
"""simple docstring"""






from google.protobuf import descriptor as _descriptor
from google.protobuf import descriptor_pool as _descriptor_pool
from google.protobuf import symbol_database as _symbol_database
from google.protobuf.internal import builder as _builder


# @@protoc_insertion_point(imports)

_lowercase							=				_symbol_database.Default()


_lowercase							=				_descriptor_pool.Default().AddSerializedFile(
    B'''\n\x19sentencepiece_model.proto\x12\rsentencepiece"\x80\x0c\n\x0bTrainerSpec\x12\r\n\x05input\x18\x01 \x03(\t\x12\x14\n\x0cinput_format\x18\x07 \x01(\t\x12\x14\n\x0cmodel_prefix\x18\x02 \x01(\t\x12\x41\n\nmodel_type\x18\x03 \x01(\x0e\x32$.sentencepiece.TrainerSpec.ModelType:\x07UNIGRAM\x12\x18\n\nvocab_size\x18\x04 \x01(\x05:\x04\x38\x30\x30\x30\x12\x17\n\x0f\x61\x63\x63\x65pt_language\x18\x05 \x03(\t\x12 \n\x15self_test_sample_size\x18\x06 \x01(\x05:\x01\x30\x12*\n\x1b\x65nable_differential_privacy\x18\x32 \x01(\x08:\x05\x66\x61lse\x12+\n differential_privacy_noise_level\x18\x33 \x01(\x02:\x01\x30\x12\x32\n\'differential_privacy_clipping_threshold\x18\x34 \x01(\x04:\x01\x30\x12"\n\x12\x63haracter_coverage\x18\n \x01(\x02:\x06\x30.9995\x12\x1e\n\x13input_sentence_size\x18\x0b \x01(\x04:\x01\x30\x12$\n\x16shuffle_input_sentence\x18\x13 \x01(\x08:\x04true\x12 \n\x14mining_sentence_size\x18\x0c \x01(\x05\x42\x02\x18\x01\x12"\n\x16training_sentence_size\x18\r \x01(\x05\x42\x02\x18\x01\x12(\n\x17seed_sentencepiece_size\x18\x0e \x01(\x05:\x07\x31\x30\x30\x30\x30\x30\x30\x12\x1e\n\x10shrinking_factor\x18\x0f \x01(\x02:\x04\x30.75\x12!\n\x13max_sentence_length\x18\x12 \x01(\x05:\x04\x34\x31\x39\x32\x12\x17\n\x0bnum_threads\x18\x10 \x01(\x05:\x02\x31\x36\x12\x1d\n\x12num_sub_iterations\x18\x11 \x01(\x05:\x01\x32\x12$\n\x18max_sentencepiece_length\x18\x14 \x01(\x05:\x02\x31\x36\x12%\n\x17split_by_unicode_script\x18\x15 \x01(\x08:\x04true\x12\x1d\n\x0fsplit_by_number\x18\x17 \x01(\x08:\x04true\x12!\n\x13split_by_whitespace\x18\x16 \x01(\x08:\x04true\x12)\n\x1atreat_whitespace_as_suffix\x18\x18 \x01(\x08:\x05\x66\x61lse\x12+\n\x1c\x61llow_whitespace_only_pieces\x18\x1a \x01(\x08:\x05\x66\x61lse\x12\x1b\n\x0csplit_digits\x18\x19 \x01(\x08:\x05\x66\x61lse\x12#\n\x19pretokenization_delimiter\x18\x35 \x01(\t:\x00\x12\x17\n\x0f\x63ontrol_symbols\x18\x1e \x03(\t\x12\x1c\n\x14user_defined_symbols\x18\x1f \x03(\t\x12\x16\n\x0erequired_chars\x18$ \x01(\t\x12\x1c\n\rbyte_fallback\x18# \x01(\x08:\x05\x66\x61lse\x12+\n\x1dvocabulary_output_piece_score\x18  \x01(\x08:\x04true\x12\x1e\n\x10hard_vocab_limit\x18! \x01(\x08:\x04true\x12\x1c\n\ruse_all_vocab\x18" \x01(\x08:\x05\x66\x61lse\x12\x11\n\x06unk_id\x18( \x01(\x05:\x01\x30\x12\x11\n\x06\x62os_id\x18) \x01(\x05:\x01\x31\x12\x11\n\x06\x65os_id\x18* \x01(\x05:\x01\x32\x12\x12\n\x06pad_id\x18+ \x01(\x05:\x02-1\x12\x18\n\tunk_piece\x18- \x01(\t:\x05<unk>\x12\x16\n\tbos_piece\x18. \x01(\t:\x03<s>\x12\x17\n\teos_piece\x18/ \x01(\t:\x04</s>\x12\x18\n\tpad_piece\x18\x30 \x01(\t:\x05<pad>\x12\x1a\n\x0bunk_surface\x18, \x01(\t:\x05 \xe2\x81\x87 \x12+\n\x1ctrain_extremely_large_corpus\x18\x31 \x01(\x08:\x05\x66\x61lse"5\n\tModelType\x12\x0b\n\x07UNIGRAM\x10\x01\x12\x07\n\x03\x42PE\x10\x02\x12\x08\n\x04WORD\x10\x03\x12\x08\n\x04\x43HAR\x10\x04*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02"\xd1\x01\n\x0eNormalizerSpec\x12\x0c\n\x04name\x18\x01 \x01(\t\x12\x1c\n\x14precompiled_charsmap\x18\x02 \x01(\x0c\x12\x1e\n\x10\x61\x64\x64_dummy_prefix\x18\x03 \x01(\x08:\x04true\x12&\n\x18remove_extra_whitespaces\x18\x04 \x01(\x08:\x04true\x12 \n\x12\x65scape_whitespaces\x18\x05 \x01(\x08:\x04true\x12\x1e\n\x16normalization_rule_tsv\x18\x06 \x01(\t*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02"y\n\x0cSelfTestData\x12\x33\n\x07samples\x18\x01 \x03(\x0b\x32".sentencepiece.SelfTestData.Sample\x1a)\n\x06Sample\x12\r\n\x05input\x18\x01 \x01(\t\x12\x10\n\x08\x65xpected\x18\x02 \x01(\t*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02"\xfe\x03\n\nModelProto\x12\x37\n\x06pieces\x18\x01 \x03(\x0b\x32\'.sentencepiece.ModelProto.SentencePiece\x12\x30\n\x0ctrainer_spec\x18\x02 \x01(\x0b\x32\x1a.sentencepiece.TrainerSpec\x12\x36\n\x0fnormalizer_spec\x18\x03 \x01(\x0b\x32\x1d.sentencepiece.NormalizerSpec\x12\x33\n\x0eself_test_data\x18\x04 \x01(\x0b\x32\x1b.sentencepiece.SelfTestData\x12\x38\n\x11\x64\x65normalizer_spec\x18\x05 \x01(\x0b\x32\x1d.sentencepiece.NormalizerSpec\x1a\xd2\x01\n\rSentencePiece\x12\r\n\x05piece\x18\x01 \x01(\t\x12\r\n\x05score\x18\x02 \x01(\x02\x12\x42\n\x04type\x18\x03 \x01(\x0e\x32,.sentencepiece.ModelProto.SentencePiece.Type:\x06NORMAL"T\n\x04Type\x12\n\n\x06NORMAL\x10\x01\x12\x0b\n\x07UNKNOWN\x10\x02\x12\x0b\n\x07\x43ONTROL\x10\x03\x12\x10\n\x0cUSER_DEFINED\x10\x04\x12\x08\n\x04\x42YTE\x10\x06\x12\n\n\x06UNUSED\x10\x05*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02\x42\x02H\x03'''
)

