Generate: fix TF doctests (#20159)

src/transformers/generation/tf_utils.py

@@ -2399,8 +2399,8 @@ class TFGenerationMixin:
         ... )
 
         >>> outputs = model.greedy_search(input_ids, logits_processor=logits_processor)
-
-        >>> print("Generated:", tokenizer.batch_decode(outputs, skip_special_tokens=True))
+        >>> tokenizer.batch_decode(outputs, skip_special_tokens=True)
+        ["Today is a beautiful day, and I'm so happy to be here. I'm so happy to"]
         ```"""
 
         # 1. init greedy_search values
@@ -2634,6 +2634,7 @@ class TFGenerationMixin:
         Examples:
 
         ```python
+        >>> import tensorflow as tf
         >>> from transformers import (
         ...     AutoTokenizer,
         ...     TFAutoModelForCausalLM,
@@ -2666,9 +2667,11 @@ class TFGenerationMixin:
         ...     ]
         ... )
 
+        >>> tf.random.set_seed(0)
         >>> outputs = model.sample(input_ids, logits_processor=logits_processor, logits_warper=logits_warper)
 
-        >>> print("Generated:", tokenizer.batch_decode(outputs, skip_special_tokens=True))
+        >>> tokenizer.batch_decode(outputs, skip_special_tokens=True)
+        ['Today is a beautiful day, and it\'s all about the future."\n\nThe announcement comes three']
         ```"""
 
         # 1. init greedy_search values
@@ -2927,15 +2930,15 @@ class TFGenerationMixin:
         >>> # lets run beam search using 3 beams
         >>> num_beams = 3
         >>> # define decoder start token ids
-        >>> input_ids = tf.ones((num_beams, 1), dtype=tf.int64)
+        >>> input_ids = tf.ones((1, num_beams, 1), dtype=tf.int32)
         >>> input_ids = input_ids * model.config.decoder_start_token_id
 
         >>> # add encoder_outputs to model keyword arguments
-        >>> model_kwargs = {
-        ...     "encoder_outputs": model.get_encoder()(
-        ...         tf.repeat(encoder_input_ids, num_beams, axis=0), return_dict=True
-        ...     )
-        ... }
+        >>> encoder_outputs = model.get_encoder()(encoder_input_ids, return_dict=True)
+        >>> encoder_outputs.last_hidden_state = tf.repeat(
+        ...     tf.expand_dims(encoder_outputs.last_hidden_state, axis=0), num_beams, axis=1
+        ... )
+        >>> model_kwargs = {"encoder_outputs": encoder_outputs}
 
         >>> # instantiate logits processors
         >>> logits_processor = TFLogitsProcessorList(
@@ -2944,7 +2947,8 @@ class TFGenerationMixin:
 
         >>> outputs = model.beam_search(input_ids, logits_processor=logits_processor, **model_kwargs)
 
-        >>> print("Generated:", tokenizer.batch_decode(outputs, skip_special_tokens=True))
+        >>> tokenizer.batch_decode(outputs, skip_special_tokens=True)
+        ['Wie alt bist du?']
         ```"""
 
         def flatten_beam_dim(tensor, batch_axis=0):