mirror of
https://github.com/dogkeeper886/ollama37.git
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This commit represents a complete rework after pulling the latest changes from official ollama/ollama repository and re-applying Tesla K80 compatibility patches. ## Key Changes ### CUDA Compute Capability 3.7 Support (Tesla K80) - Added sm_37 (compute 3.7) to CMAKE_CUDA_ARCHITECTURES in CMakeLists.txt - Updated CMakePresets.json to include compute 3.7 in "CUDA 11" preset - Using 37-virtual (PTX with JIT compilation) for maximum compatibility ### Legacy Toolchain Compatibility - **NVIDIA Driver**: 470.256.02 (last version supporting Kepler/K80) - **CUDA Version**: 11.4.4 (last CUDA 11.x supporting compute 3.7) - **GCC Version**: 10.5.0 (required by CUDA 11.4 host_config.h) ### CPU Architecture Trade-offs Due to GCC 10.5 limitation, sacrificed newer CPU optimizations: - Alderlake CPU variant enabled WITHOUT AVX_VNNI (requires GCC 11+) - Still supports: SSE4.2, AVX, F16C, AVX2, BMI2, FMA - Performance impact: ~3-7% on newer CPUs (acceptable for K80 compatibility) ### Build System Updates - Modified ml/backend/ggml/ggml/src/ggml-cuda/CMakeLists.txt for compute 3.7 - Added -Wno-deprecated-gpu-targets flag to suppress warnings - Updated ml/backend/ggml/ggml/src/CMakeLists.txt for Alderlake without AVX_VNNI ### Upstream Sync Merged latest llama.cpp changes including: - Enhanced KV cache management with ISWA and hybrid memory support - Improved multi-modal support (mtmd framework) - New model architectures (Gemma3, Llama4, Qwen3, etc.) - GPU backend improvements for CUDA, Metal, and ROCm - Updated quantization support and GGUF format handling ### Documentation - Updated CLAUDE.md with comprehensive build instructions - Documented toolchain constraints and CPU architecture trade-offs - Removed outdated CI/CD workflows (tesla-k80-*.yml) - Cleaned up temporary development artifacts ## Rationale This fork maintains Tesla K80 GPU support (compute 3.7) which was dropped in official Ollama due to legacy driver/CUDA requirements. The toolchain constraint creates a deadlock: - K80 → Driver 470 → CUDA 11.4 → GCC 10 → No AVX_VNNI We accept the loss of cutting-edge CPU optimizations to enable running modern LLMs on legacy but still capable Tesla K80 hardware (12GB VRAM per GPU). 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
221 lines
5.7 KiB
Go
221 lines
5.7 KiB
Go
package middleware
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import (
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"encoding/base64"
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"encoding/json"
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"net/http"
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"net/http/httptest"
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"strings"
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"testing"
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"github.com/gin-gonic/gin"
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"github.com/ollama/ollama/api"
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"github.com/ollama/ollama/openai"
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)
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func TestEmbeddingsMiddleware_EncodingFormats(t *testing.T) {
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testCases := []struct {
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name string
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encodingFormat string
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expectType string // "array" or "string"
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verifyBase64 bool
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}{
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{"float format", "float", "array", false},
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{"base64 format", "base64", "string", true},
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{"default format", "", "array", false},
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}
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gin.SetMode(gin.TestMode)
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endpoint := func(c *gin.Context) {
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resp := api.EmbedResponse{
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Embeddings: [][]float32{{0.1, -0.2, 0.3}},
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PromptEvalCount: 5,
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}
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c.JSON(http.StatusOK, resp)
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}
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router := gin.New()
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router.Use(EmbeddingsMiddleware())
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router.Handle(http.MethodPost, "/api/embed", endpoint)
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for _, tc := range testCases {
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t.Run(tc.name, func(t *testing.T) {
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body := `{"input": "test", "model": "test-model"`
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if tc.encodingFormat != "" {
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body += `, "encoding_format": "` + tc.encodingFormat + `"`
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}
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body += `}`
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req, _ := http.NewRequest(http.MethodPost, "/api/embed", strings.NewReader(body))
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req.Header.Set("Content-Type", "application/json")
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resp := httptest.NewRecorder()
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router.ServeHTTP(resp, req)
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if resp.Code != http.StatusOK {
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t.Fatalf("expected status 200, got %d", resp.Code)
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}
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var result openai.EmbeddingList
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if err := json.Unmarshal(resp.Body.Bytes(), &result); err != nil {
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t.Fatalf("failed to unmarshal response: %v", err)
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}
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if len(result.Data) != 1 {
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t.Fatalf("expected 1 embedding, got %d", len(result.Data))
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}
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switch tc.expectType {
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case "array":
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if _, ok := result.Data[0].Embedding.([]interface{}); !ok {
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t.Errorf("expected array, got %T", result.Data[0].Embedding)
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}
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case "string":
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embStr, ok := result.Data[0].Embedding.(string)
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if !ok {
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t.Errorf("expected string, got %T", result.Data[0].Embedding)
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} else if tc.verifyBase64 {
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decoded, err := base64.StdEncoding.DecodeString(embStr)
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if err != nil {
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t.Errorf("invalid base64: %v", err)
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} else if len(decoded) != 12 {
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t.Errorf("expected 12 bytes, got %d", len(decoded))
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}
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}
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}
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})
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}
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}
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func TestEmbeddingsMiddleware_BatchWithBase64(t *testing.T) {
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gin.SetMode(gin.TestMode)
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endpoint := func(c *gin.Context) {
