


'''simple docstring'''






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


_A      =     logging.get_logger(__name__)

_A      =     {
    """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      SCREAMING_SNAKE_CASE_	( snake_case		):
			__a					:      str					=  '''reformer'''
			__a					:      Tuple					=  ['''past_buckets_states''']
			__a					:      Tuple					=  {}




			def __init__(      self					,      lowercase=6_4					,      lowercase=["local", "lsh", "local", "lsh", "local", "lsh"]					,      lowercase=1.0					,      lowercase=True					,      lowercase=[6_4, 6_4]					,      lowercase=[6_4, 1_9_2]					,      lowercase=0					,      lowercase=2					,      lowercase=5_1_2					,      lowercase=None					,      lowercase="relu"					,      lowercase=0.0_5					,      lowercase=2_5_6					,      lowercase=0.0_2					,      lowercase=False					,      lowercase=1e-12					,      lowercase=1					,      lowercase=0					,      lowercase=0.0_5					,      lowercase=6_4					,      lowercase=6_4					,      lowercase=0.0					,      lowercase=1					,      lowercase=0					,      lowercase=4_0_9_6					,      lowercase=1_2					,      lowercase=None					,      lowercase=1					,      lowercase=0					,      lowercase=3_2_0					,      lowercase=False					,      lowercase=True					,      lowercase=None					,      **lowercase					,      )			->						Optional[Any]:


								'''simple docstring'''






								__SCREAMING_SNAKE_CASE					:							List[Any]			   = hash_seed
								__SCREAMING_SNAKE_CASE					:							Dict			   = vocab_size
								__SCREAMING_SNAKE_CASE					:							Tuple			   = attention_head_size
								__SCREAMING_SNAKE_CASE					:							str			   = hidden_size
								__SCREAMING_SNAKE_CASE					:							List[Any]			   = num_attention_heads
								__SCREAMING_SNAKE_CASE					:							Any			   = num_hashes
								__SCREAMING_SNAKE_CASE					:							Tuple			   = len(lowercase       )
								__SCREAMING_SNAKE_CASE					:							Optional[int]			   = tuple(lowercase       ) if isinstance(lowercase					,      lowercase       ) else num_buckets
								__SCREAMING_SNAKE_CASE					:							List[str]			   = lsh_attn_chunk_length
								__SCREAMING_SNAKE_CASE					:							Tuple			   = local_attn_chunk_length
								__SCREAMING_SNAKE_CASE					:							Optional[int]			   = lsh_num_chunks_after
								__SCREAMING_SNAKE_CASE					:							Tuple			   = lsh_num_chunks_before
								__SCREAMING_SNAKE_CASE					:							Tuple			   = local_num_chunks_after
								__SCREAMING_SNAKE_CASE					:							Dict			   = local_num_chunks_before
								__SCREAMING_SNAKE_CASE					:							Union[str, Any]			   = hidden_act
								__SCREAMING_SNAKE_CASE					:							Any			   = feed_forward_size
								__SCREAMING_SNAKE_CASE					:							Any			   = hidden_dropout_prob
								__SCREAMING_SNAKE_CASE					:							List[Any]			   = lsh_attention_probs_dropout_prob
								__SCREAMING_SNAKE_CASE					:							Tuple			   = local_attention_probs_dropout_prob
								__SCREAMING_SNAKE_CASE					:							Union[str, Any]			   = max_position_embeddings
								__SCREAMING_SNAKE_CASE					:							Optional[Any]			   = initializer_range
								__SCREAMING_SNAKE_CASE					:							Any			   = layer_norm_eps
								__SCREAMING_SNAKE_CASE					:							List[str]			   = axial_pos_embds
								__SCREAMING_SNAKE_CASE					:							Optional[Any]			   = tuple(lowercase       )
								__SCREAMING_SNAKE_CASE					:							List[Any]			   = tuple(lowercase       )
								__SCREAMING_SNAKE_CASE					:							Dict			   = axial_norm_std
								__SCREAMING_SNAKE_CASE					:							Any			   = chunk_size_lm_head
								__SCREAMING_SNAKE_CASE					:							Dict			   = attn_layers
								__SCREAMING_SNAKE_CASE					:							str			   = use_cache
								__SCREAMING_SNAKE_CASE					:							Tuple			   = classifier_dropout
								super().__init__(
								    pad_token_id=lowercase					,      eos_token_id=lowercase					,      is_decoder=lowercase					,      tie_word_embeddings=lowercase					,      **lowercase					,      )






'''simple docstring'''






# 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/


