# coding=utf-8
# Copyright 2025 The HuggingFace Inc. 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
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"""Testing suite for the ColQwen2 processor."""

import shutil
import tempfile
import unittest

import torch

from transformers import AutoProcessor, Qwen2VLProcessor
from transformers.models.colqwen2.processing_colqwen2 import ColQwen2Processor
from transformers.testing_utils import get_tests_dir, require_torch, require_vision
from transformers.utils import is_vision_available

from ...test_processing_common import ProcessorTesterMixin


if is_vision_available():
    from transformers import (
        ColQwen2Processor,
    )

SAMPLE_VOCAB = get_tests_dir("fixtures/test_sentencepiece.model")


@require_torch
@require_vision
class ColQwen2ProcessorTest(ProcessorTesterMixin, unittest.TestCase):
    processor_class = ColQwen2Processor

    @classmethod
    def setUpClass(cls):
        cls.tmpdirname = tempfile.mkdtemp()
        processor = Qwen2VLProcessor.from_pretrained("Qwen/Qwen2-VL-2B-Instruct")
        processor.save_pretrained(cls.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

    @classmethod
    def tearDownClass(cls):
        shutil.rmtree(cls.tmpdirname)

    # Copied from tests.models.llava.test_processing_llava.LlavaProcessorTest.test_get_num_vision_tokens
    def test_get_num_vision_tokens(self):
        "Tests general functionality of the helper used internally in vLLM"

        processor = self.get_processor()

        output = processor._get_num_multimodal_tokens(image_sizes=[(100, 100), (300, 100), (500, 30)])
        self.assertTrue("num_image_tokens" in output)
        self.assertEqual(len(output["num_image_tokens"]), 3)

        self.assertTrue("num_image_patches" in output)
        self.assertEqual(len(output["num_image_patches"]), 3)

    def test_process_images(self):
        # Processor configuration
        image_input = self.prepare_image_inputs()
        image_processor = self.get_component("image_processor")
        tokenizer = self.get_component("tokenizer", max_length=112, padding="max_length")
        image_processor.image_seq_length = 14

        # Get the processor
        processor = self.processor_class(
            tokenizer=tokenizer,
            image_processor=image_processor,
        )

        # Process the image
        batch_feature = processor.process_images(images=image_input, return_tensors="pt")

        # Assertions
        self.assertIn("pixel_values", batch_feature)
        self.assertEqual(batch_feature["pixel_values"].shape, torch.Size([1, 56, 1176]))

    def test_process_queries(self):
        # Inputs
        queries = [
            "Is attention really all you need?",
            "Are Benjamin, Antoine, Merve, and Jo best friends?",
        ]

        # Processor configuration
        image_processor = self.get_component("image_processor")
        tokenizer = self.get_component("tokenizer", max_length=112, padding="max_length")
        image_processor.image_seq_length = 14

        # Get the processor
        processor = self.processor_class(
            tokenizer=tokenizer,
            image_processor=image_processor,
        )

        # Process the image
        batch_feature = processor.process_queries(text=queries, return_tensors="pt")

        # Assertions
        self.assertIn("input_ids", batch_feature)
        self.assertIsInstance(batch_feature["input_ids"], torch.Tensor)
        self.assertEqual(batch_feature["input_ids"].shape[0], len(queries))

    # The following tests override the parent tests because ColQwen2Processor can only take one of images or text as input at a time.

    def test_tokenizer_defaults_preserved_by_kwargs(self):
        if "image_processor" not in self.processor_class.attributes:
            self.skipTest(f"image_processor attribute not present in {self.processor_class}")
        processor_components = self.prepare_components()
        processor_components["tokenizer"] = self.get_component("tokenizer", max_length=117, padding="max_length")

        processor = self.processor_class(**processor_components)
        self.skip_processor_without_typed_kwargs(processor)
        input_str = self.prepare_text_inputs()
        inputs = processor(text=input_str, return_tensors="pt")
        self.assertEqual(inputs[self.text_input_name].shape[-1], 117)

    def test_image_processor_defaults_preserved_by_image_kwargs(self):
        """
        We use do_rescale=True, rescale_factor=-1.0 to ensure that image_processor kwargs are preserved in the processor.
        We then check that the mean of the pixel_values is less than or equal to 0 after processing.
        Since the original pixel_values are in [0, 255], this is a good indicator that the rescale_factor is indeed applied.
        """
        if "image_processor" not in self.processor_class.attributes:
            self.skipTest(f"image_processor attribute not present in {self.processor_class}")
        processor_components = self.prepare_components()
        processor_components["image_processor"] = self.get_component(
            "image_processor", do_rescale=True, rescale_factor=-1.0
        )
        processor_components["tokenizer"] = self.get_component("tokenizer", max_length=117, padding="max_length")

        processor = self.processor_class(**processor_components)
        self.skip_processor_without_typed_kwargs(processor)

        image_input = self.prepare_image_inputs()

        inputs = processor(images=image_input, return_tensors="pt")
        self.assertLessEqual(inputs[self.images_input_name][0][0].mean(), 0)

