu"       , lowercase__=0.05       , lowercase__=256       , lowercase__=0.02       , lowercase__=False       , lowercase__=1E-12       , lowercase__=1       , lowercase__=0       , lowercase__=0.05       , lowercase__=64       , lowercase__=64       , lowercase__=0.0       , lowercase__=1       , lowercase__=0       , lowercase__=4_096       , lowercase__=12       , lowercase__=None       , lowercase__=1       , lowercase__=0       , lowercase__=320       , lowercase__=False       , lowercase__=True       , lowercase__=None       , **lowercase__       , )       -> Optional[Any]:
        __UpperCAmelCase			   =     hash_seed
        __UpperCAmelCase			   =     vocab_size
        __UpperCAmelCase			   =     attention_head_size
        __UpperCAmelCase			   =     hidden_size
        __UpperCAmelCase			   =     num_attention_heads
        __UpperCAmelCase			   =     num_hashes
        __UpperCAmelCase			   =     len(lowercase__					)
        __UpperCAmelCase			   =     tuple(lowercase__					) if isinstance(lowercase__       , lowercase__					) else num_buckets
        __UpperCAmelCase			   =     lsh_attn_chunk_length
        __UpperCAmelCase			   =     local_attn_chunk_length
        __UpperCAmelCase			   =     lsh_num_chunks_after
        __UpperCAmelCase			   =     lsh_num_chunks_before
        __UpperCAmelCase			   =     local_num_chunks_after
        __UpperCAmelCase			   =     local_num_chunks_before
        __UpperCAmelCase			   =     hidden_act
        __UpperCAmelCase			   =     feed_forward_size
        __UpperCAmelCase			   =     hidden_dropout_prob
        __UpperCAmelCase			   =     lsh_attention_probs_dropout_prob
        __UpperCAmelCase			   =     local_attention_probs_dropout_prob
        __UpperCAmelCase			   =     max_position_embeddings
        __UpperCAmelCase			   =     initializer_range
        __UpperCAmelCase			   =     layer_norm_eps
        __UpperCAmelCase			   =     axial_pos_embds
        __UpperCAmelCase			   =     tuple(lowercase__					)
        __UpperCAmelCase			   =     tuple(lowercase__					)
        __UpperCAmelCase			   =     axial_norm_std
        __UpperCAmelCase			   =     chunk_size_lm_head
        __UpperCAmelCase			   =     attn_layers
        __UpperCAmelCase			   =     use_cache
        __UpperCAmelCase			   =     classifier_dropout
        super().__init__(
            pad_token_id=lowercase__       , eos_token_id=lowercase__       , is_decoder=lowercase__       , tie_word_embeddings=lowercase__       , **lowercase__       , )







# 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/




