






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)

lowerCamelCase			:					List[str]         =	_symbol_database.Default()


lowerCamelCase			:					Union[str, Any]         =	_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'''
)

lowerCamelCase			:					List[str]         =	globals()
_builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, _globals)
_builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, '''sentencepiece_model_pb2''', _globals)
if _descriptor._USE_C_DESCRIPTORS is False:
 lowerCamelCase			:					Optional[Any]         =	None
 lowerCamelCase			:					int         =	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"
 lowerCamelCase			:					Tuple         =	45
 lowerCamelCase			:					int         =	1581
 lowerCamelCase			:					Union[str, Any]         =	1517
 lowerCamelCase			:					str         =	1570
 lowerCamelCase			:					Any         =	1584
 lowerCamelCase			:					Any         =	1793
 lowerCamelCase			:					Any         =	1795
 lowerCamelCase			:					Optional[Any]         =	1916
 lowerCamelCase			:					int         =	1864
 lowerCamelCase			:					Optional[int]         =	1905
 lowerCamelCase			:					List[Any]         =	1919
 lowerCamelCase			:					Dict         =	2429
 lowerCamelCase			:					List[Any]         =	2208
 lowerCamelCase			:					Any         =	2418
 lowerCamelCase			:					Optional[int]         =	2323
 lowerCamelCase			:					Optional[Any]         =	2407
# @@protoc_insertion_point(module_scope)














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

from packaging import version


if TYPE_CHECKING:
 from ... import PreTrainedTokenizer, TensorType

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


lowerCamelCase			:					List[str]         =	logging.get_logger(__name__)

lowerCamelCase			:					Any         =	{
    '''bigscience/bloom''': '''https://huggingface.co/bigscience/bloom/resolve/main/config.json''',
    '''bigscience/bloom-560m''': '''https://huggingface.co/bigscience/bloom-560m/blob/main/config.json''',
    '''bigscience/bloom-1b1''': '''https://huggingface.co/bigscience/bloom-1b1/blob/main/config.json''',
    '''bigscience/bloom-1b7''': '''https://huggingface.co/bigscience/bloom-1b7/blob/main/config.json''',
    '''bigscience/bloom-3b''': '''https://huggingface.co/bigscience/bloom-3b/blob/main/config.json''',
    '''bigscience/bloom-7b1''': '''https://huggingface.co/bigscience/bloom-7b1/blob/main/config.json''',
}



class 							_UpperCamelCase			(a_			):
      snake_case_  =							"""bloom"""
      snake_case_  =							["""past_key_values"""]
      snake_case_  =							{
          """num_hidden_layers""": """n_layer""",
          """num_attention_heads""": """n_head""",
      }


      def __init__(	self					,						__UpperCamelCase=2_5_0_8_8_0					,						__UpperCamelCase=6_4					,						__UpperCamelCase=2					,						__UpperCamelCase=8					,						__UpperCamelCase=1e-5					,						__UpperCamelCase=0.0_2					,						__UpperCamelCase=True					,						__UpperCamelCase=1					,						__UpperCamelCase=2					,						__UpperCamelCase=False					,						__UpperCamelCase=0.0					,						__UpperCamelCase=0.0					,						__UpperCamelCase=1					,						__UpperCamelCase=False					,						**__UpperCamelCase					,						)->	Dict:
           __lowerCAmelCase								=					vocab_size
           # Backward compatibility with n_embed kwarg
           __lowerCAmelCase								=					kwargs.pop("n_embed"					,						__UpperCamelCase	)
           __lowerCAmelCase								=					hidden_size if n_embed is None else n_embed
           __lowerCAmelCase								=					n_layer
           __lowerCAmelCase								=					n_head
           __lowerCAmelCase								=					layer_norm_epsilon
           __lowerCAmelCase								=					initializer_range
           __lowerCAmelCase								=					use_cache
           __lowerCAmelCase								=					pretraining_tp
           __lowerCAmelCase								=					apply_residual_connection_post_layernorm
           __lowerCAmelCase								=					hidden_dropout
           __lowerCAmelCase								=					attention_dropout

           __lowerCAmelCase								=					bos_token_id
           __lowerCAmelCase								=					eos_token_id
           __lowerCAmelCase								=					slow_but_exact

           super().__init__(bos_token_id=__UpperCamelCase					,						eos_token_id=__UpperCamelCase					,						**__UpperCamelCase	)



class 							_UpperCamelCase			(a_			):
      snake_case_  =							version.parse("""1.12"""			)


      def __init__(	self					,						__UpperCamelCase					,						__UpperCamelCase = "default"					,						__UpperCamelCase = None					,						__UpperCamelCase = False					,						)->	str:
           super().__init__(__UpperCamelCase					,						task=__UpperCamelCase					,						patching_specs=__UpperCamelCase					,						use_past=__UpperCamelCase	)
           if not getattr(self._config					,						"pad_token_id"					,						__UpperCamelCase	):
                # TODO: how to do that better?
                __lowerCAmelCase								=					0


      @property
      def  __UpperCAmelCase		(	self	)->	Mapping[str, Mapping[int, str]]:
           __lowerCAmelCase								=					OrderedDict({"input_ids": {0: "batch", 1: "sequence"}}	)
           if self.use_past:
                # BLOOM stores values on dynamic axis 2. For more details see: https://github.com/huggingface/transformers/pull/18344
                self.fill_with_past_key_values_(__UpperCamelCase					,						direction="inputs"					,						inverted_values_shape=__UpperCamelCase	)
                __lowerCAmelCase								=					{0: "batch", 1: "past_sequence + sequence"}
           else:
                __lowerCAmelCase								=					{0: "batch", 1: "sequence"}

           return common_inputs


      @property
      def  __UpperCAmelCase		(	self	)->	int:
           return self._config.n_layer


      @property
      def  __UpperCAmelCase		(	self	)->	int:
           return self._config.n_head


      @property
      def  __UpperCAmelCase		(	self	)->	float:
           return 1e-3


      def  __UpperCAmelCase		(	self					,						__UpperCamelCase					,						__UpperCamelCase = -1					,						__UpperCamelCase = -1					,						__UpperCamelCase = False					,						__UpperCamelCase = None					,						)->	Mapping[str, Any]:
           __lowerCAmelCase								=					super(__UpperCamelCase					,						self	).generate_dummy_inputs(
               __UpperCamelCase					,						batch_size=__UpperCamelCase					,						seq_length=__UpperCamelCase					,						is_pair=__UpperCamelCase					,						framework=__UpperCamelCase	)

           # We need to order the input in the way they appears in the forward()
           __lowerCAmelCase								=					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

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

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

           return ordered_inputs


      @property
      def  __UpperCAmelCase		(	self	)->	int:
           return 1_3






