style: Regroup import statements at the top of app.py
Browse files
app.py
CHANGED
@@ -1,11 +1,8 @@
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#%%
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from text_processing import split_into_words, Word
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import torch
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from transformers import AutoTokenizer, AutoModelForCausalLM
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from pprint import pprint
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#%%
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def load_model_and_tokenizer(model_name):
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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model = AutoModelForCausalLM.from_pretrained(model_name)
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@@ -27,7 +24,6 @@ def calculate_log_probabilities(model, tokenizer, inputs, input_ids):
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tokens = tokenizer.convert_ids_to_tokens(input_ids[0])
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return list(zip(tokens[1:], token_log_probs.tolist()))
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from transformers import PreTrainedModel, PreTrainedTokenizer
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def generate_replacements(model: PreTrainedModel, tokenizer: PreTrainedTokenizer, prefix: str, device: torch.device, num_samples: int = 5) -> list[str]:
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input_context = tokenizer(prefix, return_tensors="pt").to(device)
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from text_processing import split_into_words, Word
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import torch
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from transformers import AutoTokenizer, AutoModelForCausalLM, PreTrainedModel, PreTrainedTokenizer
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from pprint import pprint
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def load_model_and_tokenizer(model_name):
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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model = AutoModelForCausalLM.from_pretrained(model_name)
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tokens = tokenizer.convert_ids_to_tokens(input_ids[0])
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return list(zip(tokens[1:], token_log_probs.tolist()))
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def generate_replacements(model: PreTrainedModel, tokenizer: PreTrainedTokenizer, prefix: str, device: torch.device, num_samples: int = 5) -> list[str]:
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input_context = tokenizer(prefix, return_tensors="pt").to(device)
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