

"""simple docstring"""





import torch
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss

from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.bert.modeling_bert import (
    BERT_INPUTS_DOCSTRING,
    BERT_START_DOCSTRING,
    BertEmbeddings,
    BertLayer,
    BertPooler,
    BertPreTrainedModel,
)

def   __SCREAMING_SNAKE_CASE  (      A_ ):
	lowerCAmelCase__	:						List[str]  				=	torch.exp(A_ )
	lowerCAmelCase__	:						Tuple  				=	torch.sum(A_						,     dim=1 )  # sum of exp(x_i)
	lowerCAmelCase__	:						Any  				=	torch.sum(x * exp_x						,     dim=1 )  # sum of x_i * exp(x_i)
	return torch.log(A_ ) - B / A







class 				SCREAMING_SNAKE_CASE					(		nn.Module       ):






					"""simple docstring"""



					def __init__(       self			:				str   ,lowercase_			:				Optional[int]		):
						super().__init__()
						lowerCAmelCase__	:						Tuple  				=	config.output_attentions
						lowerCAmelCase__	:						List[str]  				=	config.output_hidden_states
						lowerCAmelCase__	:						Union[str, Any]  				=	nn.ModuleList([BertLayer(lowercase_		) for _ in range(config.num_hidden_layers		)]		)
						lowerCAmelCase__	:						int  				=	nn.ModuleList([BertHighway(lowercase_		) for _ in range(config.num_hidden_layers		)]		)

						lowerCAmelCase__	:						Any  				=	[-1 for _ in range(config.num_hidden_layers		)]







					def        __lowerCAmelCase (       self			:				List[str]   ,lowercase_			:				List[Any]		):
						if (type(lowercase_		) is float) or (type(lowercase_		) is int):
							for i in range(len(self.early_exit_entropy		)		):
								lowerCAmelCase__	:						List[str]  				=	x
						else:
							lowerCAmelCase__	:						Optional[Any]  				=	x







					def        __lowerCAmelCase (       self			:				Any   ,lowercase_			:				int		):
						lowerCAmelCase__	:						Any  				=	pooler.state_dict()
						for highway in self.highway:
							for name, param in highway.pooler.state_dict().items():
								param.copy_(loaded_model[name]		)







					def        __lowerCAmelCase (       self			:				str   ,lowercase_			:				Optional[int]   ,lowercase_			:				Union[str, Any]=None   ,lowercase_			:				Any=None   ,lowercase_			:				Union[str, Any]=None   ,lowercase_			:				Optional[Any]=None   ,):
						lowerCAmelCase__	:						Tuple  				=	()
						lowerCAmelCase__	:						Optional[int]  				=	()
						lowerCAmelCase__	:						str  				=	()
						for i, layer_module in enumerate(self.layer		):
							if self.output_hidden_states:
								lowerCAmelCase__	:						Dict  				=	all_hidden_states + (hidden_states,)

							lowerCAmelCase__	:						str  				=	layer_module(
							    lowercase_   ,lowercase_   ,head_mask[i]   ,lowercase_   ,lowercase_		)
							lowerCAmelCase__	:						int  				=	layer_outputs[0]

							if self.output_attentions:
								lowerCAmelCase__	:						Optional[int]  				=	all_attentions + (layer_outputs[1],)

							lowerCAmelCase__	:						str  				=	(hidden_states,)
							if self.output_hidden_states:
								lowerCAmelCase__	:						Tuple  				=	current_outputs + (all_hidden_states,)
							if self.output_attentions:
								lowerCAmelCase__	:						Any  				=	current_outputs + (all_attentions,)

							lowerCAmelCase__	:						Dict  				=	self.highway[i](lowercase_		)
							# logits, pooled_output

							if not self.training:
								lowerCAmelCase__	:						str  				=	highway_exit[0]
								lowerCAmelCase__	:						str  				=	entropy(lowercase_		)
								lowerCAmelCase__	:						str  				=	highway_exit + (highway_entropy,)  # logits, hidden_states(?), entropy
								lowerCAmelCase__	:						Dict  				=	all_highway_exits + (highway_exit,)

								if highway_entropy < self.early_exit_entropy[i]:
									lowerCAmelCase__	:						str  				=	(highway_logits,) + current_outputs[1:] + (all_highway_exits,)
									raise HighwayException(lowercase_   ,i + 1		)
							else:
								lowerCAmelCase__	:						Union[str, Any]  				=	all_highway_exits + (highway_exit,)

