codingutf8 iz beltagy matthew e peters arman cohan and the huggingface inc team licensed under the apache license version 2 0 the license you may not use this file except in compliance with the license you may obtain a copy of the license at http www apache orglicenseslicense2 0 unless required by applicable law or agreed to in writing software distributed under the license is distributed on an as is basis without warranties or conditions of any kind either express or implied see the license for the specific language governing permissions and limitations under the license modeltestermixin testattentionoutputs is expecting attention tensors to be of size numattentionheads encoderseqlength encoderkeylength but tflongformerselfattention returns attention of shape numattentionheads encoderseqlength self attentionwindow 1 because its local attention only attends to self attentionwindow and one before and one after because of padding encoderseqlength is different from seqlength relevant for the testattentionoutputs and testhiddenstatesoutput tests first forward pass create hypothetical next token and extent to nextinputids append to next inputids and select random slice test that outputs are equal for slice check that output attentions can also be changed via the config check attention is always last and order is fine todo headmasking not yet implement change to intended input here change to expected output here change to intended input here change to expected output here coding utf 8 iz beltagy matthew e peters arman cohan and the huggingface inc team licensed under the apache license version 2 0 the license you may not use this file except in compliance with the license you may obtain a copy of the license at http www apache org licenses license 2 0 unless required by applicable law or agreed to in writing software distributed under the license is distributed on an as is basis without warranties or conditions of any kind either express or implied see the license for the specific language governing permissions and limitations under the license modeltestermixin test_attention_outputs is expecting attention tensors to be of size num_attention_heads encoder_seq_length encoder_key_length but tflongformerselfattention returns attention of shape num_attention_heads encoder_seq_length self attention_window 1 because its local attention only attends to self attention_window and one before and one after because of padding encoder_seq_length is different from seq_length relevant for the test_attention_outputs and test_hidden_states_output tests first forward pass create hypothetical next token and extent to next_input_ids append to next input_ids and select random slice test that outputs are equal for slice check that output attentions can also be changed via the config check attention is always last and order is fine todo head masking not yet implement change to intended input here change to expected output here change to intended input here change to expected output here
from __future__ import annotations

import unittest

from transformers import LEDConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
    import tensorflow as tf

    from transformers import TFLEDForConditionalGeneration, TFLEDModel


@require_tf
class TFLEDModelTester:
    config_cls = LEDConfig
    config_updates = {}
    hidden_act = "gelu"

    def __init__(
        self,
        parent,
        batch_size=13,
        seq_length=7,
        is_training=True,
        use_labels=False,
        vocab_size=99,
        hidden_size=32,
        num_hidden_layers=2,
        num_attention_heads=4,
        intermediate_size=37,
        hidden_dropout_prob=0.1,
        attention_probs_dropout_prob=0.1,
        max_position_embeddings=20,
        eos_token_id=2,
        pad_token_id=1,
        bos_token_id=0,
        attention_window=4,
    ):
        self.parent = parent
        self.batch_size = batch_size
        self.seq_length = seq_length
        self.is_training = is_training
        self.use_labels = use_labels
        self.vocab_size = vocab_size
        self.hidden_size = hidden_size
        self.num_hidden_layers = num_hidden_layers
        self.num_attention_heads = num_attention_heads
        self.intermediate_size = intermediate_size

        self.hidden_dropout_prob = hidden_dropout_prob
        self.attention_probs_dropout_prob = attention_probs_dropout_prob
        self.max_position_embeddings = max_position_embeddings
        self.eos_token_id = eos_token_id
        self.pad_token_id = pad_token_id
        self.bos_token_id = bos_token_id
        self.attention_window = attention_window

        
        
        
        
        self.key_length = self.attention_window + 2

        
        
        self.encoder_seq_length = (
            self.seq_length + (self.attention_window - self.seq_length % self.attention_window) % self.attention_window
        )

    def prepare_config_and_inputs_for_common(self):
        input_ids = ids_tensor([self.batch_size, self.seq_length - 1], self.vocab_size)
        eos_tensor = tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size), 1)
        input_ids = tf.concat([input_ids, eos_tensor], axis=1)

        decoder_input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)

        config = self.config_cls(
            vocab_size=self.vocab_size,
            d_model=self.hidden_size,
            encoder_layers=self.num_hidden_layers,
            decoder_layers=self.num_hidden_layers,
            encoder_attention_heads=self.num_attention_heads,
            decoder_attention_heads=self.num_attention_heads,
            encoder_ffn_dim=self.intermediate_size,
            decoder_ffn_dim=self.intermediate_size,
            dropout=self.hidden_dropout_prob,
            attention_dropout=self.attention_probs_dropout_prob,
            max_position_embeddings=self.max_position_embeddings,
            eos_token_ids=[2],
            bos_token_id=self.bos_token_id,
            pad_token_id=self.pad_token_id,
            decoder_start_token_id=self.pad_token_id,
            attention_window=self.attention_window,
            **self.config_updates,
        )
        inputs_dict = prepare_led_inputs_dict(config, input_ids, decoder_input_ids)
        global_attention_mask = tf.concat(
            [tf.zeros_like(input_ids)[:, :-1], tf.ones_like(input_ids)[:, -1:]],
            axis=-1,
        )
        inputs_dict["global_attention_mask"] = global_attention_mask
        return config, inputs_dict

