# Copyright 2021 The HuggingFace Team. All rights reserved.
# Copyright (c) 2022 Graphcore Ltd. 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_IMAGE_CLASSIFICATION_MAPPING, PreTrainedTokenizer, is_vision_available
from transformers.testing_utils import nested_simplify, require_torch, require_vision

from optimum.graphcore import pipeline

from .test_pipelines_common import ANY, PipelineTestCaseMeta


if is_vision_available():
    from PIL import Image
else:

    class Image:
        @staticmethod
        def open(*args, **kwargs):
            pass


@require_torch
@require_vision
class ImageClassificationPipelineTests(unittest.TestCase, metaclass=PipelineTestCaseMeta):
    model_mapping = MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING

    def get_test_pipeline(self, model, ipu_config, tokenizer, feature_extractor):
        image_classifier = pipeline(
            task="image-classification",
            model=model,
            ipu_config=ipu_config,
            feature_extractor=feature_extractor,
            top_k=2,
        )
        examples = [
            Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"),
            "http://images.cocodataset.org/val2017/000000039769.jpg",
        ]
        return image_classifier, examples

    def run_pipeline_test(self, image_classifier, examples):
        outputs = image_classifier("./tests/fixtures/tests_samples/COCO/000000039769.png")

        self.assertEqual(
            outputs,
            [
                {"score": ANY(float), "label": ANY(str)},
                {"score": ANY(float), "label": ANY(str)},
            ],
        )

        import datasets

        dataset = datasets.load_dataset("hf-internal-testing/fixtures_image_utils", "image", split="test")

        # Accepts URL + PIL.Image + lists
        outputs = image_classifier(
            [
                Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"),
                "http://images.cocodataset.org/val2017/000000039769.jpg",
                # RGBA
                dataset[0]["file"],
                # LA
                dataset[1]["file"],
                # L
                dataset[2]["file"],
            ]
        )
        self.assertEqual(
            outputs,
            [
                [
                    {"score": ANY(float), "label": ANY(str)},
                    {"score": ANY(float), "label": ANY(str)},
                ],
                [
                    {"score": ANY(float), "label": ANY(str)},
                    {"score": ANY(float), "label": ANY(str)},
                ],
                [
                    {"score": ANY(float), "label": ANY(str)},
                    {"score": ANY(float), "label": ANY(str)},
                ],
                [
                    {"score": ANY(float), "label": ANY(str)},
                    {"score": ANY(float), "label": ANY(str)},
                ],
                [
                    {"score": ANY(float), "label": ANY(str)},
                    {"score": ANY(float), "label": ANY(str)},
                ],
            ],
        )

    @require_torch
    def test_small_model_pt(self):
        small_model = "hf-internal-testing/tiny-random-vit"
        image_classifier = pipeline(
            "image-classification",
            model=small_model,
            ipu_config={"layers_per_ipu": [5], "ipus_per_replica": 1},
        )

        outputs = image_classifier("http://images.cocodataset.org/val2017/000000039769.jpg")
        self.assertEqual(
            nested_simplify(outputs, decimals=4),
            [{"label": "LABEL_1", "score": 0.574}, {"label": "LABEL_0", "score": 0.426}],
        )

        outputs = image_classifier(
            [
                "http://images.cocodataset.org/val2017/000000039769.jpg",
                "http://images.cocodataset.org/val2017/000000039769.jpg",
            ],
            top_k=2,
        )
        self.assertEqual(
            nested_simplify(outputs, decimals=4),
            [
                [{"label": "LABEL_1", "score": 0.574}, {"label": "LABEL_0", "score": 0.426}],
                [{"label": "LABEL_1", "score": 0.574}, {"label": "LABEL_0", "score": 0.426}],
            ],
        )

    def test_custom_tokenizer(self):
        tokenizer = PreTrainedTokenizer()

        # Assert that the pipeline can be initialized with a feature extractor that is not in any mapping
        image_classifier = pipeline(
            "image-classification",
            model="hf-internal-testing/tiny-random-vit",
            ipu_config={"layers_per_ipu": [5], "ipus_per_replica": 1},
            tokenizer=tokenizer,
        )

        self.assertIs(image_classifier.tokenizer, tokenizer)

    # @slow
    # @require_torch
    # def test_perceiver(self):
    #     # Perceiver is not tested by `run_pipeline_test` properly.
    #     # That is because the type of feature_extractor and model preprocessor need to be kept
    #     # in sync, which is not the case in the current design
    #     image_classifier = pipeline("image-classification", model="deepmind/vision-perceiver-conv")
    #     outputs = image_classifier("http://images.cocodataset.org/val2017/000000039769.jpg")
    #     self.assertEqual(
    #         nested_simplify(outputs, decimals=4),
    #         [
    #             {"score": 0.4385, "label": "tabby, tabby cat"},
    #             {"score": 0.321, "label": "tiger cat"},
    #             {"score": 0.0502, "label": "Egyptian cat"},
    #             {"score": 0.0137, "label": "crib, cot"},
    #             {"score": 0.007, "label": "radiator"},
    #         ],
    #     )

    #     image_classifier = pipeline("image-classification", model="deepmind/vision-perceiver-fourier")
    #     outputs = image_classifier("http://images.cocodataset.org/val2017/000000039769.jpg")
    #     self.assertEqual(
    #         nested_simplify(outputs, decimals=4),
    #         [
    #             {"score": 0.5658, "label": "tabby, tabby cat"},
    #             {"score": 0.1309, "label": "tiger cat"},
    #             {"score": 0.0722, "label": "Egyptian cat"},
    #             {"score": 0.0707, "label": "remote control, remote"},
    #             {"score": 0.0082, "label": "computer keyboard, keypad"},
    #         ],
    #     )

    #     image_classifier = pipeline("image-classification", model="deepmind/vision-perceiver-learned")
    #     outputs = image_classifier("http://images.cocodataset.org/val2017/000000039769.jpg")
    #     self.assertEqual(
    #         nested_simplify(outputs, decimals=4),
    #         [
    #             {"score": 0.3022, "label": "tabby, tabby cat"},
    #             {"score": 0.2362, "label": "Egyptian cat"},
    #             {"score": 0.1856, "label": "tiger cat"},
    #             {"score": 0.0324, "label": "remote control, remote"},
    #             {"score": 0.0096, "label": "quilt, comforter, comfort, puff"},
    #         ],
    #     )