", "()V", FF_JNI_METHOD, offsetof(struct JNIAMediaCodecFields, init_id), 1 },
- { "android/media/MediaCodec.BufferInfo", "flags", "I", FF_JNI_FIELD, offsetof(struct JNIAMediaCodecFields, flags_id), 1 },
- { "android/media/MediaCodec.BufferInfo", "offset", "I", FF_JNI_FIELD, offsetof(struct JNIAMediaCodecFields, offset_id), 1 },
- { "android/media/MediaCodec.BufferInfo", "presentationTimeUs", "J", FF_JNI_FIELD, offsetof(struct JNIAMediaCodecFields, presentation_time_us_id), 1 },
- { "android/media/MediaCodec.BufferInfo", "size", "I", FF_JNI_FIELD, offsetof(struct JNIAMediaCodecFields, size_id), 1 },
-
- { NULL }
-};
-
-static const AVClass amediacodec_class = {
- .class_name = "amediacodec",
- .item_name = av_default_item_name,
- .version = LIBAVUTIL_VERSION_INT,
-};
-
-typedef struct FFAMediaCodecJni {
- FFAMediaCodec api;
-
- struct JNIAMediaCodecFields jfields;
-
- jobject object;
- jobject buffer_info;
-
- jobject input_buffers;
- jobject output_buffers;
-
- int INFO_TRY_AGAIN_LATER;
- int INFO_OUTPUT_BUFFERS_CHANGED;
- int INFO_OUTPUT_FORMAT_CHANGED;
-
- int BUFFER_FLAG_CODEC_CONFIG;
- int BUFFER_FLAG_END_OF_STREAM;
- int BUFFER_FLAG_KEY_FRAME;
-
- int CONFIGURE_FLAG_ENCODE;
-
- int has_get_i_o_buffer;
-} FFAMediaCodecJni;
-
-static const FFAMediaCodec media_codec_jni;
-
-#define JNI_GET_ENV_OR_RETURN(env, log_ctx, ret) do { \
- (env) = ff_jni_get_env(log_ctx); \
- if (!(env)) { \
- return ret; \
- } \
-} while (0)
-
-#define JNI_GET_ENV_OR_RETURN_VOID(env, log_ctx) do { \
- (env) = ff_jni_get_env(log_ctx); \
- if (!(env)) { \
- return; \
- } \
-} while (0)
-
-int ff_AMediaCodecProfile_getProfileFromAVCodecContext(AVCodecContext *avctx)
-{
- // Copy and modified from MediaCodecInfo.java
- static const int AVCProfileBaseline = 0x01;
- static const int AVCProfileMain = 0x02;
- static const int AVCProfileExtended = 0x04;
- static const int AVCProfileHigh = 0x08;
- static const int AVCProfileHigh10 = 0x10;
- static const int AVCProfileHigh422 = 0x20;
- static const int AVCProfileHigh444 = 0x40;
- static const int AVCProfileConstrainedBaseline = 0x10000;
- static const int AVCProfileConstrainedHigh = 0x80000;
-
- static const int HEVCProfileMain = 0x01;
- static const int HEVCProfileMain10 = 0x02;
- static const int HEVCProfileMainStill = 0x04;
- static const int HEVCProfileMain10HDR10 = 0x1000;
- static const int HEVCProfileMain10HDR10Plus = 0x2000;
-
- static const int VP9Profile0 = 0x01;
- static const int VP9Profile1 = 0x02;
- static const int VP9Profile2 = 0x04;
- static const int VP9Profile3 = 0x08;
- static const int VP9Profile2HDR = 0x1000;
- static const int VP9Profile3HDR = 0x2000;
- static const int VP9Profile2HDR10Plus = 0x4000;
- static const int VP9Profile3HDR10Plus = 0x8000;
-
- static const int MPEG4ProfileSimple = 0x01;
- static const int MPEG4ProfileSimpleScalable = 0x02;
- static const int MPEG4ProfileCore = 0x04;
- static const int MPEG4ProfileMain = 0x08;
- static const int MPEG4ProfileNbit = 0x10;
- static const int MPEG4ProfileScalableTexture = 0x20;
- static const int MPEG4ProfileSimpleFBA = 0x80;
- static const int MPEG4ProfileSimpleFace = 0x40;
- static const int MPEG4ProfileBasicAnimated = 0x100;
- static const int MPEG4ProfileHybrid = 0x200;
- static const int MPEG4ProfileAdvancedRealTime = 0x400;
- static const int MPEG4ProfileCoreScalable = 0x800;
- static const int MPEG4ProfileAdvancedCoding = 0x1000;
- static const int MPEG4ProfileAdvancedCore = 0x2000;
- static const int MPEG4ProfileAdvancedScalable = 0x4000;
- static const int MPEG4ProfileAdvancedSimple = 0x8000;
-
- // Unused yet.
- (void)AVCProfileConstrainedHigh;
- (void)HEVCProfileMain10HDR10;
- (void)HEVCProfileMain10HDR10Plus;
- (void)VP9Profile2HDR;
- (void)VP9Profile3HDR;
- (void)VP9Profile2HDR10Plus;
- (void)VP9Profile3HDR10Plus;
-
- if (avctx->codec_id == AV_CODEC_ID_H264) {
- switch(avctx->profile) {
- case FF_PROFILE_H264_BASELINE:
- return AVCProfileBaseline;
- case FF_PROFILE_H264_CONSTRAINED_BASELINE:
- return AVCProfileConstrainedBaseline;
- case FF_PROFILE_H264_MAIN:
- return AVCProfileMain;
- break;
- case FF_PROFILE_H264_EXTENDED:
- return AVCProfileExtended;
- case FF_PROFILE_H264_HIGH:
- return AVCProfileHigh;
- case FF_PROFILE_H264_HIGH_10:
- case FF_PROFILE_H264_HIGH_10_INTRA:
- return AVCProfileHigh10;
- case FF_PROFILE_H264_HIGH_422:
- case FF_PROFILE_H264_HIGH_422_INTRA:
- return AVCProfileHigh422;
- case FF_PROFILE_H264_HIGH_444:
- case FF_PROFILE_H264_HIGH_444_INTRA:
- case FF_PROFILE_H264_HIGH_444_PREDICTIVE:
- return AVCProfileHigh444;
- }
- } else if (avctx->codec_id == AV_CODEC_ID_HEVC) {
- switch (avctx->profile) {
- case FF_PROFILE_HEVC_MAIN:
- return HEVCProfileMain;
- case FF_PROFILE_HEVC_MAIN_STILL_PICTURE:
- return HEVCProfileMainStill;
- case FF_PROFILE_HEVC_MAIN_10:
- return HEVCProfileMain10;
- }
- } else if (avctx->codec_id == AV_CODEC_ID_VP9) {
- switch (avctx->profile) {
- case FF_PROFILE_VP9_0:
- return VP9Profile0;
- case FF_PROFILE_VP9_1:
- return VP9Profile1;
- case FF_PROFILE_VP9_2:
- return VP9Profile2;
- case FF_PROFILE_VP9_3:
- return VP9Profile3;
- }
- } else if(avctx->codec_id == AV_CODEC_ID_MPEG4) {
- switch (avctx->profile)
- {
- case FF_PROFILE_MPEG4_SIMPLE:
- return MPEG4ProfileSimple;
- case FF_PROFILE_MPEG4_SIMPLE_SCALABLE:
- return MPEG4ProfileSimpleScalable;
- case FF_PROFILE_MPEG4_CORE:
- return MPEG4ProfileCore;
- case FF_PROFILE_MPEG4_MAIN:
- return MPEG4ProfileMain;
- case FF_PROFILE_MPEG4_N_BIT:
- return MPEG4ProfileNbit;
- case FF_PROFILE_MPEG4_SCALABLE_TEXTURE:
- return MPEG4ProfileScalableTexture;
- case FF_PROFILE_MPEG4_SIMPLE_FACE_ANIMATION:
- return MPEG4ProfileSimpleFBA;
- case FF_PROFILE_MPEG4_BASIC_ANIMATED_TEXTURE:
- return MPEG4ProfileBasicAnimated;
- case FF_PROFILE_MPEG4_HYBRID:
- return MPEG4ProfileHybrid;
- case FF_PROFILE_MPEG4_ADVANCED_REAL_TIME:
- return MPEG4ProfileAdvancedRealTime;
- case FF_PROFILE_MPEG4_CORE_SCALABLE:
- return MPEG4ProfileCoreScalable;
- case FF_PROFILE_MPEG4_ADVANCED_CODING:
- return MPEG4ProfileAdvancedCoding;
- case FF_PROFILE_MPEG4_ADVANCED_CORE:
- return MPEG4ProfileAdvancedCore;
- case FF_PROFILE_MPEG4_ADVANCED_SCALABLE_TEXTURE:
- return MPEG4ProfileAdvancedScalable;
- case FF_PROFILE_MPEG4_ADVANCED_SIMPLE:
- return MPEG4ProfileAdvancedSimple;
- case FF_PROFILE_MPEG4_SIMPLE_STUDIO:
- // Studio profiles are not supported by mediacodec.
- default:
- break;
- }
- }
-
- return -1;
-}
-
-char *ff_AMediaCodecList_getCodecNameByType(const char *mime, int profile, int encoder, void *log_ctx)
-{
- int ret;
- int i;
- int codec_count;
- int found_codec = 0;
- char *name = NULL;
- char *supported_type = NULL;
-
- JNIEnv *env = NULL;
- struct JNIAMediaCodecListFields jfields = { 0 };
- struct JNIAMediaFormatFields mediaformat_jfields = { 0 };
-
- jobject codec_name = NULL;
-
- jobject info = NULL;
- jobject type = NULL;
- jobjectArray types = NULL;
-
- jobject capabilities = NULL;
- jobject profile_level = NULL;
- jobjectArray profile_levels = NULL;
-
- JNI_GET_ENV_OR_RETURN(env, log_ctx, NULL);
-
- if ((ret = ff_jni_init_jfields(env, &jfields, jni_amediacodeclist_mapping, 0, log_ctx)) < 0) {
- goto done;
- }
-
- if ((ret = ff_jni_init_jfields(env, &mediaformat_jfields, jni_amediaformat_mapping, 0, log_ctx)) < 0) {
- goto done;
- }
-
- codec_count = (*env)->CallStaticIntMethod(env, jfields.mediacodec_list_class, jfields.get_codec_count_id);
- if (ff_jni_exception_check(env, 1, log_ctx) < 0) {
- goto done;
- }
-
- for(i = 0; i < codec_count; i++) {
- int j;
- int type_count;
- int is_encoder;
-
- info = (*env)->CallStaticObjectMethod(env, jfields.mediacodec_list_class, jfields.get_codec_info_at_id, i);
- if (ff_jni_exception_check(env, 1, log_ctx) < 0) {
- goto done;
- }
-
- types = (*env)->CallObjectMethod(env, info, jfields.get_supported_types_id);
- if (ff_jni_exception_check(env, 1, log_ctx) < 0) {
- goto done;
- }
-
- is_encoder = (*env)->CallBooleanMethod(env, info, jfields.is_encoder_id);
- if (ff_jni_exception_check(env, 1, log_ctx) < 0) {
- goto done;
- }
-
- if (is_encoder != encoder) {
- goto done_with_info;
- }
-
- if (jfields.is_software_only_id) {
- int is_software_only = (*env)->CallBooleanMethod(env, info, jfields.is_software_only_id);
- if (ff_jni_exception_check(env, 1, log_ctx) < 0) {
- goto done;
- }
-
- if (is_software_only) {
- goto done_with_info;
- }
- }
-
- codec_name = (*env)->CallObjectMethod(env, info, jfields.get_name_id);
- if (ff_jni_exception_check(env, 1, log_ctx) < 0) {
- goto done;
- }
-
- name = ff_jni_jstring_to_utf_chars(env, codec_name, log_ctx);
- if (!name) {
- goto done;
- }
-
- if (codec_name) {
- (*env)->DeleteLocalRef(env, codec_name);
- codec_name = NULL;
- }
-
- /* Skip software decoders */
- if (
- strstr(name, "OMX.google") ||
- strstr(name, "OMX.ffmpeg") ||
- (strstr(name, "OMX.SEC") && strstr(name, ".sw.")) ||
- !strcmp(name, "OMX.qcom.video.decoder.hevcswvdec")) {
- goto done_with_info;
- }
-
- type_count = (*env)->GetArrayLength(env, types);
- for (j = 0; j < type_count; j++) {
- int k;
- int profile_count;
-
- type = (*env)->GetObjectArrayElement(env, types, j);
- if (ff_jni_exception_check(env, 1, log_ctx) < 0) {
- goto done;
- }
-
- supported_type = ff_jni_jstring_to_utf_chars(env, type, log_ctx);
- if (!supported_type) {
- goto done;
- }
-
- if (av_strcasecmp(supported_type, mime)) {
- goto done_with_type;
- }
-
- capabilities = (*env)->CallObjectMethod(env, info, jfields.get_codec_capabilities_id, type);
- if (ff_jni_exception_check(env, 1, log_ctx) < 0) {
- goto done;
- }
-
- profile_levels = (*env)->GetObjectField(env, capabilities, jfields.profile_levels_id);
- if (ff_jni_exception_check(env, 1, log_ctx) < 0) {
- goto done;
- }
-
- profile_count = (*env)->GetArrayLength(env, profile_levels);
- if (!profile_count) {
- found_codec = 1;
- }
- for (k = 0; k < profile_count; k++) {
- int supported_profile = 0;
-
- if (profile < 0) {
- found_codec = 1;
- break;
- }
-
- profile_level = (*env)->GetObjectArrayElement(env, profile_levels, k);
- if (ff_jni_exception_check(env, 1, log_ctx) < 0) {
- goto done;
- }
-
- supported_profile = (*env)->GetIntField(env, profile_level, jfields.profile_id);
- if (ff_jni_exception_check(env, 1, log_ctx) < 0) {
- goto done;
- }
-
- found_codec = profile == supported_profile;
-
- if (profile_level) {
- (*env)->DeleteLocalRef(env, profile_level);
- profile_level = NULL;
- }
-
- if (found_codec) {
- break;
- }
- }
-
-done_with_type:
- if (profile_levels) {
- (*env)->DeleteLocalRef(env, profile_levels);
- profile_levels = NULL;
- }
-
- if (capabilities) {
- (*env)->DeleteLocalRef(env, capabilities);
- capabilities = NULL;
- }
-
- if (type) {
- (*env)->DeleteLocalRef(env, type);
- type = NULL;
- }
-
- av_freep(&supported_type);
-
- if (found_codec) {
- break;
- }
- }
-
-done_with_info:
- if (info) {
- (*env)->DeleteLocalRef(env, info);
- info = NULL;
- }
-
- if (types) {
- (*env)->DeleteLocalRef(env, types);
- types = NULL;
- }
-
- if (found_codec) {
- break;
- }
-
- av_freep(&name);
- }
-
-done:
- if (codec_name) {
- (*env)->DeleteLocalRef(env, codec_name);
- }
-
- if (info) {
- (*env)->DeleteLocalRef(env, info);
- }
-
- if (type) {
- (*env)->DeleteLocalRef(env, type);
- }
-
- if (types) {
- (*env)->DeleteLocalRef(env, types);
- }
-
- if (capabilities) {
- (*env)->DeleteLocalRef(env, capabilities);
- }
-
- if (profile_level) {
- (*env)->DeleteLocalRef(env, profile_level);
- }
-
- if (profile_levels) {
- (*env)->DeleteLocalRef(env, profile_levels);
- }
-
- av_freep(&supported_type);
-
- ff_jni_reset_jfields(env, &jfields, jni_amediacodeclist_mapping, 0, log_ctx);
- ff_jni_reset_jfields(env, &mediaformat_jfields, jni_amediaformat_mapping, 0, log_ctx);
-
- if (!found_codec) {
- av_freep(&name);
- }
-
- return name;
-}
-
-static FFAMediaFormat *mediaformat_jni_new(void)
-{
- JNIEnv *env = NULL;
- FFAMediaFormatJni *format = NULL;
- jobject object = NULL;
-
- format = av_mallocz(sizeof(*format));
- if (!format) {
- return NULL;
- }
- format->api = media_format_jni;
-
- env = ff_jni_get_env(format);
- if (!env) {
- av_freep(&format);
- return NULL;
- }
-
- if (ff_jni_init_jfields(env, &format->jfields, jni_amediaformat_mapping, 1, format) < 0) {
- goto fail;
- }
-
- object = (*env)->NewObject(env, format->jfields.mediaformat_class, format->jfields.init_id);
- if (!object) {
- goto fail;
- }
-
- format->object = (*env)->NewGlobalRef(env, object);
- if (!format->object) {
- goto fail;
- }
-
-fail:
- if (object) {
- (*env)->DeleteLocalRef(env, object);
- }
-
- if (!format->object) {
- ff_jni_reset_jfields(env, &format->jfields, jni_amediaformat_mapping, 1, format);
- av_freep(&format);
- }
-
- return (FFAMediaFormat *)format;
-}
-
-static FFAMediaFormat *mediaformat_jni_newFromObject(void *object)
-{
- JNIEnv *env = NULL;
- FFAMediaFormatJni *format = NULL;
-
- format = av_mallocz(sizeof(*format));
- if (!format) {
- return NULL;
- }
- format->api = media_format_jni;
-
- env = ff_jni_get_env(format);
- if (!env) {
- av_freep(&format);
- return NULL;
- }
-
- if (ff_jni_init_jfields(env, &format->jfields, jni_amediaformat_mapping, 1, format) < 0) {
- goto fail;
- }
-
- format->object = (*env)->NewGlobalRef(env, object);
- if (!format->object) {
- goto fail;
- }
-
- return (FFAMediaFormat *)format;
-fail:
- ff_jni_reset_jfields(env, &format->jfields, jni_amediaformat_mapping, 1, format);
-
- av_freep(&format);
-
- return NULL;
-}
-
-static int mediaformat_jni_delete(FFAMediaFormat* ctx)
-{
- int ret = 0;
- FFAMediaFormatJni *format = (FFAMediaFormatJni *)ctx;
- JNIEnv *env = NULL;
-
- if (!format) {
- return 0;
- }
-
- JNI_GET_ENV_OR_RETURN(env, format, AVERROR_EXTERNAL);
-
- (*env)->DeleteGlobalRef(env, format->object);
- format->object = NULL;
-
- ff_jni_reset_jfields(env, &format->jfields, jni_amediaformat_mapping, 1, format);
-
- av_freep(&format);
-
- return ret;
-}
-
-static char* mediaformat_jni_toString(FFAMediaFormat* ctx)
-{
- char *ret = NULL;
- FFAMediaFormatJni *format = (FFAMediaFormatJni *)ctx;
- JNIEnv *env = NULL;
- jstring description = NULL;
-
- av_assert0(format != NULL);
-
- JNI_GET_ENV_OR_RETURN(env, format, NULL);
-
- description = (*env)->CallObjectMethod(env, format->object, format->jfields.to_string_id);
- if (ff_jni_exception_check(env, 1, NULL) < 0) {
- goto fail;
- }
-
- ret = ff_jni_jstring_to_utf_chars(env, description, format);
-fail:
- if (description) {
- (*env)->DeleteLocalRef(env, description);
- }
-
- return ret;
-}
-
-static int mediaformat_jni_getInt32(FFAMediaFormat* ctx, const char *name, int32_t *out)
-{
- int ret = 1;
- FFAMediaFormatJni *format = (FFAMediaFormatJni *)ctx;
- JNIEnv *env = NULL;
- jstring key = NULL;
- jboolean contains_key;
-
- av_assert0(format != NULL);
-
- JNI_GET_ENV_OR_RETURN(env, format, 0);
-
- key = ff_jni_utf_chars_to_jstring(env, name, format);
- if (!key) {
- ret = 0;
- goto fail;
- }
-
- contains_key = (*env)->CallBooleanMethod(env, format->object, format->jfields.contains_key_id, key);
- if (!contains_key || (ret = ff_jni_exception_check(env, 1, format)) < 0) {
- ret = 0;
- goto fail;
- }
-
- *out = (*env)->CallIntMethod(env, format->object, format->jfields.get_integer_id, key);
- if ((ret = ff_jni_exception_check(env, 1, format)) < 0) {
- ret = 0;
- goto fail;
- }
-
- ret = 1;
-fail:
- if (key) {
- (*env)->DeleteLocalRef(env, key);
- }
-
- return ret;
-}
-
-static int mediaformat_jni_getInt64(FFAMediaFormat* ctx, const char *name, int64_t *out)
-{
- int ret = 1;
- FFAMediaFormatJni *format = (FFAMediaFormatJni *)ctx;
- JNIEnv *env = NULL;
- jstring key = NULL;
- jboolean contains_key;
-
- av_assert0(format != NULL);
-
- JNI_GET_ENV_OR_RETURN(env, format, 0);
-
- key = ff_jni_utf_chars_to_jstring(env, name, format);
- if (!key) {
- ret = 0;
- goto fail;
- }
-
- contains_key = (*env)->CallBooleanMethod(env, format->object, format->jfields.contains_key_id, key);
- if (!contains_key || (ret = ff_jni_exception_check(env, 1, format)) < 0) {
- ret = 0;
- goto fail;
- }
-
- *out = (*env)->CallLongMethod(env, format->object, format->jfields.get_long_id, key);
- if ((ret = ff_jni_exception_check(env, 1, format)) < 0) {
- ret = 0;
- goto fail;
- }
-
- ret = 1;
-fail:
- if (key) {
- (*env)->DeleteLocalRef(env, key);
- }
-
- return ret;
-}
-
-static int mediaformat_jni_getFloat(FFAMediaFormat* ctx, const char *name, float *out)
-{
- int ret = 1;
- FFAMediaFormatJni *format = (FFAMediaFormatJni *)ctx;
- JNIEnv *env = NULL;
- jstring key = NULL;
- jboolean contains_key;
-
- av_assert0(format != NULL);
-
- JNI_GET_ENV_OR_RETURN(env, format, 0);
-
- key = ff_jni_utf_chars_to_jstring(env, name, format);
- if (!key) {
- ret = 0;
- goto fail;
- }
-
- contains_key = (*env)->CallBooleanMethod(env, format->object, format->jfields.contains_key_id, key);
- if (!contains_key || (ret = ff_jni_exception_check(env, 1, format)) < 0) {
- ret = 0;
- goto fail;
- }
-
- *out = (*env)->CallFloatMethod(env, format->object, format->jfields.get_float_id, key);
- if ((ret = ff_jni_exception_check(env, 1, format)) < 0) {
- ret = 0;
- goto fail;
- }
-
- ret = 1;
-fail:
- if (key) {
- (*env)->DeleteLocalRef(env, key);
- }
-
- return ret;
-}
-
-static int mediaformat_jni_getBuffer(FFAMediaFormat* ctx, const char *name, void** data, size_t *size)
-{
- int ret = 1;
- FFAMediaFormatJni *format = (FFAMediaFormatJni *)ctx;
- JNIEnv *env = NULL;
- jstring key = NULL;
- jboolean contains_key;
- jobject result = NULL;
-
- av_assert0(format != NULL);
-
- JNI_GET_ENV_OR_RETURN(env, format, 0);
-
- key = ff_jni_utf_chars_to_jstring(env, name, format);
- if (!key) {
- ret = 0;
- goto fail;
- }
-
- contains_key = (*env)->CallBooleanMethod(env, format->object, format->jfields.contains_key_id, key);
- if (!contains_key || (ret = ff_jni_exception_check(env, 1, format)) < 0) {
- ret = 0;
- goto fail;
- }
-
- result = (*env)->CallObjectMethod(env, format->object, format->jfields.get_bytebuffer_id, key);
- if ((ret = ff_jni_exception_check(env, 1, format)) < 0) {
- ret = 0;
- goto fail;
- }
-
- *data = (*env)->GetDirectBufferAddress(env, result);
- *size = (*env)->GetDirectBufferCapacity(env, result);
-
- if (*data && *size) {
- void *src = *data;
- *data = av_malloc(*size);
- if (!*data) {
- ret = 0;
- goto fail;
- }
-
- memcpy(*data, src, *size);
- }
-
- ret = 1;
-fail:
- if (key) {
- (*env)->DeleteLocalRef(env, key);
- }
-
- if (result) {
- (*env)->DeleteLocalRef(env, result);
- }
-
- return ret;
-}
-
-static int mediaformat_jni_getString(FFAMediaFormat* ctx, const char *name, const char **out)
-{
- int ret = 1;
- FFAMediaFormatJni *format = (FFAMediaFormatJni *)ctx;
- JNIEnv *env = NULL;
- jstring key = NULL;
- jboolean contains_key;
- jstring result = NULL;
-
- av_assert0(format != NULL);
-
- JNI_GET_ENV_OR_RETURN(env, format, 0);
-
- key = ff_jni_utf_chars_to_jstring(env, name, format);
- if (!key) {
- ret = 0;
- goto fail;
- }
-
- contains_key = (*env)->CallBooleanMethod(env, format->object, format->jfields.contains_key_id, key);
- if (!contains_key || (ret = ff_jni_exception_check(env, 1, format)) < 0) {
- ret = 0;
- goto fail;
- }
-
- result = (*env)->CallObjectMethod(env, format->object, format->jfields.get_string_id, key);
- if ((ret = ff_jni_exception_check(env, 1, format)) < 0) {
- ret = 0;
- goto fail;
- }
-
- *out = ff_jni_jstring_to_utf_chars(env, result, format);
- if (!*out) {
- ret = 0;
- goto fail;
- }
-
- ret = 1;
-fail:
- if (key) {
- (*env)->DeleteLocalRef(env, key);
- }
-
- if (result) {
- (*env)->DeleteLocalRef(env, result);
- }
-
- return ret;
-}
-
-static void mediaformat_jni_setInt32(FFAMediaFormat* ctx, const char* name, int32_t value)
-{
- JNIEnv *env = NULL;
- jstring key = NULL;
- FFAMediaFormatJni *format = (FFAMediaFormatJni *)ctx;
-
- av_assert0(format != NULL);
-
- JNI_GET_ENV_OR_RETURN_VOID(env, format);
-
- key = ff_jni_utf_chars_to_jstring(env, name, format);
- if (!key) {
- goto fail;
- }
-
- (*env)->CallVoidMethod(env, format->object, format->jfields.set_integer_id, key, value);
- if (ff_jni_exception_check(env, 1, format) < 0) {
- goto fail;
- }
-
-fail:
- if (key) {
- (*env)->DeleteLocalRef(env, key);
- }
-}
-
-static void mediaformat_jni_setInt64(FFAMediaFormat* ctx, const char* name, int64_t value)
-{
- JNIEnv *env = NULL;
- jstring key = NULL;
- FFAMediaFormatJni *format = (FFAMediaFormatJni *)ctx;
-
- av_assert0(format != NULL);
-
- JNI_GET_ENV_OR_RETURN_VOID(env, format);
-
- key = ff_jni_utf_chars_to_jstring(env, name, format);
- if (!key) {
- goto fail;
- }
-
- (*env)->CallVoidMethod(env, format->object, format->jfields.set_long_id, key, value);
- if (ff_jni_exception_check(env, 1, format) < 0) {
- goto fail;
- }
-
-fail:
- if (key) {
- (*env)->DeleteLocalRef(env, key);
- }
-}
-
-static void mediaformat_jni_setFloat(FFAMediaFormat* ctx, const char* name, float value)
-{
- JNIEnv *env = NULL;
- jstring key = NULL;
- FFAMediaFormatJni *format = (FFAMediaFormatJni *)ctx;
-
- av_assert0(format != NULL);
-
- JNI_GET_ENV_OR_RETURN_VOID(env, format);
-
- key = ff_jni_utf_chars_to_jstring(env, name, format);
- if (!key) {
- goto fail;
- }
-
- (*env)->CallVoidMethod(env, format->object, format->jfields.set_float_id, key, value);
- if (ff_jni_exception_check(env, 1, format) < 0) {
- goto fail;
- }
-
-fail:
- if (key) {
- (*env)->DeleteLocalRef(env, key);
- }
-}
-
-static void mediaformat_jni_setString(FFAMediaFormat* ctx, const char* name, const char* value)
-{
- JNIEnv *env = NULL;
- jstring key = NULL;
- jstring string = NULL;
- FFAMediaFormatJni *format = (FFAMediaFormatJni *)ctx;
-
- av_assert0(format != NULL);
-
- JNI_GET_ENV_OR_RETURN_VOID(env, format);
-
- key = ff_jni_utf_chars_to_jstring(env, name, format);
- if (!key) {
- goto fail;
- }
-
- string = ff_jni_utf_chars_to_jstring(env, value, format);
- if (!string) {
- goto fail;
- }
-
- (*env)->CallVoidMethod(env, format->object, format->jfields.set_string_id, key, string);
- if (ff_jni_exception_check(env, 1, format) < 0) {
- goto fail;
- }
-
-fail:
- if (key) {
- (*env)->DeleteLocalRef(env, key);
- }
-
- if (string) {
- (*env)->DeleteLocalRef(env, string);
- }
-}
-
-static void mediaformat_jni_setBuffer(FFAMediaFormat* ctx, const char* name, void* data, size_t size)
-{
- JNIEnv *env = NULL;
- jstring key = NULL;
- jobject buffer = NULL;
- void *buffer_data = NULL;
- FFAMediaFormatJni *format = (FFAMediaFormatJni *)ctx;
-
- av_assert0(format != NULL);
-
- JNI_GET_ENV_OR_RETURN_VOID(env, format);
-
- key = ff_jni_utf_chars_to_jstring(env, name, format);
- if (!key) {
- goto fail;
- }
-
- if (!data || !size) {
- goto fail;
- }
-
- buffer_data = av_malloc(size);
- if (!buffer_data) {
- goto fail;
- }
-
- memcpy(buffer_data, data, size);
-
- buffer = (*env)->NewDirectByteBuffer(env, buffer_data, size);
- if (!buffer) {
- goto fail;
- }
-
- (*env)->CallVoidMethod(env, format->object, format->jfields.set_bytebuffer_id, key, buffer);
- if (ff_jni_exception_check(env, 1, format) < 0) {
- goto fail;
- }
-
-fail:
- if (key) {
- (*env)->DeleteLocalRef(env, key);
- }
-
- if (buffer) {
- (*env)->DeleteLocalRef(env, buffer);
- }
-}
-
-static int codec_init_static_fields(FFAMediaCodecJni *codec)
-{
- int ret = 0;
- JNIEnv *env = NULL;
-
- JNI_GET_ENV_OR_RETURN(env, codec, AVERROR_EXTERNAL);
-
- codec->INFO_TRY_AGAIN_LATER = (*env)->GetStaticIntField(env, codec->jfields.mediacodec_class, codec->jfields.info_try_again_later_id);
- if ((ret = ff_jni_exception_check(env, 1, codec)) < 0) {
- goto fail;
- }
-
- codec->BUFFER_FLAG_CODEC_CONFIG = (*env)->GetStaticIntField(env, codec->jfields.mediacodec_class, codec->jfields.buffer_flag_codec_config_id);
- if ((ret = ff_jni_exception_check(env, 1, codec)) < 0) {
- goto fail;
- }
-
- codec->BUFFER_FLAG_END_OF_STREAM = (*env)->GetStaticIntField(env, codec->jfields.mediacodec_class, codec->jfields.buffer_flag_end_of_stream_id);
- if ((ret = ff_jni_exception_check(env, 1, codec)) < 0) {
- goto fail;
- }
-
- if (codec->jfields.buffer_flag_key_frame_id) {
- codec->BUFFER_FLAG_KEY_FRAME = (*env)->GetStaticIntField(env, codec->jfields.mediacodec_class, codec->jfields.buffer_flag_key_frame_id);
- if ((ret = ff_jni_exception_check(env, 1, codec)) < 0) {
- goto fail;
- }
- }
-
- codec->CONFIGURE_FLAG_ENCODE = (*env)->GetStaticIntField(env, codec->jfields.mediacodec_class, codec->jfields.configure_flag_encode_id);
- if ((ret = ff_jni_exception_check(env, 1, codec)) < 0) {
- goto fail;
- }
-
- codec->INFO_TRY_AGAIN_LATER = (*env)->GetStaticIntField(env, codec->jfields.mediacodec_class, codec->jfields.info_try_again_later_id);
- if ((ret = ff_jni_exception_check(env, 1, codec)) < 0) {
- goto fail;
- }
-
- codec->INFO_OUTPUT_BUFFERS_CHANGED = (*env)->GetStaticIntField(env, codec->jfields.mediacodec_class, codec->jfields.info_output_buffers_changed_id);
- if ((ret = ff_jni_exception_check(env, 1, codec)) < 0) {
- goto fail;
- }
-
- codec->INFO_OUTPUT_FORMAT_CHANGED = (*env)->GetStaticIntField(env, codec->jfields.mediacodec_class, codec->jfields.info_output_format_changed_id);
- if ((ret = ff_jni_exception_check(env, 1, codec)) < 0) {
- goto fail;
- }
-
-fail:
-
- return ret;
-}
-
-#define CREATE_CODEC_BY_NAME 0
-#define CREATE_DECODER_BY_TYPE 1
-#define CREATE_ENCODER_BY_TYPE 2
-
-static inline FFAMediaCodec *codec_create(int method, const char *arg)
-{
- int ret = -1;
- JNIEnv *env = NULL;
- FFAMediaCodecJni *codec = NULL;
- jstring jarg = NULL;
- jobject object = NULL;
- jobject buffer_info = NULL;
- jmethodID create_id = NULL;
-
- codec = av_mallocz(sizeof(*codec));
- if (!codec) {
- return NULL;
- }
- codec->api = media_codec_jni;
-
- env = ff_jni_get_env(codec);
- if (!env) {
- av_freep(&codec);
- return NULL;
- }
-
- if (ff_jni_init_jfields(env, &codec->jfields, jni_amediacodec_mapping, 1, codec) < 0) {
- goto fail;
- }
-
- jarg = ff_jni_utf_chars_to_jstring(env, arg, codec);
- if (!jarg) {
- goto fail;
- }
-
- switch (method) {
- case CREATE_CODEC_BY_NAME: create_id = codec->jfields.create_by_codec_name_id; break;
- case CREATE_DECODER_BY_TYPE: create_id = codec->jfields.create_decoder_by_type_id; break;
- case CREATE_ENCODER_BY_TYPE: create_id = codec->jfields.create_encoder_by_type_id; break;
- default:
- av_assert0(0);
- }
-
- object = (*env)->CallStaticObjectMethod(env,
- codec->jfields.mediacodec_class,
- create_id,
- jarg);
- if (ff_jni_exception_check(env, 1, codec) < 0) {
- goto fail;
- }
-
- codec->object = (*env)->NewGlobalRef(env, object);
- if (!codec->object) {
- goto fail;
- }
-
- if (codec_init_static_fields(codec) < 0) {
- goto fail;
- }
-
- if (codec->jfields.get_input_buffer_id && codec->jfields.get_output_buffer_id) {
- codec->has_get_i_o_buffer = 1;
- }
-
- buffer_info = (*env)->NewObject(env, codec->jfields.mediainfo_class, codec->jfields.init_id);
- if (ff_jni_exception_check(env, 1, codec) < 0) {
- goto fail;
- }
-
- codec->buffer_info = (*env)->NewGlobalRef(env, buffer_info);
- if (!codec->buffer_info) {
- goto fail;
- }
-
- ret = 0;
-fail:
- if (jarg) {
- (*env)->DeleteLocalRef(env, jarg);
- }
-
- if (object) {
- (*env)->DeleteLocalRef(env, object);
- }
-
- if (buffer_info) {
- (*env)->DeleteLocalRef(env, buffer_info);
- }
-
- if (ret < 0) {
- if (codec->object) {
- (*env)->DeleteGlobalRef(env, codec->object);
- }
-
- if (codec->buffer_info) {
- (*env)->DeleteGlobalRef(env, codec->buffer_info);
- }
-
- ff_jni_reset_jfields(env, &codec->jfields, jni_amediacodec_mapping, 1, codec);
- av_freep(&codec);
- }
-
- return (FFAMediaCodec *)codec;
-}
-
-#define DECLARE_FF_AMEDIACODEC_CREATE_FUNC(name, method) \
-static FFAMediaCodec *mediacodec_jni_##name(const char *arg) \
-{ \
- return codec_create(method, arg); \
-} \
-
-DECLARE_FF_AMEDIACODEC_CREATE_FUNC(createCodecByName, CREATE_CODEC_BY_NAME)
-DECLARE_FF_AMEDIACODEC_CREATE_FUNC(createDecoderByType, CREATE_DECODER_BY_TYPE)
-DECLARE_FF_AMEDIACODEC_CREATE_FUNC(createEncoderByType, CREATE_ENCODER_BY_TYPE)
-
-static int mediacodec_jni_delete(FFAMediaCodec* ctx)
-{
- int ret = 0;
- FFAMediaCodecJni *codec = (FFAMediaCodecJni *)ctx;
- JNIEnv *env = NULL;
-
- if (!codec) {
- return 0;
- }
-
- JNI_GET_ENV_OR_RETURN(env, codec, AVERROR_EXTERNAL);
-
- (*env)->CallVoidMethod(env, codec->object, codec->jfields.release_id);
- if (ff_jni_exception_check(env, 1, codec) < 0) {
- ret = AVERROR_EXTERNAL;
- }
-
- (*env)->DeleteGlobalRef(env, codec->input_buffers);
- codec->input_buffers = NULL;
-
- (*env)->DeleteGlobalRef(env, codec->output_buffers);
- codec->output_buffers = NULL;
-
- (*env)->DeleteGlobalRef(env, codec->object);
- codec->object = NULL;
-
- (*env)->DeleteGlobalRef(env, codec->buffer_info);
- codec->buffer_info = NULL;
-
- ff_jni_reset_jfields(env, &codec->jfields, jni_amediacodec_mapping, 1, codec);
-
- av_freep(&codec);
-
- return ret;
-}
-
-static char *mediacodec_jni_getName(FFAMediaCodec *ctx)
-{
- char *ret = NULL;
- JNIEnv *env = NULL;
- jobject *name = NULL;
- FFAMediaCodecJni *codec = (FFAMediaCodecJni *)ctx;
-
- JNI_GET_ENV_OR_RETURN(env, codec, NULL);
-
- name = (*env)->CallObjectMethod(env, codec->object, codec->jfields.get_name_id);
- if (ff_jni_exception_check(env, 1, codec) < 0) {
- goto fail;
- }
-
- ret = ff_jni_jstring_to_utf_chars(env, name, codec);
-
-fail:
- if (name) {
- (*env)->DeleteLocalRef(env, name);
- }
-
- return ret;
-}
-
-static int mediacodec_jni_configure(FFAMediaCodec *ctx,
- const FFAMediaFormat* format_ctx,
- FFANativeWindow* window,
- void *crypto,
- uint32_t flags)
-{
- int ret = 0;
- JNIEnv *env = NULL;
- FFAMediaCodecJni *codec = (FFAMediaCodecJni *)ctx;
- const FFAMediaFormatJni *format = (FFAMediaFormatJni *)format_ctx;
- jobject *surface = window ? window->surface : NULL;
-
- JNI_GET_ENV_OR_RETURN(env, codec, AVERROR_EXTERNAL);
-
- if (flags & codec->CONFIGURE_FLAG_ENCODE) {
- if (surface && !codec->jfields.set_input_surface_id) {
- av_log(ctx, AV_LOG_ERROR, "System doesn't support setInputSurface\n");
- return AVERROR_EXTERNAL;
- }
-
- (*env)->CallVoidMethod(env, codec->object, codec->jfields.configure_id, format->object, NULL, NULL, flags);
- if (ff_jni_exception_check(env, 1, codec) < 0)
- return AVERROR_EXTERNAL;
-
- if (!surface)
- return 0;
-
- (*env)->CallVoidMethod(env, codec->object, codec->jfields.set_input_surface_id, surface);
- if (ff_jni_exception_check(env, 1, codec) < 0)
- return AVERROR_EXTERNAL;
- return 0;
- } else {
- (*env)->CallVoidMethod(env, codec->object, codec->jfields.configure_id, format->object, surface, NULL, flags);
- }
- if (ff_jni_exception_check(env, 1, codec) < 0) {
- ret = AVERROR_EXTERNAL;
- goto fail;
- }
-
-fail:
- return ret;
-}
-
-static int mediacodec_jni_start(FFAMediaCodec* ctx)
-{
- int ret = 0;
- JNIEnv *env = NULL;
- FFAMediaCodecJni *codec = (FFAMediaCodecJni *)ctx;
-
- JNI_GET_ENV_OR_RETURN(env, codec, AVERROR_EXTERNAL);
-
- (*env)->CallVoidMethod(env, codec->object, codec->jfields.start_id);
- if (ff_jni_exception_check(env, 1, codec) < 0) {
- ret = AVERROR_EXTERNAL;
- goto fail;
- }
-
-fail:
- return ret;
-}
-
-static int mediacodec_jni_stop(FFAMediaCodec* ctx)
-{
- int ret = 0;
- JNIEnv *env = NULL;
- FFAMediaCodecJni *codec = (FFAMediaCodecJni *)ctx;
-
- JNI_GET_ENV_OR_RETURN(env, codec, AVERROR_EXTERNAL);
-
- (*env)->CallVoidMethod(env, codec->object, codec->jfields.stop_id);
- if (ff_jni_exception_check(env, 1, codec) < 0) {
- ret = AVERROR_EXTERNAL;
- goto fail;
- }
-
-fail:
- return ret;
-}
-
-static int mediacodec_jni_flush(FFAMediaCodec* ctx)
-{
- int ret = 0;
- JNIEnv *env = NULL;
- FFAMediaCodecJni *codec = (FFAMediaCodecJni *)ctx;
-
- JNI_GET_ENV_OR_RETURN(env, codec, AVERROR_EXTERNAL);
-
- (*env)->CallVoidMethod(env, codec->object, codec->jfields.flush_id);
- if (ff_jni_exception_check(env, 1, codec) < 0) {
- ret = AVERROR_EXTERNAL;
- goto fail;
- }
-
-fail:
- return ret;
-}
-
-static int mediacodec_jni_releaseOutputBuffer(FFAMediaCodec* ctx, size_t idx, int render)
-{
- int ret = 0;
- JNIEnv *env = NULL;
- FFAMediaCodecJni *codec = (FFAMediaCodecJni *)ctx;
-
- JNI_GET_ENV_OR_RETURN(env, codec, AVERROR_EXTERNAL);
-
- (*env)->CallVoidMethod(env, codec->object, codec->jfields.release_output_buffer_id, (jint)idx, (jboolean)render);
- if (ff_jni_exception_check(env, 1, codec) < 0) {
- ret = AVERROR_EXTERNAL;
- goto fail;
- }
-
-fail:
- return ret;
-}
-
-static int mediacodec_jni_releaseOutputBufferAtTime(FFAMediaCodec *ctx, size_t idx, int64_t timestampNs)
-{
- int ret = 0;
- JNIEnv *env = NULL;
- FFAMediaCodecJni *codec = (FFAMediaCodecJni *)ctx;
-
- JNI_GET_ENV_OR_RETURN(env, codec, AVERROR_EXTERNAL);
-
- (*env)->CallVoidMethod(env, codec->object, codec->jfields.release_output_buffer_at_time_id, (jint)idx, (jlong)timestampNs);
- if (ff_jni_exception_check(env, 1, codec) < 0) {
- ret = AVERROR_EXTERNAL;
- goto fail;
- }
-
-fail:
- return ret;
-}
-
-static ssize_t mediacodec_jni_dequeueInputBuffer(FFAMediaCodec* ctx, int64_t timeoutUs)
-{
- int ret = 0;
- JNIEnv *env = NULL;
- FFAMediaCodecJni *codec = (FFAMediaCodecJni *)ctx;
-
- JNI_GET_ENV_OR_RETURN(env, codec, AVERROR_EXTERNAL);
-
- ret = (*env)->CallIntMethod(env, codec->object, codec->jfields.dequeue_input_buffer_id, timeoutUs);
- if (ff_jni_exception_check(env, 1, codec) < 0) {
- ret = AVERROR_EXTERNAL;
- goto fail;
- }
-
-fail:
- return ret;
-}
-
-static int mediacodec_jni_queueInputBuffer(FFAMediaCodec* ctx, size_t idx, off_t offset, size_t size, uint64_t time, uint32_t flags)
-{
- int ret = 0;
- JNIEnv *env = NULL;
- FFAMediaCodecJni *codec = (FFAMediaCodecJni *)ctx;
-
- JNI_GET_ENV_OR_RETURN(env, codec, AVERROR_EXTERNAL);
-
- (*env)->CallVoidMethod(env, codec->object, codec->jfields.queue_input_buffer_id, (jint)idx, (jint)offset, (jint)size, time, flags);
- if ((ret = ff_jni_exception_check(env, 1, codec)) < 0) {
- ret = AVERROR_EXTERNAL;
- goto fail;
- }
-
-fail:
- return ret;
-}
-
-static ssize_t mediacodec_jni_dequeueOutputBuffer(FFAMediaCodec* ctx, FFAMediaCodecBufferInfo *info, int64_t timeoutUs)
-{
- int ret = 0;
- JNIEnv *env = NULL;
- FFAMediaCodecJni *codec = (FFAMediaCodecJni *)ctx;
-
- JNI_GET_ENV_OR_RETURN(env, codec, AVERROR_EXTERNAL);
-
- ret = (*env)->CallIntMethod(env, codec->object, codec->jfields.dequeue_output_buffer_id, codec->buffer_info, timeoutUs);
- if (ff_jni_exception_check(env, 1, codec) < 0) {
- return AVERROR_EXTERNAL;
- }
-
- info->flags = (*env)->GetIntField(env, codec->buffer_info, codec->jfields.flags_id);
- if (ff_jni_exception_check(env, 1, codec) < 0) {
- return AVERROR_EXTERNAL;
- }
-
- info->offset = (*env)->GetIntField(env, codec->buffer_info, codec->jfields.offset_id);
- if (ff_jni_exception_check(env, 1, codec) < 0) {
- return AVERROR_EXTERNAL;
- }
-
- info->presentationTimeUs = (*env)->GetLongField(env, codec->buffer_info, codec->jfields.presentation_time_us_id);
- if (ff_jni_exception_check(env, 1, codec) < 0) {
- return AVERROR_EXTERNAL;
- }
-
- info->size = (*env)->GetIntField(env, codec->buffer_info, codec->jfields.size_id);
- if (ff_jni_exception_check(env, 1, codec) < 0) {
- return AVERROR_EXTERNAL;
- }
-
- return ret;
-}
-
-static uint8_t* mediacodec_jni_getInputBuffer(FFAMediaCodec* ctx, size_t idx, size_t *out_size)
-{
- uint8_t *ret = NULL;
- JNIEnv *env = NULL;
- FFAMediaCodecJni *codec = (FFAMediaCodecJni *)ctx;
- jobject buffer = NULL;
- jobject input_buffers = NULL;
-
- JNI_GET_ENV_OR_RETURN(env, codec, NULL);
-
- if (codec->has_get_i_o_buffer) {
- buffer = (*env)->CallObjectMethod(env, codec->object, codec->jfields.get_input_buffer_id, (jint)idx);
- if (ff_jni_exception_check(env, 1, codec) < 0) {
- goto fail;
- }
- } else {
- if (!codec->input_buffers) {
- input_buffers = (*env)->CallObjectMethod(env, codec->object, codec->jfields.get_input_buffers_id);
- if (ff_jni_exception_check(env, 1, codec) < 0) {
- goto fail;
- }
-
- codec->input_buffers = (*env)->NewGlobalRef(env, input_buffers);
- if (ff_jni_exception_check(env, 1, codec) < 0) {
- goto fail;
- }
- }
-
- buffer = (*env)->GetObjectArrayElement(env, codec->input_buffers, idx);
- if (ff_jni_exception_check(env, 1, codec) < 0) {
- goto fail;
- }
- }
-
- ret = (*env)->GetDirectBufferAddress(env, buffer);
- *out_size = (*env)->GetDirectBufferCapacity(env, buffer);
-fail:
- if (buffer) {
- (*env)->DeleteLocalRef(env, buffer);
- }
-
- if (input_buffers) {
- (*env)->DeleteLocalRef(env, input_buffers);
- }
-
- return ret;
-}
-
-static uint8_t* mediacodec_jni_getOutputBuffer(FFAMediaCodec* ctx, size_t idx, size_t *out_size)
-{
- uint8_t *ret = NULL;
- JNIEnv *env = NULL;
- FFAMediaCodecJni *codec = (FFAMediaCodecJni *)ctx;
- jobject buffer = NULL;
- jobject output_buffers = NULL;
-
- JNI_GET_ENV_OR_RETURN(env, codec, NULL);
-
- if (codec->has_get_i_o_buffer) {
- buffer = (*env)->CallObjectMethod(env, codec->object, codec->jfields.get_output_buffer_id, (jint)idx);
- if (ff_jni_exception_check(env, 1, codec) < 0) {
- goto fail;
- }
- } else {
- if (!codec->output_buffers) {
- output_buffers = (*env)->CallObjectMethod(env, codec->object, codec->jfields.get_output_buffers_id);
- if (ff_jni_exception_check(env, 1, codec) < 0) {
- goto fail;
- }
-
- codec->output_buffers = (*env)->NewGlobalRef(env, output_buffers);
- if (ff_jni_exception_check(env, 1, codec) < 0) {
- goto fail;
- }
- }
-
- buffer = (*env)->GetObjectArrayElement(env, codec->output_buffers, idx);
- if (ff_jni_exception_check(env, 1, codec) < 0) {
- goto fail;
- }
- }
-
- ret = (*env)->GetDirectBufferAddress(env, buffer);
- *out_size = (*env)->GetDirectBufferCapacity(env, buffer);
-fail:
- if (buffer) {
- (*env)->DeleteLocalRef(env, buffer);
- }
-
- if (output_buffers) {
- (*env)->DeleteLocalRef(env, output_buffers);
- }
-
- return ret;
-}
-
-static FFAMediaFormat* mediacodec_jni_getOutputFormat(FFAMediaCodec* ctx)
-{
- FFAMediaFormat *ret = NULL;
- JNIEnv *env = NULL;
- FFAMediaCodecJni *codec = (FFAMediaCodecJni *)ctx;
-
- jobject mediaformat = NULL;
-
- JNI_GET_ENV_OR_RETURN(env, codec, NULL);
-
- mediaformat = (*env)->CallObjectMethod(env, codec->object, codec->jfields.get_output_format_id);
- if (ff_jni_exception_check(env, 1, codec) < 0) {
- goto fail;
- }
-
- ret = mediaformat_jni_newFromObject(mediaformat);
-fail:
- if (mediaformat) {
- (*env)->DeleteLocalRef(env, mediaformat);
- }
-
- return ret;
-}
-
-static int mediacodec_jni_infoTryAgainLater(FFAMediaCodec *ctx, ssize_t idx)
-{
- FFAMediaCodecJni *codec = (FFAMediaCodecJni *)ctx;
- return idx == codec->INFO_TRY_AGAIN_LATER;
-}
-
-static int mediacodec_jni_infoOutputBuffersChanged(FFAMediaCodec *ctx, ssize_t idx)
-{
- FFAMediaCodecJni *codec = (FFAMediaCodecJni *)ctx;
- return idx == codec->INFO_OUTPUT_BUFFERS_CHANGED;
-}
-
-static int mediacodec_jni_infoOutputFormatChanged(FFAMediaCodec *ctx, ssize_t idx)
-{
- FFAMediaCodecJni *codec = (FFAMediaCodecJni *)ctx;
- return idx == codec->INFO_OUTPUT_FORMAT_CHANGED;
-}
-
-static int mediacodec_jni_getBufferFlagCodecConfig(FFAMediaCodec *ctx)
-{
- FFAMediaCodecJni *codec = (FFAMediaCodecJni *)ctx;
- return codec->BUFFER_FLAG_CODEC_CONFIG;
-}
-
-static int mediacodec_jni_getBufferFlagEndOfStream(FFAMediaCodec *ctx)
-{
- FFAMediaCodecJni *codec = (FFAMediaCodecJni *)ctx;
- return codec->BUFFER_FLAG_END_OF_STREAM;
-}
-
-static int mediacodec_jni_getBufferFlagKeyFrame(FFAMediaCodec *ctx)
-{
- FFAMediaCodecJni *codec = (FFAMediaCodecJni *)ctx;
- return codec->BUFFER_FLAG_KEY_FRAME;
-}
-
-static int mediacodec_jni_getConfigureFlagEncode(FFAMediaCodec *ctx)
-{
- FFAMediaCodecJni *codec = (FFAMediaCodecJni *)ctx;
- return codec->CONFIGURE_FLAG_ENCODE;
-}
-
-static int mediacodec_jni_cleanOutputBuffers(FFAMediaCodec *ctx)
-{
- int ret = 0;
- FFAMediaCodecJni *codec = (FFAMediaCodecJni *)ctx;
-
- if (!codec->has_get_i_o_buffer) {
- if (codec->output_buffers) {
- JNIEnv *env = NULL;
-
- env = ff_jni_get_env(codec);
- if (!env) {
- ret = AVERROR_EXTERNAL;
- goto fail;
- }
-
- (*env)->DeleteGlobalRef(env, codec->output_buffers);
- codec->output_buffers = NULL;
- }
- }
-
-fail:
- return ret;
-}
-
-static int mediacodec_jni_signalEndOfInputStream(FFAMediaCodec *ctx)
-{
- JNIEnv *env = NULL;
- FFAMediaCodecJni *codec = (FFAMediaCodecJni *)ctx;
-
- JNI_GET_ENV_OR_RETURN(env, codec, AVERROR_EXTERNAL);
-
- (*env)->CallVoidMethod(env, codec->object, codec->jfields.signal_end_of_input_stream_id);
- if (ff_jni_exception_check(env, 1, codec) < 0) {
- return AVERROR_EXTERNAL;
- }
-
- return 0;
-}
-
-static const FFAMediaFormat media_format_jni = {
- .class = &amediaformat_class,
-
- .create = mediaformat_jni_new,
- .delete = mediaformat_jni_delete,
-
- .toString = mediaformat_jni_toString,
-
- .getInt32 = mediaformat_jni_getInt32,
- .getInt64 = mediaformat_jni_getInt64,
- .getFloat = mediaformat_jni_getFloat,
- .getBuffer = mediaformat_jni_getBuffer,
- .getString = mediaformat_jni_getString,
-
- .setInt32 = mediaformat_jni_setInt32,
- .setInt64 = mediaformat_jni_setInt64,
- .setFloat = mediaformat_jni_setFloat,
- .setString = mediaformat_jni_setString,
- .setBuffer = mediaformat_jni_setBuffer,
-};
-
-static const FFAMediaCodec media_codec_jni = {
- .class = &amediacodec_class,
-
- .getName = mediacodec_jni_getName,
-
- .createCodecByName = mediacodec_jni_createCodecByName,
- .createDecoderByType = mediacodec_jni_createDecoderByType,
- .createEncoderByType = mediacodec_jni_createEncoderByType,
- .delete = mediacodec_jni_delete,
-
- .configure = mediacodec_jni_configure,
- .start = mediacodec_jni_start,
- .stop = mediacodec_jni_stop,
- .flush = mediacodec_jni_flush,
-
- .getInputBuffer = mediacodec_jni_getInputBuffer,
- .getOutputBuffer = mediacodec_jni_getOutputBuffer,
-
- .dequeueInputBuffer = mediacodec_jni_dequeueInputBuffer,
- .queueInputBuffer = mediacodec_jni_queueInputBuffer,
-
- .dequeueOutputBuffer = mediacodec_jni_dequeueOutputBuffer,
- .getOutputFormat = mediacodec_jni_getOutputFormat,
-
- .releaseOutputBuffer = mediacodec_jni_releaseOutputBuffer,
- .releaseOutputBufferAtTime = mediacodec_jni_releaseOutputBufferAtTime,
-
- .infoTryAgainLater = mediacodec_jni_infoTryAgainLater,
- .infoOutputBuffersChanged = mediacodec_jni_infoOutputBuffersChanged,
- .infoOutputFormatChanged = mediacodec_jni_infoOutputFormatChanged,
-
- .getBufferFlagCodecConfig = mediacodec_jni_getBufferFlagCodecConfig,
- .getBufferFlagEndOfStream = mediacodec_jni_getBufferFlagEndOfStream,
- .getBufferFlagKeyFrame = mediacodec_jni_getBufferFlagKeyFrame,
-
- .getConfigureFlagEncode = mediacodec_jni_getConfigureFlagEncode,
- .cleanOutputBuffers = mediacodec_jni_cleanOutputBuffers,
- .signalEndOfInputStream = mediacodec_jni_signalEndOfInputStream,
-};
-
-typedef struct FFAMediaFormatNdk {
- FFAMediaFormat api;
-
- void *libmedia;
- AMediaFormat *impl;
-
- AMediaFormat *(*new)(void);
- media_status_t (*delete)(AMediaFormat*);
-
- const char* (*toString)(AMediaFormat*);
-
- bool (*getInt32)(AMediaFormat*, const char *name, int32_t *out);
- bool (*getInt64)(AMediaFormat*, const char *name, int64_t *out);
- bool (*getFloat)(AMediaFormat*, const char *name, float *out);
- bool (*getSize)(AMediaFormat*, const char *name, size_t *out);
- bool (*getBuffer)(AMediaFormat*, const char *name, void** data, size_t *size);
- bool (*getString)(AMediaFormat*, const char *name, const char **out);
- bool (*getRect)(AMediaFormat *, const char *name,
- int32_t *left, int32_t *top, int32_t *right, int32_t *bottom);
-
- void (*setInt32)(AMediaFormat*, const char* name, int32_t value);
- void (*setInt64)(AMediaFormat*, const char* name, int64_t value);
- void (*setFloat)(AMediaFormat*, const char* name, float value);
- void (*setString)(AMediaFormat*, const char* name, const char* value);
- void (*setBuffer)(AMediaFormat*, const char* name, const void* data, size_t size);
- void (*setRect)(AMediaFormat *, const char *name,
- int32_t left, int32_t top, int32_t right, int32_t bottom);
-} FFAMediaFormatNdk;
-
-typedef struct FFAMediaCodecNdk {
- FFAMediaCodec api;
-
- void *libmedia;
- AMediaCodec *impl;
- ANativeWindow *window;
-
- AMediaCodec* (*createCodecByName)(const char *name);
- AMediaCodec* (*createDecoderByType)(const char *mime_type);
- AMediaCodec* (*createEncoderByType)(const char *mime_type);
- media_status_t (*delete)(AMediaCodec*);
-
- media_status_t (*configure)(AMediaCodec *,
- const AMediaFormat *format,
- ANativeWindow *surface,
- AMediaCrypto *crypto,
- uint32_t flags);
- media_status_t (*start)(AMediaCodec*);
- media_status_t (*stop)(AMediaCodec*);
- media_status_t (*flush)(AMediaCodec*);
-
- uint8_t* (*getInputBuffer)(AMediaCodec*, size_t idx, size_t *out_size);
- uint8_t* (*getOutputBuffer)(AMediaCodec*, size_t idx, size_t *out_size);
-
- ssize_t (*dequeueInputBuffer)(AMediaCodec*, int64_t timeoutUs);
- media_status_t (*queueInputBuffer)(AMediaCodec*, size_t idx,
- long offset, size_t size,
- uint64_t time, uint32_t flags);
-
- ssize_t (*dequeueOutputBuffer)(AMediaCodec*, AMediaCodecBufferInfo *info, int64_t timeoutUs);
- AMediaFormat* (*getOutputFormat)(AMediaCodec*);
-
- media_status_t (*releaseOutputBuffer)(AMediaCodec*, size_t idx, bool render);
- media_status_t (*releaseOutputBufferAtTime)(AMediaCodec *mData, size_t idx, int64_t timestampNs);
-
- // Available since API level 28.
- media_status_t (*getName)(AMediaCodec*, char** out_name);
- void (*releaseName)(AMediaCodec*, char* name);
-
- // Available since API level 26.
