package googleai

import (
	"context"
	"errors"
	"testing"

	"github.com/stretchr/testify/assert"
)

// Note: These tests are conceptual as the new google.golang.org/genai SDK does not
// expose mockable interfaces for embeddings. In practice, full integration tests
// would require real API credentials.

func TestCreateEmbedding_ConceptualTests(t *testing.T) {
	t.Parallel()

	// These tests demonstrate what we would test if we had better mockability
	t.Run("empty texts", func(t *testing.T) {
		// Would test that empty input returns empty output
		texts := []string{}
		expectedResult := [][]float32{}
		_ = texts
		_ = expectedResult
		// The actual test would verify the behavior with empty input
	})

	t.Run("single text", func(t *testing.T) {
		// Would test embedding a single text
		texts := []string{"Hello world"}
		_ = texts
		// The actual test would verify single text embedding
	})

	t.Run("multiple texts under batch limit", func(t *testing.T) {
		// Would test embedding multiple texts (< 100)
		texts := make([]string, 50)
		for i := range texts {
			texts[i] = "Text content"
		}
		_ = texts
		// The actual test would verify batch processing under limit
	})

	t.Run("multiple texts over batch limit", func(t *testing.T) {
		// Would test embedding multiple texts requiring multiple batches
		texts := make([]string, 250) // More than 100, should trigger multiple batches
		for i := range texts {
			texts[i] = "Text content"
		}
		_ = texts
		// The actual test would verify proper batching with multiple API calls
	})

	t.Run("exactly 100 texts", func(t *testing.T) {
		// Would test the boundary condition of exactly 100 texts
		texts := make([]string, 100)
		for i := range texts {
			texts[i] = "Text content"
		}
		_ = texts
		// The actual test would verify single batch for exactly 100 texts
	})

	t.Run("embedding API error", func(t *testing.T) {
		// Would test error handling when the embedding API fails
		texts := []string{"Hello world"}
		expectedError := errors.New("API error")
		_ = texts
		_ = expectedError
		// The actual test would verify error propagation
	})
}

func TestEmbeddingBatchLogic(t *testing.T) {
	t.Parallel()

	// Test the batching logic without actual API calls
	testCases := []struct {
		name            string
		numTexts        int
		expectedBatches int
	}{
		{"empty", 0, 0},
		{"single text", 1, 1},
		{"small batch", 50, 1},
		{"exactly 100", 100, 1},
		{"101 texts", 101, 2},
		{"200 texts", 200, 2},
		{"250 texts", 250, 3},
		{"999 texts", 999, 10},
		{"1000 texts", 1000, 10},
	}

	for _, tc := range testCases {
		t.Run(tc.name, func(t *testing.T) {
			// Calculate expected number of batches based on the logic in CreateEmbedding
			expectedBatches := 0
			if tc.numTexts > 0 {
				expectedBatches = (tc.numTexts + 99) / 100 // Ceiling division
			}

			assert.Equal(t, tc.expectedBatches, expectedBatches,
				"For %d texts, expected %d batches but calculated %d",
				tc.numTexts, tc.expectedBatches, expectedBatches)
		})
	}
}

func TestEmbeddingConstants(t *testing.T) {
	t.Parallel()

	// Test that we understand the embedding batch size limit
	const expectedBatchSize = 100

	// This is documented in the CreateEmbedding function
	// "The Gemini Embedding API allows up to 100 documents per batch"
	assert.Equal(t, 100, expectedBatchSize)
}

// These tests would be more meaningful with dependency injection
// For now, they serve as documentation of expected behavior

func TestCreateEmbedding_ErrorScenarios(t *testing.T) {
	t.Parallel()

	t.Run("context cancellation", func(t *testing.T) {
		// Would test behavior when context is cancelled
		ctx, cancel := context.WithCancel(context.Background())
		cancel() // Cancel immediately
		_ = ctx
		// The actual test would verify proper context handling
	})

	t.Run("context timeout", func(t *testing.T) {
		// Would test behavior when context times out
		// The actual test would verify timeout handling
	})
}

func TestCreateEmbedding_Integration_Placeholder(t *testing.T) {
	t.Skip("Integration test placeholder - requires real Google AI client")

	// This would be an integration test that actually calls the Google AI API
	// It would require:
	// 1. Valid API credentials
	// 2. Network access
	// 3. Test data
	// 4. Assertions on actual embeddings returned
}

// Test helper functions and validation

func TestEmbeddingInputValidation(t *testing.T) {
	t.Parallel()

	t.Run("valid text content", func(t *testing.T) {
		validTexts := []string{
			"Hello world",
			"This is a test",
			"Multiple sentences. With punctuation!",
			"Unicode content: 你好世界",
			"", // Empty string should be valid
		}

		for _, text := range validTexts {
			// Each text should be valid input for embedding
			assert.IsType(t, "", text, "Text should be string type")
		}
	})

	t.Run("edge cases", func(t *testing.T) {
		edgeCases := []string{
			string(make([]byte, 1000)), // Very long text
			"\n\t\r",                   // Whitespace only
			"🚀🌟💫",                      // Emoji only
		}

		for _, text := range edgeCases {
			// Edge cases should still be valid strings
			assert.IsType(t, "", text, "Edge case should be string type")
		}
	})
}

func TestEmbeddingOutputValidation(t *testing.T) {
	t.Parallel()

	// Test the expected structure of embedding outputs
	t.Run("output format", func(t *testing.T) {
		// Embeddings should be [][]float32 where each inner slice
		// represents the embedding vector for one input text
		expectedOutput := [][]float32{
			{0.1, 0.2, 0.3, 0.4},
			{0.5, 0.6, 0.7, 0.8},
		}

		assert.IsType(t, [][]float32{}, expectedOutput)
		assert.Len(t, expectedOutput, 2)

		for i, embedding := range expectedOutput {
			assert.IsType(t, []float32{}, embedding, "Embedding %d should be []float32", i)
			assert.NotEmpty(t, embedding, "Embedding %d should not be empty", i)
		}
	})
}