        # Add last layer
						if self.output_hidden_states:
							lowerCAmelCase__	:						List[Any]  				=	all_hidden_states + (hidden_states,)

						lowerCAmelCase__	:						Dict  				=	(hidden_states,)
						if self.output_hidden_states:
							lowerCAmelCase__	:						List[Any]  				=	outputs + (all_hidden_states,)
						if self.output_attentions:
							lowerCAmelCase__	:						List[Any]  				=	outputs + (all_attentions,)

						lowerCAmelCase__	:						List[Any]  				=	outputs + (all_highway_exits,)
						return outputs  # last-layer hidden state, (all hidden states), (all attentions), all highway exits







@add_start_docstrings(
    "The Bert Model transformer with early exiting (DeeBERT). "    ,							a_    ,							)
class 				SCREAMING_SNAKE_CASE					(		a_       ):






					"""simple docstring"""



					def __init__(       self			:				List[str]   ,lowercase_			:				Tuple		):
						super().__init__(lowercase_		)
						lowerCAmelCase__	:						List[Any]  				=	config

						lowerCAmelCase__	:						int  				=	BertEmbeddings(lowercase_		)
						lowerCAmelCase__	:						Union[str, Any]  				=	DeeBertEncoder(lowercase_		)
						lowerCAmelCase__	:						Union[str, Any]  				=	BertPooler(lowercase_		)

						self.init_weights()







					def        __lowerCAmelCase (       self			:				str		):
						self.encoder.init_highway_pooler(self.pooler		)







					def        __lowerCAmelCase (       self			:				Tuple		):
						return self.embeddings.word_embeddings







					def        __lowerCAmelCase (       self			:				List[str]   ,lowercase_			:				Optional[Any]		):
						lowerCAmelCase__	:						Any  				=	value







					def        __lowerCAmelCase (       self			:				str   ,lowercase_			:				Tuple		):
						for layer, heads in heads_to_prune.items():
							self.encoder.layer[layer].attention.prune_heads(lowercase_		)







					@add_start_docstrings_to_model_forward(lowercase_		)
					def        __lowerCAmelCase (       self			:				Tuple   ,lowercase_			:				int=None   ,lowercase_			:				int=None   ,lowercase_			:				Tuple=None   ,lowercase_			:				Dict=None   ,lowercase_			:				Dict=None   ,lowercase_			:				Optional[int]=None   ,lowercase_			:				Optional[int]=None   ,lowercase_			:				str=None   ,):
						if input_ids is not None and inputs_embeds is not None:
							raise ValueError('''You cannot specify both input_ids and inputs_embeds at the same time'''		)
						elif input_ids is not None:
							lowerCAmelCase__	:						List[str]  				=	input_ids.size()
						elif inputs_embeds is not None:
							lowerCAmelCase__	:						Optional[Any]  				=	inputs_embeds.size()[:-1]
						else:
							raise ValueError('''You have to specify either input_ids or inputs_embeds'''		)

						lowerCAmelCase__	:						Union[str, Any]  				=	input_ids.device if input_ids is not None else inputs_embeds.device

						if attention_mask is None:
							lowerCAmelCase__	:						Union[str, Any]  				=	torch.ones(lowercase_   ,device=lowercase_		)
						if encoder_attention_mask is None:
							lowerCAmelCase__	:						Tuple  				=	torch.ones(lowercase_   ,device=lowercase_		)
						if token_type_ids is None:
							lowerCAmelCase__	:						int  				=	torch.zeros(lowercase_   ,dtype=torch.long   ,device=lowercase_		)

						# We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
						# ourselves in which case we just need to make it broadcastable to all heads.
						lowerCAmelCase__	:						torch.Tensor  				=	self.get_extended_attention_mask(lowercase_   ,lowercase_   ,lowercase_		)