- media_status_t (*setInputSurface)(AMediaCodec*, ANativeWindow *);
- media_status_t (*signalEndOfInputStream)(AMediaCodec *);
-} FFAMediaCodecNdk;
-
-static const FFAMediaFormat media_format_ndk;
-static const FFAMediaCodec media_codec_ndk;
-
-static const AVClass amediaformat_ndk_class = {
- .class_name = "amediaformat_ndk",
- .item_name = av_default_item_name,
- .version = LIBAVUTIL_VERSION_INT,
-};
-
-static const AVClass amediacodec_ndk_class = {
- .class_name = "amediacodec_ndk",
- .item_name = av_default_item_name,
- .version = LIBAVUTIL_VERSION_INT,
-};
-
-static FFAMediaFormat *mediaformat_ndk_create(AMediaFormat *impl)
-{
- FFAMediaFormatNdk *format = av_mallocz(sizeof(*format));
- if (!format)
- return NULL;
-
- format->api = media_format_ndk;
-
- format->libmedia = dlopen("libmediandk.so", RTLD_NOW);
- if (!format->libmedia)
- goto error;
-
-#define GET_OPTIONAL_SYMBOL(sym) \
- format->sym = dlsym(format->libmedia, "AMediaFormat_" #sym);
-
-#define GET_SYMBOL(sym) \
- GET_OPTIONAL_SYMBOL(sym) \
- if (!format->sym) \
- goto error;
-
- GET_SYMBOL(new)
- GET_SYMBOL(delete)
-
- GET_SYMBOL(toString)
-
- GET_SYMBOL(getInt32)
- GET_SYMBOL(getInt64)
- GET_SYMBOL(getFloat)
- GET_SYMBOL(getSize)
- GET_SYMBOL(getBuffer)
- GET_SYMBOL(getString)
- GET_OPTIONAL_SYMBOL(getRect)
-
- GET_SYMBOL(setInt32)
- GET_SYMBOL(setInt64)
- GET_SYMBOL(setFloat)
- GET_SYMBOL(setString)
- GET_SYMBOL(setBuffer)
- GET_OPTIONAL_SYMBOL(setRect)
-
-#undef GET_SYMBOL
-#undef GET_OPTIONAL_SYMBOL
-
- if (impl) {
- format->impl = impl;
- } else {
- format->impl = format->new();
- if (!format->impl)
- goto error;
- }
-
- return (FFAMediaFormat *)format;
-
-error:
- if (format->libmedia)
- dlclose(format->libmedia);
- av_freep(&format);
- return NULL;
-}
-
-static FFAMediaFormat *mediaformat_ndk_new(void)
-{
- return mediaformat_ndk_create(NULL);
-}
-
-static int mediaformat_ndk_delete(FFAMediaFormat* ctx)
-{
- FFAMediaFormatNdk *format = (FFAMediaFormatNdk *)ctx;
- int ret = 0;
- if (!format)
- return 0;
-
- av_assert0(format->api.class == &amediaformat_ndk_class);
-
- if (format->impl && (format->delete(format->impl) != AMEDIA_OK))
- ret = AVERROR_EXTERNAL;
- if (format->libmedia)
- dlclose(format->libmedia);
- av_free(format);
-
- return ret;
-}
-
-static char* mediaformat_ndk_toString(FFAMediaFormat* ctx)
-{
- FFAMediaFormatNdk *format = (FFAMediaFormatNdk *)ctx;
- const char *str = format->toString(format->impl);
- return av_strdup(str);
-}
-
-static int mediaformat_ndk_getInt32(FFAMediaFormat* ctx, const char *name, int32_t *out)
-{
- FFAMediaFormatNdk *format = (FFAMediaFormatNdk *)ctx;
- return format->getInt32(format->impl, name, out);
-}
-
-static int mediaformat_ndk_getInt64(FFAMediaFormat* ctx, const char *name, int64_t *out)
-{
- FFAMediaFormatNdk *format = (FFAMediaFormatNdk *)ctx;
- return format->getInt64(format->impl, name, out);
-}
-
-static int mediaformat_ndk_getFloat(FFAMediaFormat* ctx, const char *name, float *out)
-{
- FFAMediaFormatNdk *format = (FFAMediaFormatNdk *)ctx;
- return format->getFloat(format->impl, name, out);
-}
-
-static int mediaformat_ndk_getBuffer(FFAMediaFormat* ctx, const char *name, void** data, size_t *size)
-{
- FFAMediaFormatNdk *format = (FFAMediaFormatNdk *)ctx;
- return format->getBuffer(format->impl, name, data, size);
-}
-
-static int mediaformat_ndk_getString(FFAMediaFormat* ctx, const char *name, const char **out)
-{
- FFAMediaFormatNdk *format = (FFAMediaFormatNdk *)ctx;
- const char *tmp = NULL;
- int ret = format->getString(format->impl, name, &tmp);
-
- if (tmp)
- *out = av_strdup(tmp);
- return ret;
-}
-
-static int mediaformat_ndk_getRect(FFAMediaFormat *ctx, const char *name,
- int32_t *left, int32_t *top, int32_t *right, int32_t *bottom)
-{
- FFAMediaFormatNdk *format = (FFAMediaFormatNdk *)ctx;
- if (!format->getRect)
- return AVERROR_EXTERNAL;
- return format->getRect(format->impl, name, left, top, right, bottom);
-}
-
-static void mediaformat_ndk_setInt32(FFAMediaFormat* ctx, const char* name, int32_t value)
-{
- FFAMediaFormatNdk *format = (FFAMediaFormatNdk *)ctx;
- format->setInt32(format->impl, name, value);
-}
-
-static void mediaformat_ndk_setInt64(FFAMediaFormat* ctx, const char* name, int64_t value)
-{
- FFAMediaFormatNdk *format = (FFAMediaFormatNdk *)ctx;
- format->setInt64(format->impl, name, value);
-}
-
-static void mediaformat_ndk_setFloat(FFAMediaFormat* ctx, const char* name, float value)
-{
- FFAMediaFormatNdk *format = (FFAMediaFormatNdk *)ctx;
- format->setFloat(format->impl, name, value);
-}
-
-static void mediaformat_ndk_setString(FFAMediaFormat* ctx, const char* name, const char* value)
-{
- FFAMediaFormatNdk *format = (FFAMediaFormatNdk *)ctx;
- format->setString(format->impl, name, value);
-}
-
-static void mediaformat_ndk_setBuffer(FFAMediaFormat* ctx, const char* name, void* data, size_t size)
-{
- FFAMediaFormatNdk *format = (FFAMediaFormatNdk *)ctx;
- format->setBuffer(format->impl, name, data, size);
-}
-
-static void mediaformat_ndk_setRect(FFAMediaFormat *ctx, const char *name,
- int32_t left, int32_t top, int32_t right, int32_t bottom)
-{
- FFAMediaFormatNdk *format = (FFAMediaFormatNdk *)ctx;
- if (!format->setRect) {
- av_log(ctx, AV_LOG_WARNING, "Doesn't support setRect\n");
- return;
- }
- format->setRect(format->impl, name, left, top, right, bottom);
-}
-
-static char *mediacodec_ndk_getName(FFAMediaCodec *ctx)
-{
- FFAMediaCodecNdk *codec = (FFAMediaCodecNdk *)ctx;
- char *ret = NULL;
- char *name = NULL;
-
- if (!codec->getName || !codec->releaseName) {
- av_log(ctx, AV_LOG_DEBUG, "getName() unavailable\n");
- return ret;
- }
-
- codec->getName(codec->impl, &name);
- if (name) {
- ret = av_strdup(name);
- codec->releaseName(codec->impl, name);
- }
-
- return ret;
-}
-
-static inline FFAMediaCodec *ndk_codec_create(int method, const char *arg) {
- FFAMediaCodecNdk *codec = av_mallocz(sizeof(*codec));
- const char *lib_name = "libmediandk.so";
-
- if (!codec)
- return NULL;
-
- codec->api = media_codec_ndk;
- codec->libmedia = dlopen(lib_name, RTLD_NOW);
- if (!codec->libmedia)
- goto error;
-
-#define GET_SYMBOL(sym, required) \
- codec->sym = dlsym(codec->libmedia, "AMediaCodec_" #sym); \
- if (!codec->sym) { \
- av_log(codec, required ? AV_LOG_ERROR : AV_LOG_INFO, \
- #sym "() unavailable from %s\n", lib_name); \
- if (required) \
- goto error; \
- }
-
- GET_SYMBOL(createCodecByName, 1)
- GET_SYMBOL(createDecoderByType, 1)
- GET_SYMBOL(createEncoderByType, 1)
- GET_SYMBOL(delete, 1)
-
- GET_SYMBOL(configure, 1)
- GET_SYMBOL(start, 1)
- GET_SYMBOL(stop, 1)
- GET_SYMBOL(flush, 1)
-
- GET_SYMBOL(getInputBuffer, 1)
- GET_SYMBOL(getOutputBuffer, 1)
-
- GET_SYMBOL(dequeueInputBuffer, 1)
- GET_SYMBOL(queueInputBuffer, 1)
-
- GET_SYMBOL(dequeueOutputBuffer, 1)
- GET_SYMBOL(getOutputFormat, 1)
-
- GET_SYMBOL(releaseOutputBuffer, 1)
- GET_SYMBOL(releaseOutputBufferAtTime, 1)
-
- GET_SYMBOL(getName, 0)
- GET_SYMBOL(releaseName, 0)
-
- GET_SYMBOL(setInputSurface, 0)
- GET_SYMBOL(signalEndOfInputStream, 0)
-
-#undef GET_SYMBOL
-
- switch (method) {
- case CREATE_CODEC_BY_NAME:
- codec->impl = codec->createCodecByName(arg);
- break;
- case CREATE_DECODER_BY_TYPE:
- codec->impl = codec->createDecoderByType(arg);
- break;
- case CREATE_ENCODER_BY_TYPE:
- codec->impl = codec->createEncoderByType(arg);
- break;
- default:
- av_assert0(0);
- }
- if (!codec->impl)
- goto error;
-
- return (FFAMediaCodec *)codec;
-
-error:
- if (codec->libmedia)
- dlclose(codec->libmedia);
- av_freep(&codec);
- return NULL;
-}
-
-#define DECLARE_NDK_AMEDIACODEC_CREATE_FUNC(name, method) \
-static FFAMediaCodec *mediacodec_ndk_##name(const char *arg) \
-{ \
- return ndk_codec_create(method, arg); \
-} \
-
-DECLARE_NDK_AMEDIACODEC_CREATE_FUNC(createCodecByName, CREATE_CODEC_BY_NAME)
-DECLARE_NDK_AMEDIACODEC_CREATE_FUNC(createDecoderByType, CREATE_DECODER_BY_TYPE)
-DECLARE_NDK_AMEDIACODEC_CREATE_FUNC(createEncoderByType, CREATE_ENCODER_BY_TYPE)
-
-static int mediacodec_ndk_delete(FFAMediaCodec* ctx)
-{
- FFAMediaCodecNdk *codec = (FFAMediaCodecNdk *)ctx;
- int ret = 0;
-
- if (!codec)
- return 0;
-
- av_assert0(codec->api.class == &amediacodec_ndk_class);
-
- if (codec->impl && (codec->delete(codec->impl) != AMEDIA_OK))
- ret = AVERROR_EXTERNAL;
- if (codec->window)
- ANativeWindow_release(codec->window);
- if (codec->libmedia)
- dlclose(codec->libmedia);
- av_free(codec);
-
- return ret;
-}
-
-static int mediacodec_ndk_configure(FFAMediaCodec* ctx,
- const FFAMediaFormat* format_ctx,
- FFANativeWindow* window,
- void *crypto,
- uint32_t flags)
-{
- FFAMediaCodecNdk *codec = (FFAMediaCodecNdk *)ctx;
- FFAMediaFormatNdk *format = (FFAMediaFormatNdk *)format_ctx;
- media_status_t status;
- ANativeWindow *native_window = NULL;
-
- if (window) {
- if (window->surface) {
- JNIEnv *env = NULL;
- JNI_GET_ENV_OR_RETURN(env, ctx, -1);
- native_window = ANativeWindow_fromSurface(env, window->surface);
- // Save for release
- codec->window = native_window;
- } else if (window->native_window) {
- native_window = window->native_window;
- }
- }
-
- if (format_ctx->class != &amediaformat_ndk_class) {
- av_log(ctx, AV_LOG_ERROR, "invalid media format\n");
- return AVERROR(EINVAL);
- }
-
- if (flags & AMEDIACODEC_CONFIGURE_FLAG_ENCODE) {
- if (native_window && !codec->setInputSurface) {
- av_log(ctx, AV_LOG_ERROR, "System doesn't support setInputSurface\n");
- return AVERROR_EXTERNAL;
- }
-
- status = codec->configure(codec->impl, format->impl, NULL, NULL, flags);
- if (status != AMEDIA_OK) {
- av_log(codec, AV_LOG_ERROR, "Encoder configure failed, %d\n", status);
- return AVERROR_EXTERNAL;
- }
-
- if (!native_window)
- return 0;
-
- status = codec->setInputSurface(codec->impl, native_window);
- if (status != AMEDIA_OK) {
- av_log(codec, AV_LOG_ERROR, "Encoder set input surface failed, %d\n", status);
- return AVERROR_EXTERNAL;
- }
- } else {
- status = codec->configure(codec->impl, format->impl, native_window, NULL, flags);
- if (status != AMEDIA_OK) {
- av_log(codec, AV_LOG_ERROR, "Decoder configure failed, %d\n", status);
- return AVERROR_EXTERNAL;
- }
- }
-
- return 0;
-}
-
-#define MEDIACODEC_NDK_WRAPPER(method) \
-static int mediacodec_ndk_ ## method(FFAMediaCodec* ctx) \
-{ \
- FFAMediaCodecNdk *codec = (FFAMediaCodecNdk *)ctx; \
- media_status_t status = codec->method(codec->impl); \
- \
- if (status != AMEDIA_OK) { \
- av_log(codec, AV_LOG_ERROR, #method " failed, %d\n", status); \
- return AVERROR_EXTERNAL; \
- } \
- \
- return 0; \
-} \
-
-MEDIACODEC_NDK_WRAPPER(start)
-MEDIACODEC_NDK_WRAPPER(stop)
-MEDIACODEC_NDK_WRAPPER(flush)
-
-static uint8_t* mediacodec_ndk_getInputBuffer(FFAMediaCodec* ctx, size_t idx, size_t *out_size)
-{
- FFAMediaCodecNdk *codec = (FFAMediaCodecNdk *)ctx;
- return codec->getInputBuffer(codec->impl, idx, out_size);
-}
-
-static uint8_t* mediacodec_ndk_getOutputBuffer(FFAMediaCodec* ctx, size_t idx, size_t *out_size)
-{
- FFAMediaCodecNdk *codec = (FFAMediaCodecNdk *)ctx;
- return codec->getOutputBuffer(codec->impl, idx, out_size);
-}
-
-static ssize_t mediacodec_ndk_dequeueInputBuffer(FFAMediaCodec* ctx, int64_t timeoutUs)
-{
- FFAMediaCodecNdk *codec = (FFAMediaCodecNdk *)ctx;
- return codec->dequeueInputBuffer(codec->impl, timeoutUs);
-}
-
-static int mediacodec_ndk_queueInputBuffer(FFAMediaCodec *ctx, size_t idx,
- off_t offset, size_t size,
- uint64_t time, uint32_t flags)
-{
- FFAMediaCodecNdk *codec = (FFAMediaCodecNdk *)ctx;
- return codec->queueInputBuffer(codec->impl, idx, offset, size, time, flags);
-}
-
-static ssize_t mediacodec_ndk_dequeueOutputBuffer(FFAMediaCodec* ctx, FFAMediaCodecBufferInfo *info, int64_t timeoutUs)
-{
- FFAMediaCodecNdk *codec = (FFAMediaCodecNdk *)ctx;
- AMediaCodecBufferInfo buf_info = {0};
- ssize_t ret;
-
- ret = codec->dequeueOutputBuffer(codec->impl, &buf_info, timeoutUs);
- info->offset = buf_info.offset;
- info->size = buf_info.size;
- info->presentationTimeUs = buf_info.presentationTimeUs;
- info->flags = buf_info.flags;
-
- return ret;
-}
-
-static FFAMediaFormat* mediacodec_ndk_getOutputFormat(FFAMediaCodec* ctx)
-{
- FFAMediaCodecNdk *codec = (FFAMediaCodecNdk *)ctx;
- AMediaFormat *format = codec->getOutputFormat(codec->impl);
-
- if (!format)
- return NULL;
- return mediaformat_ndk_create(format);
-}
-
-static int mediacodec_ndk_releaseOutputBuffer(FFAMediaCodec* ctx, size_t idx, int render)
-{
- FFAMediaCodecNdk *codec = (FFAMediaCodecNdk *)ctx;
- media_status_t status;
-
- status = codec->releaseOutputBuffer(codec->impl, idx, render);
- if (status != AMEDIA_OK) {
- av_log(codec, AV_LOG_ERROR, "release output buffer failed, %d\n", status);
- return AVERROR_EXTERNAL;
- }
-
- return 0;
-}
-
-static int mediacodec_ndk_releaseOutputBufferAtTime(FFAMediaCodec *ctx, size_t idx, int64_t timestampNs)
-{
- FFAMediaCodecNdk *codec = (FFAMediaCodecNdk *)ctx;
- media_status_t status;
-
- status = codec->releaseOutputBufferAtTime(codec->impl, idx, timestampNs);
- if (status != AMEDIA_OK) {
- av_log(codec, AV_LOG_ERROR, "releaseOutputBufferAtTime failed, %d\n", status);
- return AVERROR_EXTERNAL;
- }
-
- return 0;
-}
-
-static int mediacodec_ndk_infoTryAgainLater(FFAMediaCodec *ctx, ssize_t idx)
-{
- return idx == AMEDIACODEC_INFO_TRY_AGAIN_LATER;
-}
-
-static int mediacodec_ndk_infoOutputBuffersChanged(FFAMediaCodec *ctx, ssize_t idx)
-{
- return idx == AMEDIACODEC_INFO_OUTPUT_BUFFERS_CHANGED;
-}
-
-static int mediacodec_ndk_infoOutputFormatChanged(FFAMediaCodec *ctx, ssize_t idx)
-{
- return idx == AMEDIACODEC_INFO_OUTPUT_FORMAT_CHANGED;
-}
-
-static int mediacodec_ndk_getBufferFlagCodecConfig(FFAMediaCodec *ctx)
-{
- return AMEDIACODEC_BUFFER_FLAG_CODEC_CONFIG;
-}
-
-static int mediacodec_ndk_getBufferFlagEndOfStream(FFAMediaCodec *ctx)
-{
- return AMEDIACODEC_BUFFER_FLAG_END_OF_STREAM;
-}
-
-static int mediacodec_ndk_getBufferFlagKeyFrame(FFAMediaCodec *ctx)
-{
- return 1;
-}
-
-static int mediacodec_ndk_getConfigureFlagEncode(FFAMediaCodec *ctx)
-{
- return AMEDIACODEC_CONFIGURE_FLAG_ENCODE;
-}
-
-static int mediacodec_ndk_cleanOutputBuffers(FFAMediaCodec *ctx)
-{
- return 0;
-}
-
-static int mediacodec_ndk_signalEndOfInputStream(FFAMediaCodec *ctx)
-{
- FFAMediaCodecNdk *codec = (FFAMediaCodecNdk *)ctx;
- media_status_t status;
-
- if (!codec->signalEndOfInputStream) {
- av_log(codec, AV_LOG_ERROR, "signalEndOfInputStream unavailable\n");
- return AVERROR_EXTERNAL;
- }
-
- status = codec->signalEndOfInputStream(codec->impl);
- if (status != AMEDIA_OK) {
- av_log(codec, AV_LOG_ERROR, "signalEndOfInputStream failed, %d\n", status);
- return AVERROR_EXTERNAL;
- }
- av_log(codec, AV_LOG_DEBUG, "signalEndOfInputStream success\n");
-
- return 0;
-}
-
-static const FFAMediaFormat media_format_ndk = {
- .class = &amediaformat_ndk_class,
-
- .create = mediaformat_ndk_new,
- .delete = mediaformat_ndk_delete,
-
- .toString = mediaformat_ndk_toString,
-
- .getInt32 = mediaformat_ndk_getInt32,
- .getInt64 = mediaformat_ndk_getInt64,
- .getFloat = mediaformat_ndk_getFloat,
- .getBuffer = mediaformat_ndk_getBuffer,
- .getString = mediaformat_ndk_getString,
- .getRect = mediaformat_ndk_getRect,
-
- .setInt32 = mediaformat_ndk_setInt32,
- .setInt64 = mediaformat_ndk_setInt64,
- .setFloat = mediaformat_ndk_setFloat,
- .setString = mediaformat_ndk_setString,
- .setBuffer = mediaformat_ndk_setBuffer,
- .setRect = mediaformat_ndk_setRect,
-};
-
-static const FFAMediaCodec media_codec_ndk = {
- .class = &amediacodec_ndk_class,
-
- .getName = mediacodec_ndk_getName,
-
- .createCodecByName = mediacodec_ndk_createCodecByName,
- .createDecoderByType = mediacodec_ndk_createDecoderByType,
- .createEncoderByType = mediacodec_ndk_createEncoderByType,
- .delete = mediacodec_ndk_delete,
-
- .configure = mediacodec_ndk_configure,
- .start = mediacodec_ndk_start,
- .stop = mediacodec_ndk_stop,
- .flush = mediacodec_ndk_flush,
-
- .getInputBuffer = mediacodec_ndk_getInputBuffer,
- .getOutputBuffer = mediacodec_ndk_getOutputBuffer,
-
- .dequeueInputBuffer = mediacodec_ndk_dequeueInputBuffer,
- .queueInputBuffer = mediacodec_ndk_queueInputBuffer,
-
- .dequeueOutputBuffer = mediacodec_ndk_dequeueOutputBuffer,
- .getOutputFormat = mediacodec_ndk_getOutputFormat,
-
- .releaseOutputBuffer = mediacodec_ndk_releaseOutputBuffer,
- .releaseOutputBufferAtTime = mediacodec_ndk_releaseOutputBufferAtTime,
-
- .infoTryAgainLater = mediacodec_ndk_infoTryAgainLater,
- .infoOutputBuffersChanged = mediacodec_ndk_infoOutputBuffersChanged,
- .infoOutputFormatChanged = mediacodec_ndk_infoOutputFormatChanged,
-
- .getBufferFlagCodecConfig = mediacodec_ndk_getBufferFlagCodecConfig,
- .getBufferFlagEndOfStream = mediacodec_ndk_getBufferFlagEndOfStream,
- .getBufferFlagKeyFrame = mediacodec_ndk_getBufferFlagKeyFrame,
-
- .getConfigureFlagEncode = mediacodec_ndk_getConfigureFlagEncode,
- .cleanOutputBuffers = mediacodec_ndk_cleanOutputBuffers,
- .signalEndOfInputStream = mediacodec_ndk_signalEndOfInputStream,
-};
-
-FFAMediaFormat *ff_AMediaFormat_new(int ndk)
-{
- if (ndk)
- return media_format_ndk.create();
- return media_format_jni.create();
-}
-
-FFAMediaCodec* ff_AMediaCodec_createCodecByName(const char *name, int ndk)
-{
- if (ndk)
- return media_codec_ndk.createCodecByName(name);
- return media_codec_jni.createCodecByName(name);
-}
-
-FFAMediaCodec* ff_AMediaCodec_createDecoderByType(const char *mime_type, int ndk)
-{
- if (ndk)
- return media_codec_ndk.createDecoderByType(mime_type);
- return media_codec_jni.createDecoderByType(mime_type);
-}
-
-FFAMediaCodec* ff_AMediaCodec_createEncoderByType(const char *mime_type, int ndk)
-{
- if (ndk)
- return media_codec_ndk.createEncoderByType(mime_type);
- return media_codec_jni.createEncoderByType(mime_type);
-}
-
-int ff_Build_SDK_INT(AVCodecContext *avctx)
-{
- int ret = -1;
-
-#if __ANDROID_API__ >= 24
- // android_get_device_api_level() is a static inline before API level 29.
- // dlsym() might doesn't work.
- //
- // We can implement android_get_device_api_level() by
- // __system_property_get(), but __system_property_get() has created a lot of
- // troubles and is deprecated. So avoid using __system_property_get() for
- // now.
- //
- // Hopy we can remove the conditional compilation finally by bumping the
- // required API level.
- //
- ret = android_get_device_api_level();
-#else
- JNIEnv *env = NULL;
- jclass versionClass;
- jfieldID sdkIntFieldID;
- JNI_GET_ENV_OR_RETURN(env, avctx, -1);
-
- versionClass = (*env)->FindClass(env, "android/os/Build$VERSION");
- sdkIntFieldID = (*env)->GetStaticFieldID(env, versionClass, "SDK_INT", "I");
- ret = (*env)->GetStaticIntField(env, versionClass, sdkIntFieldID);
- (*env)->DeleteLocalRef(env, versionClass);
-#endif
- av_log(avctx, AV_LOG_DEBUG, "device api level %d\n", ret);
-
- return ret;
-}
-
-static struct {
- enum FFAMediaFormatColorRange mf_range;
- enum AVColorRange range;
-} color_range_map[] = {
- { COLOR_RANGE_FULL, AVCOL_RANGE_JPEG },
- { COLOR_RANGE_LIMITED, AVCOL_RANGE_MPEG },
-};
-
-static struct {
- enum FFAMediaFormatColorStandard mf_standard;
- enum AVColorSpace space;
-} color_space_map[] = {
- { COLOR_STANDARD_BT709, AVCOL_SPC_BT709 },
- { COLOR_STANDARD_BT601_PAL, AVCOL_SPC_BT470BG },
- { COLOR_STANDARD_BT601_NTSC, AVCOL_SPC_SMPTE170M },
- { COLOR_STANDARD_BT2020, AVCOL_SPC_BT2020_NCL },
-};
-
-static struct {
- enum FFAMediaFormatColorStandard mf_standard;
- enum AVColorPrimaries primaries;
-} color_primaries_map[] = {
- { COLOR_STANDARD_BT709, AVCOL_PRI_BT709 },
- { COLOR_STANDARD_BT601_PAL, AVCOL_PRI_BT470BG },
- { COLOR_STANDARD_BT601_NTSC, AVCOL_PRI_SMPTE170M },
- { COLOR_STANDARD_BT2020, AVCOL_PRI_BT2020 },
-};
-
-static struct {
- enum FFAMediaFormatColorTransfer mf_transfer;
- enum AVColorTransferCharacteristic transfer;
-} color_transfer_map[] = {
- { COLOR_TRANSFER_LINEAR, AVCOL_TRC_LINEAR },
- { COLOR_TRANSFER_SDR_VIDEO, AVCOL_TRC_SMPTE170M },
- { COLOR_TRANSFER_ST2084, AVCOL_TRC_SMPTEST2084 },
- { COLOR_TRANSFER_HLG, AVCOL_TRC_ARIB_STD_B67 },
-};
-
-enum AVColorRange ff_AMediaFormatColorRange_to_AVColorRange(int color_range)
-{
- for (int i = 0; i < FF_ARRAY_ELEMS(color_range_map); i++)
- if (color_range_map[i].mf_range == color_range)
- return color_range_map[i].range;
-
- return AVCOL_RANGE_UNSPECIFIED;
-}
-
-int ff_AMediaFormatColorRange_from_AVColorRange(enum AVColorRange color_range)
-{
- for (int i = 0; i < FF_ARRAY_ELEMS(color_range_map); i++)
- if (color_range_map[i].range == color_range)
- return color_range_map[i].mf_range;
- return COLOR_RANGE_UNSPECIFIED;
-}
-
-enum AVColorSpace ff_AMediaFormatColorStandard_to_AVColorSpace(int color_standard)
-{
- for (int i = 0; i < FF_ARRAY_ELEMS(color_space_map); i++)
- if (color_space_map[i].mf_standard == color_standard)
- return color_space_map[i].space;
-
- return AVCOL_SPC_UNSPECIFIED;
-}
-
-int ff_AMediaFormatColorStandard_from_AVColorSpace(enum AVColorSpace color_space)
-{
- for (int i = 0; i < FF_ARRAY_ELEMS(color_space_map); i++)
- if (color_space_map[i].space == color_space)
- return color_space_map[i].mf_standard;
-
- return COLOR_STANDARD_UNSPECIFIED;
-}
-
-enum AVColorPrimaries ff_AMediaFormatColorStandard_to_AVColorPrimaries(int color_standard)
-{
- for (int i = 0; i < FF_ARRAY_ELEMS(color_primaries_map); i++)
- if (color_primaries_map[i].mf_standard == color_standard)
- return color_primaries_map[i].primaries;
-
- return AVCOL_PRI_UNSPECIFIED;
-}
-
-enum AVColorTransferCharacteristic
-ff_AMediaFormatColorTransfer_to_AVColorTransfer(int color_transfer)
-{
- for (int i = 0; i < FF_ARRAY_ELEMS(color_transfer_map); i++)
- if (color_transfer_map[i].mf_transfer == color_transfer)
- return color_transfer_map[i].transfer;
-
- return AVCOL_TRC_UNSPECIFIED;
-}
-
-int ff_AMediaFormatColorTransfer_from_AVColorTransfer(
- enum AVColorTransferCharacteristic color_transfer)
-{
- for (int i = 0; i < FF_ARRAY_ELEMS(color_transfer_map); i++)
- if (color_transfer_map[i].transfer == color_transfer)
- return color_transfer_map[i].mf_transfer;
-
- return COLOR_TRANSFER_UNSPECIFIED;
-}
diff --git a/spaces/congsaPfin/Manga-OCR/logs/ABC Kids Play Games Watch Videos and Learn the Alphabet with Your 3 Year Old.md b/spaces/congsaPfin/Manga-OCR/logs/ABC Kids Play Games Watch Videos and Learn the Alphabet with Your 3 Year Old.md
deleted file mode 100644
index 1600c0015d38044b07e7b4f829fb95d9252c69f7..0000000000000000000000000000000000000000
--- a/spaces/congsaPfin/Manga-OCR/logs/ABC Kids Play Games Watch Videos and Learn the Alphabet with Your 3 Year Old.md
+++ /dev/null
@@ -1,56 +0,0 @@
-ABC Games for 3 Year Olds: Fun, Easy, and Educational
-
-Learning the alphabet is one of the most important skills for preschoolers. It lays the foundation for reading, writing, and communication. However, learning the alphabet can also be boring and frustrating for some kids. That's why playing games is a great way to make it fun and easy. Games can help your 3 year old learn the letters, sounds, and words in a playful and interactive way. In this article, we will share with you some of the benefits of playing ABC games with your 3 year old, some of the best ABC games you can try, and some tips on how to choose the right game for your child.
- 5 Benefits of Playing ABC Games with Your 3 Year Old
-
-Playing ABC games with your 3 year old can have many positive effects on their development. Here are some of the benefits you can expect from playing these games:
-abc games for 3 year olds DOWNLOAD ➡ https://urlca.com/2uO7Xt
-Improves letter recognition and phonemic awareness. Playing ABC games can help your child recognize the shapes and names of the letters, as well as their sounds and positions in words. This can help them develop phonemic awareness, which is the ability to hear and manipulate the sounds in words. Phonemic awareness is essential for learning to read and spell.Enhances vocabulary and language skills. Playing ABC games can expose your child to new words and meanings, as well as reinforce their existing vocabulary. This can help them expand their language skills and comprehension. Playing ABC games can also encourage your child to use words in sentences and conversations, which can improve their communication skills.Boosts memory and concentration. Playing ABC games can challenge your child's memory and attention span, as they have to recall the letters, sounds, and words they have learned. This can help them improve their cognitive skills and mental abilities.Fosters creativity and imagination. Playing ABC games can stimulate your child's creativity and imagination, as they have to use their senses, emotions, and thoughts to play. For example, they can make up stories or songs with the letters or words they have learned, or they can draw or act out the letters or words they have learned.Strengthens bonding and communication. Playing ABC games with your 3 year old can be a fun and enjoyable activity that can strengthen your bond and communication with your child. You can share your feedback, praise, and encouragement with your child, as well as listen to their ideas and opinions. You can also learn more about your child's personality, interests, and abilities through playing ABC games.
- 10 Best ABC Games for 3 Year Olds
-
-There are many ABC games that you can play with your 3 year old, either online or offline. Here are some of the best ones that we recommend:
-
-Alphabet BINGO . This is an online game that teaches your child to identify the uppercase and lowercase letters of the alphabet. Your child has to match the letters on the bingo card with the ones that are spoken by the narrator. The game has four levels of difficulty, from easy to hard.Letter Bubble - Alphabetical Order . This is another online game that teaches your child to arrange the letters of the alphabet in order. Your child has to pop the bubbles that contain the letters in the correct sequence. The game has three modes: easy, medium, and hard.Alphabet Hunt . This is an offline game that involves finding and collecting objects that start with each letter of the alphabet. You can use any objects that you have at home or outside, such as toys, books, clothes, fruits, etc. You can make a list of the objects for each letter, or you can let your child choose them. You can also make it more fun by setting a timer or hiding the objects.Alphabet Slider Puzzle . This is an online game that challenges your child to solve a puzzle that contains a picture and a word that starts with a certain letter. Your child has to slide the tiles to form the picture and the word. The game has four levels of difficulty, from easy to hard.Alphabats - Alliteration . This is an online game that teaches your child to recognize and produce words that have the same initial sound. Your child has to help the bats catch the bugs that have words that start with the same letter as the bat. The game has three levels of difficulty, from easy to hard.Letter Sort with Sticky Notes . This is an offline game that teaches your child to sort the letters of the alphabet into different categories. You can use sticky notes or paper to write the letters on them. You can then ask your child to sort them by color, shape, size, or sound. For example, you can ask your child to sort the letters by vowels and consonants, or by curved and straight lines.Write Letters in Shaving Cream . This is an offline game that teaches your child to write the letters of the alphabet using their fingers. You can use shaving cream or any other sensory material, such as whipped cream, sand, or paint. You can spread the material on a tray or a table, and then ask your child to write the letters on it. You can also write the letters first and ask your child to trace them.Sponge the Alphabet . This is an offline game that teaches your child to recognize and name the letters of the alphabet using sponges. You can use sponges or any other soft material, such as cotton balls or pom poms. You can cut out the shapes of the letters from the sponges, or you can buy them ready-made. You can then ask your child to sponge paint the letters on a paper or a wall.Make an Alphabet Book . This is an offline game that teaches your child to create their own book with pictures and words for each letter of the alphabet. You can use paper or cardboard to make the pages of the book, and then ask your child to draw or paste pictures that start with each letter on them. You can also help your child write the words under each picture.Play Musical Alphabet . This is an offline game that teaches your child to sing and dance along with the alphabet song. You can use music or sing it yourself, and then ask your child to join you. You can also make it more fun by adding some actions or movements for each letter, such as clapping, jumping, or spinning.
- How to Choose the Right ABC Game for Your 3 Year Old
-
-Not all ABC games are suitable for every 3 year old. Some games may be too easy or too hard for your child, depending on their level of development and learning style. Here are some tips on how to choose the right ABC game for your 3 year old:
-
-Consider your child's interests and preferences. Choose games that match your child's hobbies, passions, and personality. For example, if your child loves animals, you can choose games that feature animal pictures or sounds. If your child is adventurous, you can choose games that involve exploration or discovery. If your child is artistic, you can choose games that involve drawing or painting.Choose games that are age-appropriate and challenging. Choose games that are suitable for your child's level of development and learning ability. For example, if your child is just starting to learn the alphabet, you can choose games that focus on letter recognition and naming. If your child already knows the letters, you can choose games that focus on letter sounds and words. You can also choose games that have different levels of difficulty, so you can adjust them according to your child's progress.Look for games that are interactive and engaging. Choose games that require your child to use their senses, motor skills, and cognitive skills to play. For example, you can choose games that involve touching, moving, listening, speaking, or thinking. You can also choose games that have feedback, rewards, or surprises to keep your child motivated and interested.Mix up different types of games to keep it fun and varied. Choose games that have different formats, themes, and objectives to avoid boredom and repetition. For example, you can mix up online and offline games, or games that use letters, pictures, sounds, or words. You can also mix up games that are individual and cooperative, or games that are competitive and cooperative.
- Conclusion
-
-Playing ABC games with your 3 year old can be a fun and effective way to help them learn the alphabet. ABC games can improve your child's letter recognition, phonemic awareness, vocabulary, language skills, memory, concentration, creativity, imagination, bonding, and communication. There are many ABC games that you can choose from, either online or offline. You can choose the right game for your child by considering their interests, preferences, level of development, learning style, and personality. You can also mix up different types of games to keep it fun and varied. Playing ABC games with your 3 year old can be a rewarding and enjoyable experience for both of you.
- FAQs
-
-Here are some frequently asked questions about playing ABC games with your 3 year old:
-
-What are some tips to make ABC games more fun for 3 year olds? Some tips to make ABC games more fun for 3 year olds are: How often should I play ABC games with my 3 year old? There is no fixed rule on how often you should play ABC games with your 3 year old. It depends on your child's interest, attention span, and mood. However, a general guideline is to play ABC games with your 3 year old for about 10 to 15 minutes a day, or as long as they are having fun and learning.How can I track my 3 year old's progress in learning the alphabet? You can track your 3 year old's progress in learning the alphabet by observing their performance in playing ABC games. You can also use informal assessments such as quizzes, tests, or checklists to measure your child's knowledge and skills in learning the alphabet. You can also use formal assessments such as standardized tests or evaluations to compare your child's progress with other children of the same age and grade level.What are some other resources to help my 3 year old learn the alphabet? Some other resources to help your 3 year old learn the alphabet are: What are some common mistakes to avoid when playing ABC games with my 3 year old? Some common mistakes to avoid when playing ABC games with your 3 year old are:
-
-
197e85843dAble Cee Live Performance Vol 3 MP3 Download
-If you are looking for a gospel album that will uplift your spirit and fill your heart with joy, then you should check out Able Cee Live Performance Vol 3 . This is a live recording of Able Cee, a Nigerian gospel singer, producer, and songwriter, who delivers a powerful performance of praise and worship songs. In this article, we will tell you everything you need to know about Able Cee Live Performance Vol 3, including who is Able Cee, what is Live Performance Vol 3, why should you download it, how to download it, and where to download it. We will also answer some frequently asked questions about the album. So, let's get started!
-able cee live performance vol 3 mp3 download Download Zip ⚙ https://urlca.com/2uO738
Who is Able Cee?
-Able Cee, whose real name is Chikaodiri Okpara, is a Nigerian contemporary gospel singer, producer, and songwriter from Ebonyi State. He is also known as Nothing Mega, which is the title of his debut album with his group C-JEC International in 2006. Able Cee has been singing since he was a child, and he has a passion for spreading the gospel through music. He has released several albums and singles, such as Gospel Live Performance Praise Vol 1, Gospel Live Performance Praise Vol 2, Gospel Live Performance Praise Vol 4, Nothing Mega Reloaded, Ihe Dikwuru Bu Ndu (Life), and many more. He is also a music director and a keyboardist. He has performed in many churches and events across Nigeria and beyond.
- What is Live Performance Vol 3?
-Live Performance Vol 3 is the third installment of Able Cee's live performance series, which he started in 2017. It is a live recording of Able Cee performing various gospel songs in a church setting, accompanied by a band and a choir. The album consists of two tracks, Track A and Track B, each lasting about 30 minutes. The songs are a mix of Nigerian/Naija gospel praise and worship songs, some of which are original compositions by Able Cee, and some of which are covers of popular songs by other artists. The album showcases Able Cee's vocal skills, musical talent, and spiritual fervor.
- Why should you download Live Performance Vol 3?
-There are many reasons why you should download Live Performance Vol 3. Here are some of them:
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-It is a great source of inspiration and motivation for your daily life. The songs are uplifting, encouraging, and edifying. They will help you to praise God, thank Him for His blessings, trust Him in times of trouble, and seek His guidance.
-It is a great way to enjoy gospel music at its best. The songs are well-produced, well-arranged, and well-performed. The sound quality is excellent, the instrumentation is rich, and the vocals are clear. You will feel like you are in the presence of Able Cee and his band.
-It is a great way to support Able Cee and his ministry. By downloading the album, you are showing your appreciation for his work and his message. You are also helping him to reach more people with the gospel through his music.
-
- How to download Live Performance Vol 3?
-Downloading Live Performance Vol 3 is easy and simple. Here are the steps you need to follow:
-
-Go to Go to https://www.ablecee.com , the official website of Able Cee. You will see a banner that says "Download Live Performance Vol 3 Now". Click on it and you will be redirected to a page where you can choose your preferred format and quality. You can download the album as MP3, WAV, or FLAC files, and you can choose between high, medium, or low quality. The higher the quality, the larger the file size.
-Select the format and quality that you want and click on the "Download" button. You will be asked to enter your name and email address. This is to verify that you are a human and not a robot, and also to send you a confirmation email with the download link. Enter your details and click on "Submit".
-Check your email inbox for the confirmation email from Able Cee. It should arrive within a few minutes. If you don't see it, check your spam or junk folder. Open the email and click on the download link. You will be taken to a page where you can download the album as a ZIP file. Save the file to your device and extract it using a ZIP extractor software.
-Enjoy listening to Live Performance Vol 3 on your device. You can also transfer it to other devices or burn it to a CD if you want.
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- Where to download Live Performance Vol 3?
-Besides the official website of Able Cee, there are other sources where you can download Live Performance Vol 3. However, you should be careful about the quality and authenticity of these sources, as some of them may contain viruses, malware, or fake files. Here are some of the reputable sources where you can download Live Performance Vol 3 safely and legally:
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- Conclusion
-We hope that this article has given you all the information you need about Able Cee Live Performance Vol 3 MP3 Download. This is a wonderful gospel album that will bless your soul and make you dance for joy. You can download it from the official website of Able Cee or from other sources that we have mentioned above. You can also share it with your friends and family who love gospel music. Thank you for reading this article and may God bless you!
-Able Cee gospel live performance praise vol 3 track B
- FAQs
-Q1: What is the genre of Live Performance Vol 3?
-A1: The genre of Live Performance Vol 3 is gospel music, specifically Nigerian/Naija gospel praise and worship music.
- Q2: How long is Live Performance Vol 3?
-A2: Live Performance Vol 3 is about one hour long, divided into two tracks of about 30 minutes each.
- Q3: How much does Live Performance Vol 3 cost?
-A3: Live Performance Vol 3 is free to download from the official website of Able Cee or from other sources that we have mentioned above. However, if you want to support Able Cee and his ministry, you can make a donation through his website or buy his other albums.
- Q4: Is Live Performance Vol 3 available on other platforms?
-A4: Yes, Live Performance Vol 3 is also available on other platforms such as YouTube, Spotify, Apple Music, Deezer, Boomplay, and more.
- Q5: What are some other albums by Able Cee?
-A5: Some other albums by Able Cee are Gospel Live Performance Praise Vol 1, Gospel Live Performance Praise Vol 2, Gospel Live Performance Praise Vol 4, Nothing Mega Reloaded, Ihe Dikwuru Bu Ndu ( Life), and many more. You can find them on his website or on other platforms that we have mentioned above.
197e85843dAgar.io Free Skins Download: How to Customize Your Cell and Play with Style
-Do you love playing online games that are simple yet addictive? Do you want to customize your character and stand out from the crowd? If you answered yes to both questions, then you should try agar.io, a popular browser game that lets you control a cell and eat other cells to grow bigger. In this article, we will tell you everything you need to know about agar.io, including how to get free skins for your cell and how to install and use them.
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What is Agar.io and Why You Should Play It
-Agar.io is a multiplayer online game that was released in 2015 by a Brazilian developer named Matheus Valadares. The game is inspired by the agar, a substance used in microbiology to culture bacteria. The game is simple: you control a cell on a map filled with other cells, both players and bots. Your goal is to eat smaller cells and avoid being eaten by bigger ones. You can also split your cell into smaller pieces to move faster or to escape from predators. The game has different modes, such as free-for-all, teams, experimental, and battle royale.
- The Basics of Agar.io Gameplay
-To play agar.io, you just need a web browser and an internet connection. You can choose a nickname for your cell, or leave it blank if you want to be anonymous. You can also choose a region and a mode to play in. Once you enter the game, you will see your cell on the map, along with other cells of different colors and sizes. You can move your cell with your mouse cursor, and use the space bar to split it or the W key to eject some mass. You can also zoom in and out with the mouse wheel or the + and - keys.
- The Benefits of Playing Agar.io
-Agar.io is not only fun, but also beneficial for your brain. Playing agar.io can help you improve your reflexes, concentration, strategy, and spatial awareness. It can also help you relieve stress, boredom, and anxiety. Moreover, playing agar.io can help you socialize with other players from around the world. You can chat with them using the chat box, or join clans and groups with them. You can also make friends or enemies with other players based on your interactions.
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- What are Agar.io Skins and How to Get Them
-One of the best features of agar.io is that you can customize your cell with different skins. Skins are images or patterns that cover your cell and make it look more unique and attractive. There are hundreds of skins available for agar.io, ranging from flags, logos, memes, celebrities, animals, cartoons, and more. Some skins are official and can be used by anyone, while others are unofficial and require some tricks to use.
- The Types of Agar.io Skins
-There are three main types of agar.io skins: default skins, premium skins, and custom skins.
-
-Default skins are the official skins that are available for everyone. You can use them by typing their name as your nickname in the game. For example, if you type "doge" as your nickname, your cell will have the doge meme skin. You can find a list of all default skins here .Premium skins are the official skins that require some coins or coupons to unlock. You can get coins or coupons by playing the game, watching ads, completing surveys, or buying them with real money. You can use them to buy skins from the in-game shop. Premium skins are usually more detailed and exclusive than default skins. You can find a list of all premium skins here .Custom skins are the unofficial skins that are created by users or third-party websites. They are not supported by the game developers, but they can be used with some hacks or extensions. Custom skins can be any image or design that you want, as long as it follows the game rules and does not violate any copyrights or trademarks. You can find a list of some custom skins here .
- The Methods of Getting Agar.io Skins
-There are three main methods of getting agar.io skins: using the in-game shop, using user scripts, and using custom skins.
- Using the In-Game Shop
-The easiest and safest way to get agar.io skins is to use the in-game shop. The shop offers a variety of premium skins that you can buy with coins or coupons. You can also get some free skins by watching ads or completing surveys. To access the shop, you need to create an account and log in to the game. Then, you can click on the shop icon on the main menu and browse through the available skins. You can preview the skins before buying them, and you can also see how many coins or coupons they cost. Once you buy a skin, you can use it by selecting it from your inventory.
- Using User Scripts
-Another way to get agar.io skins is to use user scripts. User scripts are pieces of code that modify the game's behavior or appearance. They can be used to add features, change settings, or enable custom skins. To use user scripts, you need to install a browser extension that supports them, such as Tampermonkey or Greasemonkey. Then, you need to find a user script that offers agar.io skins, such as Agar Tool or Legend Mod . You can install the user script by clicking on its link and following the instructions. Once you install the user script, you can access its settings and choose the skin that you want to use.
- Using Custom Skins
-The third way to get agar.io skins is to use custom skins. Custom skins are images that you upload to a third-party website and then use in the game. To use custom skins, you need to find a website that offers them, such as Agar.io Skins or Agario Skins . Then, you need to upload your image or choose one from the gallery. You can also edit your image with filters, stickers, text, and more. Once you have your image ready, you need to copy its URL and paste it as your nickname in the game. Your cell will then have the custom skin that you chose.
- How to Install and Use Agar.io Skins
-Now that you know how to get agar.io skins, let's see how to install and use them.
- How to Install User Scripts for Agar.io Skins
-To install user scripts for agar.io skins, you need to follow these steps:
-
-Download and install a browser extension that supports user scripts, such as Tampermonkey or Greasemonkey.
-Find a user script that offers agar.io skins, such as Agar Tool or Legend Mod.
-Click on the link of the user script and follow the instructions to install it.
-Restart your browser and open agar.io.
-Access the settings of the user script and choose the skin that you want to use.
-Enjoy playing with your new skin!
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- How to Use Custom Skins for Agar.io
-To use custom skins for agar.io, you need to follow these steps:
-
-Find a website that offers custom skins for agar.io, such as Agar.io Skins or Agario Skins.
-Upload your image or choose one from the gallery.
-Edit your image with filters, stickers, text, and more if you want.
-Copy the URL of your image.
-Paste it as your nickname in agar.io.
-Enjoy playing with your custom skin!Enjoy playing with your custom skin!
-
- Conclusion
-Agar.io is a fun and addictive online game that lets you control a cell and eat other cells to grow bigger. You can also customize your cell with different skins that make it look more unique and attractive. There are many ways to get agar.io skins, such as using the in-game shop, using user scripts, or using custom skins. You can also install and use agar.io skins easily by following the steps we explained in this article. We hope you enjoyed reading this article and learned something new about agar.io skins. Now, go ahead and try them out for yourself and see how they change your gaming experience!
- Summary of the Main Points
-
-Agar.io is a multiplayer online game that lets you control a cell and eat other cells to grow bigger.
-You can customize your cell with different skins that cover your cell and make it look more unique and attractive.
-There are three types of agar.io skins: default skins, premium skins, and custom skins.
-There are three methods of getting agar.io skins: using the in-game shop, using user scripts, and using custom skins.
-You can install and use agar.io skins by following the steps we explained in this article.
-
- FAQs
-
-What is the best skin for agar.io? How do I change my skin in agar.io? Are agar.io skins safe to use? Can I create my own skin for agar.io? Can I share my skin with other players? 401be4b1e0Animal Revolt Battle Simulator Godzilla Mod Download: How to Unleash the King of the Monsters
- Do you love animals, simulators, and giant monsters? If so, you might want to check out Animal Revolt Battle Simulator, a game that lets you create funny and chaotic battles between all sorts of ragdoll creatures. And if you want to spice things up even more, you can download the Godzilla mod, which adds the legendary kaiju as a playable unit in the game. In this article, we will tell you everything you need to know about Animal Revolt Battle Simulator and the Godzilla mod, including how to download and install it, how to customize it, and how to use it in the game.
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What is Animal Revolt Battle Simulator?
- Animal Revolt Battle Simulator is a physics-based sandbox game where you can create epic battles between ragdoll creatures. You can build your own maps or pick from a selection of ready-made ones, place up to seven opposing armies made of different types of beasts, and watch them tear each other apart in an explosive spectacle. You can also join the fight yourself in the first-person mode and blow the enemy away with some powerful guns.
- A game with a huge selection of animals, weapons, maps, and custom units
- One of the best features of Animal Revolt Battle Simulator is its variety. You can choose from more than 70 creatures to use in your battles, ranging from dinosaurs, dragons, sharks, spiders, elephants, lions, bears, wolves, gorillas, humans, zombies, robots, aliens, and more. You can also equip them with different weapons, such as swords, axes, spears, guns, rockets, lasers, flamethrowers, grenades, mines, etc. You can also create your own custom monsters by combining different body parts and weapons. The possibilities are endless!
- The game also offers a wide range of maps to play on. You can use realistic environments like forests, deserts, islands, mountains, cities, farms, etc., or fantasy settings like castles, temples, pyramids, spaceships, etc. You can also build your own maps using various props and objects. You can even download custom maps created by other players from the Steam Workshop.
- A game with a campaign mode, a first-person mode, and a Steam Workshop integration
- If you want to test your tactical and strategic skills in Animal Revolt Battle Simulator
If you want to test your tactical and strategic skills in Animal Revolt Battle Simulator, you can try the campaign mode, where you have to face different challenges and scenarios with limited resources and units. You can also play the game in the first-person mode, where you can control one of the animals or weapons and join the battle yourself. You can switch between different units and weapons during the fight, and experience the action from different perspectives.
- Another great feature of Animal Revolt Battle Simulator is its Steam Workshop integration, which allows you to download and upload custom content created by other players. You can find hundreds of mods, maps, units, weapons, skins, and more on the Workshop, and easily add them to your game. You can also share your own creations with the community and get feedback and ratings.
- What is the Godzilla mod?
- If you are a fan of Godzilla, the king of the monsters, you will love the Godzilla mod for Animal Revolt Battle Simulator. This mod adds Godzilla as a weaponized animal unit that you can use in your battles. You can unleash his atomic breath, his tail swipe, his roar, and his sheer size and strength against your enemies. You can also customize his appearance and abilities to suit your preferences. And you can fight against or alongside Godzilla in various scenarios inspired by his movies.
-How to change Godzilla skin in Animal Revolt Battle Simulator
- A mod that adds Godzilla as a weaponized animal unit
- The Godzilla mod is one of the most popular and impressive mods for Animal Revolt Battle Simulator. It adds Godzilla as a new animal unit that you can place on your maps and use in your battles. Godzilla is a massive creature that towers over most other units in the game. He has a lot of health and armor, making him very durable and resistant to damage. He also has a lot of power and speed, making him very deadly and agile.
- But what makes Godzilla truly unique is his arsenal of attacks. He can use his atomic breath, a powerful beam of energy that he shoots from his mouth, to destroy anything in his path. He can also use his tail swipe, a sweeping motion that he does with his long tail, to knock down and crush his foes. He can also use his roar, a loud sound that he emits from his throat, to stun and scare his enemies. And he can also use his claws, teeth, and body to bite, scratch, and slam his opponents.
- A mod that lets you customize Godzilla's appearance and abilities
- One of the coolest features of the Godzilla mod is that it lets you customize Godzilla's appearance and abilities to your liking. You can change his texture and skin color, making him look more realistic or more cartoonish. You can also change his size, making him bigger or smaller. You can also change his stats, such as his health, armor, speed, power, etc., making him stronger or weaker. And you can also change his attacks, such as their damage, range, cooldown, etc., making them more or less effective.
- To customize Godzilla's appearance and abilities, you need to use the customizer tool that comes with the mod. You can access it by clicking on the "Customize" button on the bottom right corner of the screen when you select Godzilla as a unit. You will see a menu with different options that you can adjust using sliders or buttons. You can also save your customizations as presets that you can load later.
- A mod that lets you fight against or alongside Godzilla in various scenarios
- Another fun feature of the Godzilla mod is that it lets you fight against or alongside Godzilla in various scenarios based on his movies. You can recreate some of the iconic battles that Godzilla had with other monsters or humans in different settings. For example, you can pit Godzilla against King Kong on Skull Island, or against Mechagodzilla in Tokyo, or against Mothra in Hawaii. You can also team up with Godzilla and help him defeat his enemies or protect him from harm.
- To fight against or alongside Godzilla in various scenarios, you need to use the scenario tool that comes with the mod. You can access it by clicking on the "Scenario" button on the bottom right corner of the screen when you select Godzilla as a unit. You will see a list of scenarios that you can choose from, each with a description and a preview image. You can also create your own scenarios by using the map editor and placing different units and props on your map.
- How to download and install the Godzilla mod?
- If you want to download and install the Godzilla mod for Animal Revolt Battle Simulator
If you want to download and install the Godzilla mod for Animal Revolt Battle Simulator, you need to follow these simple steps:
- Step 1: Subscribe to the mod on Steam Workshop
- The easiest way to get the Godzilla mod is to subscribe to it on Steam Workshop, the online platform where you can find and share custom content for various games. To do this, you need to have Animal Revolt Battle Simulator installed on your computer and have a Steam account. Then, you need to go to the Godzilla mod page on Steam Workshop and click on the "Subscribe" button. This will automatically download the mod and add it to your game.
- Step 2: Launch Animal Revolt Battle Simulator and select "Workshop" from the main menu
- Once you have subscribed to the Godzilla mod, you need to launch Animal Revolt Battle Simulator and select "Workshop" from the main menu. This will take you to a screen where you can see all the mods that you have subscribed to on Steam Workshop. You can also browse and search for other mods that you might be interested in.
- Step 3: Find the Godzilla mod and click on "Load"
- On the Workshop screen, you need to find the Godzilla mod and click on the "Load" button. This will load the mod into your game and make it available for use. You will see a confirmation message that says "Mod loaded successfully". You can also unload the mod by clicking on the "Unload" button if you want to disable it.
- Step 4: Enjoy playing with Godzilla in the game
- Now that you have loaded the Godzilla mod, you can enjoy playing with Godzilla in the game. You can find him in the animal selection menu under the category "Weaponized Animals". You can place him on your maps and use him in your battles. You can also customize his appearance and abilities using the customizer tool, and fight against or alongside him in various scenarios using the scenario tool.
- Tips and tricks for using the Godzilla mod
- The Godzilla mod is a lot of fun to use, but it can also be challenging and tricky at times. Here are some tips and tricks that can help you get the most out of it:
- How to change Godzilla's texture and skin
- If you want to change Godzilla's texture and skin, you need to use the customizer tool that comes with the mod. You can access it by clicking on the "Customize" button on the bottom right corner of the screen when you select Godzilla as a unit. Then, you need to click on the "Texture" tab on the top left corner of the menu. You will see a list of different textures that you can choose from, such as classic, realistic, cartoon, etc. You can also adjust the color of each texture using sliders or buttons. You can preview your changes on a 3D model of Godzilla on the right side of the menu.
- How to attach weapons and body parts to Godzilla
- If you want to attach weapons and body parts to Godzilla, you need to use the customizer tool that comes with the mod. You can access it by clicking on the "Customize" button on the bottom right corner of the screen when you select Godzilla as a unit. Then, you need to click on the "Attachments" tab on the top left corner of
If you want to attach weapons and body parts to Godzilla, you need to use the customizer tool that comes with the mod. You can access it by clicking on the "Customize" button on the bottom right corner of the screen when you select Godzilla as a unit. Then, you need to click on the "Attachments" tab on the top left corner of the menu. You will see a list of different attachments that you can choose from, such as horns, spikes, wings, guns, rockets, lasers, etc. You can also adjust the size, position, and rotation of each attachment using sliders or buttons. You can preview your changes on a 3D model of Godzilla on the right side of the menu.
- How to control Godzilla in the first-person mode
- If you want to control Godzilla in the first-person mode, you need to use the first-person tool that comes with the mod. You can access it by clicking on the "First-Person" button on the bottom right corner of the screen when you select Godzilla as a unit. This will switch your view to Godzilla's perspective and let you control him using your keyboard and mouse. You can move around using the W, A, S, and D keys, and look around using your mouse. You can also use your mouse wheel to zoom in and out. You can use your left mouse button to use your atomic breath, your right mouse button to use your tail swipe, and your space bar to use your roar. You can also switch between different units and weapons during the fight by pressing the Q and E keys.
- Conclusion and FAQs
- Animal Revolt Battle Simulator is a fun and crazy game that lets you create epic battles between ragdoll creatures. And if you want to make it even more fun and crazy, you can download the Godzilla mod, which adds the king of the monsters as a playable unit in the game. You can customize his appearance and abilities, and fight against or alongside him in various scenarios. You can also control him in the first-person mode and unleash his power on your enemies.
- If you are interested in downloading and installing the Godzilla mod for Animal Revolt Battle Simulator, you can follow the steps that we have explained in this article. You can also check out some tips and tricks that we have shared to help you get the most out of it. We hope that you have enjoyed this article and that you have learned something new.
- Here are some FAQs that you might have about Animal Revolt Battle Simulator and the Godzilla mod:
- Q: Is Animal Revolt Battle Simulator free to play?
-A: No, Animal Revolt Battle Simulator is not free to play. It is a paid game that you can buy on Steam for $14.99 USD.
- Q: Is the Godzilla mod free to download?
-A: Yes, the Godzilla mod is free to download. You can subscribe to it on Steam Workshop and add it to your game.
- Q: Can I play Animal Revolt Battle Simulator online with other players?
-A: No, Animal Revolt Battle Simulator does not have an online multiplayer mode. It is a single-player game that you can play offline or online with Steam Cloud support.
- Q: Can I play Animal Revolt Battle Simulator on other platforms besides PC?
-A: No, Animal Revolt Battle Simulator is only available for PC. It is not compatible with other platforms such as consoles or mobile devices.
- Q: Can I request or suggest new features or improvements for Animal Revolt Battle Simulator or the Godzilla mod?
-A: Yes, you can request or suggest new features or improvements for Animal Revolt Battle Simulator or the Godzilla mod by contacting the developers or the modders through their Steam pages or social media accounts. They are always open to feedback and ideas from their fans.
401be4b1e0How to Download GTA 5 for Free
-Grand Theft Auto V, or GTA 5, is one of the most popular and successful video games of all time. It is an open-world action-adventure game that lets you explore the city of Los Santos and its surrounding areas, as well as engage in various missions, heists, activities, and online multiplayer modes. GTA 5 has been praised for its stunning graphics, immersive gameplay, rich story, diverse characters, hilarious satire, and endless replay value.
