2020 the huggingface 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 these are encoder decoder they don t just append to incoming string dosamplefalse necessary for reproducibility dosamplefalse necessary for reproducibility 2020 the huggingface 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 these are encoder decoder they don t just append to incoming string do_sample false necessary for reproducibility do_sample false necessary for reproducibility
import unittest

from transformers import (
    MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    Text2TextGenerationPipeline,
    pipeline,
)
from transformers.testing_utils import is_pipeline_test, require_tf, require_torch
from transformers.utils import is_torch_available

from .test_pipelines_common import ANY


if is_torch_available():
    import torch


@is_pipeline_test
class Text2TextGenerationPipelineTests(unittest.TestCase):
    model_mapping = MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
    tf_model_mapping = TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING

    def get_test_pipeline(self, model, tokenizer, processor):
        generator = Text2TextGenerationPipeline(model=model, tokenizer=tokenizer)
        return generator, ["Something to write", "Something else"]

    def run_pipeline_test(self, generator, _):
        outputs = generator("Something there")
        self.assertEqual(outputs, [{"generated_text": ANY(str)}])
        
        self.assertFalse(outputs[0]["generated_text"].startswith("Something there"))

        outputs = generator(["This is great !", "Something else"], num_return_sequences=2, do_sample=True)
        self.assertEqual(
            outputs,
            [
                [{"generated_text": ANY(str)}, {"generated_text": ANY(str)}],
                [{"generated_text": ANY(str)}, {"generated_text": ANY(str)}],
            ],
        )

        outputs = generator(
            ["This is great !", "Something else"], num_return_sequences=2, batch_size=2, do_sample=True
        )
        self.assertEqual(
            outputs,
            [
                [{"generated_text": ANY(str)}, {"generated_text": ANY(str)}],
                [{"generated_text": ANY(str)}, {"generated_text": ANY(str)}],
            ],
        )

        with self.assertRaises(ValueError):
            generator(4)

    @require_torch
    def test_small_model_pt(self):
        generator = pipeline("text2text-generation", model="patrickvonplaten/t5-tiny-random", framework="pt")
        
        outputs = generator("Something there", do_sample=False)
        self.assertEqual(outputs, [{"generated_text": ""}])

        num_return_sequences = 3
        outputs = generator(
            "Something there",
            num_return_sequences=num_return_sequences,
            num_beams=num_return_sequences,
        )
        target_outputs = [
            {"generated_text": "Beide Beide Beide Beide Beide Beide Beide Beide Beide"},
            {"generated_text": "Beide Beide Beide Beide Beide Beide Beide Beide"},
            {"generated_text": ""},
        ]
        self.assertEqual(outputs, target_outputs)

        outputs = generator("This is a test", do_sample=True, num_return_sequences=2, return_tensors=True)
        self.assertEqual(
            outputs,
            [
                {"generated_token_ids": ANY(torch.Tensor)},
                {"generated_token_ids": ANY(torch.Tensor)},
            ],
        )
        generator.tokenizer.pad_token_id = generator.model.config.eos_token_id
        generator.tokenizer.pad_token = "<pad>"
        outputs = generator(
            ["This is a test", "This is a second test"],
            do_sample=True,
            num_return_sequences=2,
            batch_size=2,
            return_tensors=True,
        )
        self.assertEqual(
            outputs,
            [
                [
                    {"generated_token_ids": ANY(torch.Tensor)},
                    {"generated_token_ids": ANY(torch.Tensor)},
                ],
                [
                    {"generated_token_ids": ANY(torch.Tensor)},
                    {"generated_token_ids": ANY(torch.Tensor)},
                ],
            ],
        )

    @require_tf
    def test_small_model_tf(self):
        generator = pipeline("text2text-generation", model="patrickvonplaten/t5-tiny-random", framework="tf")
        
        outputs = generator("Something there", do_sample=False)
        self.assertEqual(outputs, [{"generated_text": ""}])