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resp := api.EmbedResponse{
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Embeddings: [][]float32{
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{0.1, 0.2},
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{0.3, 0.4},
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{0.5, 0.6},
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},
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PromptEvalCount: 10,
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}
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c.JSON(http.StatusOK, resp)
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}
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router := gin.New()
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router.Use(EmbeddingsMiddleware())
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router.Handle(http.MethodPost, "/api/embed", endpoint)
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body := `{
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"input": ["hello", "world", "test"],
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"model": "test-model",
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"encoding_format": "base64"
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}`
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req, _ := http.NewRequest(http.MethodPost, "/api/embed", strings.NewReader(body))
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req.Header.Set("Content-Type", "application/json")
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resp := httptest.NewRecorder()
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router.ServeHTTP(resp, req)
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if resp.Code != http.StatusOK {
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t.Fatalf("expected status 200, got %d", resp.Code)
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}
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var result openai.EmbeddingList
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if err := json.Unmarshal(resp.Body.Bytes(), &result); err != nil {
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t.Fatalf("failed to unmarshal response: %v", err)
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}
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if len(result.Data) != 3 {
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t.Fatalf("expected 3 embeddings, got %d", len(result.Data))
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}
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// All should be base64 strings
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for i := range 3 {
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embeddingStr, ok := result.Data[i].Embedding.(string)
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if !ok {
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t.Errorf("embedding %d: expected string, got %T", i, result.Data[i].Embedding)
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continue
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}
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// Verify it's valid base64
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if _, err := base64.StdEncoding.DecodeString(embeddingStr); err != nil {
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t.Errorf("embedding %d: invalid base64: %v", i, err)
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}
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// Check index
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if result.Data[i].Index != i {
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t.Errorf("embedding %d: expected index %d, got %d", i, i, result.Data[i].Index)
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}
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}
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}
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func TestEmbeddingsMiddleware_InvalidEncodingFormat(t *testing.T) {
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gin.SetMode(gin.TestMode)
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endpoint := func(c *gin.Context) {
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c.Status(http.StatusOK)
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}
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router := gin.New()
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router.Use(EmbeddingsMiddleware())
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router.Handle(http.MethodPost, "/api/embed", endpoint)
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testCases := []struct {
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name string
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encodingFormat string
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shouldFail bool
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}{
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{"valid: float", "float", false},
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{"valid: base64", "base64", false},
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{"valid: FLOAT (uppercase)", "FLOAT", false},
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{"valid: BASE64 (uppercase)", "BASE64", false},
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{"valid: Float (mixed)", "Float", false},
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{"valid: Base64 (mixed)", "Base64", false},
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{"invalid: json", "json", true},
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{"invalid: hex", "hex", true},
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{"invalid: invalid_format", "invalid_format", true},
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}
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for _, tc := range testCases {
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t.Run(tc.name, func(t *testing.T) {
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body := `{
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"input": "test",
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"model": "test-model",
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"encoding_format": "` + tc.encodingFormat + `"
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}`
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req, _ := http.NewRequest(http.MethodPost, "/api/embed", strings.NewReader(body))
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req.Header.Set("Content-Type", "application/json")
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resp := httptest.NewRecorder()
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router.ServeHTTP(resp, req)
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if tc.shouldFail {
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if resp.Code != http.StatusBadRequest {
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t.Errorf("expected status 400, got %d", resp.Code)
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}
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var errResp openai.ErrorResponse
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if err := json.Unmarshal(resp.Body.Bytes(), &errResp); err != nil {
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t.Fatalf("failed to unmarshal error response: %v", err)
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}
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if errResp.Error.Type != "invalid_request_error" {
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t.Errorf("expected error type 'invalid_request_error', got %q", errResp.Error.Type)
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}
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if !strings.Contains(errResp.Error.Message, "encoding_format") {
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t.Errorf("expected error message to mention encoding_format, got %q", errResp.Error.Message)
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}
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} else {
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if resp.Code != http.StatusOK {
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t.Errorf("expected status 200, got %d: %s", resp.Code, resp.Body.String())
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}
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}
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})
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}
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}
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