-If you are a fan of GTA 5 or want to experience it for yourself, you might be wondering how to download it for free. After all, GTA 5 is not a cheap game, and buying it from official sources can cost you anywhere from $20 to $60, depending on the platform and edition. However, there are some ways to get GTA 5 for free or at a lower price, if you know where to look.
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In this article, we will show you how to download GTA 5 for free from different platforms and sources, as well as what are the system requirements, installation guide, and reviews of the game. By the end of this article, you will be able to enjoy GTA 5 on your PC without spending a dime.
- GTA 5 System Requirements
-Before you download GTA 5 for free, you need to make sure that your PC can run it smoothly. GTA 5 is a demanding game that requires a powerful system to run at its full potential. Here are the minimum and recommended system requirements for running GTA 5 on PC:
-
-Minimum System Requirements Recommended System Requirements OS: Windows 8.1 64 Bit, Windows 8 64 Bit, Windows 7 64 Bit Service Pack 1 OS: Windows 10 - April Update (v1803)
-If your PC meets or exceeds these requirements, you should be able to run GTA 5 without any major issues. However, if your PC falls short of these requirements, you might experience low frame rates, crashes, glitches, or other problems. In that case, you might want to upgrade your PC or lower the graphics settings in the game.
- GTA ... GTA 5 Download Options
- Now that you know the system requirements for GTA 5, you might be wondering where to download it for free. There are several platforms and sources that offer GTA 5 for free or at a discounted price, but they also have their own advantages and disadvantages. In this section, we will compare the different options and help you choose the best one for you.
- Steam
-Steam is one of the most popular and trusted platforms for buying and downloading PC games. It has a huge library of games, including GTA 5, and it also offers various features such as cloud saves, achievements, community, and more. Steam also has frequent sales and discounts, so you might be able to get GTA 5 for a lower price than usual.
-To download GTA 5 from Steam, you need to have a Steam account and the Steam client installed on your PC. You can create a Steam account for free on their website, and download the Steam client from there as well. Once you have Steam on your PC, you can search for GTA 5 in the store and purchase it with your preferred payment method. After that, you can download and install GTA 5 on your PC through Steam.
-How to download GTA 5 PC digital copy for free
-The benefits of using Steam are that you get a legitimate copy of GTA 5 that is updated and supported by the developers, and that you can access all the features and modes of the game, including online multiplayer. The drawbacks are that you have to pay for the game, unless it is on sale or free for a limited time, and that you have to use Steam to launch and play the game, which might affect your performance or privacy.
- Epic Games Store
-Epic Games Store is another platform that sells and distributes PC games. It is owned by Epic Games, the creators of Fortnite and Unreal Engine, and it competes with Steam by offering exclusive games and free games every week. In fact, GTA 5 was one of the free games that Epic Games Store gave away in May 2020, and millions of users claimed it during that period.
-To download GTA 5 from Epic Games Store, you need to have an Epic Games account and the Epic Games Launcher installed on your PC. You can create an Epic Games account for free on their website, and download the Epic Games Launcher from there as well. Once you have Epic Games Launcher on your PC, you can search for GTA 5 in the store and purchase it with your preferred payment method. After that, you can download and install GTA 5 on your PC through Epic Games Launcher.
-The benefits of using Epic Games Store are similar to Steam, except that you might be able to get GTA 5 for free if it is part of their weekly giveaways. The drawbacks are also similar to Steam, except that some users have reported issues with Epic Games Launcher such as crashes, errors, or slow downloads.
- Rockstar Games Launcher
-Rockstar Games Launcher is the official platform for buying and downloading games from Rockstar Games, the developers of GTA 5. It is a relatively new platform that was launched in September 2019, and it offers some exclusive bonuses and rewards for Rockstar Games fans. For example, if you download Rockstar Games Launcher before October 8th 2019, you can get a free copy of GTA San Andreas on your PC.
-To download GTA 5 from Rockstar Games Launcher, you need to have a Rockstar Games Social Club account and the Rockstar Games Launcher installed on your PC. You can create a Rockstar Games Social Club account for free on their website, and download the Rockstar Games Launcher from there as well. Once you have Rockstar Games Launcher on your PC, you can search for GTA 5 in the store and purchase it with your preferred payment method. After that, you can download and install GTA 5 on your PC through Rockstar Games Launcher.
-The benefits of using Rockstar Games Launcher are that you get a direct connection to the developers of GTA 5, and that you can access some exclusive content and offers for Rockstar Games titles. The drawbacks are that you have to pay full price for GTA 5, unless it is on sale or free for a limited time, and that you have to use Rockstar Games Launcher to launch and play the game, which might affect your performance or privacy.
- Torrent Sites
-Torrent sites are websites that allow users to share files through peer-to-peer networks. They are often used to distribute pirated or illegal content, such as movies, music, software, or games. GTA 5 is one of the most pirated games in history, and there are many torrent sites that offer cracked versions of GTA 5 for free.
-To download GTA 5 from torrent sites ... [ To download GTA 5 from torrent sites, you need to have a torrent client and a VPN installed on your PC. A torrent client is a software that allows you to download files from torrent sites, such as uTorrent, BitTorrent, or qBittorrent. A VPN is a service that encrypts and hides your online activity, such as NordVPN, ExpressVPN, or CyberGhost. You can download and install these tools from their respective websites.
-Once you have a torrent client and a VPN on your PC, you can search for GTA 5 on torrent sites such as The Pirate Bay, RARBG, or 1337x. You can choose the torrent that has the most seeders, leechers, and positive comments, and download it to your PC. After that, you can open the torrent file with your torrent client and start downloading GTA 5 on your PC. You should also activate your VPN before downloading GTA 5 from torrent sites, to protect your privacy and avoid legal issues.
-The benefits of using torrent sites are that you can get GTA 5 for free, and that you can download it faster than other sources, depending on the number of seeders and leechers. The drawbacks are that you are breaking the law and risking fines or lawsuits, that you are exposing your PC to viruses, malware, or spyware, that you are not supporting the developers of GTA 5, and that you might not be able to access all the features and modes of the game, especially online multiplayer.
- GTA 5 Installation Guide
-After you have downloaded GTA 5 from your preferred source, you need to install it on your PC. The installation process might vary depending on the source, but here are some general steps that you can follow:
-
-Locate the GTA 5 setup file on your PC. It might be in a folder named GTA 5 or Grand Theft Auto V, or in your Downloads folder.
-Double-click on the GTA 5 setup file to launch the installation wizard. You might need to run it as administrator or enter a password if prompted.
-Follow the instructions on the screen to choose the installation location, language, and options for GTA 5. You might need to agree to the terms and conditions or enter a product key if required.
-Wait for the installation to complete. It might take several minutes or hours depending on the size of GTA 5 and the speed of your PC.
-Once the installation is done, you can launch GTA 5 from your desktop shortcut or start menu. You might need to sign in to your Steam, Epic Games Store, Rockstar Games Social Club, or other accounts if necessary.
-
-Congratulations! You have successfully installed GTA 5 on your PC. You can now enjoy playing one of the best games ever made.
- GTA 5 Reviews
-GTA 5 is not only a popular game, but also a critically acclaimed one. It has received universal praise from critics and gamers alike, and it has won numerous awards and accolades. Here are some of the reviews of GTA 5 from reputable sources:
-
-"Grand Theft Auto V is not only a preposterously enjoyable video game, but also an intelligent and sharp-tongued satire of contemporary America." - The Guardian
-"GTA V is an incredible achievement in world design ... It represents a refinement of everything that GTA IV brought to the table five years ago." - IGN
-"GTA V is one of the most exhilarating experiences this console generation ... It's beautiful, funny, heartbreaking and anarchic." - GamesRadar
-"GTA V is an immensely ambitious game that lives up to every ounce of hype it received over the last few years." - GameSpot
-"GTA V is one of those rare games where I found myself caring about everything I did ... It's a remarkable achievement in open-world gaming." - Polygon
-
-As you can see, GTA 5 is a masterpiece of gaming that deserves all the praise it gets. It is a game that you should not miss out on.
- Conclusion
-In this article, we have shown you how to download GTA 5 for free from different platforms and sources. We have also covered the system requirements, installation guide, and reviews of GTA 5. We hope that this article has been helpful and informative for you.
-GTA 5 is a game that you should definitely try at least once in your life. It is a game that will keep you entertained for hours with its amazing gameplay, story, characters, graphics, and modes. It is a game that is a game that will make you feel like you are living in a different world. It is a game that you can download for free from various sources, as we have shown you in this article.
- So, what are you waiting for? Download GTA 5 for free today and start your adventure in Los Santos. You won't regret it.
- FAQs
-Here are some of the frequently asked questions about GTA 5 and their answers:
-
-Is GTA 5 free? Is GTA 5 online free? Is GTA 5 safe to download? How long does it take to download GTA 5? How much space does GTA 5 take on PC? 401be4b1e0Merge Ragdoll Fighting Download: How to Create and Fight with Mutant Monsters
- Introduction
- If you are looking for a fun and addictive fighting game that lets you unleash your creativity and imagination, then you should try Merge Ragdoll Fighting. This game is a unique blend of action, arcade, and casual genres that will keep you entertained for hours. In this game, you can merge DNA of different monsters and create the ultimate fighter. You can then use your mutant fighter to battle against ragdoll enemies in epic fights for survival. You can also customize your fighter with different weapons, skins, and special skills. The game has amazing graphics, bright animations, and hilarious ways to die. It is suitable for players of all ages who enjoy fighting games with a twist.
- Why should you play Merge Ragdoll Fighting?
- There are many reasons why you should play Merge Ragdoll Fighting, but here are some of the main ones:
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-Addictive gameplay : The game is easy to play but hard to master. You can merge DNA of different monsters and create endless combinations of fighters. You can also upgrade your fighters and unlock new weapons and skills. The game has a lot of levels and challenges to complete, as well as achievements and rewards to collect.Amazing graphics : The game has stunning graphics that will make you feel like you are in a lab full of monsters. The game also has bright animations that will make the fights more exciting and dynamic. The game has a stylized art style that gives it a unique charm.Bright animations : The game has hilarious animations that will make you laugh out loud. You can see your fighters perform different moves and combos, as well as use special skills and weapons. You can also see your enemies react in funny ways when they get hit or die. The game has a lot of dumb and funny ways to die that will make you want to replay the levels.Great variety of weapons : The game has a lot of weapons that you can use to enhance your fighters and make them more powerful. You can use swords, axes, hammers, guns, rockets, lasers, bombs, and more. You can also unlock new weapons as you progress in the game.Different ragdoll stickman skins : The game has a lot of skins that you can use to customize your fighters and make them look more cool or funny. You can use skins of animals, superheroes, zombies, pirates, ninjas, robots, aliens, and more. You can also unlock new skins as you progress in the game.Epic fights : The game has epic fights that will test your skills and strategy. You can fight against different types of ragdoll enemies, such as boxers, wrestlers, ninjas, zombies, robots, aliens, and more. You can also fight against bosses that have special abilities and attacks. The fights are fast-paced and intense, and you will need to use your fighters' skills and weapons wisely.Dumb & funny ways to die : The game has a lot of dumb and funny ways to die that will make you laugh out loud. You can see your fighters die in hilarious ways, such as getting crushed by a giant hammer, getting blown up by a bomb, getting electrocuted by a laser, getting impaled by a sword, getting eaten by a monster, and more. You can also see your enemies die in funny ways, such as getting knocked out by a punch, getting sliced by an axe, getting shot by a gun, getting burned by a rocket , and more. The game has a lot of ragdoll physics and blood effects that make the deaths more realistic and fun.
- How to download and install Merge Ragdoll Fighting on your device
- If you want to play Merge Ragdoll Fighting, you will need to download and install it on your device. The game is available for free on different platforms, such as Android, iOS, and PC. Here are the steps to download and install the game on your device:
- For Android users
-
-Go to the Google Play Store and search for Merge Ragdoll Fighting.
-Select the game from the list of results and tap on Install.
-Wait for the game to download and install on your device.
-Once the game is installed, tap on Open to launch the game and start playing.
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- For iOS users
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-Go to the App Store and search for Merge Ragdoll Fighting.
-Select the game from the list of results and tap on Get.
-Enter your Apple ID and password if prompted.
-Wait for the game to download and install on your device.
-Once the game is installed, tap on Open to launch the game and start playing.
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- For PC users
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-Go to the official website of Merge Ragdoll Fighting and click on Download for PC.
-Select the version of the game that is compatible with your operating system (Windows or Mac).
-Save the file to your computer and run it to start the installation process.
-Follow the instructions on the screen to complete the installation.
-Once the game is installed, double-click on the game icon to launch the game and start playing.
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- How to play Merge Ragdoll Fighting
- Merge Ragdoll Fighting is a simple but fun game that anyone can play. However, if you want to master the game and become the best fighter, you will need to learn some tips and tricks. Here are some of the basics of how to play Merge Ragdoll Fighting:
- The basics of the game
- The game has two main modes: Lab Mode and Arena Mode. In Lab Mode, you can merge DNA of different monsters and create your own fighters. In Arena Mode, you can use your fighters to battle against ragdoll enemies in different levels. You can switch between the modes by tapping on the buttons at the bottom of the screen.
- In Lab Mode, you will see a grid of DNA tubes that contain different monsters. You can tap on a tube to select a monster, and then drag it to another tube to merge them. The result will be a new monster that has a combination of traits from both parents. You can also tap on a tube to see more information about a monster, such as its name, type, stats, skills, and weapons. You can also upgrade your monsters by spending coins or gems that you earn from playing Arena Mode.
- In Arena Mode, you will see a list of levels that you can play. Each level has a different theme, such as forest, desert, city, space, etc. You can also see the difficulty level, the number of enemies, and the rewards for completing each level. You can select a level by tapping on it, and then choose up to three fighters that you want to use in that level. You can also change your fighters' weapons and skins by tapping on them before starting a level.
- Once you start a level, you will see your fighters and enemies on opposite sides of the screen. You can control your fighters by tapping on them and dragging them around. You can also use their skills and weapons by tapping on the buttons at the bottom of the screen. Your goal is to defeat all enemies before they defeat you. You can also collect coins, gems, health packs, and power-ups that appear randomly on the screen during a fight. You can use these items to improve your fighters' performance or heal them if they get injured.
- How to merge DNA and create mutant fighters
- Merging DNA is one of the most fun and creative aspects of Merge Ragdoll Fighting. You can create endless combinations of fighters by merging different monsters' DNA. Here are some tips on how to merge DNA and create mutant fighters:
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-Experiment with different combinations : The best way to create unique and powerful fighters is to experiment with different combinations of monsters' DNA. You can try merging monsters of different types, such as fire, water, earth, air, etc., or monsters of similar types, such as zombies, robots, aliens, etc. You can also merge monsters of different sizes, shapes, colors, etc. You can also merge monsters that have special traits, such as wings, horns, tails, etc. The more you experiment, the more you will discover new and amazing fighters.Pay attention to the stats and skills : When you merge monsters' DNA, you will create a new monster that has a combination of stats and skills from both parents. The stats include health, attack, defense, and speed. The skills include passive and active abilities that can help you in a fight. You can see the stats and skills of a monster by tapping on its tube in Lab Mode. You should pay attention to these factors when merging DNA, as they will affect your fighters' performance in Arena Mode. You should try to create fighters that have balanced stats and useful skills.Upgrade your fighters : You can upgrade your fighters by spending coins or gems that you earn from playing Arena Mode. You can upgrade your fighters' stats, skills, and weapons by tapping on the Upgrade button in Lab Mode. Upgrading your fighters will make them stronger and more effective in a fight. You should upgrade your fighters regularly to keep up with the increasing difficulty of the levels.Customize your fighters : You can customize your fighters by changing their weapons and skins. You can change your fighters' weapons by tapping on the Weapon button in Lab Mode. You can choose from a variety of weapons that have different effects and damage types. You can also unlock new weapons as you progress in the game. You can change your fighters' skins by tapping on the Skin button in Lab Mode. You can choose from a variety of skins that have different looks and themes. You can also unlock new skins as you progress in the game. Customizing your fighters will make them look more cool or funny, and also give them some advantages or disadvantages in a fight.
- How to fight and win against ragdoll enemies
- Fighting against ragdoll enemies is one of the most exciting and challenging aspects of Merge Ragdoll Fighting. You will face different types of enemies that have different abilities and attacks. You will also face bosses that are more powerful and have special moves. Here are some tips on how to fight and win against ragdoll enemies:
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-Use your fighters' skills and weapons wisely : Your fighters have skills and weapons that can help you in a fight. You can use their skills by tapping on the buttons at the bottom of the screen. You can use their weapons by dragging them to the enemies. However, you should use these abilities wisely, as they have cooldowns or limited ammo. You should also consider the effects and damage types of your skills and weapons, as they may be more or less effective against certain enemies.Avoid enemy attacks : Your enemies have attacks that can hurt or kill your fighters. You can see their attacks by looking at their animations or indicators on the screen. You should avoid their attacks by moving your fighters around or using their skills or weapons to block or dodge them. You should also pay attention to the environment, as it may have hazards or obstacles that can harm your fighters.Collect items : During a fight, you may see items that appear randomly on the screen. These items include coins, gems, health packs, and power-ups. You should collect these items by dragging your fighters to them or using their skills or weapons to grab them. These items can help you improve your fighters' performance or heal them if they get injured.Use strategy : Fighting against ragdoll enemies requires strategy and planning. You should choose your fighters carefully based on their stats, skills, weapons, and skins. You should also choose your enemies wisely based on their types, abilities, and weaknesses. You should also use your fighters' skills and weapons strategically based on the situation and the enemy's behavior. You should also use items smartly based on your needs and goals.
- Tips and tricks to master the game
- Merge Ragdoll Fighting is a game that is easy to play but hard to master. If you want to become the best fighter and complete all levels with ease, you will need some tips and tricks to master the game. Here are some of them:
-
-Play regularly : The best way to master the game is to play it regularly and practice your skills. The more you play, the more you will learn about the game mechanics, the monsters, the enemies, the levels, etc. You will also earn more coins and gems that you can use to upgrade and customize your fighters.Watch ads : The game has ads that you can watch voluntarily to get some benefits. You can watch ads to get free coins, gems , or health packs. You can also watch ads to double your rewards after completing a level. You can also watch ads to revive your fighters if they die in a fight. Watching ads can help you progress faster and easier in the game.Complete achievements and daily quests : The game has achievements and daily quests that you can complete to get extra coins and gems. You can see the achievements and daily quests by tapping on the buttons at the top of the screen. The achievements are based on your overall progress and performance in the game, such as creating a certain number of fighters, completing a certain number of levels, killing a certain number of enemies, etc. The daily quests are based on specific tasks that you have to do within a day, such as merging a certain type of monster, using a certain weapon, collecting a certain amount of coins, etc. Completing achievements and daily quests can help you earn more coins and gems that you can use to upgrade and customize your fighters.Join the community : The game has a community of players that you can join and interact with. You can see the community by tapping on the button at the bottom right of the screen. The community has a chat room where you can chat with other players, share your tips and tricks, ask for help, or just have fun. The community also has a leaderboard where you can see your rank and compare your score with other players. You can also see other players' profiles and their fighters. Joining the community can help you learn more about the game, make new friends, and have more fun.
- Conclusion
- Merge Ragdoll Fighting is a fun and addictive fighting game that lets you create and fight with mutant monsters. You can merge DNA of different monsters and create endless combinations of fighters. You can also customize your fighters with different weapons and skins. You can then use your fighters to battle against ragdoll enemies in epic fights for survival. The game has amazing graphics, bright animations, and hilarious ways to die. It is suitable for players of all ages who enjoy fighting games with a twist.
- If you want to play Merge Ragdoll Fighting, you can download and install it on your device for free. The game is available for Android, iOS, and PC platforms. You can also follow some tips and tricks to master the game and become the best fighter.
- Merge Ragdoll Fighting is a game that will keep you entertained for hours. It is a game that will challenge your skills and strategy. It is a game that will unleash your creativity and imagination. It is a game that will make you laugh out loud. It is a game that you should try today.
- FAQs
- Here are some of the frequently asked questions about Merge Ragdoll Fighting:
-
-How many monsters are there in Merge Ragdoll Fighting? There are over 100 monsters that you can merge and create in Merge Ragdoll Fighting. Each monster has its own name, type, stats, skills, and weapons. You can see all the monsters that you have created by tapping on the Collection button in Lab Mode.
-How do I unlock new weapons and skins in Merge Ragdoll Fighting? You can unlock new weapons and skins in Merge Ragdoll Fighting by completing levels, achievements, and daily quests. You can also buy them with coins or gems that you earn from playing Arena Mode or watching ads.
-What are the power-ups in Merge Ragdoll Fighting? The power-ups are items that appear randomly on the screen during a fight. They have different effects that can help you or hinder your enemies. Some of the power-ups are:
-
-Bomb : Explodes and damages nearby enemies.Freeze : Freezes all enemies for a few seconds.Fire : Sets all enemies on fire for a few seconds.Poison : Poisons all enemies for a few seconds.Shield : Protects your fighters from enemy attacks for a few seconds.Speed : Increases your fighters' speed for a few seconds.Strength : Increases your fighters' attack for a few seconds.
-How do I revive my fighters if they die in a fight? If your fighters die in a fight, you have two options to revive them: watch an ad or spend gems. If you watch an ad, you can revive one fighter for free. If you spend gems, you can revive all fighters at once. However, you can only revive your fighters once per level , so you should use this option wisely.
-How do I contact the developers of Merge Ragdoll Fighting? If you have any questions, feedback, or suggestions about Merge Ragdoll Fighting, you can contact the developers by tapping on the Settings button at the top right of the screen. You can then tap on the Contact Us button and fill out the form with your message. You can also follow the developers on their social media accounts, such as Facebook, Twitter, Instagram, and YouTube.
- 197e85843dMonster Box Mod APK: A Fun and Addictive Action Game
-If you are looking for a simple yet challenging action game that will keep you entertained for hours, then you should try Monster Box. Monster Box is a game where you have to collect and upgrade various monsters and use them to fight against waves of enemies. You can also customize your monsters with different skins, weapons, and accessories. Monster Box is a game that will test your reflexes, strategy, and creativity.
-What is Monster Box?
-Monster Box is a game developed by PONOS Corporation, the same company that created the popular Battle Cats series. Monster Box was released in 2019 and has been downloaded over 10 million times on Google Play. The game has a rating of 4.4 out of 5 stars and has received positive reviews from players.
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Features of Monster Box
-Monster Box has many features that make it a fun and addictive game. Some of these features are:
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-Simple and intuitive controls: You can control your monsters with just one finger. Tap to move, swipe to attack, and hold to charge your special skill.Cute and colorful graphics: The game has a pixel art style that is charming and nostalgic. The monsters are adorable and have unique designs and animations.Various modes and stages: The game has different modes and stages that offer different challenges and rewards. You can play the normal mode, the hard mode, the endless mode, or the special events. You can also unlock new stages by clearing the previous ones.Diverse and upgradeable monsters: The game has over 100 monsters that you can collect and upgrade. Each monster has its own stats, skills, and personality. You can also equip them with different items to boost their performance.Social features: The game allows you to interact with other players through the chat system, the friend system, or the guild system. You can also compete with other players in the ranking system or cooperate with them in the raid system.
-How to play Monster Box
-The gameplay of Monster Box is simple but challenging. Here are the basic steps to play the game:
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-Select a stage and a team of monsters to start the game.
-Tap on the screen to move your monsters and swipe to attack the enemies.
-Use your special skills when they are fully charged.
-Defeat all the enemies and clear the stage.
-Earn coins, gems, items, and new monsters as rewards.
-Use your rewards to upgrade your monsters and items.
-Repeat the process and enjoy the game.
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-Why download Monster Box Mod APK?
-Monster Box is a free-to-play game, but it also has some in-app purchases that can enhance your gaming experience. However, if you don't want to spend real money on the game, you can download Monster Box Mod APK instead. Monster Box Mod APK is a modified version of the original game that gives you unlimited access to all the features and resources of the game without any cost.
-Benefits of Monster Box Mod APK
-Some of the benefits of downloading Monster Box Mod APK are:
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-Unlimited money: You can get unlimited coins and gems in the game, which you can use to buy new monsters, items, skins, and more.All unlocked: You can unlock all the modes, stages, monsters, items, skins, and more in the game without any restrictions.No ads: You can enjoy the game without any annoying ads that interrupt your gameplay.No root required: You don't need to root your device to install or run Monster Box Mod APK.
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-Downloading and installing Monster Box Mod APK is easy and fast. Here are the steps to follow:
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-Click on the download button below to download the Monster Box Mod APK file on your device.
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-Conclusion
-Monster Box is a fun and addictive action game that will keep you hooked for hours. You can collect and upgrade various monsters and use them to fight against waves of enemies. You can also customize your monsters with different skins, weapons, and accessories. Monster Box has simple and intuitive controls, cute and colorful graphics, various modes and stages, diverse and upgradeable monsters, and social features. You can download Monster Box Mod APK to get unlimited money, all unlocked, no ads, and no root required. Monster Box Mod APK is a great way to enjoy the game without any limitations. Download it now and have fun.
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-Q: Is Monster Box Mod APK safe to use? A: Yes, Monster Box Mod APK is safe to use. It does not contain any viruses or malware that can harm your device or data. It is also compatible with most Android devices.
-Q: Can I play Monster Box online with other players? A: Yes, you can play Monster Box online with other players. You can chat with them, add them as friends, join or create a guild, compete in the ranking system, or cooperate in the raid system.
-Q: How can I get more coins and gems in Monster Box? A: You can get more coins and gems in Monster Box by clearing stages, completing quests, participating in events, watching ads, or buying them with real money. You can also download Monster Box Mod APK to get unlimited coins and gems for free.
-Q: How can I unlock new monsters and items in Monster Box? A: You can unlock new monsters and items in Monster Box by clearing stages, completing quests, participating in events, or buying them with coins or gems. You can also download Monster Box Mod APK to unlock all the monsters and items for free.
-Q: How can I update Monster Box Mod APK? A: You can update Monster Box Mod APK by downloading the latest version from this website. You don't need to uninstall the previous version, just install the new one over it.
- 401be4b1e0Sniper Assassin 3D Mod APK: A Fun and Thrilling Shooting Game
-If you are looking for a free online multiplayer FPS game that lets you fight in a global war on crime and become the ultimate sniper assassin, then you should try Sniper Assassin 3D. This game has great gameplay, awesome visuals, and entertaining missions that will keep you hooked for hours. And if you want to make your gaming experience even more exciting, you should download Sniper Assassin 3D Mod APK. This mod apk will give you access to unlimited money, menu, premium features, and more. In this article, we will tell you everything you need to know about Sniper Assassin 3D and its mod apk.
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What is Sniper Assassin 3D?
-Sniper Assassin 3D is a fun shooting game developed by Fun Games For Free. It is available for Android and iOS devices, as well as Windows and macOS computers. In this game, you play as a professional sniper who has to take down various targets in different locations around the world. You can choose from over 150+ snipers and rifles, customize them with upgrades and evolutions, and use them in over 850+ thrilling missions. You can also play in multiple battlegrounds, from huge cities to beautiful beaches. You can also compete with other players online in PvP mode or join a clan and cooperate with your teammates.
- What is Sniper Assassin 3D Mod APK?
-Sniper Assassin 3D Mod APK is a modified version of the original game that gives you some extra benefits that are not available in the official version. Mod APK stands for modified application package file. It is a file that contains the installation data of an app that has been altered by someone other than the original developer. By installing a mod apk file, you can bypass some restrictions or limitations imposed by the developer or the platform. For example, you can get unlimited money, unlock all weapons, access premium features, etc.
- How to Download and Install Sniper Assassin 3D Mod APK?
-If you want to download and install Sniper Assassin 3D Mod APK on your device, you need to follow these steps:
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-First, you need to find a reliable source that provides the mod apk file. You can search for it on Google or use one of these links: or . Make sure you download the latest version of the mod apk that is compatible with your device.
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-What are the Features of Sniper Assassin 3D Mod APK?
-Sniper Assassin 3D Mod APK has many features that make it more fun and thrilling than the original game. Here are some of the main features of the mod apk:
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-Unlimited Money: You can get unlimited money in the game, which you can use to buy and upgrade weapons, items, skills, etc. You can also unlock all the snipers and rifles without spending any real money.Mod Menu: You can access a mod menu that lets you enable or disable various options, such as god mode, one hit kill, no recoil, no reload, etc. You can also change the game speed, zoom level, aim assist, etc.Premium Features: You can enjoy all the premium features of the game, such as no ads, unlimited energy, VIP access, exclusive weapons, etc. You can also get free diamonds and coins every day.Anti-Ban: You can play the game without worrying about getting banned by the developers or the platform. The mod apk has an anti-ban system that protects your account and device from detection and suspension.
-What are the Tips and Tricks for Playing Sniper Assassin 3D?
-Sniper Assassin 3D is a challenging game that requires skill, strategy, and patience. Here are some tips and tricks that can help you improve your performance and enjoy the game more:
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-Aim Carefully: The most important skill in sniping is aiming. You need to aim carefully and accurately at your target, taking into account factors such as distance, wind, gravity, movement, etc. You can use the scope to zoom in and out and adjust the crosshair. You can also use the aim assist feature in the mod apk to help you with aiming.Choose the Right Weapon: The game offers a variety of weapons to choose from, each with its own strengths and weaknesses. You need to choose the right weapon for each mission and situation. Some weapons have higher damage, range, stability, fire rate, etc. than others. You can also upgrade your weapons with evolutions and attachments to enhance their performance.Complete Missions: The game has over 850+ missions to complete, each with its own objectives, challenges, and rewards. You need to complete missions to progress in the game and earn money, diamonds, coins, etc. You can also replay missions to improve your score and rank. Some missions have special requirements or conditions that you need to fulfill.Play Online: The game has an online mode that lets you compete with other players around the world in PvP mode or join a clan and cooperate with your teammates. You can also chat with other players and share your achievements. Playing online can help you improve your skills, earn more rewards, and have more fun.
-What are the Reviews of Sniper Assassin 3D?
-Sniper Assassin 3D is a popular game that has received many positive reviews from players and critics alike. Here are some of the reviews of the game from various sources:
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-Source Rating Review [Google Play Store] 4.4/5 "This is one of my favorite games ever! It's so realistic and fun! I love how you can customize your weapons and upgrade them. The graphics are amazing and the missions are challenging. I highly recommend this game to anyone who loves shooting games!" [App Store] 4.6/5 "This game is awesome! It's very addictive and entertaining! I like how you can play online with other people and join clans. The mod apk is also great! It gives you unlimited money and premium features! This game is a must-have for sniping fans!" [PCMag] 4/5 "Sniper Assassin 3D is a fun and thrilling shooting game that offers a lot of content and variety. The game has great gameplay, awesome visuals, and entertaining missions that will keep you hooked for hours. The mod apk adds more excitement and convenience to the game." Conclusion
-Sniper Assassin 3D is a fun and thrilling shooting game that lets you become the ultimate sniper assassin. You can enjoy the game with its original features, or you can download Sniper Assassin 3D Mod APK to get unlimited money, menu, premium features, and more. You can also follow some tips and tricks to improve your performance and enjoy the game more. Sniper Assassin 3D has received many positive reviews from players and critics alike, and it is one of the best sniping games available. If you are looking for a free online multiplayer FPS game that offers a lot of content and variety, you should try Sniper Assassin 3D.
- FAQs
-Here are some of the frequently asked questions and answers about Sniper Assassin 3D and its mod apk:
-
-Is Sniper Assassin 3D Mod APK safe to use? Can I play Sniper Assassin 3D offline? How can I get more diamonds and coins in Sniper Assassin 3D? How can I update Sniper Assassin 3D Mod APK? How can I contact the developers of Sniper Assassin 3D? 197e85843d=!&|~$:]/,t;function p(e,n){t=null;var r=e.next();if(r=="#")return e.skipToEnd(),"comment";if(r=="0"&&e.eat("x"))return e.eatWhile(/[\da-f]/i),"number";if(r=="."&&e.eat(/\d/))return e.match(/\d*(?:e[+\-]?\d+)?/),"number";if(/\d/.test(r))return e.match(/\d*(?:\.\d+)?(?:e[+\-]\d+)?L?/),"number";if(r=="'"||r=='"')return n.tokenize=E(r),"string";if(r=="`")return e.match(/[^`]+`/),"string.special";if(r=="."&&e.match(/.(?:[.]|\d+)/))return"keyword";if(/[a-zA-Z\.]/.test(r)){e.eatWhile(/[\w\.]/);var i=e.current();return h.propertyIsEnumerable(i)?"atom":N.propertyIsEnumerable(i)?(A.propertyIsEnumerable(i)&&!e.match(/\s*if(\s+|$)/,!1)&&(t="block"),"keyword"):m.propertyIsEnumerable(i)?"builtin":"variable"}else return r=="%"?(e.skipTo("%")&&e.next(),"variableName.special"):r=="<"&&e.eat("-")||r=="<"&&e.match("<-")||r=="-"&&e.match(/>>?/)||r=="="&&n.ctx.argList?"operator":k.test(r)?(r=="$"||e.eatWhile(k),"operator"):/[\(\){}\[\];]/.test(r)?(t=r,r==";"?"punctuation":null):null}function E(e){return function(n,r){if(n.eat("\\")){var i=n.next();return i=="x"?n.match(/^[a-f0-9]{2}/i):(i=="u"||i=="U")&&n.eat("{")&&n.skipTo("}")?n.next():i=="u"?n.match(/^[a-f0-9]{4}/i):i=="U"?n.match(/^[a-f0-9]{8}/i):/[0-7]/.test(i)&&n.match(/^[0-7]{1,2}/),"string.special"}else{for(var l;(l=n.next())!=null;){if(l==e){r.tokenize=p;break}if(l=="\\"){n.backUp(1);break}}return"string"}}}var v=1,u=2,c=4;function o(e,n,r){e.ctx={type:n,indent:e.indent,flags:0,column:r.column(),prev:e.ctx}}function x(e,n){var r=e.ctx;e.ctx={type:r.type,indent:r.indent,flags:r.flags|n,column:r.column,prev:r.prev}}function a(e){e.indent=e.ctx.indent,e.ctx=e.ctx.prev}const I={name:"r",startState:function(e){return{tokenize:p,ctx:{type:"top",indent:-e,flags:u},indent:0,afterIdent:!1}},token:function(e,n){if(e.sol()&&(n.ctx.flags&3||(n.ctx.flags|=u),n.ctx.flags&c&&a(n),n.indent=e.indentation()),e.eatSpace())return null;var r=n.tokenize(e,n);return r!="comment"&&!(n.ctx.flags&u)&&x(n,v),(t==";"||t=="{"||t=="}")&&n.ctx.type=="block"&&a(n),t=="{"?o(n,"}",e):t=="("?(o(n,")",e),n.afterIdent&&(n.ctx.argList=!0)):t=="["?o(n,"]",e):t=="block"?o(n,"block",e):t==n.ctx.type?a(n):n.ctx.type=="block"&&r!="comment"&&x(n,c),n.afterIdent=r=="variable"||r=="keyword",r},indent:function(e,n,r){if(e.tokenize!=p)return 0;var i=n&&n.charAt(0),l=e.ctx,d=i==l.type;return l.flags&c&&(l=l.prev),l.type=="block"?l.indent+(i=="{"?0:r.unit):l.flags&v?l.column+(d?0:1):l.indent+(d?0:r.unit)},languageData:{wordChars:".",commentTokens:{line:"#"},autocomplete:b.concat(g,s)}};export{I as r};
-//# sourceMappingURL=r-3ca97919.js.map
diff --git a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/markdown_it/__init__.py b/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/markdown_it/__init__.py
deleted file mode 100644
index 6606868a7aaaef5ba3b85507bd0cc6f34806d947..0000000000000000000000000000000000000000
--- a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/markdown_it/__init__.py
+++ /dev/null
@@ -1,5 +0,0 @@
-"""A Python port of Markdown-It"""
-__all__ = ("MarkdownIt",)
-__version__ = "3.0.0"
-
-from .main import MarkdownIt
diff --git a/spaces/ddiddi/bhasha.dev/README.md b/spaces/ddiddi/bhasha.dev/README.md
deleted file mode 100644
index 90f22e0a17d0c541ef1e140e9a27d384eb750df1..0000000000000000000000000000000000000000
--- a/spaces/ddiddi/bhasha.dev/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Bhasha.dev
-emoji: 🚀
-colorFrom: red
-colorTo: pink
-sdk: gradio
-sdk_version: 3.4.1
-app_file: app.py
-pinned: false
-license: other
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/declare-lab/tango/diffusers/examples/community/clip_guided_stable_diffusion_img2img.py b/spaces/declare-lab/tango/diffusers/examples/community/clip_guided_stable_diffusion_img2img.py
deleted file mode 100644
index c3dee5aa9e9a159849ba260ccb706496af5ae84b..0000000000000000000000000000000000000000
--- a/spaces/declare-lab/tango/diffusers/examples/community/clip_guided_stable_diffusion_img2img.py
+++ /dev/null
@@ -1,496 +0,0 @@
-import inspect
-from typing import List, Optional, Union
-
-import numpy as np
-import PIL
-import torch
-from torch import nn
-from torch.nn import functional as F
-from torchvision import transforms
-from transformers import CLIPFeatureExtractor, CLIPModel, CLIPTextModel, CLIPTokenizer
-
-from diffusers import (
- AutoencoderKL,
- DDIMScheduler,
- DiffusionPipeline,
- DPMSolverMultistepScheduler,
- LMSDiscreteScheduler,
- PNDMScheduler,
- UNet2DConditionModel,
-)
-from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipelineOutput
-from diffusers.utils import (
- PIL_INTERPOLATION,
- deprecate,
- randn_tensor,
-)
-
-
-EXAMPLE_DOC_STRING = """
- Examples:
- ```
- from io import BytesIO
-
- import requests
- import torch
- from diffusers import DiffusionPipeline
- from PIL import Image
- from transformers import CLIPFeatureExtractor, CLIPModel
-
- feature_extractor = CLIPFeatureExtractor.from_pretrained(
- "laion/CLIP-ViT-B-32-laion2B-s34B-b79K"
- )
- clip_model = CLIPModel.from_pretrained(
- "laion/CLIP-ViT-B-32-laion2B-s34B-b79K", torch_dtype=torch.float16
- )
-
-
- guided_pipeline = DiffusionPipeline.from_pretrained(
- "CompVis/stable-diffusion-v1-4",
- # custom_pipeline="clip_guided_stable_diffusion",
- custom_pipeline="/home/njindal/diffusers/examples/community/clip_guided_stable_diffusion.py",
- clip_model=clip_model,
- feature_extractor=feature_extractor,
- torch_dtype=torch.float16,
- )
- guided_pipeline.enable_attention_slicing()
- guided_pipeline = guided_pipeline.to("cuda")
-
- prompt = "fantasy book cover, full moon, fantasy forest landscape, golden vector elements, fantasy magic, dark light night, intricate, elegant, sharp focus, illustration, highly detailed, digital painting, concept art, matte, art by WLOP and Artgerm and Albert Bierstadt, masterpiece"
-
- url = "https://raw.githubusercontent.com/CompVis/stable-diffusion/main/assets/stable-samples/img2img/sketch-mountains-input.jpg"
-
- response = requests.get(url)
- init_image = Image.open(BytesIO(response.content)).convert("RGB")
-
- image = guided_pipeline(
- prompt=prompt,
- num_inference_steps=30,
- image=init_image,
- strength=0.75,
- guidance_scale=7.5,
- clip_guidance_scale=100,
- num_cutouts=4,
- use_cutouts=False,
- ).images[0]
- display(image)
- ```
-"""
-
-
-def preprocess(image, w, h):
- if isinstance(image, torch.Tensor):
- return image
- elif isinstance(image, PIL.Image.Image):
- image = [image]
-
- if isinstance(image[0], PIL.Image.Image):
- image = [np.array(i.resize((w, h), resample=PIL_INTERPOLATION["lanczos"]))[None, :] for i in image]
- image = np.concatenate(image, axis=0)
- image = np.array(image).astype(np.float32) / 255.0
- image = image.transpose(0, 3, 1, 2)
- image = 2.0 * image - 1.0
- image = torch.from_numpy(image)
- elif isinstance(image[0], torch.Tensor):
- image = torch.cat(image, dim=0)
- return image
-
-
-class MakeCutouts(nn.Module):
- def __init__(self, cut_size, cut_power=1.0):
- super().__init__()
-
- self.cut_size = cut_size
- self.cut_power = cut_power
-
- def forward(self, pixel_values, num_cutouts):
- sideY, sideX = pixel_values.shape[2:4]
- max_size = min(sideX, sideY)
- min_size = min(sideX, sideY, self.cut_size)
- cutouts = []
- for _ in range(num_cutouts):
- size = int(torch.rand([]) ** self.cut_power * (max_size - min_size) + min_size)
- offsetx = torch.randint(0, sideX - size + 1, ())
- offsety = torch.randint(0, sideY - size + 1, ())
- cutout = pixel_values[:, :, offsety : offsety + size, offsetx : offsetx + size]
- cutouts.append(F.adaptive_avg_pool2d(cutout, self.cut_size))
- return torch.cat(cutouts)
-
-
-def spherical_dist_loss(x, y):
- x = F.normalize(x, dim=-1)
- y = F.normalize(y, dim=-1)
- return (x - y).norm(dim=-1).div(2).arcsin().pow(2).mul(2)
-
-
-def set_requires_grad(model, value):
- for param in model.parameters():
- param.requires_grad = value
-
-
-class CLIPGuidedStableDiffusion(DiffusionPipeline):
- """CLIP guided stable diffusion based on the amazing repo by @crowsonkb and @Jack000
- - https://github.com/Jack000/glid-3-xl
- - https://github.dev/crowsonkb/k-diffusion
- """
-
- def __init__(
- self,
- vae: AutoencoderKL,
- text_encoder: CLIPTextModel,
- clip_model: CLIPModel,
- tokenizer: CLIPTokenizer,
- unet: UNet2DConditionModel,
- scheduler: Union[PNDMScheduler, LMSDiscreteScheduler, DDIMScheduler, DPMSolverMultistepScheduler],
- feature_extractor: CLIPFeatureExtractor,
- ):
- super().__init__()
- self.register_modules(
- vae=vae,
- text_encoder=text_encoder,
- clip_model=clip_model,
- tokenizer=tokenizer,
- unet=unet,
- scheduler=scheduler,
- feature_extractor=feature_extractor,
- )
-
- self.normalize = transforms.Normalize(mean=feature_extractor.image_mean, std=feature_extractor.image_std)
- self.cut_out_size = (
- feature_extractor.size
- if isinstance(feature_extractor.size, int)
- else feature_extractor.size["shortest_edge"]
- )
- self.make_cutouts = MakeCutouts(self.cut_out_size)
-
- set_requires_grad(self.text_encoder, False)
- set_requires_grad(self.clip_model, False)
-
- def enable_attention_slicing(self, slice_size: Optional[Union[str, int]] = "auto"):
- if slice_size == "auto":
- # half the attention head size is usually a good trade-off between
- # speed and memory
- slice_size = self.unet.config.attention_head_dim // 2
- self.unet.set_attention_slice(slice_size)
-
- def disable_attention_slicing(self):
- self.enable_attention_slicing(None)
-
- def freeze_vae(self):
- set_requires_grad(self.vae, False)
-
- def unfreeze_vae(self):
- set_requires_grad(self.vae, True)
-
- def freeze_unet(self):
- set_requires_grad(self.unet, False)
-
- def unfreeze_unet(self):
- set_requires_grad(self.unet, True)
-
- def get_timesteps(self, num_inference_steps, strength, device):
- # get the original timestep using init_timestep
- init_timestep = min(int(num_inference_steps * strength), num_inference_steps)
-
- t_start = max(num_inference_steps - init_timestep, 0)
- timesteps = self.scheduler.timesteps[t_start:]
-
- return timesteps, num_inference_steps - t_start
-
- def prepare_latents(self, image, timestep, batch_size, num_images_per_prompt, dtype, device, generator=None):
- if not isinstance(image, (torch.Tensor, PIL.Image.Image, list)):
- raise ValueError(
- f"`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(image)}"
- )
-
- image = image.to(device=device, dtype=dtype)
-
- batch_size = batch_size * num_images_per_prompt
- if isinstance(generator, list) and len(generator) != batch_size:
- raise ValueError(
- f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
- f" size of {batch_size}. Make sure the batch size matches the length of the generators."
- )
-
- if isinstance(generator, list):
- init_latents = [
- self.vae.encode(image[i : i + 1]).latent_dist.sample(generator[i]) for i in range(batch_size)
- ]
- init_latents = torch.cat(init_latents, dim=0)
- else:
- init_latents = self.vae.encode(image).latent_dist.sample(generator)
-
- init_latents = self.vae.config.scaling_factor * init_latents
-
- if batch_size > init_latents.shape[0] and batch_size % init_latents.shape[0] == 0:
- # expand init_latents for batch_size
- deprecation_message = (
- f"You have passed {batch_size} text prompts (`prompt`), but only {init_latents.shape[0]} initial"
- " images (`image`). Initial images are now duplicating to match the number of text prompts. Note"
- " that this behavior is deprecated and will be removed in a version 1.0.0. Please make sure to update"
- " your script to pass as many initial images as text prompts to suppress this warning."
- )
- deprecate("len(prompt) != len(image)", "1.0.0", deprecation_message, standard_warn=False)
- additional_image_per_prompt = batch_size // init_latents.shape[0]
- init_latents = torch.cat([init_latents] * additional_image_per_prompt, dim=0)
- elif batch_size > init_latents.shape[0] and batch_size % init_latents.shape[0] != 0:
- raise ValueError(
- f"Cannot duplicate `image` of batch size {init_latents.shape[0]} to {batch_size} text prompts."
- )
- else:
- init_latents = torch.cat([init_latents], dim=0)
-
- shape = init_latents.shape
- noise = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
-
- # get latents
- init_latents = self.scheduler.add_noise(init_latents, noise, timestep)
- latents = init_latents
-
- return latents
-
- @torch.enable_grad()
- def cond_fn(
- self,
- latents,
- timestep,
- index,
- text_embeddings,
- noise_pred_original,
- text_embeddings_clip,
- clip_guidance_scale,
- num_cutouts,
- use_cutouts=True,
- ):
- latents = latents.detach().requires_grad_()
-
- latent_model_input = self.scheduler.scale_model_input(latents, timestep)
-
- # predict the noise residual
- noise_pred = self.unet(latent_model_input, timestep, encoder_hidden_states=text_embeddings).sample
-
- if isinstance(self.scheduler, (PNDMScheduler, DDIMScheduler, DPMSolverMultistepScheduler)):
- alpha_prod_t = self.scheduler.alphas_cumprod[timestep]
- beta_prod_t = 1 - alpha_prod_t
- # compute predicted original sample from predicted noise also called
- # "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
- pred_original_sample = (latents - beta_prod_t ** (0.5) * noise_pred) / alpha_prod_t ** (0.5)
-
- fac = torch.sqrt(beta_prod_t)
- sample = pred_original_sample * (fac) + latents * (1 - fac)
- elif isinstance(self.scheduler, LMSDiscreteScheduler):
- sigma = self.scheduler.sigmas[index]
- sample = latents - sigma * noise_pred
- else:
- raise ValueError(f"scheduler type {type(self.scheduler)} not supported")
-
- sample = 1 / self.vae.config.scaling_factor * sample
- image = self.vae.decode(sample).sample
- image = (image / 2 + 0.5).clamp(0, 1)
-
- if use_cutouts:
- image = self.make_cutouts(image, num_cutouts)
- else:
- image = transforms.Resize(self.cut_out_size)(image)
- image = self.normalize(image).to(latents.dtype)
-
- image_embeddings_clip = self.clip_model.get_image_features(image)
- image_embeddings_clip = image_embeddings_clip / image_embeddings_clip.norm(p=2, dim=-1, keepdim=True)
-
- if use_cutouts:
- dists = spherical_dist_loss(image_embeddings_clip, text_embeddings_clip)
- dists = dists.view([num_cutouts, sample.shape[0], -1])
- loss = dists.sum(2).mean(0).sum() * clip_guidance_scale
- else:
- loss = spherical_dist_loss(image_embeddings_clip, text_embeddings_clip).mean() * clip_guidance_scale
-
- grads = -torch.autograd.grad(loss, latents)[0]
-
- if isinstance(self.scheduler, LMSDiscreteScheduler):
- latents = latents.detach() + grads * (sigma**2)
- noise_pred = noise_pred_original
- else:
- noise_pred = noise_pred_original - torch.sqrt(beta_prod_t) * grads
- return noise_pred, latents
-
- @torch.no_grad()
- def __call__(
- self,
- prompt: Union[str, List[str]],
- height: Optional[int] = 512,
- width: Optional[int] = 512,
- image: Union[torch.FloatTensor, PIL.Image.Image] = None,
- strength: float = 0.8,
- num_inference_steps: Optional[int] = 50,
- guidance_scale: Optional[float] = 7.5,
- num_images_per_prompt: Optional[int] = 1,
- eta: float = 0.0,
- clip_guidance_scale: Optional[float] = 100,
- clip_prompt: Optional[Union[str, List[str]]] = None,
- num_cutouts: Optional[int] = 4,
- use_cutouts: Optional[bool] = True,
- generator: Optional[torch.Generator] = None,
- latents: Optional[torch.FloatTensor] = None,
- output_type: Optional[str] = "pil",
- return_dict: bool = True,
- ):
- if isinstance(prompt, str):
- batch_size = 1
- elif isinstance(prompt, list):
- batch_size = len(prompt)
- else:
- raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
-
- if height % 8 != 0 or width % 8 != 0:
- raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")
-
- # get prompt text embeddings
- text_input = self.tokenizer(
- prompt,
- padding="max_length",
- max_length=self.tokenizer.model_max_length,
- truncation=True,
- return_tensors="pt",
- )
- text_embeddings = self.text_encoder(text_input.input_ids.to(self.device))[0]
- # duplicate text embeddings for each generation per prompt
- text_embeddings = text_embeddings.repeat_interleave(num_images_per_prompt, dim=0)
-
- # set timesteps
- accepts_offset = "offset" in set(inspect.signature(self.scheduler.set_timesteps).parameters.keys())
- extra_set_kwargs = {}
- if accepts_offset:
- extra_set_kwargs["offset"] = 1
-
- self.scheduler.set_timesteps(num_inference_steps, **extra_set_kwargs)
- # Some schedulers like PNDM have timesteps as arrays
- # It's more optimized to move all timesteps to correct device beforehand
- self.scheduler.timesteps.to(self.device)
-
- timesteps, num_inference_steps = self.get_timesteps(num_inference_steps, strength, self.device)
- latent_timestep = timesteps[:1].repeat(batch_size * num_images_per_prompt)
-
- # Preprocess image
- image = preprocess(image, width, height)
- latents = self.prepare_latents(
- image, latent_timestep, batch_size, num_images_per_prompt, text_embeddings.dtype, self.device, generator
- )
-
- if clip_guidance_scale > 0:
- if clip_prompt is not None:
- clip_text_input = self.tokenizer(
- clip_prompt,
- padding="max_length",
- max_length=self.tokenizer.model_max_length,
- truncation=True,
- return_tensors="pt",
- ).input_ids.to(self.device)
- else:
- clip_text_input = text_input.input_ids.to(self.device)
- text_embeddings_clip = self.clip_model.get_text_features(clip_text_input)
- text_embeddings_clip = text_embeddings_clip / text_embeddings_clip.norm(p=2, dim=-1, keepdim=True)
- # duplicate text embeddings clip for each generation per prompt
- text_embeddings_clip = text_embeddings_clip.repeat_interleave(num_images_per_prompt, dim=0)
-
- # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
- # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
- # corresponds to doing no classifier free guidance.
- do_classifier_free_guidance = guidance_scale > 1.0
- # get unconditional embeddings for classifier free guidance
- if do_classifier_free_guidance:
- max_length = text_input.input_ids.shape[-1]
- uncond_input = self.tokenizer([""], padding="max_length", max_length=max_length, return_tensors="pt")
- uncond_embeddings = self.text_encoder(uncond_input.input_ids.to(self.device))[0]
- # duplicate unconditional embeddings for each generation per prompt
- uncond_embeddings = uncond_embeddings.repeat_interleave(num_images_per_prompt, dim=0)
-
- # For classifier free guidance, we need to do two forward passes.
- # Here we concatenate the unconditional and text embeddings into a single batch
- # to avoid doing two forward passes
- text_embeddings = torch.cat([uncond_embeddings, text_embeddings])
-
- # get the initial random noise unless the user supplied it
-
- # Unlike in other pipelines, latents need to be generated in the target device
- # for 1-to-1 results reproducibility with the CompVis implementation.
- # However this currently doesn't work in `mps`.
- latents_shape = (batch_size * num_images_per_prompt, self.unet.in_channels, height // 8, width // 8)
- latents_dtype = text_embeddings.dtype
- if latents is None:
- if self.device.type == "mps":
- # randn does not work reproducibly on mps
- latents = torch.randn(latents_shape, generator=generator, device="cpu", dtype=latents_dtype).to(
- self.device
- )
- else:
- latents = torch.randn(latents_shape, generator=generator, device=self.device, dtype=latents_dtype)
- else:
- if latents.shape != latents_shape:
- raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {latents_shape}")
- latents = latents.to(self.device)
-
- # scale the initial noise by the standard deviation required by the scheduler
- latents = latents * self.scheduler.init_noise_sigma
-
- # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
- # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
- # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
- # and should be between [0, 1]
- accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
- extra_step_kwargs = {}
- if accepts_eta:
- extra_step_kwargs["eta"] = eta
-
- # check if the scheduler accepts generator
- accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys())
- if accepts_generator:
- extra_step_kwargs["generator"] = generator
-
- with self.progress_bar(total=num_inference_steps):
- for i, t in enumerate(timesteps):
- # expand the latents if we are doing classifier free guidance
- latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
- latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
-
- # predict the noise residual
- noise_pred = self.unet(latent_model_input, t, encoder_hidden_states=text_embeddings).sample
-
- # perform classifier free guidance
- if do_classifier_free_guidance:
- noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
- noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
-
- # perform clip guidance
- if clip_guidance_scale > 0:
- text_embeddings_for_guidance = (
- text_embeddings.chunk(2)[1] if do_classifier_free_guidance else text_embeddings
- )
- noise_pred, latents = self.cond_fn(
- latents,
- t,
- i,
- text_embeddings_for_guidance,
- noise_pred,
- text_embeddings_clip,
- clip_guidance_scale,
- num_cutouts,
- use_cutouts,
- )
-
- # compute the previous noisy sample x_t -> x_t-1
- latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample
-
- # scale and decode the image latents with vae
- latents = 1 / self.vae.config.scaling_factor * latents
- image = self.vae.decode(latents).sample
-
- image = (image / 2 + 0.5).clamp(0, 1)
- image = image.cpu().permute(0, 2, 3, 1).numpy()
-
- if output_type == "pil":
- image = self.numpy_to_pil(image)
-
- if not return_dict:
- return (image, None)
-
- return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=None)
diff --git a/spaces/declare-lab/tango/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_stable_unclip.py b/spaces/declare-lab/tango/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_stable_unclip.py
deleted file mode 100644
index ce41572e683c4a59f4a6c2335875986ca18d9358..0000000000000000000000000000000000000000
--- a/spaces/declare-lab/tango/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_stable_unclip.py
+++ /dev/null
@@ -1,933 +0,0 @@
-# Copyright 2023 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 inspect
-from typing import Any, Callable, Dict, List, Optional, Tuple, Union
-
-import torch
-from transformers import CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
-from transformers.models.clip.modeling_clip import CLIPTextModelOutput
-
-from ...loaders import TextualInversionLoaderMixin
-from ...models import AutoencoderKL, PriorTransformer, UNet2DConditionModel
-from ...models.embeddings import get_timestep_embedding
-from ...schedulers import KarrasDiffusionSchedulers
-from ...utils import is_accelerate_available, is_accelerate_version, logging, randn_tensor, replace_example_docstring
-from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
-from .stable_unclip_image_normalizer import StableUnCLIPImageNormalizer
-
-
-logger = logging.get_logger(__name__) # pylint: disable=invalid-name
-
-EXAMPLE_DOC_STRING = """
- Examples:
- ```py
- >>> import torch
- >>> from diffusers import StableUnCLIPPipeline
-
- >>> pipe = StableUnCLIPPipeline.from_pretrained(
- ... "fusing/stable-unclip-2-1-l", torch_dtype=torch.float16
- ... ) # TODO update model path
- >>> pipe = pipe.to("cuda")
-
- >>> prompt = "a photo of an astronaut riding a horse on mars"
- >>> images = pipe(prompt).images
- >>> images[0].save("astronaut_horse.png")
- ```
-"""
-
-
-class StableUnCLIPPipeline(DiffusionPipeline, TextualInversionLoaderMixin):
- """
- Pipeline for text-to-image generation using stable unCLIP.
-
- This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
- library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)
-
- Args:
- prior_tokenizer ([`CLIPTokenizer`]):
- Tokenizer of class
- [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
- prior_text_encoder ([`CLIPTextModelWithProjection`]):
- Frozen text-encoder.
- prior ([`PriorTransformer`]):
- The canonincal unCLIP prior to approximate the image embedding from the text embedding.
- prior_scheduler ([`KarrasDiffusionSchedulers`]):
- Scheduler used in the prior denoising process.
- image_normalizer ([`StableUnCLIPImageNormalizer`]):
- Used to normalize the predicted image embeddings before the noise is applied and un-normalize the image
- embeddings after the noise has been applied.
- image_noising_scheduler ([`KarrasDiffusionSchedulers`]):
- Noise schedule for adding noise to the predicted image embeddings. The amount of noise to add is determined
- by `noise_level` in `StableUnCLIPPipeline.__call__`.
- tokenizer (`CLIPTokenizer`):
- Tokenizer of class
- [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
- text_encoder ([`CLIPTextModel`]):
- Frozen text-encoder.
- unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents.
- scheduler ([`KarrasDiffusionSchedulers`]):
- A scheduler to be used in combination with `unet` to denoise the encoded image latents.
- vae ([`AutoencoderKL`]):
- Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
- """
-
- # prior components
- prior_tokenizer: CLIPTokenizer
- prior_text_encoder: CLIPTextModelWithProjection
- prior: PriorTransformer
- prior_scheduler: KarrasDiffusionSchedulers
-
- # image noising components
- image_normalizer: StableUnCLIPImageNormalizer
- image_noising_scheduler: KarrasDiffusionSchedulers
-
- # regular denoising components
- tokenizer: CLIPTokenizer
- text_encoder: CLIPTextModel
- unet: UNet2DConditionModel
- scheduler: KarrasDiffusionSchedulers
-
- vae: AutoencoderKL
-
- def __init__(
- self,
- # prior components
- prior_tokenizer: CLIPTokenizer,
- prior_text_encoder: CLIPTextModelWithProjection,
- prior: PriorTransformer,
- prior_scheduler: KarrasDiffusionSchedulers,
- # image noising components
- image_normalizer: StableUnCLIPImageNormalizer,
- image_noising_scheduler: KarrasDiffusionSchedulers,
- # regular denoising components
- tokenizer: CLIPTokenizer,
- text_encoder: CLIPTextModelWithProjection,
- unet: UNet2DConditionModel,
- scheduler: KarrasDiffusionSchedulers,
- # vae
- vae: AutoencoderKL,
- ):
- super().__init__()
-
- self.register_modules(
- prior_tokenizer=prior_tokenizer,
- prior_text_encoder=prior_text_encoder,
- prior=prior,
- prior_scheduler=prior_scheduler,
- image_normalizer=image_normalizer,
- image_noising_scheduler=image_noising_scheduler,
- tokenizer=tokenizer,
- text_encoder=text_encoder,
- unet=unet,
- scheduler=scheduler,
- vae=vae,
- )
-
- self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
-
- # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.enable_vae_slicing
- def enable_vae_slicing(self):
- r"""
- Enable sliced VAE decoding.
-
- When this option is enabled, the VAE will split the input tensor in slices to compute decoding in several
- steps. This is useful to save some memory and allow larger batch sizes.
