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from typing import Dict, List, Any
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from transformers import AutoTokenizer, AutoModelForCausalLM
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from transformers.generation.logits_process import LogitsProcessorList, InfNanRemoveLogitsProcessor
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from transformers_gad.grammar_utils import IncrementalGrammarConstraint
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from transformers_gad.generation.logits_process import GrammarAlignedOracleLogitsProcessor
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class EndpointHandler():
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def __init__(self, path=""):
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self.tokenizer = AutoTokenizer.from_pretrained(path)
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self.model = AutoModelForCausalLM.from_pretrained(path)
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def __call__(self, data: Dict[str, Any]) -> List[Dict[str, Any]]:
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inputs = data.get("inputs",data)
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grammar_str = data.get("grammar", "")
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MAX_NEW_TOKENS=4096
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MAX_TIME=300
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print(grammar_str)
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grammar = IncrementalGrammarConstraint(grammar_str, "root", self.tokenizer)
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gad_oracle_processor = GrammarAlignedOracleLogitsProcessor(grammar)
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inf_nan_remove_processor = InfNanRemoveLogitsProcessor()
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logits_processors = LogitsProcessorList([
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inf_nan_remove_processor,
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gad_oracle_processor,
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])
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input_ids = self.tokenizer([inputs], add_special_tokens=False, return_tensors="pt")["input_ids"]
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output = self.model.generate(
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input_ids,
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do_sample=True,
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max_time=MAX_TIME,
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max_new_tokens=MAX_NEW_TOKENS,
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logits_processor=logits_processors
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)
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gad_oracle_processor.reset()
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input_length = 1 if self.model.config.is_encoder_decoder else input_ids.shape[1]
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if (hasattr(output, "sequences")):
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generated_tokens = output.sequences[:, input_length:]
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else:
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generated_tokens = output[:, input_length:]
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generations = self.tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)
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return generations |