-
-#include "libavutil/channel_layout.h"
-#include "libavutil/common.h"
-#include "libavutil/mem.h"
-#include "libavutil/opt.h"
-
-#include "avcodec.h"
-#include "celp_math.h"
-#include "codec_internal.h"
-#include "encode.h"
-#include "g723_1.h"
-
-#define BITSTREAM_WRITER_LE
-#include "put_bits.h"
-
-/**
- * Hamming window coefficients scaled by 2^15
- */
-static const int16_t hamming_window[LPC_FRAME] = {
- 2621, 2631, 2659, 2705, 2770, 2853, 2955, 3074, 3212, 3367,
- 3541, 3731, 3939, 4164, 4405, 4663, 4937, 5226, 5531, 5851,
- 6186, 6534, 6897, 7273, 7661, 8062, 8475, 8899, 9334, 9780,
- 10235, 10699, 11172, 11653, 12141, 12636, 13138, 13645, 14157, 14673,
- 15193, 15716, 16242, 16769, 17298, 17827, 18356, 18884, 19411, 19935,
- 20457, 20975, 21489, 21999, 22503, 23002, 23494, 23978, 24455, 24924,
- 25384, 25834, 26274, 26704, 27122, 27529, 27924, 28306, 28675, 29031,
- 29373, 29700, 30012, 30310, 30592, 30857, 31107, 31340, 31557, 31756,
- 31938, 32102, 32249, 32377, 32488, 32580, 32654, 32710, 32747, 32766,
- 32766, 32747, 32710, 32654, 32580, 32488, 32377, 32249, 32102, 31938,
- 31756, 31557, 31340, 31107, 30857, 30592, 30310, 30012, 29700, 29373,
- 29031, 28675, 28306, 27924, 27529, 27122, 26704, 26274, 25834, 25384,
- 24924, 24455, 23978, 23494, 23002, 22503, 21999, 21489, 20975, 20457,
- 19935, 19411, 18884, 18356, 17827, 17298, 16769, 16242, 15716, 15193,
- 14673, 14157, 13645, 13138, 12636, 12141, 11653, 11172, 10699, 10235,
- 9780, 9334, 8899, 8475, 8062, 7661, 7273, 6897, 6534, 6186,
- 5851, 5531, 5226, 4937, 4663, 4405, 4164, 3939, 3731, 3541,
- 3367, 3212, 3074, 2955, 2853, 2770, 2705, 2659, 2631, 2621
-};
-
-/**
- * Binomial window coefficients scaled by 2^15
- */
-static const int16_t binomial_window[LPC_ORDER] = {
- 32749, 32695, 32604, 32477, 32315, 32118, 31887, 31622, 31324, 30995
-};
-
-/**
- * 0.994^i scaled by 2^15
- */
-static const int16_t bandwidth_expand[LPC_ORDER] = {
- 32571, 32376, 32182, 31989, 31797, 31606, 31416, 31228, 31040, 30854
-};
-
-/**
- * 0.5^i scaled by 2^15
- */
-static const int16_t percept_flt_tbl[2][LPC_ORDER] = {
- /* Zero part */
- {29491, 26542, 23888, 21499, 19349, 17414, 15673, 14106, 12695, 11425},
- /* Pole part */
- {16384, 8192, 4096, 2048, 1024, 512, 256, 128, 64, 32}
-};
-
-static av_cold int g723_1_encode_init(AVCodecContext *avctx)
-{
- G723_1_Context *s = avctx->priv_data;
- G723_1_ChannelContext *p = &s->ch[0];
-
- if (avctx->sample_rate != 8000) {
- av_log(avctx, AV_LOG_ERROR, "Only 8000Hz sample rate supported\n");
- return AVERROR(EINVAL);
- }
-
- if (avctx->bit_rate == 6300) {
- p->cur_rate = RATE_6300;
- } else if (avctx->bit_rate == 5300) {
- av_log(avctx, AV_LOG_ERROR, "Use bitrate 6300 instead of 5300.\n");
- avpriv_report_missing_feature(avctx, "Bitrate 5300");
- return AVERROR_PATCHWELCOME;
- } else {
- av_log(avctx, AV_LOG_ERROR, "Bitrate not supported, use 6300\n");
- return AVERROR(EINVAL);
- }
- avctx->frame_size = 240;
- memcpy(p->prev_lsp, dc_lsp, LPC_ORDER * sizeof(int16_t));
-
- return 0;
-}
-
-/**
- * Remove DC component from the input signal.
- *
- * @param buf input signal
- * @param fir zero memory
- * @param iir pole memory
- */
-static void highpass_filter(int16_t *buf, int16_t *fir, int *iir)
-{
- int i;
- for (i = 0; i < FRAME_LEN; i++) {
- *iir = (buf[i] - *fir) * (1 << 15) + MULL2(*iir, 0x7f00);
- *fir = buf[i];
- buf[i] = av_clipl_int32((int64_t)*iir + (1 << 15)) >> 16;
- }
-}
-
-/**
- * Estimate autocorrelation of the input vector.
- *
- * @param buf input buffer
- * @param autocorr autocorrelation coefficients vector
- */
-static void comp_autocorr(int16_t *buf, int16_t *autocorr)
-{
- int i, scale, temp;
- int16_t vector[LPC_FRAME];
-
- ff_g723_1_scale_vector(vector, buf, LPC_FRAME);
-
- /* Apply the Hamming window */
- for (i = 0; i < LPC_FRAME; i++)
- vector[i] = (vector[i] * hamming_window[i] + (1 << 14)) >> 15;
-
- /* Compute the first autocorrelation coefficient */
- temp = ff_dot_product(vector, vector, LPC_FRAME);
-
- /* Apply a white noise correlation factor of (1025/1024) */
- temp += temp >> 10;
-
- /* Normalize */
- scale = ff_g723_1_normalize_bits(temp, 31);
- autocorr[0] = av_clipl_int32((int64_t) (temp << scale) +
- (1 << 15)) >> 16;
-
- /* Compute the remaining coefficients */
- if (!autocorr[0]) {
- memset(autocorr + 1, 0, LPC_ORDER * sizeof(int16_t));
- } else {
- for (i = 1; i <= LPC_ORDER; i++) {
- temp = ff_dot_product(vector, vector + i, LPC_FRAME - i);
- temp = MULL2(temp * (1 << scale), binomial_window[i - 1]);
- autocorr[i] = av_clipl_int32((int64_t) temp + (1 << 15)) >> 16;
- }
- }
-}
-
-/**
- * Use Levinson-Durbin recursion to compute LPC coefficients from
- * autocorrelation values.
- *
- * @param lpc LPC coefficients vector
- * @param autocorr autocorrelation coefficients vector
- * @param error prediction error
- */
-static void levinson_durbin(int16_t *lpc, int16_t *autocorr, int16_t error)
-{
- int16_t vector[LPC_ORDER];
- int16_t partial_corr;
- int i, j, temp;
-
- memset(lpc, 0, LPC_ORDER * sizeof(int16_t));
-
- for (i = 0; i < LPC_ORDER; i++) {
- /* Compute the partial correlation coefficient */
- temp = 0;
- for (j = 0; j < i; j++)
- temp -= lpc[j] * autocorr[i - j - 1];
- temp = (autocorr[i] * (1 << 13) + temp) * (1 << 3);
-
- if (FFABS(temp) >= (error << 16))
- break;
-
- partial_corr = temp / (error << 1);
-
- lpc[i] = (partial_corr + (1 << 1)) >> 2;
-
- /* Update the prediction error */
- temp = MULL2(temp, partial_corr);
- error = av_clipl_int32((int64_t) (error << 16) - temp +
- (1 << 15)) >> 16;
-
- memcpy(vector, lpc, i * sizeof(int16_t));
- for (j = 0; j < i; j++) {
- temp = partial_corr * vector[i - j - 1] * 2;
- lpc[j] = av_clipl_int32((int64_t) (lpc[j] * (1 << 16)) - temp +
- (1 << 15)) >> 16;
- }
- }
-}
-
-/**
- * Calculate LPC coefficients for the current frame.
- *
- * @param buf current frame
- * @param prev_data 2 trailing subframes of the previous frame
- * @param lpc LPC coefficients vector
- */
-static void comp_lpc_coeff(int16_t *buf, int16_t *lpc)
-{
- int16_t autocorr[(LPC_ORDER + 1) * SUBFRAMES];
- int16_t *autocorr_ptr = autocorr;
- int16_t *lpc_ptr = lpc;
- int i, j;
-
- for (i = 0, j = 0; j < SUBFRAMES; i += SUBFRAME_LEN, j++) {
- comp_autocorr(buf + i, autocorr_ptr);
- levinson_durbin(lpc_ptr, autocorr_ptr + 1, autocorr_ptr[0]);
-
- lpc_ptr += LPC_ORDER;
- autocorr_ptr += LPC_ORDER + 1;
- }
-}
-
-static void lpc2lsp(int16_t *lpc, int16_t *prev_lsp, int16_t *lsp)
-{
- int f[LPC_ORDER + 2]; ///< coefficients of the sum and difference
- ///< polynomials (F1, F2) ordered as
- ///< f1[0], f2[0], ...., f1[5], f2[5]
-
- int max, shift, cur_val, prev_val, count, p;
- int i, j;
- int64_t temp;
-
- /* Initialize f1[0] and f2[0] to 1 in Q25 */
- for (i = 0; i < LPC_ORDER; i++)
- lsp[i] = (lpc[i] * bandwidth_expand[i] + (1 << 14)) >> 15;
-
- /* Apply bandwidth expansion on the LPC coefficients */
- f[0] = f[1] = 1 << 25;
-
- /* Compute the remaining coefficients */
- for (i = 0; i < LPC_ORDER / 2; i++) {
- /* f1 */
- f[2 * i + 2] = -f[2 * i] - (lsp[i] + lsp[LPC_ORDER - 1 - i]) * (1 << 12);
- /* f2 */
- f[2 * i + 3] = f[2 * i + 1] - (lsp[i] - lsp[LPC_ORDER - 1 - i]) * (1 << 12);
- }
-
- /* Divide f1[5] and f2[5] by 2 for use in polynomial evaluation */
- f[LPC_ORDER] >>= 1;
- f[LPC_ORDER + 1] >>= 1;
-
- /* Normalize and shorten */
- max = FFABS(f[0]);
- for (i = 1; i < LPC_ORDER + 2; i++)
- max = FFMAX(max, FFABS(f[i]));
-
- shift = ff_g723_1_normalize_bits(max, 31);
-
- for (i = 0; i < LPC_ORDER + 2; i++)
- f[i] = av_clipl_int32((int64_t) (f[i] * (1 << shift)) + (1 << 15)) >> 16;
-
- /**
- * Evaluate F1 and F2 at uniform intervals of pi/256 along the
- * unit circle and check for zero crossings.
- */
- p = 0;
- temp = 0;
- for (i = 0; i <= LPC_ORDER / 2; i++)
- temp += f[2 * i] * G723_1_COS_TAB_FIRST_ELEMENT;
- prev_val = av_clipl_int32(temp << 1);
- count = 0;
- for (i = 1; i < COS_TBL_SIZE / 2; i++) {
- /* Evaluate */
- temp = 0;
- for (j = 0; j <= LPC_ORDER / 2; j++)
- temp += f[LPC_ORDER - 2 * j + p] * ff_g723_1_cos_tab[i * j % COS_TBL_SIZE];
- cur_val = av_clipl_int32(temp * 2);
-
- /* Check for sign change, indicating a zero crossing */
- if ((cur_val ^ prev_val) < 0) {
- int abs_cur = FFABS(cur_val);
- int abs_prev = FFABS(prev_val);
- int sum = abs_cur + abs_prev;
-
- shift = ff_g723_1_normalize_bits(sum, 31);
- sum <<= shift;
- abs_prev = abs_prev << shift >> 8;
- lsp[count++] = ((i - 1) << 7) + (abs_prev >> 1) / (sum >> 16);
-
- if (count == LPC_ORDER)
- break;
-
- /* Switch between sum and difference polynomials */
- p ^= 1;
-
- /* Evaluate */
- temp = 0;
- for (j = 0; j <= LPC_ORDER / 2; j++)
- temp += f[LPC_ORDER - 2 * j + p] *
- ff_g723_1_cos_tab[i * j % COS_TBL_SIZE];
- cur_val = av_clipl_int32(temp * 2);
- }
- prev_val = cur_val;
- }
-
- if (count != LPC_ORDER)
- memcpy(lsp, prev_lsp, LPC_ORDER * sizeof(int16_t));
-}
-
-/**
- * Quantize the current LSP subvector.
- *
- * @param num band number
- * @param offset offset of the current subvector in an LPC_ORDER vector
- * @param size size of the current subvector
- */
-#define get_index(num, offset, size) \
-{ \
- int error, max = -1; \
- int16_t temp[4]; \
- int i, j; \
- \
- for (i = 0; i < LSP_CB_SIZE; i++) { \
- for (j = 0; j < size; j++){ \
- temp[j] = (weight[j + (offset)] * ff_g723_1_lsp_band##num[i][j] + \
- (1 << 14)) >> 15; \
- } \
- error = ff_g723_1_dot_product(lsp + (offset), temp, size) * 2; \
- error -= ff_g723_1_dot_product(ff_g723_1_lsp_band##num[i], temp, size); \
- if (error > max) { \
- max = error; \
- lsp_index[num] = i; \
- } \
- } \
-}
-
-/**
- * Vector quantize the LSP frequencies.
- *
- * @param lsp the current lsp vector
- * @param prev_lsp the previous lsp vector
- */
-static void lsp_quantize(uint8_t *lsp_index, int16_t *lsp, int16_t *prev_lsp)
-{
- int16_t weight[LPC_ORDER];
- int16_t min, max;
- int shift, i;
-
- /* Calculate the VQ weighting vector */
- weight[0] = (1 << 20) / (lsp[1] - lsp[0]);
- weight[LPC_ORDER - 1] = (1 << 20) /
- (lsp[LPC_ORDER - 1] - lsp[LPC_ORDER - 2]);
-
- for (i = 1; i < LPC_ORDER - 1; i++) {
- min = FFMIN(lsp[i] - lsp[i - 1], lsp[i + 1] - lsp[i]);
- if (min > 0x20)
- weight[i] = (1 << 20) / min;
- else
- weight[i] = INT16_MAX;
- }
-
- /* Normalize */
- max = 0;
- for (i = 0; i < LPC_ORDER; i++)
- max = FFMAX(weight[i], max);
-
- shift = ff_g723_1_normalize_bits(max, 15);
- for (i = 0; i < LPC_ORDER; i++) {
- weight[i] <<= shift;
- }
-
- /* Compute the VQ target vector */
- for (i = 0; i < LPC_ORDER; i++) {
- lsp[i] -= dc_lsp[i] +
- (((prev_lsp[i] - dc_lsp[i]) * 12288 + (1 << 14)) >> 15);
- }
-
- get_index(0, 0, 3);
- get_index(1, 3, 3);
- get_index(2, 6, 4);
-}
-
-/**
- * Perform IIR filtering.
- *
- * @param fir_coef FIR coefficients
- * @param iir_coef IIR coefficients
- * @param src source vector
- * @param dest destination vector
- */
-static void iir_filter(int16_t *fir_coef, int16_t *iir_coef,
- int16_t *src, int16_t *dest)
-{
- int m, n;
-
- for (m = 0; m < SUBFRAME_LEN; m++) {
- int64_t filter = 0;
- for (n = 1; n <= LPC_ORDER; n++) {
- filter -= fir_coef[n - 1] * src[m - n] -
- iir_coef[n - 1] * dest[m - n];
- }
-
- dest[m] = av_clipl_int32(src[m] * (1 << 16) + filter * (1 << 3) +
- (1 << 15)) >> 16;
- }
-}
-
-/**
- * Apply the formant perceptual weighting filter.
- *
- * @param flt_coef filter coefficients
- * @param unq_lpc unquantized lpc vector
- */
-static void perceptual_filter(G723_1_ChannelContext *p, int16_t *flt_coef,
- int16_t *unq_lpc, int16_t *buf)
-{
- int16_t vector[FRAME_LEN + LPC_ORDER];
- int i, j, k, l = 0;
-
- memcpy(buf, p->iir_mem, sizeof(int16_t) * LPC_ORDER);
- memcpy(vector, p->fir_mem, sizeof(int16_t) * LPC_ORDER);
- memcpy(vector + LPC_ORDER, buf + LPC_ORDER, sizeof(int16_t) * FRAME_LEN);
-
- for (i = LPC_ORDER, j = 0; j < SUBFRAMES; i += SUBFRAME_LEN, j++) {
- for (k = 0; k < LPC_ORDER; k++) {
- flt_coef[k + 2 * l] = (unq_lpc[k + l] * percept_flt_tbl[0][k] +
- (1 << 14)) >> 15;
- flt_coef[k + 2 * l + LPC_ORDER] = (unq_lpc[k + l] *
- percept_flt_tbl[1][k] +
- (1 << 14)) >> 15;
- }
- iir_filter(flt_coef + 2 * l, flt_coef + 2 * l + LPC_ORDER,
- vector + i, buf + i);
- l += LPC_ORDER;
- }
- memcpy(p->iir_mem, buf + FRAME_LEN, sizeof(int16_t) * LPC_ORDER);
- memcpy(p->fir_mem, vector + FRAME_LEN, sizeof(int16_t) * LPC_ORDER);
-}
-
-/**
- * Estimate the open loop pitch period.