    def check_decoder_model_past_large_inputs(self, config, inputs_dict):
        model = TFLEDModel(config=config).get_decoder()
        input_ids = inputs_dict["input_ids"]

        input_ids = input_ids[:1, :]
        attention_mask = inputs_dict["attention_mask"][:1, :]
        self.batch_size = 1

        
        outputs = model(input_ids, attention_mask=attention_mask, use_cache=True)

        output, past_key_values = outputs.to_tuple()

        
        next_tokens = ids_tensor((self.batch_size, 3), config.vocab_size)
        next_attn_mask = tf.cast(ids_tensor((self.batch_size, 3), 2), tf.int8)

        
        next_input_ids = tf.concat([input_ids, next_tokens], axis=-1)
        next_attention_mask = tf.concat([attention_mask, next_attn_mask], axis=-1)

        output_from_no_past = model(next_input_ids, attention_mask=next_attention_mask)[0]
        output_from_past = model(next_tokens, attention_mask=next_attention_mask, past_key_values=past_key_values)[0]

        self.parent.assertEqual(next_tokens.shape[1], output_from_past.shape[1])

        
        random_slice_idx = int(ids_tensor((1,), output_from_past.shape[-1]))
        output_from_no_past_slice = output_from_no_past[:, -3:, random_slice_idx]
        output_from_past_slice = output_from_past[:, :, random_slice_idx]

        
        tf.debugging.assert_near(output_from_past_slice, output_from_no_past_slice, rtol=1e-3)


def prepare_led_inputs_dict(
    config,
    input_ids,
    decoder_input_ids,
    attention_mask=None,
    decoder_attention_mask=None,
    head_mask=None,
    decoder_head_mask=None,
):
    if attention_mask is None:
        attention_mask = tf.cast(tf.math.not_equal(input_ids, config.pad_token_id), tf.int8)
    if decoder_attention_mask is None:
        decoder_attention_mask = tf.concat(
            [
                tf.ones(decoder_input_ids[:, :1].shape, dtype=tf.int8),
                tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:], config.pad_token_id), tf.int8),
            ],
            axis=-1,
        )
    if head_mask is None:
        head_mask = tf.ones((config.encoder_layers, config.encoder_attention_heads))
    if decoder_head_mask is None:
        decoder_head_mask = tf.ones((config.decoder_layers, config.decoder_attention_heads))
    return {
        "input_ids": input_ids,
        "attention_mask": attention_mask,
        "decoder_input_ids": decoder_input_ids,
        "decoder_attention_mask": decoder_attention_mask,
        "head_mask": head_mask,
        "decoder_head_mask": decoder_head_mask,
    }


@require_tf
class TFLEDModelTest(TFModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
    all_model_classes = (TFLEDForConditionalGeneration, TFLEDModel) if is_tf_available() else ()
    all_generative_model_classes = (TFLEDForConditionalGeneration,) if is_tf_available() else ()
    pipeline_model_mapping = (
        {
            "conversational": TFLEDForConditionalGeneration,
            "feature-extraction": TFLEDModel,
            "summarization": TFLEDForConditionalGeneration,
            "text2text-generation": TFLEDForConditionalGeneration,
            "translation": TFLEDForConditionalGeneration,
        }
        if is_tf_available()
        else {}
    )
    is_encoder_decoder = True
    test_pruning = False
    test_head_masking = False
    test_onnx = False

    def setUp(self):
        self.model_tester = TFLEDModelTester(self)
        self.config_tester = ConfigTester(self, config_class=LEDConfig)

    def test_config(self):
        self.config_tester.run_common_tests()

    def test_decoder_model_past_large_inputs(self):
        config_and_inputs = self.model_tester.prepare_config_and_inputs_for_common()
        self.model_tester.check_decoder_model_past_large_inputs(*config_and_inputs)

    def test_attention_outputs(self):
        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
        inputs_dict["global_attention_mask"] = tf.zeros_like(inputs_dict["attention_mask"])
        num_global_attn_indices = 2
        inputs_dict["global_attention_mask"] = tf.where(
            tf.range(self.model_tester.seq_length)[None, :] < num_global_attn_indices,
            1,
            inputs_dict["global_attention_mask"],
        )

