# 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 2023 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 shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available


if is_vision_available():
    from PIL import Image

    from transformers import (
        AutoProcessor,
        BertTokenizerFast,
        BlipImageProcessor,
        GPT2Tokenizer,
        InstructBlipProcessor,
        PreTrainedTokenizerFast,
    )


@require_vision
class InstructBlipProcessorTest(unittest.TestCase):
    def setUp(self):
        self.tmpdirname = tempfile.mkdtemp()

        image_processor = BlipImageProcessor()
        tokenizer = GPT2Tokenizer.from_pretrained("hf-internal-testing/tiny-random-GPT2Model")
        qformer_tokenizer = BertTokenizerFast.from_pretrained("hf-internal-testing/tiny-random-bert")

        processor = InstructBlipProcessor(image_processor, tokenizer, qformer_tokenizer)

        processor.save_pretrained(self.tmpdirname)

    def get_tokenizer(self, **kwargs):
        return AutoProcessor.from_pretrained(self.tmpdirname, **kwargs).tokenizer

    def get_image_processor(self, **kwargs):
        return AutoProcessor.from_pretrained(self.tmpdirname, **kwargs).image_processor

    def get_qformer_tokenizer(self, **kwargs):
        return AutoProcessor.from_pretrained(self.tmpdirname, **kwargs).qformer_tokenizer

    def tearDown(self):
        shutil.rmtree(self.tmpdirname)

    def prepare_image_inputs(self):
        """This function prepares a list of PIL images, or a list of numpy arrays if one specifies numpify=True,
        or a list of PyTorch tensors if one specifies torchify=True.
        """

        image_inputs = [np.random.randint(255, size=(3, 30, 400), dtype=np.uint8)]

        image_inputs = [Image.fromarray(np.moveaxis(x, 0, -1)) for x in image_inputs]

        return image_inputs

    def test_save_load_pretrained_additional_features(self):
        processor = InstructBlipProcessor(
            tokenizer=self.get_tokenizer(),
            image_processor=self.get_image_processor(),
            qformer_tokenizer=self.get_qformer_tokenizer(),
        )
        processor.save_pretrained(self.tmpdirname)

        tokenizer_add_kwargs = self.get_tokenizer(bos_token="(BOS)", eos_token="(EOS)")
        image_processor_add_kwargs = self.get_image_processor(do_normalize=False, padding_value=1.0)

        processor = InstructBlipProcessor.from_pretrained(
            self.tmpdirname, bos_token="(BOS)", eos_token="(EOS)", do_normalize=False, padding_value=1.0
        )

        self.assertEqual(processor.tokenizer.get_vocab(), tokenizer_add_kwargs.get_vocab())
        self.assertIsInstance(processor.tokenizer, PreTrainedTokenizerFast)

        self.assertEqual(processor.image_processor.to_json_string(), image_processor_add_kwargs.to_json_string())
        self.assertIsInstance(processor.image_processor, BlipImageProcessor)
        self.assertIsInstance(processor.qformer_tokenizer, BertTokenizerFast)

    def test_image_processor(self):
        image_processor = self.get_image_processor()
        tokenizer = self.get_tokenizer()
        qformer_tokenizer = self.get_qformer_tokenizer()

        processor = InstructBlipProcessor(
            tokenizer=tokenizer, image_processor=image_processor, qformer_tokenizer=qformer_tokenizer
        )

        image_input = self.prepare_image_inputs()

        input_feat_extract = image_processor(image_input, return_tensors="np")
        input_processor = processor(images=image_input, return_tensors="np")

        for key in input_feat_extract.keys():
            self.assertAlmostEqual(input_feat_extract[key].sum(), input_processor[key].sum(), delta=1e-2)

    def test_tokenizer(self):
        image_processor = self.get_image_processor()
        tokenizer = self.get_tokenizer()
        qformer_tokenizer = self.get_qformer_tokenizer()

        processor = InstructBlipProcessor(
            tokenizer=tokenizer, image_processor=image_processor, qformer_tokenizer=qformer_tokenizer
        )

        input_str = "lower newer"

        encoded_processor = processor(text=input_str)

        encoded_tokens = tokenizer(input_str, return_token_type_ids=False)
        encoded_tokens_qformer = qformer_tokenizer(input_str, return_token_type_ids=False)