- *
- * @param buf perceptually weighted speech
- * @param start estimation is carried out from this position
- */
-static int estimate_pitch(int16_t *buf, int start)
-{
- int max_exp = 32;
- int max_ccr = 0x4000;
- int max_eng = 0x7fff;
- int index = PITCH_MIN;
- int offset = start - PITCH_MIN + 1;
-
- int ccr, eng, orig_eng, ccr_eng, exp;
- int diff, temp;
-
- int i;
-
- orig_eng = ff_dot_product(buf + offset, buf + offset, HALF_FRAME_LEN);
-
- for (i = PITCH_MIN; i <= PITCH_MAX - 3; i++) {
- offset--;
-
- /* Update energy and compute correlation */
- orig_eng += buf[offset] * buf[offset] -
- buf[offset + HALF_FRAME_LEN] * buf[offset + HALF_FRAME_LEN];
- ccr = ff_dot_product(buf + start, buf + offset, HALF_FRAME_LEN);
- if (ccr <= 0)
- continue;
-
- /* Split into mantissa and exponent to maintain precision */
- exp = ff_g723_1_normalize_bits(ccr, 31);
- ccr = av_clipl_int32((int64_t) (ccr << exp) + (1 << 15)) >> 16;
- exp <<= 1;
- ccr *= ccr;
- temp = ff_g723_1_normalize_bits(ccr, 31);
- ccr = ccr << temp >> 16;
- exp += temp;
-
- temp = ff_g723_1_normalize_bits(orig_eng, 31);
- eng = av_clipl_int32((int64_t) (orig_eng << temp) + (1 << 15)) >> 16;
- exp -= temp;
-
- if (ccr >= eng) {
- exp--;
- ccr >>= 1;
- }
- if (exp > max_exp)
- continue;
-
- if (exp + 1 < max_exp)
- goto update;
-
- /* Equalize exponents before comparison */
- if (exp + 1 == max_exp)
- temp = max_ccr >> 1;
- else
- temp = max_ccr;
- ccr_eng = ccr * max_eng;
- diff = ccr_eng - eng * temp;
- if (diff > 0 && (i - index < PITCH_MIN || diff > ccr_eng >> 2)) {
-update:
- index = i;
- max_exp = exp;
- max_ccr = ccr;
- max_eng = eng;
- }
- }
- return index;
-}
-
-/**
- * Compute harmonic noise filter parameters.
- *
- * @param buf perceptually weighted speech
- * @param pitch_lag open loop pitch period
- * @param hf harmonic filter parameters
- */
-static void comp_harmonic_coeff(int16_t *buf, int16_t pitch_lag, HFParam *hf)
-{
- int ccr, eng, max_ccr, max_eng;
- int exp, max, diff;
- int energy[15];
- int i, j;
-
- for (i = 0, j = pitch_lag - 3; j <= pitch_lag + 3; i++, j++) {
- /* Compute residual energy */
- energy[i << 1] = ff_dot_product(buf - j, buf - j, SUBFRAME_LEN);
- /* Compute correlation */
- energy[(i << 1) + 1] = ff_dot_product(buf, buf - j, SUBFRAME_LEN);
- }
-
- /* Compute target energy */
- energy[14] = ff_dot_product(buf, buf, SUBFRAME_LEN);
-
- /* Normalize */
- max = 0;
- for (i = 0; i < 15; i++)
- max = FFMAX(max, FFABS(energy[i]));
-
- exp = ff_g723_1_normalize_bits(max, 31);
- for (i = 0; i < 15; i++) {
- energy[i] = av_clipl_int32((int64_t)(energy[i] * (1 << exp)) +
- (1 << 15)) >> 16;
- }
-
- hf->index = -1;
- hf->gain = 0;
- max_ccr = 1;
- max_eng = 0x7fff;
-
- for (i = 0; i <= 6; i++) {
- eng = energy[i << 1];
- ccr = energy[(i << 1) + 1];
-
- if (ccr <= 0)
- continue;
-
- ccr = (ccr * ccr + (1 << 14)) >> 15;
- diff = ccr * max_eng - eng * max_ccr;
- if (diff > 0) {
- max_ccr = ccr;
- max_eng = eng;
- hf->index = i;
- }
- }
-
- if (hf->index == -1) {
- hf->index = pitch_lag;
- return;
- }
-
- eng = energy[14] * max_eng;
- eng = (eng >> 2) + (eng >> 3);
- ccr = energy[(hf->index << 1) + 1] * energy[(hf->index << 1) + 1];
- if (eng < ccr) {
- eng = energy[(hf->index << 1) + 1];
-
- if (eng >= max_eng)
- hf->gain = 0x2800;
- else
- hf->gain = ((eng << 15) / max_eng * 0x2800 + (1 << 14)) >> 15;
- }
- hf->index += pitch_lag - 3;
-}
-
-/**
- * Apply the harmonic noise shaping filter.
- *
- * @param hf filter parameters
- */
-static void harmonic_filter(HFParam *hf, const int16_t *src, int16_t *dest)
-{
- int i;
-
- for (i = 0; i < SUBFRAME_LEN; i++) {
- int64_t temp = hf->gain * src[i - hf->index] * 2;
- dest[i] = av_clipl_int32(src[i] * (1 << 16) - temp + (1 << 15)) >> 16;
- }
-}
-
-static void harmonic_noise_sub(HFParam *hf, const int16_t *src, int16_t *dest)
-{
- int i;
- for (i = 0; i < SUBFRAME_LEN; i++) {
- int64_t temp = hf->gain * src[i - hf->index] * 2;
- dest[i] = av_clipl_int32((dest[i] - src[i]) * (1 << 16) + temp +
- (1 << 15)) >> 16;
- }
-}
-
-/**
- * Combined synthesis and formant perceptual weighting filer.
- *
- * @param qnt_lpc quantized lpc coefficients
- * @param perf_lpc perceptual filter coefficients
- * @param perf_fir perceptual filter fir memory
- * @param perf_iir perceptual filter iir memory
- * @param scale the filter output will be scaled by 2^scale
- */
-static void synth_percept_filter(int16_t *qnt_lpc, int16_t *perf_lpc,
- int16_t *perf_fir, int16_t *perf_iir,
- const int16_t *src, int16_t *dest, int scale)
-{
- int i, j;
- int16_t buf_16[SUBFRAME_LEN + LPC_ORDER];
- int64_t buf[SUBFRAME_LEN];
-
- int16_t *bptr_16 = buf_16 + LPC_ORDER;
-
- memcpy(buf_16, perf_fir, sizeof(int16_t) * LPC_ORDER);
- memcpy(dest - LPC_ORDER, perf_iir, sizeof(int16_t) * LPC_ORDER);
-
- for (i = 0; i < SUBFRAME_LEN; i++) {
- int64_t temp = 0;
- for (j = 1; j <= LPC_ORDER; j++)
- temp -= qnt_lpc[j - 1] * bptr_16[i - j];
-
- buf[i] = src[i] * (1 << 15) + temp * (1 << 3);
- bptr_16[i] = av_clipl_int32(buf[i] + (1 << 15)) >> 16;
- }
-
- for (i = 0; i < SUBFRAME_LEN; i++) {
- int64_t fir = 0, iir = 0;
- for (j = 1; j <= LPC_ORDER; j++) {
- fir -= perf_lpc[j - 1] * bptr_16[i - j];
- iir += perf_lpc[j + LPC_ORDER - 1] * dest[i - j];
- }
- dest[i] = av_clipl_int32((buf[i] + fir * (1 << 3)) * (1 << scale) + iir * (1 << 3) +
- (1 << 15)) >> 16;
- }
- memcpy(perf_fir, buf_16 + SUBFRAME_LEN, sizeof(int16_t) * LPC_ORDER);
- memcpy(perf_iir, dest + SUBFRAME_LEN - LPC_ORDER,
- sizeof(int16_t) * LPC_ORDER);
-}
-
-/**
- * Compute the adaptive codebook contribution.
- *
- * @param buf input signal
- * @param index the current subframe index
- */
-static void acb_search(G723_1_ChannelContext *p, int16_t *residual,
- int16_t *impulse_resp, const int16_t *buf,
- int index)
-{
- int16_t flt_buf[PITCH_ORDER][SUBFRAME_LEN];
-
- const int16_t *cb_tbl = ff_g723_1_adaptive_cb_gain85;
-
- int ccr_buf[PITCH_ORDER * SUBFRAMES << 2];
-
- int pitch_lag = p->pitch_lag[index >> 1];
- int acb_lag = 1;
- int acb_gain = 0;
- int odd_frame = index & 1;
- int iter = 3 + odd_frame;
- int count = 0;
- int tbl_size = 85;
-
- int i, j, k, l, max;
- int64_t temp;
-
- if (!odd_frame) {
- if (pitch_lag == PITCH_MIN)
- pitch_lag++;
- else
- pitch_lag = FFMIN(pitch_lag, PITCH_MAX - 5);
- }
-
- for (i = 0; i < iter; i++) {
- ff_g723_1_get_residual(residual, p->prev_excitation, pitch_lag + i - 1);
-
- for (j = 0; j < SUBFRAME_LEN; j++) {
- temp = 0;
- for (k = 0; k <= j; k++)
- temp += residual[PITCH_ORDER - 1 + k] * impulse_resp[j - k];
- flt_buf[PITCH_ORDER - 1][j] = av_clipl_int32(temp * 2 + (1 << 15)) >> 16;
- }
-
- for (j = PITCH_ORDER - 2; j >= 0; j--) {
- flt_buf[j][0] = (residual[j] + (1 << 1)) >> 2;
- for (k = 1; k < SUBFRAME_LEN; k++) {
- temp = flt_buf[j + 1][k - 1] * (1 << 15) +
- residual[j] * impulse_resp[k];
- flt_buf[j][k] = av_clipl_int32(temp * 2 + (1 << 15)) >> 16;
- }
- }
-
- /* Compute crosscorrelation with the signal */
- for (j = 0; j < PITCH_ORDER; j++) {
- temp = ff_dot_product(buf, flt_buf[j], SUBFRAME_LEN);
- ccr_buf[count++] = av_clipl_int32(temp * 2);
- }
-
- /* Compute energies */
- for (j = 0; j < PITCH_ORDER; j++) {
- ccr_buf[count++] = ff_g723_1_dot_product(flt_buf[j], flt_buf[j],
- SUBFRAME_LEN);
- }
-
- for (j = 1; j < PITCH_ORDER; j++) {
- for (k = 0; k < j; k++) {
- temp = ff_dot_product(flt_buf[j], flt_buf[k], SUBFRAME_LEN);
- ccr_buf[count++] = av_clipl_int32(temp * (1 << 2));
- }
- }
- }
-
- /* Normalize and shorten */
- max = 0;
- for (i = 0; i < 20 * iter; i++)
- max = FFMAX(max, FFABS(ccr_buf[i]));
-
- temp = ff_g723_1_normalize_bits(max, 31);
-
- for (i = 0; i < 20 * iter; i++)
- ccr_buf[i] = av_clipl_int32((int64_t) (ccr_buf[i] * (1 << temp)) +
- (1 << 15)) >> 16;
-
- max = 0;
- for (i = 0; i < iter; i++) {
- /* Select quantization table */
- if (!odd_frame && pitch_lag + i - 1 >= SUBFRAME_LEN - 2 ||
- odd_frame && pitch_lag >= SUBFRAME_LEN - 2) {
- cb_tbl = ff_g723_1_adaptive_cb_gain170;
- tbl_size = 170;
- }
-
- for (j = 0, k = 0; j < tbl_size; j++, k += 20) {
- temp = 0;
- for (l = 0; l < 20; l++)
- temp += ccr_buf[20 * i + l] * cb_tbl[k + l];
- temp = av_clipl_int32(temp);
-
- if (temp > max) {
- max = temp;
- acb_gain = j;
- acb_lag = i;
- }
- }
- }
-
- if (!odd_frame) {
- pitch_lag += acb_lag - 1;
- acb_lag = 1;
- }
-
- p->pitch_lag[index >> 1] = pitch_lag;
- p->subframe[index].ad_cb_lag = acb_lag;
- p->subframe[index].ad_cb_gain = acb_gain;
-}
-
-/**
- * Subtract the adaptive codebook contribution from the input
- * to obtain the residual.
- *
- * @param buf target vector
- */
-static void sub_acb_contrib(const int16_t *residual, const int16_t *impulse_resp,
- int16_t *buf)
-{
- int i, j;
- /* Subtract adaptive CB contribution to obtain the residual */
- for (i = 0; i < SUBFRAME_LEN; i++) {
- int64_t temp = buf[i] * (1 << 14);
- for (j = 0; j <= i; j++)
- temp -= residual[j] * impulse_resp[i - j];
-
- buf[i] = av_clipl_int32(temp * (1 << 2) + (1 << 15)) >> 16;
- }
-}
-
-/**
- * Quantize the residual signal using the fixed codebook (MP-MLQ).
- *
- * @param optim optimized fixed codebook parameters
- * @param buf excitation vector
- */
-static void get_fcb_param(FCBParam *optim, int16_t *impulse_resp,
- int16_t *buf, int pulse_cnt, int pitch_lag)
-{
- FCBParam param;
- int16_t impulse_r[SUBFRAME_LEN];
- int16_t temp_corr[SUBFRAME_LEN];
- int16_t impulse_corr[SUBFRAME_LEN];
-
- int ccr1[SUBFRAME_LEN];
- int ccr2[SUBFRAME_LEN];
- int amp, err, max, max_amp_index, min, scale, i, j, k, l;
-
- int64_t temp;
-
- /* Update impulse response */
- memcpy(impulse_r, impulse_resp, sizeof(int16_t) * SUBFRAME_LEN);
- param.dirac_train = 0;
- if (pitch_lag < SUBFRAME_LEN - 2) {
- param.dirac_train = 1;
- ff_g723_1_gen_dirac_train(impulse_r, pitch_lag);
- }
-
- for (i = 0; i < SUBFRAME_LEN; i++)
- temp_corr[i] = impulse_r[i] >> 1;
-
- /* Compute impulse response autocorrelation */
- temp = ff_g723_1_dot_product(temp_corr, temp_corr, SUBFRAME_LEN);
-
- scale = ff_g723_1_normalize_bits(temp, 31);
- impulse_corr[0] = av_clipl_int32((temp << scale) + (1 << 15)) >> 16;
-
- for (i = 1; i < SUBFRAME_LEN; i++) {
- temp = ff_g723_1_dot_product(temp_corr + i, temp_corr,
- SUBFRAME_LEN - i);
- impulse_corr[i] = av_clipl_int32(temp * (1 << scale) + (1 << 15)) >> 16;
- }
-
- /* Compute crosscorrelation of impulse response with residual signal */
- scale -= 4;
- for (i = 0; i < SUBFRAME_LEN; i++) {
- temp = ff_g723_1_dot_product(buf + i, impulse_r, SUBFRAME_LEN - i);
- if (scale < 0)
- ccr1[i] = temp >> -scale;
- else
- ccr1[i] = av_clipl_int32(temp * (1 << scale));
- }
-
- /* Search loop */
- for (i = 0; i < GRID_SIZE; i++) {
- /* Maximize the crosscorrelation */
- max = 0;
- for (j = i; j < SUBFRAME_LEN; j += GRID_SIZE) {
- temp = FFABS(ccr1[j]);
- if (temp >= max) {
- max = temp;
- param.pulse_pos[0] = j;
- }
- }
-
- /* Quantize the gain (max crosscorrelation/impulse_corr[0]) */
- amp = max;
- min = 1 << 30;
- max_amp_index = GAIN_LEVELS - 2;
- for (j = max_amp_index; j >= 2; j--) {
- temp = av_clipl_int32((int64_t) ff_g723_1_fixed_cb_gain[j] *
- impulse_corr[0] << 1);
- temp = FFABS(temp - amp);
- if (temp < min) {
- min = temp;
- max_amp_index = j;
- }
- }
-
- max_amp_index--;
- /* Select additional gain values */
- for (j = 1; j < 5; j++) {
- for (k = i; k < SUBFRAME_LEN; k += GRID_SIZE) {
- temp_corr[k] = 0;
- ccr2[k] = ccr1[k];
- }
- param.amp_index = max_amp_index + j - 2;
- amp = ff_g723_1_fixed_cb_gain[param.amp_index];
-
- param.pulse_sign[0] = (ccr2[param.pulse_pos[0]] < 0) ? -amp : amp;
- temp_corr[param.pulse_pos[0]] = 1;
-
- for (k = 1; k < pulse_cnt; k++) {
- max = INT_MIN;
- for (l = i; l < SUBFRAME_LEN; l += GRID_SIZE) {
- if (temp_corr[l])
- continue;
- temp = impulse_corr[FFABS(l - param.pulse_pos[k - 1])];
- temp = av_clipl_int32((int64_t) temp *
- param.pulse_sign[k - 1] * 2);
- ccr2[l] -= temp;
- temp = FFABS(ccr2[l]);
- if (temp > max) {
- max = temp;
- param.pulse_pos[k] = l;
- }
- }
-
- param.pulse_sign[k] = (ccr2[param.pulse_pos[k]] < 0) ?