        config.return_dict = True
        seq_length = self.model_tester.seq_length
        encoder_seq_length = self.model_tester.encoder_seq_length

        def check_decoder_attentions_output(outputs):
            decoder_attentions = outputs.decoder_attentions
            self.assertEqual(len(decoder_attentions), self.model_tester.num_hidden_layers)
            self.assertListEqual(
                list(decoder_attentions[0].shape[-3:]),
                [self.model_tester.num_attention_heads, seq_length, seq_length],
            )

        def check_encoder_attentions_output(outputs):
            attentions = [t.numpy() for t in outputs.encoder_attentions]
            global_attentions = [t.numpy() for t in outputs.encoder_global_attentions]
            self.assertEqual(len(attentions), self.model_tester.num_hidden_layers)
            self.assertEqual(len(global_attentions), self.model_tester.num_hidden_layers)
            self.assertListEqual(
                list(attentions[0].shape[-3:]),
                [self.model_tester.num_attention_heads, seq_length, seq_length],
            )
            self.assertListEqual(
                list(global_attentions[0].shape[-3:]),
                [self.model_tester.num_attention_heads, encoder_seq_length, num_global_attn_indices],
            )

        for model_class in self.all_model_classes:
            inputs_dict["output_attentions"] = True
            inputs_dict["use_cache"] = False
            config.output_hidden_states = False
            model = model_class(config)
            outputs = model(self._prepare_for_class(inputs_dict, model_class))
            out_len = len(outputs)
            self.assertEqual(config.output_hidden_states, False)
            check_encoder_attentions_output(outputs)

            if self.is_encoder_decoder:
                model = model_class(config)
                outputs = model(self._prepare_for_class(inputs_dict, model_class))
                self.assertEqual(config.output_hidden_states, False)
                check_decoder_attentions_output(outputs)

            
            del inputs_dict["output_attentions"]
            config.output_attentions = True
            model = model_class(config)
            outputs = model(self._prepare_for_class(inputs_dict, model_class))
            self.assertEqual(config.output_hidden_states, False)
            check_encoder_attentions_output(outputs)

            
            inputs_dict["output_attentions"] = True
            config.output_hidden_states = True
            model = model_class(config)
            outputs = model(self._prepare_for_class(inputs_dict, model_class))

            self.assertEqual(out_len + (2 if self.is_encoder_decoder else 1), len(outputs))
            self.assertEqual(model.config.output_hidden_states, True)
            check_encoder_attentions_output(outputs)

    @unittest.skip("LED keeps using potentially symbolic tensors in conditionals and breaks tracing.")
    def test_saved_model_creation(self):
        pass

    def test_generate_with_headmasking(self):
        
        pass


def _long_tensor(tok_lst):
    return tf.constant(tok_lst, dtype=tf.int32)


TOLERANCE = 1e-4


@slow
@require_tf
class TFLEDModelIntegrationTest(unittest.TestCase):
    def test_inference_no_head(self):
        model = TFLEDForConditionalGeneration.from_pretrained("allenai/led-base-16384").led

        
        input_ids = _long_tensor([512 * [0, 31414, 232, 328, 740, 1140, 12695, 69]])
        decoder_input_ids = _long_tensor([128 * [0, 31414, 232, 328, 740, 1140, 12695, 69]])
        inputs_dict = prepare_led_inputs_dict(model.config, input_ids, decoder_input_ids)
        output = model(**inputs_dict)[0]
        expected_shape = (1, 1024, 768)
        self.assertEqual(output.shape, expected_shape)
        
        expected_slice = tf.convert_to_tensor(
            [[2.3050, 2.8279, 0.6531], [-1.8457, -0.1455, -3.5661], [-1.0186, 0.4586, -2.2043]],
        )
        tf.debugging.assert_near(output[:, :3, :3], expected_slice, atol=1e-3)

    def test_inference_with_head(self):
        model = TFLEDForConditionalGeneration.from_pretrained("allenai/led-base-16384")

        
        input_ids = _long_tensor([512 * [0, 31414, 232, 328, 740, 1140, 12695, 69]])
        decoder_input_ids = _long_tensor([128 * [0, 31414, 232, 328, 740, 1140, 12695, 69]])
        inputs_dict = prepare_led_inputs_dict(model.config, input_ids, decoder_input_ids)
        output = model(**inputs_dict)[0]
        expected_shape = (1, 1024, model.config.vocab_size)
        self.assertEqual(output.shape, expected_shape)
        
        expected_slice = tf.convert_to_tensor(
            [[33.6507, 6.4572, 16.8089], [5.8739, -2.4238, 11.2902], [-3.2139, -4.3149, 4.2783]],
        )
        tf.debugging.assert_near(output[:, :3, :3], expected_slice, atol=1e-3, rtol=1e-3)