- """
- self.vae.enable_slicing()
-
- # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.disable_vae_slicing
- def disable_vae_slicing(self):
- r"""
- Disable sliced VAE decoding. If `enable_vae_slicing` was previously invoked, this method will go back to
- computing decoding in one step.
- """
- self.vae.disable_slicing()
-
- def enable_sequential_cpu_offload(self, gpu_id=0):
- r"""
- Offloads all models to CPU using accelerate, significantly reducing memory usage. When called, the pipeline's
- models have their state dicts saved to CPU and then are moved to a `torch.device('meta') and loaded to GPU only
- when their specific submodule has its `forward` method called.
- """
- if is_accelerate_available():
- from accelerate import cpu_offload
- else:
- raise ImportError("Please install accelerate via `pip install accelerate`")
-
- device = torch.device(f"cuda:{gpu_id}")
-
- # TODO: self.prior.post_process_latents and self.image_noiser.{scale,unscale} are not covered by the offload hooks, so they fails if added to the list
- models = [
- self.prior_text_encoder,
- self.text_encoder,
- self.unet,
- self.vae,
- ]
- for cpu_offloaded_model in models:
- if cpu_offloaded_model is not None:
- cpu_offload(cpu_offloaded_model, device)
-
- def enable_model_cpu_offload(self, gpu_id=0):
- r"""
- Offloads all models to CPU using accelerate, reducing memory usage with a low impact on performance. Compared
- to `enable_sequential_cpu_offload`, this method moves one whole model at a time to the GPU when its `forward`
- method is called, and the model remains in GPU until the next model runs. Memory savings are lower than with
- `enable_sequential_cpu_offload`, but performance is much better due to the iterative execution of the `unet`.
- """
- if is_accelerate_available() and is_accelerate_version(">=", "0.17.0.dev0"):
- from accelerate import cpu_offload_with_hook
- else:
- raise ImportError("`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.")
-
- device = torch.device(f"cuda:{gpu_id}")
-
- if self.device.type != "cpu":
- self.to("cpu", silence_dtype_warnings=True)
- torch.cuda.empty_cache() # otherwise we don't see the memory savings (but they probably exist)
-
- hook = None
- for cpu_offloaded_model in [self.text_encoder, self.prior_text_encoder, self.unet, self.vae]:
- _, hook = cpu_offload_with_hook(cpu_offloaded_model, device, prev_module_hook=hook)
-
- # We'll offload the last model manually.
- self.final_offload_hook = hook
-
- @property
- # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
- def _execution_device(self):
- r"""
- Returns the device on which the pipeline's models will be executed. After calling
- `pipeline.enable_sequential_cpu_offload()` the execution device can only be inferred from Accelerate's module
- hooks.
- """
- if not hasattr(self.unet, "_hf_hook"):
- return self.device
- for module in self.unet.modules():
- if (
- hasattr(module, "_hf_hook")
- and hasattr(module._hf_hook, "execution_device")
- and module._hf_hook.execution_device is not None
- ):
- return torch.device(module._hf_hook.execution_device)
- return self.device
-
- # Copied from diffusers.pipelines.unclip.pipeline_unclip.UnCLIPPipeline._encode_prompt with _encode_prompt->_encode_prior_prompt, tokenizer->prior_tokenizer, text_encoder->prior_text_encoder
- def _encode_prior_prompt(
- self,
- prompt,
- device,
- num_images_per_prompt,
- do_classifier_free_guidance,
- text_model_output: Optional[Union[CLIPTextModelOutput, Tuple]] = None,
- text_attention_mask: Optional[torch.Tensor] = None,
- ):
- if text_model_output is None:
- batch_size = len(prompt) if isinstance(prompt, list) else 1
- # get prompt text embeddings
- text_inputs = self.prior_tokenizer(
- prompt,
- padding="max_length",
- max_length=self.prior_tokenizer.model_max_length,
- truncation=True,
- return_tensors="pt",
- )
- text_input_ids = text_inputs.input_ids
- text_mask = text_inputs.attention_mask.bool().to(device)
-
- untruncated_ids = self.prior_tokenizer(prompt, padding="longest", return_tensors="pt").input_ids
-
- if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(
- text_input_ids, untruncated_ids
- ):
- removed_text = self.prior_tokenizer.batch_decode(
- untruncated_ids[:, self.prior_tokenizer.model_max_length - 1 : -1]
- )
- logger.warning(
- "The following part of your input was truncated because CLIP can only handle sequences up to"
- f" {self.prior_tokenizer.model_max_length} tokens: {removed_text}"
- )
- text_input_ids = text_input_ids[:, : self.prior_tokenizer.model_max_length]
-
- prior_text_encoder_output = self.prior_text_encoder(text_input_ids.to(device))
-
- prompt_embeds = prior_text_encoder_output.text_embeds
- prior_text_encoder_hidden_states = prior_text_encoder_output.last_hidden_state
-
- else:
- batch_size = text_model_output[0].shape[0]
- prompt_embeds, prior_text_encoder_hidden_states = text_model_output[0], text_model_output[1]
- text_mask = text_attention_mask
-
- prompt_embeds = prompt_embeds.repeat_interleave(num_images_per_prompt, dim=0)
- prior_text_encoder_hidden_states = prior_text_encoder_hidden_states.repeat_interleave(
- num_images_per_prompt, dim=0
- )
- text_mask = text_mask.repeat_interleave(num_images_per_prompt, dim=0)
-
- if do_classifier_free_guidance:
- uncond_tokens = [""] * batch_size
-
- uncond_input = self.prior_tokenizer(
- uncond_tokens,
- padding="max_length",
- max_length=self.prior_tokenizer.model_max_length,
- truncation=True,
- return_tensors="pt",
- )
- uncond_text_mask = uncond_input.attention_mask.bool().to(device)
- negative_prompt_embeds_prior_text_encoder_output = self.prior_text_encoder(
- uncond_input.input_ids.to(device)
- )
-
- negative_prompt_embeds = negative_prompt_embeds_prior_text_encoder_output.text_embeds
- uncond_prior_text_encoder_hidden_states = (
- negative_prompt_embeds_prior_text_encoder_output.last_hidden_state
- )
-
- # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
-
- seq_len = negative_prompt_embeds.shape[1]
- negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt)
- negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len)
-
- seq_len = uncond_prior_text_encoder_hidden_states.shape[1]
- uncond_prior_text_encoder_hidden_states = uncond_prior_text_encoder_hidden_states.repeat(
- 1, num_images_per_prompt, 1
- )
- uncond_prior_text_encoder_hidden_states = uncond_prior_text_encoder_hidden_states.view(
- batch_size * num_images_per_prompt, seq_len, -1
- )
- uncond_text_mask = uncond_text_mask.repeat_interleave(num_images_per_prompt, dim=0)
-
- # done duplicates
-
- # For classifier free guidance, we need to do two forward passes.
- # Here we concatenate the unconditional and text embeddings into a single batch
- # to avoid doing two forward passes
- prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])
- prior_text_encoder_hidden_states = torch.cat(
- [uncond_prior_text_encoder_hidden_states, prior_text_encoder_hidden_states]
- )
-
- text_mask = torch.cat([uncond_text_mask, text_mask])
-
- return prompt_embeds, prior_text_encoder_hidden_states, text_mask
-
- # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._encode_prompt
- def _encode_prompt(
- self,
- prompt,
- device,
- num_images_per_prompt,
- do_classifier_free_guidance,
- negative_prompt=None,
- prompt_embeds: Optional[torch.FloatTensor] = None,
- negative_prompt_embeds: Optional[torch.FloatTensor] = None,
- ):
- r"""
- Encodes the prompt into text encoder hidden states.
-
- Args:
- prompt (`str` or `List[str]`, *optional*):
- prompt to be encoded
- device: (`torch.device`):
- torch device
- num_images_per_prompt (`int`):
- number of images that should be generated per prompt
- do_classifier_free_guidance (`bool`):
- whether to use classifier free guidance or not
- negative_prompt (`str` or `List[str]`, *optional*):
- The prompt or prompts not to guide the image generation. If not defined, one has to pass
- `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
- less than `1`).
- prompt_embeds (`torch.FloatTensor`, *optional*):
- Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
- provided, text embeddings will be generated from `prompt` input argument.
- negative_prompt_embeds (`torch.FloatTensor`, *optional*):
- Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
- weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
- argument.
- """
- if prompt is not None and isinstance(prompt, str):
- batch_size = 1
- elif prompt is not None and isinstance(prompt, list):
- batch_size = len(prompt)
- else:
- batch_size = prompt_embeds.shape[0]
-
- if prompt_embeds is None:
- # textual inversion: procecss multi-vector tokens if necessary
- if isinstance(self, TextualInversionLoaderMixin):
- prompt = self.maybe_convert_prompt(prompt, self.tokenizer)
-
- text_inputs = self.tokenizer(
- prompt,
- padding="max_length",
- max_length=self.tokenizer.model_max_length,
- truncation=True,
- return_tensors="pt",
- )
- text_input_ids = text_inputs.input_ids
- untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="pt").input_ids
-
- if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(
- text_input_ids, untruncated_ids
- ):
- removed_text = self.tokenizer.batch_decode(
- untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]
- )
- logger.warning(
- "The following part of your input was truncated because CLIP can only handle sequences up to"
- f" {self.tokenizer.model_max_length} tokens: {removed_text}"
- )
-
- if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask:
- attention_mask = text_inputs.attention_mask.to(device)
- else:
- attention_mask = None
-
- prompt_embeds = self.text_encoder(
- text_input_ids.to(device),
- attention_mask=attention_mask,
- )
- prompt_embeds = prompt_embeds[0]
-
- prompt_embeds = prompt_embeds.to(dtype=self.text_encoder.dtype, device=device)
-
- bs_embed, seq_len, _ = prompt_embeds.shape
- # duplicate text embeddings for each generation per prompt, using mps friendly method
- prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)
- prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)
-
- # get unconditional embeddings for classifier free guidance
- if do_classifier_free_guidance and negative_prompt_embeds is None:
- uncond_tokens: List[str]
- if negative_prompt is None:
- uncond_tokens = [""] * batch_size
- elif type(prompt) is not type(negative_prompt):
- raise TypeError(
- f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
- f" {type(prompt)}."
- )
- elif isinstance(negative_prompt, str):
- uncond_tokens = [negative_prompt]
- elif batch_size != len(negative_prompt):
- raise ValueError(
- f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
- f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
- " the batch size of `prompt`."
- )
- else:
- uncond_tokens = negative_prompt
-
- # textual inversion: procecss multi-vector tokens if necessary
- if isinstance(self, TextualInversionLoaderMixin):
- uncond_tokens = self.maybe_convert_prompt(uncond_tokens, self.tokenizer)
-
- max_length = prompt_embeds.shape[1]
- uncond_input = self.tokenizer(
- uncond_tokens,
- padding="max_length",
- max_length=max_length,
- truncation=True,
- return_tensors="pt",
- )
-
- if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask:
- attention_mask = uncond_input.attention_mask.to(device)
- else:
- attention_mask = None
-
- negative_prompt_embeds = self.text_encoder(
- uncond_input.input_ids.to(device),
- attention_mask=attention_mask,
- )
- negative_prompt_embeds = negative_prompt_embeds[0]
-
- if do_classifier_free_guidance:
- # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
- seq_len = negative_prompt_embeds.shape[1]
-
- negative_prompt_embeds = negative_prompt_embeds.to(dtype=self.text_encoder.dtype, device=device)
-
- negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)
- negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)
-
- # For classifier free guidance, we need to do two forward passes.
- # Here we concatenate the unconditional and text embeddings into a single batch
- # to avoid doing two forward passes
- prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])
-
- return prompt_embeds
-
- # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.decode_latents
- def decode_latents(self, latents):
- latents = 1 / self.vae.config.scaling_factor * latents
- image = self.vae.decode(latents).sample
- image = (image / 2 + 0.5).clamp(0, 1)
- # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
- image = image.cpu().permute(0, 2, 3, 1).float().numpy()
- return image
-
- # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs with prepare_extra_step_kwargs->prepare_prior_extra_step_kwargs, scheduler->prior_scheduler
- def prepare_prior_extra_step_kwargs(self, generator, eta):
- # prepare extra kwargs for the prior_scheduler step, since not all prior_schedulers have the same signature
- # eta (η) is only used with the DDIMScheduler, it will be ignored for other prior_schedulers.
- # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
- # and should be between [0, 1]
-
- accepts_eta = "eta" in set(inspect.signature(self.prior_scheduler.step).parameters.keys())
- extra_step_kwargs = {}
- if accepts_eta:
- extra_step_kwargs["eta"] = eta
-
- # check if the prior_scheduler accepts generator
- accepts_generator = "generator" in set(inspect.signature(self.prior_scheduler.step).parameters.keys())
- if accepts_generator:
- extra_step_kwargs["generator"] = generator
- return extra_step_kwargs
-
- # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs
- def prepare_extra_step_kwargs(self, generator, eta):
- # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
- # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
- # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
- # and should be between [0, 1]
-
- accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
- extra_step_kwargs = {}
- if accepts_eta:
- extra_step_kwargs["eta"] = eta
-
- # check if the scheduler accepts generator
- accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys())
- if accepts_generator:
- extra_step_kwargs["generator"] = generator
- return extra_step_kwargs
-
- def check_inputs(
- self,
- prompt,
- height,
- width,
- callback_steps,
- noise_level,
- negative_prompt=None,
- prompt_embeds=None,
- negative_prompt_embeds=None,
- ):
- if height % 8 != 0 or width % 8 != 0:
- raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")
-
- if (callback_steps is None) or (
- callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)
- ):
- raise ValueError(
- f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
- f" {type(callback_steps)}."
- )
-
- if prompt is not None and prompt_embeds is not None:
- raise ValueError(
- "Provide either `prompt` or `prompt_embeds`. Please make sure to define only one of the two."
- )
-
- if prompt is None and prompt_embeds is None:
- raise ValueError(
- "Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined."
- )
-
- if prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):
- raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
-
- if negative_prompt is not None and negative_prompt_embeds is not None:
- raise ValueError(
- "Provide either `negative_prompt` or `negative_prompt_embeds`. Cannot leave both `negative_prompt` and `negative_prompt_embeds` undefined."
- )
-
- if prompt is not None and negative_prompt is not None:
- if type(prompt) is not type(negative_prompt):
- raise TypeError(
- f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
- f" {type(prompt)}."
- )
-
- if prompt_embeds is not None and negative_prompt_embeds is not None:
- if prompt_embeds.shape != negative_prompt_embeds.shape:
- raise ValueError(
- "`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but"
- f" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`"
- f" {negative_prompt_embeds.shape}."
- )
-
- if noise_level < 0 or noise_level >= self.image_noising_scheduler.config.num_train_timesteps:
- raise ValueError(
- f"`noise_level` must be between 0 and {self.image_noising_scheduler.config.num_train_timesteps - 1}, inclusive."
- )
-
- # Copied from diffusers.pipelines.unclip.pipeline_unclip.UnCLIPPipeline.prepare_latents
- def prepare_latents(self, shape, dtype, device, generator, latents, scheduler):
- if latents is None:
- latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
- else:
- if latents.shape != shape:
- raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {shape}")
- latents = latents.to(device)
-
- latents = latents * scheduler.init_noise_sigma
- return latents
-
- def noise_image_embeddings(
- self,
- image_embeds: torch.Tensor,
- noise_level: int,
- noise: Optional[torch.FloatTensor] = None,
- generator: Optional[torch.Generator] = None,
- ):
- """
- Add noise to the image embeddings. The amount of noise is controlled by a `noise_level` input. A higher
- `noise_level` increases the variance in the final un-noised images.
-
- The noise is applied in two ways
- 1. A noise schedule is applied directly to the embeddings
- 2. A vector of sinusoidal time embeddings are appended to the output.
-
- In both cases, the amount of noise is controlled by the same `noise_level`.
-
- The embeddings are normalized before the noise is applied and un-normalized after the noise is applied.
- """
- if noise is None:
- noise = randn_tensor(
- image_embeds.shape, generator=generator, device=image_embeds.device, dtype=image_embeds.dtype
- )
-
- noise_level = torch.tensor([noise_level] * image_embeds.shape[0], device=image_embeds.device)
-
- self.image_normalizer.to(image_embeds.device)
- image_embeds = self.image_normalizer.scale(image_embeds)
-
- image_embeds = self.image_noising_scheduler.add_noise(image_embeds, timesteps=noise_level, noise=noise)
-
- image_embeds = self.image_normalizer.unscale(image_embeds)
-
- noise_level = get_timestep_embedding(
- timesteps=noise_level, embedding_dim=image_embeds.shape[-1], flip_sin_to_cos=True, downscale_freq_shift=0
- )
-
- # `get_timestep_embeddings` does not contain any weights and will always return f32 tensors,
- # but we might actually be running in fp16. so we need to cast here.
- # there might be better ways to encapsulate this.
- noise_level = noise_level.to(image_embeds.dtype)
-
- image_embeds = torch.cat((image_embeds, noise_level), 1)
-
- return image_embeds
-
- @torch.no_grad()
- @replace_example_docstring(EXAMPLE_DOC_STRING)
- def __call__(
- self,
- # regular denoising process args
- prompt: Optional[Union[str, List[str]]] = None,
- height: Optional[int] = None,
- width: Optional[int] = None,
- num_inference_steps: int = 20,
- guidance_scale: float = 10.0,
- negative_prompt: Optional[Union[str, List[str]]] = None,
- num_images_per_prompt: Optional[int] = 1,
- eta: float = 0.0,
- generator: Optional[torch.Generator] = None,
- latents: Optional[torch.FloatTensor] = None,
- prompt_embeds: Optional[torch.FloatTensor] = None,
- negative_prompt_embeds: Optional[torch.FloatTensor] = None,
- output_type: Optional[str] = "pil",
- return_dict: bool = True,
- callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
- callback_steps: int = 1,
- cross_attention_kwargs: Optional[Dict[str, Any]] = None,
- noise_level: int = 0,
- # prior args
- prior_num_inference_steps: int = 25,
- prior_guidance_scale: float = 4.0,
- prior_latents: Optional[torch.FloatTensor] = None,
- ):
- """
- Function invoked when calling the pipeline for generation.
-
- Args:
- prompt (`str` or `List[str]`, *optional*):
- The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.
- instead.
- height (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
- The height in pixels of the generated image.
- width (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
- The width in pixels of the generated image.
- num_inference_steps (`int`, *optional*, defaults to 20):
- The number of denoising steps. More denoising steps usually lead to a higher quality image at the
- expense of slower inference.
- guidance_scale (`float`, *optional*, defaults to 10.0):
- Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
- `guidance_scale` is defined as `w` of equation 2. of [Imagen
- Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
- 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
- usually at the expense of lower image quality.
- negative_prompt (`str` or `List[str]`, *optional*):
- The prompt or prompts not to guide the image generation. If not defined, one has to pass
- `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
- less than `1`).
- num_images_per_prompt (`int`, *optional*, defaults to 1):
- The number of images to generate per prompt.
- eta (`float`, *optional*, defaults to 0.0):
- Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
- [`schedulers.DDIMScheduler`], will be ignored for others.
- generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
- One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)
- to make generation deterministic.
- latents (`torch.FloatTensor`, *optional*):
- Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
- generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
- tensor will ge generated by sampling using the supplied random `generator`.
- prompt_embeds (`torch.FloatTensor`, *optional*):
- Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
- provided, text embeddings will be generated from `prompt` input argument.
- negative_prompt_embeds (`torch.FloatTensor`, *optional*):
- Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
- weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
- argument.
- output_type (`str`, *optional*, defaults to `"pil"`):
- The output format of the generate image. Choose between
- [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
- return_dict (`bool`, *optional*, defaults to `True`):
- Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
- plain tuple.
- callback (`Callable`, *optional*):
- A function that will be called every `callback_steps` steps during inference. The function will be
- called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
- callback_steps (`int`, *optional*, defaults to 1):
- The frequency at which the `callback` function will be called. If not specified, the callback will be
- called at every step.
- cross_attention_kwargs (`dict`, *optional*):
- A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
- `self.processor` in
- [diffusers.cross_attention](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/cross_attention.py).
- noise_level (`int`, *optional*, defaults to `0`):
- The amount of noise to add to the image embeddings. A higher `noise_level` increases the variance in
- the final un-noised images. See `StableUnCLIPPipeline.noise_image_embeddings` for details.
- prior_num_inference_steps (`int`, *optional*, defaults to 25):
- The number of denoising steps in the prior denoising process. More denoising steps usually lead to a
- higher quality image at the expense of slower inference.
- prior_guidance_scale (`float`, *optional*, defaults to 4.0):
- Guidance scale for the prior denoising process as defined in [Classifier-Free Diffusion
- Guidance](https://arxiv.org/abs/2207.12598). `prior_guidance_scale` is defined as `w` of equation 2. of
- [Imagen Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting
- `guidance_scale > 1`. Higher guidance scale encourages to generate images that are closely linked to
- the text `prompt`, usually at the expense of lower image quality.
- prior_latents (`torch.FloatTensor`, *optional*):
- Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
- embedding generation in the prior denoising process. Can be used to tweak the same generation with
- different prompts. If not provided, a latents tensor will ge generated by sampling using the supplied
- random `generator`.
-
- Examples:
-
- Returns:
- [`~pipelines.ImagePipelineOutput`] or `tuple`: [`~ pipeline_utils.ImagePipelineOutput`] if `return_dict` is
- True, otherwise a `tuple`. When returning a tuple, the first element is a list with the generated images.
- """
- # 0. Default height and width to unet
- height = height or self.unet.config.sample_size * self.vae_scale_factor
- width = width or self.unet.config.sample_size * self.vae_scale_factor
-
- # 1. Check inputs. Raise error if not correct
- self.check_inputs(
- prompt=prompt,
- height=height,
- width=width,
- callback_steps=callback_steps,
- noise_level=noise_level,
- negative_prompt=negative_prompt,
- prompt_embeds=prompt_embeds,
- negative_prompt_embeds=negative_prompt_embeds,
- )
-
- # 2. Define call parameters
- if prompt is not None and isinstance(prompt, str):
- batch_size = 1
- elif prompt is not None and isinstance(prompt, list):
- batch_size = len(prompt)
- else:
- batch_size = prompt_embeds.shape[0]
-
- batch_size = batch_size * num_images_per_prompt
-
- device = self._execution_device
-
- # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
- # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
- # corresponds to doing no classifier free guidance.
- prior_do_classifier_free_guidance = prior_guidance_scale > 1.0
-
- # 3. Encode input prompt
- prior_prompt_embeds, prior_text_encoder_hidden_states, prior_text_mask = self._encode_prior_prompt(
- prompt=prompt,
- device=device,
- num_images_per_prompt=num_images_per_prompt,
- do_classifier_free_guidance=prior_do_classifier_free_guidance,
- )
-
- # 4. Prepare prior timesteps
- self.prior_scheduler.set_timesteps(prior_num_inference_steps, device=device)
- prior_timesteps_tensor = self.prior_scheduler.timesteps
-
- # 5. Prepare prior latent variables
- embedding_dim = self.prior.config.embedding_dim
- prior_latents = self.prepare_latents(
- (batch_size, embedding_dim),
- prior_prompt_embeds.dtype,
- device,
- generator,
- prior_latents,
- self.prior_scheduler,
- )
-
- # 6. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
- prior_extra_step_kwargs = self.prepare_prior_extra_step_kwargs(generator, eta)
-
- # 7. Prior denoising loop
- for i, t in enumerate(self.progress_bar(prior_timesteps_tensor)):
- # expand the latents if we are doing classifier free guidance
- latent_model_input = torch.cat([prior_latents] * 2) if prior_do_classifier_free_guidance else prior_latents
- latent_model_input = self.prior_scheduler.scale_model_input(latent_model_input, t)
-
- predicted_image_embedding = self.prior(
- latent_model_input,
- timestep=t,
- proj_embedding=prior_prompt_embeds,
- encoder_hidden_states=prior_text_encoder_hidden_states,
- attention_mask=prior_text_mask,
- ).predicted_image_embedding
-
- if prior_do_classifier_free_guidance:
- predicted_image_embedding_uncond, predicted_image_embedding_text = predicted_image_embedding.chunk(2)
- predicted_image_embedding = predicted_image_embedding_uncond + prior_guidance_scale * (
- predicted_image_embedding_text - predicted_image_embedding_uncond
- )
-
- prior_latents = self.prior_scheduler.step(
- predicted_image_embedding,
- timestep=t,
- sample=prior_latents,
- **prior_extra_step_kwargs,
- ).prev_sample
-
- if callback is not None and i % callback_steps == 0:
- callback(i, t, prior_latents)
-
- prior_latents = self.prior.post_process_latents(prior_latents)
-
- image_embeds = prior_latents
-
- # done prior
-
- # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
- # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
- # corresponds to doing no classifier free guidance.
- do_classifier_free_guidance = guidance_scale > 1.0
-
- # 8. Encode input prompt
- prompt_embeds = self._encode_prompt(
- prompt=prompt,
- device=device,
- num_images_per_prompt=num_images_per_prompt,
- do_classifier_free_guidance=do_classifier_free_guidance,
- negative_prompt=negative_prompt,
- prompt_embeds=prompt_embeds,
- negative_prompt_embeds=negative_prompt_embeds,
- )
-
- # 9. Prepare image embeddings
- image_embeds = self.noise_image_embeddings(
- image_embeds=image_embeds,
- noise_level=noise_level,
- generator=generator,
- )
-
- if do_classifier_free_guidance:
- negative_prompt_embeds = torch.zeros_like(image_embeds)
-
- # For classifier free guidance, we need to do two forward passes.
- # Here we concatenate the unconditional and text embeddings into a single batch
- # to avoid doing two forward passes
- image_embeds = torch.cat([negative_prompt_embeds, image_embeds])
-
- # 10. Prepare timesteps
- self.scheduler.set_timesteps(num_inference_steps, device=device)
- timesteps = self.scheduler.timesteps
-
- # 11. Prepare latent variables
- num_channels_latents = self.unet.in_channels
- shape = (batch_size, num_channels_latents, height // self.vae_scale_factor, width // self.vae_scale_factor)
- latents = self.prepare_latents(
- shape=shape,
- dtype=prompt_embeds.dtype,
- device=device,
- generator=generator,
- latents=latents,
- scheduler=self.scheduler,
- )
-
- # 12. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
- extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
-
- # 13. Denoising loop
- for i, t in enumerate(self.progress_bar(timesteps)):
- latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
- latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
-
- # predict the noise residual
- noise_pred = self.unet(
- latent_model_input,
- t,
- encoder_hidden_states=prompt_embeds,
- class_labels=image_embeds,
- cross_attention_kwargs=cross_attention_kwargs,
- ).sample
-
- # perform guidance
- if do_classifier_free_guidance:
- noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
- noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
-
- # compute the previous noisy sample x_t -> x_t-1
- latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample
-
- if callback is not None and i % callback_steps == 0:
- callback(i, t, latents)
-
- # 14. Post-processing
- image = self.decode_latents(latents)
-
- # Offload last model to CPU
- if hasattr(self, "final_offload_hook") and self.final_offload_hook is not None:
- self.final_offload_hook.offload()
-
- # 15. Convert to PIL
- if output_type == "pil":
- image = self.numpy_to_pil(image)
-
- if not return_dict:
- return (image,)
-
- return ImagePipelineOutput(images=image)
diff --git a/spaces/deepset/retrieval-augmentation-svb/utils/__init__.py b/spaces/deepset/retrieval-augmentation-svb/utils/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/deepskyreal/ai-mixer-hotchpotch/sad_talker/src/face3d/extract_kp_videos.py b/spaces/deepskyreal/ai-mixer-hotchpotch/sad_talker/src/face3d/extract_kp_videos.py
deleted file mode 100644
index 21616a3b4b5077ffdce99621395237b4edcff58c..0000000000000000000000000000000000000000
--- a/spaces/deepskyreal/ai-mixer-hotchpotch/sad_talker/src/face3d/extract_kp_videos.py
+++ /dev/null
@@ -1,108 +0,0 @@
-import os
-import cv2
-import time
-import glob
-import argparse
-import face_alignment
-import numpy as np
-from PIL import Image
-from tqdm import tqdm
-from itertools import cycle
-
-from torch.multiprocessing import Pool, Process, set_start_method
-
-class KeypointExtractor():
- def __init__(self, device):
- self.detector = face_alignment.FaceAlignment(face_alignment.LandmarksType._2D,
- device=device)
-
- def extract_keypoint(self, images, name=None, info=True):
- if isinstance(images, list):
- keypoints = []
- if info:
- i_range = tqdm(images,desc='landmark Det:')
- else:
- i_range = images
-
- for image in i_range:
- current_kp = self.extract_keypoint(image)
- if np.mean(current_kp) == -1 and keypoints:
- keypoints.append(keypoints[-1])
- else:
- keypoints.append(current_kp[None])
-
- keypoints = np.concatenate(keypoints, 0)
- np.savetxt(os.path.splitext(name)[0]+'.txt', keypoints.reshape(-1))
- return keypoints
- else:
- while True:
- try:
- keypoints = self.detector.get_landmarks_from_image(np.array(images))[0]
- break
- except RuntimeError as e:
- if str(e).startswith('CUDA'):
- print("Warning: out of memory, sleep for 1s")
- time.sleep(1)
- else:
- print(e)
- break
- except TypeError:
- print('No face detected in this image')
- shape = [68, 2]
- keypoints = -1. * np.ones(shape)
- break
- if name is not None:
- np.savetxt(os.path.splitext(name)[0]+'.txt', keypoints.reshape(-1))
- return keypoints
-
-def read_video(filename):
- frames = []
- cap = cv2.VideoCapture(filename)
- while cap.isOpened():
- ret, frame = cap.read()
- if ret:
- frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
- frame = Image.fromarray(frame)
- frames.append(frame)
- else:
- break
- cap.release()
- return frames
-
-def run(data):
- filename, opt, device = data
- os.environ['CUDA_VISIBLE_DEVICES'] = device
- kp_extractor = KeypointExtractor()
- images = read_video(filename)
- name = filename.split('/')[-2:]
- os.makedirs(os.path.join(opt.output_dir, name[-2]), exist_ok=True)
- kp_extractor.extract_keypoint(
- images,
- name=os.path.join(opt.output_dir, name[-2], name[-1])
- )
-
-if __name__ == '__main__':
- set_start_method('spawn')
- parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)
- parser.add_argument('--input_dir', type=str, help='the folder of the input files')
- parser.add_argument('--output_dir', type=str, help='the folder of the output files')
- parser.add_argument('--device_ids', type=str, default='0,1')
- parser.add_argument('--workers', type=int, default=4)
-
- opt = parser.parse_args()
- filenames = list()
- VIDEO_EXTENSIONS_LOWERCASE = {'mp4'}
- VIDEO_EXTENSIONS = VIDEO_EXTENSIONS_LOWERCASE.union({f.upper() for f in VIDEO_EXTENSIONS_LOWERCASE})
- extensions = VIDEO_EXTENSIONS
-
- for ext in extensions:
- os.listdir(f'{opt.input_dir}')
- print(f'{opt.input_dir}/*.{ext}')
- filenames = sorted(glob.glob(f'{opt.input_dir}/*.{ext}'))
- print('Total number of videos:', len(filenames))
- pool = Pool(opt.workers)
- args_list = cycle([opt])
- device_ids = opt.device_ids.split(",")
- device_ids = cycle(device_ids)
- for data in tqdm(pool.imap_unordered(run, zip(filenames, args_list, device_ids))):
- None
diff --git a/spaces/deepwisdom/MetaGPT/tests/metagpt/utils/test_pycst.py b/spaces/deepwisdom/MetaGPT/tests/metagpt/utils/test_pycst.py
deleted file mode 100644
index 07352eac26238080861007e946d8832d7e3d85cb..0000000000000000000000000000000000000000
--- a/spaces/deepwisdom/MetaGPT/tests/metagpt/utils/test_pycst.py
+++ /dev/null
@@ -1,136 +0,0 @@
-from metagpt.utils import pycst
-
-code = '''
-#!/usr/bin/env python
-# -*- coding: utf-8 -*-
-from typing import overload
-
-@overload
-def add_numbers(a: int, b: int):
- ...
-
-@overload
-def add_numbers(a: float, b: float):
- ...
-
-def add_numbers(a: int, b: int):
- return a + b
-
-
-class Person:
- def __init__(self, name: str, age: int):
- self.name = name
- self.age = age
-
- def greet(self):
- return f"Hello, my name is {self.name} and I am {self.age} years old."
-'''
-
-documented_code = '''
-"""
-This is an example module containing a function and a class definition.
-"""
-
-
-def add_numbers(a: int, b: int):
- """This function is used to add two numbers and return the result.
-
- Parameters:
- a: The first integer.
- b: The second integer.
-
- Returns:
- int: The sum of the two numbers.
- """
- return a + b
-
-class Person:
- """This class represents a person's information, including name and age.
-
- Attributes:
- name: The person's name.
- age: The person's age.
- """
-
- def __init__(self, name: str, age: int):
- """Creates a new instance of the Person class.
-
- Parameters:
- name: The person's name.
- age: The person's age.
- """
- ...
-
- def greet(self):
- """
- Returns a greeting message including the name and age.
-
- Returns:
- str: The greeting message.
- """
- ...
-'''
-
-
-merged_code = '''
-#!/usr/bin/env python
-# -*- coding: utf-8 -*-
-"""
-This is an example module containing a function and a class definition.
-"""
-
-from typing import overload
-
-@overload
-def add_numbers(a: int, b: int):
- ...
-
-@overload
-def add_numbers(a: float, b: float):
- ...
-
-def add_numbers(a: int, b: int):
- """This function is used to add two numbers and return the result.
-
- Parameters:
- a: The first integer.
- b: The second integer.
-
- Returns:
- int: The sum of the two numbers.
- """
- return a + b
-
-
-class Person:
- """This class represents a person's information, including name and age.
-
- Attributes:
- name: The person's name.
- age: The person's age.
- """
- def __init__(self, name: str, age: int):
- """Creates a new instance of the Person class.
-
- Parameters:
- name: The person's name.
- age: The person's age.
- """
- self.name = name
- self.age = age
-
- def greet(self):
- """
- Returns a greeting message including the name and age.
-
- Returns:
- str: The greeting message.
- """
- return f"Hello, my name is {self.name} and I am {self.age} years old."
-'''
-
-
-def test_merge_docstring():
- data = pycst.merge_docstring(code, documented_code)
- print(data)
- assert data == merged_code
diff --git a/spaces/diacanFperku/AutoGPT/ESET NOD32 Antivirus 12.2.29.0 Crack License Key (Lifetime) ((FULL)).md b/spaces/diacanFperku/AutoGPT/ESET NOD32 Antivirus 12.2.29.0 Crack License Key (Lifetime) ((FULL)).md
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--- a/spaces/diacanFperku/AutoGPT/ESET NOD32 Antivirus 12.2.29.0 Crack License Key (Lifetime) ((FULL)).md
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diff --git a/spaces/diacanFperku/AutoGPT/Ek Tha Tiger 3 1080p Full Movie Download Fixed.md b/spaces/diacanFperku/AutoGPT/Ek Tha Tiger 3 1080p Full Movie Download Fixed.md
deleted file mode 100644
index 061b42cb2508c00d466596eabe5dee77e0709115..0000000000000000000000000000000000000000
--- a/spaces/diacanFperku/AutoGPT/Ek Tha Tiger 3 1080p Full Movie Download Fixed.md
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diff --git a/spaces/digitalxingtong/Azuma-Bert-VITS2/models.py b/spaces/digitalxingtong/Azuma-Bert-VITS2/models.py
deleted file mode 100644
index d4afe44d883691610c5903e602a3ca245fcb3a5c..0000000000000000000000000000000000000000
--- a/spaces/digitalxingtong/Azuma-Bert-VITS2/models.py
+++ /dev/null
@@ -1,707 +0,0 @@
-import copy
-import math
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-import commons
-import modules
-import attentions
-import monotonic_align
-
-from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d
-from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm
-
-from commons import init_weights, get_padding
-from text import symbols, num_tones, num_languages
-class DurationDiscriminator(nn.Module): #vits2
- def __init__(self, in_channels, filter_channels, kernel_size, p_dropout, gin_channels=0):
- super().__init__()
-
- self.in_channels = in_channels
- self.filter_channels = filter_channels
- self.kernel_size = kernel_size
- self.p_dropout = p_dropout
- self.gin_channels = gin_channels
-
- self.drop = nn.Dropout(p_dropout)
- self.conv_1 = nn.Conv1d(in_channels, filter_channels, kernel_size, padding=kernel_size//2)
- self.norm_1 = modules.LayerNorm(filter_channels)
- self.conv_2 = nn.Conv1d(filter_channels, filter_channels, kernel_size, padding=kernel_size//2)
- self.norm_2 = modules.LayerNorm(filter_channels)
- self.dur_proj = nn.Conv1d(1, filter_channels, 1)
-
- self.pre_out_conv_1 = nn.Conv1d(2*filter_channels, filter_channels, kernel_size, padding=kernel_size//2)
- self.pre_out_norm_1 = modules.LayerNorm(filter_channels)
- self.pre_out_conv_2 = nn.Conv1d(filter_channels, filter_channels, kernel_size, padding=kernel_size//2)
- self.pre_out_norm_2 = modules.LayerNorm(filter_channels)
-
- if gin_channels != 0:
- self.cond = nn.Conv1d(gin_channels, in_channels, 1)
-
- self.output_layer = nn.Sequential(
- nn.Linear(filter_channels, 1),
- nn.Sigmoid()
- )
-
- def forward_probability(self, x, x_mask, dur, g=None):
- dur = self.dur_proj(dur)
- x = torch.cat([x, dur], dim=1)
- x = self.pre_out_conv_1(x * x_mask)
- x = torch.relu(x)
- x = self.pre_out_norm_1(x)
- x = self.drop(x)
- x = self.pre_out_conv_2(x * x_mask)
- x = torch.relu(x)
- x = self.pre_out_norm_2(x)
- x = self.drop(x)
- x = x * x_mask
- x = x.transpose(1, 2)
- output_prob = self.output_layer(x)
- return output_prob
-
- def forward(self, x, x_mask, dur_r, dur_hat, g=None):
- x = torch.detach(x)
- if g is not None:
- g = torch.detach(g)
- x = x + self.cond(g)
- x = self.conv_1(x * x_mask)
- x = torch.relu(x)
- x = self.norm_1(x)
- x = self.drop(x)
- x = self.conv_2(x * x_mask)
- x = torch.relu(x)
- x = self.norm_2(x)
- x = self.drop(x)
-
- output_probs = []
- for dur in [dur_r, dur_hat]:
- output_prob = self.forward_probability(x, x_mask, dur, g)
- output_probs.append(output_prob)
-
- return output_probs
-
-class TransformerCouplingBlock(nn.Module):
- def __init__(self,
- channels,
- hidden_channels,
- filter_channels,
- n_heads,
- n_layers,
- kernel_size,
- p_dropout,
- n_flows=4,
- gin_channels=0,
- share_parameter=False
- ):
-
- super().__init__()
- self.channels = channels
- self.hidden_channels = hidden_channels
- self.kernel_size = kernel_size
- self.n_layers = n_layers
- self.n_flows = n_flows
- self.gin_channels = gin_channels
-
- self.flows = nn.ModuleList()
-
- self.wn = attentions.FFT(hidden_channels, filter_channels, n_heads, n_layers, kernel_size, p_dropout, isflow = True, gin_channels = self.gin_channels) if share_parameter else None
-
- for i in range(n_flows):
- self.flows.append(
- modules.TransformerCouplingLayer(channels, hidden_channels, kernel_size, n_layers, n_heads, p_dropout, filter_channels, mean_only=True, wn_sharing_parameter=self.wn, gin_channels = self.gin_channels))
- self.flows.append(modules.Flip())
-
- def forward(self, x, x_mask, g=None, reverse=False):
- if not reverse:
- for flow in self.flows:
- x, _ = flow(x, x_mask, g=g, reverse=reverse)
- else:
- for flow in reversed(self.flows):
- x = flow(x, x_mask, g=g, reverse=reverse)
- return x
-
-class StochasticDurationPredictor(nn.Module):
- def __init__(self, in_channels, filter_channels, kernel_size, p_dropout, n_flows=4, gin_channels=0):
- super().__init__()
- filter_channels = in_channels # it needs to be removed from future version.
- self.in_channels = in_channels
- self.filter_channels = filter_channels
- self.kernel_size = kernel_size
- self.p_dropout = p_dropout
- self.n_flows = n_flows
- self.gin_channels = gin_channels
-
- self.log_flow = modules.Log()
- self.flows = nn.ModuleList()
- self.flows.append(modules.ElementwiseAffine(2))
- for i in range(n_flows):
- self.flows.append(modules.ConvFlow(2, filter_channels, kernel_size, n_layers=3))
- self.flows.append(modules.Flip())
-
- self.post_pre = nn.Conv1d(1, filter_channels, 1)
- self.post_proj = nn.Conv1d(filter_channels, filter_channels, 1)
- self.post_convs = modules.DDSConv(filter_channels, kernel_size, n_layers=3, p_dropout=p_dropout)
- self.post_flows = nn.ModuleList()
- self.post_flows.append(modules.ElementwiseAffine(2))
- for i in range(4):
- self.post_flows.append(modules.ConvFlow(2, filter_channels, kernel_size, n_layers=3))
- self.post_flows.append(modules.Flip())
-
- self.pre = nn.Conv1d(in_channels, filter_channels, 1)
- self.proj = nn.Conv1d(filter_channels, filter_channels, 1)
- self.convs = modules.DDSConv(filter_channels, kernel_size, n_layers=3, p_dropout=p_dropout)
- if gin_channels != 0:
- self.cond = nn.Conv1d(gin_channels, filter_channels, 1)
-
- def forward(self, x, x_mask, w=None, g=None, reverse=False, noise_scale=1.0):
- x = torch.detach(x)
- x = self.pre(x)
- if g is not None:
- g = torch.detach(g)
- x = x + self.cond(g)
- x = self.convs(x, x_mask)
- x = self.proj(x) * x_mask
-
- if not reverse:
- flows = self.flows
- assert w is not None
-
- logdet_tot_q = 0
- h_w = self.post_pre(w)
- h_w = self.post_convs(h_w, x_mask)
- h_w = self.post_proj(h_w) * x_mask
- e_q = torch.randn(w.size(0), 2, w.size(2)).to(device=x.device, dtype=x.dtype) * x_mask
- z_q = e_q
- for flow in self.post_flows:
- z_q, logdet_q = flow(z_q, x_mask, g=(x + h_w))
- logdet_tot_q += logdet_q
- z_u, z1 = torch.split(z_q, [1, 1], 1)
- u = torch.sigmoid(z_u) * x_mask
- z0 = (w - u) * x_mask
- logdet_tot_q += torch.sum((F.logsigmoid(z_u) + F.logsigmoid(-z_u)) * x_mask, [1, 2])
- logq = torch.sum(-0.5 * (math.log(2 * math.pi) + (e_q ** 2)) * x_mask, [1, 2]) - logdet_tot_q
-
- logdet_tot = 0
- z0, logdet = self.log_flow(z0, x_mask)
- logdet_tot += logdet
- z = torch.cat([z0, z1], 1)
- for flow in flows:
- z, logdet = flow(z, x_mask, g=x, reverse=reverse)
- logdet_tot = logdet_tot + logdet
- nll = torch.sum(0.5 * (math.log(2 * math.pi) + (z ** 2)) * x_mask, [1, 2]) - logdet_tot
- return nll + logq # [b]
- else:
- flows = list(reversed(self.flows))
- flows = flows[:-2] + [flows[-1]] # remove a useless vflow
- z = torch.randn(x.size(0), 2, x.size(2)).to(device=x.device, dtype=x.dtype) * noise_scale
- for flow in flows:
- z = flow(z, x_mask, g=x, reverse=reverse)
- z0, z1 = torch.split(z, [1, 1], 1)
- logw = z0
- return logw
-
-
-class DurationPredictor(nn.Module):
- def __init__(self, in_channels, filter_channels, kernel_size, p_dropout, gin_channels=0):
- super().__init__()
-
- self.in_channels = in_channels
- self.filter_channels = filter_channels
- self.kernel_size = kernel_size
- self.p_dropout = p_dropout
- self.gin_channels = gin_channels
-
- self.drop = nn.Dropout(p_dropout)
- self.conv_1 = nn.Conv1d(in_channels, filter_channels, kernel_size, padding=kernel_size // 2)
- self.norm_1 = modules.LayerNorm(filter_channels)
- self.conv_2 = nn.Conv1d(filter_channels, filter_channels, kernel_size, padding=kernel_size // 2)
- self.norm_2 = modules.LayerNorm(filter_channels)
- self.proj = nn.Conv1d(filter_channels, 1, 1)
-
- if gin_channels != 0:
- self.cond = nn.Conv1d(gin_channels, in_channels, 1)
-
- def forward(self, x, x_mask, g=None):
- x = torch.detach(x)
- if g is not None:
- g = torch.detach(g)
- x = x + self.cond(g)
- x = self.conv_1(x * x_mask)
- x = torch.relu(x)
- x = self.norm_1(x)
- x = self.drop(x)
- x = self.conv_2(x * x_mask)
- x = torch.relu(x)
- x = self.norm_2(x)
- x = self.drop(x)
- x = self.proj(x * x_mask)
- return x * x_mask
-
-
-class TextEncoder(nn.Module):
- def __init__(self,
- n_vocab,
- out_channels,
- hidden_channels,
- filter_channels,
- n_heads,
- n_layers,
- kernel_size,
- p_dropout,
- gin_channels=0):
- super().__init__()
- self.n_vocab = n_vocab
- self.out_channels = out_channels
- self.hidden_channels = hidden_channels
- self.filter_channels = filter_channels
- self.n_heads = n_heads
- self.n_layers = n_layers
- self.kernel_size = kernel_size
- self.p_dropout = p_dropout
- self.gin_channels = gin_channels
- self.emb = nn.Embedding(len(symbols), hidden_channels)
- nn.init.normal_(self.emb.weight, 0.0, hidden_channels ** -0.5)
- self.tone_emb = nn.Embedding(num_tones, hidden_channels)
- nn.init.normal_(self.tone_emb.weight, 0.0, hidden_channels ** -0.5)
- self.language_emb = nn.Embedding(num_languages, hidden_channels)
- nn.init.normal_(self.language_emb.weight, 0.0, hidden_channels ** -0.5)
- self.bert_proj = nn.Conv1d(1024, hidden_channels, 1)
-
- self.encoder = attentions.Encoder(
- hidden_channels,
- filter_channels,
- n_heads,
- n_layers,
- kernel_size,
- p_dropout,
- gin_channels=self.gin_channels)
- self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
-
- def forward(self, x, x_lengths, tone, language, bert, g=None):
- x = (self.emb(x)+ self.tone_emb(tone)+ self.language_emb(language)+self.bert_proj(bert).transpose(1,2)) * math.sqrt(self.hidden_channels) # [b, t, h]
- x = torch.transpose(x, 1, -1) # [b, h, t]
- x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(x.dtype)
-
- x = self.encoder(x * x_mask, x_mask, g=g)
- stats = self.proj(x) * x_mask
-
- m, logs = torch.split(stats, self.out_channels, dim=1)
- return x, m, logs, x_mask
-
-
-class ResidualCouplingBlock(nn.Module):
- def __init__(self,
- channels,
- hidden_channels,
- kernel_size,
- dilation_rate,
- n_layers,
- n_flows=4,
- gin_channels=0):
- super().__init__()
- self.channels = channels
- self.hidden_channels = hidden_channels
- self.kernel_size = kernel_size
- self.dilation_rate = dilation_rate
- self.n_layers = n_layers
- self.n_flows = n_flows
- self.gin_channels = gin_channels
-
- self.flows = nn.ModuleList()
- for i in range(n_flows):
- self.flows.append(
- modules.ResidualCouplingLayer(channels, hidden_channels, kernel_size, dilation_rate, n_layers,
- gin_channels=gin_channels, mean_only=True))
- self.flows.append(modules.Flip())
-
- def forward(self, x, x_mask, g=None, reverse=False):
- if not reverse:
- for flow in self.flows:
- x, _ = flow(x, x_mask, g=g, reverse=reverse)
- else:
- for flow in reversed(self.flows):
- x = flow(x, x_mask, g=g, reverse=reverse)
- return x
-
-
-class PosteriorEncoder(nn.Module):
- def __init__(self,
- in_channels,
- out_channels,
- hidden_channels,
- kernel_size,
- dilation_rate,
- n_layers,
- gin_channels=0):
- super().__init__()
- self.in_channels = in_channels
- self.out_channels = out_channels
- self.hidden_channels = hidden_channels
- self.kernel_size = kernel_size
- self.dilation_rate = dilation_rate
- self.n_layers = n_layers
- self.gin_channels = gin_channels
-
- self.pre = nn.Conv1d(in_channels, hidden_channels, 1)
- self.enc = modules.WN(hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=gin_channels)
- self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
-
- def forward(self, x, x_lengths, g=None):
- x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(x.dtype)
- x = self.pre(x) * x_mask
- x = self.enc(x, x_mask, g=g)
- stats = self.proj(x) * x_mask
- m, logs = torch.split(stats, self.out_channels, dim=1)
- z = (m + torch.randn_like(m) * torch.exp(logs)) * x_mask
- return z, m, logs, x_mask
-
-
-class Generator(torch.nn.Module):
- def __init__(self, initial_channel, resblock, resblock_kernel_sizes, resblock_dilation_sizes, upsample_rates,
- upsample_initial_channel, upsample_kernel_sizes, gin_channels=0):
- super(Generator, self).__init__()
- self.num_kernels = len(resblock_kernel_sizes)
- self.num_upsamples = len(upsample_rates)
- self.conv_pre = Conv1d(initial_channel, upsample_initial_channel, 7, 1, padding=3)
- resblock = modules.ResBlock1 if resblock == '1' else modules.ResBlock2
-
- self.ups = nn.ModuleList()
- for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)):
- self.ups.append(weight_norm(
- ConvTranspose1d(upsample_initial_channel // (2 ** i), upsample_initial_channel // (2 ** (i + 1)),
- k, u, padding=(k - u) // 2)))
-
- self.resblocks = nn.ModuleList()
- for i in range(len(self.ups)):
- ch = upsample_initial_channel // (2 ** (i + 1))
- for j, (k, d) in enumerate(zip(resblock_kernel_sizes, resblock_dilation_sizes)):
- self.resblocks.append(resblock(ch, k, d))
-
- self.conv_post = Conv1d(ch, 1, 7, 1, padding=3, bias=False)
- self.ups.apply(init_weights)
-
- if gin_channels != 0:
- self.cond = nn.Conv1d(gin_channels, upsample_initial_channel, 1)
-
- def forward(self, x, g=None):
- x = self.conv_pre(x)
- if g is not None:
- x = x + self.cond(g)
-
- for i in range(self.num_upsamples):
- x = F.leaky_relu(x, modules.LRELU_SLOPE)
- x = self.ups[i](x)
- xs = None
- for j in range(self.num_kernels):
- if xs is None:
- xs = self.resblocks[i * self.num_kernels + j](x)
- else:
- xs += self.resblocks[i * self.num_kernels + j](x)
- x = xs / self.num_kernels
- x = F.leaky_relu(x)
- x = self.conv_post(x)
- x = torch.tanh(x)
-
- return x
-
- def remove_weight_norm(self):
- print('Removing weight norm...')
- for l in self.ups:
- remove_weight_norm(l)
- for l in self.resblocks:
- l.remove_weight_norm()
-
-
-class DiscriminatorP(torch.nn.Module):
- def __init__(self, period, kernel_size=5, stride=3, use_spectral_norm=False):
- super(DiscriminatorP, self).__init__()
- self.period = period
- self.use_spectral_norm = use_spectral_norm
- norm_f = weight_norm if use_spectral_norm == False else spectral_norm
- self.convs = nn.ModuleList([
- norm_f(Conv2d(1, 32, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
- norm_f(Conv2d(32, 128, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
- norm_f(Conv2d(128, 512, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
- norm_f(Conv2d(512, 1024, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
- norm_f(Conv2d(1024, 1024, (kernel_size, 1), 1, padding=(get_padding(kernel_size, 1), 0))),
- ])
- self.conv_post = norm_f(Conv2d(1024, 1, (3, 1), 1, padding=(1, 0)))
-
- def forward(self, x):
- fmap = []
-
- # 1d to 2d
- b, c, t = x.shape
- if t % self.period != 0: # pad first
- n_pad = self.period - (t % self.period)
- x = F.pad(x, (0, n_pad), "reflect")
- t = t + n_pad
- x = x.view(b, c, t // self.period, self.period)
-
- for l in self.convs:
- x = l(x)
- x = F.leaky_relu(x, modules.LRELU_SLOPE)
- fmap.append(x)
- x = self.conv_post(x)
- fmap.append(x)
- x = torch.flatten(x, 1, -1)
-
- return x, fmap
-
-
-class DiscriminatorS(torch.nn.Module):
- def __init__(self, use_spectral_norm=False):
- super(DiscriminatorS, self).__init__()
- norm_f = weight_norm if use_spectral_norm == False else spectral_norm
- self.convs = nn.ModuleList([
- norm_f(Conv1d(1, 16, 15, 1, padding=7)),
- norm_f(Conv1d(16, 64, 41, 4, groups=4, padding=20)),
- norm_f(Conv1d(64, 256, 41, 4, groups=16, padding=20)),
- norm_f(Conv1d(256, 1024, 41, 4, groups=64, padding=20)),
- norm_f(Conv1d(1024, 1024, 41, 4, groups=256, padding=20)),
- norm_f(Conv1d(1024, 1024, 5, 1, padding=2)),
- ])
- self.conv_post = norm_f(Conv1d(1024, 1, 3, 1, padding=1))
-
- def forward(self, x):
- fmap = []
-
- for l in self.convs:
- x = l(x)
- x = F.leaky_relu(x, modules.LRELU_SLOPE)
- fmap.append(x)
- x = self.conv_post(x)
- fmap.append(x)
- x = torch.flatten(x, 1, -1)
-
- return x, fmap
-
-
-class MultiPeriodDiscriminator(torch.nn.Module):
- def __init__(self, use_spectral_norm=False):
- super(MultiPeriodDiscriminator, self).__init__()
- periods = [2, 3, 5, 7, 11]
-
- discs = [DiscriminatorS(use_spectral_norm=use_spectral_norm)]
- discs = discs + [DiscriminatorP(i, use_spectral_norm=use_spectral_norm) for i in periods]
- self.discriminators = nn.ModuleList(discs)
-
- def forward(self, y, y_hat):
- y_d_rs = []
- y_d_gs = []
- fmap_rs = []
- fmap_gs = []
- for i, d in enumerate(self.discriminators):
- y_d_r, fmap_r = d(y)
- y_d_g, fmap_g = d(y_hat)
- y_d_rs.append(y_d_r)
- y_d_gs.append(y_d_g)
- fmap_rs.append(fmap_r)
- fmap_gs.append(fmap_g)
-
- return y_d_rs, y_d_gs, fmap_rs, fmap_gs
-
-class ReferenceEncoder(nn.Module):
- '''
- inputs --- [N, Ty/r, n_mels*r] mels
- outputs --- [N, ref_enc_gru_size]
- '''
-
- def __init__(self, spec_channels, gin_channels=0):
-
- super().__init__()
- self.spec_channels = spec_channels
- ref_enc_filters = [32, 32, 64, 64, 128, 128]
- K = len(ref_enc_filters)
- filters = [1] + ref_enc_filters
- convs = [weight_norm(nn.Conv2d(in_channels=filters[i],
- out_channels=filters[i + 1],
- kernel_size=(3, 3),
- stride=(2, 2),
- padding=(1, 1))) for i in range(K)]
- self.convs = nn.ModuleList(convs)
- # self.wns = nn.ModuleList([weight_norm(num_features=ref_enc_filters[i]) for i in range(K)])
-
- out_channels = self.calculate_channels(spec_channels, 3, 2, 1, K)
- self.gru = nn.GRU(input_size=ref_enc_filters[-1] * out_channels,
- hidden_size=256 // 2,
- batch_first=True)
- self.proj = nn.Linear(128, gin_channels)
-
- def forward(self, inputs, mask=None):
- N = inputs.size(0)
- out = inputs.view(N, 1, -1, self.spec_channels) # [N, 1, Ty, n_freqs]
- for conv in self.convs:
- out = conv(out)
- # out = wn(out)
- out = F.relu(out) # [N, 128, Ty//2^K, n_mels//2^K]
-
- out = out.transpose(1, 2) # [N, Ty//2^K, 128, n_mels//2^K]
- T = out.size(1)
- N = out.size(0)
- out = out.contiguous().view(N, T, -1) # [N, Ty//2^K, 128*n_mels//2^K]
-
- self.gru.flatten_parameters()
- memory, out = self.gru(out) # out --- [1, N, 128]
-
- return self.proj(out.squeeze(0))
-
- def calculate_channels(self, L, kernel_size, stride, pad, n_convs):
- for i in range(n_convs):
- L = (L - kernel_size + 2 * pad) // stride + 1
- return L
-
-
-class SynthesizerTrn(nn.Module):
- """
- Synthesizer for Training
- """
-
- def __init__(self,
- n_vocab,
- spec_channels,
- segment_size,
- inter_channels,
- hidden_channels,
- filter_channels,
- n_heads,
- n_layers,
- kernel_size,
- p_dropout,
- resblock,
- resblock_kernel_sizes,
- resblock_dilation_sizes,
- upsample_rates,
- upsample_initial_channel,
- upsample_kernel_sizes,
- n_speakers=256,
- gin_channels=256,
- use_sdp=True,
- n_flow_layer = 4,
- n_layers_trans_flow = 3,
- flow_share_parameter = False,
- use_transformer_flow = True,
- **kwargs):
-
- super().__init__()
- self.n_vocab = n_vocab
- self.spec_channels = spec_channels
- self.inter_channels = inter_channels
- self.hidden_channels = hidden_channels
- self.filter_channels = filter_channels
- self.n_heads = n_heads
- self.n_layers = n_layers
- self.kernel_size = kernel_size
- self.p_dropout = p_dropout
- self.resblock = resblock
- self.resblock_kernel_sizes = resblock_kernel_sizes
- self.resblock_dilation_sizes = resblock_dilation_sizes
- self.upsample_rates = upsample_rates
- self.upsample_initial_channel = upsample_initial_channel
- self.upsample_kernel_sizes = upsample_kernel_sizes
- self.segment_size = segment_size
- self.n_speakers = n_speakers
- self.gin_channels = gin_channels
- self.n_layers_trans_flow = n_layers_trans_flow
- self.use_spk_conditioned_encoder = kwargs.get("use_spk_conditioned_encoder", True)
- self.use_sdp = use_sdp
- self.use_noise_scaled_mas = kwargs.get("use_noise_scaled_mas", False)
- self.mas_noise_scale_initial = kwargs.get("mas_noise_scale_initial", 0.01)
- self.noise_scale_delta = kwargs.get("noise_scale_delta", 2e-6)
- self.current_mas_noise_scale = self.mas_noise_scale_initial
- if self.use_spk_conditioned_encoder and gin_channels > 0:
- self.enc_gin_channels = gin_channels
- self.enc_p = TextEncoder(n_vocab,
- inter_channels,
- hidden_channels,
- filter_channels,
- n_heads,
- n_layers,
- kernel_size,
- p_dropout,
- gin_channels=self.enc_gin_channels)
- self.dec = Generator(inter_channels, resblock, resblock_kernel_sizes, resblock_dilation_sizes, upsample_rates,
- upsample_initial_channel, upsample_kernel_sizes, gin_channels=gin_channels)
- self.enc_q = PosteriorEncoder(spec_channels, inter_channels, hidden_channels, 5, 1, 16,
- gin_channels=gin_channels)
- if use_transformer_flow:
- self.flow = TransformerCouplingBlock(inter_channels, hidden_channels, filter_channels, n_heads, n_layers_trans_flow, 5, p_dropout, n_flow_layer, gin_channels=gin_channels,share_parameter= flow_share_parameter)
- else:
- self.flow = ResidualCouplingBlock(inter_channels, hidden_channels, 5, 1, n_flow_layer, gin_channels=gin_channels)
- self.sdp = StochasticDurationPredictor(hidden_channels, 192, 3, 0.5, 4, gin_channels=gin_channels)
- self.dp = DurationPredictor(hidden_channels, 256, 3, 0.5, gin_channels=gin_channels)
-
- if n_speakers >= 1:
- self.emb_g = nn.Embedding(n_speakers, gin_channels)
- else:
- self.ref_enc = ReferenceEncoder(spec_channels, gin_channels)
-
- def forward(self, x, x_lengths, y, y_lengths, sid, tone, language, bert):
- if self.n_speakers > 0:
- g = self.emb_g(sid).unsqueeze(-1) # [b, h, 1]
- else:
- g = self.ref_enc(y.transpose(1,2)).unsqueeze(-1)
- x, m_p, logs_p, x_mask = self.enc_p(x, x_lengths, tone, language, bert,g=g)
- z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=g)
- z_p = self.flow(z, y_mask, g=g)
-
- with torch.no_grad():
- # negative cross-entropy
- s_p_sq_r = torch.exp(-2 * logs_p) # [b, d, t]
- neg_cent1 = torch.sum(-0.5 * math.log(2 * math.pi) - logs_p, [1], keepdim=True) # [b, 1, t_s]
- neg_cent2 = torch.matmul(-0.5 * (z_p ** 2).transpose(1, 2),
- s_p_sq_r) # [b, t_t, d] x [b, d, t_s] = [b, t_t, t_s]
- neg_cent3 = torch.matmul(z_p.transpose(1, 2), (m_p * s_p_sq_r)) # [b, t_t, d] x [b, d, t_s] = [b, t_t, t_s]
- neg_cent4 = torch.sum(-0.5 * (m_p ** 2) * s_p_sq_r, [1], keepdim=True) # [b, 1, t_s]
- neg_cent = neg_cent1 + neg_cent2 + neg_cent3 + neg_cent4
- if self.use_noise_scaled_mas:
- epsilon = torch.std(neg_cent) * torch.randn_like(neg_cent) * self.current_mas_noise_scale
- neg_cent = neg_cent + epsilon
-
- attn_mask = torch.unsqueeze(x_mask, 2) * torch.unsqueeze(y_mask, -1)
- attn = monotonic_align.maximum_path(neg_cent, attn_mask.squeeze(1)).unsqueeze(1).detach()
-
- w = attn.sum(2)
-
- l_length_sdp = self.sdp(x, x_mask, w, g=g)
- l_length_sdp = l_length_sdp / torch.sum(x_mask)
-
- logw_ = torch.log(w + 1e-6) * x_mask
- logw = self.dp(x, x_mask, g=g)
- l_length_dp = torch.sum((logw - logw_) ** 2, [1, 2]) / torch.sum(x_mask) # for averaging
-
- l_length = l_length_dp + l_length_sdp
-
- # expand prior
- m_p = torch.matmul(attn.squeeze(1), m_p.transpose(1, 2)).transpose(1, 2)
- logs_p = torch.matmul(attn.squeeze(1), logs_p.transpose(1, 2)).transpose(1, 2)
-
- z_slice, ids_slice = commons.rand_slice_segments(z, y_lengths, self.segment_size)
- o = self.dec(z_slice, g=g)
- return o, l_length, attn, ids_slice, x_mask, y_mask, (z, z_p, m_p, logs_p, m_q, logs_q), (x, logw, logw_)
-
- def infer(self, x, x_lengths, sid, tone, language, bert, noise_scale=.667, length_scale=1, noise_scale_w=0.8, max_len=None, sdp_ratio=0,y=None):
- #x, m_p, logs_p, x_mask = self.enc_p(x, x_lengths, tone, language, bert)
- # g = self.gst(y)
- if self.n_speakers > 0:
- g = self.emb_g(sid).unsqueeze(-1) # [b, h, 1]
- else:
- g = self.ref_enc(y.transpose(1,2)).unsqueeze(-1)
- x, m_p, logs_p, x_mask = self.enc_p(x, x_lengths, tone, language, bert,g=g)
- logw = self.sdp(x, x_mask, g=g, reverse=True, noise_scale=noise_scale_w) * (sdp_ratio) + self.dp(x, x_mask, g=g) * (1 - sdp_ratio)
- w = torch.exp(logw) * x_mask * length_scale
- w_ceil = torch.ceil(w)
- y_lengths = torch.clamp_min(torch.sum(w_ceil, [1, 2]), 1).long()
- y_mask = torch.unsqueeze(commons.sequence_mask(y_lengths, None), 1).to(x_mask.dtype)
- attn_mask = torch.unsqueeze(x_mask, 2) * torch.unsqueeze(y_mask, -1)
- attn = commons.generate_path(w_ceil, attn_mask)
-
- m_p = torch.matmul(attn.squeeze(1), m_p.transpose(1, 2)).transpose(1, 2) # [b, t', t], [b, t, d] -> [b, d, t']
- logs_p = torch.matmul(attn.squeeze(1), logs_p.transpose(1, 2)).transpose(1,
- 2) # [b, t', t], [b, t, d] -> [b, d, t']
-
- z_p = m_p + torch.randn_like(m_p) * torch.exp(logs_p) * noise_scale
- z = self.flow(z_p, y_mask, g=g, reverse=True)
- o = self.dec((z * y_mask)[:, :, :max_len], g=g)
- return o, attn, y_mask, (z, z_p, m_p, logs_p)
diff --git a/spaces/digitalxingtong/Jiaran-Bert-VITS2/text/chinese.py b/spaces/digitalxingtong/Jiaran-Bert-VITS2/text/chinese.py
deleted file mode 100644
index 276753880b73de2e8889dcb2101cd98c09e0710b..0000000000000000000000000000000000000000
--- a/spaces/digitalxingtong/Jiaran-Bert-VITS2/text/chinese.py
+++ /dev/null
@@ -1,193 +0,0 @@
-import os
-import re
-
-import cn2an
-from pypinyin import lazy_pinyin, Style
-
-from text import symbols
-from text.symbols import punctuation
-from text.tone_sandhi import ToneSandhi
-
-current_file_path = os.path.dirname(__file__)
-pinyin_to_symbol_map = {line.split("\t")[0]: line.strip().split("\t")[1] for line in
- open(os.path.join(current_file_path, 'opencpop-strict.txt')).readlines()}
-
-import jieba.posseg as psg
-
-
-rep_map = {
- '：': ',',
- '；': ',',
- '，': ',',
- '。': '.',
- '！': '!',
- '？': '?',
- '\n': '.',
- "·": ",",
- '、': ",",
- '...': '…',
- '$': '.',
- '“': "'",
- '”': "'",
- '‘': "'",
- '’': "'",
- '（': "'",
- '）': "'",
- '(': "'",
- ')': "'",
- '《': "'",
- '》': "'",
- '【': "'",
- '】': "'",
- '[': "'",
- ']': "'",
- '—': "-",
- '～': "-",
- '~': "-",
- '「': "'",
- '」': "'",
-
-}
-
-tone_modifier = ToneSandhi()
-
-def replace_punctuation(text):
- text = text.replace("嗯", "恩").replace("呣","母")
- pattern = re.compile('|'.join(re.escape(p) for p in rep_map.keys()))
-
- replaced_text = pattern.sub(lambda x: rep_map[x.group()], text)
-
- replaced_text = re.sub(r'[^\u4e00-\u9fa5'+"".join(punctuation)+r']+', '', replaced_text)
-
- return replaced_text
-
-def g2p(text):
- pattern = r'(?<=[{0}])\s*'.format(''.join(punctuation))
- sentences = [i for i in re.split(pattern, text) if i.strip()!='']
- phones, tones, word2ph = _g2p(sentences)
- assert sum(word2ph) == len(phones)
- assert len(word2ph) == len(text) #Sometimes it will crash,you can add a try-catch.
