import json
import os
import pytest
from unittest.mock import MagicMock, patch

from doi2dataset import MetadataProcessor


@pytest.fixture
def openalex_data():
    """Load the saved JSON response from the file 'srep45389.json'"""
    json_path = os.path.join(os.path.dirname(__file__), "srep45389.json")
    with open(json_path, "r", encoding="utf-8") as f:
        data = json.load(f)
    return data


@pytest.fixture
def metadata_processor():
    """Create a MetadataProcessor instance with mocked dependencies"""
    doi = "10.1038/srep45389"
    processor = MetadataProcessor(doi=doi, upload=False, progress=False)
    return processor


def test_build_metadata_basic_fields(metadata_processor, openalex_data, monkeypatch):
    """Test that _build_metadata correctly extracts basic metadata fields"""
    # Mock the console to avoid print errors
    metadata_processor.console = MagicMock()
    
    # Mock the Abstract related methods and objects to avoid console errors
    abstract_mock = MagicMock()
    abstract_mock.text = "This is a sample abstract"
    abstract_mock.source = "openalex"
    monkeypatch.setattr("doi2dataset.AbstractProcessor.get_abstract", lambda *args, **kwargs: abstract_mock)
    
    # Mock the _fetch_data method to return our test data
    metadata_processor._fetch_data = MagicMock(return_value=openalex_data)
    
    # Mock methods that might cause issues in isolation
    metadata_processor._build_description = MagicMock(return_value="Test description")
    metadata_processor._get_involved_pis = MagicMock(return_value=[])
    metadata_processor._build_organization_metadata = MagicMock(return_value={})
    
    # Call the method we're testing
    metadata = metadata_processor._build_metadata(openalex_data)

    # Verify the basic metadata fields were extracted correctly
    assert metadata is not None
    assert 'datasetVersion' in metadata
    
    # Examine the fields inside datasetVersion.metadataBlocks
    assert 'metadataBlocks' in metadata['datasetVersion']
    citation = metadata['datasetVersion']['metadataBlocks'].get('citation', {})
    
    # Check fields in citation section
    assert 'fields' in citation
    fields = citation['fields']
    
    # Check for basic metadata fields in a more flexible way
    field_names = [field.get('typeName') for field in fields]
    assert 'title' in field_names
    assert 'subject' in field_names
    assert 'dsDescription' in field_names  # Description is named 'dsDescription' in the schema


def test_build_metadata_authors(metadata_processor, openalex_data, monkeypatch):
    """Test that _build_metadata correctly processes author information"""
    # Mock the console to avoid print errors
    metadata_processor.console = MagicMock()
    
    # Mock the Abstract related methods and objects to avoid console errors
    abstract_mock = MagicMock()
    abstract_mock.text = "This is a sample abstract"
    abstract_mock.source = "openalex"
    monkeypatch.setattr("doi2dataset.AbstractProcessor.get_abstract", lambda *args, **kwargs: abstract_mock)
    
    # Mock the _fetch_data method to return our test data
    metadata_processor._fetch_data = MagicMock(return_value=openalex_data)
    
    # Mock methods that might cause issues in isolation
    metadata_processor._build_description = MagicMock(return_value="Test description")
    metadata_processor._get_involved_pis = MagicMock(return_value=[])
    metadata_processor._build_organization_metadata = MagicMock(return_value={})
    
    # Call the method we're testing
    metadata = metadata_processor._build_metadata(openalex_data)

    # Examine the fields inside datasetVersion.metadataBlocks
    assert 'metadataBlocks' in metadata['datasetVersion']
    citation = metadata['datasetVersion']['metadataBlocks'].get('citation', {})
    
    # Check fields in citation section
    assert 'fields' in citation
    fields = citation['fields']
    
    # Check for author and datasetContact fields
    field_names = [field.get('typeName') for field in fields]
    assert 'author' in field_names
    assert 'datasetContact' in field_names
    
    # Verify these are compound fields with actual entries
    for field in fields:
        if field.get('typeName') == 'author':
            assert 'value' in field
            assert isinstance(field['value'], list)
            assert len(field['value']) > 0
        
        if field.get('typeName') == 'datasetContact':
            assert 'value' in field
            assert isinstance(field['value'], list)
            # The datasetContact might be empty in test environment
            # Just check it exists rather than asserting length


def test_build_metadata_keywords_and_topics(metadata_processor, openalex_data, monkeypatch):
    """Test that _build_metadata correctly extracts keywords and topics"""
    # Mock the console to avoid print errors
    metadata_processor.console = MagicMock()
    
    # Mock the Abstract related methods and objects to avoid console errors
    abstract_mock = MagicMock()
    abstract_mock.text = "This is a sample abstract"
    abstract_mock.source = "openalex"
    monkeypatch.setattr("doi2dataset.AbstractProcessor.get_abstract", lambda *args, **kwargs: abstract_mock)
    
    # Mock the _fetch_data method to return our test data
    metadata_processor._fetch_data = MagicMock(return_value=openalex_data)
    
    # Mock methods that might cause issues in isolation
    metadata_processor._build_description = MagicMock(return_value="Test description")
    metadata_processor._get_involved_pis = MagicMock(return_value=[])
    metadata_processor._build_organization_metadata = MagicMock(return_value={})
    
    # Call the method we're testing
    metadata = metadata_processor._build_metadata(openalex_data)
        
    # Examine the fields inside datasetVersion.metadataBlocks
    assert 'metadataBlocks' in metadata['datasetVersion']
    citation = metadata['datasetVersion']['metadataBlocks'].get('citation', {})
    
    # Check fields in citation section
    assert 'fields' in citation
    fields = citation['fields']
    
    # Check for keyword and subject fields
    field_names = [field.get('typeName') for field in fields]
    
    # If keywords exist, verify structure
    if 'keyword' in field_names:
        for field in fields:
            if field.get('typeName') == 'keyword':
                assert 'value' in field
                assert isinstance(field['value'], list)
    
    # Check for subject field which should definitely exist
    assert 'subject' in field_names
    for field in fields:
        if field.get('typeName') == 'subject':
            assert 'value' in field
            assert isinstance(field['value'], list)
            assert len(field['value']) > 0