        for key in encoded_tokens.keys():
            self.assertListEqual(encoded_tokens[key], encoded_processor[key])

        for key in encoded_tokens_qformer.keys():
            self.assertListEqual(encoded_tokens_qformer[key], encoded_processor["qformer_" + key])

    def test_processor(self):
        image_processor = self.get_image_processor()
        tokenizer = self.get_tokenizer()
        qformer_tokenizer = self.get_qformer_tokenizer()

        processor = InstructBlipProcessor(
            tokenizer=tokenizer, image_processor=image_processor, qformer_tokenizer=qformer_tokenizer
        )

        input_str = "lower newer"
        image_input = self.prepare_image_inputs()

        inputs = processor(text=input_str, images=image_input)

        self.assertListEqual(
            list(inputs.keys()),
            ["input_ids", "attention_mask", "qformer_input_ids", "qformer_attention_mask", "pixel_values"],
        )

        # test if it raises when no input is passed
        with pytest.raises(ValueError):
            processor()

    def test_tokenizer_decode(self):
        image_processor = self.get_image_processor()
        tokenizer = self.get_tokenizer()
        qformer_tokenizer = self.get_qformer_tokenizer()

        processor = InstructBlipProcessor(
            tokenizer=tokenizer, image_processor=image_processor, qformer_tokenizer=qformer_tokenizer
        )

        predicted_ids = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

        decoded_processor = processor.batch_decode(predicted_ids)
        decoded_tok = tokenizer.batch_decode(predicted_ids)

        self.assertListEqual(decoded_tok, decoded_processor)

    def test_model_input_names(self):
        image_processor = self.get_image_processor()
        tokenizer = self.get_tokenizer()
        qformer_tokenizer = self.get_qformer_tokenizer()

        processor = InstructBlipProcessor(
            tokenizer=tokenizer, image_processor=image_processor, qformer_tokenizer=qformer_tokenizer
        )

        input_str = "lower newer"
        image_input = self.prepare_image_inputs()

        inputs = processor(text=input_str, images=image_input)

        self.assertListEqual(
            list(inputs.keys()),
            ["input_ids", "attention_mask", "qformer_input_ids", "qformer_attention_mask", "pixel_values"],
        )

# Copyright 2023 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 shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available


if is_vision_available():
    from PIL import Image

    from transformers import (
        AutoProcessor,
        BertTokenizerFast,
        BlipImageProcessor,
        GPT2Tokenizer,
        InstructBlipProcessor,
        PreTrainedTokenizerFast,
    )


@require_vision
class InstructBlipProcessorTest(unittest.TestCase):
    def setUp(self):
        self.tmpdirname = tempfile.mkdtemp()

        image_processor = BlipImageProcessor()
        tokenizer = GPT2Tokenizer.from_pretrained("hf-internal-testing/tiny-random-GPT2Model")
        qformer_tokenizer = BertTokenizerFast.from_pretrained("hf-internal-testing/tiny-random-bert")

        processor = InstructBlipProcessor(image_processor, tokenizer, qformer_tokenizer)

        processor.save_pretrained(self.tmpdirname)

    def get_tokenizer(self, **kwargs):
        return AutoProcessor.from_pretrained(self.tmpdirname, **kwargs).tokenizer

    def get_image_processor(self, **kwargs):
        return AutoProcessor.from_pretrained(self.tmpdirname, **kwargs).image_processor

    def get_qformer_tokenizer(self, **kwargs):
        return AutoProcessor.from_pretrained(self.tmpdirname, **kwargs).qformer_tokenizer

    def tearDown(self):
        shutil.rmtree(self.tmpdirname)

    def prepare_image_inputs(self):
        """This function prepares a list of PIL images, or a list of numpy arrays if one specifies numpify=True,
        or a list of PyTorch tensors if one specifies torchify=True.
        """

        image_inputs = [np.random.randint(255, size=(3, 30, 400), dtype=np.uint8)]

        image_inputs = [Image.fromarray(np.moveaxis(x, 0, -1)) for x in image_inputs]

        return image_inputs

    def test_save_load_pretrained_additional_features(self):
        processor = InstructBlipProcessor(
            tokenizer=self.get_tokenizer(),
            image_processor=self.get_image_processor(),
            qformer_tokenizer=self.get_qformer_tokenizer(),
        )
        processor.save_pretrained(self.tmpdirname)