- phones = ['_'] + phones + ["_"]
- tones = [0] + tones + [0]
- word2ph = [1] + word2ph + [1]
- return phones, tones, word2ph
-
-
-def _get_initials_finals(word):
- initials = []
- finals = []
- orig_initials = lazy_pinyin(
- word, neutral_tone_with_five=True, style=Style.INITIALS)
- orig_finals = lazy_pinyin(
- word, neutral_tone_with_five=True, style=Style.FINALS_TONE3)
- for c, v in zip(orig_initials, orig_finals):
- initials.append(c)
- finals.append(v)
- return initials, finals
-
-
-def _g2p(segments):
- phones_list = []
- tones_list = []
- word2ph = []
- for seg in segments:
- pinyins = []
- # Replace all English words in the sentence
- seg = re.sub('[a-zA-Z]+', '', seg)
- seg_cut = psg.lcut(seg)
- initials = []
- finals = []
- seg_cut = tone_modifier.pre_merge_for_modify(seg_cut)
- for word, pos in seg_cut:
- if pos == 'eng':
- continue
- sub_initials, sub_finals = _get_initials_finals(word)
- sub_finals = tone_modifier.modified_tone(word, pos,
- sub_finals)
- initials.append(sub_initials)
- finals.append(sub_finals)
-
- # assert len(sub_initials) == len(sub_finals) == len(word)
- initials = sum(initials, [])
- finals = sum(finals, [])
- #
- for c, v in zip(initials, finals):
- raw_pinyin = c+v
- # NOTE: post process for pypinyin outputs
- # we discriminate i, ii and iii
- if c == v:
- assert c in punctuation
- phone = [c]
- tone = '0'
- word2ph.append(1)
- else:
- v_without_tone = v[:-1]
- tone = v[-1]
-
- pinyin = c+v_without_tone
- assert tone in '12345'
-
- if c:
- # 多音节
- v_rep_map = {
- "uei": 'ui',
- 'iou': 'iu',
- 'uen': 'un',
- }
- if v_without_tone in v_rep_map.keys():
- pinyin = c+v_rep_map[v_without_tone]
- else:
- # 单音节
- pinyin_rep_map = {
- 'ing': 'ying',
- 'i': 'yi',
- 'in': 'yin',
- 'u': 'wu',
- }
- if pinyin in pinyin_rep_map.keys():
- pinyin = pinyin_rep_map[pinyin]
- else:
- single_rep_map = {
- 'v': 'yu',
- 'e': 'e',
- 'i': 'y',
- 'u': 'w',
- }
- if pinyin[0] in single_rep_map.keys():
- pinyin = single_rep_map[pinyin[0]]+pinyin[1:]
-
- assert pinyin in pinyin_to_symbol_map.keys(), (pinyin, seg, raw_pinyin)
- phone = pinyin_to_symbol_map[pinyin].split(' ')
- word2ph.append(len(phone))
-
- phones_list += phone
- tones_list += [int(tone)] * len(phone)
- return phones_list, tones_list, word2ph
-
-
-
-def text_normalize(text):
- numbers = re.findall(r'\d+(?:\.?\d+)?', text)
- for number in numbers:
- text = text.replace(number, cn2an.an2cn(number), 1)
- text = replace_punctuation(text)
- return text
-
-def get_bert_feature(text, word2ph):
- from text import chinese_bert
- return chinese_bert.get_bert_feature(text, word2ph)
-
-if __name__ == '__main__':
- from text.chinese_bert import get_bert_feature
- text = "啊！但是《原神》是由,米哈\游自主， [研发]的一款全.新开放世界.冒险游戏"
- text = text_normalize(text)
- print(text)
- phones, tones, word2ph = g2p(text)
- bert = get_bert_feature(text, word2ph)
-
- print(phones, tones, word2ph, bert.shape)
-
-
-# # 示例用法
-# text = "这是一个示例文本：,你好！这是一个测试...."
-# print(g2p_paddle(text)) # 输出: 这是一个示例文本你好这是一个测试
diff --git a/spaces/ds520/bingo/postcss.config.js b/spaces/ds520/bingo/postcss.config.js
deleted file mode 100644
index 33ad091d26d8a9dc95ebdf616e217d985ec215b8..0000000000000000000000000000000000000000
--- a/spaces/ds520/bingo/postcss.config.js
+++ /dev/null
@@ -1,6 +0,0 @@
-module.exports = {
- plugins: {
- tailwindcss: {},
- autoprefixer: {},
- },
-}
diff --git a/spaces/eIysia/VITS-Umamusume-voice-synthesizer/text/ngu_dialect.py b/spaces/eIysia/VITS-Umamusume-voice-synthesizer/text/ngu_dialect.py
deleted file mode 100644
index ce3e12bbf0469426872eed5f681985d3e1be9b26..0000000000000000000000000000000000000000
--- a/spaces/eIysia/VITS-Umamusume-voice-synthesizer/text/ngu_dialect.py
+++ /dev/null
@@ -1,30 +0,0 @@
-import re
-import opencc
-
-
-dialects = {'SZ': 'suzhou', 'WX': 'wuxi', 'CZ': 'changzhou', 'HZ': 'hangzhou',
- 'SX': 'shaoxing', 'NB': 'ningbo', 'JJ': 'jingjiang', 'YX': 'yixing',
- 'JD': 'jiading', 'ZR': 'zhenru', 'PH': 'pinghu', 'TX': 'tongxiang',
- 'JS': 'jiashan', 'HN': 'xiashi', 'LP': 'linping', 'XS': 'xiaoshan',
- 'FY': 'fuyang', 'RA': 'ruao', 'CX': 'cixi', 'SM': 'sanmen',
- 'TT': 'tiantai', 'WZ': 'wenzhou', 'SC': 'suichang', 'YB': 'youbu'}
-
-converters = {}
-
-for dialect in dialects.values():
- try:
- converters[dialect] = opencc.OpenCC(dialect)
- except:
- pass
-
-
-def ngu_dialect_to_ipa(text, dialect):
- dialect = dialects[dialect]
- text = converters[dialect].convert(text).replace('-','').replace('$',' ')
- text = re.sub(r'[、；：]', '，', text)
- text = re.sub(r'\s*，\s*', ', ', text)
- text = re.sub(r'\s*。\s*', '. ', text)
- text = re.sub(r'\s*？\s*', '? ', text)
- text = re.sub(r'\s*！\s*', '! ', text)
- text = re.sub(r'\s*$', '', text)
- return text
diff --git a/spaces/eaglelandsonce/UploadaDocAskaQuestion/utils.py b/spaces/eaglelandsonce/UploadaDocAskaQuestion/utils.py
deleted file mode 100644
index e919dd00dfafa5a6b5449907bb54e8421bd54577..0000000000000000000000000000000000000000
--- a/spaces/eaglelandsonce/UploadaDocAskaQuestion/utils.py
+++ /dev/null
@@ -1,65 +0,0 @@
-import os
-from langchain.embeddings import OpenAIEmbeddings
-from langchain.vectorstores import Chroma
-
-
-# stack up loading methods using elif statments for loading PDF, DOCX, TXT, and CSV files into LangChain Documents
-def load_document(file):
- import os
- name, extension = os.path.splitext(file)
-
- if extension == '.pdf':
- from langchain.document_loaders import PyPDFLoader
- print(f'Loading {file}')
- loader = PyPDFLoader(file)
- elif extension == '.docx':
- from langchain.document_loaders import Docx2txtLoader
- print(f'Loading {file}')
- loader = Docx2txtLoader(file)
- elif extension == '.txt':
- from langchain.document_loaders import TextLoader
- loader = TextLoader(file)
- elif extension == '.csv':
- from langchain.document_loaders import CSVLoader
- loader = CSVLoader(file)
- else:
- print('Document format is not supported!')
- return None
-
- data = loader.load()
- return data
-
-
-# chunck your data for embedding
-def chunk_data(data, chunk_size=256, chunk_overlap=20):
- from langchain.text_splitter import RecursiveCharacterTextSplitter
- text_splitter = RecursiveCharacterTextSplitter(chunk_size=chunk_size, chunk_overlap=chunk_overlap)
- chunks = text_splitter.split_documents(data)
- return chunks
-
-
-# using OpenAIEmbeddings() create your embeddings and save to the Chroma vector store
-def create_embeddings(chunks):
- embeddings = OpenAIEmbeddings()
- vector_store = Chroma.from_documents(chunks, embeddings)
- return vector_store
-
-# here where you ask your question, here we use a combination of RetrievalQA and ChatOpenAI but his is not the only way to do this
-def ask_and_get_answer(vector_store, q, k=3):
- from langchain.chains import RetrievalQA
- from langchain.chat_models import ChatOpenAI
- # choose the 3.5 turbo model which is default and set the temperature to 1 which is maximum
- llm = ChatOpenAI(model='gpt-3.5-turbo', temperature=1)
- #VectorStoreRetrieverMemory stores memories in a VectorDB and queries the top-K most "salient" docs every time it is called.
- retriever = vector_store.as_retriever(search_type='similarity', search_kwargs={'k': k})
- chain = RetrievalQA.from_chain_type(llm=llm, chain_type="stuff", retriever=retriever)
- answer = chain.run(q)
- return answer
-
-
-# return the embedding cost (using tiktoken)
-def calculate_embedding_cost(texts):
- import tiktoken
- enc = tiktoken.encoding_for_model('text-embedding-ada-002')
- total_tokens = sum([len(enc.encode(page.page_content)) for page in texts])
- return total_tokens, total_tokens / 1000 * 0.0004
diff --git a/spaces/elonmuskceo/persistent-data/README.md b/spaces/elonmuskceo/persistent-data/README.md
deleted file mode 100644
index 5daa71cbf1491b74952c1b9304911007f359f8d9..0000000000000000000000000000000000000000
--- a/spaces/elonmuskceo/persistent-data/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Persistent Data
-emoji: 🐨
-colorFrom: pink
-colorTo: gray
-sdk: gradio
-sdk_version: 2.4.2
-app_file: app.py
-pinned: false
----
-
-# Configuration
diff --git a/spaces/epexVfeibi/Imagedeblurr/24 Season 5 Complete Dvdrip X264 Mkv By Riddlera.md b/spaces/epexVfeibi/Imagedeblurr/24 Season 5 Complete Dvdrip X264 Mkv By Riddlera.md
deleted file mode 100644
index 5ca21955d463207f0b394b95b13916855253b6b6..0000000000000000000000000000000000000000
--- a/spaces/epexVfeibi/Imagedeblurr/24 Season 5 Complete Dvdrip X264 Mkv By Riddlera.md
+++ /dev/null
@@ -1,6 +0,0 @@
-24 Season 5 Complete Dvdrip X264 Mkv By Riddlera DOWNLOAD >>> https://jinyurl.com/2uEph8
diff --git a/spaces/erbanku/gpt-academic/config.py b/spaces/erbanku/gpt-academic/config.py
deleted file mode 100644
index 4e79c2ad91504bb7b3de2fef86d7096679290b18..0000000000000000000000000000000000000000
--- a/spaces/erbanku/gpt-academic/config.py
+++ /dev/null
@@ -1,74 +0,0 @@
-# [step 1]>> 例如： API_KEY = "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" （此key无效）
-API_KEY = "sk-此处填API密钥" # 可同时填写多个API-KEY，用英文逗号分割，例如API_KEY = "sk-openaikey1,sk-openaikey2,fkxxxx-api2dkey1,fkxxxx-api2dkey2"
-
-# [step 2]>> 改为True应用代理，如果直接在海外服务器部署，此处不修改
-USE_PROXY = False
-if USE_PROXY:
- # 填写格式是 [协议]:// [地址] :[端口]，填写之前不要忘记把USE_PROXY改成True，如果直接在海外服务器部署，此处不修改
- # 例如 "socks5h://localhost:11284"
- # [协议] 常见协议无非socks5h/http; 例如 v2**y 和 ss* 的默认本地协议是socks5h; 而cl**h 的默认本地协议是http
- # [地址] 懂的都懂，不懂就填localhost或者127.0.0.1肯定错不了（localhost意思是代理软件安装在本机上）
- # [端口] 在代理软件的设置里找。虽然不同的代理软件界面不一样，但端口号都应该在最显眼的位置上
-
- # 代理网络的地址，打开你的*学*网软件查看代理的协议(socks5/http)、地址(localhost)和端口(11284)
- proxies = {
- # [协议]:// [地址] :[端口]
- "http": "socks5h://localhost:11284",
- "https": "socks5h://localhost:11284",
- }
-else:
- proxies = None
-
-# [step 3]>> 多线程函数插件中，默认允许多少路线程同时访问OpenAI。Free trial users的限制是每分钟3次，Pay-as-you-go users的限制是每分钟3500次
-# 一言以蔽之：免费用户填3，OpenAI绑了信用卡的用户可以填 16 或者更高。提高限制请查询：https://platform.openai.com/docs/guides/rate-limits/overview
-DEFAULT_WORKER_NUM = 3
-
-
-# [step 4]>> 以下配置可以优化体验，但大部分场合下并不需要修改
-# 对话窗的高度
-CHATBOT_HEIGHT = 1115
-
-# 代码高亮
-CODE_HIGHLIGHT = True
-
-# 窗口布局
-LAYOUT = "LEFT-RIGHT" # "LEFT-RIGHT"（左右布局） # "TOP-DOWN"（上下布局）
-DARK_MODE = True # "LEFT-RIGHT"（左右布局） # "TOP-DOWN"（上下布局）
-
-# 发送请求到OpenAI后，等待多久判定为超时
-TIMEOUT_SECONDS = 30
-
-# 网页的端口, -1代表随机端口
-WEB_PORT = -1
-
-# 如果OpenAI不响应（网络卡顿、代理失败、KEY失效），重试的次数限制
-MAX_RETRY = 2
-
-# OpenAI模型选择是（gpt4现在只对申请成功的人开放）
-LLM_MODEL = "gpt-3.5-turbo" # 可选 "chatglm"
-AVAIL_LLM_MODELS = ["gpt-3.5-turbo", "gpt-4", "api2d-gpt-4", "api2d-gpt-3.5-turbo"]
-
-# 本地LLM模型如ChatGLM的执行方式 CPU/GPU
-LOCAL_MODEL_DEVICE = "cpu" # 可选 "cuda"
-
-# 设置gradio的并行线程数（不需要修改）
-CONCURRENT_COUNT = 100
-
-# 设置用户名和密码（不需要修改）（相关功能不稳定，与gradio版本和网络都相关，如果本地使用不建议加这个）
-# [("username", "password"), ("username2", "password2"), ...]
-AUTHENTICATION = []
-
-# 重新URL重新定向，实现更换API_URL的作用（常规情况下，不要修改!!）
-# （高危设置！通过修改此设置，您将把您的API-KEY和对话隐私完全暴露给您设定的中间人！）
-# 格式 {"https://api.openai.com/v1/chat/completions": "在这里填写重定向的api.openai.com的URL"}
-# 例如 API_URL_REDIRECT = {"https://api.openai.com/v1/chat/completions": "https://ai.open.com/api/conversation"}
-API_URL_REDIRECT = {}
-
-# 如果需要在二级路径下运行（常规情况下，不要修改!!）（需要配合修改main.py才能生效!）
-CUSTOM_PATH = "/"
-
-# 如果需要使用newbing，把newbing的长长的cookie放到这里
-NEWBING_STYLE = "creative" # ["creative", "balanced", "precise"]
-NEWBING_COOKIES = """
-your bing cookies here
-"""
\ No newline at end of file
diff --git a/spaces/exbert-project/exbert/client/src/ts/data/AttentionCapsule.ts b/spaces/exbert-project/exbert/client/src/ts/data/AttentionCapsule.ts
deleted file mode 100644
index e945f36da58583984e752ed2b00ce3f58c693e5a..0000000000000000000000000000000000000000
--- a/spaces/exbert-project/exbert/client/src/ts/data/AttentionCapsule.ts
+++ /dev/null
@@ -1,124 +0,0 @@
-import * as _ from 'lodash'
-import * as x_ from '../etc/_Tools'
-import * as tp from '../etc/types'
-import * as tf from '@tensorflow/tfjs'
-
-/**
- * Notes:
- *
- * - Also encapsulate the CLS/SEP info vs. no CLS/SEP info
- * - When layer format changes from list, drop the index into conf.layer
- */
-
-const bpeTokens = ["[CLS]", "[SEP]", "", " ", "<|endoftext|>"]
-const findBadIndexes = (x: tp.FullSingleTokenInfo[]) => x_.findAllIndexes(x.map(t => t.text), (a) => _.includes(bpeTokens, a))
-
-export function makeFromMetaResponse(r:tp.AttentionResponse, isZeroed){
- const key = 'aa' // Change this if backend response changes to be simpler
- const currPair = r[key]
- const left = currPair.left
- const right = currPair.right
- const leftZero = x_.findAllIndexes(left.map(t => t.text), (a) => _.includes(bpeTokens, a))
- const rightZero = x_.findAllIndexes(right.map(t => t.text), (a) => _.includes(bpeTokens, a))
- return new AttentionWrapper(currPair.att, [leftZero, rightZero], isZeroed)
-}
-
-export class AttentionWrapper {
- protected _att:number[][][]
- protected _attTensor:tf.Tensor3D
- protected _zeroedAttTensor:tf.Tensor3D
-
- badToks:[number[], number[]] // Indexes for the CLS and SEP tokens
- isZeroed: boolean
- nLayers = 12;
- nHeads = 12;
-
- constructor(att:number[][][], badToks:[number[], number[]]=[[],[]], isZeroed=true){
- this.init(att, badToks, isZeroed)
- }
-
- init(att:number[][][], badToks:[number[], number[]]=[[],[]], isZeroed) {
- this.isZeroed = isZeroed
- this._att = att;
- this._zeroedAttTensor = zeroRowCol(tf.tensor3d(att), badToks[0], badToks[1])
- this._attTensor = tf.tensor3d(att) // If I put this first, buffer modifications change this too.
- this.badToks = badToks;
- }
-
- updateFromNormal(r:tp.AttentionResponse, isZeroed){
- const key = 'aa' // Change this if backend response changes to be simpler
- const currPair = r[key]
- const left = currPair.left
- const right = currPair.right
-
- const leftZero = findBadIndexes(left)
- const rightZero = findBadIndexes(right)
- this.init(currPair.att, [leftZero, rightZero], isZeroed)
- }
-
- get attTensor() {
- const tens = this.isZeroed ? this._zeroedAttTensor : this._attTensor
- return tens
- }
-
- get att() {
- return this.attTensor.arraySync()
- }
-
- zeroed(): boolean
- zeroed(val:boolean): this
- zeroed(val?) {
- if (val == null) return this.isZeroed
- this.isZeroed = val
- return this
- }
-
- toggleZeroing() {
- this.zeroed(!this.zeroed())
- }
-
- protected _byHeads(heads:number[]):tf.Tensor2D {
- if (heads.length == 0) {
- return tf.zerosLike(this._byHead(0))
- }
-
- return (this.attTensor.gather(heads, 0).sum(0))
- }
-
- protected _byHead(head:number):tf.Tensor2D {
- return (this.attTensor.gather([head], 0).squeeze([0]))
- }
-
- byHeads(heads:number[]):number[][] {
- return this._byHeads(heads).arraySync()
- }
-
- byHead(head:number):number[][] {
- return this._byHead(head).arraySync()
- }
-}
-
-function zeroRowCol(tens:tf.Tensor3D, rows:number[], cols:number[]):tf.Tensor3D {
- let outTens = tens.clone()
- let atb = outTens.bufferSync()
- _.range(atb.shape[0]).forEach((head) => {
- _.range(atb.shape[1]).forEach((i) => {
- // Set rows to 0
- if (_.includes(rows, i)) {
- _.range(atb.shape[2]).forEach((j) => {
- atb.set(0, head, i, j)
- })
- }
-
- // Set cols to 0
- _.range(atb.shape[2]).forEach((j) => {
- if (_.includes(cols, j))
- _.range(atb.shape[1]).forEach((i) => {
- atb.set(0, head, i, j)
- })
- })
- })
- })
-
- return outTens
-}
\ No newline at end of file
diff --git a/spaces/falterWliame/Face_Mask_Detection/Fileviewpro Lisans Anahtar.md b/spaces/falterWliame/Face_Mask_Detection/Fileviewpro Lisans Anahtar.md
deleted file mode 100644
index 4b408380fb0ed43f7e0c94da5511b905f8072e8d..0000000000000000000000000000000000000000
--- a/spaces/falterWliame/Face_Mask_Detection/Fileviewpro Lisans Anahtar.md
+++ /dev/null
@@ -1,49 +0,0 @@
-
-FileViewPro Lisans Anahtar: Dosya Uzantılarını Açmanın Kolay Yolu
-FileViewPro, Windows'ta herhangi bir dosya uzantısını açmanıza yardımcı olan bir programdır. PDF, RAR, Word, Excel, JPG, MP4, MP3 ve daha fazlası dahil olmak üzere 120'den fazla dosya formatını destekler. Ayrıca dosyaları düzenlemenize, dönüÅtürmenize ve paylaÅmanıza da olanak tanır. FileViewPro ile bilgisayarınızda açamadıÄınız bir dosya kalmaz!
-Bu yazıda FileViewPro'yu nasıl ücretsiz indirebileceÄinizi, nasıl kurabileceÄinizi ve nasıl ücretsiz lisans anahtarı alabileceÄinizi anlatacaÄız. Ayrıca FileViewPro'yu nasıl kırabileceÄinizi de öÄreneceksiniz. Hadi baÅlayalım!
-fileviewpro lisans anahtar DOWNLOAD ✔✔✔ https://urlca.com/2uDc1N
FileViewPro'yu Nasıl Ãcretsiz Ä°ndirir ve Kurarım?
-FileViewPro'yu resmi web sitesinden indirebilirsiniz: https://www.fileviewpro.com/en/download
-Ä°ndirdiÄiniz "Setup_2016.exe" dosyasını çalıÅtırın ve kurulum dilini seçin. "Next" butonuna tıklayın ve kurulum sihirbazının adımlarını takip edin. FileViewPro size bilgisayarınızı hızlandırmak için WinThruster programını kurmanızı önerebilir. Bu adımı atlamak istiyorsanız "Skip" butonuna tıklayın.
-Kurulum bittiÄinde, FileViewPro'nun tüm özelliklerini kullanabilmek için kullanıcı arayüzünün saÄ Ã¼st köÅesindeki "Register" butonuna tıklayabilirsiniz.
-FileViewPro 2016 Lisans Anahtarı Nasıl Alırım?
-FileViewPro'yu kurduktan sonra, ücretsiz olarak etkinleÅtirmek isteyebilirsiniz. EndiÅelenmeyin, burada size ücretsiz FileViewPro 2016 Lisans Anahtarı sunuyoruz.
-FileViewPro 2016 Lisans Anahtarı:
-
-8765F4D3S45F678765F4
-7B6V5C4XC5V6B7N87B6
-B6V5C45V6B7N8 76V5B7
-8N7B6V5C4XC5V6B7NB6
-7NB6V5C4C5V6B7N8N7B6
-
-Bu lisans anahtarlarından birini kopyalayın ve FileViewPro'nun kayıt penceresine yapıÅtırın. Ardından "Activate" butonuna tıklayın ve programın etkinleÅtirildiÄini göreceksiniz.
-
-FileViewPro'yu Nasıl Kırarım?
-EÄer ücretsiz FileViewPro 2016 Lisans Anahtarı iÅe yaramazsa, FileViewPro 2016 kırma programını indirebileceÄiniz birçok web sitesi vardır. Ancak bu sitelerin güvenilir olmadıÄını ve virüs veya zararlı yazılım içerebileceÄini unutmayın. Bu nedenle kendi sorumluluÄunuzda indirin ve kullanın.
-Bu sitelerden biri
-
-
FileViewPro'nun Avantajları ve Dezavantajları Nelerdir?
-FileViewPro, birçok dosya formatını açmanıza yardımcı olan kullanıÅlı bir programdır. Ancak her program gibi, FileViewPro'nun da avantajları ve dezavantajları vardır. Ä°Åte bunlardan bazıları:
-Avantajları
-
-FileViewPro ile birden fazla program indirmenize gerek kalmaz. Tek bir program ile tüm dosyaları açabilirsiniz.
-FileViewPro, dosyaları görüntülemenin yanı sıra düzenlemenize, dönüÅtürmenize ve paylaÅmanıza da olanak tanır. Böylece dosyalarınız üzerinde tam kontrol sahibi olursunuz.
-FileViewPro, kullanıcı dostu bir arayüze sahiptir. Dosyalarınızı sürükleyip bırakarak veya açmak istediÄiniz dosyayı seçerek kolayca açabilirsiniz.
-FileViewPro, 34 farklı dilde destek sunar. Böylece kendi dilinizde dosyalarınızı açabilir ve yönetebilirsiniz.
-
-Dezavantajları
-
-FileViewPro ücretsiz deÄildir. Tüm özelliklerini kullanabilmek için yıllık 39.95 dolar ödemeniz gerekir.
-FileViewPro bazen diÄer potansiyel olarak istenmeyen programlarla birlikte gelir. Bu programlar bilgisayarınızın performansını düÅürebilir veya güvenliÄini tehlikeye atabilir.
-FileViewPro her ne kadar 120'den fazla dosya formatını desteklese de, bazı nadir veya yeni dosya formatlarını açamayabilir. Bu durumda baÅka bir program aramak zorunda kalabilirsiniz.
-FileViewPro bazen hatalar veya çökmeler yaÅayabilir. Bu da dosyalarınızın zarar görmesine veya kaybolmasına neden olabilir.
-
-FileViewPro Alternatifleri Nelerdir?
-EÄer FileViewPro'yu beÄenmediyseniz veya baÅka bir program arıyorsanız, size FileViewPro alternatifleri olarak sunabileceÄimiz birkaç program vardır. Bunlar:
-
-File Viewer Plus : File Viewer Plus 4, 400'den fazla dosya formatını açabilen bir programdır. Ofis belgeleri, PDF'ler, resim dosyaları gibi yaygın dosya türlerini tek bir programla görüntüleyebilirsiniz. Ayrıca dosyaları düzenleyebilir, dönüÅtürebilir ve kaydedebilirsiniz.The Unarchiver : The Unarchiver, Mac için dünyanın en sevilen RAR açıcı programıdır. Mac'in yerel aracından daha Åık olan bu program, bilinen tüm arÅiv türlerini destekler. Ayrıca Åifreli veya bozuk arÅivleri de açabilir.Free File Viewer : Free File Viewer, 200'den fazla dosya formatını açabilen ücretsiz bir programdır. PDF, DOCX, XLSX, PNG, JPG, MP3, MP4 gibi popüler dosya türlerini görüntüleyebilirsiniz. Ayrıca dosyaları yazdırabilir veya e-posta ile gönderebilirsiniz.' if e.mod is module else submodule_names[e.mod]
- param_size = sum(t.numel() for t in e.unique_params)
- buffer_size = sum(t.numel() for t in e.unique_buffers)
- output_shapes = [str(list(e.outputs[0].shape)) for t in e.outputs]
- output_dtypes = [str(t.dtype).split('.')[-1] for t in e.outputs]
- rows += [[
- name + (':0' if len(e.outputs) >= 2 else ''),
- str(param_size) if param_size else '-',
- str(buffer_size) if buffer_size else '-',
- (output_shapes + ['-'])[0],
- (output_dtypes + ['-'])[0],
- ]]
- for idx in range(1, len(e.outputs)):
- rows += [[name + f':{idx}', '-', '-', output_shapes[idx], output_dtypes[idx]]]
- param_total += param_size
- buffer_total += buffer_size
- rows += [['---'] * len(rows[0])]
- rows += [['Total', str(param_total), str(buffer_total), '-', '-']]
-
- # Print table.
- widths = [max(len(cell) for cell in column) for column in zip(*rows)]
- print()
- for row in rows:
- print(' '.join(cell + ' ' * (width - len(cell)) for cell, width in zip(row, widths)))
- print()
- return outputs
-
-#----------------------------------------------------------------------------
diff --git a/spaces/feregVcuzo/sanity-test-midi/checkpoint/Download Instagram 6.17 0 APK - The Most Popular Social Media App for Android.md b/spaces/feregVcuzo/sanity-test-midi/checkpoint/Download Instagram 6.17 0 APK - The Most Popular Social Media App for Android.md
deleted file mode 100644
index cab2bcf226d4125bccf7b3dd8631542dedc29d52..0000000000000000000000000000000000000000
--- a/spaces/feregVcuzo/sanity-test-midi/checkpoint/Download Instagram 6.17 0 APK - The Most Popular Social Media App for Android.md
+++ /dev/null
@@ -1,142 +0,0 @@
-
-Instagram 6.17 0 APK Download: How to Get the Latest Version of the Popular Social Media App
- Instagram is one of the most popular social media apps in the world, with over one billion monthly active users. It allows you to share photos and videos with your friends, family, and followers, as well as discover new content from people you may not know. Whether you want to showcase your creativity, promote your business, or just have fun, Instagram is the app for you.
- What is Instagram and why do you need it?
- Instagram features and benefits
- Instagram has many features that make it a great app for social media lovers. Some of them are:
-instagram 6.17 0 apk download Download >>> https://gohhs.com/2uPuXw
-You can post photos and videos to your profile, or share them as stories that disappear after 24 hours.
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-You can watch live videos from your favorite accounts or broadcast your own live video to your followers.
-You can create reels, which are short videos that you can add music and effects to.
-You can shop from your favorite brands and creators directly on the app.
-
- Instagram requirements and compatibility
- To use Instagram, you need a smartphone or tablet that runs on Android or iOS operating systems. You also need an internet connection, either Wi-Fi or mobile data. The minimum Android version required for Instagram is 4.4 (KitKat), while the minimum iOS version required is 11.0.
- What is an APK file and why do you need it?
- APK file definition and advantages
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-
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-instagram 6.17 0 apk download for android
-
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-
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-
- Step 4: Launch and enjoy the Instagram app
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- How to update the Instagram app to the latest version?
- Update from the Google Play Store
- If you have installed the Instagram app from the Google Play Store, you can easily update it to the latest version by following these steps:
-
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-Tap on My apps & games.
-Find the Instagram app in the list of installed apps.
-If there is an update available, tap on Update to download and install it.
-
- Update from the APK file
- If you have installed the Instagram app from an APK file, you can also update it to the latest version by following these steps:
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- Conclusion
- In this article, we have explained what is Instagram and why do you need it, what is an APK file and why do you need it, how to download and install Instagram 6.17 0 APK file on your Android device, and how to update the app to the latest version. We hope that this article has been helpful and informative for you. If you have any questions or feedback, please feel free to leave a comment below. Thank you for reading and happy Instagramming!
- FAQs
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-Is Instagram 6.17 0 APK file safe to download and install?
-Yes, as long as you download it from a reputable and trusted source, such as [APKPure]. However, you should always scan the file with an antivirus or malware detector before installing it, and backup your data and device in case something goes wrong.
-What are the benefits of downloading Instagram 6.17 0 APK file?
-Some of the benefits are that you can access apps that are not available in your region or country, get apps that are not compatible with your device or operating system, get apps that are not updated or supported by their developers anymore, and get apps that have extra features or modifications that are not available in the official versions.
-What are the risks of downloading Instagram 6.17 0 APK file?
-Some of the risks are that you may download malicious or infected files that can harm your device or compromise your privacy, violate the terms and conditions of the app developers or Google Play Store by installing unauthorized or modified apps, encounter bugs, errors, or compatibility issues that can affect the performance or functionality of the app, and need to update the app manually or frequently to get the latest features and fixes.
-How can I uninstall Instagram 6.17 0 APK file from my device?
-You can uninstall Instagram 6.17 0 APK file from your device by following these steps:
-
-Go to Settings > Apps > Instagram.
-Tap on Uninstall.
-A confirmation message will appear. Tap OK to proceed.
-
- How can I contact the Instagram support team if I have any issues with the app?
-You can contact the Instagram support team by visiting their [Help Center] or by sending them an email at [support@instagram.com]. You can also report any bugs, errors, or feedback through the app by going to Settings > Help > Report a Problem.
- 401be4b1e0How to Download APK Snapchat for Android Devices
-Do you love using Snapchat to share your snaps and stories with your friends and family? Do you want to enjoy more features and options than the official app offers? If yes, then you might want to download APK Snapchat for your Android device.
-download apk snapchat Download Zip 🔗 https://gohhs.com/2uPum4
APK Snapchat is a modified version of the original Snapchat app that allows you to access more filters, stickers, emojis, lenses, and other fun stuff. You can also save snaps and stories without notifying the sender, view deleted messages, spoof your location, and more.
-In this article, we will show you how to download APK Snapchat for your Android device from a trusted source called Uptodown. We will also guide you through the installation process and give you some tips on how to use APK Snapchat safely and effectively.
-What is Snapchat?
-Snapchat is a popular social media app that lets you send and receive photos and videos (called snaps) with your contacts. The interesting thing about Snapchat is that all the content disappears after it's viewed. You can also create stories that last for 24 hours and showcase your daily activities.
-Snapchat also has a lot of features that make it fun and engaging. You can use filters, stickers, emojis, lenses, bitmojis, and other effects to spice up your snaps. You can also chat with your friends, make video calls, play games, watch shows, discover news, and more.
-Why Download APK Snapchat?
-APK Snapchat is a modified version of the original Snapchat app that gives you more control and freedom over your snaps. With APK Snapchat, you can:
-
-Access more filters, stickers, emojis, lenses, bitmojis, and other effects that are not available in the official app.
- Save snaps and stories without notifying the sender or the app. You can also view deleted messages and replay snaps as many times as you want.
-Spoof your location and use a fake GPS to access geo-restricted filters and content.
-Disable ads, screenshots, typing notifications, and other annoyances.
-Customize your app's appearance, layout, font, color, and more.
-
-However, downloading APK Snapchat also comes with some risks. For one thing, APK Snapchat is not authorized by Snapchat Inc., the official developer of the app. This means that APK Snapchat may not be safe, secure, or compatible with your device. It may also contain malware, viruses, or spyware that can harm your device or compromise your privacy.
-Another risk of downloading APK Snapchat is that you may get banned from using the official app. Snapchat has a strict policy against using third-party apps or modifications that violate its terms of service. If Snapchat detects that you are using APK Snapchat, it may lock your account temporarily or permanently.
-Therefore, if you decide to download APK Snapchat, you should do so at your own risk and discretion. You should also make sure that you download APK Snapchat from a trusted source that offers the latest and safest version of the app.
-download snapchat apk for android
-How to Download APK Snapchat from Uptodown
-One of the best sources to download APK Snapchat is Uptodown. Uptodown is a reputable website that offers millions of free and legal apps for Android devices. Uptodown also scans all the apps for viruses and malware before uploading them to its platform. You can also read user reviews, ratings, and comments about the apps on Uptodown.
-To download APK Snapchat from Uptodown, follow these simple steps:
-
-Go to Uptodown's website on your browser.
-Type "Snapchat" in the search bar and hit enter.
-Select the app named "Snapchat" with the yellow icon and the description "Share your day using pictures".
-Scroll down to the bottom of the page and click on "Download".
-Choose the version of APK Snapchat that you want to download. You can see the file size, date, and changelog of each version.
-Click on "Download" again and wait for the file to be downloaded to your device.
-
-You can also use this comparison table to see the differences between APK Snapchat and official Snapchat:
- | Feature | APK Snapchat | Official Snapchat | |---------|--------------|-------------------| | Filters | More | Less | | Stickers | More | Less | | Emojis | More | Less | | Lenses | More | Less | | Bitmojis | More | Less | | Save snaps | Yes | No | | View deleted messages | Yes | No | | Replay snaps | Unlimited | Limited | | Spoof location | Yes | No | | Disable ads | Yes | No | | Disable screenshots | Yes | No | | Disable typing notifications | Yes | No | | Customize appearance | Yes | No | | Safety | Low | High | | Compatibility | Low | High | How to Install APK Snapchat on Your Android Device
- After you download APK Snapchat from Uptodown, you need to install it on your Android device. To do that, follow these steps:
-
-Locate the downloaded file on your device's file manager or downloads folder.
-Tap on the file and select "Install".
-If you see a warning message that says "For your security, your phone is not allowed to install unknown apps from this source", you need to enable unknown sources on your device. To do that, go to Settings > Security > Unknown sources and toggle it on.
-Go back to the file and tap on "Install" again.
-Wait for the installation process to finish.
-You can now launch APK Snapchat from your app drawer or home screen.
-
- How to Use APK Snapchat on Your Android Device
- Using APK Snapchat on your Android device is similar to using the official app. You can sign in with your existing account or create a new one. You can also sync your contacts and add friends from other social media platforms.
- To use APK Snapchat on your Android device, you can follow these tips:
-
-To access more filters, stickers, emojis, lenses, bitmojis, and other effects, swipe left or right on the camera screen. You can also tap on the smiley face icon at the bottom of the screen to see more options. To save snaps and stories without notifying the sender or the app, tap on the download icon at the bottom of the screen. You can also long-press on the snap or story to save it.
-To view deleted messages and replay snaps, go to the chat screen and tap on the message or snap that you want to see. You can also swipe up on the screen to see more options.
-To spoof your location and use a fake GPS, go to Settings > Location > Fake GPS and toggle it on. You can also select the location that you want to use from the map.
-To disable ads, screenshots, typing notifications, and other annoyances, go to Settings > Privacy > Advanced and toggle off the options that you don't want.
-To customize your app's appearance, layout, font, color, and more, go to Settings > Appearance and choose the options that you like.
-
- Conclusion
- APK Snapchat is a great way to enjoy more features and options than the official app. You can access more filters, stickers, emojis, lenses, bitmojis, and other effects. You can also save snaps and stories without notifying the sender, view deleted messages, spoof your location, and more.
- However, APK Snapchat also comes with some risks. It is not authorized by Snapchat Inc., the official developer of the app. It may not be safe, secure, or compatible with your device. It may also contain malware, viruses, or spyware that can harm your device or compromise your privacy. Moreover, you may get banned from using the official app if Snapchat detects that you are using APK Snapchat.
- Therefore, if you decide to download APK Snapchat, you should do so at your own risk and discretion. You should also make sure that you download APK Snapchat from a trusted source like Uptodown that offers the latest and safest version of the app.
- If you are ready to download APK Snapchat for your Android device, click on the link below and follow the instructions. Have fun snapping!
- Download APK Snapchat from Uptodown
- FAQs
- Here are some frequently asked questions about APK Snapchat and their answers:
-
-What is APK Snapchat? APK Snapchat is a modified version of the original Snapchat app that allows you to access more features and options than the official app.
-What are the benefits of downloading APK Snapchat? With APK Snapchat, you can access more filters, stickers, emojis, lenses, bitmojis, and other effects. You can also save snaps and stories without notifying the sender, view deleted messages, spoof your location, and more.
-What are the risks of downloading APK Snapchat? APK Snapchat is not authorized by Snapchat Inc., the official developer of the app. It may not be safe, secure, or compatible with your device. It may also contain malware, viruses, or spyware that can harm your device or compromise your privacy. Moreover, you may get banned from using the official app if Snapchat detects that you are using APK Snapchat.
-Where can I download APK Snapchat? You can download APK Snapchat from a trusted source like Uptodown that offers the latest and safest version of the app.
-How can I install APK Snapchat on my Android device? You need to enable unknown sources on your device and then tap on the downloaded file to install it.
- 197e85843dImage to Story
- Upload an image, get a story made by Llama2 !
- """
- )
- with gr.Row():
- with gr.Column():
- image_in = gr.Image(label="Image input", type="filepath", elem_id="image-in", height=420)
- audience = gr.Radio(label="Target Audience", choices=["Children", "Adult"], value="Children")
- submit_btn = gr.Button('Tell me a story')
- with gr.Column():
- #caption = gr.Textbox(label="Generated Caption")
- story = gr.Textbox(label="generated Story", elem_id="story", height=420)
-
- with gr.Group(elem_id="share-btn-container", visible=False) as share_group:
- community_icon = gr.HTML(community_icon_html)
- loading_icon = gr.HTML(loading_icon_html)
- share_button = gr.Button("Share with community", elem_id="share-btn")
-
- gr.Examples(examples=[["./examples/crabby.png", "Children"],["./examples/hopper.jpeg", "Adult"]],
- fn=infer,
- inputs=[image_in, audience],
- outputs=[story, share_group],
- cache_examples=True
- )
-
- submit_btn.click(fn=infer, inputs=[image_in, audience], outputs=[story, share_group])
- share_button.click(None, [], [], _js=share_js)
-
-demo.queue(max_size=12).launch()
diff --git a/spaces/figsfidds/moody_nana_classifier/app.py b/spaces/figsfidds/moody_nana_classifier/app.py
deleted file mode 100644
index db767db9630ad89d3a886b9c895fcc8096e6005e..0000000000000000000000000000000000000000
--- a/spaces/figsfidds/moody_nana_classifier/app.py
+++ /dev/null
@@ -1,33 +0,0 @@
-from fastai.vision.all import *
-import gradio as gr
-
-learn = load_learner('export.pkl')
-
-labels = learn.dls.vocab
-def predict(img_fnm):
- img = PILImage.create(img_fnm)
- pred, pred_idx, probs = learn.predict(img)
- return {label: float(prob) for label, prob in zip(labels, probs)}
-
-title = "Moody-nana Classifier"
-description = "Classifies an image as either happy, angry, or banana."
-examples = [
- "examples/angry-banana.jpg",
- "examples/angry.jpg",
- "examples/happy.jpg",
- "examples/banana.jpg"
-]
-interpretation = "default"
-enable_queue = True
-
-intf = gr.Interface(
- fn=predict,
- inputs=gr.components.Image(shape=(512, 512)),
- outputs=gr.components.Label(),
- title=title,
- description=description,
- examples=examples,
- interpretation=interpretation
-)
-
-intf.launch()
diff --git a/spaces/fiz2/cloudy/greeting.md b/spaces/fiz2/cloudy/greeting.md
deleted file mode 100644
index 53c7c4bc1e4e1ad87e25f400b685536ea4b8a816..0000000000000000000000000000000000000000
--- a/spaces/fiz2/cloudy/greeting.md
+++ /dev/null
@@ -1,2 +0,0 @@
-thank you antigonus <3
-pass: _cosmos_
\ No newline at end of file
diff --git a/spaces/flynster/FeinbergQuizNotes/question_generation/utils.py b/spaces/flynster/FeinbergQuizNotes/question_generation/utils.py
deleted file mode 100644
index 37fb834f59cc9a031fee5e36cc2e0249f932ba55..0000000000000000000000000000000000000000
--- a/spaces/flynster/FeinbergQuizNotes/question_generation/utils.py
+++ /dev/null
@@ -1,49 +0,0 @@
-from typing import Callable, Dict, Iterable, List
-from torch import nn
-
-# these functions are taken from transformers repo
-def grad_status(model: nn.Module) -> Iterable:
- return (par.requires_grad for par in model.parameters())
-
-def freeze_params(model: nn.Module):
- for par in model.parameters():
- par.requires_grad = False
-
-def freeze_embeds(model: nn.Module):
- """Freeze token embeddings and positional embeddings for bart, just token embeddings for t5."""
- try:
- freeze_params(model.model.shared)
- for d in [model.model.encoder, model.model.decoder]:
- freeze_params(d.embed_positions)
- freeze_params(d.embed_tokens)
- except AttributeError:
- freeze_params(model.shared)
- for d in [model.encoder, model.decoder]:
- freeze_params(d.embed_tokens)
-
-def assert_not_all_frozen(model):
- model_grads: List[bool] = list(grad_status(model))
- npars = len(model_grads)
- assert any(model_grads), f"none of {npars} weights require grad"
-
-def label_smoothed_nll_loss(lprobs, target, epsilon, ignore_index=-100):
- """From fairseq"""
- if target.dim() == lprobs.dim() - 1:
- target = target.unsqueeze(-1)
- nll_loss = -lprobs.gather(dim=-1, index=target)
- smooth_loss = -lprobs.sum(dim=-1, keepdim=True)
- if ignore_index is not None:
- pad_mask = target.eq(ignore_index)
- nll_loss.masked_fill_(pad_mask, 0.0)
- smooth_loss.masked_fill_(pad_mask, 0.0)
- bs = pad_mask.long().sum()
- else:
- nll_loss = nll_loss.squeeze(-1)
- smooth_loss = smooth_loss.squeeze(-1)
- bs = lprobs.shape[0]
-
- nll_loss = nll_loss.sum() # mean()? Scared to break other math.
- smooth_loss = smooth_loss.sum()
- eps_i = epsilon / lprobs.size(-1)
- loss = (1.0 - epsilon) * nll_loss + eps_i * smooth_loss
- return loss / bs, nll_loss / bs
\ No newline at end of file
diff --git a/spaces/frapochetti/blurry-faces/kornia_benchmark.py b/spaces/frapochetti/blurry-faces/kornia_benchmark.py
deleted file mode 100644
index 9317c8517e60b3b72f6409566db6be705f334aa6..0000000000000000000000000000000000000000
--- a/spaces/frapochetti/blurry-faces/kornia_benchmark.py
+++ /dev/null
@@ -1,63 +0,0 @@
-import cv2
-import gradio as gr
-from PIL import Image
-import numpy as np
-import torch
-import kornia as K
-from kornia.contrib import FaceDetector, FaceDetectorResult
-import time
-
-device = torch.device('cpu')
-face_detection = FaceDetector().to(device)
-
-def scale_image(img: np.ndarray, size: int) -> np.ndarray:
- h, w = img.shape[:2]
- scale = 1. * size / w
- return cv2.resize(img, (int(w * scale), int(h * scale)))
-
-
-def apply_blur_face(img: torch.Tensor, img_vis: np.ndarray, det: FaceDetectorResult):
- # crop the face
- x1, y1 = det.xmin.int(), det.ymin.int()
- x2, y2 = det.xmax.int(), det.ymax.int()
- roi = img[..., y1:y2, x1:x2]
- #print(roi.shape)
- if roi.shape[-1]==0 or roi.shape[-2]==0:
- return
-
- # apply blurring and put back to the visualisation image
- roi = K.filters.gaussian_blur2d(roi, (21, 21), (100., 100.))
- roi = K.color.rgb_to_bgr(roi)
- img_vis[y1:y2, x1:x2] = K.tensor_to_image(roi)
-
-
-def run(image):
- image.thumbnail((1280, 1280))
- img_raw = np.array(image)
-
- # preprocess
- img = K.image_to_tensor(img_raw, keepdim=False).to(device)
- img = K.color.bgr_to_rgb(img.float())
-
- with torch.no_grad():
- dets = face_detection(img)
- dets = [FaceDetectorResult(o) for o in dets]
-
- img_vis = img_raw.copy()
-
- for b in dets:
- if b.score < 0.5:
- continue
-
- apply_blur_face(img, img_vis, b)
-
- return Image.fromarray(img_vis)
-
-if __name__ == "__main__":
-
- start = time.time()
- for _ in range(100):
- image = Image.open("./images/crowd.jpeg")
- _ = run(image)
-
- print('It took', (time.time()-start)/100, 'seconds.')
\ No newline at end of file
diff --git a/spaces/g4f/freegpt-webui/g4f/Provider/Providers/You.py b/spaces/g4f/freegpt-webui/g4f/Provider/Providers/You.py
deleted file mode 100644
index 02a2774ce62bae33612a73272d584dc2acaf3eb0..0000000000000000000000000000000000000000
--- a/spaces/g4f/freegpt-webui/g4f/Provider/Providers/You.py
+++ /dev/null
@@ -1,24 +0,0 @@
-import os
-import json
-import time
-import subprocess
-
-from ...typing import sha256, Dict, get_type_hints
-
-url = 'https://you.com'
-model = 'gpt-3.5-turbo'
-supports_stream = True
-needs_auth = False
-
-def _create_completion(model: str, messages: list, stream: bool, **kwargs):
-
- path = os.path.dirname(os.path.realpath(__file__))
- config = json.dumps({
- 'messages': messages}, separators=(',', ':'))
-
- cmd = ['python3', f'{path}/helpers/you.py', config]
-
- p = subprocess.Popen(cmd, stdout=subprocess.PIPE, stderr=subprocess.STDOUT)
-
- for line in iter(p.stdout.readline, b''):
- yield line.decode('utf-8') #[:-1]
\ No newline at end of file
diff --git a/spaces/gauravahuja/nlpconnect-vit-gpt2-image-captioning/README.md b/spaces/gauravahuja/nlpconnect-vit-gpt2-image-captioning/README.md
deleted file mode 100644
index 9fa34c47d9d0ce4ce93f35db1f9e1b593726619b..0000000000000000000000000000000000000000
--- a/spaces/gauravahuja/nlpconnect-vit-gpt2-image-captioning/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Nlpconnect Vit Gpt2 Image Captioning
-emoji: 📉
-colorFrom: purple
-colorTo: yellow
-sdk: gradio
-sdk_version: 3.18.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/gaurxvreddy/Xtinguish/utils.py b/spaces/gaurxvreddy/Xtinguish/utils.py
deleted file mode 100644
index fd2c30eca632f02e0701bfa8689bcb2d5333b5cf..0000000000000000000000000000000000000000
--- a/spaces/gaurxvreddy/Xtinguish/utils.py
+++ /dev/null
@@ -1,39 +0,0 @@
-import cv2
-import numpy as np
-
-def pixel_counter(img, lower_limit, upper_limit):
- img = np.array(img)
-
- rimg = cv2.resize(img, (1000, 600))
- blur = cv2.GaussianBlur(rimg, (15, 15), 0)
- hsv = cv2.cvtColor(blur, cv2.COLOR_RGB2HSV)
-
- lower = np.array(lower_limit, dtype='uint8') # Fire Color Ranges
- upper = np.array(upper_limit, dtype='uint8') # Fire Color Ranges
-
- # Counting "fire-like" pixels in the image
- mask = cv2.inRange(hsv, lower, upper)
- count = cv2.countNonZero(mask)
-
- return count
-
-def find_intensity(image, time):
- fire_pixels = pixel_counter(image, [0, 100, 110], [70, 250, 255])
-
- if (time == 'Night'):
- value = fire_pixels/150000
- if value>=1:
- return "High Intensity", 1
- elif value<0.01:
- return "Low Intensity", value
- else:
- return "Medium Intensity", value
-
- else:
- value = fire_pixels/80000
- if value>=1:
- return "High Intensity", 1
- elif value<0.25:
- return "Low Intensity", value
- else:
- return "Medium Intensity", value
\ No newline at end of file
diff --git a/spaces/geniusguy777/Face_Recognition/README.md b/spaces/geniusguy777/Face_Recognition/README.md
deleted file mode 100644
index 6dee63dbffaeb0c6a9b8a78ba4e1311144f9c00e..0000000000000000000000000000000000000000
--- a/spaces/geniusguy777/Face_Recognition/README.md
+++ /dev/null
@@ -1,14 +0,0 @@
----
-title: FaceRecognition
-emoji: 🚀
-colorFrom: red
-colorTo: red
-sdk: gradio
-sdk_version: 3.1.1
-app_file: app.py
-pinned: false
-license: gpl-3.0
-duplicated_from: Zengyf-CVer/FaceRecognition
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/georgefen/Face-Landmark-ControlNet/annotator/uniformer/mmcv/cnn/bricks/conv.py b/spaces/georgefen/Face-Landmark-ControlNet/annotator/uniformer/mmcv/cnn/bricks/conv.py
deleted file mode 100644
index cf54491997a48ac3e7fadc4183ab7bf3e831024c..0000000000000000000000000000000000000000
--- a/spaces/georgefen/Face-Landmark-ControlNet/annotator/uniformer/mmcv/cnn/bricks/conv.py
+++ /dev/null
@@ -1,44 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-from torch import nn
-
-from .registry import CONV_LAYERS
-
-CONV_LAYERS.register_module('Conv1d', module=nn.Conv1d)
-CONV_LAYERS.register_module('Conv2d', module=nn.Conv2d)
-CONV_LAYERS.register_module('Conv3d', module=nn.Conv3d)
-CONV_LAYERS.register_module('Conv', module=nn.Conv2d)
-
-
-def build_conv_layer(cfg, *args, **kwargs):
- """Build convolution layer.
-
- Args:
- cfg (None or dict): The conv layer config, which should contain:
- - type (str): Layer type.
- - layer args: Args needed to instantiate an conv layer.
- args (argument list): Arguments passed to the `__init__`
- method of the corresponding conv layer.
- kwargs (keyword arguments): Keyword arguments passed to the `__init__`
- method of the corresponding conv layer.
-
- Returns:
- nn.Module: Created conv layer.