_lowercase							=				globals()
_builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, _globals)
_builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, '''sentencepiece_model_pb2''', _globals)
if _descriptor._USE_C_DESCRIPTORS is False:
					_lowercase							=				None
					_lowercase							=				B'''H\003'''
					# (generated by protobuf compiler, but `_TRAINERSPEC` is not defined)
					# _TRAINERSPEC.fields_by_name["mining_sentence_size"]._options = None
					# _TRAINERSPEC.fields_by_name["mining_sentence_size"]._serialized_options = b"\030\001"
					# _TRAINERSPEC.fields_by_name["training_sentence_size"]._options = None
					# _TRAINERSPEC.fields_by_name["training_sentence_size"]._serialized_options = b"\030\001"
					_lowercase							=				45
					_lowercase							=				1_581
					_lowercase							=				1_517
					_lowercase							=				1_570
					_lowercase							=				1_584
					_lowercase							=				1_793
					_lowercase							=				1_795
					_lowercase							=				1_916
					_lowercase							=				1_864
					_lowercase							=				1_905
					_lowercase							=				1_919
					_lowercase							=				2_429
					_lowercase							=				2_208
					_lowercase							=				2_418
					_lowercase							=				2_323
					_lowercase							=				2_407
# @@protoc_insertion_point(module_scope)