- -amp : amp;
- temp_corr[param.pulse_pos[k]] = 1;
- }
-
- /* Create the error vector */
- memset(temp_corr, 0, sizeof(int16_t) * SUBFRAME_LEN);
-
- for (k = 0; k < pulse_cnt; k++)
- temp_corr[param.pulse_pos[k]] = param.pulse_sign[k];
-
- for (k = SUBFRAME_LEN - 1; k >= 0; k--) {
- temp = 0;
- for (l = 0; l <= k; l++) {
- int prod = av_clipl_int32((int64_t) temp_corr[l] *
- impulse_r[k - l] * 2);
- temp = av_clipl_int32(temp + prod);
- }
- temp_corr[k] = temp >> 14;
- }
-
- /* Compute square of error */
- err = 0;
- for (k = 0; k < SUBFRAME_LEN; k++) {
- int64_t prod;
- prod = av_clipl_int32((int64_t) buf[k] * temp_corr[k] * 2);
- err = av_clipl_int32(err - prod);
- prod = av_clipl_int32((int64_t) temp_corr[k] * temp_corr[k]);
- err = av_clipl_int32(err + prod);
- }
-
- /* Minimize */
- if (err < optim->min_err) {
- optim->min_err = err;
- optim->grid_index = i;
- optim->amp_index = param.amp_index;
- optim->dirac_train = param.dirac_train;
-
- for (k = 0; k < pulse_cnt; k++) {
- optim->pulse_sign[k] = param.pulse_sign[k];
- optim->pulse_pos[k] = param.pulse_pos[k];
- }
- }
- }
- }
-}
-
-/**
- * Encode the pulse position and gain of the current subframe.
- *
- * @param optim optimized fixed CB parameters
- * @param buf excitation vector
- */
-static void pack_fcb_param(G723_1_Subframe *subfrm, FCBParam *optim,
- int16_t *buf, int pulse_cnt)
-{
- int i, j;
-
- j = PULSE_MAX - pulse_cnt;
-
- subfrm->pulse_sign = 0;
- subfrm->pulse_pos = 0;
-
- for (i = 0; i < SUBFRAME_LEN >> 1; i++) {
- int val = buf[optim->grid_index + (i << 1)];
- if (!val) {
- subfrm->pulse_pos += ff_g723_1_combinatorial_table[j][i];
- } else {
- subfrm->pulse_sign <<= 1;
- if (val < 0)
- subfrm->pulse_sign++;
- j++;
-
- if (j == PULSE_MAX)
- break;
- }
- }
- subfrm->amp_index = optim->amp_index;
- subfrm->grid_index = optim->grid_index;
- subfrm->dirac_train = optim->dirac_train;
-}
-
-/**
- * Compute the fixed codebook excitation.
- *
- * @param buf target vector
- * @param impulse_resp impulse response of the combined filter
- */
-static void fcb_search(G723_1_ChannelContext *p, int16_t *impulse_resp,
- int16_t *buf, int index)
-{
- FCBParam optim;
- int pulse_cnt = pulses[index];
- int i;
-
- optim.min_err = 1 << 30;
- get_fcb_param(&optim, impulse_resp, buf, pulse_cnt, SUBFRAME_LEN);
-
- if (p->pitch_lag[index >> 1] < SUBFRAME_LEN - 2) {
- get_fcb_param(&optim, impulse_resp, buf, pulse_cnt,
- p->pitch_lag[index >> 1]);
- }
-
- /* Reconstruct the excitation */
- memset(buf, 0, sizeof(int16_t) * SUBFRAME_LEN);
- for (i = 0; i < pulse_cnt; i++)
- buf[optim.pulse_pos[i]] = optim.pulse_sign[i];
-
- pack_fcb_param(&p->subframe[index], &optim, buf, pulse_cnt);
-
- if (optim.dirac_train)
- ff_g723_1_gen_dirac_train(buf, p->pitch_lag[index >> 1]);
-}
-
-/**
- * Pack the frame parameters into output bitstream.
- *
- * @param frame output buffer
- * @param size size of the buffer
- */
-static void pack_bitstream(G723_1_ChannelContext *p, AVPacket *avpkt, int info_bits)
-{
- PutBitContext pb;
- int i, temp;
-
- init_put_bits(&pb, avpkt->data, avpkt->size);
-
- put_bits(&pb, 2, info_bits);
-
- put_bits(&pb, 8, p->lsp_index[2]);
- put_bits(&pb, 8, p->lsp_index[1]);
- put_bits(&pb, 8, p->lsp_index[0]);
-
- put_bits(&pb, 7, p->pitch_lag[0] - PITCH_MIN);
- put_bits(&pb, 2, p->subframe[1].ad_cb_lag);
- put_bits(&pb, 7, p->pitch_lag[1] - PITCH_MIN);
- put_bits(&pb, 2, p->subframe[3].ad_cb_lag);
-
- /* Write 12 bit combined gain */
- for (i = 0; i < SUBFRAMES; i++) {
- temp = p->subframe[i].ad_cb_gain * GAIN_LEVELS +
- p->subframe[i].amp_index;
- if (p->cur_rate == RATE_6300)
- temp += p->subframe[i].dirac_train << 11;
- put_bits(&pb, 12, temp);
- }
-
- put_bits(&pb, 1, p->subframe[0].grid_index);
- put_bits(&pb, 1, p->subframe[1].grid_index);
- put_bits(&pb, 1, p->subframe[2].grid_index);
- put_bits(&pb, 1, p->subframe[3].grid_index);
-
- if (p->cur_rate == RATE_6300) {
- put_bits(&pb, 1, 0); /* reserved bit */
-
- /* Write 13 bit combined position index */
- temp = (p->subframe[0].pulse_pos >> 16) * 810 +
- (p->subframe[1].pulse_pos >> 14) * 90 +
- (p->subframe[2].pulse_pos >> 16) * 9 +
- (p->subframe[3].pulse_pos >> 14);
- put_bits(&pb, 13, temp);
-
- put_bits(&pb, 16, p->subframe[0].pulse_pos & 0xffff);
- put_bits(&pb, 14, p->subframe[1].pulse_pos & 0x3fff);
- put_bits(&pb, 16, p->subframe[2].pulse_pos & 0xffff);
- put_bits(&pb, 14, p->subframe[3].pulse_pos & 0x3fff);
-
- put_bits(&pb, 6, p->subframe[0].pulse_sign);
- put_bits(&pb, 5, p->subframe[1].pulse_sign);
- put_bits(&pb, 6, p->subframe[2].pulse_sign);
- put_bits(&pb, 5, p->subframe[3].pulse_sign);
- }
-
- flush_put_bits(&pb);
-}
-
-static int g723_1_encode_frame(AVCodecContext *avctx, AVPacket *avpkt,
- const AVFrame *frame, int *got_packet_ptr)
-{
- G723_1_Context *s = avctx->priv_data;
- G723_1_ChannelContext *p = &s->ch[0];
- int16_t unq_lpc[LPC_ORDER * SUBFRAMES];
- int16_t qnt_lpc[LPC_ORDER * SUBFRAMES];
- int16_t cur_lsp[LPC_ORDER];
- int16_t weighted_lpc[LPC_ORDER * SUBFRAMES << 1];
- int16_t vector[FRAME_LEN + PITCH_MAX];
- int offset, ret, i, j, info_bits = 0;
- int16_t *in, *start;
- HFParam hf[4];
-
- /* duplicate input */
- start = in = av_memdup(frame->data[0], frame->nb_samples * sizeof(int16_t));
- if (!in)
- return AVERROR(ENOMEM);
-
- highpass_filter(in, &p->hpf_fir_mem, &p->hpf_iir_mem);
-
- memcpy(vector, p->prev_data, HALF_FRAME_LEN * sizeof(int16_t));
- memcpy(vector + HALF_FRAME_LEN, in, FRAME_LEN * sizeof(int16_t));
-
- comp_lpc_coeff(vector, unq_lpc);
- lpc2lsp(&unq_lpc[LPC_ORDER * 3], p->prev_lsp, cur_lsp);
- lsp_quantize(p->lsp_index, cur_lsp, p->prev_lsp);
-
- /* Update memory */
- memcpy(vector + LPC_ORDER, p->prev_data + SUBFRAME_LEN,
- sizeof(int16_t) * SUBFRAME_LEN);
- memcpy(vector + LPC_ORDER + SUBFRAME_LEN, in,
- sizeof(int16_t) * (HALF_FRAME_LEN + SUBFRAME_LEN));
- memcpy(p->prev_data, in + HALF_FRAME_LEN,
- sizeof(int16_t) * HALF_FRAME_LEN);
- memcpy(in, vector + LPC_ORDER, sizeof(int16_t) * FRAME_LEN);
-
- perceptual_filter(p, weighted_lpc, unq_lpc, vector);
-
- memcpy(in, vector + LPC_ORDER, sizeof(int16_t) * FRAME_LEN);
- memcpy(vector, p->prev_weight_sig, sizeof(int16_t) * PITCH_MAX);
- memcpy(vector + PITCH_MAX, in, sizeof(int16_t) * FRAME_LEN);
-
- ff_g723_1_scale_vector(vector, vector, FRAME_LEN + PITCH_MAX);
-
- p->pitch_lag[0] = estimate_pitch(vector, PITCH_MAX);
- p->pitch_lag[1] = estimate_pitch(vector, PITCH_MAX + HALF_FRAME_LEN);
-
- for (i = PITCH_MAX, j = 0; j < SUBFRAMES; i += SUBFRAME_LEN, j++)
- comp_harmonic_coeff(vector + i, p->pitch_lag[j >> 1], hf + j);
-
- memcpy(vector, p->prev_weight_sig, sizeof(int16_t) * PITCH_MAX);
- memcpy(vector + PITCH_MAX, in, sizeof(int16_t) * FRAME_LEN);
- memcpy(p->prev_weight_sig, vector + FRAME_LEN, sizeof(int16_t) * PITCH_MAX);
-
- for (i = 0, j = 0; j < SUBFRAMES; i += SUBFRAME_LEN, j++)
- harmonic_filter(hf + j, vector + PITCH_MAX + i, in + i);
-
- ff_g723_1_inverse_quant(cur_lsp, p->prev_lsp, p->lsp_index, 0);
- ff_g723_1_lsp_interpolate(qnt_lpc, cur_lsp, p->prev_lsp);
-
- memcpy(p->prev_lsp, cur_lsp, sizeof(int16_t) * LPC_ORDER);
-
- offset = 0;
- for (i = 0; i < SUBFRAMES; i++) {
- int16_t impulse_resp[SUBFRAME_LEN];
- int16_t residual[SUBFRAME_LEN + PITCH_ORDER - 1];
- int16_t flt_in[SUBFRAME_LEN];
- int16_t zero[LPC_ORDER], fir[LPC_ORDER], iir[LPC_ORDER];
-
- /**
- * Compute the combined impulse response of the synthesis filter,
- * formant perceptual weighting filter and harmonic noise shaping filter
- */
- memset(zero, 0, sizeof(int16_t) * LPC_ORDER);
- memset(vector, 0, sizeof(int16_t) * PITCH_MAX);
- memset(flt_in, 0, sizeof(int16_t) * SUBFRAME_LEN);
-
- flt_in[0] = 1 << 13; /* Unit impulse */
- synth_percept_filter(qnt_lpc + offset, weighted_lpc + (offset << 1),
- zero, zero, flt_in, vector + PITCH_MAX, 1);
- harmonic_filter(hf + i, vector + PITCH_MAX, impulse_resp);
-
- /* Compute the combined zero input response */
- flt_in[0] = 0;
- memcpy(fir, p->perf_fir_mem, sizeof(int16_t) * LPC_ORDER);
- memcpy(iir, p->perf_iir_mem, sizeof(int16_t) * LPC_ORDER);
-
- synth_percept_filter(qnt_lpc + offset, weighted_lpc + (offset << 1),
- fir, iir, flt_in, vector + PITCH_MAX, 0);
- memcpy(vector, p->harmonic_mem, sizeof(int16_t) * PITCH_MAX);
- harmonic_noise_sub(hf + i, vector + PITCH_MAX, in);
-
- acb_search(p, residual, impulse_resp, in, i);
- ff_g723_1_gen_acb_excitation(residual, p->prev_excitation,
- p->pitch_lag[i >> 1], &p->subframe[i],
- p->cur_rate);
- sub_acb_contrib(residual, impulse_resp, in);
-
- fcb_search(p, impulse_resp, in, i);
-
- /* Reconstruct the excitation */
- ff_g723_1_gen_acb_excitation(impulse_resp, p->prev_excitation,
- p->pitch_lag[i >> 1], &p->subframe[i],
- RATE_6300);
-
- memmove(p->prev_excitation, p->prev_excitation + SUBFRAME_LEN,
- sizeof(int16_t) * (PITCH_MAX - SUBFRAME_LEN));
- for (j = 0; j < SUBFRAME_LEN; j++)
- in[j] = av_clip_int16(in[j] * 2 + impulse_resp[j]);
- memcpy(p->prev_excitation + PITCH_MAX - SUBFRAME_LEN, in,
- sizeof(int16_t) * SUBFRAME_LEN);
-
- /* Update filter memories */
- synth_percept_filter(qnt_lpc + offset, weighted_lpc + (offset << 1),
- p->perf_fir_mem, p->perf_iir_mem,
- in, vector + PITCH_MAX, 0);
- memmove(p->harmonic_mem, p->harmonic_mem + SUBFRAME_LEN,
- sizeof(int16_t) * (PITCH_MAX - SUBFRAME_LEN));
- memcpy(p->harmonic_mem + PITCH_MAX - SUBFRAME_LEN, vector + PITCH_MAX,
- sizeof(int16_t) * SUBFRAME_LEN);
-
- in += SUBFRAME_LEN;
- offset += LPC_ORDER;
- }
-
- av_free(start);
-
- ret = ff_get_encode_buffer(avctx, avpkt, frame_size[info_bits], 0);
- if (ret < 0)
- return ret;
-
- *got_packet_ptr = 1;
- pack_bitstream(p, avpkt, info_bits);
- return 0;
-}
-
-static const FFCodecDefault defaults[] = {
- { "b", "6300" },
- { NULL },
-};
-
-const FFCodec ff_g723_1_encoder = {
- .p.name = "g723_1",
- CODEC_LONG_NAME("G.723.1"),
- .p.type = AVMEDIA_TYPE_AUDIO,
- .p.id = AV_CODEC_ID_G723_1,
- .p.capabilities = AV_CODEC_CAP_DR1 | AV_CODEC_CAP_ENCODER_REORDERED_OPAQUE,
- .priv_data_size = sizeof(G723_1_Context),
- .init = g723_1_encode_init,
- FF_CODEC_ENCODE_CB(g723_1_encode_frame),
- .defaults = defaults,
- .p.sample_fmts = (const enum AVSampleFormat[]) {
- AV_SAMPLE_FMT_S16, AV_SAMPLE_FMT_NONE
- },
- .p.ch_layouts = (const AVChannelLayout[]){
- AV_CHANNEL_LAYOUT_MONO, { 0 }
- },
-};
diff --git a/spaces/congsaPfin/Manga-OCR/app.py b/spaces/congsaPfin/Manga-OCR/app.py
deleted file mode 100644
index de00755086ecc26f8d74b91f9297ffebd14be115..0000000000000000000000000000000000000000
--- a/spaces/congsaPfin/Manga-OCR/app.py
+++ /dev/null
@@ -1,44 +0,0 @@
-#!/usr/bin/env python
-
-from __future__ import annotations
-
-import functools
-import pathlib
-import urllib.request
-
-import gradio as gr
-import PIL.Image
-from manga_ocr import MangaOcr
-
-TITLE = 'Manga OCR'
-DESCRIPTION = 'This is an unofficial demo for https://github.com/kha-white/manga-ocr.'