- """
- if cfg is None:
- cfg_ = dict(type='Conv2d')
- else:
- if not isinstance(cfg, dict):
- raise TypeError('cfg must be a dict')
- if 'type' not in cfg:
- raise KeyError('the cfg dict must contain the key "type"')
- cfg_ = cfg.copy()
-
- layer_type = cfg_.pop('type')
- if layer_type not in CONV_LAYERS:
- raise KeyError(f'Unrecognized norm type {layer_type}')
- else:
- conv_layer = CONV_LAYERS.get(layer_type)
-
- layer = conv_layer(*args, **kwargs, **cfg_)
-
- return layer
diff --git a/spaces/georgefen/Face-Landmark-ControlNet/annotator/uniformer/mmcv/ops/__init__.py b/spaces/georgefen/Face-Landmark-ControlNet/annotator/uniformer/mmcv/ops/__init__.py
deleted file mode 100644
index 999e090a458ee148ceca0649f1e3806a40e909bd..0000000000000000000000000000000000000000
--- a/spaces/georgefen/Face-Landmark-ControlNet/annotator/uniformer/mmcv/ops/__init__.py
+++ /dev/null
@@ -1,81 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-from .assign_score_withk import assign_score_withk
-from .ball_query import ball_query
-from .bbox import bbox_overlaps
-from .border_align import BorderAlign, border_align
-from .box_iou_rotated import box_iou_rotated
-from .carafe import CARAFE, CARAFENaive, CARAFEPack, carafe, carafe_naive
-from .cc_attention import CrissCrossAttention
-from .contour_expand import contour_expand
-from .corner_pool import CornerPool
-from .correlation import Correlation
-from .deform_conv import DeformConv2d, DeformConv2dPack, deform_conv2d
-from .deform_roi_pool import (DeformRoIPool, DeformRoIPoolPack,
- ModulatedDeformRoIPoolPack, deform_roi_pool)
-from .deprecated_wrappers import Conv2d_deprecated as Conv2d
-from .deprecated_wrappers import ConvTranspose2d_deprecated as ConvTranspose2d
-from .deprecated_wrappers import Linear_deprecated as Linear
-from .deprecated_wrappers import MaxPool2d_deprecated as MaxPool2d
-from .focal_loss import (SigmoidFocalLoss, SoftmaxFocalLoss,
- sigmoid_focal_loss, softmax_focal_loss)
-from .furthest_point_sample import (furthest_point_sample,
- furthest_point_sample_with_dist)
-from .fused_bias_leakyrelu import FusedBiasLeakyReLU, fused_bias_leakyrelu
-from .gather_points import gather_points
-from .group_points import GroupAll, QueryAndGroup, grouping_operation
-from .info import (get_compiler_version, get_compiling_cuda_version,
- get_onnxruntime_op_path)
-from .iou3d import boxes_iou_bev, nms_bev, nms_normal_bev
-from .knn import knn
-from .masked_conv import MaskedConv2d, masked_conv2d
-from .modulated_deform_conv import (ModulatedDeformConv2d,
- ModulatedDeformConv2dPack,
- modulated_deform_conv2d)
-from .multi_scale_deform_attn import MultiScaleDeformableAttention
-from .nms import batched_nms, nms, nms_match, nms_rotated, soft_nms
-from .pixel_group import pixel_group
-from .point_sample import (SimpleRoIAlign, point_sample,
- rel_roi_point_to_rel_img_point)
-from .points_in_boxes import (points_in_boxes_all, points_in_boxes_cpu,
- points_in_boxes_part)
-from .points_sampler import PointsSampler
-from .psa_mask import PSAMask
-from .roi_align import RoIAlign, roi_align
-from .roi_align_rotated import RoIAlignRotated, roi_align_rotated
-from .roi_pool import RoIPool, roi_pool
-from .roiaware_pool3d import RoIAwarePool3d
-from .roipoint_pool3d import RoIPointPool3d
-from .saconv import SAConv2d
-from .scatter_points import DynamicScatter, dynamic_scatter
-from .sync_bn import SyncBatchNorm
-from .three_interpolate import three_interpolate
-from .three_nn import three_nn
-from .tin_shift import TINShift, tin_shift
-from .upfirdn2d import upfirdn2d
-from .voxelize import Voxelization, voxelization
-
-__all__ = [
- 'bbox_overlaps', 'CARAFE', 'CARAFENaive', 'CARAFEPack', 'carafe',
- 'carafe_naive', 'CornerPool', 'DeformConv2d', 'DeformConv2dPack',
- 'deform_conv2d', 'DeformRoIPool', 'DeformRoIPoolPack',
- 'ModulatedDeformRoIPoolPack', 'deform_roi_pool', 'SigmoidFocalLoss',
- 'SoftmaxFocalLoss', 'sigmoid_focal_loss', 'softmax_focal_loss',
- 'get_compiler_version', 'get_compiling_cuda_version',
- 'get_onnxruntime_op_path', 'MaskedConv2d', 'masked_conv2d',
- 'ModulatedDeformConv2d', 'ModulatedDeformConv2dPack',
- 'modulated_deform_conv2d', 'batched_nms', 'nms', 'soft_nms', 'nms_match',
- 'RoIAlign', 'roi_align', 'RoIPool', 'roi_pool', 'SyncBatchNorm', 'Conv2d',
- 'ConvTranspose2d', 'Linear', 'MaxPool2d', 'CrissCrossAttention', 'PSAMask',
- 'point_sample', 'rel_roi_point_to_rel_img_point', 'SimpleRoIAlign',
- 'SAConv2d', 'TINShift', 'tin_shift', 'assign_score_withk',
- 'box_iou_rotated', 'RoIPointPool3d', 'nms_rotated', 'knn', 'ball_query',
- 'upfirdn2d', 'FusedBiasLeakyReLU', 'fused_bias_leakyrelu',
- 'RoIAlignRotated', 'roi_align_rotated', 'pixel_group', 'QueryAndGroup',
- 'GroupAll', 'grouping_operation', 'contour_expand', 'three_nn',
- 'three_interpolate', 'MultiScaleDeformableAttention', 'BorderAlign',
- 'border_align', 'gather_points', 'furthest_point_sample',
- 'furthest_point_sample_with_dist', 'PointsSampler', 'Correlation',
- 'boxes_iou_bev', 'nms_bev', 'nms_normal_bev', 'Voxelization',
- 'voxelization', 'dynamic_scatter', 'DynamicScatter', 'RoIAwarePool3d',
- 'points_in_boxes_part', 'points_in_boxes_cpu', 'points_in_boxes_all'
-]
diff --git a/spaces/gojiteji/SDTextTransmitter/README.md b/spaces/gojiteji/SDTextTransmitter/README.md
deleted file mode 100644
index 92583628b040094f122a256450c1556ced34a389..0000000000000000000000000000000000000000
--- a/spaces/gojiteji/SDTextTransmitter/README.md
+++ /dev/null
@@ -1,14 +0,0 @@
----
-title: 📡SDTextTransmitter📡
-emoji: 📡
-colorFrom: purple
-colorTo: purple
-sdk: gradio
-sdk_version: 3.28.0
-app_file: app.py
-pinned: false
-tags:
-- jax-diffusers-event
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/gotiQspiryo/whisper-ui/examples/Astute Graphics Plugins Bundle 1.2.2 Crack How to Install and Activate the Best Tools for Vector Sketching.md b/spaces/gotiQspiryo/whisper-ui/examples/Astute Graphics Plugins Bundle 1.2.2 Crack How to Install and Activate the Best Tools for Vector Sketching.md
deleted file mode 100644
index b5bf29a598467f9c9927e84f884e9bd63b7de29f..0000000000000000000000000000000000000000
--- a/spaces/gotiQspiryo/whisper-ui/examples/Astute Graphics Plugins Bundle 1.2.2 Crack How to Install and Activate the Best Tools for Vector Sketching.md
+++ /dev/null
@@ -1,6 +0,0 @@
-Astute Graphics Plugins Bundle 1.2.2 Crack Download File ❤❤❤ https://urlgoal.com/2uyMY1
diff --git a/spaces/gotiQspiryo/whisper-ui/examples/Mastercam X8 Full Crack 19 How to Download and Install the Best CADCAM Software.md b/spaces/gotiQspiryo/whisper-ui/examples/Mastercam X8 Full Crack 19 How to Download and Install the Best CADCAM Software.md
deleted file mode 100644
index 1375660716848352116b2b2ea0d7430d982efdc3..0000000000000000000000000000000000000000
--- a/spaces/gotiQspiryo/whisper-ui/examples/Mastercam X8 Full Crack 19 How to Download and Install the Best CADCAM Software.md
+++ /dev/null
@@ -1,6 +0,0 @@
-mastercam x8 full crack 19 Download Zip ★★★ https://urlgoal.com/2uyM3o
diff --git a/spaces/gptjx/02/Dockerfile b/spaces/gptjx/02/Dockerfile
deleted file mode 100644
index 8cbd335b09b1d1975bfd83a053b5fcaf398147ea..0000000000000000000000000000000000000000
--- a/spaces/gptjx/02/Dockerfile
+++ /dev/null
@@ -1,14 +0,0 @@
-FROM python:3.9 as builder
-RUN apt-get update && apt-get install -y build-essential
-COPY requirements.txt .
-RUN pip install --user -r requirements.txt
-
-FROM python:3.9
-MAINTAINER iskoldt
-COPY --from=builder /root/.local /root/.local
-ENV PATH=/root/.local/bin:$PATH
-COPY . /app
-WORKDIR /app
-ENV my_api_key empty
-ENV dockerrun yes
-CMD ["python3", "-u", "ChuanhuChatbot.py", "2>&1", "|", "tee", "/var/log/application.log"]
diff --git a/spaces/gradio/HuBERT/examples/conv_seq2seq/README.md b/spaces/gradio/HuBERT/examples/conv_seq2seq/README.md
deleted file mode 100644
index 95fe7e7909a77ee0e50fe31d4b8be38daa8f3be7..0000000000000000000000000000000000000000
--- a/spaces/gradio/HuBERT/examples/conv_seq2seq/README.md
+++ /dev/null
@@ -1,25 +0,0 @@
-# Convolutional Sequence to Sequence Learning (Gehring et al., 2017)
-
-## Pre-trained models
-
-Description | Dataset | Model | Test set(s)
----|---|---|---
-Convolutional {milestone.title} """
- if desc['status'] not in all_status:
- all_status.append(desc['status'])
- df.append(dict(Task=task_name,
- Start=desc['start date'],
- Finish=milestone.due_on.strftime('%Y-%m-%d'),
- Resource=desc['status'],
- Description=desc['leader']))
- except:
- num_milestones -= 1
- task_name = f"""{milestone.title} """
- bad_milestones.append(task_name)
- name2num_milestones[name] = num_milestones
-
-copy_df = copy.deepcopy(df)
-colors = {'not started': 'rgb(217, 217, 217)',
- 'in progress': 'rgb(147, 196, 125)',
- 'high priority - on track': 'rgb(234, 153, 153)',
- 'high priority - help needed': 'rgb(255, 0, 0)',
- 'completed': 'rgb(56, 118, 29)'}
-
-if len(all_status) == 0:
- task_name = "None"
-
-for key in colors.keys():
- if key not in all_status:
- copy_df.append(dict(Task=task_name,
- Start='2023-04-09',
- Finish='2023-04-09',
- Resource=key))
-
-fig = ff.create_gantt(copy_df, colors=colors,
- index_col='Resource',
- show_colorbar=True,
- show_hover_fill=True,
- group_tasks=True,
- title="BigCode planning")
-
-fig.update_xaxes(ticks= "outside",
- ticklabelmode= "period",
- tickformat="%b",
- tickcolor= "black",
- ticklen=10,
- range=[datetime.datetime(2023, 8, 25),
- datetime.datetime(2023, 11, 16)],
- minor=dict(
- ticklen=4,
- dtick=7*24*60*60*1000,
- tick0="2023-09-01",
- griddash='dot',
- gridcolor='white')
- )
-
-fig.update_layout(margin=go.layout.Margin(l=250))
-fig.layout.xaxis.rangeselector = None # remove range selector on top
-
-# Add today line
-fig.add_vline(x=datetime.datetime.now().strftime('%Y-%m-%d'), line_width=3, line_dash="dash", line_color="black")
-fig.add_annotation({
- "x": datetime.datetime.now().strftime('%Y-%m-%d'),
- "y": fig.layout.yaxis['range'][1],
- "yshift": 10,
- "text": "Today",
- "showarrow": False,
- })
-
-# Add The Stack 1.2
-fig.add_vline(x='2023-03-05', line_width=3, line_dash="dash", line_color="red")
-fig.add_annotation({
- "x": '2023-03-05',
- "y": fig.layout.yaxis['range'][1],
- "yshift": 10,
- "text": "The Stack 1.2",
- "showarrow": False,
- })
-
-# Add PII
-fig.add_vline(x='2023-10-01', line_width=3, line_dash="dash", line_color="red")
-fig.add_annotation({
- "x": '2023-10-01',
- "y": fig.layout.yaxis['range'][1],
- "yshift": 10,
- "text": "Model training",
- "showarrow": False,
- })
-
-# Add release line
-fig.add_vline(x='2023-10-31', line_width=3, line_dash="dash", line_color="red")
-fig.add_annotation({
- "x": '2023-10-31',
- "y": fig.layout.yaxis['range'][1],
- "yshift": 10,
- "text": "Model release",
- "showarrow": False,
- })
-
-# Add point of contacts
-fig.add_annotation({
- "x": "2023-08-25",
- "y": fig.layout.yaxis['range'][1],
- "yshift": 10,
- "xanchor": "left",
- "text": "Contact",
- "showarrow": False})
-for i, entry in enumerate(df[::-1]):
- fig.add_annotation(x='2023-08-25', y=i,
- text=entry['Description'],
- showarrow=False,
- xanchor="left",
- xref="x")
-
-# Add working group annotations
-fig.add_hline(y=-0.5, line_width=1, line_color="grey")
-height = -0.5
-for name, _ in name2repo[::-1]:
- if name2num_milestones[name] > 0:
- fig.add_annotation(x='2023-11-13', y=height + name2num_milestones[name]/2,
- text=name,
- showarrow=False,
- align="center",
- textangle=-90)
- height += name2num_milestones[name]
- fig.add_hline(y=height, line_width=1, line_color="grey")
-
-st.plotly_chart(fig, use_container_width=True)
-
-if len(bad_milestones):
- with st.expander("Bad Milestones"):
- for bms in bad_milestones:
- st.markdown(bms + "\n\n", unsafe_allow_html=True)
-
-if st.button("Refresh"):
- st.experimental_rerun()
\ No newline at end of file
diff --git a/spaces/hrnph/rvc-models/vc_infer_pipeline.py b/spaces/hrnph/rvc-models/vc_infer_pipeline.py
deleted file mode 100644
index c26d45068f9b6bf2b194b13c3c89f8a06347c124..0000000000000000000000000000000000000000
--- a/spaces/hrnph/rvc-models/vc_infer_pipeline.py
+++ /dev/null
@@ -1,306 +0,0 @@
-import numpy as np, parselmouth, torch, pdb
-from time import time as ttime
-import torch.nn.functional as F
-from config import x_pad, x_query, x_center, x_max
-import scipy.signal as signal
-import pyworld, os, traceback, faiss
-from scipy import signal
-
-bh, ah = signal.butter(N=5, Wn=48, btype="high", fs=16000)
-
-
-class VC(object):
- def __init__(self, tgt_sr, device, is_half):
- self.sr = 16000 # hubert输入采样率
- self.window = 160 # 每帧点数
- self.t_pad = self.sr * x_pad # 每条前后pad时间
- self.t_pad_tgt = tgt_sr * x_pad
- self.t_pad2 = self.t_pad * 2
- self.t_query = self.sr * x_query # 查询切点前后查询时间
- self.t_center = self.sr * x_center # 查询切点位置
- self.t_max = self.sr * x_max # 免查询时长阈值
- self.device = device
- self.is_half = is_half
-
- def get_f0(self, x, p_len, f0_up_key, f0_method, inp_f0=None):
- time_step = self.window / self.sr * 1000
- f0_min = 50
- f0_max = 1100
- f0_mel_min = 1127 * np.log(1 + f0_min / 700)
- f0_mel_max = 1127 * np.log(1 + f0_max / 700)
- if f0_method == "pm":
- f0 = (
- parselmouth.Sound(x, self.sr)
- .to_pitch_ac(
- time_step=time_step / 1000,
- voicing_threshold=0.6,
- pitch_floor=f0_min,
- pitch_ceiling=f0_max,
- )
- .selected_array["frequency"]
- )
- pad_size = (p_len - len(f0) + 1) // 2
- if pad_size > 0 or p_len - len(f0) - pad_size > 0:
- f0 = np.pad(
- f0, [[pad_size, p_len - len(f0) - pad_size]], mode="constant"
- )
- elif f0_method == "harvest":
- f0, t = pyworld.harvest(
- x.astype(np.double),
- fs=self.sr,
- f0_ceil=f0_max,
- f0_floor=f0_min,
- frame_period=10,
- )
- f0 = pyworld.stonemask(x.astype(np.double), f0, t, self.sr)
- f0 = signal.medfilt(f0, 3)
- f0 *= pow(2, f0_up_key / 12)
- # with open("test.txt","w")as f:f.write("\n".join([str(i)for i in f0.tolist()]))
- tf0 = self.sr // self.window # 每秒f0点数
- if inp_f0 is not None:
- delta_t = np.round(
- (inp_f0[:, 0].max() - inp_f0[:, 0].min()) * tf0 + 1
- ).astype("int16")
- replace_f0 = np.interp(
- list(range(delta_t)), inp_f0[:, 0] * 100, inp_f0[:, 1]
- )
- shape = f0[x_pad * tf0 : x_pad * tf0 + len(replace_f0)].shape[0]
- f0[x_pad * tf0 : x_pad * tf0 + len(replace_f0)] = replace_f0[:shape]
- # with open("test_opt.txt","w")as f:f.write("\n".join([str(i)for i in f0.tolist()]))
- f0bak = f0.copy()
- f0_mel = 1127 * np.log(1 + f0 / 700)
- f0_mel[f0_mel > 0] = (f0_mel[f0_mel > 0] - f0_mel_min) * 254 / (
- f0_mel_max - f0_mel_min
- ) + 1
- f0_mel[f0_mel <= 1] = 1
- f0_mel[f0_mel > 255] = 255
- f0_coarse = np.rint(f0_mel).astype(np.int)
- return f0_coarse, f0bak # 1-0
-
- def vc(
- self,
- model,
- net_g,
- sid,
- audio0,
- pitch,
- pitchf,
- times,
- index,
- big_npy,
- index_rate,
- ): # ,file_index,file_big_npy
- feats = torch.from_numpy(audio0)
- if self.is_half:
- feats = feats.half()
- else:
- feats = feats.float()
- if feats.dim() == 2: # double channels
- feats = feats.mean(-1)
- assert feats.dim() == 1, feats.dim()
- feats = feats.view(1, -1)
- padding_mask = torch.BoolTensor(feats.shape).to(self.device).fill_(False)
-
- inputs = {
- "source": feats.to(self.device),
- "padding_mask": padding_mask,
- "output_layer": 9, # layer 9
- }
- t0 = ttime()
- with torch.no_grad():
- logits = model.extract_features(**inputs)
- feats = model.final_proj(logits[0])
-
- if (
- isinstance(index, type(None)) == False
- and isinstance(big_npy, type(None)) == False
- and index_rate != 0
- ):
- npy = feats[0].cpu().numpy()
- if self.is_half:
- npy = npy.astype("float32")
- _, I = index.search(npy, 1)
- npy = big_npy[I.squeeze()]
- if self.is_half:
- npy = npy.astype("float16")
- feats = (
- torch.from_numpy(npy).unsqueeze(0).to(self.device) * index_rate
- + (1 - index_rate) * feats
- )
-
- feats = F.interpolate(feats.permute(0, 2, 1), scale_factor=2).permute(0, 2, 1)
- t1 = ttime()
- p_len = audio0.shape[0] // self.window
- if feats.shape[1] < p_len:
- p_len = feats.shape[1]
- if pitch != None and pitchf != None:
- pitch = pitch[:, :p_len]
- pitchf = pitchf[:, :p_len]
- p_len = torch.tensor([p_len], device=self.device).long()
- with torch.no_grad():
- if pitch != None and pitchf != None:
- audio1 = (
- (net_g.infer(feats, p_len, pitch, pitchf, sid)[0][0, 0] * 32768)
- .data.cpu()
- .float()
- .numpy()
- .astype(np.int16)
- )
- else:
- audio1 = (
- (net_g.infer(feats, p_len, sid)[0][0, 0] * 32768)
- .data.cpu()
- .float()
- .numpy()
- .astype(np.int16)
- )
- del feats, p_len, padding_mask
- if torch.cuda.is_available():
- torch.cuda.empty_cache()
- t2 = ttime()
- times[0] += t1 - t0
- times[2] += t2 - t1
- return audio1
-
- def pipeline(
- self,
- model,
- net_g,
- sid,
- audio,
- times,
- f0_up_key,
- f0_method,
- file_index,
- file_big_npy,
- index_rate,
- if_f0,
- f0_file=None,
- ):
- if (
- file_big_npy != ""
- and file_index != ""
- and os.path.exists(file_big_npy) == True
- and os.path.exists(file_index) == True
- and index_rate != 0
- ):
- try:
- index = faiss.read_index(file_index)
- big_npy = np.load(file_big_npy)
- except:
- traceback.print_exc()
- index = big_npy = None
- else:
- index = big_npy = None
- print("Feature retrieval library doesn't exist or ratio is 0")
- audio = signal.filtfilt(bh, ah, audio)
- audio_pad = np.pad(audio, (self.window // 2, self.window // 2), mode="reflect")
- opt_ts = []
- if audio_pad.shape[0] > self.t_max:
- audio_sum = np.zeros_like(audio)
- for i in range(self.window):
- audio_sum += audio_pad[i : i - self.window]
- for t in range(self.t_center, audio.shape[0], self.t_center):
- opt_ts.append(
- t
- - self.t_query
- + np.where(
- np.abs(audio_sum[t - self.t_query : t + self.t_query])
- == np.abs(audio_sum[t - self.t_query : t + self.t_query]).min()
- )[0][0]
- )
- s = 0
- audio_opt = []
- t = None
- t1 = ttime()
- audio_pad = np.pad(audio, (self.t_pad, self.t_pad), mode="reflect")
- p_len = audio_pad.shape[0] // self.window
- inp_f0 = None
- if hasattr(f0_file, "name") == True:
- try:
- with open(f0_file.name, "r") as f:
- lines = f.read().strip("\n").split("\n")
- inp_f0 = []
- for line in lines:
- inp_f0.append([float(i) for i in line.split(",")])
- inp_f0 = np.array(inp_f0, dtype="float32")
- except:
- traceback.print_exc()
- sid = torch.tensor(sid, device=self.device).unsqueeze(0).long()
- pitch, pitchf = None, None
- if if_f0 == 1:
- pitch, pitchf = self.get_f0(audio_pad, p_len, f0_up_key, f0_method, inp_f0)
- pitch = pitch[:p_len]
- pitchf = pitchf[:p_len]
- pitch = torch.tensor(pitch, device=self.device).unsqueeze(0).long()
- pitchf = torch.tensor(pitchf, device=self.device).unsqueeze(0).float()
- t2 = ttime()
- times[1] += t2 - t1
- for t in opt_ts:
- t = t // self.window * self.window
- if if_f0 == 1:
- audio_opt.append(
- self.vc(
- model,
- net_g,
- sid,
- audio_pad[s : t + self.t_pad2 + self.window],
- pitch[:, s // self.window : (t + self.t_pad2) // self.window],
- pitchf[:, s // self.window : (t + self.t_pad2) // self.window],
- times,
- index,
- big_npy,
- index_rate,
- )[self.t_pad_tgt : -self.t_pad_tgt]
- )
- else:
- audio_opt.append(
- self.vc(
- model,
- net_g,
- sid,
- audio_pad[s : t + self.t_pad2 + self.window],
- None,
- None,
- times,
- index,
- big_npy,
- index_rate,
- )[self.t_pad_tgt : -self.t_pad_tgt]
- )
- s = t
- if if_f0 == 1:
- audio_opt.append(
- self.vc(
- model,
- net_g,
- sid,
- audio_pad[t:],
- pitch[:, t // self.window :] if t is not None else pitch,
- pitchf[:, t // self.window :] if t is not None else pitchf,
- times,
- index,
- big_npy,
- index_rate,
- )[self.t_pad_tgt : -self.t_pad_tgt]
- )
- else:
- audio_opt.append(
- self.vc(
- model,
- net_g,
- sid,
- audio_pad[t:],
- None,
- None,
- times,
- index,
- big_npy,
- index_rate,
- )[self.t_pad_tgt : -self.t_pad_tgt]
- )
- audio_opt = np.concatenate(audio_opt)
- del pitch, pitchf, sid
- if torch.cuda.is_available():
- torch.cuda.empty_cache()
- return audio_opt
diff --git a/spaces/huaiji3y/bingo-Public/src/components/chat-suggestions.tsx b/spaces/huaiji3y/bingo-Public/src/components/chat-suggestions.tsx
deleted file mode 100644
index 48aec7c84e4407c482acdfcc7857fb0f660d12d3..0000000000000000000000000000000000000000
--- a/spaces/huaiji3y/bingo-Public/src/components/chat-suggestions.tsx
+++ /dev/null
@@ -1,45 +0,0 @@
-import React, { useMemo } from 'react'
-import Image from 'next/image'
-import HelpIcon from '@/assets/images/help.svg'
-import { SuggestedResponse } from '@/lib/bots/bing/types'
-import { useBing } from '@/lib/hooks/use-bing'
-import { atom, useAtom } from 'jotai'
-
-type Suggestions = SuggestedResponse[]
-const helpSuggestions = ['为什么不回应某些主题', '告诉我更多关于必应的资迅', '必应如何使用 AI?'].map((text) => ({ text }))
-const suggestionsAtom = atom([])
-
-type ChatSuggestionsProps = React.ComponentProps<'div'> & Pick, 'setInput'> & { suggestions?: Suggestions }
-
-export function ChatSuggestions({ setInput, suggestions = [] }: ChatSuggestionsProps) {
- const [currentSuggestions, setSuggestions] = useAtom(suggestionsAtom)
- const toggleSuggestions = (() => {
- if (currentSuggestions === helpSuggestions) {
- setSuggestions(suggestions)
- } else {
- setSuggestions(helpSuggestions)
- }
- })
-
- useMemo(() => {
- setSuggestions(suggestions)
- window.scrollBy(0, 2000)
- }, [suggestions.length, setSuggestions])
-
- return currentSuggestions?.length ? (
-
-
-
-
- {
- currentSuggestions.map(suggestion => (
- setInput(suggestion.text)}>
- {suggestion.text}
-
- ))
- }
-
-
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-If you are looking for a fun and family-friendly animated movie to watch, you might want to check out Alibaba Aur 41 Chor. This is a 2011 movie based on the classic Arabian Nights tale of Alibaba and the Forty Thieves. The movie features the voices of John Abraham, Priyanka Chopra, Ashutosh Rana, and Prem Chopra. The movie follows the adventures of Alibaba, a poor woodcutter who stumbles upon a secret cave filled with treasure belonging to a band of robbers. He decides to take some of the treasure for himself, but soon gets into trouble with the ruthless leader of the robbers, Abu Hassan.
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In this article, we will tell you more about Alibaba Aur 41 Chor and how you can watch it in HD quality. We will also give you some tips on how to download or stream the movie online safely and legally.
-What is Alibaba Aur 41 Chor About?
-Alibaba Aur 41 Chor is a movie that combines comedy, action, romance, and musical elements. The movie is set in the Middle East and showcases the culture and history of the region. The movie also has a lot of humor and references to modern pop culture.
-The movie starts with Alibaba, a poor woodcutter who lives with his brother Kasim and his wife Marjina. One day, he witnesses a group of robbers entering a secret cave using a magic password. He follows them and discovers that the cave is full of gold and jewels. He decides to take some of the treasure for himself and his family.
-However, his brother Kasim gets greedy and tries to take more treasure from the cave. He forgets the password and gets trapped inside. The robbers find out that someone has stolen their treasure and they vow to find and kill him. They also kidnap Marjina and threaten to kill her unless Alibaba surrenders.
-Alibaba has to use his wit and courage to save his brother, his wife, and himself from the robbers. He also gets help from Morgiana, a slave girl who works for Abu Hassan. She falls in love with Alibaba and helps him escape from many dangers.
-The movie ends with Alibaba defeating Abu Hassan and freeing Morgiana from slavery. He also shares his treasure with the poor people of his town and becomes a hero.
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-How to Watch Movies Online Safely and Legally?
-If you want to watch movies online safely and legally, you should use a VPN service or an ad-blocker to protect yourself from legal and security risks. A VPN service is a tool that encrypts your internet traffic and hides your IP address from prying eyes. This way, you can access any site without being tracked or blocked by your ISP or government. An ad-blocker is a tool that blocks unwanted ads or pop-ups that may interfere with your viewing experience.
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-Conclusion
-Alibaba Aur 41 Chor is a fun and family-friendly animated movie that you can watch in HD quality on your device. You can choose from different options to download or stream the movie online, but you should be aware of the legal and security risks involved in using torrent or free streaming sites. You should use a VPN service or an ad-blocker to protect yourself from these risks.
-We hope this article has helped you find the best way to watch Alibaba Aur 41 Chor in HD quality. If you have any questions or suggestions, please leave them in the comments below.
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-If you prefer to download the movie and watch it offline, you may want to find some reliable and fast download links for Alibaba Aur 41 Chor 720p. You can find many sites that offer this option, such as MoviesCounter, FilmyZilla, or WorldFree4u. These sites provide direct download links for the movie in 720p resolution with subtitles. You can also choose from different formats, such as MKV, MP4, or AVI.
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-Alibaba Aur 41 Chor: A Bollywood Adventure You Don't Want to Miss
-Alibaba Aur 41 Chor is a movie that will entertain you and your family with its comedy, action, romance, and musical elements. The movie is a modern adaptation of the classic Arabian Nights tale of Alibaba and the Forty Thieves. The movie showcases the culture and history of the Middle East and has a lot of humor and references to modern pop culture.
-The movie tells the story of Alibaba, a poor woodcutter who discovers a secret cave full of treasure belonging to a band of robbers. He decides to take some of the treasure for himself and his family, but soon gets into trouble with the leader of the robbers, Abu Hassan. He also gets help from Morgiana, a slave girl who works for Abu Hassan and falls in love with Alibaba.
-The movie has colorful animation, catchy songs, and a lot of humor and action. The movie also has a positive message about courage, honesty, and generosity. The movie is suitable for all ages and will make you laugh and cheer for Alibaba and his friends.
-Conclusion
-Alibaba Aur 41 Chor is a fun and family-friendly animated movie that you can watch in HD quality on your device. You can choose from different options to download or stream the movie online in 720p resolution with subtitles. However, you should be aware of the legal and security risks involved in using torrent or free streaming sites and use a VPN service or an ad-blocker to protect yourself from these risks.
-We hope this article has helped you find the best way to watch Alibaba Aur 41 Chor in HD quality. If you have any questions or suggestions, please leave them in the comments below.
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-Watching Alibaba Aur 41 Chor in HD quality has many benefits that will enhance your viewing experience. Some of these benefits are:
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-Better Picture Quality : Watching the movie in HD quality means that you will see more details and colors on the screen. The movie will look sharper and clearer, and you will not miss any important scenes or expressions. You will also enjoy the animation and the visual effects more in HD quality.Better Sound Quality : Watching the movie in HD quality also means that you will hear better sound quality on your speakers or headphones. The movie has a great soundtrack and sound effects that will make you feel like you are part of the action. You will also hear the voices of the characters more clearly and distinctly.Better Viewing Experience : Watching the movie in HD quality will make you feel more immersed and entertained by the movie. You will be able to appreciate the story, the characters, the humor, and the message of the movie more in HD quality. You will also have a more enjoyable time watching the movie with your family or friends.
-How to Optimize Your Device for Watching Alibaba Aur 41 Chor in HD Quality?
-If you want to watch Alibaba Aur 41 Chor in HD quality on your device, you should optimize your device for the best viewing experience. Here are some tips on how to do that:
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-Choose the Right Device : You should choose a device that supports HD resolution and has a good screen size and quality. For example, you can watch the movie on your laptop, tablet, or smartphone, but you should avoid watching it on a small or low-quality screen.Adjust the Settings : You should adjust the settings of your device to ensure that you get the best picture and sound quality. For example, you should set the brightness, contrast, and color of your screen to suit your preferences and environment. You should also set the volume and sound mode of your speakers or headphones to match your needs.Use a Good Internet Connection : You should use a good internet connection to download or stream the movie online without any interruptions or delays. You should also avoid using a public or shared network that may be slow or insecure. You should use a private or secure network that offers high-speed and reliable service.
-Conclusion
-Alibaba Aur 41 Chor is a fun and family-friendly animated movie that you can watch in HD quality on your device. You can choose from different options to download or stream the movie online in 720p resolution with subtitles. However, you should be aware of the legal and security risks involved in using torrent or free streaming sites and use a VPN service or an ad-blocker to protect yourself from these risks.
-You should also optimize your device for watching the movie in HD quality by choosing the right device, adjusting the settings, and using a good internet connection. This way, you will enjoy the movie more and have a better viewing experience.
-We hope this article has helped you find the best way to watch Alibaba Aur 41 Chor in HD quality. If you have any questions or suggestions, please leave them in the comments below.
-Conclusion
-Alibaba Aur 41 Chor is a fun and family-friendly animated movie that you can watch in HD quality on your device. The movie is based on a classic Arabian Nights tale and has a lot of humor, action, romance, and musical elements. The movie also showcases the culture and history of the Middle East and has a positive message about courage, honesty, and generosity.
-You can choose from different options to download or stream the movie online in 720p resolution with subtitles. However, you should be aware of the legal and security risks involved in using torrent or free streaming sites and use a VPN service or an ad-blocker to protect yourself from these risks.
-You should also optimize your device for watching the movie in HD quality by choosing the right device, adjusting the settings, and using a good internet connection. This way, you will enjoy the movie more and have a better viewing experience.
-We hope this article has helped you find the best way to watch Alibaba Aur 41 Chor in HD quality. If you have any questions or suggestions, please leave them in the comments below.
3cee63e6c2Sitemiz Ses Tarih - Eksi (2.4.8.11) B.S.A (2011) Windows 7 64bit. BilsoftMuhasebe lisans anahtar Axasoft on muhasebe crack!! better!! on racvesehyd., axasoft cari hesap takip lisans kodu, axa cari hesap.
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diff --git a/spaces/inplisQlawa/anything-midjourney-v4-1/Itisnotfoundanyfilespecifiedforisarcextractsolution NEW!.md b/spaces/inplisQlawa/anything-midjourney-v4-1/Itisnotfoundanyfilespecifiedforisarcextractsolution NEW!.md
deleted file mode 100644
index d31a3a6a3df4bd2b676bcbe9a9e945aa04a4a9ec..0000000000000000000000000000000000000000
--- a/spaces/inplisQlawa/anything-midjourney-v4-1/Itisnotfoundanyfilespecifiedforisarcextractsolution NEW!.md
+++ /dev/null
@@ -1,6 +0,0 @@
-Itisnotfoundanyfilespecifiedforisarcextractsolution Download File ===== https://urlin.us/2uEyHO
diff --git a/spaces/inreVtussa/clothingai/Examples/Av Bros Puzzle Pro 3.1 Crack.md b/spaces/inreVtussa/clothingai/Examples/Av Bros Puzzle Pro 3.1 Crack.md
deleted file mode 100644
index fc665a5073f10e7a1cf6ced8763f29bb84f9785f..0000000000000000000000000000000000000000
--- a/spaces/inreVtussa/clothingai/Examples/Av Bros Puzzle Pro 3.1 Crack.md
+++ /dev/null
@@ -1,8 +0,0 @@
-retro-futuristic puzzle games were a favorite of mine growing up. the usual layout is that one of you is the player and the other is the computer. the computer plays by establishing rules, usually a very simple set of rules, and then the player has to figure out how to play against them. how to play that game usually involves memorizing a pattern and repeating it many times. this is true for everything, but most of these games tend to be very simple.
-a puzzle game is a kind of game that is mostly played for fun, and like many other "games", requires a bit of strategy and skill to complete. puzzle games can be broadly divided into two categories: strategic and intellectual. in strategic games, the aim is to achieve specific goals within a fixed time limit, while in intellectual games, the aim is to complete a specific task or solve a problem. some puzzles require solving riddles and unriddling, while others require memorization or physical skill. other puzzles may require unusual logic or complex reasoning.
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diff --git a/spaces/inreVtussa/clothingai/Examples/Bupena Kelas 5 Sd Pdf 140 !!LINK!!.md b/spaces/inreVtussa/clothingai/Examples/Bupena Kelas 5 Sd Pdf 140 !!LINK!!.md
deleted file mode 100644
index 38ebb222a1842b79a6e4df5c9adc3442c9b2d8d3..0000000000000000000000000000000000000000
--- a/spaces/inreVtussa/clothingai/Examples/Bupena Kelas 5 Sd Pdf 140 !!LINK!!.md
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diff --git a/spaces/iqovocn/ChuanhuChatGPT/README.md b/spaces/iqovocn/ChuanhuChatGPT/README.md
deleted file mode 100644
index 79790f767ded0eb77b8129f8e960c65b8d166c14..0000000000000000000000000000000000000000
--- a/spaces/iqovocn/ChuanhuChatGPT/README.md
+++ /dev/null
@@ -1,14 +0,0 @@
----
-title: ChuanhuChatGPT
-emoji: 🐯
-colorFrom: green
-colorTo: red
-sdk: gradio
-sdk_version: 3.33.1
-app_file: ChuanhuChatbot.py
-pinned: false
-license: gpl-3.0
-duplicated_from: JohnSmith9982/ChuanhuChatGPT
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
\ No newline at end of file
diff --git a/spaces/jackli888/stable-diffusion-webui/modules/ui.py b/spaces/jackli888/stable-diffusion-webui/modules/ui.py
deleted file mode 100644
index badf4975128985ad55bb69d6ee028adc0a61f97c..0000000000000000000000000000000000000000
--- a/spaces/jackli888/stable-diffusion-webui/modules/ui.py
+++ /dev/null
@@ -1,1798 +0,0 @@
-import html
-import json
-import math
-import mimetypes
-import os
-import platform
-import random
-import sys
-import tempfile
-import time
-import traceback
-from functools import partial, reduce
-import warnings
-
-import gradio as gr
-import gradio.routes
-import gradio.utils
-import numpy as np
-from PIL import Image, PngImagePlugin
-from modules.call_queue import wrap_gradio_gpu_call, wrap_queued_call, wrap_gradio_call
-
-from modules import sd_hijack, sd_models, localization, script_callbacks, ui_extensions, deepbooru, sd_vae, extra_networks, postprocessing, ui_components, ui_common, ui_postprocessing
-from modules.ui_components import FormRow, FormGroup, ToolButton, FormHTML
-from modules.paths import script_path, data_path
-
-from modules.shared import opts, cmd_opts, restricted_opts
-
-import modules.codeformer_model
-import modules.generation_parameters_copypaste as parameters_copypaste
-import modules.gfpgan_model
-import modules.hypernetworks.ui
-import modules.scripts
-import modules.shared as shared
-import modules.styles
-import modules.textual_inversion.ui
-from modules import prompt_parser
-from modules.images import save_image
-from modules.sd_hijack import model_hijack
-from modules.sd_samplers import samplers, samplers_for_img2img
-from modules.textual_inversion import textual_inversion
-import modules.hypernetworks.ui
-from modules.generation_parameters_copypaste import image_from_url_text
-import modules.extras
-
-warnings.filterwarnings("default" if opts.show_warnings else "ignore", category=UserWarning)
-
-# this is a fix for Windows users. Without it, javascript files will be served with text/html content-type and the browser will not show any UI
-mimetypes.init()
-mimetypes.add_type('application/javascript', '.js')
-
-if not cmd_opts.share and not cmd_opts.listen:
- # fix gradio phoning home
- gradio.utils.version_check = lambda: None
- gradio.utils.get_local_ip_address = lambda: '127.0.0.1'
-
-if cmd_opts.ngrok is not None:
- import modules.ngrok as ngrok
- print('ngrok authtoken detected, trying to connect...')
- ngrok.connect(
- cmd_opts.ngrok,
- cmd_opts.port if cmd_opts.port is not None else 7860,
- cmd_opts.ngrok_region
- )
-
-
-def gr_show(visible=True):
- return {"visible": visible, "__type__": "update"}
-
-
-sample_img2img = "assets/stable-samples/img2img/sketch-mountains-input.jpg"
-sample_img2img = sample_img2img if os.path.exists(sample_img2img) else None
-
-css_hide_progressbar = """
-.wrap .m-12 svg { display:none!important; }
-.wrap .m-12::before { content:"Loading..." }
-.wrap .z-20 svg { display:none!important; }
-.wrap .z-20::before { content:"Loading..." }
-.wrap.cover-bg .z-20::before { content:"" }
-.progress-bar { display:none!important; }
-.meta-text { display:none!important; }
-.meta-text-center { display:none!important; }
-"""
-
-# Using constants for these since the variation selector isn't visible.
-# Important that they exactly match script.js for tooltip to work.
-random_symbol = '\U0001f3b2\ufe0f' # 🎲️
-reuse_symbol = '\u267b\ufe0f' # ♻️
-paste_symbol = '\u2199\ufe0f' # ↙
-refresh_symbol = '\U0001f504' # 🔄
-save_style_symbol = '\U0001f4be' # 💾
-apply_style_symbol = '\U0001f4cb' # 📋
-clear_prompt_symbol = '\U0001F5D1' # 🗑️
-extra_networks_symbol = '\U0001F3B4' # 🎴
-switch_values_symbol = '\U000021C5' # ⇅
-
-
-def plaintext_to_html(text):
- return ui_common.plaintext_to_html(text)
-
-
-def send_gradio_gallery_to_image(x):
- if len(x) == 0:
- return None
- return image_from_url_text(x[0])
-
-def visit(x, func, path=""):
- if hasattr(x, 'children'):
- for c in x.children:
- visit(c, func, path)
- elif x.label is not None:
- func(path + "/" + str(x.label), x)
-
-
-def add_style(name: str, prompt: str, negative_prompt: str):
- if name is None:
- return [gr_show() for x in range(4)]
-
- style = modules.styles.PromptStyle(name, prompt, negative_prompt)
- shared.prompt_styles.styles[style.name] = style
- # Save all loaded prompt styles: this allows us to update the storage format in the future more easily, because we
- # reserialize all styles every time we save them
- shared.prompt_styles.save_styles(shared.styles_filename)
-
- return [gr.Dropdown.update(visible=True, choices=list(shared.prompt_styles.styles)) for _ in range(2)]
-
-
-def calc_resolution_hires(enable, width, height, hr_scale, hr_resize_x, hr_resize_y):
- from modules import processing, devices
-
- if not enable:
- return ""
-
- p = processing.StableDiffusionProcessingTxt2Img(width=width, height=height, enable_hr=True, hr_scale=hr_scale, hr_resize_x=hr_resize_x, hr_resize_y=hr_resize_y)
-
- with devices.autocast():
- p.init([""], [0], [0])
-
- return f"resize: from {p.width}x{p.height} to {p.hr_resize_x or p.hr_upscale_to_x}x{p.hr_resize_y or p.hr_upscale_to_y} "
-
-
-def apply_styles(prompt, prompt_neg, styles):
- prompt = shared.prompt_styles.apply_styles_to_prompt(prompt, styles)
- prompt_neg = shared.prompt_styles.apply_negative_styles_to_prompt(prompt_neg, styles)
-
- return [gr.Textbox.update(value=prompt), gr.Textbox.update(value=prompt_neg), gr.Dropdown.update(value=[])]
-
-
-def process_interrogate(interrogation_function, mode, ii_input_dir, ii_output_dir, *ii_singles):
- if mode in {0, 1, 3, 4}:
- return [interrogation_function(ii_singles[mode]), None]
- elif mode == 2:
- return [interrogation_function(ii_singles[mode]["image"]), None]
- elif mode == 5:
- assert not shared.cmd_opts.hide_ui_dir_config, "Launched with --hide-ui-dir-config, batch img2img disabled"
- images = shared.listfiles(ii_input_dir)
- print(f"Will process {len(images)} images.")
- if ii_output_dir != "":
- os.makedirs(ii_output_dir, exist_ok=True)
- else:
- ii_output_dir = ii_input_dir
-
- for image in images:
- img = Image.open(image)
- filename = os.path.basename(image)
- left, _ = os.path.splitext(filename)
- print(interrogation_function(img), file=open(os.path.join(ii_output_dir, left + ".txt"), 'a'))
-
- return [gr.update(), None]
-
-
-def interrogate(image):
- prompt = shared.interrogator.interrogate(image.convert("RGB"))
- return gr.update() if prompt is None else prompt
-
-
-def interrogate_deepbooru(image):
- prompt = deepbooru.model.tag(image)
- return gr.update() if prompt is None else prompt
-
-
-def create_seed_inputs(target_interface):
- with FormRow(elem_id=target_interface + '_seed_row'):
- seed = (gr.Textbox if cmd_opts.use_textbox_seed else gr.Number)(label='Seed', value=-1, elem_id=target_interface + '_seed')
- seed.style(container=False)
- random_seed = gr.Button(random_symbol, elem_id=target_interface + '_random_seed')
- reuse_seed = gr.Button(reuse_symbol, elem_id=target_interface + '_reuse_seed')
-
- with gr.Group(elem_id=target_interface + '_subseed_show_box'):
- seed_checkbox = gr.Checkbox(label='Extra', elem_id=target_interface + '_subseed_show', value=False)
-
- # Components to show/hide based on the 'Extra' checkbox
- seed_extras = []
-
- with FormRow(visible=False, elem_id=target_interface + '_subseed_row') as seed_extra_row_1:
- seed_extras.append(seed_extra_row_1)
- subseed = gr.Number(label='Variation seed', value=-1, elem_id=target_interface + '_subseed')
- subseed.style(container=False)
- random_subseed = gr.Button(random_symbol, elem_id=target_interface + '_random_subseed')
- reuse_subseed = gr.Button(reuse_symbol, elem_id=target_interface + '_reuse_subseed')
- subseed_strength = gr.Slider(label='Variation strength', value=0.0, minimum=0, maximum=1, step=0.01, elem_id=target_interface + '_subseed_strength')
-
- with FormRow(visible=False) as seed_extra_row_2:
- seed_extras.append(seed_extra_row_2)
- seed_resize_from_w = gr.Slider(minimum=0, maximum=2048, step=8, label="Resize seed from width", value=0, elem_id=target_interface + '_seed_resize_from_w')
- seed_resize_from_h = gr.Slider(minimum=0, maximum=2048, step=8, label="Resize seed from height", value=0, elem_id=target_interface + '_seed_resize_from_h')
-
- random_seed.click(fn=lambda: -1, show_progress=False, inputs=[], outputs=[seed])
- random_subseed.click(fn=lambda: -1, show_progress=False, inputs=[], outputs=[subseed])
-
- def change_visibility(show):
- return {comp: gr_show(show) for comp in seed_extras}
-
- seed_checkbox.change(change_visibility, show_progress=False, inputs=[seed_checkbox], outputs=seed_extras)
-
- return seed, reuse_seed, subseed, reuse_subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, seed_checkbox
-
-
-
-def connect_clear_prompt(button):
- """Given clear button, prompt, and token_counter objects, setup clear prompt button click event"""
- button.click(
- _js="clear_prompt",
- fn=None,
- inputs=[],
- outputs=[],
- )
-
-
-def connect_reuse_seed(seed: gr.Number, reuse_seed: gr.Button, generation_info: gr.Textbox, dummy_component, is_subseed):
- """ Connects a 'reuse (sub)seed' button's click event so that it copies last used
- (sub)seed value from generation info the to the seed field. If copying subseed and subseed strength
- was 0, i.e. no variation seed was used, it copies the normal seed value instead."""