    def test_kwargs_overrides_default_tokenizer_kwargs(self):
        if "image_processor" not in self.processor_class.attributes:
            self.skipTest(f"image_processor attribute not present in {self.processor_class}")
        processor_components = self.prepare_components()
        processor_components["tokenizer"] = self.get_component("tokenizer", padding="longest")

        processor = self.processor_class(**processor_components)
        self.skip_processor_without_typed_kwargs(processor)
        input_str = self.prepare_text_inputs()
        inputs = processor(text=input_str, return_tensors="pt", max_length=112, padding="max_length")
        self.assertEqual(inputs[self.text_input_name].shape[-1], 112)

    def test_kwargs_overrides_default_image_processor_kwargs(self):
        if "image_processor" not in self.processor_class.attributes:
            self.skipTest(f"image_processor attribute not present in {self.processor_class}")
        processor_components = self.prepare_components()
        processor_components["image_processor"] = self.get_component(
            "image_processor", do_rescale=True, rescale_factor=1
        )
        processor_components["tokenizer"] = self.get_component("tokenizer", max_length=117, padding="max_length")

        processor = self.processor_class(**processor_components)
        self.skip_processor_without_typed_kwargs(processor)

        image_input = self.prepare_image_inputs()

        inputs = processor(images=image_input, do_rescale=True, rescale_factor=-1.0, return_tensors="pt")
        self.assertLessEqual(inputs[self.images_input_name][0][0].mean(), 0)

    def test_unstructured_kwargs(self):
        if "image_processor" not in self.processor_class.attributes:
            self.skipTest(f"image_processor attribute not present in {self.processor_class}")
        processor_components = self.prepare_components()
        processor = self.processor_class(**processor_components)
        self.skip_processor_without_typed_kwargs(processor)

        input_str = self.prepare_text_inputs()
        inputs = processor(
            text=input_str,
            return_tensors="pt",
            do_rescale=True,
            rescale_factor=-1.0,
            padding="max_length",
            max_length=76,
        )

        self.assertEqual(inputs[self.text_input_name].shape[-1], 76)

    def test_unstructured_kwargs_batched(self):
        if "image_processor" not in self.processor_class.attributes:
            self.skipTest(f"image_processor attribute not present in {self.processor_class}")
        processor_components = self.prepare_components()
        processor = self.processor_class(**processor_components)
        self.skip_processor_without_typed_kwargs(processor)

        image_input = self.prepare_image_inputs(batch_size=2)
        inputs = processor(
            images=image_input,
            return_tensors="pt",
            do_rescale=True,
            rescale_factor=-1.0,
            padding="longest",
            max_length=76,
        )

        self.assertLessEqual(inputs[self.images_input_name][0][0].mean(), 0)

    def test_doubly_passed_kwargs(self):
        if "image_processor" not in self.processor_class.attributes:
            self.skipTest(f"image_processor attribute not present in {self.processor_class}")
        processor_components = self.prepare_components()
        processor = self.processor_class(**processor_components)
        self.skip_processor_without_typed_kwargs(processor)

        image_input = self.prepare_image_inputs()
        with self.assertRaises(ValueError):
            _ = processor(
                images=image_input,
                images_kwargs={"do_rescale": True, "rescale_factor": -1.0},
                do_rescale=True,
                return_tensors="pt",
            )

    def test_structured_kwargs_nested(self):
        if "image_processor" not in self.processor_class.attributes:
            self.skipTest(f"image_processor attribute not present in {self.processor_class}")
        processor_components = self.prepare_components()
        processor = self.processor_class(**processor_components)
        self.skip_processor_without_typed_kwargs(processor)

        input_str = self.prepare_text_inputs()

        # Define the kwargs for each modality
        all_kwargs = {
            "common_kwargs": {"return_tensors": "pt"},
            "images_kwargs": {"do_rescale": True, "rescale_factor": -1.0},
            "text_kwargs": {"padding": "max_length", "max_length": 76},
        }

        inputs = processor(text=input_str, **all_kwargs)
        self.skip_processor_without_typed_kwargs(processor)

        self.assertEqual(inputs[self.text_input_name].shape[-1], 76)

    def test_structured_kwargs_nested_from_dict(self):
        if "image_processor" not in self.processor_class.attributes:
            self.skipTest(f"image_processor attribute not present in {self.processor_class}")
        processor_components = self.prepare_components()
        processor = self.processor_class(**processor_components)
        self.skip_processor_without_typed_kwargs(processor)
        image_input = self.prepare_image_inputs()

        # Define the kwargs for each modality
        all_kwargs = {
            "common_kwargs": {"return_tensors": "pt"},
            "images_kwargs": {"do_rescale": True, "rescale_factor": -1.0},
            "text_kwargs": {"padding": "max_length", "max_length": 76},
        }

        inputs = processor(images=image_input, **all_kwargs)
        self.assertEqual(inputs[self.text_input_name].shape[-1], 76)

    # Can process only text or images at a time
    def test_model_input_names(self):
        processor = self.get_processor()
        image_input = self.prepare_image_inputs()
        inputs = processor(images=image_input)

        self.assertSetEqual(set(inputs.keys()), set(processor.model_input_names))

    @unittest.skip("ColPali can't process text+image inputs at the same time")
    def test_processor_text_has_no_visual(self):
        pass