-
-
-def download_sample_images() -> list[pathlib.Path]:
- image_dir = pathlib.Path('images')
- if not image_dir.exists():
- image_dir.mkdir()
- for index in range(12):
- url = f'https://raw.githubusercontent.com/kha-white/manga-ocr/master/assets/examples/{index:02d}.jpg'
- urllib.request.urlretrieve(url, image_dir / f'{index:02d}.jpg')
- return sorted(image_dir.rglob('*.jpg'))
-
-
-def run(image: PIL.Image.Image, mocr: MangaOcr) -> str:
- return mocr(image)
-
-
-mocr = MangaOcr()
-func = functools.partial(run, mocr=mocr)
-
-image_paths = download_sample_images()
-examples = [[path.as_posix()] for path in image_paths]
-
-gr.Interface(
- fn=func,
- inputs=gr.Image(label='Input', type='pil'),
- outputs=gr.Text(label='Output'),
- examples=examples,
- title=TITLE,
- description=DESCRIPTION,
-).launch(show_api=False)
diff --git a/spaces/congsaPfin/Manga-OCR/logs/Brawlhalla PC Gratis Download and Play the Free-to-Play Platform Fighter with Historys Greatest Warriors.md b/spaces/congsaPfin/Manga-OCR/logs/Brawlhalla PC Gratis Download and Play the Free-to-Play Platform Fighter with Historys Greatest Warriors.md
deleted file mode 100644
index 4154ab4a0b055fdc0b6c0fcc1487e4134cfdd19c..0000000000000000000000000000000000000000
--- a/spaces/congsaPfin/Manga-OCR/logs/Brawlhalla PC Gratis Download and Play the Free-to-Play Platform Fighter with Historys Greatest Warriors.md
+++ /dev/null
@@ -1,108 +0,0 @@
-
-Download Brawlhalla PC Gratis: A Free-to-Play Platform Fighting Game
-If you are looking for a fun and exciting game that you can play with your friends or online, you should check out Brawlhalla. Brawlhalla is a free-to-play platform fighting game that supports up to 8 players online or local. You can choose from over 50 legends, each with their own unique weapons, abilities, and playstyles. You can also customize your character with skins, colors, taunts, and more. Brawlhalla is available on multiple platforms, including PC, PS5, PS4, Xbox Series X|S, Xbox One, Nintendo Switch, iOS, and Android. And the best part is, you can download Brawlhalla PC gratis and enjoy the game without spending a dime. In this article, we will tell you what Brawlhalla is, how to download it for free on your PC, why you should play it, and some tips and tricks to help you improve your skills.
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-- You can access all the game modes, features, and updates without any cost. You can play online or offline, solo or with others, casually or competitively, and join tournaments and events.
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-- Practice makes perfect. The best way to get better at the game is to practice regularly and learn from your mistakes. You can use the training mode to practice your moves, combos, timings, and dodges. You can also watch replays of your matches to analyze your strengths and weaknesses.
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-
- Conclusion
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- FAQs
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-- How do I play Brawlhalla PC gratis with my friends?
-You can play Brawlhalla PC gratis with your friends by creating or joining a custom game room. You can invite your friends to your room by sending them the room number or the invite link. You can also join your friends' rooms by entering their room number or clicking on their invite link. You can then choose the game mode, settings, and legends that you want to play with.
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-You can link your Brawlhalla accounts from different platforms by following these steps:
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-- Go to https://www.brawlhalla.com/account/ and log in with your Ubisoft account.
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-Once you have linked your accounts, you can access your progress and items across all platforms.
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-
401be4b1e0
-
-
\ No newline at end of file
diff --git a/spaces/congsaPfin/Manga-OCR/logs/Crusader Kings 2 A Game of Thrones Mod - Whats New in the Latest Version.md b/spaces/congsaPfin/Manga-OCR/logs/Crusader Kings 2 A Game of Thrones Mod - Whats New in the Latest Version.md
deleted file mode 100644
index 64639b33a22baad8fd2fc74133d8cfbfb161a88d..0000000000000000000000000000000000000000
--- a/spaces/congsaPfin/Manga-OCR/logs/Crusader Kings 2 A Game of Thrones Mod - Whats New in the Latest Version.md
+++ /dev/null
@@ -1,186 +0,0 @@
-
-Crusader Kings 2: A Game of Thrones Mod Review
-If you are a fan of both Crusader Kings 2 and A Song of Ice and Fire, then you are in for a treat. Crusader Kings 2: A Game of Thrones mod is a full-conversion mod that transforms the medieval world of Crusader Kings 2 into the fantasy realm of Westeros and beyond. You can play as any character from the books or show, from kings and queens to lords and ladies, from knights and maesters to wildlings and white walkers. You can relive the events from the series or create your own alternate history. You can fight for the Iron Throne or forge your own kingdom. You can make alliances or enemies with other players or NPCs. You can scheme, plot, duel, marry, assassinate, conquer, or die. The choice is yours.
-But how do you get started with this amazing mod? And what are some of the features that make it stand out from the vanilla game and other mods? And what are some tips and tricks to help you succeed in this brutal and unforgiving world? And finally, is this mod worth playing and recommending? In this article, I will answer all these questions and more. Let's begin.
-crusader kings 2 game of thrones mod download
Download File ===> https://urlca.com/2uOauI
- How to download and install Crusader Kings 2: A Game of Thrones mod
-The first step to enjoy this mod is to download and install it. This is not very difficult, but it requires some attention to detail. Here are the steps you need to follow:
-
-- Make sure you have Crusader Kings 2 installed on your computer. You can buy it from Steam or other platforms. The latest version of the game is 3.3.5.
-- Download the latest version of Crusader Kings 2: A Game of Thrones mod from its official website, ModDB page, or Steam workshop page. The latest version of the mod is 2.2.
-- Extract the downloaded file using a program like WinRAR or 7-Zip. You should get two folders: A Game of Thrones and A Game of Thrones Music.
-- Copy these two folders into your Crusader Kings 2 mod folder. The default location is C:\Users\YourName\Documents\Paradox Interactive\Crusader Kings II\mod.
-- Launch Crusader Kings 2 from Steam or other platforms. In the launcher window, check the boxes next to A Game of Thrones and A Game of Thrones Music under Mods.
-- Click Play and enjoy!
-
-Note: If you have any other mods installed or enabled, they may conflict with Crusader Kings 2: A Game of Thrones mod. It is recommended to disable them before playing this mod.
- Main features of Crusader Kings 2: A Game of Thrones mod
-Crusader Kings 2: A Game of Thrones mod is not just a simple reskin of the vanilla game. It adds many new features and mechanics that enhance the gameplay and immersion of the mod. Here are some of the main features of the mod:
- Different scenarios and bookmarks to choose from
-One of the most appealing aspects of this mod is that it allows you to play in different historical periods and scenarios from the books and show. You can choose from various bookmarks that correspond to major events and turning points in the story, such as:
-
-- The Bleeding Years: The aftermath of Aegon's Conquest, when the Seven Kingdoms were still in turmoil and rebellion.
-- The Conquest: The beginning of Aegon's Conquest, when he landed on Westeros with his dragons and armies.
-- The Century of Blood: The chaotic period after the Doom of Valyria, when the Free Cities fought each other for supremacy.
-- The Dance of the Dragons: The civil war between the Targaryen factions over the succession to the Iron Throne.
-- The Blackfyre Rebellion: The first of several rebellions led by the bastard sons of Aegon IV against the legitimate Targaryen line.
-- The War of the Ninepenny Kings: The last attempt by a Blackfyre pretender to claim the Iron Throne, supported by several foreign powers.
-- The Crowned Stag: The end of Robert's Rebellion, when he overthrew the Mad King and became the new king.
-- The Clash of Kings: The start of the War of the Five Kings, when several claimants rose up against Joffrey Baratheon.
-- A Feast for Crows: The aftermath of the War of the Five Kings, when new threats and challenges emerged in Westeros and beyond.
-- A Dream of Spring: The latest bookmark, based on the events from the show's final season and some speculation from the books.
-
-Each bookmark has its own set of characters, factions, events, and challenges. You can play as any character you want, from major players like Daenerys Targaryen or Jon Snow, to minor lords like Edmure Tully or Asha Greyjoy, to custom characters you create yourself. You can also create your own scenarios using the Ruler Designer or Custom Game options.
- New mechanics and events to fit the lore and flavor of A Song of Ice and Fire
-Another feature that makes this mod stand out is that it adds many new mechanics and events that are specific to the lore and flavor of A Song of Ice and Fire. These include:
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-- A new map that covers not only Westeros, but also Essos, Sothoryos, and parts of Ulthos. The map has more than 600 provinces and 80 regions, each with its own culture, religion, terrain, climate, and resources.
-- A new system of traits that reflect the personality, skills, appearance, and background of each character. Some traits are inherited, some are acquired, some are hidden, and some are unique. Traits can affect your stats, relationships, events, decisions, and actions.
-- A new system of cultures and religions that influence your gameplay and roleplay. There are more than 100 cultures and 50 religions in the mod, each with its own customs, laws, traditions, holy sites, festivals, holy orders, heresies, and special mechanics. For example, followers of the Old Gods can use weirwood trees to communicate with their ancestors or spy on their enemies; followers of R'hllor can perform blood magic or sacrifice prisoners to gain favors from their god; followers of Drowned God can drown their enemies or themselves to prove their faith; etc.
-- A new system of laws and succession that determine how your realm is governed and who inherits your titles. There are different types of laws for different regions, such as feudal laws for Westeros, nomadic laws for Dothraki, merchant republic laws for Braavos, etc. There are also different types of succession for different titles, such as primogeniture for most kingdoms , elective for the Iron Throne, seniority for the Night's Watch, etc. You can also change your laws and succession through decisions or events, such as adopting feudalism, abolishing slavery, granting women equal rights, etc.
-- A new system of events and decisions that add more depth and flavor to your gameplay and roleplay. There are hundreds of events and decisions in the mod, some of them based on the books and show, some of them original or inspired by other sources. These events and decisions can affect your character, your realm, your relations, your wars, your plots, and more. For example, you can hold a tourney, a feast, a trial by combat, a coronation, a wedding, a funeral, etc.; you can join a faction, a society, a secret cult, a rebellion, etc.; you can claim a dragon egg, hatch a dragon, tame a dragon, ride a dragon, etc.; you can explore the ruins of Valyria, the lands beyond the Wall, the mysterious islands of the Jade Sea, etc.; you can encounter characters and creatures from the lore, such as direwolves, giants, wargs, faceless men, white walkers, etc.
-
- A new dueling engine, personal interaction system, and pets
-Another feature that makes this mod unique is that it adds a new dueling engine, a personal interaction system, and pets. These features allow you to have more control and fun with your character and others. Here are some details about these features:
-
-- A new dueling engine that allows you to challenge or be challenged by other characters to a duel. You can duel for honor, for glory, for revenge, for love, or for fun. You can choose your weapon, your armor, your tactics, and your attitude. You can also choose to fight honorably or dishonorably. The outcome of the duel depends on your skills, traits, equipment, luck, and choices. You can win or lose the duel; you can wound or kill your opponent; you can gain or lose prestige and piety; you can earn or lose respect and reputation; you can make or break alliances and friendships; you can trigger or avoid wars and plots.
-- A personal interaction system that allows you to interact with other characters in more ways than before. You can send them gifts, insults, threats, requests, invitations, etc. You can also use special interactions that are specific to your culture or religion. For example, you can invite someone to a feast, a hunt, a tourney, a wedding, etc.; you can seduce someone, flirt with someone, propose to someone, etc.; you can ask someone for a favor, a loan, a claim, etc.; you can offer someone your protection, your friendship, your loyalty, etc.; you can demand someone's submission, tribute, hostage, etc.; you can blackmail someone, expose someone, slander someone, etc. These interactions can have various effects on your and their stats, relations, events, and actions.
-- A pet system that allows you to have a pet as your companion and friend. You can acquire a pet through events or decisions, such as finding a stray animal, buying an exotic creature, inheriting a family pet, etc. You can choose from various types of pets, such as dogs, cats, birds, horses, dragons, direwolves, lions, bears, etc. Each pet has its own name, traits, appearance, and personality. You can interact with your pet in various ways, such as playing with it, feeding it, training it, grooming it, etc. Your pet can also interact with other characters and pets in various ways, such as liking them, hating them, attacking them, befriending them, etc. Your pet can affect your stats, relations, events, and actions. Your pet can also have its own events and decisions, such as getting sick, getting lost, getting pregnant, etc.
-
- Tips and tricks for playing Crusader Kings 2: A Game of Thrones mod
-Crusader Kings 2: A Game of Thrones mod is not an easy mod to play. It is full of challenges, dangers, and surprises. You need to be careful, cunning, and adaptable to survive and thrive in this world. Here are some tips and tricks to help you along the way:
- How to survive as a ruler in a hostile and unpredictable world
-As a ruler, you have many responsibilities and duties, but also many enemies and threats. You need to balance your power, your prestige, your piety, and your popularity. You need to keep your realm stable, your vassals loyal, your subjects happy, and your rivals at bay. You need to be aware of the political, military, economic, and social situation of your realm and the world. You need to be prepared for any eventuality, such as wars, rebellions, plots, intrigues, assassinations, etc. Here are some tips to help you survive as a ruler:
-
-- Choose your character wisely. Depending on the scenario and bookmark you choose, you may have different options for your starting character. Some characters are more powerful, more wealthy, more influential, or more popular than others. Some characters have more allies, more enemies, more claims, or more challenges than others. Some characters have more interesting or unique stories or events than others. Choose a character that suits your playstyle, your goals, and your preferences.
-- Choose your focus wisely. As a ruler, you can choose a focus that gives you bonuses to certain stats or actions. For example, you can choose a martial focus that gives you bonuses to combat or warfare; you can choose a stewardship focus that gives you bonuses to income or management; you can choose a diplomacy focus that gives you bonuses to relations or alliances; etc. Choose a focus that matches your needs, your strengths, or your weaknesses.
-- Choose your ambitions and plots wisely. As a ruler , you can choose an ambition that gives you a goal to pursue or a plot that gives you a scheme to execute. For example, you can choose an ambition to become king, to improve your stats, to have a child, etc.; you can choose a plot to kill someone, to fabricate a claim, to kidnap someone, etc. Choose an ambition or a plot that is realistic, achievable, and beneficial for you.
-- Choose your allies and enemies wisely. As a ruler, you can make or break alliances and rivalries with other characters. For example, you can marry someone, swear fealty to someone, form a pact with someone, etc.; you can declare war on someone, rebel against someone, plot against someone, etc. Choose your allies and enemies based on your interests, your values, and your situation. Be careful not to make too many enemies or too few allies.
-- Choose your actions and reactions wisely. As a ruler, you can take various actions or react to various events that affect your gameplay and roleplay. For example, you can grant or revoke titles, raise or lower taxes, change laws or succession, etc.; you can accept or decline invitations, requests, offers, etc.; you can respond to events with different options that have different consequences. Choose your actions and reactions based on your goals, your personality, and your circumstances. Be careful not to act rashly or recklessly.
-
- How to use your council, vassals, allies, and enemies to your advantage
-As a ruler, you are not alone in this world. You have various characters that can help you or hinder you in your endeavors. You have a council of advisors that can assist you in various tasks; you have vassals that owe you allegiance and service; you have allies that can support you in times of need; and you have enemies that can oppose you in times of trouble. You need to use these characters to your advantage and avoid their disadvantages. Here are some tips to help you do that:
-
-- Use your council wisely. Your council consists of six positions: Hand of the King (or equivalent), Master of Laws, Master of Coin, Master of Arms, Master of Whispers, and Maester (or equivalent). Each position has a specific function and skill associated with it. For example, the Hand of the King helps you with general administration and diplomacy; the Master of Laws helps you with justice and intrigue; the Master of Coin helps you with economy and stewardship; etc. You can assign any character in your realm to any position on your council, as long as they meet the requirements. You can also fire or replace them at any time. Choose your councilors based on their skills, their traits, their loyalty, and their opinion of you. You can also use your councilors to perform various jobs, such as improving relations, fabricating claims, collecting taxes, training troops, spying on enemies, etc. You can also consult your council on important decisions, such as declaring war, changing laws, granting titles, etc. Your councilors can also influence your events and actions, for better or worse.
-- Use your vassals wisely. Your vassals are the lords and ladies that hold lands and titles under you. They provide you with troops, taxes, and support. They also have their own interests, ambitions, and opinions. You need to keep your vassals happy and loyal, or else they may rebel against you or join your enemies. You can improve your relations with your vassals by granting them titles, honors, favors, gifts, etc. You can also reduce their power and influence by revoking their titles, transferring their vassals, imprisoning them, executing them, etc. You can also use your vassals to expand your realm, by pressing their claims, marrying them off, or supporting them in wars.