- def copy_seed(gen_info_string: str, index):
- res = -1
-
- try:
- gen_info = json.loads(gen_info_string)
- index -= gen_info.get('index_of_first_image', 0)
-
- if is_subseed and gen_info.get('subseed_strength', 0) > 0:
- all_subseeds = gen_info.get('all_subseeds', [-1])
- res = all_subseeds[index if 0 <= index < len(all_subseeds) else 0]
- else:
- all_seeds = gen_info.get('all_seeds', [-1])
- res = all_seeds[index if 0 <= index < len(all_seeds) else 0]
-
- except json.decoder.JSONDecodeError as e:
- if gen_info_string != '':
- print("Error parsing JSON generation info:", file=sys.stderr)
- print(gen_info_string, file=sys.stderr)
-
- return [res, gr_show(False)]
-
- reuse_seed.click(
- fn=copy_seed,
- _js="(x, y) => [x, selected_gallery_index()]",
- show_progress=False,
- inputs=[generation_info, dummy_component],
- outputs=[seed, dummy_component]
- )
-
-
-def update_token_counter(text, steps):
- try:
- text, _ = extra_networks.parse_prompt(text)
-
- _, prompt_flat_list, _ = prompt_parser.get_multicond_prompt_list([text])
- prompt_schedules = prompt_parser.get_learned_conditioning_prompt_schedules(prompt_flat_list, steps)
-
- except Exception:
- # a parsing error can happen here during typing, and we don't want to bother the user with
- # messages related to it in console
- prompt_schedules = [[[steps, text]]]
-
- flat_prompts = reduce(lambda list1, list2: list1+list2, prompt_schedules)
- prompts = [prompt_text for step, prompt_text in flat_prompts]
- token_count, max_length = max([model_hijack.get_prompt_lengths(prompt) for prompt in prompts], key=lambda args: args[0])
- return f"{token_count}/{max_length} "
-
-
-def create_toprow(is_img2img):
- id_part = "img2img" if is_img2img else "txt2img"
-
- with gr.Row(elem_id=f"{id_part}_toprow", variant="compact"):
- with gr.Column(elem_id=f"{id_part}_prompt_container", scale=6):
- with gr.Row():
- with gr.Column(scale=80):
- with gr.Row():
- prompt = gr.Textbox(label="Prompt", elem_id=f"{id_part}_prompt", show_label=False, lines=3, placeholder="Prompt (press Ctrl+Enter or Alt+Enter to generate)")
-
- with gr.Row():
- with gr.Column(scale=80):
- with gr.Row():
- negative_prompt = gr.Textbox(label="Negative prompt", elem_id=f"{id_part}_neg_prompt", show_label=False, lines=2, placeholder="Negative prompt (press Ctrl+Enter or Alt+Enter to generate)")
-
- button_interrogate = None
- button_deepbooru = None
- if is_img2img:
- with gr.Column(scale=1, elem_id="interrogate_col"):
- button_interrogate = gr.Button('Interrogate\nCLIP', elem_id="interrogate")
- button_deepbooru = gr.Button('Interrogate\nDeepBooru', elem_id="deepbooru")
-
- with gr.Column(scale=1, elem_id=f"{id_part}_actions_column"):
- with gr.Row(elem_id=f"{id_part}_generate_box"):
- interrupt = gr.Button('Interrupt', elem_id=f"{id_part}_interrupt")
- skip = gr.Button('Skip', elem_id=f"{id_part}_skip")
- submit = gr.Button('Generate', elem_id=f"{id_part}_generate", variant='primary')
-
- skip.click(
- fn=lambda: shared.state.skip(),
- inputs=[],
- outputs=[],
- )
-
- interrupt.click(
- fn=lambda: shared.state.interrupt(),
- inputs=[],
- outputs=[],
- )
-
- with gr.Row(elem_id=f"{id_part}_tools"):
- paste = ToolButton(value=paste_symbol, elem_id="paste")
- clear_prompt_button = ToolButton(value=clear_prompt_symbol, elem_id=f"{id_part}_clear_prompt")
- extra_networks_button = ToolButton(value=extra_networks_symbol, elem_id=f"{id_part}_extra_networks")
- prompt_style_apply = ToolButton(value=apply_style_symbol, elem_id=f"{id_part}_style_apply")
- save_style = ToolButton(value=save_style_symbol, elem_id=f"{id_part}_style_create")
-
- token_counter = gr.HTML(value="Process images in a directory on the same machine where the server is running." +
- f"
"
- )
- img2img_batch_input_dir = gr.Textbox(label="Input directory", **shared.hide_dirs, elem_id="img2img_batch_input_dir")
- img2img_batch_output_dir = gr.Textbox(label="Output directory", **shared.hide_dirs, elem_id="img2img_batch_output_dir")
- img2img_batch_inpaint_mask_dir = gr.Textbox(label="Inpaint batch mask directory (required for inpaint batch processing only)", **shared.hide_dirs, elem_id="img2img_batch_inpaint_mask_dir")
-
- def copy_image(img):
- if isinstance(img, dict) and 'image' in img:
- return img['image']
-
- return img
-
- for button, name, elem in copy_image_buttons:
- button.click(
- fn=copy_image,
- inputs=[elem],
- outputs=[copy_image_destinations[name]],
- )
- button.click(
- fn=lambda: None,
- _js="switch_to_"+name.replace(" ", "_"),
- inputs=[],
- outputs=[],
- )
-
- with FormRow():
- resize_mode = gr.Radio(label="Resize mode", elem_id="resize_mode", choices=["Just resize", "Crop and resize", "Resize and fill", "Just resize (latent upscale)"], type="index", value="Just resize")
-
- for category in ordered_ui_categories():
- if category == "sampler":
- steps, sampler_index = create_sampler_and_steps_selection(samplers_for_img2img, "img2img")
-
- elif category == "dimensions":
- with FormRow():
- with gr.Column(elem_id="img2img_column_size", scale=4):
- width = gr.Slider(minimum=64, maximum=2048, step=8, label="Width", value=512, elem_id="img2img_width")
- height = gr.Slider(minimum=64, maximum=2048, step=8, label="Height", value=512, elem_id="img2img_height")
-
- res_switch_btn = ToolButton(value=switch_values_symbol, elem_id="img2img_res_switch_btn")
- if opts.dimensions_and_batch_together:
- with gr.Column(elem_id="img2img_column_batch"):
- batch_count = gr.Slider(minimum=1, step=1, label='Batch count', value=1, elem_id="img2img_batch_count")
- batch_size = gr.Slider(minimum=1, maximum=8, step=1, label='Batch size', value=1, elem_id="img2img_batch_size")
-
- elif category == "cfg":
- with FormGroup():
- with FormRow():
- cfg_scale = gr.Slider(minimum=1.0, maximum=30.0, step=0.5, label='CFG Scale', value=7.0, elem_id="img2img_cfg_scale")
- image_cfg_scale = gr.Slider(minimum=0, maximum=3.0, step=0.05, label='Image CFG Scale', value=1.5, elem_id="img2img_image_cfg_scale", visible=shared.sd_model and shared.sd_model.cond_stage_key == "edit")
- denoising_strength = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label='Denoising strength', value=0.75, elem_id="img2img_denoising_strength")
-
- elif category == "seed":
- seed, reuse_seed, subseed, reuse_subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, seed_checkbox = create_seed_inputs('img2img')
-
- elif category == "checkboxes":
- with FormRow(elem_id="img2img_checkboxes", variant="compact"):
- restore_faces = gr.Checkbox(label='Restore faces', value=False, visible=len(shared.face_restorers) > 1, elem_id="img2img_restore_faces")
- tiling = gr.Checkbox(label='Tiling', value=False, elem_id="img2img_tiling")
-
- elif category == "batch":
- if not opts.dimensions_and_batch_together:
- with FormRow(elem_id="img2img_column_batch"):
- batch_count = gr.Slider(minimum=1, step=1, label='Batch count', value=1, elem_id="img2img_batch_count")
- batch_size = gr.Slider(minimum=1, maximum=8, step=1, label='Batch size', value=1, elem_id="img2img_batch_size")
-
- elif category == "override_settings":
- with FormRow(elem_id="img2img_override_settings_row") as row:
- override_settings = create_override_settings_dropdown('img2img', row)
-
- elif category == "scripts":
- with FormGroup(elem_id="img2img_script_container"):
- custom_inputs = modules.scripts.scripts_img2img.setup_ui()
-
- elif category == "inpaint":
- with FormGroup(elem_id="inpaint_controls", visible=False) as inpaint_controls:
- with FormRow():
- mask_blur = gr.Slider(label='Mask blur', minimum=0, maximum=64, step=1, value=4, elem_id="img2img_mask_blur")
- mask_alpha = gr.Slider(label="Mask transparency", visible=False, elem_id="img2img_mask_alpha")
-
- with FormRow():
- inpainting_mask_invert = gr.Radio(label='Mask mode', choices=['Inpaint masked', 'Inpaint not masked'], value='Inpaint masked', type="index", elem_id="img2img_mask_mode")
-
- with FormRow():
- inpainting_fill = gr.Radio(label='Masked content', choices=['fill', 'original', 'latent noise', 'latent nothing'], value='original', type="index", elem_id="img2img_inpainting_fill")
-
- with FormRow():
- with gr.Column():
- inpaint_full_res = gr.Radio(label="Inpaint area", choices=["Whole picture", "Only masked"], type="index", value="Whole picture", elem_id="img2img_inpaint_full_res")
-
- with gr.Column(scale=4):
- inpaint_full_res_padding = gr.Slider(label='Only masked padding, pixels', minimum=0, maximum=256, step=4, value=32, elem_id="img2img_inpaint_full_res_padding")
-
- def select_img2img_tab(tab):
- return gr.update(visible=tab in [2, 3, 4]), gr.update(visible=tab == 3),
-
- for i, elem in enumerate([tab_img2img, tab_sketch, tab_inpaint, tab_inpaint_color, tab_inpaint_upload, tab_batch]):
- elem.select(
- fn=lambda tab=i: select_img2img_tab(tab),
- inputs=[],
- outputs=[inpaint_controls, mask_alpha],
- )
-
- img2img_gallery, generation_info, html_info, html_log = create_output_panel("img2img", opts.outdir_img2img_samples)
-
- connect_reuse_seed(seed, reuse_seed, generation_info, dummy_component, is_subseed=False)
- connect_reuse_seed(subseed, reuse_subseed, generation_info, dummy_component, is_subseed=True)
-
- img2img_prompt_img.change(
- fn=modules.images.image_data,
- inputs=[
- img2img_prompt_img
- ],
- outputs=[
- img2img_prompt,
- img2img_prompt_img
- ]
- )
-
- img2img_args = dict(
- fn=wrap_gradio_gpu_call(modules.img2img.img2img, extra_outputs=[None, '', '']),
- _js="submit_img2img",
- inputs=[
- dummy_component,
- dummy_component,
- img2img_prompt,
- img2img_negative_prompt,
- img2img_prompt_styles,
- init_img,
- sketch,
- init_img_with_mask,
- inpaint_color_sketch,
- inpaint_color_sketch_orig,
- init_img_inpaint,
- init_mask_inpaint,
- steps,
- sampler_index,
- mask_blur,
- mask_alpha,
- inpainting_fill,
- restore_faces,
- tiling,
- batch_count,
- batch_size,
- cfg_scale,
- image_cfg_scale,
- denoising_strength,
- seed,
- subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, seed_checkbox,
- height,
- width,
- resize_mode,
- inpaint_full_res,
- inpaint_full_res_padding,
- inpainting_mask_invert,
- img2img_batch_input_dir,
- img2img_batch_output_dir,
- img2img_batch_inpaint_mask_dir,
- override_settings,
- ] + custom_inputs,
- outputs=[
- img2img_gallery,
- generation_info,
- html_info,
- html_log,
- ],
- show_progress=False,
- )
-
- interrogate_args = dict(
- _js="get_img2img_tab_index",
- inputs=[
- dummy_component,
- img2img_batch_input_dir,
- img2img_batch_output_dir,
- init_img,
- sketch,
- init_img_with_mask,
- inpaint_color_sketch,
- init_img_inpaint,
- ],
- outputs=[img2img_prompt, dummy_component],
- )
-
- img2img_prompt.submit(**img2img_args)
- submit.click(**img2img_args)
- res_switch_btn.click(lambda w, h: (h, w), inputs=[width, height], outputs=[width, height])
-
- img2img_interrogate.click(
- fn=lambda *args: process_interrogate(interrogate, *args),
- **interrogate_args,
- )
-
- img2img_deepbooru.click(
- fn=lambda *args: process_interrogate(interrogate_deepbooru, *args),
- **interrogate_args,
- )
-
- prompts = [(txt2img_prompt, txt2img_negative_prompt), (img2img_prompt, img2img_negative_prompt)]
- style_dropdowns = [txt2img_prompt_styles, img2img_prompt_styles]
- style_js_funcs = ["update_txt2img_tokens", "update_img2img_tokens"]
-
- for button, (prompt, negative_prompt) in zip([txt2img_save_style, img2img_save_style], prompts):
- button.click(
- fn=add_style,
- _js="ask_for_style_name",
- # Have to pass empty dummy component here, because the JavaScript and Python function have to accept
- # the same number of parameters, but we only know the style-name after the JavaScript prompt
- inputs=[dummy_component, prompt, negative_prompt],
- outputs=[txt2img_prompt_styles, img2img_prompt_styles],
- )
-
- for button, (prompt, negative_prompt), styles, js_func in zip([txt2img_prompt_style_apply, img2img_prompt_style_apply], prompts, style_dropdowns, style_js_funcs):
- button.click(
- fn=apply_styles,
- _js=js_func,
- inputs=[prompt, negative_prompt, styles],
- outputs=[prompt, negative_prompt, styles],
- )
-
- token_button.click(fn=update_token_counter, inputs=[img2img_prompt, steps], outputs=[token_counter])
- negative_token_button.click(fn=wrap_queued_call(update_token_counter), inputs=[txt2img_negative_prompt, steps], outputs=[negative_token_counter])
-
- ui_extra_networks.setup_ui(extra_networks_ui_img2img, img2img_gallery)
-
- img2img_paste_fields = [
- (img2img_prompt, "Prompt"),
- (img2img_negative_prompt, "Negative prompt"),
- (steps, "Steps"),
- (sampler_index, "Sampler"),
- (restore_faces, "Face restoration"),
- (cfg_scale, "CFG scale"),
- (image_cfg_scale, "Image CFG scale"),
- (seed, "Seed"),
- (width, "Size-1"),
- (height, "Size-2"),
- (batch_size, "Batch size"),
- (subseed, "Variation seed"),
- (subseed_strength, "Variation seed strength"),
- (seed_resize_from_w, "Seed resize from-1"),
- (seed_resize_from_h, "Seed resize from-2"),
- (denoising_strength, "Denoising strength"),
- (mask_blur, "Mask blur"),
- *modules.scripts.scripts_img2img.infotext_fields
- ]
- parameters_copypaste.add_paste_fields("img2img", init_img, img2img_paste_fields, override_settings)
- parameters_copypaste.add_paste_fields("inpaint", init_img_with_mask, img2img_paste_fields, override_settings)
- parameters_copypaste.register_paste_params_button(parameters_copypaste.ParamBinding(
- paste_button=img2img_paste, tabname="img2img", source_text_component=img2img_prompt, source_image_component=None,
- ))
-
- modules.scripts.scripts_current = None
-
- with gr.Blocks(analytics_enabled=False) as extras_interface:
- ui_postprocessing.create_ui()
-
- with gr.Blocks(analytics_enabled=False) as pnginfo_interface:
- with gr.Row().style(equal_height=False):
- with gr.Column(variant='panel'):
- image = gr.Image(elem_id="pnginfo_image", label="Source", source="upload", interactive=True, type="pil")
-
- with gr.Column(variant='panel'):
- html = gr.HTML()
- generation_info = gr.Textbox(visible=False, elem_id="pnginfo_generation_info")
- html2 = gr.HTML()
- with gr.Row():
- buttons = parameters_copypaste.create_buttons(["txt2img", "img2img", "inpaint", "extras"])
-
- for tabname, button in buttons.items():
- parameters_copypaste.register_paste_params_button(parameters_copypaste.ParamBinding(
- paste_button=button, tabname=tabname, source_text_component=generation_info, source_image_component=image,
- ))
-
- image.change(
- fn=wrap_gradio_call(modules.extras.run_pnginfo),
- inputs=[image],
- outputs=[html, generation_info, html2],
- )
-
- def update_interp_description(value):
- interp_description_css = "{}
"
- interp_descriptions = {
- "No interpolation": interp_description_css.format("No interpolation will be used. Requires one model; A. Allows for format conversion and VAE baking."),
- "Weighted sum": interp_description_css.format("A weighted sum will be used for interpolation. Requires two models; A and B. The result is calculated as A * (1 - M) + B * M"),
- "Add difference": interp_description_css.format("The difference between the last two models will be added to the first. Requires three models; A, B and C. The result is calculated as A + (B - C) * M")
- }
- return interp_descriptions[value]
-
- with gr.Blocks(analytics_enabled=False) as modelmerger_interface:
- with gr.Row().style(equal_height=False):
- with gr.Column(variant='compact'):
- interp_description = gr.HTML(value=update_interp_description("Weighted sum"), elem_id="modelmerger_interp_description")
-
- with FormRow(elem_id="modelmerger_models"):
- primary_model_name = gr.Dropdown(modules.sd_models.checkpoint_tiles(), elem_id="modelmerger_primary_model_name", label="Primary model (A)")
- create_refresh_button(primary_model_name, modules.sd_models.list_models, lambda: {"choices": modules.sd_models.checkpoint_tiles()}, "refresh_checkpoint_A")
-
- secondary_model_name = gr.Dropdown(modules.sd_models.checkpoint_tiles(), elem_id="modelmerger_secondary_model_name", label="Secondary model (B)")
- create_refresh_button(secondary_model_name, modules.sd_models.list_models, lambda: {"choices": modules.sd_models.checkpoint_tiles()}, "refresh_checkpoint_B")
-
- tertiary_model_name = gr.Dropdown(modules.sd_models.checkpoint_tiles(), elem_id="modelmerger_tertiary_model_name", label="Tertiary model (C)")
- create_refresh_button(tertiary_model_name, modules.sd_models.list_models, lambda: {"choices": modules.sd_models.checkpoint_tiles()}, "refresh_checkpoint_C")
-
- custom_name = gr.Textbox(label="Custom Name (Optional)", elem_id="modelmerger_custom_name")
- interp_amount = gr.Slider(minimum=0.0, maximum=1.0, step=0.05, label='Multiplier (M) - set to 0 to get model A', value=0.3, elem_id="modelmerger_interp_amount")
- interp_method = gr.Radio(choices=["No interpolation", "Weighted sum", "Add difference"], value="Weighted sum", label="Interpolation Method", elem_id="modelmerger_interp_method")
- interp_method.change(fn=update_interp_description, inputs=[interp_method], outputs=[interp_description])
-
- with FormRow():
- checkpoint_format = gr.Radio(choices=["ckpt", "safetensors"], value="ckpt", label="Checkpoint format", elem_id="modelmerger_checkpoint_format")
- save_as_half = gr.Checkbox(value=False, label="Save as float16", elem_id="modelmerger_save_as_half")
-
- with FormRow():
- with gr.Column():
- config_source = gr.Radio(choices=["A, B or C", "B", "C", "Don't"], value="A, B or C", label="Copy config from", type="index", elem_id="modelmerger_config_method")
-
- with gr.Column():
- with FormRow():
- bake_in_vae = gr.Dropdown(choices=["None"] + list(sd_vae.vae_dict), value="None", label="Bake in VAE", elem_id="modelmerger_bake_in_vae")
- create_refresh_button(bake_in_vae, sd_vae.refresh_vae_list, lambda: {"choices": ["None"] + list(sd_vae.vae_dict)}, "modelmerger_refresh_bake_in_vae")
-
- with FormRow():
- discard_weights = gr.Textbox(value="", label="Discard weights with matching name", elem_id="modelmerger_discard_weights")
-
- with gr.Row():
- modelmerger_merge = gr.Button(elem_id="modelmerger_merge", value="Merge", variant='primary')
-
- with gr.Column(variant='compact', elem_id="modelmerger_results_container"):
- with gr.Group(elem_id="modelmerger_results_panel"):
- modelmerger_result = gr.HTML(elem_id="modelmerger_result", show_label=False)
-
- with gr.Blocks(analytics_enabled=False) as train_interface:
- with gr.Row().style(equal_height=False):
- gr.HTML(value="See wiki
")
-
- with gr.Row(variant="compact").style(equal_height=False):
- with gr.Tabs(elem_id="train_tabs"):
-
- with gr.Tab(label="Create embedding"):
- new_embedding_name = gr.Textbox(label="Name", elem_id="train_new_embedding_name")
- initialization_text = gr.Textbox(label="Initialization text", value="*", elem_id="train_initialization_text")
- nvpt = gr.Slider(label="Number of vectors per token", minimum=1, maximum=75, step=1, value=1, elem_id="train_nvpt")
- overwrite_old_embedding = gr.Checkbox(value=False, label="Overwrite Old Embedding", elem_id="train_overwrite_old_embedding")
-
- with gr.Row():
- with gr.Column(scale=3):
- gr.HTML(value="")
-
- with gr.Column():
- create_embedding = gr.Button(value="Create embedding", variant='primary', elem_id="train_create_embedding")
-
- with gr.Tab(label="Create hypernetwork"):
- new_hypernetwork_name = gr.Textbox(label="Name", elem_id="train_new_hypernetwork_name")
- new_hypernetwork_sizes = gr.CheckboxGroup(label="Modules", value=["768", "320", "640", "1280"], choices=["768", "1024", "320", "640", "1280"], elem_id="train_new_hypernetwork_sizes")
- new_hypernetwork_layer_structure = gr.Textbox("1, 2, 1", label="Enter hypernetwork layer structure", placeholder="1st and last digit must be 1. ex:'1, 2, 1'", elem_id="train_new_hypernetwork_layer_structure")
- new_hypernetwork_activation_func = gr.Dropdown(value="linear", label="Select activation function of hypernetwork. Recommended : Swish / Linear(none)", choices=modules.hypernetworks.ui.keys, elem_id="train_new_hypernetwork_activation_func")
- new_hypernetwork_initialization_option = gr.Dropdown(value = "Normal", label="Select Layer weights initialization. Recommended: Kaiming for relu-like, Xavier for sigmoid-like, Normal otherwise", choices=["Normal", "KaimingUniform", "KaimingNormal", "XavierUniform", "XavierNormal"], elem_id="train_new_hypernetwork_initialization_option")
- new_hypernetwork_add_layer_norm = gr.Checkbox(label="Add layer normalization", elem_id="train_new_hypernetwork_add_layer_norm")
- new_hypernetwork_use_dropout = gr.Checkbox(label="Use dropout", elem_id="train_new_hypernetwork_use_dropout")
- new_hypernetwork_dropout_structure = gr.Textbox("0, 0, 0", label="Enter hypernetwork Dropout structure (or empty). Recommended : 0~0.35 incrementing sequence: 0, 0.05, 0.15", placeholder="1st and last digit must be 0 and values should be between 0 and 1. ex:'0, 0.01, 0'")
- overwrite_old_hypernetwork = gr.Checkbox(value=False, label="Overwrite Old Hypernetwork", elem_id="train_overwrite_old_hypernetwork")
-
- with gr.Row():
- with gr.Column(scale=3):
- gr.HTML(value="")
-
- with gr.Column():
- create_hypernetwork = gr.Button(value="Create hypernetwork", variant='primary', elem_id="train_create_hypernetwork")
-
- with gr.Tab(label="Preprocess images"):
- process_src = gr.Textbox(label='Source directory', elem_id="train_process_src")
- process_dst = gr.Textbox(label='Destination directory', elem_id="train_process_dst")
- process_width = gr.Slider(minimum=64, maximum=2048, step=8, label="Width", value=512, elem_id="train_process_width")
- process_height = gr.Slider(minimum=64, maximum=2048, step=8, label="Height", value=512, elem_id="train_process_height")
- preprocess_txt_action = gr.Dropdown(label='Existing Caption txt Action', value="ignore", choices=["ignore", "copy", "prepend", "append"], elem_id="train_preprocess_txt_action")
-
- with gr.Row():
- process_flip = gr.Checkbox(label='Create flipped copies', elem_id="train_process_flip")
- process_split = gr.Checkbox(label='Split oversized images', elem_id="train_process_split")
- process_focal_crop = gr.Checkbox(label='Auto focal point crop', elem_id="train_process_focal_crop")
- process_multicrop = gr.Checkbox(label='Auto-sized crop', elem_id="train_process_multicrop")
- process_caption = gr.Checkbox(label='Use BLIP for caption', elem_id="train_process_caption")
- process_caption_deepbooru = gr.Checkbox(label='Use deepbooru for caption', visible=True, elem_id="train_process_caption_deepbooru")
-
- with gr.Row(visible=False) as process_split_extra_row:
- process_split_threshold = gr.Slider(label='Split image threshold', value=0.5, minimum=0.0, maximum=1.0, step=0.05, elem_id="train_process_split_threshold")
- process_overlap_ratio = gr.Slider(label='Split image overlap ratio', value=0.2, minimum=0.0, maximum=0.9, step=0.05, elem_id="train_process_overlap_ratio")
-
- with gr.Row(visible=False) as process_focal_crop_row:
- process_focal_crop_face_weight = gr.Slider(label='Focal point face weight', value=0.9, minimum=0.0, maximum=1.0, step=0.05, elem_id="train_process_focal_crop_face_weight")
- process_focal_crop_entropy_weight = gr.Slider(label='Focal point entropy weight', value=0.15, minimum=0.0, maximum=1.0, step=0.05, elem_id="train_process_focal_crop_entropy_weight")
- process_focal_crop_edges_weight = gr.Slider(label='Focal point edges weight', value=0.5, minimum=0.0, maximum=1.0, step=0.05, elem_id="train_process_focal_crop_edges_weight")
- process_focal_crop_debug = gr.Checkbox(label='Create debug image', elem_id="train_process_focal_crop_debug")
-
- with gr.Column(visible=False) as process_multicrop_col:
- gr.Markdown('Each image is center-cropped with an automatically chosen width and height.')
- with gr.Row():
- process_multicrop_mindim = gr.Slider(minimum=64, maximum=2048, step=8, label="Dimension lower bound", value=384, elem_id="train_process_multicrop_mindim")
- process_multicrop_maxdim = gr.Slider(minimum=64, maximum=2048, step=8, label="Dimension upper bound", value=768, elem_id="train_process_multicrop_maxdim")
- with gr.Row():
- process_multicrop_minarea = gr.Slider(minimum=64*64, maximum=2048*2048, step=1, label="Area lower bound", value=64*64, elem_id="train_process_multicrop_minarea")
- process_multicrop_maxarea = gr.Slider(minimum=64*64, maximum=2048*2048, step=1, label="Area upper bound", value=640*640, elem_id="train_process_multicrop_maxarea")
- with gr.Row():
- process_multicrop_objective = gr.Radio(["Maximize area", "Minimize error"], value="Maximize area", label="Resizing objective", elem_id="train_process_multicrop_objective")
- process_multicrop_threshold = gr.Slider(minimum=0, maximum=1, step=0.01, label="Error threshold", value=0.1, elem_id="train_process_multicrop_threshold")
-
- with gr.Row():
- with gr.Column(scale=3):
- gr.HTML(value="")
-
- with gr.Column():
- with gr.Row():
- interrupt_preprocessing = gr.Button("Interrupt", elem_id="train_interrupt_preprocessing")
- run_preprocess = gr.Button(value="Preprocess", variant='primary', elem_id="train_run_preprocess")
-
- process_split.change(
- fn=lambda show: gr_show(show),
- inputs=[process_split],
- outputs=[process_split_extra_row],
- )
-
- process_focal_crop.change(
- fn=lambda show: gr_show(show),
- inputs=[process_focal_crop],
- outputs=[process_focal_crop_row],
- )
-
- process_multicrop.change(
- fn=lambda show: gr_show(show),
- inputs=[process_multicrop],
- outputs=[process_multicrop_col],
- )
-
- def get_textual_inversion_template_names():
- return sorted([x for x in textual_inversion.textual_inversion_templates])
-
- with gr.Tab(label="Train"):
- gr.HTML(value="Train an embedding or Hypernetwork; you must specify a directory with a set of 1:1 ratio images [wiki]
")
- with FormRow():
- train_embedding_name = gr.Dropdown(label='Embedding', elem_id="train_embedding", choices=sorted(sd_hijack.model_hijack.embedding_db.word_embeddings.keys()))
- create_refresh_button(train_embedding_name, sd_hijack.model_hijack.embedding_db.load_textual_inversion_embeddings, lambda: {"choices": sorted(sd_hijack.model_hijack.embedding_db.word_embeddings.keys())}, "refresh_train_embedding_name")
-
- train_hypernetwork_name = gr.Dropdown(label='Hypernetwork', elem_id="train_hypernetwork", choices=[x for x in shared.hypernetworks.keys()])
- create_refresh_button(train_hypernetwork_name, shared.reload_hypernetworks, lambda: {"choices": sorted([x for x in shared.hypernetworks.keys()])}, "refresh_train_hypernetwork_name")
-
- with FormRow():
- embedding_learn_rate = gr.Textbox(label='Embedding Learning rate', placeholder="Embedding Learning rate", value="0.005", elem_id="train_embedding_learn_rate")
- hypernetwork_learn_rate = gr.Textbox(label='Hypernetwork Learning rate', placeholder="Hypernetwork Learning rate", value="0.00001", elem_id="train_hypernetwork_learn_rate")
-
- with FormRow():
- clip_grad_mode = gr.Dropdown(value="disabled", label="Gradient Clipping", choices=["disabled", "value", "norm"])
- clip_grad_value = gr.Textbox(placeholder="Gradient clip value", value="0.1", show_label=False)
-
- with FormRow():
- batch_size = gr.Number(label='Batch size', value=1, precision=0, elem_id="train_batch_size")
- gradient_step = gr.Number(label='Gradient accumulation steps', value=1, precision=0, elem_id="train_gradient_step")
-
- dataset_directory = gr.Textbox(label='Dataset directory', placeholder="Path to directory with input images", elem_id="train_dataset_directory")
- log_directory = gr.Textbox(label='Log directory', placeholder="Path to directory where to write outputs", value="textual_inversion", elem_id="train_log_directory")
-
- with FormRow():
- template_file = gr.Dropdown(label='Prompt template', value="style_filewords.txt", elem_id="train_template_file", choices=get_textual_inversion_template_names())
- create_refresh_button(template_file, textual_inversion.list_textual_inversion_templates, lambda: {"choices": get_textual_inversion_template_names()}, "refrsh_train_template_file")
-
- training_width = gr.Slider(minimum=64, maximum=2048, step=8, label="Width", value=512, elem_id="train_training_width")
- training_height = gr.Slider(minimum=64, maximum=2048, step=8, label="Height", value=512, elem_id="train_training_height")
- varsize = gr.Checkbox(label="Do not resize images", value=False, elem_id="train_varsize")
- steps = gr.Number(label='Max steps', value=100000, precision=0, elem_id="train_steps")
-
- with FormRow():
- create_image_every = gr.Number(label='Save an image to log directory every N steps, 0 to disable', value=500, precision=0, elem_id="train_create_image_every")
- save_embedding_every = gr.Number(label='Save a copy of embedding to log directory every N steps, 0 to disable', value=500, precision=0, elem_id="train_save_embedding_every")
-
- use_weight = gr.Checkbox(label="Use PNG alpha channel as loss weight", value=False, elem_id="use_weight")
-
- save_image_with_stored_embedding = gr.Checkbox(label='Save images with embedding in PNG chunks', value=True, elem_id="train_save_image_with_stored_embedding")
- preview_from_txt2img = gr.Checkbox(label='Read parameters (prompt, etc...) from txt2img tab when making previews', value=False, elem_id="train_preview_from_txt2img")
-
- shuffle_tags = gr.Checkbox(label="Shuffle tags by ',' when creating prompts.", value=False, elem_id="train_shuffle_tags")
- tag_drop_out = gr.Slider(minimum=0, maximum=1, step=0.1, label="Drop out tags when creating prompts.", value=0, elem_id="train_tag_drop_out")
-
- latent_sampling_method = gr.Radio(label='Choose latent sampling method', value="once", choices=['once', 'deterministic', 'random'], elem_id="train_latent_sampling_method")
-
- with gr.Row():
- train_embedding = gr.Button(value="Train Embedding", variant='primary', elem_id="train_train_embedding")
- interrupt_training = gr.Button(value="Interrupt", elem_id="train_interrupt_training")
- train_hypernetwork = gr.Button(value="Train Hypernetwork", variant='primary', elem_id="train_train_hypernetwork")
-
- params = script_callbacks.UiTrainTabParams(txt2img_preview_params)
-
- script_callbacks.ui_train_tabs_callback(params)
-
- with gr.Column(elem_id='ti_gallery_container'):
- ti_output = gr.Text(elem_id="ti_output", value="", show_label=False)
- ti_gallery = gr.Gallery(label='Output', show_label=False, elem_id='ti_gallery').style(grid=4)
- ti_progress = gr.HTML(elem_id="ti_progress", value="")
- ti_outcome = gr.HTML(elem_id="ti_error", value="")
-
- create_embedding.click(
- fn=modules.textual_inversion.ui.create_embedding,
- inputs=[
- new_embedding_name,
- initialization_text,
- nvpt,
- overwrite_old_embedding,
- ],
- outputs=[
- train_embedding_name,
- ti_output,
- ti_outcome,
- ]
- )
-
- create_hypernetwork.click(
- fn=modules.hypernetworks.ui.create_hypernetwork,
- inputs=[
- new_hypernetwork_name,
- new_hypernetwork_sizes,
- overwrite_old_hypernetwork,
- new_hypernetwork_layer_structure,
- new_hypernetwork_activation_func,
- new_hypernetwork_initialization_option,
- new_hypernetwork_add_layer_norm,
- new_hypernetwork_use_dropout,
- new_hypernetwork_dropout_structure
- ],
- outputs=[
- train_hypernetwork_name,
- ti_output,
- ti_outcome,
- ]
- )
-
- run_preprocess.click(
- fn=wrap_gradio_gpu_call(modules.textual_inversion.ui.preprocess, extra_outputs=[gr.update()]),
- _js="start_training_textual_inversion",
- inputs=[
- dummy_component,
- process_src,
- process_dst,
- process_width,
- process_height,
- preprocess_txt_action,
- process_flip,
- process_split,
- process_caption,
- process_caption_deepbooru,
- process_split_threshold,
- process_overlap_ratio,
- process_focal_crop,
- process_focal_crop_face_weight,
- process_focal_crop_entropy_weight,
- process_focal_crop_edges_weight,
- process_focal_crop_debug,
- process_multicrop,
- process_multicrop_mindim,
- process_multicrop_maxdim,
- process_multicrop_minarea,
- process_multicrop_maxarea,
- process_multicrop_objective,
- process_multicrop_threshold,
- ],
- outputs=[
- ti_output,
- ti_outcome,
- ],
- )
-
- train_embedding.click(
- fn=wrap_gradio_gpu_call(modules.textual_inversion.ui.train_embedding, extra_outputs=[gr.update()]),
- _js="start_training_textual_inversion",
- inputs=[
- dummy_component,
- train_embedding_name,
- embedding_learn_rate,
- batch_size,
- gradient_step,
- dataset_directory,
- log_directory,
- training_width,
- training_height,
- varsize,
- steps,
- clip_grad_mode,
- clip_grad_value,
- shuffle_tags,
- tag_drop_out,
- latent_sampling_method,
- use_weight,
- create_image_every,
- save_embedding_every,
- template_file,
- save_image_with_stored_embedding,
- preview_from_txt2img,
- *txt2img_preview_params,
- ],
- outputs=[
- ti_output,
- ti_outcome,
- ]
- )
-
- train_hypernetwork.click(
- fn=wrap_gradio_gpu_call(modules.hypernetworks.ui.train_hypernetwork, extra_outputs=[gr.update()]),
- _js="start_training_textual_inversion",
- inputs=[
- dummy_component,
- train_hypernetwork_name,
- hypernetwork_learn_rate,
- batch_size,
- gradient_step,
- dataset_directory,
- log_directory,
- training_width,
- training_height,
- varsize,
- steps,
- clip_grad_mode,
- clip_grad_value,
- shuffle_tags,
- tag_drop_out,
- latent_sampling_method,
- use_weight,
- create_image_every,
- save_embedding_every,
- template_file,
- preview_from_txt2img,
- *txt2img_preview_params,
- ],
- outputs=[
- ti_output,
- ti_outcome,
- ]
- )
-
- interrupt_training.click(
- fn=lambda: shared.state.interrupt(),
- inputs=[],
- outputs=[],
- )
-
- interrupt_preprocessing.click(
- fn=lambda: shared.state.interrupt(),
- inputs=[],
- outputs=[],
- )
-
- def create_setting_component(key, is_quicksettings=False):
- def fun():
- return opts.data[key] if key in opts.data else opts.data_labels[key].default
-
- info = opts.data_labels[key]
- t = type(info.default)
-
- args = info.component_args() if callable(info.component_args) else info.component_args
-
- if info.component is not None:
- comp = info.component
- elif t == str:
- comp = gr.Textbox
- elif t == int:
- comp = gr.Number
- elif t == bool:
- comp = gr.Checkbox
- else:
- raise Exception(f'bad options item type: {str(t)} for key {key}')
-
- elem_id = "setting_"+key
-
- if info.refresh is not None:
- if is_quicksettings:
- res = comp(label=info.label, value=fun(), elem_id=elem_id, **(args or {}))
- create_refresh_button(res, info.refresh, info.component_args, "refresh_" + key)
- else:
- with FormRow():
- res = comp(label=info.label, value=fun(), elem_id=elem_id, **(args or {}))
- create_refresh_button(res, info.refresh, info.component_args, "refresh_" + key)
- else:
- res = comp(label=info.label, value=fun(), elem_id=elem_id, **(args or {}))
-
- return res
-
- components = []
- component_dict = {}
- shared.settings_components = component_dict
-
- script_callbacks.ui_settings_callback()
- opts.reorder()
-
- def run_settings(*args):
- changed = []
-
- for key, value, comp in zip(opts.data_labels.keys(), args, components):
- assert comp == dummy_component or opts.same_type(value, opts.data_labels[key].default), f"Bad value for setting {key}: {value}; expecting {type(opts.data_labels[key].default).__name__}"
-
- for key, value, comp in zip(opts.data_labels.keys(), args, components):
- if comp == dummy_component:
- continue
-
- if opts.set(key, value):
- changed.append(key)
-
- try:
- opts.save(shared.config_filename)
- except RuntimeError:
- return opts.dumpjson(), f'{len(changed)} settings changed without save: {", ".join(changed)}.'
- return opts.dumpjson(), f'{len(changed)} settings changed{": " if len(changed) > 0 else ""}{", ".join(changed)}.'
-
- def run_settings_single(value, key):
- if not opts.same_type(value, opts.data_labels[key].default):
- return gr.update(visible=True), opts.dumpjson()
-
- if not opts.set(key, value):
- return gr.update(value=getattr(opts, key)), opts.dumpjson()
-
- opts.save(shared.config_filename)
-
- return get_value_for_setting(key), opts.dumpjson()
-
- with gr.Blocks(analytics_enabled=False) as settings_interface:
- with gr.Row():
- with gr.Column(scale=6):
- settings_submit = gr.Button(value="Apply settings", variant='primary', elem_id="settings_submit")
- with gr.Column():
- restart_gradio = gr.Button(value='Reload UI', variant='primary', elem_id="settings_restart_gradio")
-
- result = gr.HTML(elem_id="settings_result")
-
- quicksettings_names = [x.strip() for x in opts.quicksettings.split(",")]
- quicksettings_names = {x: i for i, x in enumerate(quicksettings_names) if x != 'quicksettings'}
-
- quicksettings_list = []
-
- previous_section = None
- current_tab = None
- current_row = None
- with gr.Tabs(elem_id="settings"):
- for i, (k, item) in enumerate(opts.data_labels.items()):
- section_must_be_skipped = item.section[0] is None
-
- if previous_section != item.section and not section_must_be_skipped:
- elem_id, text = item.section
-
- if current_tab is not None:
- current_row.__exit__()
- current_tab.__exit__()
-
- gr.Group()
- current_tab = gr.TabItem(elem_id="settings_{}".format(elem_id), label=text)
- current_tab.__enter__()
- current_row = gr.Column(variant='compact')
- current_row.__enter__()
-
- previous_section = item.section
-
- if k in quicksettings_names and not shared.cmd_opts.freeze_settings:
- quicksettings_list.append((i, k, item))
- components.append(dummy_component)
- elif section_must_be_skipped:
- components.append(dummy_component)
- else:
- component = create_setting_component(k)
- component_dict[k] = component
- components.append(component)
-
- if current_tab is not None:
- current_row.__exit__()
- current_tab.__exit__()
-
- with gr.TabItem("Actions"):
- request_notifications = gr.Button(value='Request browser notifications', elem_id="request_notifications")
- download_localization = gr.Button(value='Download localization template', elem_id="download_localization")
- reload_script_bodies = gr.Button(value='Reload custom script bodies (No ui updates, No restart)', variant='secondary', elem_id="settings_reload_script_bodies")
-
- with gr.TabItem("Licenses"):
- gr.HTML(shared.html("licenses.html"), elem_id="licenses")
-
- gr.Button(value="Show all pages", elem_id="settings_show_all_pages")
-
- request_notifications.click(
- fn=lambda: None,
- inputs=[],
- outputs=[],
- _js='function(){}'
- )
-
- download_localization.click(
- fn=lambda: None,
- inputs=[],
- outputs=[],
- _js='download_localization'
- )
-
- def reload_scripts():
- modules.scripts.reload_script_body_only()
- reload_javascript() # need to refresh the html page
-
- reload_script_bodies.click(
- fn=reload_scripts,
- inputs=[],
- outputs=[]
- )
-
- def request_restart():
- shared.state.interrupt()
- shared.state.need_restart = True
-
- restart_gradio.click(
- fn=request_restart,
- _js='restart_reload',
- inputs=[],
- outputs=[],
- )
-
- interfaces = [
- (txt2img_interface, "txt2img", "txt2img"),
- (img2img_interface, "img2img", "img2img"),
- (extras_interface, "Extras", "extras"),
- (pnginfo_interface, "PNG Info", "pnginfo"),
- (modelmerger_interface, "Checkpoint Merger", "modelmerger"),
- (train_interface, "Train", "ti"),
- ]
-
- css = ""
-
- for cssfile in modules.scripts.list_files_with_name("style.css"):
- if not os.path.isfile(cssfile):
- continue
-
- with open(cssfile, "r", encoding="utf8") as file:
- css += file.read() + "\n"
-
- if os.path.exists(os.path.join(data_path, "user.css")):
- with open(os.path.join(data_path, "user.css"), "r", encoding="utf8") as file:
- css += file.read() + "\n"
-
- if not cmd_opts.no_progressbar_hiding:
- css += css_hide_progressbar
-
- interfaces += script_callbacks.ui_tabs_callback()
- interfaces += [(settings_interface, "Settings", "settings")]
-
- extensions_interface = ui_extensions.create_ui()
- interfaces += [(extensions_interface, "Extensions", "extensions")]
-
- with gr.Blocks(css=css, analytics_enabled=False, title="Stable Diffusion") as demo:
- with gr.Row(elem_id="quicksettings", variant="compact"):
- for i, k, item in sorted(quicksettings_list, key=lambda x: quicksettings_names.get(x[1], x[0])):
- component = create_setting_component(k, is_quicksettings=True)
- component_dict[k] = component
-
- parameters_copypaste.connect_paste_params_buttons()
-
- with gr.Tabs(elem_id="tabs") as tabs:
- for interface, label, ifid in interfaces:
- with gr.TabItem(label, id=ifid, elem_id='tab_' + ifid):
- interface.render()
-
- if os.path.exists(os.path.join(script_path, "notification.mp3")):
- audio_notification = gr.Audio(interactive=False, value=os.path.join(script_path, "notification.mp3"), elem_id="audio_notification", visible=False)
-
- footer = shared.html("footer.html")
- footer = footer.format(versions=versions_html())
- gr.HTML(footer, elem_id="footer")
-
- text_settings = gr.Textbox(elem_id="settings_json", value=lambda: opts.dumpjson(), visible=False)
- settings_submit.click(
- fn=wrap_gradio_call(run_settings, extra_outputs=[gr.update()]),
- inputs=components,
- outputs=[text_settings, result],
- )
-
- for i, k, item in quicksettings_list:
- component = component_dict[k]
-
- component.change(
- fn=lambda value, k=k: run_settings_single(value, key=k),
- inputs=[component],
- outputs=[component, text_settings],
- )
-
- text_settings.change(
- fn=lambda: gr.update(visible=shared.sd_model and shared.sd_model.cond_stage_key == "edit"),
- inputs=[],
- outputs=[image_cfg_scale],
- )
-
- button_set_checkpoint = gr.Button('Change checkpoint', elem_id='change_checkpoint', visible=False)
- button_set_checkpoint.click(
- fn=lambda value, _: run_settings_single(value, key='sd_model_checkpoint'),
- _js="function(v){ var res = desiredCheckpointName; desiredCheckpointName = ''; return [res || v, null]; }",
- inputs=[component_dict['sd_model_checkpoint'], dummy_component],
- outputs=[component_dict['sd_model_checkpoint'], text_settings],
- )
-
- component_keys = [k for k in opts.data_labels.keys() if k in component_dict]
-
- def get_settings_values():
- return [get_value_for_setting(key) for key in component_keys]
-
- demo.load(
- fn=get_settings_values,
- inputs=[],
- outputs=[component_dict[k] for k in component_keys],
- )
-
- def modelmerger(*args):
- try:
- results = modules.extras.run_modelmerger(*args)
- except Exception as e:
- print("Error loading/saving model file:", file=sys.stderr)
- print(traceback.format_exc(), file=sys.stderr)
- modules.sd_models.list_models() # to remove the potentially missing models from the list
- return [*[gr.Dropdown.update(choices=modules.sd_models.checkpoint_tiles()) for _ in range(4)], f"Error merging checkpoints: {e}"]
- return results
-
- modelmerger_merge.click(fn=lambda: '', inputs=[], outputs=[modelmerger_result])
- modelmerger_merge.click(
- fn=wrap_gradio_gpu_call(modelmerger, extra_outputs=lambda: [gr.update() for _ in range(4)]),
- _js='modelmerger',
- inputs=[
- dummy_component,
- primary_model_name,
- secondary_model_name,
- tertiary_model_name,
- interp_method,
- interp_amount,
- save_as_half,
- custom_name,
- checkpoint_format,
- config_source,
- bake_in_vae,
- discard_weights,
- ],
- outputs=[
- primary_model_name,
- secondary_model_name,
- tertiary_model_name,
- component_dict['sd_model_checkpoint'],
- modelmerger_result,
- ]
- )
-
- ui_config_file = cmd_opts.ui_config_file
- ui_settings = {}
- settings_count = len(ui_settings)
- error_loading = False
-
- try:
- if os.path.exists(ui_config_file):
- with open(ui_config_file, "r", encoding="utf8") as file:
- ui_settings = json.load(file)
- except Exception:
- error_loading = True
- print("Error loading settings:", file=sys.stderr)
- print(traceback.format_exc(), file=sys.stderr)
-
- def loadsave(path, x):
- def apply_field(obj, field, condition=None, init_field=None):
- key = path + "/" + field
-
- if getattr(obj, 'custom_script_source', None) is not None:
- key = 'customscript/' + obj.custom_script_source + '/' + key
-
- if getattr(obj, 'do_not_save_to_config', False):
- return
-
- saved_value = ui_settings.get(key, None)
- if saved_value is None:
- ui_settings[key] = getattr(obj, field)
- elif condition and not condition(saved_value):
- pass
-
- # this warning is generally not useful;
- # print(f'Warning: Bad ui setting value: {key}: {saved_value}; Default value "{getattr(obj, field)}" will be used instead.')
- else:
- setattr(obj, field, saved_value)
- if init_field is not None:
- init_field(saved_value)
-
- if type(x) in [gr.Slider, gr.Radio, gr.Checkbox, gr.Textbox, gr.Number, gr.Dropdown] and x.visible:
- apply_field(x, 'visible')
-
- if type(x) == gr.Slider:
- apply_field(x, 'value')
- apply_field(x, 'minimum')
- apply_field(x, 'maximum')
- apply_field(x, 'step')
-
- if type(x) == gr.Radio:
- apply_field(x, 'value', lambda val: val in x.choices)
-
- if type(x) == gr.Checkbox:
- apply_field(x, 'value')
-
- if type(x) == gr.Textbox:
- apply_field(x, 'value')
-
- if type(x) == gr.Number:
- apply_field(x, 'value')
-
- if type(x) == gr.Dropdown:
- def check_dropdown(val):
- if getattr(x, 'multiselect', False):
- return all([value in x.choices for value in val])
- else:
- return val in x.choices
-
- apply_field(x, 'value', check_dropdown, getattr(x, 'init_field', None))
-
- visit(txt2img_interface, loadsave, "txt2img")
- visit(img2img_interface, loadsave, "img2img")
- visit(extras_interface, loadsave, "extras")
- visit(modelmerger_interface, loadsave, "modelmerger")
- visit(train_interface, loadsave, "train")
-
- if not error_loading and (not os.path.exists(ui_config_file) or settings_count != len(ui_settings)):
- with open(ui_config_file, "w", encoding="utf8") as file:
- json.dump(ui_settings, file, indent=4)
-
- # Required as a workaround for change() event not triggering when loading values from ui-config.json
- interp_description.value = update_interp_description(interp_method.value)
-
- return demo
-
-
-def reload_javascript():
- head = f'\n'
-
- inline = f"{localization.localization_js(shared.opts.localization)};"
- if cmd_opts.theme is not None:
- inline += f"set_theme('{cmd_opts.theme}');"
-
- for script in modules.scripts.list_scripts("javascript", ".js"):
- head += f'\n'
-
- head += f'\n'
-
- def template_response(*args, **kwargs):
- res = shared.GradioTemplateResponseOriginal(*args, **kwargs)
- res.body = res.body.replace(b'', f'{head}'.encode("utf8"))
- res.init_headers()
- return res
-
- gradio.routes.templates.TemplateResponse = template_response
-
-
-if not hasattr(shared, 'GradioTemplateResponseOriginal'):
- shared.GradioTemplateResponseOriginal = gradio.routes.templates.TemplateResponse
-
-
-def versions_html():
- import torch
- import launch
-
- python_version = ".".join([str(x) for x in sys.version_info[0:3]])
- commit = launch.commit_hash()
- short_commit = commit[0:8]
-
- if shared.xformers_available:
- import xformers
- xformers_version = xformers.__version__
- else:
- xformers_version = "N/A"
-
- return f"""
-python: {python_version}
- •
-torch: {getattr(torch, '__long_version__',torch.__version__)}
- •
-xformers: {xformers_version}
- •
-gradio: {gr.__version__}
- •
-commit: {short_commit}
- •
-checkpoint: N/A
-"""
diff --git a/spaces/jackyccl/segment-anything/groundingdino/models/GroundingDINO/transformer_vanilla.py b/spaces/jackyccl/segment-anything/groundingdino/models/GroundingDINO/transformer_vanilla.py
deleted file mode 100644
index 10c0920c1a217af5bb3e1b13077568035ab3b7b5..0000000000000000000000000000000000000000
--- a/spaces/jackyccl/segment-anything/groundingdino/models/GroundingDINO/transformer_vanilla.py
+++ /dev/null
@@ -1,123 +0,0 @@
-# ------------------------------------------------------------------------
-# Grounding DINO
-# url: https://github.com/IDEA-Research/GroundingDINO
-# Copyright (c) 2023 IDEA. All Rights Reserved.
-# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
-# ------------------------------------------------------------------------
-# Copyright (c) Aishwarya Kamath & Nicolas Carion. Licensed under the Apache License 2.0. All Rights Reserved
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
-"""
-DETR Transformer class.
-
-Copy-paste from torch.nn.Transformer with modifications:
- * positional encodings are passed in MHattention
- * extra LN at the end of encoder is removed
- * decoder returns a stack of activations from all decoding layers
-"""
-from typing import Optional
-
-import torch
-import torch.nn.functional as F
-from torch import Tensor, nn
-
-from .utils import (
- MLP,
- _get_activation_fn,
- _get_clones,
- gen_encoder_output_proposals,
- gen_sineembed_for_position,
- sigmoid_focal_loss,
-)
-
-
-class TextTransformer(nn.Module):
- def __init__(self, num_layers, d_model=256, nheads=8, dim_feedforward=2048, dropout=0.1):
- super().__init__()
- self.num_layers = num_layers
- self.d_model = d_model
- self.nheads = nheads
- self.dim_feedforward = dim_feedforward
- self.norm = None
-
- single_encoder_layer = TransformerEncoderLayer(
- d_model=d_model, nhead=nheads, dim_feedforward=dim_feedforward, dropout=dropout
- )
- self.layers = _get_clones(single_encoder_layer, num_layers)
-
- def forward(self, memory_text: torch.Tensor, text_attention_mask: torch.Tensor):
- """
-
- Args:
- text_attention_mask: bs, num_token
- memory_text: bs, num_token, d_model
-
- Raises:
- RuntimeError: _description_
-
- Returns:
- output: bs, num_token, d_model
- """
-
- output = memory_text.transpose(0, 1)
-
- for layer in self.layers:
- output = layer(output, src_key_padding_mask=text_attention_mask)
-
- if self.norm is not None:
- output = self.norm(output)
-
- return output.transpose(0, 1)
-
-
-class TransformerEncoderLayer(nn.Module):
- def __init__(
- self,
- d_model,
- nhead,
- dim_feedforward=2048,
- dropout=0.1,
- activation="relu",
- normalize_before=False,
- ):
- super().__init__()
- self.self_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout)
- # Implementation of Feedforward model
- self.linear1 = nn.Linear(d_model, dim_feedforward)
- self.dropout = nn.Dropout(dropout)
- self.linear2 = nn.Linear(dim_feedforward, d_model)
-
- self.norm1 = nn.LayerNorm(d_model)
- self.norm2 = nn.LayerNorm(d_model)
- self.dropout1 = nn.Dropout(dropout)
- self.dropout2 = nn.Dropout(dropout)
-
- self.activation = _get_activation_fn(activation)
- self.normalize_before = normalize_before
- self.nhead = nhead
-
- def with_pos_embed(self, tensor, pos: Optional[Tensor]):
- return tensor if pos is None else tensor + pos
-
- def forward(
- self,
- src,
- src_mask: Optional[Tensor] = None,
- src_key_padding_mask: Optional[Tensor] = None,
- pos: Optional[Tensor] = None,
- ):
- # repeat attn mask
- if src_mask.dim() == 3 and src_mask.shape[0] == src.shape[1]:
- # bs, num_q, num_k
- src_mask = src_mask.repeat(self.nhead, 1, 1)
-
- q = k = self.with_pos_embed(src, pos)
-
- src2 = self.self_attn(q, k, value=src, attn_mask=src_mask)[0]
-
- # src2 = self.self_attn(q, k, value=src, attn_mask=src_mask, key_padding_mask=src_key_padding_mask)[0]
- src = src + self.dropout1(src2)
- src = self.norm1(src)
- src2 = self.linear2(self.dropout(self.activation(self.linear1(src))))
- src = src + self.dropout2(src2)
- src = self.norm2(src)
- return src
diff --git a/spaces/jarvisbot/ChatImprovement/crazy_functions/test_project/python/dqn/policies.py b/spaces/jarvisbot/ChatImprovement/crazy_functions/test_project/python/dqn/policies.py
deleted file mode 100644
index 4ecf39a5fc04b24ad1b809232b186728366987b6..0000000000000000000000000000000000000000
--- a/spaces/jarvisbot/ChatImprovement/crazy_functions/test_project/python/dqn/policies.py
+++ /dev/null
@@ -1,237 +0,0 @@
-from typing import Any, Dict, List, Optional, Type
-
-import gym
-import torch as th
-from torch import nn
-
-from stable_baselines3.common.policies import BasePolicy, register_policy
-from stable_baselines3.common.torch_layers import BaseFeaturesExtractor, FlattenExtractor, NatureCNN, create_mlp
-from stable_baselines3.common.type_aliases import Schedule
-
-
-class QNetwork(BasePolicy):
- """
- Action-Value (Q-Value) network for DQN
-
- :param observation_space: Observation space
- :param action_space: Action space
- :param net_arch: The specification of the policy and value networks.
- :param activation_fn: Activation function
- :param normalize_images: Whether to normalize images or not,
- dividing by 255.0 (True by default)
- """
-
- def __init__(
- self,
- observation_space: gym.spaces.Space,
- action_space: gym.spaces.Space,
- features_extractor: nn.Module,
- features_dim: int,
- net_arch: Optional[List[int]] = None,
- activation_fn: Type[nn.Module] = nn.ReLU,
- normalize_images: bool = True,
- ):
- super(QNetwork, self).__init__(
- observation_space,
- action_space,
- features_extractor=features_extractor,
- normalize_images=normalize_images,
- )
-
- if net_arch is None:
- net_arch = [64, 64]
-
- self.net_arch = net_arch
- self.activation_fn = activation_fn
- self.features_extractor = features_extractor
- self.features_dim = features_dim
- self.normalize_images = normalize_images
- action_dim = self.action_space.n # number of actions
- q_net = create_mlp(self.features_dim, action_dim, self.net_arch, self.activation_fn)
- self.q_net = nn.Sequential(*q_net)
-
- def forward(self, obs: th.Tensor) -> th.Tensor:
- """
- Predict the q-values.
-
- :param obs: Observation
- :return: The estimated Q-Value for each action.
- """
- return self.q_net(self.extract_features(obs))
-
- def _predict(self, observation: th.Tensor, deterministic: bool = True) -> th.Tensor:
- q_values = self.forward(observation)
- # Greedy action
- action = q_values.argmax(dim=1).reshape(-1)
- return action
-
- def _get_constructor_parameters(self) -> Dict[str, Any]:
- data = super()._get_constructor_parameters()
-
- data.update(
- dict(
- net_arch=self.net_arch,
- features_dim=self.features_dim,
- activation_fn=self.activation_fn,
- features_extractor=self.features_extractor,
- )
- )
- return data
-
-
-class DQNPolicy(BasePolicy):
- """
- Policy class with Q-Value Net and target net for DQN
-
- :param observation_space: Observation space
- :param action_space: Action space
- :param lr_schedule: Learning rate schedule (could be constant)
- :param net_arch: The specification of the policy and value networks.
- :param activation_fn: Activation function
- :param features_extractor_class: Features extractor to use.
- :param features_extractor_kwargs: Keyword arguments
- to pass to the features extractor.
- :param normalize_images: Whether to normalize images or not,
- dividing by 255.0 (True by default)
- :param optimizer_class: The optimizer to use,
- ``th.optim.Adam`` by default
- :param optimizer_kwargs: Additional keyword arguments,
- excluding the learning rate, to pass to the optimizer
- """
-
- def __init__(
- self,
- observation_space: gym.spaces.Space,
- action_space: gym.spaces.Space,
- lr_schedule: Schedule,
- net_arch: Optional[List[int]] = None,
- activation_fn: Type[nn.Module] = nn.ReLU,
- features_extractor_class: Type[BaseFeaturesExtractor] = FlattenExtractor,
- features_extractor_kwargs: Optional[Dict[str, Any]] = None,
- normalize_images: bool = True,
- optimizer_class: Type[th.optim.Optimizer] = th.optim.Adam,
- optimizer_kwargs: Optional[Dict[str, Any]] = None,
- ):
- super(DQNPolicy, self).__init__(
- observation_space,
- action_space,
- features_extractor_class,
- features_extractor_kwargs,
- optimizer_class=optimizer_class,
- optimizer_kwargs=optimizer_kwargs,
- )
-
- if net_arch is None:
- if features_extractor_class == FlattenExtractor:
- net_arch = [64, 64]
- else:
- net_arch = []
-
- self.net_arch = net_arch
- self.activation_fn = activation_fn
- self.normalize_images = normalize_images
-
- self.net_args = {
- "observation_space": self.observation_space,
- "action_space": self.action_space,
- "net_arch": self.net_arch,
- "activation_fn": self.activation_fn,
- "normalize_images": normalize_images,
- }
-
- self.q_net, self.q_net_target = None, None
- self._build(lr_schedule)
-
- def _build(self, lr_schedule: Schedule) -> None:
- """
- Create the network and the optimizer.
-
- :param lr_schedule: Learning rate schedule
- lr_schedule(1) is the initial learning rate
- """
-
- self.q_net = self.make_q_net()
- self.q_net_target = self.make_q_net()
- self.q_net_target.load_state_dict(self.q_net.state_dict())
-
- # Setup optimizer with initial learning rate
- self.optimizer = self.optimizer_class(self.parameters(), lr=lr_schedule(1), **self.optimizer_kwargs)
-
- def make_q_net(self) -> QNetwork:
- # Make sure we always have separate networks for features extractors etc
- net_args = self._update_features_extractor(self.net_args, features_extractor=None)
- return QNetwork(**net_args).to(self.device)
-
- def forward(self, obs: th.Tensor, deterministic: bool = True) -> th.Tensor:
- return self._predict(obs, deterministic=deterministic)
-
- def _predict(self, obs: th.Tensor, deterministic: bool = True) -> th.Tensor:
- return self.q_net._predict(obs, deterministic=deterministic)
-
- def _get_constructor_parameters(self) -> Dict[str, Any]:
- data = super()._get_constructor_parameters()
-
- data.update(
- dict(
- net_arch=self.net_args["net_arch"],
- activation_fn=self.net_args["activation_fn"],
- lr_schedule=self._dummy_schedule, # dummy lr schedule, not needed for loading policy alone
- optimizer_class=self.optimizer_class,
- optimizer_kwargs=self.optimizer_kwargs,
- features_extractor_class=self.features_extractor_class,
- features_extractor_kwargs=self.features_extractor_kwargs,
- )
- )
- return data
-
-
-MlpPolicy = DQNPolicy
-
-
-class CnnPolicy(DQNPolicy):
- """
- Policy class for DQN when using images as input.
-
- :param observation_space: Observation space
- :param action_space: Action space
- :param lr_schedule: Learning rate schedule (could be constant)
- :param net_arch: The specification of the policy and value networks.
- :param activation_fn: Activation function
- :param features_extractor_class: Features extractor to use.
- :param normalize_images: Whether to normalize images or not,
- dividing by 255.0 (True by default)
- :param optimizer_class: The optimizer to use,
- ``th.optim.Adam`` by default
- :param optimizer_kwargs: Additional keyword arguments,
- excluding the learning rate, to pass to the optimizer
- """
-
- def __init__(
- self,
- observation_space: gym.spaces.Space,
- action_space: gym.spaces.Space,
- lr_schedule: Schedule,
- net_arch: Optional[List[int]] = None,
- activation_fn: Type[nn.Module] = nn.ReLU,
- features_extractor_class: Type[BaseFeaturesExtractor] = NatureCNN,
- features_extractor_kwargs: Optional[Dict[str, Any]] = None,
- normalize_images: bool = True,
- optimizer_class: Type[th.optim.Optimizer] = th.optim.Adam,
- optimizer_kwargs: Optional[Dict[str, Any]] = None,
- ):
- super(CnnPolicy, self).__init__(
- observation_space,
- action_space,
- lr_schedule,
- net_arch,
- activation_fn,
- features_extractor_class,
- features_extractor_kwargs,
- normalize_images,
- optimizer_class,
- optimizer_kwargs,
- )
-
-
-register_policy("MlpPolicy", MlpPolicy)
-register_policy("CnnPolicy", CnnPolicy)
diff --git a/spaces/jbilcke-hf/MusicGen/audiocraft/modules/codebooks_patterns.py b/spaces/jbilcke-hf/MusicGen/audiocraft/modules/codebooks_patterns.py
deleted file mode 100644
index c5b35cbea8cff84aa56116dbdd860fc72a913a13..0000000000000000000000000000000000000000
--- a/spaces/jbilcke-hf/MusicGen/audiocraft/modules/codebooks_patterns.py
+++ /dev/null
@@ -1,539 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-from collections import namedtuple
-from dataclasses import dataclass
-from functools import lru_cache
-import logging
-import typing as tp
-
-from abc import ABC, abstractmethod
-import torch
-
-LayoutCoord = namedtuple('LayoutCoord', ['t', 'q']) # (timestep, codebook index)
-PatternLayout = tp.List[tp.List[LayoutCoord]] # Sequence of coordinates
-logger = logging.getLogger(__name__)
-
-
-@dataclass
-class Pattern:
- """Base implementation of a pattern over a sequence with multiple codebooks.
-
- The codebook pattern consists in a layout, defining for each sequence step
- the list of coordinates of each codebook timestep in the resulting interleaved sequence.
- The first item of the pattern is always an empty list in order to properly insert a special token
- to start with. For convenience, we also keep track of ``n_q`` the number of codebooks used for the pattern
- and ``timesteps`` the number of timesteps corresponding to the original sequence.
-
- The pattern provides convenient methods to build and revert interleaved sequences from it:
- ``build_pattern_sequence`` maps a given a dense input tensor of multi-codebook sequence from [B, K, T]
- to the interleaved sequence of shape [B, K, S] applying the pattern, with S being the batch size,
- K being the number of codebooks, T the number of original timesteps and S the number of sequence steps
- for the output sequence. The unfilled positions are replaced with a special token and the built sequence
- is returned along with a mask indicating valid tokens.
- ``revert_pattern_sequence`` maps back an interleaved sequence of shape [B, K, S] to the original alignment
- of codebooks across timesteps to an output tensor of shape [B, K, T], using again a special token and a mask
- to fill and specify invalid positions if needed.
- See the dedicated methods for more details.
- """
- # Pattern layout, for each sequence step, we have a list of coordinates
- # corresponding to the original codebook timestep and position.
- # The first list is always an empty list in order to properly insert
- # a special token to start with.
- layout: PatternLayout
- timesteps: int
- n_q: int
-
- def __post_init__(self):
- assert len(self.layout) > 0
- assert self.layout[0] == []
- self._validate_layout()
- self._build_reverted_sequence_scatter_indexes = lru_cache(100)(self._build_reverted_sequence_scatter_indexes)
- self._build_pattern_sequence_scatter_indexes = lru_cache(100)(self._build_pattern_sequence_scatter_indexes)
- logger.info("New pattern, time steps: %d, sequence steps: %d", self.timesteps, len(self.layout))
-
- def _validate_layout(self):
- """Runs checks on the layout to ensure a valid pattern is defined.
- A pattern is considered invalid if:
- - Multiple timesteps for a same codebook are defined in the same sequence step
- - The timesteps for a given codebook are not in ascending order as we advance in the sequence
- (this would mean that we have future timesteps before past timesteps).
- """
- q_timesteps = {q: 0 for q in range(self.n_q)}
- for s, seq_coords in enumerate(self.layout):
- if len(seq_coords) > 0:
- qs = set()
- for coord in seq_coords:
- qs.add(coord.q)
- last_q_timestep = q_timesteps[coord.q]
- assert coord.t >= last_q_timestep, \
- f"Past timesteps are found in the sequence for codebook = {coord.q} at step {s}"
- q_timesteps[coord.q] = coord.t
- # each sequence step contains at max 1 coordinate per codebook
- assert len(qs) == len(seq_coords), \
- f"Multiple entries for a same codebook are found at step {s}"
-
- @property
- def num_sequence_steps(self):
- return len(self.layout) - 1
-
- @property
- def max_delay(self):
- max_t_in_seq_coords = 0
- for seq_coords in self.layout[1:]:
- for coords in seq_coords:
- max_t_in_seq_coords = max(max_t_in_seq_coords, coords.t + 1)
- return max_t_in_seq_coords - self.timesteps
-
- @property
- def valid_layout(self):
- valid_step = len(self.layout) - self.max_delay
- return self.layout[:valid_step]
-
- def get_sequence_coords_with_timestep(self, t: int, q: tp.Optional[int] = None):
- """Get codebook coordinates in the layout that corresponds to the specified timestep t
- and optionally to the codebook q. Coordinates are returned as a tuple with the sequence step
- and the actual codebook coordinates.
- """
- assert t <= self.timesteps, "provided timesteps is greater than the pattern's number of timesteps"
- if q is not None:
- assert q <= self.n_q, "provided number of codebooks is greater than the pattern's number of codebooks"
- coords = []
- for s, seq_codes in enumerate(self.layout):
- for code in seq_codes:
- if code.t == t and (q is None or code.q == q):
- coords.append((s, code))
- return coords
-
- def get_steps_with_timestep(self, t: int, q: tp.Optional[int] = None) -> tp.List[int]:
- return [step for step, coords in self.get_sequence_coords_with_timestep(t, q)]
-
- def get_first_step_with_timesteps(self, t: int, q: tp.Optional[int] = None) -> tp.Optional[int]:
- steps_with_timesteps = self.get_steps_with_timestep(t, q)
- return steps_with_timesteps[0] if len(steps_with_timesteps) > 0 else None
-
- def _build_pattern_sequence_scatter_indexes(self, timesteps: int, n_q: int, keep_only_valid_steps: bool,
- device: tp.Union[torch.device, str] = 'cpu'):
- """Build scatter indexes corresponding to the pattern, up to the provided sequence_steps.
-
- Args:
- timesteps (int): Maximum number of timesteps steps to consider.
- keep_only_valid_steps (bool): Restrict the pattern layout to match only valid steps.
- device (Union[torch.device, str]): Device for created tensors.
- Returns:
- indexes (torch.Tensor): Indexes corresponding to the sequence, of shape [K, S].
- mask (torch.Tensor): Mask corresponding to indexes that matches valid indexes, of shape [K, S].
- """
- assert n_q == self.n_q, f"invalid number of codebooks for the sequence and the pattern: {n_q} != {self.n_q}"
- assert timesteps <= self.timesteps, "invalid number of timesteps used to build the sequence from the pattern"
- # use the proper layout based on whether we limit ourselves to valid steps only or not,
- # note that using the valid_layout will result in a truncated sequence up to the valid steps
- ref_layout = self.valid_layout if keep_only_valid_steps else self.layout
- # single item indexing being super slow with pytorch vs. numpy, so we use numpy here
- indexes = torch.zeros(n_q, len(ref_layout), dtype=torch.long).numpy()
- mask = torch.zeros(n_q, len(ref_layout), dtype=torch.bool).numpy()
- # fill indexes with last sequence step value that will correspond to our special token
- # the last value is n_q * timesteps as we have flattened z and append special token as the last token
- # which will correspond to the index: n_q * timesteps
- indexes[:] = n_q * timesteps
- # iterate over the pattern and fill scattered indexes and mask
- for s, sequence_coords in enumerate(ref_layout):
- for coords in sequence_coords:
- if coords.t < timesteps:
- indexes[coords.q, s] = coords.t + coords.q * timesteps
- mask[coords.q, s] = 1
- indexes = torch.from_numpy(indexes).to(device)
- mask = torch.from_numpy(mask).to(device)
- return indexes, mask
-
- def build_pattern_sequence(self, z: torch.Tensor, special_token: int, keep_only_valid_steps: bool = False):
- """Build sequence corresponding to the pattern from the input tensor z.
- The sequence is built using up to sequence_steps if specified, and non-pattern
- coordinates are filled with the special token.
-
- Args:
- z (torch.Tensor): Input tensor of multi-codebooks sequence, of shape [B, K, T].
- special_token (int): Special token used to fill non-pattern coordinates in the new sequence.
- keep_only_valid_steps (bool): Build a sequence from the pattern up to valid (= fully defined) steps.
- Steps that are beyond valid steps will be replaced by the special_token in that case.
- Returns:
- values (torch.Tensor): Interleaved sequence matching the pattern, of shape [B, K, S] with S
- corresponding either to the sequence_steps if provided, otherwise to the length of the pattern.
- indexes (torch.Tensor): Indexes corresponding to the interleaved sequence, of shape [K, S].
- mask (torch.Tensor): Mask corresponding to indexes that matches valid indexes of shape [K, S].
- """
- B, K, T = z.shape
- indexes, mask = self._build_pattern_sequence_scatter_indexes(
- T, K, keep_only_valid_steps=keep_only_valid_steps, device=str(z.device)
- )
- z = z.view(B, -1)
- # we append the special token as the last index of our flattened z tensor
- z = torch.cat([z, torch.zeros_like(z[:, :1]) + special_token], dim=1)
- values = z[:, indexes.view(-1)]
- values = values.view(B, K, indexes.shape[-1])
- return values, indexes, mask
-
- def _build_reverted_sequence_scatter_indexes(self, sequence_steps: int, n_q: int,
- keep_only_valid_steps: bool = False,
- is_model_output: bool = False,
- device: tp.Union[torch.device, str] = 'cpu'):
- """Builds scatter indexes required to retrieve the original multi-codebook sequence
- from interleaving pattern.