						# If a 2D ou 3D attention mask is provided for the cross-attention
						# we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
						if encoder_attention_mask.dim() == 3:
							lowerCAmelCase__	:						Union[str, Any]  				=	encoder_attention_mask[:, None, :, :]
						if encoder_attention_mask.dim() == 2:
							lowerCAmelCase__	:						Dict  				=	encoder_attention_mask[:, None, None, :]

						lowerCAmelCase__	:						List[Any]  				=	encoder_extended_attention_mask.to(
						    dtype=next(self.parameters()		).dtype		)  # fp16 compatibility
						lowerCAmelCase__	:						Any  				=	(1.0 - encoder_extended_attention_mask) * -1_0000.0

						# Prepare head mask if needed
						# 1.0 in head_mask indicate we keep the head
						# attention_probs has shape bsz x n_heads x N x N
						# input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
						# and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
						lowerCAmelCase__	:						Tuple  				=	self.get_head_mask(lowercase_   ,self.config.num_hidden_layers		)

						lowerCAmelCase__	:						Any  				=	self.embeddings(
						    input_ids=lowercase_   ,position_ids=lowercase_   ,token_type_ids=lowercase_   ,inputs_embeds=lowercase_		)
						lowerCAmelCase__	:						Tuple  				=	self.encoder(
						    lowercase_   ,attention_mask=lowercase_   ,head_mask=lowercase_   ,encoder_hidden_states=lowercase_   ,encoder_attention_mask=lowercase_   ,)
						lowerCAmelCase__	:						str  				=	encoder_outputs[0]
						lowerCAmelCase__	:						Dict  				=	self.pooler(lowercase_		)

						lowerCAmelCase__	:						Any  				=	(
						    sequence_output,
						    pooled_output,
						) + encoder_outputs[
						    1:
						]  # add hidden_states and attentions if they are here
						return outputs  # sequence_output, pooled_output, (hidden_states), (attentions), highway exits







class 				SCREAMING_SNAKE_CASE					(		a_       ):






					"""simple docstring"""



					def __init__(       self			:				Tuple   ,lowercase_			:				Any   ,lowercase_			:				int		):
						lowerCAmelCase__	:						List[Any]  				=	message
						lowerCAmelCase__	:						Any  				=	exit_layer  # start from 1!







class 				SCREAMING_SNAKE_CASE					(		nn.Module       ):






					"""simple docstring"""



					def __init__(       self			:				Optional[Any]   ,lowercase_			:				Any		):
						super().__init__()
						lowerCAmelCase__	:						Optional[int]  				=	BertPooler(lowercase_		)
						lowerCAmelCase__	:						Optional[int]  				=	nn.Dropout(config.hidden_dropout_prob		)
						lowerCAmelCase__	:						str  				=	nn.Linear(config.hidden_size   ,config.num_labels		)







					def        __lowerCAmelCase (       self			:				Tuple   ,lowercase_			:				int		):
						# Pooler
						lowerCAmelCase__	:						Tuple  				=	encoder_outputs[0]
						lowerCAmelCase__	:						Optional[int]  				=	self.pooler(lowercase_		)
						# "return" pooler_output

						# BertModel
						lowerCAmelCase__	:						List[str]  				=	(pooler_input, pooler_output) + encoder_outputs[1:]
						# "return" bmodel_output

						# Dropout and classification
						lowerCAmelCase__	:						List[str]  				=	bmodel_output[1]

						lowerCAmelCase__	:						str  				=	self.dropout(lowercase_		)
						lowerCAmelCase__	:						List[str]  				=	self.classifier(lowercase_		)

						return logits, pooled_output







@add_start_docstrings(
    "Bert Model (with early exiting - DeeBERT) with a classifier on top,\n    also takes care of multi-layer training. "    ,							a_    ,							)
class 				SCREAMING_SNAKE_CASE					(		a_       ):






					"""simple docstring"""



					def __init__(       self			:				Optional[Any]   ,lowercase_			:				str		):
						super().__init__(lowercase_		)
						lowerCAmelCase__	:						Union[str, Any]  				=	config.num_labels
						lowerCAmelCase__	:						Optional[int]  				=	config.num_hidden_layers

						lowerCAmelCase__	:						Any  				=	DeeBertModel(lowercase_		)
						lowerCAmelCase__	:						str  				=	nn.Dropout(config.hidden_dropout_prob		)
						lowerCAmelCase__	:						Union[str, Any]  				=	nn.Linear(config.hidden_size   ,self.config.num_labels		)

						self.init_weights()