-- Use your allies wisely. Your allies are the characters that have a formal or informal alliance with you. They can be your relatives, your friends, your vassals, your liege, or your fellow faction members. They can help you in wars, plots, events, and actions. They can also ask you for help in return. You can form alliances with other characters by marrying them or their relatives, by joining their factions or societies, by swearing fealty to them or offering them protection, etc. You can also break alliances with other characters by divorcing them or their relatives, by leaving their factions or societies, by rebelling against them or refusing their protection, etc. You can also use your allies to weaken your enemies, by declaring war on them, by plotting against them, by supporting their rivals, etc.
-- Use your enemies wisely. Your enemies are the characters that have a formal or informal rivalry with you. They can be your rivals, your traitors, your rebels, your competitors, or your adversaries. They can harm you in wars, plots, events, and actions. They can also benefit you in some ways. You can use your enemies to improve your prestige, your piety, your reputation, and your skills. You can also use your enemies to unite your realm, your vassals, your allies, and your friends. You can also use your enemies to justify your actions, such as declaring war, changing laws, revoking titles, etc.
-
- Review of Crusader Kings 2: A Game of Thrones mod
-Now that we have covered some of the features and tips of Crusader Kings 2: A Game of Thrones mod, let's review it and see how it compares to the vanilla game and other mods. Here are some of the pros and cons of the mod:
- Pros
-
-- The mod is faithful and immersive to the source material. It captures the essence and atmosphere of A Song of Ice and Fire very well. It has many references and details from the books and show that fans will appreciate and enjoy.
-- The mod is rich and diverse in content and gameplay. It has many new features and mechanics that add more depth and flavor to the game. It has many different scenarios and bookmarks that offer different challenges and opportunities. It has many events and decisions that make the game more dynamic and unpredictable.
-- The mod is fun and engaging to play. It has a lot of replay value and variety. It allows you to create your own stories and histories with different characters and factions. It allows you to roleplay as your favorite or custom character with different traits and actions. It allows you to interact with other characters and pets in various ways.
-
- Cons
-
-- The mod is complex and difficult to play. It has a steep learning curve and requires a lot of attention and strategy. It has many rules and mechanics that can be confusing or overwhelming for new or casual players. It has many events and decisions that can be frustrating or unfair for unlucky or unprepared players.
-- The mod is buggy and unstable to play. It has many errors and glitches that can affect the performance and quality of the game. It may crash, freeze, lag, or corrupt your save files. It may also conflict with other mods or updates of the game. It requires constant maintenance and updates from the developers and the community.
-- The mod is incomplete and unfinished to play. It is still in development and has many features and content that are missing or incomplete. It may not reflect the latest or final version of the books or show. It may also have some inconsistencies or inaccuracies with the lore or canon.
-
- Comparison with the vanilla game and other mods
-Crusader Kings 2: A Game of Thrones mod is not the only mod for Crusader Kings 2. There are many other mods that offer different settings, themes, features, and gameplay. Some of them are historical, some of them are fantasy, some of them are original, and some of them are based on other media. Here are some of the most popular and notable mods for Crusader Kings 2:
-
-
-| Mod |
-Description |
-
-
-| CK2+ |
-A mod that enhances and expands the vanilla game with more content, features, mechanics, and balance. It aims to make the game more historical, realistic, challenging, and fun. |
-
-
-| HIP (Historical Immersion Project) |
-A mod that overhauls and improves the vanilla game with more historical accuracy, immersion, diversity, and detail. It includes several submods that focus on different aspects of the game, such as map, culture, religion, law, etc. |
-
-
-| Elder Kings |
-A mod that converts the game into the fantasy world of The Elder Scrolls series. It allows you to play as various races, factions, and characters from the lore, and experience the events and scenarios that shaped the world of Tamriel and beyond. |
-
-
-| After the End Fan Fork |
-A mod that sets the game in a post-apocalyptic North America after an unknown cataclysm. It allows you to play as various cultures, religions, and societies that emerged from the ashes of civilization. |
-
-
-| Gods! |
-A mod that adds a new layer of gameplay and roleplay by allowing you to play as a god or a demigod in a mythical world. You can create your own pantheon, influence mortals, perform miracles, fight other gods, etc. |
-
-
- How does Crusader Kings 2: A Game of Thrones mod compare to these mods? Well, it depends on your preferences and expectations. If you are looking for a more historical or realistic mod, then you may prefer CK2+ or HIP. If you are looking for a more fantasy or original mod, then you may prefer Elder Kings or After the End Fan Fork. If you are looking for a more divine or supernatural mod, then you may prefer Gods!. If you are looking for a mod that is faithful and immersive to A Song of Ice and Fire, then you may prefer Crusader Kings 2: A Game of Thrones mod.
- Personal opinion and rating of the mod
-Finally, let me share my personal opinion and rating of Crusader Kings 2: A Game of Thrones mod. I have played this mod for many hours and enjoyed it very much. I think it is one of the best mods for Crusader Kings 2 and one of the best adaptations of A Song of Ice and Fire. I love how it captures the essence and atmosphere of the books and show. I love how it adds many new features and mechanics that enhance the gameplay and immersion of the mod. I love how it allows me to create my own stories and histories with different characters and factions. I love how it is fun and engaging to play. I think it is a mod that every fan of Crusader Kings 2 and A Song of Ice and Fire should try. However, I also recognize that the mod is not perfect. It has some flaws and drawbacks that can affect the enjoyment and satisfaction of some players. I think the mod is complex and difficult to play. It requires a lot of attention and strategy. It has many rules and mechanics that can be confusing or overwhelming for new or casual players. It has many events and decisions that can be frustrating or unfair for unlucky or unprepared players. I think the mod is buggy and unstable to play. It has many errors and glitches that can affect the performance and quality of the game. It may crash, freeze, lag, or corrupt your save files. It may also conflict with other mods or updates of the game. It requires constant maintenance and updates from the developers and the community. I think the mod is incomplete and unfinished to play. It is still in development and has many features and content that are missing or incomplete. It may not reflect the latest or final version of the books or show. It may also have some inconsistencies or inaccuracies with the lore or canon. Therefore, based on my personal opinion and experience, I would rate Crusader Kings 2: A Game of Thrones mod 8 out of 10. I think it is a great mod that deserves praise and recognition, but also has room for improvement and development.
Conclusion
-In conclusion, Crusader Kings 2: A Game of Thrones mod is a full-conversion mod that transforms the medieval world of Crusader Kings 2 into the fantasy realm of Westeros and beyond. It allows you to play as various characters and factions from the books and show, and experience the events and scenarios that shaped the world of A Song of Ice and Fire. It adds many new features and mechanics that enhance the gameplay and immersion of the mod. It also has some flaws and drawbacks that can affect the enjoyment and satisfaction of some players. It is a mod that every fan of Crusader Kings 2 and A Song of Ice and Fire should try, but also be aware of its limitations and challenges.
-If you are interested in Crusader Kings 2: A Game of Thrones mod, you can download it from its official website, ModDB page, or Steam workshop page. You can also visit its forum or subreddit for more information, discussion, feedback, support, etc.
-Thank you for reading this article. I hope you found it helpful and enjoyable. Have fun playing Crusader Kings 2: A Game of Thrones mod!
- FAQs
-Here are some frequently asked questions (FAQs) about Crusader Kings 2: A Game of Thrones mod:
-
-- Q: What are the system requirements for Crusader Kings 2: A Game of Thrones mod?
-A: The system requirements for Crusader Kings 2: A Game of Thrones mod are the same as for Crusader Kings 2, plus some additional disk space for the mod files. Here are the minimum system requirements for Crusader Kings 2:
-- OS: Windows 7
-- Processor: Intel® Pentium® IV 2.4 GHz or AMD 3500+
-- Memory: 4 GB RAM
-- Hard Disk Space: 2 GB
-- Video Card: NVIDIA® GeForce 8800 or ATI Radeon® X1900, 512mb graphics memory required.
-- DirectX®: 9.0c
-- Sound: Direct X-compatible sound card
-- Additional: 3-button mouse and keyboard
-- Q: How do I update Crusader Kings 2: A Game of Thrones mod?
-A: To update Crusader Kings 2: A Game of Thrones mod, you need to download the latest version of the mod from its official website, ModDB page, or Steam workshop page. Then, you need to extract the downloaded file using a program like WinRAR or 7-Zip. You should get two folders: A Game of Thrones and A Game of Thrones Music. You need to copy these two folders into your Crusader Kings 2 mod folder, replacing the old ones. The default location is C:\Users\YourName\Documents\Paradox Interactive\Crusader Kings II\mod. Then, you need to launch Crusader Kings 2 from Steam or other platforms. In the launcher window, check the boxes next to A Game of Thrones and A Game of Thrones Music under Mods. Click Play and enjoy the updated mod.
-- Q: How do I uninstall Crusader Kings 2: A Game of Thrones mod?
-A: To uninstall Crusader Kings 2: A Game of Thrones mod, you need to delete the two folders: A Game of Thrones and A Game of Thrones Music from your Crusader Kings 2 mod folder. The default location is C:\Users\YourName\Documents\Paradox Interactive\Crusader Kings II\mod. Then, you need to launch Crusader Kings 2 from Steam or other platforms. In the launcher window, uncheck the boxes next to A Game of Thrones and A Game of Thrones Music under Mods. Click Play and enjoy the vanilla game or other mods.
-- Q: How do I play multiplayer with Crusader Kings 2: A Game of Thrones mod?
-A: To play multiplayer with Crusader Kings 2: A Game of Thrones mod, you need to have the same version of the game and the mod as your friends. You also need to disable any other mods that may conflict with Crusader Kings 2: A Game of Thrones mod. Then, you need to launch Crusader Kings 2 from Steam or other platforms. In the launcher window, check the boxes next to A Game of Thrones and A Game of Thrones Music under Mods. Click Play and then click Multiplayer. You can either host a game or join a game hosted by your friends. You can also use third-party programs like Hamachi or Evolve to create a virtual LAN network and play with your friends.
-- Q: How do I report bugs or give feedback for Crusader Kings 2: A Game of Thrones mod?
-A: To report bugs or give feedback for Crusader Kings 2: A Game of Thrones mod, you can visit its forum or subreddit. There, you can post your issues, suggestions, questions, comments, etc. You can also contact the developers and the community directly through their Discord server. Please be respectful and constructive when reporting bugs or giving feedback.
-- Q: How do I support Crusader Kings 2: A Game of Thrones mod?
-A: To support Crusader Kings 2: A Game of Thrones mod, you can do several things. You can spread the word about the mod and recommend it to your friends and other players. You can rate and review the mod on its website, ModDB page, or Steam workshop page. You can donate money to the developers and the community through their Patreon page. You can also contribute to the development and improvement of the mod by providing bug reports, feedback, suggestions, ideas, etc.
-
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-How to Download Ringtone for iPhone 12 WhatsApp
-WhatsApp is one of the most popular instant messaging apps in the world. It allows you to send text and voice messages, make voice and video calls, and share images, documents, user locations, and other content with your contacts. If you use WhatsApp frequently, you might want to customize your ringtone so that you can easily identify when you receive a notification or a call from WhatsApp.
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-In this article, we will show you how to download ringtone for iPhone 12 WhatsApp in three easy steps. You will need the following tools:
-
-- An iPhone 12 with iOS 14 or later
-- A WhatsApp account with some audio files that you want to use as ringtones
-- The GarageBand app that comes pre-installed on your iPhone 12
-- The Files app that comes pre-installed on your iPhone 12
-
-Step 1: Save the WhatsApp Audio on Your iPhone
-The first step is to save the audio file that you want to use as a ringtone on your iPhone. You cannot set an audio file as a ringtone directly from WhatsApp. You need to save it to your iPhone's storage first. Here's how:
-
-- Launch WhatsApp app and open the chat where you've received or sent the audio file that you want to use as a ringtone.
-- Tap and hold on to the audio file until you see additional options.
-- Tap on the Forward option.
-- The audio file will now be selected. Tap on the Share icon at the bottom left corner of the screen.
-- From the list of apps that appear, tap on the Files app.
-- Choose a location where you want to save the audio file, such as On My iPhone or iCloud Drive.
-- Tap on Save to confirm your action.
-
-You have now saved the audio file on your iPhone. You can find it in the Files app in the location that you chose.
-Step 2: Convert the Audio File to a Ringtone Format
-The next step is to convert the audio file to a ringtone format. You cannot set any audio file as a ringtone on your iPhone. You need to convert it to a .m4r format, which is the standard format for iPhone ringtones. You can use the GarageBand app to do this. Here's how:
-
-- Launch GarageBand app and tap on the + icon at the top right corner of the screen.
-- Tap on New Song and choose Audio Recorder as the instrument.
-- Tap on the Tracks View icon at the top left corner of the screen. It looks like three horizontal lines with circles.
-- Tap on the Loop Browser icon at the top right corner of the screen. It looks like a loop.
-- Tap on Browse Items from the Files app.
-- Navigate to the location where you saved the audio file in Step 1 and tap on it to import it to GarageBand.
-- You will see the audio file as a blue waveform on the track. You can use the tools at the top of the screen to edit it, such as trimming, splitting, looping, or adding effects.
-- When you are happy with your editing, tap on My Songs at the top left corner of the screen to save your project.
-- Tap and hold on your project until you see additional options.
-- Tap on Share and choose Ringtone as the option.
-- Enter a name for your ringtone and tap on Export.
-
-You have now converted the audio file to a ringtone format. You can find it in the Settings app under Sounds & Haptics > Ringtone.
-Step 3: Set the Ringtone for WhatsApp on Your iPhone
-The final step is to set the ringtone for WhatsApp on your iPhone. You can choose different ringtones for WhatsApp notifications and calls. Here's how:
-
-- Launch Settings app and tap on Sounds & Haptics.
-- Scroll down to find WhatsApp under Notification Sounds and tap on it.
-- You will see a list of ringtones that you can choose from. Tap on the one that you created in Step 2. You will hear a preview of how it sounds.
-- If you want to use the same ringtone for WhatsApp calls, tap on Ringtone under Sounds and Vibration Patterns and choose your ringtone from there.
-
-You have now set your ringtone for WhatsApp on your iPhone. You can test it by sending yourself a message or calling yourself from another device using WhatsApp.
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- - A: You can download more ringtones for your iPhone 12 from the iTunes Store app by tapping on More, tapping on Tones, browsing or searching for ringtones, and buying them with your Apple ID.
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-Akdi Pakdi Full Song Download: A Massy Dance Number from Liger
-If you are looking for a catchy and energetic dance song to groove to, then you might want to check out Akdi Pakdi, the first song from the upcoming movie Liger. This song has been creating a lot of buzz among the fans of Vijay Deverakonda and Ananya Panday, who are starring in this movie as the lead pair. Akdi Pakdi is a Hindi song that has been sung by Dev Negi, Pawni Pandey, and Lijo George, and composed by Lijo George-Dj Chetas. The song has a catchy hookline, upbeat music, and impressive dance moves by Vijay and Ananya. In this article, we will tell you everything you need to know about Akdi Pakdi, including its details, meaning, video, movie, and how to download it online.
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- Akdi Pakdi: The Song Details
-Akdi Pakdi is the first song from the movie Liger, which is a sports drama film directed by Puri Jagannadh and co-produced by Karan Johar. The song was released on July 11, 2022, by Sony Music India on their official YouTube channel. The song has received over 45 million views and 988 thousand likes so far. Here are some more details about the song:
- The Singers and Composers of Akdi Pakdi
-The song has been sung by three talented singers, namely Dev Negi, Pawni Pandey, and Lijo George. Dev Negi is a popular playback singer who has sung many hit songs like Badri Ki Dulhania, Sweety Tera Drama, Chalti Hai Kya 9 Se 12, etc. Pawni Pandey is also a well-known singer who has lent her voice to songs like Laila Main Laila, Balam Pichkari, Dil Ka Telephone, etc. Lijo George is not only a singer but also a music composer who has composed music for many movies like Loveyatri, Mitron, Malang, etc. He has also collaborated with Dj Chetas, who is one of the most famous DJs in India. Together, they have created some amazing remixes and original songs like Kamariya, Lamborghini, Coca Cola Tu, etc.
- The Lyrics and Meaning of Akdi Pakdi
-The lyrics of the song have been written by Mohsin Shaikh and Azeem Dayani. Mohsin Shaikh is a lyricist who has penned songs like Makhna, Dilbar Dilbar, Garmi, etc. Azeem Dayani is also a lyricist who has written songs like Bekhayali, Tujhe Kitna Chahne Lage, Tera Ban Jaunga, etc. He is also a music supervisor who has worked on many movies like Kabir Singh, Good Newwz, Kesari, etc.