-
- Args:
- sequence_steps (int): Sequence steps.
- n_q (int): Number of codebooks.
- keep_only_valid_steps (bool): Build a sequence from the pattern up to valid (= fully defined) steps.
- Steps that are beyond valid steps will be replaced by the special_token in that case.
- is_model_output (bool): Whether to keep the sequence item corresponding to initial special token or not.
- device (Union[torch.device, str]): Device for created tensors.
- Returns:
- torch.Tensor: Indexes for reconstructing the output, of shape [K, T].
- mask (torch.Tensor): Mask corresponding to indexes that matches valid indexes of shape [K, T].
- """
- ref_layout = self.valid_layout if keep_only_valid_steps else self.layout
- # TODO(jade): Do we want to further truncate to only valid timesteps here as well?
- timesteps = self.timesteps
- assert n_q == self.n_q, f"invalid number of codebooks for the sequence and the pattern: {n_q} != {self.n_q}"
- assert sequence_steps <= len(ref_layout), \
- f"sequence to revert is longer than the defined pattern: {sequence_steps} > {len(ref_layout)}"
-
- # ensure we take the appropriate indexes to keep the model output from the first special token as well
- if is_model_output:
- ref_layout = ref_layout[1:]
-
- # single item indexing being super slow with pytorch vs. numpy, so we use numpy here
- indexes = torch.zeros(n_q, timesteps, dtype=torch.long).numpy()
- mask = torch.zeros(n_q, timesteps, dtype=torch.bool).numpy()
- # fill indexes with last sequence step value that will correspond to our special token
- indexes[:] = n_q * sequence_steps
- for s, sequence_codes in enumerate(ref_layout):
- if s < sequence_steps:
- for code in sequence_codes:
- if code.t < timesteps:
- indexes[code.q, code.t] = s + code.q * sequence_steps
- mask[code.q, code.t] = 1
- indexes = torch.from_numpy(indexes).to(device)
- mask = torch.from_numpy(mask).to(device)
- return indexes, mask
-
- def revert_pattern_sequence(self, s: torch.Tensor, special_token: int, keep_only_valid_steps: bool = False):
- """Revert a sequence built from the pattern back to the original multi-codebook sequence without interleaving.
- The sequence is reverted using up to timesteps if specified, and non-pattern coordinates
- are filled with the special token.
-
- Args:
- s (torch.Tensor): Interleaved sequence tensor obtained from the pattern, of shape [B, K, S].
- special_token (int or float): Special token used to fill non-pattern coordinates in the new sequence.
- Returns:
- values (torch.Tensor): Interleaved sequence matching the pattern, of shape [B, K, T] with T
- corresponding either to the timesteps if provided, or the total timesteps in pattern otherwise.
- indexes (torch.Tensor): Indexes corresponding to the interleaved sequence, of shape [K, T].
- mask (torch.Tensor): Mask corresponding to indexes that matches valid indexes of shape [K, T].
- """
- B, K, S = s.shape
- indexes, mask = self._build_reverted_sequence_scatter_indexes(
- S, K, keep_only_valid_steps, is_model_output=False, device=str(s.device)
- )
- s = s.view(B, -1)
- # we append the special token as the last index of our flattened z tensor
- s = torch.cat([s, torch.zeros_like(s[:, :1]) + special_token], dim=1)
- values = s[:, indexes.view(-1)]
- values = values.view(B, K, indexes.shape[-1])
- return values, indexes, mask
-
- def revert_pattern_logits(self, logits: torch.Tensor, special_token: float, keep_only_valid_steps: bool = False):
- """Revert model logits obtained on a sequence built from the pattern
- back to a tensor matching the original sequence.
-
- This method is similar to ``revert_pattern_sequence`` with the following specificities:
- 1. It is designed to work with the extra cardinality dimension
- 2. We return the logits for the first sequence item that matches the special_token and
- which matching target in the original sequence is the first item of the sequence,
- while we skip the last logits as there is no matching target
- """
- B, card, K, S = logits.shape
- indexes, mask = self._build_reverted_sequence_scatter_indexes(
- S, K, keep_only_valid_steps, is_model_output=True, device=logits.device
- )
- logits = logits.reshape(B, card, -1)
- # we append the special token as the last index of our flattened z tensor
- logits = torch.cat([logits, torch.zeros_like(logits[:, :, :1]) + special_token], dim=-1) # [B, card, K x S]
- values = logits[:, :, indexes.view(-1)]
- values = values.view(B, card, K, indexes.shape[-1])
- return values, indexes, mask
-
-
-class CodebooksPatternProvider(ABC):
- """Abstraction around providing pattern for interleaving codebooks.
-
- The CodebooksPatternProvider abstraction allows to implement various strategies to
- define interleaving pattern of sequences composed of multiple codebooks. For a given
- number of codebooks `n_q`, the pattern provider can generate a specified pattern
- corresponding to a sequence of `T` timesteps with `n_q` parallel codebooks. This pattern
- can be used to construct a new sequence from the original codes respecting the specified
- pattern. The pattern is defined as a list of list of code coordinates, code coordinate
- being a tuple with the original timestep and codebook to build the new sequence.
- Note that all patterns must start with an empty list that is then used to insert a first
- sequence step of special tokens in the newly generated sequence.
-
- Args:
- n_q (int): number of codebooks.
- cached (bool): if True, patterns for a given length are cached. In general
- that should be true for efficiency reason to avoid synchronization points.
- """
- def __init__(self, n_q: int, cached: bool = True):
- assert n_q > 0
- self.n_q = n_q
- self.get_pattern = lru_cache(100)(self.get_pattern) # type: ignore
-
- @abstractmethod
- def get_pattern(self, timesteps: int) -> Pattern:
- """Builds pattern with specific interleaving between codebooks.
-
- Args:
- timesteps (int): Total numer of timesteps.
- """
- raise NotImplementedError()
-
-
-class DelayedPatternProvider(CodebooksPatternProvider):
- """Provider for delayed pattern across delayed codebooks.
- Codebooks are delayed in the sequence and sequence steps will contain codebooks
- from different timesteps.
-
- Example:
- Taking timesteps=4 and n_q=3, delays=None, the multi-codebook sequence:
- [[1, 2, 3, 4],
- [1, 2, 3, 4],
- [1, 2, 3, 4]]
- The resulting sequence obtained from the returned pattern is:
- [[S, 1, 2, 3, 4],
- [S, S, 1, 2, 3],
- [S, S, S, 1, 2]]
- (with S being a special token)
-
- Args:
- n_q (int): Number of codebooks.
- delays (Optional[List[int]]): Delay for each of the codebooks.
- If delays not defined, each codebook is delayed by 1 compared to the previous one.
- flatten_first (int): Flatten the first N timesteps.
- empty_initial (int): Prepend with N empty list of coordinates.
- """
- def __init__(self, n_q: int, delays: tp.Optional[tp.List[int]] = None,
- flatten_first: int = 0, empty_initial: int = 0):
- super().__init__(n_q)
- if delays is None:
- delays = list(range(n_q))
- self.delays = delays
- self.flatten_first = flatten_first
- self.empty_initial = empty_initial
- assert len(self.delays) == self.n_q
- assert sorted(self.delays) == self.delays
-
- def get_pattern(self, timesteps: int) -> Pattern:
- out: PatternLayout = [[]]
- max_delay = max(self.delays)
- if self.empty_initial:
- out += [[] for _ in range(self.empty_initial)]
- if self.flatten_first:
- for t in range(min(timesteps, self.flatten_first)):
- for q in range(self.n_q):
- out.append([LayoutCoord(t, q)])
- for t in range(self.flatten_first, timesteps + max_delay):
- v = []
- for q, delay in enumerate(self.delays):
- t_for_q = t - delay
- if t_for_q >= self.flatten_first:
- v.append(LayoutCoord(t_for_q, q))
- out.append(v)
- return Pattern(out, n_q=self.n_q, timesteps=timesteps)
-
-
-class ParallelPatternProvider(DelayedPatternProvider):
- """Provider for parallel pattern across codebooks.
- This pattern provider is a special case of the delayed pattern with actually no delay,
- hence delays=repeat(0, n_q).
-
- Args:
- n_q (int): Number of codebooks.
- """
- def __init__(self, n_q: int):
- super().__init__(n_q, [0] * n_q)
-
-
-class UnrolledPatternProvider(CodebooksPatternProvider):
- """Provider for unrolling codebooks pattern.
- This pattern provider enables to represent the codebook flattened completely or only to some extend
- while also specifying a given delay between the flattened codebooks representation, allowing to
- unroll the codebooks in the sequence.
-
- Example:
- 1. Flattening of the codebooks.
- By default, the pattern provider will fully flatten the codebooks such as flattening=range(n_q),
- taking n_q = 3 and timesteps = 4:
- [[1, 2, 3, 4],
- [1, 2, 3, 4],
- [1, 2, 3, 4]]
- will result into:
- [[S, S, 1, S, S, 2, S, S, 3, S, S, 4],
- [S, 1, S, S, 2, S, S, 3, S, S, 4, S],
- [1, S, S, 2, S, S, 3, S, S, 4, S, S]]
- 2. Partial flattening of the codebooks. The ``flattening`` parameter allows to specify the inner step
- for each of the codebook, allowing to define which codebook to flatten (or keep in parallel), for example
- taking n_q = 3, timesteps = 4 and flattening = [0, 1, 1]:
- [[1, 2, 3, 4],
- [1, 2, 3, 4],
- [1, 2, 3, 4]]
- will result into:
- [[S, 1, S, S, 2, S, S, 3, S, S, 4, S],
- [S, 1, S, S, 2, S, S, 3, S, S, 4, S],
- [1, S, S, 2, S, S, 3, S, S, 4, S, S]]
- 3. Flattening with delay. The ``delay`` parameter allows to further unroll the sequence of codebooks
- allowing to specify the delay per codebook. Note that the delay between codebooks flattened to the
- same inner timestep should be coherent. For example, taking n_q = 3, timesteps = 4, flattening = [0, 1, 1]
- and delays = [0, 3, 3]:
- [[1, 2, 3, 4],
- [1, 2, 3, 4],
- [1, 2, 3, 4]]
- will result into:
- [[S, S, S, 1, S, 2, S, 3, S, 4],
- [S, S, S, 1, S, 2, S, 3, S, 4],
- [1, 2, 3, S, 4, S, 5, S, 6, S]]
-
- Args:
- n_q (int): Number of codebooks.
- flattening (Optional[List[int]]): Flattening schema over the codebooks. If not defined,
- the codebooks will be flattened to 1 codebook per step, meaning that the sequence will
- have n_q extra steps for each timestep.
- delays (Optional[List[int]]): Delay for each of the codebooks. If not defined,
- no delay is added and therefore will default to [0] * ``n_q``.
- Note that two codebooks that will be flattened to the same inner step
- should have the same delay, otherwise the pattern is considered as invalid.
- """
- FlattenedCodebook = namedtuple('FlattenedCodebook', ['codebooks', 'delay'])
-
- def __init__(self, n_q: int, flattening: tp.Optional[tp.List[int]] = None,
- delays: tp.Optional[tp.List[int]] = None):
- super().__init__(n_q)
- if flattening is None:
- flattening = list(range(n_q))
- if delays is None:
- delays = [0] * n_q
- assert len(flattening) == n_q
- assert len(delays) == n_q
- assert sorted(flattening) == flattening
- assert sorted(delays) == delays
- self._flattened_codebooks = self._build_flattened_codebooks(delays, flattening)
- self.max_delay = max(delays)
-
- def _build_flattened_codebooks(self, delays: tp.List[int], flattening: tp.List[int]):
- """Build a flattened codebooks representation as a dictionary of inner step
- and the actual codebook indices corresponding to the flattened codebook. For convenience, we
- also store the delay associated to the flattened codebook to avoid maintaining an extra mapping.
- """
- flattened_codebooks: dict = {}
- for q, (inner_step, delay) in enumerate(zip(flattening, delays)):
- if inner_step not in flattened_codebooks:
- flat_codebook = UnrolledPatternProvider.FlattenedCodebook(codebooks=[q], delay=delay)
- else:
- flat_codebook = flattened_codebooks[inner_step]
- assert flat_codebook.delay == delay, (
- "Delay and flattening between codebooks is inconsistent: ",
- "two codebooks flattened to the same position should have the same delay."
- )
- flat_codebook.codebooks.append(q)
- flattened_codebooks[inner_step] = flat_codebook
- return flattened_codebooks
-
- @property
- def _num_inner_steps(self):
- """Number of inner steps to unroll between timesteps in order to flatten the codebooks.
- """
- return max([inner_step for inner_step in self._flattened_codebooks.keys()]) + 1
-
- def num_virtual_steps(self, timesteps: int) -> int:
- return timesteps * self._num_inner_steps + 1
-
- def get_pattern(self, timesteps: int) -> Pattern:
- """Builds pattern for delay across codebooks.
-
- Args:
- timesteps (int): Total numer of timesteps.
- """
- # the PatternLayout is built as a tuple of sequence position and list of coordinates
- # so that it can be reordered properly given the required delay between codebooks of given timesteps
- indexed_out: list = [(-1, [])]
- max_timesteps = timesteps + self.max_delay
- for t in range(max_timesteps):
- # for each timestep, we unroll the flattened codebooks,
- # emitting the sequence step with the corresponding delay
- for step in range(self._num_inner_steps):
- if step in self._flattened_codebooks:
- # we have codebooks at this virtual step to emit
- step_codebooks = self._flattened_codebooks[step]
- t_for_q = t + step_codebooks.delay
- coords = [LayoutCoord(t, q) for q in step_codebooks.codebooks]
- if t_for_q < max_timesteps and t < max_timesteps:
- indexed_out.append((t_for_q, coords))
- else:
- # there is no codebook in this virtual step so we emit an empty list
- indexed_out.append((t, []))
- out = [coords for _, coords in sorted(indexed_out)]
- return Pattern(out, n_q=self.n_q, timesteps=timesteps)
-
-
-class VALLEPattern(CodebooksPatternProvider):
- """Almost VALL-E style pattern. We futher allow some delays for the
- codebooks other than the first one.
-
- Args:
- n_q (int): Number of codebooks.
- delays (Optional[List[int]]): Delay for each of the codebooks.
- If delays not defined, each codebook is delayed by 1 compared to the previous one.
- """
- def __init__(self, n_q: int, delays: tp.Optional[tp.List[int]] = None):
- super().__init__(n_q)
- if delays is None:
- delays = [0] * (n_q - 1)
- self.delays = delays
- assert len(self.delays) == self.n_q - 1
- assert sorted(self.delays) == self.delays
-
- def get_pattern(self, timesteps: int) -> Pattern:
- out: PatternLayout = [[]]
- for t in range(timesteps):
- out.append([LayoutCoord(t, 0)])
- max_delay = max(self.delays)
- for t in range(timesteps + max_delay):
- v = []
- for q, delay in enumerate(self.delays):
- t_for_q = t - delay
- if t_for_q >= 0:
- v.append(LayoutCoord(t_for_q, q + 1))
- out.append(v)
- return Pattern(out, n_q=self.n_q, timesteps=timesteps)
-
-
-class MusicLMPattern(CodebooksPatternProvider):
- """Almost MusicLM style pattern. This is equivalent to full flattening
- but in a different order.
-
- Args:
- n_q (int): Number of codebooks.
- group_by (int): Number of codebooks to group together.
- """
- def __init__(self, n_q: int, group_by: int = 2):
- super().__init__(n_q)
- self.group_by = group_by
-
- def get_pattern(self, timesteps: int) -> Pattern:
- out: PatternLayout = [[]]
- for offset in range(0, self.n_q, self.group_by):
- for t in range(timesteps):
- for q in range(offset, offset + self.group_by):
- out.append([LayoutCoord(t, q)])
- return Pattern(out, n_q=self.n_q, timesteps=timesteps)
diff --git a/spaces/jbilcke-hf/media-server/test.sh b/spaces/jbilcke-hf/media-server/test.sh
deleted file mode 100644
index a4653ad33613542efdd798195d5f001be282378f..0000000000000000000000000000000000000000
--- a/spaces/jbilcke-hf/media-server/test.sh
+++ /dev/null
@@ -1,2 +0,0 @@
-echo "$FOO$BAR"
-
diff --git a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/Crypto/SelfTest/Cipher/test_CFB.py b/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/Crypto/SelfTest/Cipher/test_CFB.py
deleted file mode 100644
index cb0c35295ce51cf3cc8be4d85b66b52ac85353f4..0000000000000000000000000000000000000000
--- a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/Crypto/SelfTest/Cipher/test_CFB.py
+++ /dev/null
@@ -1,411 +0,0 @@
-# ===================================================================
-#
-# Copyright (c) 2014, Legrandin
-# All rights reserved.
-#
-# Redistribution and use in source and binary forms, with or without
-# modification, are permitted provided that the following conditions
-# are met:
-#
-# 1. Redistributions of source code must retain the above copyright
-# notice, this list of conditions and the following disclaimer.
-# 2. Redistributions in binary form must reproduce the above copyright
-# notice, this list of conditions and the following disclaimer in
-# the documentation and/or other materials provided with the
-# distribution.
-#
-# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
-# "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
-# LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
-# FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE
-# COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,
-# INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
-# BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
-# LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
-# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
-# LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN
-# ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
-# POSSIBILITY OF SUCH DAMAGE.
-# ===================================================================
-
-import unittest
-from binascii import unhexlify
-
-from Crypto.SelfTest.loader import load_test_vectors
-from Crypto.SelfTest.st_common import list_test_cases
-from Crypto.Util.py3compat import tobytes, is_string
-from Crypto.Cipher import AES, DES3, DES
-from Crypto.Hash import SHAKE128
-
-from Crypto.SelfTest.Cipher.test_CBC import BlockChainingTests
-
-
-def get_tag_random(tag, length):
- return SHAKE128.new(data=tobytes(tag)).read(length)
-
-
-class CfbTests(BlockChainingTests):
-
- aes_mode = AES.MODE_CFB
- des3_mode = DES3.MODE_CFB
-
- # Redefine test_unaligned_data_128/64
-
- def test_unaligned_data_128(self):
- plaintexts = [ b"7777777" ] * 100
-
- cipher = AES.new(self.key_128, AES.MODE_CFB, self.iv_128, segment_size=8)
- ciphertexts = [ cipher.encrypt(x) for x in plaintexts ]
- cipher = AES.new(self.key_128, AES.MODE_CFB, self.iv_128, segment_size=8)
- self.assertEqual(b"".join(ciphertexts), cipher.encrypt(b"".join(plaintexts)))
-
- cipher = AES.new(self.key_128, AES.MODE_CFB, self.iv_128, segment_size=128)
- ciphertexts = [ cipher.encrypt(x) for x in plaintexts ]
- cipher = AES.new(self.key_128, AES.MODE_CFB, self.iv_128, segment_size=128)
- self.assertEqual(b"".join(ciphertexts), cipher.encrypt(b"".join(plaintexts)))
-
- def test_unaligned_data_64(self):
- plaintexts = [ b"7777777" ] * 100
- cipher = DES3.new(self.key_192, DES3.MODE_CFB, self.iv_64, segment_size=8)
- ciphertexts = [ cipher.encrypt(x) for x in plaintexts ]
- cipher = DES3.new(self.key_192, DES3.MODE_CFB, self.iv_64, segment_size=8)
- self.assertEqual(b"".join(ciphertexts), cipher.encrypt(b"".join(plaintexts)))
-
- cipher = DES3.new(self.key_192, DES3.MODE_CFB, self.iv_64, segment_size=64)
- ciphertexts = [ cipher.encrypt(x) for x in plaintexts ]
- cipher = DES3.new(self.key_192, DES3.MODE_CFB, self.iv_64, segment_size=64)
- self.assertEqual(b"".join(ciphertexts), cipher.encrypt(b"".join(plaintexts)))
-
- # Extra
-
- def test_segment_size_128(self):
- for bits in range(8, 129, 8):
- cipher = AES.new(self.key_128, AES.MODE_CFB, self.iv_128,
- segment_size=bits)
-
- for bits in 0, 7, 9, 127, 129:
- self.assertRaises(ValueError, AES.new, self.key_128, AES.MODE_CFB,
- self.iv_128,
- segment_size=bits)
-
- def test_segment_size_64(self):
- for bits in range(8, 65, 8):
- cipher = DES3.new(self.key_192, DES3.MODE_CFB, self.iv_64,
- segment_size=bits)
-
- for bits in 0, 7, 9, 63, 65:
- self.assertRaises(ValueError, DES3.new, self.key_192, AES.MODE_CFB,
- self.iv_64,
- segment_size=bits)
-
-
-class NistCfbVectors(unittest.TestCase):
-
- def _do_kat_aes_test(self, file_name, segment_size):
-
- test_vectors = load_test_vectors(("Cipher", "AES"),
- file_name,
- "AES CFB%d KAT" % segment_size,
- { "count" : lambda x: int(x) } )
- if test_vectors is None:
- return
-
- direction = None
- for tv in test_vectors:
-
- # The test vector file contains some directive lines
- if is_string(tv):
- direction = tv
- continue
-
- self.description = tv.desc
- cipher = AES.new(tv.key, AES.MODE_CFB, tv.iv,
- segment_size=segment_size)
- if direction == "[ENCRYPT]":
- self.assertEqual(cipher.encrypt(tv.plaintext), tv.ciphertext)
- elif direction == "[DECRYPT]":
- self.assertEqual(cipher.decrypt(tv.ciphertext), tv.plaintext)
- else:
- assert False
-
- # See Section 6.4.5 in AESAVS
- def _do_mct_aes_test(self, file_name, segment_size):
-
- test_vectors = load_test_vectors(("Cipher", "AES"),
- file_name,
- "AES CFB%d Montecarlo" % segment_size,
- { "count" : lambda x: int(x) } )
- if test_vectors is None:
- return
-
- assert(segment_size in (8, 128))
-
- direction = None
- for tv in test_vectors:
-
- # The test vector file contains some directive lines
- if is_string(tv):
- direction = tv
- continue
-
- self.description = tv.desc
- cipher = AES.new(tv.key, AES.MODE_CFB, tv.iv,
- segment_size=segment_size)
-
- def get_input(input_text, output_seq, j):
- # CFB128
- if segment_size == 128:
- if j >= 2:
- return output_seq[-2]
- return [input_text, tv.iv][j]
- # CFB8
- if j == 0:
- return input_text
- elif j <= 16:
- return tv.iv[j - 1:j]
- return output_seq[j - 17]
-
- if direction == '[ENCRYPT]':
- cts = []
- for j in range(1000):
- plaintext = get_input(tv.plaintext, cts, j)
- cts.append(cipher.encrypt(plaintext))
- self.assertEqual(cts[-1], tv.ciphertext)
- elif direction == '[DECRYPT]':
- pts = []
- for j in range(1000):
- ciphertext = get_input(tv.ciphertext, pts, j)
- pts.append(cipher.decrypt(ciphertext))
- self.assertEqual(pts[-1], tv.plaintext)
- else:
- assert False
-
- def _do_tdes_test(self, file_name, segment_size):
-
- test_vectors = load_test_vectors(("Cipher", "TDES"),
- file_name,
- "TDES CFB%d KAT" % segment_size,
- { "count" : lambda x: int(x) } )
- if test_vectors is None:
- return
-
- direction = None
- for tv in test_vectors:
-
- # The test vector file contains some directive lines
- if is_string(tv):
- direction = tv
- continue
-
- self.description = tv.desc
- if hasattr(tv, "keys"):
- cipher = DES.new(tv.keys, DES.MODE_CFB, tv.iv,
- segment_size=segment_size)
- else:
- if tv.key1 != tv.key3:
- key = tv.key1 + tv.key2 + tv.key3 # Option 3
- else:
- key = tv.key1 + tv.key2 # Option 2
- cipher = DES3.new(key, DES3.MODE_CFB, tv.iv,
- segment_size=segment_size)
- if direction == "[ENCRYPT]":
- self.assertEqual(cipher.encrypt(tv.plaintext), tv.ciphertext)
- elif direction == "[DECRYPT]":
- self.assertEqual(cipher.decrypt(tv.ciphertext), tv.plaintext)
- else:
- assert False
-
-
-# Create one test method per file
-nist_aes_kat_mmt_files = (
- # KAT
- "CFB?GFSbox128.rsp",
- "CFB?GFSbox192.rsp",
- "CFB?GFSbox256.rsp",
- "CFB?KeySbox128.rsp",
- "CFB?KeySbox192.rsp",
- "CFB?KeySbox256.rsp",
- "CFB?VarKey128.rsp",
- "CFB?VarKey192.rsp",
- "CFB?VarKey256.rsp",
- "CFB?VarTxt128.rsp",
- "CFB?VarTxt192.rsp",
- "CFB?VarTxt256.rsp",
- # MMT
- "CFB?MMT128.rsp",
- "CFB?MMT192.rsp",
- "CFB?MMT256.rsp",
- )
-nist_aes_mct_files = (
- "CFB?MCT128.rsp",
- "CFB?MCT192.rsp",
- "CFB?MCT256.rsp",
- )
-
-for file_gen_name in nist_aes_kat_mmt_files:
- for bits in "8", "128":
- file_name = file_gen_name.replace("?", bits)
- def new_func(self, file_name=file_name, bits=bits):
- self._do_kat_aes_test(file_name, int(bits))
- setattr(NistCfbVectors, "test_AES_" + file_name, new_func)
-
-for file_gen_name in nist_aes_mct_files:
- for bits in "8", "128":
- file_name = file_gen_name.replace("?", bits)
- def new_func(self, file_name=file_name, bits=bits):
- self._do_mct_aes_test(file_name, int(bits))
- setattr(NistCfbVectors, "test_AES_" + file_name, new_func)
-del file_name, new_func
-
-nist_tdes_files = (
- "TCFB?MMT2.rsp", # 2TDES
- "TCFB?MMT3.rsp", # 3TDES
- "TCFB?invperm.rsp", # Single DES
- "TCFB?permop.rsp",
- "TCFB?subtab.rsp",
- "TCFB?varkey.rsp",
- "TCFB?vartext.rsp",
- )
-
-for file_gen_name in nist_tdes_files:
- for bits in "8", "64":
- file_name = file_gen_name.replace("?", bits)
- def new_func(self, file_name=file_name, bits=bits):
- self._do_tdes_test(file_name, int(bits))
- setattr(NistCfbVectors, "test_TDES_" + file_name, new_func)
-
-# END OF NIST CBC TEST VECTORS
-
-
-class SP800TestVectors(unittest.TestCase):
- """Class exercising the CFB test vectors found in Section F.3
- of NIST SP 800-3A"""
-
- def test_aes_128_cfb8(self):
- plaintext = '6bc1bee22e409f96e93d7e117393172aae2d'
- ciphertext = '3b79424c9c0dd436bace9e0ed4586a4f32b9'
- key = '2b7e151628aed2a6abf7158809cf4f3c'
- iv = '000102030405060708090a0b0c0d0e0f'
-
- key = unhexlify(key)
- iv = unhexlify(iv)
- plaintext = unhexlify(plaintext)
- ciphertext = unhexlify(ciphertext)
-
- cipher = AES.new(key, AES.MODE_CFB, iv, segment_size=8)
- self.assertEqual(cipher.encrypt(plaintext), ciphertext)
- cipher = AES.new(key, AES.MODE_CFB, iv, segment_size=8)
- self.assertEqual(cipher.decrypt(ciphertext), plaintext)
-
- def test_aes_192_cfb8(self):
- plaintext = '6bc1bee22e409f96e93d7e117393172aae2d'
- ciphertext = 'cda2521ef0a905ca44cd057cbf0d47a0678a'
- key = '8e73b0f7da0e6452c810f32b809079e562f8ead2522c6b7b'
- iv = '000102030405060708090a0b0c0d0e0f'
-
- key = unhexlify(key)
- iv = unhexlify(iv)
- plaintext = unhexlify(plaintext)
- ciphertext = unhexlify(ciphertext)
-
- cipher = AES.new(key, AES.MODE_CFB, iv, segment_size=8)
- self.assertEqual(cipher.encrypt(plaintext), ciphertext)
- cipher = AES.new(key, AES.MODE_CFB, iv, segment_size=8)
- self.assertEqual(cipher.decrypt(ciphertext), plaintext)
-
- def test_aes_256_cfb8(self):
- plaintext = '6bc1bee22e409f96e93d7e117393172aae2d'
- ciphertext = 'dc1f1a8520a64db55fcc8ac554844e889700'
- key = '603deb1015ca71be2b73aef0857d77811f352c073b6108d72d9810a30914dff4'
- iv = '000102030405060708090a0b0c0d0e0f'
-
- key = unhexlify(key)
- iv = unhexlify(iv)
- plaintext = unhexlify(plaintext)
- ciphertext = unhexlify(ciphertext)
-
- cipher = AES.new(key, AES.MODE_CFB, iv, segment_size=8)
- self.assertEqual(cipher.encrypt(plaintext), ciphertext)
- cipher = AES.new(key, AES.MODE_CFB, iv, segment_size=8)
- self.assertEqual(cipher.decrypt(ciphertext), plaintext)
-
- def test_aes_128_cfb128(self):
- plaintext = '6bc1bee22e409f96e93d7e117393172a' +\
- 'ae2d8a571e03ac9c9eb76fac45af8e51' +\
- '30c81c46a35ce411e5fbc1191a0a52ef' +\
- 'f69f2445df4f9b17ad2b417be66c3710'
- ciphertext = '3b3fd92eb72dad20333449f8e83cfb4a' +\
- 'c8a64537a0b3a93fcde3cdad9f1ce58b' +\
- '26751f67a3cbb140b1808cf187a4f4df' +\
- 'c04b05357c5d1c0eeac4c66f9ff7f2e6'
- key = '2b7e151628aed2a6abf7158809cf4f3c'
- iv = '000102030405060708090a0b0c0d0e0f'
-
- key = unhexlify(key)
- iv = unhexlify(iv)
- plaintext = unhexlify(plaintext)
- ciphertext = unhexlify(ciphertext)
-
- cipher = AES.new(key, AES.MODE_CFB, iv, segment_size=128)
- self.assertEqual(cipher.encrypt(plaintext), ciphertext)
- cipher = AES.new(key, AES.MODE_CFB, iv, segment_size=128)
- self.assertEqual(cipher.decrypt(ciphertext), plaintext)
-
- def test_aes_192_cfb128(self):
- plaintext = '6bc1bee22e409f96e93d7e117393172a' +\
- 'ae2d8a571e03ac9c9eb76fac45af8e51' +\
- '30c81c46a35ce411e5fbc1191a0a52ef' +\
- 'f69f2445df4f9b17ad2b417be66c3710'
- ciphertext = 'cdc80d6fddf18cab34c25909c99a4174' +\
- '67ce7f7f81173621961a2b70171d3d7a' +\
- '2e1e8a1dd59b88b1c8e60fed1efac4c9' +\
- 'c05f9f9ca9834fa042ae8fba584b09ff'
- key = '8e73b0f7da0e6452c810f32b809079e562f8ead2522c6b7b'
- iv = '000102030405060708090a0b0c0d0e0f'
-
- key = unhexlify(key)
- iv = unhexlify(iv)
- plaintext = unhexlify(plaintext)
- ciphertext = unhexlify(ciphertext)
-
- cipher = AES.new(key, AES.MODE_CFB, iv, segment_size=128)
- self.assertEqual(cipher.encrypt(plaintext), ciphertext)
- cipher = AES.new(key, AES.MODE_CFB, iv, segment_size=128)
- self.assertEqual(cipher.decrypt(ciphertext), plaintext)
-
- def test_aes_256_cfb128(self):
- plaintext = '6bc1bee22e409f96e93d7e117393172a' +\
- 'ae2d8a571e03ac9c9eb76fac45af8e51' +\
- '30c81c46a35ce411e5fbc1191a0a52ef' +\
- 'f69f2445df4f9b17ad2b417be66c3710'
-
- ciphertext = 'dc7e84bfda79164b7ecd8486985d3860' +\
- '39ffed143b28b1c832113c6331e5407b' +\
- 'df10132415e54b92a13ed0a8267ae2f9' +\
- '75a385741ab9cef82031623d55b1e471'
- key = '603deb1015ca71be2b73aef0857d77811f352c073b6108d72d9810a30914dff4'
- iv = '000102030405060708090a0b0c0d0e0f'
-
- key = unhexlify(key)
- iv = unhexlify(iv)
- plaintext = unhexlify(plaintext)
- ciphertext = unhexlify(ciphertext)
-
- cipher = AES.new(key, AES.MODE_CFB, iv, segment_size=128)
- self.assertEqual(cipher.encrypt(plaintext), ciphertext)
- cipher = AES.new(key, AES.MODE_CFB, iv, segment_size=128)
- self.assertEqual(cipher.decrypt(ciphertext), plaintext)
-
-
-def get_tests(config={}):
- tests = []
- tests += list_test_cases(CfbTests)
- if config.get('slow_tests'):
- tests += list_test_cases(NistCfbVectors)
- tests += list_test_cases(SP800TestVectors)
- return tests
-
-
-if __name__ == '__main__':
- suite = lambda: unittest.TestSuite(get_tests())
- unittest.main(defaultTest='suite')
diff --git a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/aiohttp/web_server.py b/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/aiohttp/web_server.py
deleted file mode 100644
index fa46e905caa307f30a242951610193ee2a98692e..0000000000000000000000000000000000000000
--- a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/aiohttp/web_server.py
+++ /dev/null
@@ -1,62 +0,0 @@
-"""Low level HTTP server."""
-import asyncio
-from typing import Any, Awaitable, Callable, Dict, List, Optional # noqa
-
-from .abc import AbstractStreamWriter
-from .helpers import get_running_loop
-from .http_parser import RawRequestMessage
-from .streams import StreamReader
-from .web_protocol import RequestHandler, _RequestFactory, _RequestHandler
-from .web_request import BaseRequest
-
-__all__ = ("Server",)
-
-
-class Server:
- def __init__(
- self,
- handler: _RequestHandler,
- *,
- request_factory: Optional[_RequestFactory] = None,
- loop: Optional[asyncio.AbstractEventLoop] = None,
- **kwargs: Any
- ) -> None:
- self._loop = get_running_loop(loop)
- self._connections: Dict[RequestHandler, asyncio.Transport] = {}
- self._kwargs = kwargs
- self.requests_count = 0
- self.request_handler = handler
- self.request_factory = request_factory or self._make_request
-
- @property
- def connections(self) -> List[RequestHandler]:
- return list(self._connections.keys())
-
- def connection_made(
- self, handler: RequestHandler, transport: asyncio.Transport
- ) -> None:
- self._connections[handler] = transport
-
- def connection_lost(
- self, handler: RequestHandler, exc: Optional[BaseException] = None
- ) -> None:
- if handler in self._connections:
- del self._connections[handler]
-
- def _make_request(
- self,
- message: RawRequestMessage,
- payload: StreamReader,
- protocol: RequestHandler,
- writer: AbstractStreamWriter,
- task: "asyncio.Task[None]",
- ) -> BaseRequest:
- return BaseRequest(message, payload, protocol, writer, task, self._loop)
-
- async def shutdown(self, timeout: Optional[float] = None) -> None:
- coros = [conn.shutdown(timeout) for conn in self._connections]
- await asyncio.gather(*coros)
- self._connections.clear()
-
- def __call__(self) -> RequestHandler:
- return RequestHandler(self, loop=self._loop, **self._kwargs)
diff --git a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/gpt_index/indices/knowledge_graph/base.py b/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/gpt_index/indices/knowledge_graph/base.py
deleted file mode 100644
index d6d758488aa4f117d92c3a487ff31b70388a0842..0000000000000000000000000000000000000000
--- a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/gpt_index/indices/knowledge_graph/base.py
+++ /dev/null
@@ -1,196 +0,0 @@
-"""Keyword-table based index.
-
-Similar to a "hash table" in concept. LlamaIndex first tries
-to extract keywords from the source text, and stores the
-keywords as keys per item. It similarly extracts keywords
-from the query text. Then, it tries to match those keywords to
-existing keywords in the table.
-
-"""
-
-import logging
-from typing import Any, Dict, List, Optional, Sequence, Tuple, Type
-
-from gpt_index.data_structs.data_structs import KG
-from gpt_index.indices.base import DOCUMENTS_INPUT, BaseGPTIndex
-from gpt_index.indices.query.base import BaseGPTIndexQuery
-from gpt_index.indices.query.knowledge_graph.query import GPTKGTableQuery, KGQueryMode
-from gpt_index.indices.query.schema import QueryMode
-from gpt_index.langchain_helpers.chain_wrapper import LLMPredictor
-from gpt_index.langchain_helpers.text_splitter import TextSplitter
-from gpt_index.prompts.default_prompts import (
- DEFAULT_KG_TRIPLET_EXTRACT_PROMPT,
- DEFAULT_QUERY_KEYWORD_EXTRACT_TEMPLATE,
-)
-from gpt_index.prompts.prompts import KnowledgeGraphPrompt
-from gpt_index.schema import BaseDocument
-from gpt_index.utils import get_new_id
-
-DQKET = DEFAULT_QUERY_KEYWORD_EXTRACT_TEMPLATE
-
-
-class GPTKnowledgeGraphIndex(BaseGPTIndex[KG]):
- """GPT Knowledge Graph Index.
-
- Build a KG by extracting triplets, and leveraging the KG during query-time.
-
- Args:
- kg_triple_extract_template (KnowledgeGraphPrompt): The prompt to use for
- extracting triplets.
- max_triplets_per_chunk (int): The maximum number of triplets to extract.
-
- """
-
- index_struct_cls = KG
-
- def __init__(
- self,
- documents: Optional[Sequence[DOCUMENTS_INPUT]] = None,
- index_struct: Optional[KG] = None,
- kg_triple_extract_template: Optional[KnowledgeGraphPrompt] = None,
- max_triplets_per_chunk: int = 10,
- llm_predictor: Optional[LLMPredictor] = None,
- text_splitter: Optional[TextSplitter] = None,
- include_embeddings: bool = False,
- **kwargs: Any,
- ) -> None:
- """Initialize params."""
- # need to set parameters before building index in base class.
- self.include_embeddings = include_embeddings
- self.max_triplets_per_chunk = max_triplets_per_chunk
- self.kg_triple_extract_template = (
- kg_triple_extract_template or DEFAULT_KG_TRIPLET_EXTRACT_PROMPT
- )
- # NOTE: Partially format keyword extract template here.
- self.kg_triple_extract_template = (
- self.kg_triple_extract_template.partial_format(
- max_knowledge_triplets=self.max_triplets_per_chunk
- )
- )
- super().__init__(
- documents=documents,
- index_struct=index_struct,
- llm_predictor=llm_predictor,
- text_splitter=text_splitter,
- **kwargs,
- )
-
- @classmethod
- def get_query_map(self) -> Dict[str, Type[BaseGPTIndexQuery]]:
- """Get query map."""
- return {
- QueryMode.DEFAULT: GPTKGTableQuery,
- }
-
- def _extract_triplets(self, text: str) -> List[Tuple[str, str, str]]:
- """Extract keywords from text."""
- response, _ = self._llm_predictor.predict(
- self.kg_triple_extract_template,
- text=text,
- )
- return self._parse_triplet_response(response)
-
- @staticmethod
- def _parse_triplet_response(response: str) -> List[Tuple[str, str, str]]:
- knowledge_strs = response.strip().split("\n")
- results = []
- for text in knowledge_strs:
- tokens = text[1:-1].split(",")
- if len(tokens) != 3:
- continue
- subj, pred, obj = tokens
- results.append((subj.strip(), pred.strip(), obj.strip()))
- return results
-
- def _build_fallback_text_splitter(self) -> TextSplitter:
- # if not specified, use "smart" text splitter to ensure chunks fit in prompt
- return self._prompt_helper.get_text_splitter_given_prompt(
- self.kg_triple_extract_template, 1
- )
-
- def _build_index_from_documents(self, documents: Sequence[BaseDocument]) -> KG:
- """Build the index from documents."""
- # do simple concatenation
- index_struct = KG(table={})
- for d in documents:
- nodes = self._get_nodes_from_document(d)
- for n in nodes:
- # set doc id
- node_id = get_new_id(set())
- n.doc_id = node_id
-
- triplets = self._extract_triplets(n.get_text())
- logging.debug(f"> Extracted triplets: {triplets}")
- for triplet in triplets:
- index_struct.upsert_triplet(triplet, n)
-
- if self.include_embeddings:
- for i, triplet in enumerate(triplets):
- self._embed_model.queue_text_for_embeddding(
- str(triplet), str(triplet)
- )
-
- embed_outputs = self._embed_model.get_queued_text_embeddings()
- for (rel_text, rel_embed) in zip(*embed_outputs):
- index_struct.add_to_embedding_dict(rel_text, rel_embed)
-
- return index_struct
-
- def _insert(self, document: BaseDocument, **insert_kwargs: Any) -> None:
- """Insert a document."""
- nodes = self._get_nodes_from_document(document)
- for n in nodes:
- # set doc id
- node_id = get_new_id(set())
- n.doc_id = node_id
-
- triplets = self._extract_triplets(n.get_text())
- logging.debug(f"Extracted triplets: {triplets}")
- for triplet in triplets:
- triplet_str = str(triplet)
- self._index_struct.upsert_triplet(triplet, n)
- if (
- self.include_embeddings
- and triplet_str not in self._index_struct.embedding_dict
- ):
- rel_embedding = self._embed_model.get_text_embedding(triplet_str)
- self.index_struct.add_to_embedding_dict(triplet_str, rel_embedding)
-
- def _delete(self, doc_id: str, **delete_kwargs: Any) -> None:
- """Delete a document."""
- raise NotImplementedError("Delete is not supported for KG index yet.")
-
- def _preprocess_query(self, mode: QueryMode, query_kwargs: Dict) -> None:
- """Set the default embedding mode during query based on current index."""
- if (
- len(self.index_struct.embedding_dict) > 0
- and "embedding_mode" not in query_kwargs
- ):
- query_kwargs["embedding_mode"] = KGQueryMode.HYBRID
-
- def get_networkx_graph(self) -> Any:
- """Get networkx representation of the graph structure.
-
- NOTE: This function requires networkx to be installed.
- NOTE: This is a beta feature.
-
- """
- try:
- import networkx as nx
- except ImportError:
- raise ImportError(
- "Please install networkx to visualize the graph: `pip install networkx`"
- )
-
- g = nx.Graph()
- # add nodes
- for node_name in self.index_struct.table.keys():
- g.add_node(node_name)
-
- # add edges
- rel_map = self.index_struct.rel_map
- for keyword in rel_map.keys():
- for obj, rel in rel_map[keyword]:
- g.add_edge(keyword, obj, title=rel)
-
- return g
diff --git a/spaces/justest/gpt4free/g4f/.v1/testing/hpgptai_test.py b/spaces/justest/gpt4free/g4f/.v1/testing/hpgptai_test.py
deleted file mode 100644
index cdd146dd381346d689266ce05b6fa9e12f574b1b..0000000000000000000000000000000000000000
--- a/spaces/justest/gpt4free/g4f/.v1/testing/hpgptai_test.py
+++ /dev/null
@@ -1,41 +0,0 @@
-import hpgptai
-
-#single completion
-res = hpgptai.Completion.create("你是谁","127.0.0.1:7890")
-print(res["reply"])
-
-
-#chat completion
-messages = [
- {
- "content": "你是谁",
- "html": "你是谁",
- "id": hpgptai.ChatCompletion.randomStr(),
- "role": "user",
- "who": "User: ",
- },
- {
- "content": "我是一位AI助手，专门为您提供各种服务和支持。我可以回答您的问题，帮助您解决问题，提供相关信息，并执行一些任务。请随时告诉我您需要什么帮助。",
- "html": "我是一位AI助手，专门为您提供各种服务和支持。我可以回答您的问题，帮助您解决问题，提供相关信息，并执行一些任务。请随时告诉我您需要什么帮助。",
- "id": hpgptai.ChatCompletion.randomStr(),
- "role": "assistant",
- "who": "AI: ",
- },
- {
- "content": "我上一句问的是什么？",
- "html": "我上一句问的是什么？",
- "id": hpgptai.ChatCompletion.randomStr(),
- "role": "user",
- "who": "User: ",
- },
-]
-res = hpgptai.ChatCompletion.create(messages,proxy="127.0.0.1:7890")
-print(res["reply"])
-
-
-
-
-
-
-
-
diff --git a/spaces/kaicheng/ChatGPT_ad/locale/extract_locale.py b/spaces/kaicheng/ChatGPT_ad/locale/extract_locale.py
deleted file mode 100644
index 32b0924bd6dffe150cb3e481ddadef836b91b83c..0000000000000000000000000000000000000000
--- a/spaces/kaicheng/ChatGPT_ad/locale/extract_locale.py
+++ /dev/null
@@ -1,26 +0,0 @@
-import os
-import json
-import re
-
-# Define regular expression patterns
-pattern = r'i18n\((\"{3}.*?\"{3}|\".*?\")\)'
-
-# Load the .py file
-with open('ChuanhuChatbot.py', 'r', encoding='utf-8') as f:
- contents = f.read()
-
-# Load the .py files in the modules folder
-for filename in os.listdir("modules"):
- if filename.endswith(".py"):
- with open(os.path.join("modules", filename), "r", encoding="utf-8") as f:
- contents += f.read()
-
-# Matching with regular expressions
-matches = re.findall(pattern, contents, re.DOTALL)
-
-# Convert to key/value pairs
-data = {match.strip('()"'): '' for match in matches}
-
-# Save as a JSON file
-with open('labels.json', 'w', encoding='utf-8') as f:
- json.dump(data, f, ensure_ascii=False, indent=4)
\ No newline at end of file
diff --git a/spaces/kbora/minerva-generate-docker/README.md b/spaces/kbora/minerva-generate-docker/README.md
deleted file mode 100644
index eaf41d5d34dc4a1b08457af08e107fb5a3b8c3eb..0000000000000000000000000000000000000000
--- a/spaces/kbora/minerva-generate-docker/README.md
+++ /dev/null
@@ -1,8 +0,0 @@
----
-title: Minerva Docker
-sdk: docker
-emoji: 📊
-colorFrom: indigo
-colorTo: green
-pinned: false
----
\ No newline at end of file
diff --git a/spaces/kenhugs/dsed/Encoder.py b/spaces/kenhugs/dsed/Encoder.py
deleted file mode 100644
index eda71aace262cf2b7dbc608b0e4e8e17e90c80a7..0000000000000000000000000000000000000000
--- a/spaces/kenhugs/dsed/Encoder.py
+++ /dev/null
@@ -1,836 +0,0 @@
-"""
-
-"""
-from collections.abc import Iterable
-
-import numpy as np
-
-
-class Encoder:
- """ Allow the localization in time of sound events using temporal prediction.
-
- In a sound event detection task, the output of the prediction model is
- often a temporal prediction. Different segmentation algorithm exist in order
- to translate this curves into a list of segment. This encoder give you access
- to most of them in a fairly easy way.
-
- """
-
- def __init__(self, classes: list, temporal_precision: int, clip_length: int,
- minimal_segment_step: int, t_collar: float = 0.200,
- percentage_of_length: float = 0.2,
- time_resolution: float = 1.00,
- method: str = "event_based_metrics"):
- """ Initialization of the encoder.
-
- To initialize the encoder, you must provide the list of the classes that
- each curve will represent, in the same order along with information
- about the curves and the precision of the segmentation.
-
-
- Args:
- classes (list):
- The list of class that each curves will represent
- It is require for the function parse.
- temporal_precision (int):
- The temporal prediction for each classes.
- clip_length (int):
- The length of the audio file (in seconds).
- minimal_segment_step (int):
- The minimum space in between two segments.
-
- .. todo:: Implement alternative way to provide the data (dict)
- """
- self.classes = classes
- self.temporal_precision = temporal_precision
- self.clip_length = clip_length
- self.minimal_segment_step = minimal_segment_step
- self.t_collar = t_collar
- self.percentage_of_length = percentage_of_length
- self.time_resolution = time_resolution
- self.method = method
-
- # Attribute that are not initialize with the constructor
- self.frame_length = None
- self.nb_frame = None
- self.class_correspondence = dict(zip(classes, range(len(classes))))
- self.class_correspondence_reverse = dict(zip(range(len(classes)), classes))
-
- def encode(self, temporal_prediction: np.array, method: str = "threshold",
- smooth: str = None, **kwargs) -> list:
- """Perform the localization of the sound event present in the file.
-
- Using the temporal prediction provided y the last step of the system,
- it will "localize" the sound event inside the file under the form of a
- strongly annotated line. (see DCASE2018 task 4 strong label exemple).
- There is two methods implemented here, one using a simple threshold
- based segmentation and an other using a modulation system based on the
- variance of the prediction over the time.
-
- Args:
- temporal_prediction (np.array): The complete set for
- probabilities that need to segmented. must be a three dimensional
- numpy array (, , )
- method (str):
- The segmentation method to use
- [threshold | hysteresis | derivative | primitive |
- mean_threshold | global_mean_threshold | median_threshold |
- gobal_median_threshold].
- smooth (str):
- The smoothing method to use [smoothMovingAvg].
- kwargs:
- See the segmentation method parameters.
-
- Returns:
- Return a list of positive and negative segments with their size. A
- segment is a tuple where the first value that represent the segment
- value (1) for positive, (0) for negative and the second values is
- the width of the segment (number of frame).
-
- :Exemple:
-
- ```YOTsn73eqbfc_10.000_20.000.wav 0.163 0.665 Alarm_bell_ringing```
- """
- # parameters verification
- _methods = ["threshold", "hysteresis", "derivative", "mean_threshold",
- "median_threshold", "dynamic_threshold",
- "global_mean_threshold", "global_median_threshold"]
-
- if method not in _methods:
- raise ValueError("Method %s doesn't exist. Only %s are available" %
- (method, _methods))
-
- # Depending on the method selected, the proper function will be selected
- encoder = None
-
- if method == _methods[0]:
- encoder = self.__encode_using_threshold
- elif method == _methods[2]:
- encoder = self._encode_using_derivative
- elif method == _methods[1]:
- encoder = self.__encode_using_hysteresis
- elif method == _methods[3]:
- encoder = self.__encode_using_mean_threshold
- elif method == _methods[6]:
- encoder = self.__encode_using_gmean_threshold
- elif method == _methods[4]:
- encoder = self.__encode_using_median_treshold
- elif method == _methods[5]:
- encoder = self.__encode_using_dynamic_threshold
- elif method == _methods[7]:
- encoder = self.__encode_using_gmedian_threshold
-
- # Apply smoothing if requested
- if smooth is not None:
- temporal_prediction = self.__smooth(temporal_prediction,
- method=smooth, **kwargs)
-
- # Now that we have the strong prediction, we can assign the value to the
- # two attributes nb_frame and frame_length
- self.nb_frame = temporal_prediction.shape[1]
- self.frame_length = self.clip_length / self.nb_frame
-
- # Execute the selected segmentation algorithm and recover its results
- return encoder(temporal_prediction, **kwargs)
-
- def parse(self, all_segments: list, test_files_name: list) -> str:
- """Transform a list of segment into a string ready for evaluation with
- sed_eval.
-
- Args:
- all_segments (list): a list of dict of 10 key. the list length is
- equal to the number of file, the dict number test_files_name
- (list): The list of the file names in the same
- """
- output = ""
-
- for clipIndex in range(len(all_segments)):
- clip = all_segments[clipIndex]
-
- empty_cls = 0
- for cls in clip:
- if len(clip[cls]) == 1 and clip[cls][0][0] == 0:
- empty_cls += 1
-
- if empty_cls == 10:
- output += "%s\n" % test_files_name[clipIndex]
- else:
-
- for cls in clip:
- start = 0
- for segment in clip[cls]:
- if segment[0] == 1.0:
- output += "%s\t%f\t%f\t%s\n" % (
- test_files_name[clipIndex],
- start * self.frame_length,
- (start + segment[1]) * self.frame_length,
- self.class_correspondence_reverse[cls]
- )
- start += segment[1]
-
- return output
-
- # ==================================================================================================================
- #
- # ENCODING METHODS
- #
- # ==================================================================================================================
- def _encode_using_derivative(self, temporal_prediction: np.array,
- rising: float = 0.5, decreasing: float = -0.5,
- window_size: int = 5, high: float = 0.8,
- padding: str = "same", **kwargs) -> list:
- """The derivative create segment based on the intensity of the variation
- of the temporal prediction curve. If the prediction rise above a certain
- threshold `rising` then a valid segment start. If it decrease faster
- than the `decreasing` threshold, then a valid segment finish. If the
- prediction start with a high value, of rise slowly but high, then an
- absolute (and global) threshold `high` is used. (it works like a normal
- threhsold)
-
- Args:
- temporal_prediction (np.array): The complete set for
- probabilities that need to segmented. must be a three dimensional
- numpy array (, , )
- rising (float): Must be between 0 and 1, rising threshold. When the
- decreasing (float): Must be between 0 and 1, decreasing threshold
- window_size (int): size of the processing window
- high (float): minimum prediction value that trigger a valid
- padding (str): The padding method to used on the curves
-
- Returns:
- The result of the system under the form of a strong annotation text
- where each represent on timed event
- """
-
- output = []
-
- # for class-dependant parameters
- window_sizes = window_size
- if not isinstance(window_size, Iterable):
- window_sizes = [window_size] * len(self.classes)
-
- risings = rising
- if not isinstance(rising, Iterable):
- risings = [rising] * len(self.classes)
-
- decreasings = decreasing
- if not isinstance(decreasing, Iterable):
- decreasings = [decreasing] * len(self.classes)
-
- highs = high
- if not isinstance(high, Iterable):
- highs = [high] * len(self.classes)
-
-
- for clip in temporal_prediction:
- cls = 0
- labeled = dict()
-
- for cls_ind, prediction_per_class in enumerate(clip.T):
- # get class-dependant parameters
- _window_size = int(window_sizes[cls_ind])
- _rising = risings[cls_ind]
- _decreasing = decreasings[cls_ind]
- _high = highs[cls_ind]
-
- padded_prediction_per_class = self.__pad(prediction_per_class,
- _window_size,
- method=padding)
-
- nb_segment = 1
- segments = []
- segment = [0.0, 0]
- for i in range(len(padded_prediction_per_class) - _window_size):
- window = padded_prediction_per_class[i:i + _window_size]
- slope = (window[-1] - window[0]) / _window_size
-
- # first element
- if i == 0:
- if window[0] > _high:
- segment = [1.0, 1]
- else:
- segment = [0.0, 1]
-
- # if on "high" segment
- if segment[0] == 1:
-
- # if above high threshol
- if window[0] > _high:
- segment[1] += 1
-
- else:
- # if decreasing threshold is reach
- if slope < _decreasing:
- segments.append(segment)
- nb_segment += 1
- segment = [0.0, 1]
- else:
- segment[1] += 1
-
- # if on "low" segment
- else:
-
- # if above high threshold
- if window[0] > _high:
- segments.append(segment)
- nb_segment += 1
- segment = [1.0, 1]
-
- else:
- if slope > _rising:
- segments.append(segment)
- nb_segment += 1
- segment = [1.0, 1]
- else:
- segment[1] += 1
-
- segments.append(segment.copy())
-
- labeled[cls] = segments
- cls += 1
-
- output.append(labeled)
- return output
-
- def __encode_using_hysteresis(self, temporal_prediction: np.array,
- low: float = 0.4, high: float = 0.6,
- **kwargs) -> list:
- """The hysteresis based segmentation algorithm require two threhsolds. A
- high value to decided when the segment should start and a low value to
- decided when to finish the segment. It perform better when the temporal
- prediction is noisy
-
- Args:
- temporal_prediction (np.array): The complete set for
- probabilities that need to segmented. must be a three dimensional
- numpy array (, , )
- low (float): low threshold (can be a list for class-dependant
- thresholding)
- high (float): high threshold (can ve a list for class-dependant
- thresholding)
- kwargs: Extra arguments
-
- Returns:
- the result of the system under the form of a strong annotation text
- where each line represent on timed event
- """
-
- # In case of class dependant thresholding
- lows = low
- if not isinstance(low, Iterable):
- lows = [low] * len(self.classes)
-
- highs = high
- if not isinstance(high, Iterable):
- highs = [high] * len(self.classes)
-
- prediction = temporal_prediction
-
- output = []
-
- for clip in prediction:
- labeled = dict()
-
- cls = 0
- for cls_ind, prediction_per_class in enumerate(clip.T):
- converted = list()
- segment = [0, 0]
- nb_segment = 1
- for i in range(len(prediction_per_class)):
- element = prediction_per_class[i]
-
- # first element
- if i == 0:
- if element > highs[cls_ind]:
- segment = [1.0, 1]
- else:
- segment = [0.0, 1]
-
- # then
- if element > highs[cls_ind]:
- if segment[0] == 1:
- segment[1] += 1
- else:
- converted.append(segment)
- nb_segment += 1
- segment = [1.0, 1]
-
- elif lows[cls_ind] <= element:
- segment[1] += 1
-
- else:
- if segment[0] == 0:
- segment[1] += 1
- else:
- converted.append(segment)
- nb_segment += 1
- segment = [0.0, 0]
-
- converted.append(segment)
-
- labeled[cls] = converted.copy()
- cls += 1
-
- output.append(labeled)
-
- return output
-
- def __encode_using_threshold(self, temporal_prediction: np.array,
- threshold: float or list, **kwargs) -> list:
- """A basic threshold segmentation algorithm.
-
- For each frame where the probability is above the given threshold,
- will be part of a valid segment, an invalid one otherwise. The
- threshold can be set globally (one unique threshold for all the
- classes) or independantely (one threshold for each classes)
-
- Args:
- temporal_prediction (np.array): The complete set for
- probabilities that need to segmented. must be a three dimensional
- numpy array (, , )
- threshold (float or list): One unique threshold or a list of
- threhsold. If using a list, it must define one threshold for
- each class
- **kwargs:
- """
- output = []
-
- thresholds = threshold
- if not isinstance(threshold, Iterable):
- thresholds = [threshold] * len(self.classes)
-
- bin_prediction = temporal_prediction.copy()
- bin_prediction[bin_prediction > thresholds] = 1
- bin_prediction[bin_prediction <= thresholds] = 0
-
- # Merging "hole" that are smaller than 200 ms
- step_length = self.clip_length / temporal_prediction.shape[1] * 1000
- max_hole_size = int(self.minimal_segment_step / step_length)
-
- for clip in bin_prediction:
- labeled = dict()
-
- cls = 0
- for bin_prediction_per_class in clip.T:
- # convert the binarized list into a list of tuple representing
- # the element and it's number of occurrence. The order is
- # conserved and the total sum should be equal to 10s
-
- # first pass --> Fill the holes
- for i in range(len(bin_prediction_per_class) - max_hole_size):
- window = bin_prediction_per_class[i: i + max_hole_size]
-
- if window[0] == window[-1] == 1:
- window[:] = [window[0]] * max_hole_size
-
- # second pass --> split into segments
- converted = []
- cpt = 0
- nb_segment = 0
- previous_elt = None
- for element in bin_prediction_per_class:
- if previous_elt is None:
- previous_elt = element
- cpt += 1
- nb_segment = 1
- continue
-
- if element == previous_elt:
- cpt += 1
-
- else:
- converted.append((previous_elt, cpt))
- previous_elt = element
- nb_segment += 1
- cpt = 1
-
- # case where the class is detect during the whole clip
- # if nbSegment == 1:
- converted.append((previous_elt, cpt))
-
- labeled[cls] = converted.copy()
- cls += 1
-
- output.append(labeled)
-
- return output
-
- def __encode_using_gmean_threshold(self, temporal_prediction: np.array,
- independent: bool = False, **kwargs
- ) -> list:
- """Using all the temporal prediction, the mean of each curve and for
- each class is computed and will be choose as threshold. Then call the
- `__encode_using_threshold` function to apply it.
-
- Args:
- temporal_prediction (np.array): The complete set for
- probabilities that need to segmented. must be a three dimensional
- numpy array (
-
-
-
-