# What does this PR do?

This PR fixes the last batch of models that are too big for common tests. After it, only two classes override the common test to skip it:
- timm-backbone
- layoutlmv2
In the first case, it's because there is no timm model small enough to work (I did switch to the smallest resnet though) and in the second detectron2 does not let us configure a smaller backbone (I did try with the constant exposed by they don't seem to have any effect).
Fix last models for common tests that are too big.. # What does this PR do?

This PR fixes the last batch of models that are too big for common tests. After it, only two classes override the common test to skip it:
- timm-backbone
- layoutlmv2
In the first case, it's because there is no timm model small enough to work (I did switch to the smallest resnet though) and in the second detectron2 does not let us configure a smaller backbone (I did try with the constant exposed by they don't seem to have any effect).
# Copyright 2020 The HuggingFace Team. All rights reserved.
#
# 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.

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)}])
        # These are encoder decoder, they don't just append to incoming string
        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")
        # do_sample=False necessary for reproducibility
        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")
        # do_sample=False necessary for reproducibility
        outputs = generator("Something there", do_sample=False)
        self.assertEqual(outputs, [{"generated_text": ""}])

# Copyright 2020 The HuggingFace Team. All rights reserved.
#
# 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.

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)}])
        # These are encoder decoder, they don't just append to incoming string
        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")
        # do_sample=False necessary for reproducibility
        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")
        # do_sample=False necessary for reproducibility
        outputs = generator("Something there", do_sample=False)
        self.assertEqual(outputs, [{"generated_text": ""}])