-The song is a fun and flirty track that revolves around Vijay's character trying to woo Ananya's character. The title of the song em>Akdi Pakdi means one-two in Hindi, and it is a common phrase used in children's games like hopscotch or tag. However, in the context of the song, it also implies a playful chase between the lovers, who tease and flirt with each other. The song has some catchy lines like Akdi pakdi akdi pakdi, tu bhaage main pakdu (One-two one-two, you run and I catch you), Tere nakhre hain anmol, main toh khareedu (Your tantrums are priceless, I want to buy them), Tu hai meri lollipop, main toh chakdu (You are my lollipop, I want to lick you), etc.
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- The Music Video and Choreography of Akdi Pakdi
-The music video of the song has been directed by Puri Jagannadh, who is also the director of the movie Liger. The video features Vijay Deverakonda and Ananya Panday in a colorful and vibrant setting, where they dance and romance with each other. The video also showcases some glimpses of the movie, where Vijay plays a mixed martial arts fighter and Ananya plays his love interest.
-The choreography of the song has been done by Raju Sundaram, who is a renowned choreographer and actor in the South Indian film industry. He has won three National Film Awards for Best Choreography for his work in movies like Minsara Kanavu, Janatha Garage, and Rowdy Rathore. He has also choreographed songs for many Bollywood movies like Chennai Express, Kick, Ra.One, etc. He has given some energetic and stylish dance moves to Vijay and Ananya, who have performed them with grace and confidence.
- Akdi Pakdi: The Movie Details
-Akdi Pakdi is not just a song, but also a part of a much-awaited movie called Liger. Liger is a sports drama film that marks the debut of Vijay Deverakonda in Bollywood. The movie is also a pan-Indian project that will be released in five languages: Hindi, Telugu, Tamil, Kannada, and Malayalam. Here are some more details about the movie:
- Liger: The Plot and Genre of the Movie
-Liger is a movie that revolves around the life of a mixed martial arts fighter named Ranveer, played by Vijay Deverakonda. Ranveer is a street fighter who has a rare blood group that makes him aggressive and powerful. He is also known as Liger, which is a hybrid of a lion and a tiger. He falls in love with Mona, played by Ananya Panday, who is a bubbly and cheerful girl who supports him in his dreams. However, Ranveer's life takes a turn when he gets involved in the dark world of underground fighting, where he has to face many challenges and enemies.
-Liger is a movie that combines action, romance, drama, and comedy. It is a movie that showcases the journey of a fighter who wants to achieve his goals and prove his worth. It is also a movie that explores the themes of love, friendship, family, and identity.
- Liger: The Cast and Crew of the Movie
-Liger has an ensemble cast that includes some of the most talented actors from different film industries. Apart from Vijay Deverakonda and Ananya Panday, the movie also stars Ramya Krishnan, Ronit Roy, Vishu Reddy, Ali, Makarand Deshpande, Abdul Quadir Amin, etc. The movie also features some special appearances by Jackie Shroff, Suniel Shetty, Chunky Pandey, etc.
-Liger has been directed by Puri Jagannadh, who is one of the most successful directors in Telugu cinema. He has directed movies like Pokiri, Businessman, Temper, iSmart Shankar, etc. He has also co-produced the movie with Karan Johar, who is one of the most influential producers in Bollywood. He has produced movies like Kuch Kuch Hota Hai, Kabhi Khushi Kabhie Gham, Dostana, Yeh Jawaani Hai Deewani, etc. The movie has been written by Puri Jagannadh and Charmme Kaur, who is also one of the co-producers of the movie. The music of the movie has been composed by Lijo George-Dj Chetas, who have also composed the song Akdi Pakdi. The cinematography of the movie has been done by Vishnu Sarma, who has worked on movies like Mardaani 2, Bala, etc. The editing of the movie has been done by Junaid Siddiqui, who has worked on movies like Dabangg 3, Race 3, etc.
- Liger: The Release Date and Languages of the Movie
-Liger is a movie that has been in the making for a long time. The movie was announced in January 2020, and the shooting began in February 2020. However, due to the COVID-19 pandemic, the shooting was halted and resumed several times. The movie was initially scheduled to release on September 9, 2021, but it was postponed due to the second wave of the pandemic. The makers have now announced that the movie will release on January 26, 2022, on the occasion of Republic Day.
-Liger is a movie that will be released in five languages: Hindi, Telugu, Tamil, Kannada, and Malayalam. The movie will also have dubbed versions in other languages like English, French, German, etc. The movie will be distributed by Zee Studios and Dharma Productions in India and by AA Films and Phars Film internationally.
- Akdi Pakdi: How to Download the Full Song Online
-If you are a fan of Akdi Pakdi and want to download the full song online, then you might be wondering how to do it. There are many ways to download songs online, but not all of them are legal and safe. In this section, we will tell you how to download Akdi Pakdi online in a legal and safe manner. We will also tell you the benefits and drawbacks of downloading songs online, and the alternatives and options you have.
- The Legal and Safe Ways to Download Akdi Pakdi
-The best way to download Akdi Pakdi online is to use a legal and safe platform that has the rights to stream or download the song. Some of these platforms are:
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-- YouTube Music: YouTube Music is a music streaming service that allows you to listen to songs online or download them offline. You can access YouTube Music through the YouTube app or website, or through a separate app called YouTube Music. To download songs offline, you need to have a YouTube Premium subscription, which costs Rs. 129 per month or Rs. 399 for three months. You can also get a free trial for one month before subscribing.
-- Spotify: Spotify is another music streaming service that allows you to listen to songs online or download them offline. You can access Spotify through the Spotify app or website, or through other devices like smart speakers, TVs, etc. To download songs offline, you need to have a Spotify Premium subscription, which costs Rs. 119 per month or Rs. 699 for a year. You can also get a free trial for one month before subscribing.
-- JioSaavn: JioSaavn is an Indian music streaming service that allows you to listen to songs online or download them offline. You can access JioSaavn through the JioSaavn app or website, or through other devices like smart speakers, TVs, etc. To download songs offline, you need to have a JioSaavn Pro subscription, which costs Rs. 99 per month or Rs. 399 for a year. You can also get a free trial for one month before subscribing.
-- Gaana: Gaana is another Indian music streaming service that allows you to listen to songs online or download them offline. You can access Gaana through the Gaana app or website, or through other devices like smart speakers, TVs, etc. To download songs offline, you need to have a Gaana Plus subscription, which costs Rs. 99 per month or Rs. 399 for a year. You can also get a free trial for one month before subscribing.
-
-These are some of the legal and safe ways to download Akdi Pakdi online. However, there are some benefits and drawbacks of downloading songs online that you should be aware of.
- The Benefits and Drawbacks of Downloading Akdi Pakdi
-Downloading Akdi Pakdi online has some benefits and drawbacks that you should consider before doing it. Some of these are:
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-
-| Benefits |
-Drawbacks |
-
-
-| You can listen to the song anytime and anywhere without internet connection. |
-You need to pay for a subscription fee or buy the song from an online store, which can be expensive or inconvenient. |
-
-
-| You can enjoy the high-quality sound and video of the song without buffering or interruptions. |
-You need to have enough storage space on your device to download the song, which can take up a lot of memory. |
-
-
-| You can share the song with your friends and family through Bluetooth, USB, or other methods. |
-You might face legal issues or penalties if you download the song from an illegal or pirated source, which can harm your device or data. |
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- These are some of the benefits and drawbacks of downloading Akdi Pakdi online. You should weigh them carefully before deciding to download the song online.
- The Alternatives and Options to Download Akdi Pakdi
-If you do not want to download Akdi Pakdi online, or if you are not satisfied with the legal and safe ways to download it, then you have some alternatives and options to enjoy the song. Some of these are:
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-- Watch the song on YouTube: You can watch the song on YouTube for free, without downloading it. You can also watch the song in different languages, as it has been dubbed in Telugu, Tamil, Kannada, and Malayalam. However, you will need an internet connection to watch the song, and you might face ads or interruptions while watching it.
-- Listen to the song on FM radio: You can listen to the song on FM radio for free, without downloading it. You can also listen to the song in different languages, as it has been played on various radio stations across India. However, you will not be able to choose when and how many times you want to listen to the song, and you might miss some parts of the song due to signal issues or other noises.
-- Buy the CD or DVD of the song: You can buy the CD or DVD of the song from an online or offline store, and play it on your device. You can also buy the CD or DVD of the movie Liger, which will include the song Akdi Pakdi. However, you will have to pay for the CD or DVD, which can be costly or unavailable. You will also need a compatible device and player to play the CD or DVD.
-- Watch the movie Liger in theatres: You can watch the movie Liger in theatres when it releases on January 26, 2022. You can also watch the movie in different languages, as it will be released in five languages: Hindi, Telugu, Tamil, Kannada, and Malayalam. However, you will have to wait for the movie to release, and book your tickets in advance. You will also have to follow the COVID-19 safety protocols and guidelines while watching the movie in theatres.
-
-These are some of the alternatives and options to download Akdi Pakdi online. You can choose any of them according to your preference and convenience.
- Conclusion
-Akdi Pakdi is a massy dance number from the movie Liger that has been loved by many people. The song has been sung by Dev Negi, Pawni Pandey, and Lijo George, and composed by Lijo George-Dj Chetas. The song has been written by Mohsin Shaikh and Azeem Dayani. The song has a catchy hookline, upbeat music, and impressive dance moves by Vijay Deverakonda and Ananya Panday. The song has been directed by Puri Jagannadh and choreographed by Raju Sundaram.
-Liger is a sports drama film that marks the debut of Vijay Deverakonda in Bollywood. The movie is also a pan-Indian project that will be released in five languages: Hindi, Telugu, Tamil, Kannada, and Malayalam. The movie is directed by Puri Jagannadh and co-produced by Karan Johar. The movie stars Vijay Deverakonda as a mixed martial arts fighter and Ananya Panday as his love interest. The movie also features Ramya Krishnan, Ronit Roy, Vishu Reddy, Ali, Makarand Deshpande, etc. The movie will release on January 26 , 2022, on the occasion of Republic Day.
-If you want to download Akdi Pakdi online, you can use a legal and safe platform like YouTube Music, Spotify, JioSaavn, or Gaana. You can also watch the song on YouTube, listen to it on FM radio, buy the CD or DVD of the song, or watch the movie Liger in theatres. However, you should be aware of the benefits and drawbacks of downloading songs online, and choose the best option for you.
-We hope you enjoyed this article and learned something new about Akdi Pakdi and Liger. If you have any questions or feedback, please feel free to leave a comment below. And don't forget to share this article with your friends and family who might be interested in this topic. Thank you for reading!
- FAQs
-Here are some frequently asked questions and answers related to Akdi Pakdi:
-
-- What is the meaning of Liger?
-Liger is a hybrid of a lion and a tiger, which is also the nickname of Vijay Deverakonda's character in the movie. It symbolizes his strength, power, and aggression as a fighter.
-- Who is the director of Liger?
-Liger is directed by Puri Jagannadh, who is one of the most successful directors in Telugu cinema. He has directed movies like Pokiri, Businessman, Temper, iSmart Shankar, etc.
-- Who is the producer of Liger?
-Liger is co-produced by Karan Johar, who is one of the most influential producers in Bollywood. He has produced movies like Kuch Kuch Hota Hai, Kabhi Khushi Kabhie Gham, Dostana, Yeh Jawaani Hai Deewani, etc.
-- Who are the singers of Akdi Pakdi?
-Akdi Pakdi is sung by Dev Negi, Pawni Pandey, and Lijo George. Dev Negi is a popular playback singer who has sung many hit songs like Badri Ki Dulhania, Sweety Tera Drama, Chalti Hai Kya 9 Se 12, etc. Pawni Pandey is also a well-known singer who has lent her voice to songs like Laila Main Laila, Balam Pichkari, Dil Ka Telephone, etc. Lijo George is not only a singer but also a music composer who has composed music for many movies like Loveyatri, Mitron, Malang, etc.
-- Who are the composers of Akdi Pakdi?
-Akdi Pakdi is composed by Lijo George-Dj Chetas. They are a duo of music composers and DJs who have created some amazing remixes and original songs like Kamariya, Lamborghini, Coca Cola Tu, etc.
-
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diff --git a/spaces/congsaPfin/Manga-OCR/logs/MGlobal Live MOD APK Watch and Interact with Streamers from All Over the World without Restrictions!.md b/spaces/congsaPfin/Manga-OCR/logs/MGlobal Live MOD APK Watch and Interact with Streamers from All Over the World without Restrictions!.md
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Do you love action games with superheroes and villains? Do you want to swing around the city like Spider-Man and fight crime with your rope skills? If yes, then you should try Spider Stickman Rope Hero, a fun and exciting game where you can become a stickman hero with amazing abilities. But wait, there's more! You can also download the mod apk version of this game and enjoy unlimited money, unlocked items, no ads, and more. In this article, we will tell you everything you need to know about Spider Stickman Rope Hero and how to download and install the mod apk on your device. Let's get started!
What is Spider Stickman Rope Hero?
Spider Stickman Rope Hero is an adventure game developed by Dino Games Studio. It is available for Android devices on Google Play Store. In this game, you can create your own stickman character and customize it with different costumes, masks, weapons, and accessories. You can also use your rope to swing around the city, climb buildings, jump from rooftops, and explore different locations. You can also fight against various enemies, such as gangsters, zombies, robots, aliens, and even dinosaurs. You can use your fists, guns, grenades, swords, hammers, axes, and more to defeat them. You can also drive different vehicles, such as cars, bikes, helicopters, tanks, etc., and perform stunts and crashes. The game has a lot of missions to complete and rewards to collect. You can also play in free mode and do whatever you want in the open world.
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The game has stunning 3D graphics and realistic physics that make it look like a real stickman movie. The game also has awesome sound effects and music that enhance the atmosphere and mood of the game. The game has simple and intuitive controls that make it easy to play for anyone. You can use the virtual joystick to move your character and the buttons to perform actions.
Why download the mod apk?
Spider Stickman Rope Hero is a free game that you can download from Google Play Store. However, it also has some limitations and drawbacks that may affect your gaming experience. For example:
- The game has ads that may interrupt your gameplay or consume your data.
- The game has in-app purchases that may require you to spend real money to unlock some items or features.
- The game has a limited amount of money that you can earn by completing missions or collecting coins.
- The game has some items or features that are locked or unavailable until you reach a certain level or complete a certain mission.
If you want to enjoy Spider Stickman Rope Hero without any restrictions or hassles, then you should download the mod apk version of this game. The mod apk is a modified version of the original game that has some extra features and benefits that are not available in the original version. For example:
-Conclusion
-Spider Stickman Rope Hero is a game that will make you feel like a superhero with amazing rope skills. You can swing around the city, fight against enemies, drive vehicles, and complete missions. You can also download the mod apk version of this game and enjoy unlimited money, unlocked items, no ads, and more. If you are looking for a game that is fun, exciting, and addictive, then you should try Spider Stickman Rope Hero. Download it now and become the stickman hero you always wanted to be!
-FAQs
-What is the latest version of Spider Stickman Rope Hero mod apk?
-The latest version of Spider Stickman Rope Hero mod apk is 1.0.5. It was updated on June 15, 2023. It has some bug fixes and improvements.
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-Yes, Spider Stickman Rope Hero mod apk is safe to use. It does not contain any viruses or malware that may harm your device or data. However, you should always download it from a trusted source and scan it before installing it.
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-You can update Spider Stickman Rope Hero mod apk by downloading the latest version from our website and installing it over the existing one. You do not need to uninstall the previous version.
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diff --git a/spaces/congsaPfin/Manga-OCR/logs/Top 10 Sites to Download Video Games for Free in 2023.md b/spaces/congsaPfin/Manga-OCR/logs/Top 10 Sites to Download Video Games for Free in 2023.md
deleted file mode 100644
index d35e18b1928c3e6b16de2b9300e70cca41cd8157..0000000000000000000000000000000000000000
--- a/spaces/congsaPfin/Manga-OCR/logs/Top 10 Sites to Download Video Games for Free in 2023.md
+++ /dev/null
@@ -1,116 +0,0 @@
-
-
- Access a variety of games
- Enjoy offline gaming | | H2: Risks of Downloading Video Games for Free | - Legal issues and penalties
- Malware and viruses
- Poor quality and performance | | H2: Tips for Downloading Video Games for Free Safely and Legally | - Use trusted and reputable sites
- Check the game ratings and reviews
- Scan the files before installing
- Avoid pirated and cracked games | | H2: Best Sites to Download Video Games for Free | - Ocean of Games
- Steam
- ThePcGames.Net
- OvaGames
- GameJolt | | H1: Conclusion | Summary of the main points and recommendations | Table 2: Article with HTML formatting How to Download Video Games for Free
-If you are a gaming enthusiast, you might be wondering how to download video games for free. After all, buying new games can be expensive and time-consuming, especially if you have a limited budget and a busy schedule. Fortunately, there are many ways to enjoy your favorite games without spending a dime. In this article, we will explore the benefits and risks of downloading video games for free, as well as some tips and tricks to do it safely and legally. We will also share some of the best sites to download PC games for free in 2023.