					@add_start_docstrings_to_model_forward(lowercase_		)
					def        __lowerCAmelCase (       self			:				Any   ,lowercase_			:				Tuple=None   ,lowercase_			:				Optional[int]=None   ,lowercase_			:				List[Any]=None   ,lowercase_			:				int=None   ,lowercase_			:				Tuple=None   ,lowercase_			:				Optional[Any]=None   ,lowercase_			:				Optional[int]=None   ,lowercase_			:				Dict=-1   ,lowercase_			:				Dict=False   ,):
						lowerCAmelCase__	:						Tuple  				=	self.num_layers
						try:
							lowerCAmelCase__	:						Dict  				=	self.bert(
							    lowercase_   ,attention_mask=lowercase_   ,token_type_ids=lowercase_   ,position_ids=lowercase_   ,head_mask=lowercase_   ,inputs_embeds=lowercase_   ,)
							# sequence_output, pooled_output, (hidden_states), (attentions), highway exits

							lowerCAmelCase__	:						Any  				=	outputs[1]

							lowerCAmelCase__	:						str  				=	self.dropout(lowercase_		)
							lowerCAmelCase__	:						List[Any]  				=	self.classifier(lowercase_		)
							lowerCAmelCase__	:						Dict  				=	(logits,) + outputs[2:]  # add hidden states and attention if they are here
						except HighwayException as e:
							lowerCAmelCase__	:						Dict  				=	e.message
							lowerCAmelCase__	:						Dict  				=	e.exit_layer
							lowerCAmelCase__	:						int  				=	outputs[0]

						if not self.training:
							lowerCAmelCase__	:						Union[str, Any]  				=	entropy(lowercase_		)
							lowerCAmelCase__	:						str  				=	[]
							lowerCAmelCase__	:						Optional[int]  				=	[]
						if labels is not None:
							if self.num_labels == 1:
								#  We are doing regression
								lowerCAmelCase__	:						Optional[Any]  				=	MSELoss()
								lowerCAmelCase__	:						str  				=	loss_fct(logits.view(-1		)   ,labels.view(-1		)		)
							else:
								lowerCAmelCase__	:						int  				=	CrossEntropyLoss()
								lowerCAmelCase__	:						Union[str, Any]  				=	loss_fct(logits.view(-1   ,self.num_labels		)   ,labels.view(-1		)		)

							# work with highway exits
							lowerCAmelCase__	:						Union[str, Any]  				=	[]
							for highway_exit in outputs[-1]:
								lowerCAmelCase__	:						List[Any]  				=	highway_exit[0]
								if not self.training:
									highway_logits_all.append(lowercase_		)
									highway_entropy.append(highway_exit[2]		)
								if self.num_labels == 1:
									#  We are doing regression
									lowerCAmelCase__	:						int  				=	MSELoss()
									lowerCAmelCase__	:						str  				=	loss_fct(highway_logits.view(-1		)   ,labels.view(-1		)		)
								else:
									lowerCAmelCase__	:						Optional[int]  				=	CrossEntropyLoss()
									lowerCAmelCase__	:						Optional[int]  				=	loss_fct(highway_logits.view(-1   ,self.num_labels		)   ,labels.view(-1		)		)
								highway_losses.append(lowercase_		)

							if train_highway:
								lowerCAmelCase__	:						Optional[int]  				=	(sum(highway_losses[:-1]		),) + outputs
								# exclude the final highway, of course
							else:
								lowerCAmelCase__	:						Any  				=	(loss,) + outputs
						if not self.training:
							lowerCAmelCase__	:						Union[str, Any]  				=	outputs + ((original_entropy, highway_entropy), exit_layer)
							if output_layer >= 0:
								lowerCAmelCase__	:						Optional[Any]  				=	(
								    (outputs[0],) + (highway_logits_all[output_layer],) + outputs[2:]
								)  # use the highway of the last layer

						return outputs  # (loss), logits, (hidden_states), (attentions), (highway_exits)












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	:							Tuple				=_symbol_database.Default()


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