"""simple docstring"""






from collections import OrderedDict
from typing import Any, List, Mapping, Optional

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging


_lowercase							=				logging.get_logger(__name__)


_lowercase							=				{
    '''Salesforce/codegen-350M-nl''': '''https://huggingface.co/Salesforce/codegen-350M-nl/resolve/main/config.json''',
    '''Salesforce/codegen-350M-multi''': '''https://huggingface.co/Salesforce/codegen-350M-multi/resolve/main/config.json''',
    '''Salesforce/codegen-350M-mono''': '''https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/config.json''',
    '''Salesforce/codegen-2B-nl''': '''https://huggingface.co/Salesforce/codegen-2B-nl/resolve/main/config.json''',
    '''Salesforce/codegen-2B-multi''': '''https://huggingface.co/Salesforce/codegen-2B-multi/resolve/main/config.json''',
    '''Salesforce/codegen-2B-mono''': '''https://huggingface.co/Salesforce/codegen-2B-mono/resolve/main/config.json''',
    '''Salesforce/codegen-6B-nl''': '''https://huggingface.co/Salesforce/codegen-6B-nl/resolve/main/config.json''',
    '''Salesforce/codegen-6B-multi''': '''https://huggingface.co/Salesforce/codegen-6B-multi/resolve/main/config.json''',
    '''Salesforce/codegen-6B-mono''': '''https://huggingface.co/Salesforce/codegen-6B-mono/resolve/main/config.json''',
    '''Salesforce/codegen-16B-nl''': '''https://huggingface.co/Salesforce/codegen-16B-nl/resolve/main/config.json''',
    '''Salesforce/codegen-16B-multi''': '''https://huggingface.co/Salesforce/codegen-16B-multi/resolve/main/config.json''',
    '''Salesforce/codegen-16B-mono''': '''https://huggingface.co/Salesforce/codegen-16B-mono/resolve/main/config.json''',
}

class 					__a     ( __a       ):



							'''simple docstring'''







							_lowerCamelCase					:			List[Any]          =   """codegen"""
							_lowerCamelCase					:			List[str]          =   {
							    """max_position_embeddings""": """n_positions""",
							    """hidden_size""": """n_embd""",
							    """num_attention_heads""": """n_head""",
							    """num_hidden_layers""": """n_layer""",
							}
							def __init__(     self  ,    _lowerCamelCase=50_400  ,    _lowerCamelCase=2_048  ,    _lowerCamelCase=2_048  ,    _lowerCamelCase=4_096  ,    _lowerCamelCase=28  ,    _lowerCamelCase=16  ,    _lowerCamelCase=64  ,    _lowerCamelCase=None  ,    _lowerCamelCase="gelu_new"  ,    _lowerCamelCase=0.0  ,    _lowerCamelCase=0.0  ,    _lowerCamelCase=0.0  ,    _lowerCamelCase=1e-5  ,    _lowerCamelCase=0.02  ,    _lowerCamelCase=True  ,    _lowerCamelCase=50_256  ,    _lowerCamelCase=50_256  ,    _lowerCamelCase=False  ,    **_lowerCamelCase  ,    )			->							Union[str, Any]:




										'''simple docstring'''
										__lowercase       =       vocab_size
										__lowercase       =       n_ctx
										__lowercase       =       n_positions
										__lowercase       =       n_embd
										__lowercase       =       n_layer
										__lowercase       =       n_head
										__lowercase       =       n_inner
										__lowercase       =       rotary_dim
										__lowercase       =       activation_function
										__lowercase       =       resid_pdrop
										__lowercase       =       embd_pdrop
										__lowercase       =       attn_pdrop
										__lowercase       =       layer_norm_epsilon
										__lowercase       =       initializer_range
										__lowercase       =       use_cache

										__lowercase       =       bos_token_id
										__lowercase       =       eos_token_id

										super().__init__(
										    bos_token_id=_lowerCamelCase  ,    eos_token_id=_lowerCamelCase  ,    tie_word_embeddings=_lowerCamelCase  ,    **_lowerCamelCase       )





class 					__a     ( __a       ):



							'''simple docstring'''







							def __init__(     self  ,    _lowerCamelCase  ,    _lowerCamelCase = "default"  ,    _lowerCamelCase = None  ,    _lowerCamelCase = False  ,    )			->							str:




										'''simple docstring'''
										super().__init__(_lowerCamelCase  ,    task=_lowerCamelCase  ,    patching_specs=_lowerCamelCase  ,    use_past=_lowerCamelCase       )
										if not getattr(self._config  ,    "pad_token_id"  ,    _lowerCamelCase       ):
													# TODO: how to do that better?
													__lowercase       =       0
							@property
							def   SCREAMING_SNAKE_CASE						(     self       )			->							Mapping[str, Mapping[int, str]]:




										'''simple docstring'''
										__lowercase       =       OrderedDict({"input_ids": {0: "batch", 1: "sequence"}}       )
										if self.use_past:
													self.fill_with_past_key_values_(_lowerCamelCase  ,    direction="inputs"       )
													__lowercase       =       {0: "batch", 1: "past_sequence + sequence"}
										else:
													__lowercase       =       {0: "batch", 1: "sequence"}

										return common_inputs
							@property
							def   SCREAMING_SNAKE_CASE						(     self       )			->							int:




										'''simple docstring'''
										return self._config.n_layer
							@property
							def   SCREAMING_SNAKE_CASE						(     self       )			->							int:




										'''simple docstring'''
										return self._config.n_head
							def   SCREAMING_SNAKE_CASE						(     self  ,    _lowerCamelCase  ,    _lowerCamelCase = -1  ,    _lowerCamelCase = -1  ,    _lowerCamelCase = False  ,    _lowerCamelCase = None  ,    )			->							Mapping[str, Any]:




										'''simple docstring'''
										__lowercase       =       super(_lowerCamelCase  ,    self       ).generate_dummy_inputs(
										    _lowerCamelCase  ,    batch_size=_lowerCamelCase  ,    seq_length=_lowerCamelCase  ,    is_pair=_lowerCamelCase  ,    framework=_lowerCamelCase       )

										# We need to order the input in the way they appears in the forward()
										__lowercase       =       OrderedDict({"input_ids": common_inputs["input_ids"]}       )

										# Need to add the past_keys
										if self.use_past:
													if not is_torch_available():
																raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed."       )
													else:
																import torch

																__lowercase   ,					__lowercase       =       common_inputs["input_ids"].shape
																# Not using the same length for past_key_values
																__lowercase       =       seqlen + 2
																__lowercase       =       (
																    batch,
																    self.num_attention_heads,
																    past_key_values_length,
																    self._config.hidden_size // self.num_attention_heads,
																)
																__lowercase       =       [
																    (torch.zeros(_lowerCamelCase       ), torch.zeros(_lowerCamelCase       )) for _ in range(self.num_layers       )
																]

										__lowercase       =       common_inputs["attention_mask"]
										if self.use_past:
													__lowercase       =       ordered_inputs["attention_mask"].dtype
													__lowercase       =       torch.cat(
													    [ordered_inputs["attention_mask"], torch.ones(_lowerCamelCase  ,    _lowerCamelCase  ,    dtype=_lowerCamelCase       )]  ,    dim=1       )

										return ordered_inputs





							@property
							def   SCREAMING_SNAKE_CASE						(     self       )			->							int:




										'''simple docstring'''
										return 13