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- Benefits of Downloading Video Games for Free
-Downloading video games for free has many advantages, such as:
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-- Save money and time: You don't have to pay for the games or wait for them to be delivered. You can simply download them from the comfort of your home and start playing right away.
-- Access a variety of games: You can choose from a wide range of genres, styles, and themes, from action and adventure to puzzle and strategy. You can also find indie games, retro games, and fan-made games that are not available in stores.
-- Enjoy offline gaming: You don't need an internet connection to play the games once they are downloaded. You can play them anytime and anywhere, even when you are offline.
-
- Risks of Downloading Video Games for Free
-However, downloading video games for free also has some drawbacks, such as:
-
-- Legal issues and penalties: Some games are protected by copyright laws and require a license to play. Downloading them without permission is considered piracy and can result in fines or even jail time. You might also face lawsuits from the game developers or publishers.
-- Malware and viruses: Some sites that offer free games are not safe or secure. They might contain malicious software that can harm your computer or steal your personal information. You might also encounter pop-ups, ads, or redirects that can compromise your browsing experience.
-- Poor quality and performance: Some free games are not well-made or optimized. They might have bugs, glitches, errors, or compatibility issues. They might also lack features, updates, or support from the developers.
-
- Tips for Downloading Video Games for Free Safely and Legally
-To avoid the risks of downloading video games for free, you should follow these tips:
-
-- Use trusted and reputable sites: You should only download games from sites that are reliable and legitimate. You can check their reputation by reading user reviews, ratings, comments, or feedback. You can also look for signs of security, such as HTTPS, SSL certificates, or badges.
-- Check the game ratings and reviews: You should only download games that are suitable for your age group and preferences. You can check their ratings by organizations such as ESRB, PEGI, or USK. You can also read their reviews by critics or players to get an idea of their quality and content.
-- Scan the files before installing: You should always scan the files you download with an antivirus or anti-malware program before installing them on your computer. This will help you detect and remove any potential threats or infections.
-- A. - Avoid pirated and cracked games: You should never download games that are illegally copied or modified. They might contain malware or viruses, as well as damage the game industry and the rights of the creators. You should always respect the intellectual property and the terms of service of the games.
-
- Best Sites to Download Video Games for Free
-Here are some of the best sites to download PC games for free in 2023:
-
-
-| Site |
-Description |
-
-
-| Ocean of Games |
-This site offers a huge collection of games from various genres and categories. You can download them easily and quickly, without any registration or payment. You can also find the latest releases and updates, as well as game guides and tutorials. |
-
-
-| Steam |
-This site is one of the most popular and trusted platforms for PC gaming. It has thousands of games, both free and paid, from indie developers to big publishers. You can also enjoy features such as cloud saving, achievements, multiplayer, chat, and more. |
-
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-| ThePcGames.Net |
-This site provides high-quality and full-version PC games for free. You can download them directly or through torrent links, without any ads or surveys. You can also request games that are not available on the site. |
-
-
-| OvaGames |
-This site specializes in free PC games with single links or parts. You can download them from various file hosting services, such as Google Drive, Mega, or MediaFire. You can also find DLCs, patches, cracks, and repacks. |
-
-
-| GameJolt |
-This site is a community-driven platform for indie games. You can download and play thousands of games for free, as well as rate, review, and comment on them. You can also create your own games and share them with others. |
-
-
- Conclusion
-Downloading video games for free is a great way to enjoy your gaming hobby without breaking the bank. However, you should also be aware of the risks and challenges involved, such as legal issues, malware, and poor quality. To download video games for free safely and legally, you should use trusted and reputable sites, check the game ratings and reviews, scan the files before installing, and avoid pirated and cracked games. Some of the best sites to download PC games for free in 2023 are Ocean of Games, Steam, ThePcGames.Net, OvaGames, and GameJolt.
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-- Q: Is it illegal to download video games for free?
-- A: It depends on the source and the license of the games. Some games are free to download and play legally, while others are protected by copyright laws and require a license to play. Downloading them without permission is considered piracy and can result in legal consequences.
-- Q: How can I download video games for free faster?
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-- Q: How can I play video games for free online?
-- A: You can play video games for free online by using a web browser or a streaming service. Some examples of web browser games are Agar.io, Slither.io, or Krunker.io. Some examples of streaming services are Google Stadia, GeForce Now, or PlayStation Now.
-- Q: How can I get paid video games for free?
-- A: You can get paid video games for free by taking advantage of promotions, giveaways, or discounts. Some sites that offer these are Humble Bundle, Epic Games Store, or GOG.com. You can also join online communities or forums that share free game codes or keys.
-- Q: How can I support the game developers if I download video games for free?
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diff --git a/spaces/contluForse/HuggingGPT/assets/AnyDVD HD V7.4.8.0 Final-BRD Utorrent.md b/spaces/contluForse/HuggingGPT/assets/AnyDVD HD V7.4.8.0 Final-BRD Utorrent.md
deleted file mode 100644
index c1094a419c885b638f32f416bd0eee79c599f960..0000000000000000000000000000000000000000
--- a/spaces/contluForse/HuggingGPT/assets/AnyDVD HD V7.4.8.0 Final-BRD Utorrent.md
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diff --git a/spaces/cooelf/Multimodal-CoT/timm/models/nasnet.py b/spaces/cooelf/Multimodal-CoT/timm/models/nasnet.py
deleted file mode 100644
index 2afe82c3f374dd4790bc940289c8e3794497fbbc..0000000000000000000000000000000000000000
--- a/spaces/cooelf/Multimodal-CoT/timm/models/nasnet.py
+++ /dev/null
@@ -1,567 +0,0 @@
-""" NasNet-A (Large)
- nasnetalarge implementation grabbed from Cadene's pretrained models
- https://github.com/Cadene/pretrained-models.pytorch
-"""
-from functools import partial
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-from .helpers import build_model_with_cfg
-from .layers import ConvBnAct, create_conv2d, create_pool2d, create_classifier
-from .registry import register_model
-
-__all__ = ['NASNetALarge']
-
-default_cfgs = {
- 'nasnetalarge': {
- 'url': 'http://data.lip6.fr/cadene/pretrainedmodels/nasnetalarge-a1897284.pth',
- 'input_size': (3, 331, 331),
- 'pool_size': (11, 11),
- 'crop_pct': 0.911,
- 'interpolation': 'bicubic',
- 'mean': (0.5, 0.5, 0.5),
- 'std': (0.5, 0.5, 0.5),
- 'num_classes': 1000,
- 'first_conv': 'conv0.conv',
- 'classifier': 'last_linear',
- 'label_offset': 1, # 1001 classes in pretrained weights
- },
-}
-
-
-class ActConvBn(nn.Module):
-
- def __init__(self, in_channels, out_channels, kernel_size, stride=1, padding=''):
- super(ActConvBn, self).__init__()
- self.act = nn.ReLU()
- self.conv = create_conv2d(
- in_channels, out_channels, kernel_size=kernel_size, stride=stride, padding=padding)
- self.bn = nn.BatchNorm2d(out_channels, eps=0.001, momentum=0.1)
-
- def forward(self, x):
- x = self.act(x)
- x = self.conv(x)
- x = self.bn(x)
- return x
-
-
-class SeparableConv2d(nn.Module):
-
- def __init__(self, in_channels, out_channels, kernel_size, stride, padding=''):
- super(SeparableConv2d, self).__init__()
- self.depthwise_conv2d = create_conv2d(
- in_channels, in_channels, kernel_size=kernel_size,
- stride=stride, padding=padding, groups=in_channels)
- self.pointwise_conv2d = create_conv2d(
- in_channels, out_channels, kernel_size=1, padding=0)
-
- def forward(self, x):
- x = self.depthwise_conv2d(x)
- x = self.pointwise_conv2d(x)
- return x
-
-
-class BranchSeparables(nn.Module):
-
- def __init__(self, in_channels, out_channels, kernel_size, stride=1, pad_type='', stem_cell=False):
- super(BranchSeparables, self).__init__()
- middle_channels = out_channels if stem_cell else in_channels
- self.act_1 = nn.ReLU()
- self.separable_1 = SeparableConv2d(
- in_channels, middle_channels, kernel_size, stride=stride, padding=pad_type)
- self.bn_sep_1 = nn.BatchNorm2d(middle_channels, eps=0.001, momentum=0.1)
- self.act_2 = nn.ReLU(inplace=True)
- self.separable_2 = SeparableConv2d(
- middle_channels, out_channels, kernel_size, stride=1, padding=pad_type)
- self.bn_sep_2 = nn.BatchNorm2d(out_channels, eps=0.001, momentum=0.1)
-
- def forward(self, x):
- x = self.act_1(x)
- x = self.separable_1(x)
- x = self.bn_sep_1(x)
- x = self.act_2(x)
- x = self.separable_2(x)
- x = self.bn_sep_2(x)
- return x
-
-
-class CellStem0(nn.Module):
- def __init__(self, stem_size, num_channels=42, pad_type=''):
- super(CellStem0, self).__init__()
- self.num_channels = num_channels
- self.stem_size = stem_size
- self.conv_1x1 = ActConvBn(self.stem_size, self.num_channels, 1, stride=1)
-
- self.comb_iter_0_left = BranchSeparables(self.num_channels, self.num_channels, 5, 2, pad_type)
- self.comb_iter_0_right = BranchSeparables(self.stem_size, self.num_channels, 7, 2, pad_type, stem_cell=True)
-
- self.comb_iter_1_left = create_pool2d('max', 3, 2, padding=pad_type)
- self.comb_iter_1_right = BranchSeparables(self.stem_size, self.num_channels, 7, 2, pad_type, stem_cell=True)
-
- self.comb_iter_2_left = create_pool2d('avg', 3, 2, count_include_pad=False, padding=pad_type)
- self.comb_iter_2_right = BranchSeparables(self.stem_size, self.num_channels, 5, 2, pad_type, stem_cell=True)
-
- self.comb_iter_3_right = create_pool2d('avg', 3, 1, count_include_pad=False, padding=pad_type)
-
- self.comb_iter_4_left = BranchSeparables(self.num_channels, self.num_channels, 3, 1, pad_type)
- self.comb_iter_4_right = create_pool2d('max', 3, 2, padding=pad_type)
-
- def forward(self, x):
- x1 = self.conv_1x1(x)
-
- x_comb_iter_0_left = self.comb_iter_0_left(x1)
- x_comb_iter_0_right = self.comb_iter_0_right(x)
- x_comb_iter_0 = x_comb_iter_0_left + x_comb_iter_0_right
-
- x_comb_iter_1_left = self.comb_iter_1_left(x1)
- x_comb_iter_1_right = self.comb_iter_1_right(x)
- x_comb_iter_1 = x_comb_iter_1_left + x_comb_iter_1_right
-
- x_comb_iter_2_left = self.comb_iter_2_left(x1)
- x_comb_iter_2_right = self.comb_iter_2_right(x)
- x_comb_iter_2 = x_comb_iter_2_left + x_comb_iter_2_right
-
- x_comb_iter_3_right = self.comb_iter_3_right(x_comb_iter_0)
- x_comb_iter_3 = x_comb_iter_3_right + x_comb_iter_1
-
- x_comb_iter_4_left = self.comb_iter_4_left(x_comb_iter_0)
- x_comb_iter_4_right = self.comb_iter_4_right(x1)
- x_comb_iter_4 = x_comb_iter_4_left + x_comb_iter_4_right
-
- x_out = torch.cat([x_comb_iter_1, x_comb_iter_2, x_comb_iter_3, x_comb_iter_4], 1)
- return x_out
-
-
-class CellStem1(nn.Module):
-
- def __init__(self, stem_size, num_channels, pad_type=''):
- super(CellStem1, self).__init__()
- self.num_channels = num_channels
- self.stem_size = stem_size
- self.conv_1x1 = ActConvBn(2 * self.num_channels, self.num_channels, 1, stride=1)
-
- self.act = nn.ReLU()
- self.path_1 = nn.Sequential()
- self.path_1.add_module('avgpool', nn.AvgPool2d(1, stride=2, count_include_pad=False))
- self.path_1.add_module('conv', nn.Conv2d(self.stem_size, self.num_channels // 2, 1, stride=1, bias=False))
-
- self.path_2 = nn.Sequential()
- self.path_2.add_module('pad', nn.ZeroPad2d((-1, 1, -1, 1)))
- self.path_2.add_module('avgpool', nn.AvgPool2d(1, stride=2, count_include_pad=False))
- self.path_2.add_module('conv', nn.Conv2d(self.stem_size, self.num_channels // 2, 1, stride=1, bias=False))
-
- self.final_path_bn = nn.BatchNorm2d(self.num_channels, eps=0.001, momentum=0.1)
-
- self.comb_iter_0_left = BranchSeparables(self.num_channels, self.num_channels, 5, 2, pad_type)
- self.comb_iter_0_right = BranchSeparables(self.num_channels, self.num_channels, 7, 2, pad_type)
-
- self.comb_iter_1_left = create_pool2d('max', 3, 2, padding=pad_type)
- self.comb_iter_1_right = BranchSeparables(self.num_channels, self.num_channels, 7, 2, pad_type)
-
- self.comb_iter_2_left = create_pool2d('avg', 3, 2, count_include_pad=False, padding=pad_type)
- self.comb_iter_2_right = BranchSeparables(self.num_channels, self.num_channels, 5, 2, pad_type)
-
- self.comb_iter_3_right = create_pool2d('avg', 3, 1, count_include_pad=False, padding=pad_type)
-
- self.comb_iter_4_left = BranchSeparables(self.num_channels, self.num_channels, 3, 1, pad_type)
- self.comb_iter_4_right = create_pool2d('max', 3, 2, padding=pad_type)
-
- def forward(self, x_conv0, x_stem_0):
- x_left = self.conv_1x1(x_stem_0)
-
- x_relu = self.act(x_conv0)
- # path 1
- x_path1 = self.path_1(x_relu)
- # path 2
- x_path2 = self.path_2(x_relu)
- # final path
- x_right = self.final_path_bn(torch.cat([x_path1, x_path2], 1))
-
- x_comb_iter_0_left = self.comb_iter_0_left(x_left)
- x_comb_iter_0_right = self.comb_iter_0_right(x_right)
- x_comb_iter_0 = x_comb_iter_0_left + x_comb_iter_0_right
-
- x_comb_iter_1_left = self.comb_iter_1_left(x_left)
- x_comb_iter_1_right = self.comb_iter_1_right(x_right)
- x_comb_iter_1 = x_comb_iter_1_left + x_comb_iter_1_right
-
- x_comb_iter_2_left = self.comb_iter_2_left(x_left)