        tokenizer_add_kwargs = self.get_tokenizer(bos_token="(BOS)", eos_token="(EOS)")
        image_processor_add_kwargs = self.get_image_processor(do_normalize=False, padding_value=1.0)

        processor = InstructBlipProcessor.from_pretrained(
            self.tmpdirname, bos_token="(BOS)", eos_token="(EOS)", do_normalize=False, padding_value=1.0
        )

        self.assertEqual(processor.tokenizer.get_vocab(), tokenizer_add_kwargs.get_vocab())
        self.assertIsInstance(processor.tokenizer, PreTrainedTokenizerFast)

        self.assertEqual(processor.image_processor.to_json_string(), image_processor_add_kwargs.to_json_string())
        self.assertIsInstance(processor.image_processor, BlipImageProcessor)
        self.assertIsInstance(processor.qformer_tokenizer, BertTokenizerFast)

    def test_image_processor(self):
        image_processor = self.get_image_processor()
        tokenizer = self.get_tokenizer()
        qformer_tokenizer = self.get_qformer_tokenizer()

        processor = InstructBlipProcessor(
            tokenizer=tokenizer, image_processor=image_processor, qformer_tokenizer=qformer_tokenizer
        )

        image_input = self.prepare_image_inputs()

        input_feat_extract = image_processor(image_input, return_tensors="np")
        input_processor = processor(images=image_input, return_tensors="np")

        for key in input_feat_extract.keys():
            self.assertAlmostEqual(input_feat_extract[key].sum(), input_processor[key].sum(), delta=1e-2)

    def test_tokenizer(self):
        image_processor = self.get_image_processor()
        tokenizer = self.get_tokenizer()
        qformer_tokenizer = self.get_qformer_tokenizer()

        processor = InstructBlipProcessor(
            tokenizer=tokenizer, image_processor=image_processor, qformer_tokenizer=qformer_tokenizer
        )

        input_str = "lower newer"

        encoded_processor = processor(text=input_str)

        encoded_tokens = tokenizer(input_str, return_token_type_ids=False)
        encoded_tokens_qformer = qformer_tokenizer(input_str, return_token_type_ids=False)

        for key in encoded_tokens.keys():
            self.assertListEqual(encoded_tokens[key], encoded_processor[key])

        for key in encoded_tokens_qformer.keys():
            self.assertListEqual(encoded_tokens_qformer[key], encoded_processor["qformer_" + key])

    def test_processor(self):
        image_processor = self.get_image_processor()
        tokenizer = self.get_tokenizer()
        qformer_tokenizer = self.get_qformer_tokenizer()

        processor = InstructBlipProcessor(
            tokenizer=tokenizer, image_processor=image_processor, qformer_tokenizer=qformer_tokenizer
        )

        input_str = "lower newer"
        image_input = self.prepare_image_inputs()

        inputs = processor(text=input_str, images=image_input)

        self.assertListEqual(
            list(inputs.keys()),
            ["input_ids", "attention_mask", "qformer_input_ids", "qformer_attention_mask", "pixel_values"],
        )

        # test if it raises when no input is passed
        with pytest.raises(ValueError):
            processor()

    def test_tokenizer_decode(self):
        image_processor = self.get_image_processor()
        tokenizer = self.get_tokenizer()
        qformer_tokenizer = self.get_qformer_tokenizer()

        processor = InstructBlipProcessor(
            tokenizer=tokenizer, image_processor=image_processor, qformer_tokenizer=qformer_tokenizer
        )

        predicted_ids = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

        decoded_processor = processor.batch_decode(predicted_ids)
        decoded_tok = tokenizer.batch_decode(predicted_ids)

        self.assertListEqual(decoded_tok, decoded_processor)

    def test_model_input_names(self):
        image_processor = self.get_image_processor()
        tokenizer = self.get_tokenizer()
        qformer_tokenizer = self.get_qformer_tokenizer()

        processor = InstructBlipProcessor(
            tokenizer=tokenizer, image_processor=image_processor, qformer_tokenizer=qformer_tokenizer
        )

        input_str = "lower newer"
        image_input = self.prepare_image_inputs()

        inputs = processor(text=input_str, images=image_input)

        self.assertListEqual(
            list(inputs.keys()),
            ["input_ids", "attention_mask", "qformer_input_ids", "qformer_attention_mask", "pixel_values"],
        )
