From f3feaf7f2266b830be7e1bd5d70bbfc80952e43b Mon Sep 17 00:00:00 2001 From: Sayak Paul Date: Tue, 17 Jan 2023 17:29:23 +0100 Subject: [PATCH] Change variable name to prevent shadowing (#21153) fix: input -> input_string. --- docs/source/en/tf_xla.mdx | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/docs/source/en/tf_xla.mdx b/docs/source/en/tf_xla.mdx index 2cab715f32..9b6018b57f 100644 --- a/docs/source/en/tf_xla.mdx +++ b/docs/source/en/tf_xla.mdx @@ -83,12 +83,12 @@ check_min_version("4.21.0") tokenizer = AutoTokenizer.from_pretrained("gpt2", padding_side="left", pad_token="") model = TFAutoModelForCausalLM.from_pretrained("gpt2") -input = ["TensorFlow is"] +input_string = ["TensorFlow is"] # One line to create an XLA generation function xla_generate = tf.function(model.generate, jit_compile=True) -tokenized_input = tokenizer(input, return_tensors="tf") +tokenized_input = tokenizer(input_string, return_tensors="tf") generated_tokens = xla_generate(**tokenized_input, num_beams=2) decoded_text = tokenizer.decode(generated_tokens[0], skip_special_tokens=True) @@ -112,12 +112,12 @@ from transformers import AutoTokenizer, TFAutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("gpt2", padding_side="left", pad_token="") model = TFAutoModelForCausalLM.from_pretrained("gpt2") -input = ["TensorFlow is"] +input_string = ["TensorFlow is"] xla_generate = tf.function(model.generate, jit_compile=True) # Here, we call the tokenizer with padding options. -tokenized_input = tokenizer(input, pad_to_multiple_of=8, padding=True, return_tensors="tf") +tokenized_input = tokenizer(input_string, pad_to_multiple_of=8, padding=True, return_tensors="tf") generated_tokens = xla_generate(**tokenized_input, num_beams=2) decoded_text = tokenizer.decode(generated_tokens[0], skip_special_tokens=True) @@ -136,8 +136,8 @@ model = TFAutoModelForCausalLM.from_pretrained("gpt2") xla_generate = tf.function(model.generate, jit_compile=True) -for input in ["TensorFlow is", "TensorFlow is a", "TFLite is a"]: - tokenized_input = tokenizer(input, pad_to_multiple_of=8, padding=True, return_tensors="tf") +for input_string in ["TensorFlow is", "TensorFlow is a", "TFLite is a"]: + tokenized_input = tokenizer(input_string, pad_to_multiple_of=8, padding=True, return_tensors="tf") start = time.time_ns() generated_tokens = xla_generate(**tokenized_input, num_beams=2) end = time.time_ns()