# Copyright 2024 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 GotOcr2Processor
from transformers.testing_utils import require_vision

from ...test_processing_common import ProcessorTesterMixin


@require_vision
class GotOcr2ProcessorTest(ProcessorTesterMixin, unittest.TestCase):
    processor_class = GotOcr2Processor
    # Tiny processor created with make_tiny_processor.py from "stepfun-ai/GOT-OCR-2.0-hf"
    tiny_model_id = "hf-internal-testing/tiny-processor-got_ocr2"

    @classmethod
    def _setup_image_processor(cls):
        # Instantiate directly to avoid loading the full 384×384 image processor from Hub.
        image_processor_class = cls._get_component_class_from_processor("image_processor")
        return image_processor_class()

    @unittest.skip("GotOcr2Processor pop the image processor output 'num_patches'")
    def test_image_processor_defaults(self):
        pass

    def test_ocr_queries(self):
        processor = self.get_processor()
        image_input = self.prepare_image_inputs()
        inputs = processor(image_input, return_tensors="pt")
        self.assertEqual(inputs["input_ids"].shape, (1, 324))
        self.assertEqual(inputs["pixel_values"].shape, (1, 3, 384, 384))

        inputs = processor(image_input, return_tensors="pt", format=True)
        self.assertEqual(inputs["input_ids"].shape, (1, 328))
        self.assertEqual(inputs["pixel_values"].shape, (1, 3, 384, 384))

        inputs = processor(image_input, return_tensors="pt", color="red")
        self.assertEqual(inputs["input_ids"].shape, (1, 329))
        self.assertEqual(inputs["pixel_values"].shape, (1, 3, 384, 384))

        inputs = processor(image_input, return_tensors="pt", box=[0, 0, 100, 100])
        self.assertEqual(inputs["input_ids"].shape, (1, 341))
        self.assertEqual(inputs["pixel_values"].shape, (1, 3, 384, 384))

        inputs = processor([image_input, image_input], return_tensors="pt", multi_page=True, format=True)
        self.assertEqual(inputs["input_ids"].shape, (1, 595))
        self.assertEqual(inputs["pixel_values"].shape, (2, 3, 384, 384))

        inputs = processor(image_input, return_tensors="pt", crop_to_patches=True, max_patches=6)
        self.assertEqual(inputs["input_ids"].shape, (1, 1872))
        self.assertEqual(inputs["pixel_values"].shape, (7, 3, 384, 384))

    def test_processor_text_has_no_visual(self):
        # Overwritten: requires `multi_page` kwarg to process nested vision inputs
        processor = self.get_processor()

        text = self.prepare_text_inputs(batch_size=3, modalities="image")
        image_inputs = self.prepare_image_inputs(batch_size=3)
        processing_kwargs = {"return_tensors": "pt", "padding": True, "multi_page": True}

        # Call with nested list of vision inputs
        image_inputs_nested = [[image] if not isinstance(image, list) else image for image in image_inputs]
        inputs_dict_nested = {"text": text, "images": image_inputs_nested}
        inputs = processor(**inputs_dict_nested, **processing_kwargs)
        self.assertTrue(self.text_input_name in inputs)

        # Call with one of the samples with no associated vision input
        plain_text = "lower newer"
        image_inputs_nested[0] = []
        text[0] = plain_text
        inputs_dict_no_vision = {"text": text, "images": image_inputs_nested}
        inputs_nested = processor(**inputs_dict_no_vision, **processing_kwargs)
        self.assertListEqual(
            inputs[self.text_input_name][1:].tolist(), inputs_nested[self.text_input_name][1:].tolist()
        )