- x_comb_iter_2_right = self.comb_iter_2_right(x_right)
- x_comb_iter_2 = x_comb_iter_2_left + x_comb_iter_2_right
-
- x_comb_iter_3_right = self.comb_iter_3_right(x_comb_iter_0)
- x_comb_iter_3 = x_comb_iter_3_right + x_comb_iter_1
-
- x_comb_iter_4_left = self.comb_iter_4_left(x_comb_iter_0)
- x_comb_iter_4_right = self.comb_iter_4_right(x_left)
- x_comb_iter_4 = x_comb_iter_4_left + x_comb_iter_4_right
-
- x_out = torch.cat([x_comb_iter_1, x_comb_iter_2, x_comb_iter_3, x_comb_iter_4], 1)
- return x_out
-
-
-class FirstCell(nn.Module):
-
- def __init__(self, in_chs_left, out_chs_left, in_chs_right, out_chs_right, pad_type=''):
- super(FirstCell, self).__init__()
- self.conv_1x1 = ActConvBn(in_chs_right, out_chs_right, 1, stride=1)
-
- self.act = nn.ReLU()
- self.path_1 = nn.Sequential()
- self.path_1.add_module('avgpool', nn.AvgPool2d(1, stride=2, count_include_pad=False))
- self.path_1.add_module('conv', nn.Conv2d(in_chs_left, out_chs_left, 1, stride=1, bias=False))
-
- self.path_2 = nn.Sequential()
- self.path_2.add_module('pad', nn.ZeroPad2d((-1, 1, -1, 1)))
- self.path_2.add_module('avgpool', nn.AvgPool2d(1, stride=2, count_include_pad=False))
- self.path_2.add_module('conv', nn.Conv2d(in_chs_left, out_chs_left, 1, stride=1, bias=False))
-
- self.final_path_bn = nn.BatchNorm2d(out_chs_left * 2, eps=0.001, momentum=0.1)
-
- self.comb_iter_0_left = BranchSeparables(out_chs_right, out_chs_right, 5, 1, pad_type)
- self.comb_iter_0_right = BranchSeparables(out_chs_right, out_chs_right, 3, 1, pad_type)
-
- self.comb_iter_1_left = BranchSeparables(out_chs_right, out_chs_right, 5, 1, pad_type)
- self.comb_iter_1_right = BranchSeparables(out_chs_right, out_chs_right, 3, 1, pad_type)
-
- self.comb_iter_2_left = create_pool2d('avg', 3, 1, count_include_pad=False, padding=pad_type)
-
- self.comb_iter_3_left = create_pool2d('avg', 3, 1, count_include_pad=False, padding=pad_type)
- self.comb_iter_3_right = create_pool2d('avg', 3, 1, count_include_pad=False, padding=pad_type)
-
- self.comb_iter_4_left = BranchSeparables(out_chs_right, out_chs_right, 3, 1, pad_type)
-
- def forward(self, x, x_prev):
- x_relu = self.act(x_prev)
- x_path1 = self.path_1(x_relu)
- x_path2 = self.path_2(x_relu)
- x_left = self.final_path_bn(torch.cat([x_path1, x_path2], 1))
- x_right = self.conv_1x1(x)
-
- x_comb_iter_0_left = self.comb_iter_0_left(x_right)
- x_comb_iter_0_right = self.comb_iter_0_right(x_left)
- x_comb_iter_0 = x_comb_iter_0_left + x_comb_iter_0_right
-
- x_comb_iter_1_left = self.comb_iter_1_left(x_left)
- x_comb_iter_1_right = self.comb_iter_1_right(x_left)
- x_comb_iter_1 = x_comb_iter_1_left + x_comb_iter_1_right
-
- x_comb_iter_2_left = self.comb_iter_2_left(x_right)
- x_comb_iter_2 = x_comb_iter_2_left + x_left
-
- x_comb_iter_3_left = self.comb_iter_3_left(x_left)
- x_comb_iter_3_right = self.comb_iter_3_right(x_left)
- x_comb_iter_3 = x_comb_iter_3_left + x_comb_iter_3_right
-
- x_comb_iter_4_left = self.comb_iter_4_left(x_right)
- x_comb_iter_4 = x_comb_iter_4_left + x_right
-
- x_out = torch.cat([x_left, x_comb_iter_0, x_comb_iter_1, x_comb_iter_2, x_comb_iter_3, x_comb_iter_4], 1)
- return x_out
-
-
-class NormalCell(nn.Module):
-
- def __init__(self, in_chs_left, out_chs_left, in_chs_right, out_chs_right, pad_type=''):
- super(NormalCell, self).__init__()
- self.conv_prev_1x1 = ActConvBn(in_chs_left, out_chs_left, 1, stride=1, padding=pad_type)
- self.conv_1x1 = ActConvBn(in_chs_right, out_chs_right, 1, stride=1, padding=pad_type)
-
- self.comb_iter_0_left = BranchSeparables(out_chs_right, out_chs_right, 5, 1, pad_type)
- self.comb_iter_0_right = BranchSeparables(out_chs_left, out_chs_left, 3, 1, pad_type)
-
- self.comb_iter_1_left = BranchSeparables(out_chs_left, out_chs_left, 5, 1, pad_type)
- self.comb_iter_1_right = BranchSeparables(out_chs_left, out_chs_left, 3, 1, pad_type)
-
- self.comb_iter_2_left = create_pool2d('avg', 3, 1, count_include_pad=False, padding=pad_type)
-
- self.comb_iter_3_left = create_pool2d('avg', 3, 1, count_include_pad=False, padding=pad_type)
- self.comb_iter_3_right = create_pool2d('avg', 3, 1, count_include_pad=False, padding=pad_type)
-
- self.comb_iter_4_left = BranchSeparables(out_chs_right, out_chs_right, 3, 1, pad_type)
-
- def forward(self, x, x_prev):
- x_left = self.conv_prev_1x1(x_prev)
- x_right = self.conv_1x1(x)
-
- x_comb_iter_0_left = self.comb_iter_0_left(x_right)
- x_comb_iter_0_right = self.comb_iter_0_right(x_left)
- x_comb_iter_0 = x_comb_iter_0_left + x_comb_iter_0_right
-
- x_comb_iter_1_left = self.comb_iter_1_left(x_left)
- x_comb_iter_1_right = self.comb_iter_1_right(x_left)
- x_comb_iter_1 = x_comb_iter_1_left + x_comb_iter_1_right
-
- x_comb_iter_2_left = self.comb_iter_2_left(x_right)
- x_comb_iter_2 = x_comb_iter_2_left + x_left
-
- x_comb_iter_3_left = self.comb_iter_3_left(x_left)
- x_comb_iter_3_right = self.comb_iter_3_right(x_left)
- x_comb_iter_3 = x_comb_iter_3_left + x_comb_iter_3_right
-
- x_comb_iter_4_left = self.comb_iter_4_left(x_right)
- x_comb_iter_4 = x_comb_iter_4_left + x_right
-
- x_out = torch.cat([x_left, x_comb_iter_0, x_comb_iter_1, x_comb_iter_2, x_comb_iter_3, x_comb_iter_4], 1)
- return x_out
-
-
-class ReductionCell0(nn.Module):
-
- def __init__(self, in_chs_left, out_chs_left, in_chs_right, out_chs_right, pad_type=''):
- super(ReductionCell0, self).__init__()
- self.conv_prev_1x1 = ActConvBn(in_chs_left, out_chs_left, 1, stride=1, padding=pad_type)
- self.conv_1x1 = ActConvBn(in_chs_right, out_chs_right, 1, stride=1, padding=pad_type)
-
- self.comb_iter_0_left = BranchSeparables(out_chs_right, out_chs_right, 5, 2, pad_type)
- self.comb_iter_0_right = BranchSeparables(out_chs_right, out_chs_right, 7, 2, pad_type)
-
- self.comb_iter_1_left = create_pool2d('max', 3, 2, padding=pad_type)
- self.comb_iter_1_right = BranchSeparables(out_chs_right, out_chs_right, 7, 2, pad_type)
-
- self.comb_iter_2_left = create_pool2d('avg', 3, 2, count_include_pad=False, padding=pad_type)
- self.comb_iter_2_right = BranchSeparables(out_chs_right, out_chs_right, 5, 2, pad_type)
-
- self.comb_iter_3_right = create_pool2d('avg', 3, 1, count_include_pad=False, padding=pad_type)
-
- self.comb_iter_4_left = BranchSeparables(out_chs_right, out_chs_right, 3, 1, pad_type)
- self.comb_iter_4_right = create_pool2d('max', 3, 2, padding=pad_type)
-
- def forward(self, x, x_prev):
- x_left = self.conv_prev_1x1(x_prev)
- x_right = self.conv_1x1(x)
-
- x_comb_iter_0_left = self.comb_iter_0_left(x_right)
- x_comb_iter_0_right = self.comb_iter_0_right(x_left)
- x_comb_iter_0 = x_comb_iter_0_left + x_comb_iter_0_right
-
- x_comb_iter_1_left = self.comb_iter_1_left(x_right)
- x_comb_iter_1_right = self.comb_iter_1_right(x_left)
- x_comb_iter_1 = x_comb_iter_1_left + x_comb_iter_1_right
-
- x_comb_iter_2_left = self.comb_iter_2_left(x_right)
- x_comb_iter_2_right = self.comb_iter_2_right(x_left)
- x_comb_iter_2 = x_comb_iter_2_left + x_comb_iter_2_right
-
- x_comb_iter_3_right = self.comb_iter_3_right(x_comb_iter_0)
- x_comb_iter_3 = x_comb_iter_3_right + x_comb_iter_1
-
- x_comb_iter_4_left = self.comb_iter_4_left(x_comb_iter_0)
- x_comb_iter_4_right = self.comb_iter_4_right(x_right)
- x_comb_iter_4 = x_comb_iter_4_left + x_comb_iter_4_right
-
- x_out = torch.cat([x_comb_iter_1, x_comb_iter_2, x_comb_iter_3, x_comb_iter_4], 1)
- return x_out
-
-
-class ReductionCell1(nn.Module):
-
- def __init__(self, in_chs_left, out_chs_left, in_chs_right, out_chs_right, pad_type=''):
- super(ReductionCell1, self).__init__()
- self.conv_prev_1x1 = ActConvBn(in_chs_left, out_chs_left, 1, stride=1, padding=pad_type)
- self.conv_1x1 = ActConvBn(in_chs_right, out_chs_right, 1, stride=1, padding=pad_type)
-
- self.comb_iter_0_left = BranchSeparables(out_chs_right, out_chs_right, 5, 2, pad_type)
- self.comb_iter_0_right = BranchSeparables(out_chs_right, out_chs_right, 7, 2, pad_type)
-
- self.comb_iter_1_left = create_pool2d('max', 3, 2, padding=pad_type)
- self.comb_iter_1_right = BranchSeparables(out_chs_right, out_chs_right, 7, 2, pad_type)
-
- self.comb_iter_2_left = create_pool2d('avg', 3, 2, count_include_pad=False, padding=pad_type)
- self.comb_iter_2_right = BranchSeparables(out_chs_right, out_chs_right, 5, 2, pad_type)
-
- self.comb_iter_3_right = create_pool2d('avg', 3, 1, count_include_pad=False, padding=pad_type)
-
- self.comb_iter_4_left = BranchSeparables(out_chs_right, out_chs_right, 3, 1, pad_type)
- self.comb_iter_4_right = create_pool2d('max', 3, 2, padding=pad_type)
-
- def forward(self, x, x_prev):
- x_left = self.conv_prev_1x1(x_prev)
- x_right = self.conv_1x1(x)
-
- x_comb_iter_0_left = self.comb_iter_0_left(x_right)
- x_comb_iter_0_right = self.comb_iter_0_right(x_left)
- x_comb_iter_0 = x_comb_iter_0_left + x_comb_iter_0_right
-
- x_comb_iter_1_left = self.comb_iter_1_left(x_right)
- x_comb_iter_1_right = self.comb_iter_1_right(x_left)
- x_comb_iter_1 = x_comb_iter_1_left + x_comb_iter_1_right
-
- x_comb_iter_2_left = self.comb_iter_2_left(x_right)
- x_comb_iter_2_right = self.comb_iter_2_right(x_left)
- x_comb_iter_2 = x_comb_iter_2_left + x_comb_iter_2_right
-
- x_comb_iter_3_right = self.comb_iter_3_right(x_comb_iter_0)
- x_comb_iter_3 = x_comb_iter_3_right + x_comb_iter_1
-
- x_comb_iter_4_left = self.comb_iter_4_left(x_comb_iter_0)
- x_comb_iter_4_right = self.comb_iter_4_right(x_right)
- x_comb_iter_4 = x_comb_iter_4_left + x_comb_iter_4_right
-
- x_out = torch.cat([x_comb_iter_1, x_comb_iter_2, x_comb_iter_3, x_comb_iter_4], 1)
- return x_out
-
-
-class NASNetALarge(nn.Module):
- """NASNetALarge (6 @ 4032) """
-
- def __init__(self, num_classes=1000, in_chans=3, stem_size=96, channel_multiplier=2,
- num_features=4032, output_stride=32, drop_rate=0., global_pool='avg', pad_type='same'):
- super(NASNetALarge, self).__init__()
- self.num_classes = num_classes
- self.stem_size = stem_size
- self.num_features = num_features
- self.channel_multiplier = channel_multiplier
- self.drop_rate = drop_rate
- assert output_stride == 32
-
- channels = self.num_features // 24
- # 24 is default value for the architecture
-
- self.conv0 = ConvBnAct(
- in_channels=in_chans, out_channels=self.stem_size, kernel_size=3, padding=0, stride=2,
- norm_layer=partial(nn.BatchNorm2d, eps=0.001, momentum=0.1), apply_act=False)
-
- self.cell_stem_0 = CellStem0(
- self.stem_size, num_channels=channels // (channel_multiplier ** 2), pad_type=pad_type)
- self.cell_stem_1 = CellStem1(
- self.stem_size, num_channels=channels // channel_multiplier, pad_type=pad_type)
-
- self.cell_0 = FirstCell(
- in_chs_left=channels, out_chs_left=channels // 2,
- in_chs_right=2 * channels, out_chs_right=channels, pad_type=pad_type)
- self.cell_1 = NormalCell(
- in_chs_left=2 * channels, out_chs_left=channels,
- in_chs_right=6 * channels, out_chs_right=channels, pad_type=pad_type)
- self.cell_2 = NormalCell(
- in_chs_left=6 * channels, out_chs_left=channels,
- in_chs_right=6 * channels, out_chs_right=channels, pad_type=pad_type)
- self.cell_3 = NormalCell(
- in_chs_left=6 * channels, out_chs_left=channels,
- in_chs_right=6 * channels, out_chs_right=channels, pad_type=pad_type)
- self.cell_4 = NormalCell(
- in_chs_left=6 * channels, out_chs_left=channels,
- in_chs_right=6 * channels, out_chs_right=channels, pad_type=pad_type)
- self.cell_5 = NormalCell(
- in_chs_left=6 * channels, out_chs_left=channels,
- in_chs_right=6 * channels, out_chs_right=channels, pad_type=pad_type)
-
- self.reduction_cell_0 = ReductionCell0(
- in_chs_left=6 * channels, out_chs_left=2 * channels,
- in_chs_right=6 * channels, out_chs_right=2 * channels, pad_type=pad_type)
- self.cell_6 = FirstCell(
- in_chs_left=6 * channels, out_chs_left=channels,
- in_chs_right=8 * channels, out_chs_right=2 * channels, pad_type=pad_type)
- self.cell_7 = NormalCell(
- in_chs_left=8 * channels, out_chs_left=2 * channels,
- in_chs_right=12 * channels, out_chs_right=2 * channels, pad_type=pad_type)
- self.cell_8 = NormalCell(
- in_chs_left=12 * channels, out_chs_left=2 * channels,
- in_chs_right=12 * channels, out_chs_right=2 * channels, pad_type=pad_type)
- self.cell_9 = NormalCell(
- in_chs_left=12 * channels, out_chs_left=2 * channels,
- in_chs_right=12 * channels, out_chs_right=2 * channels, pad_type=pad_type)
- self.cell_10 = NormalCell(
- in_chs_left=12 * channels, out_chs_left=2 * channels,
- in_chs_right=12 * channels, out_chs_right=2 * channels, pad_type=pad_type)
- self.cell_11 = NormalCell(
- in_chs_left=12 * channels, out_chs_left=2 * channels,
- in_chs_right=12 * channels, out_chs_right=2 * channels, pad_type=pad_type)
-
- self.reduction_cell_1 = ReductionCell1(
- in_chs_left=12 * channels, out_chs_left=4 * channels,
- in_chs_right=12 * channels, out_chs_right=4 * channels, pad_type=pad_type)
- self.cell_12 = FirstCell(
- in_chs_left=12 * channels, out_chs_left=2 * channels,
- in_chs_right=16 * channels, out_chs_right=4 * channels, pad_type=pad_type)
- self.cell_13 = NormalCell(
- in_chs_left=16 * channels, out_chs_left=4 * channels,
- in_chs_right=24 * channels, out_chs_right=4 * channels, pad_type=pad_type)
- self.cell_14 = NormalCell(
- in_chs_left=24 * channels, out_chs_left=4 * channels,
- in_chs_right=24 * channels, out_chs_right=4 * channels, pad_type=pad_type)
- self.cell_15 = NormalCell(
- in_chs_left=24 * channels, out_chs_left=4 * channels,
- in_chs_right=24 * channels, out_chs_right=4 * channels, pad_type=pad_type)
- self.cell_16 = NormalCell(
- in_chs_left=24 * channels, out_chs_left=4 * channels,
- in_chs_right=24 * channels, out_chs_right=4 * channels, pad_type=pad_type)
- self.cell_17 = NormalCell(
- in_chs_left=24 * channels, out_chs_left=4 * channels,
- in_chs_right=24 * channels, out_chs_right=4 * channels, pad_type=pad_type)
- self.act = nn.ReLU(inplace=True)
- self.feature_info = [
- dict(num_chs=96, reduction=2, module='conv0'),
- dict(num_chs=168, reduction=4, module='cell_stem_1.conv_1x1.act'),
- dict(num_chs=1008, reduction=8, module='reduction_cell_0.conv_1x1.act'),
- dict(num_chs=2016, reduction=16, module='reduction_cell_1.conv_1x1.act'),
- dict(num_chs=4032, reduction=32, module='act'),
- ]
-
- self.global_pool, self.last_linear = create_classifier(
- self.num_features, self.num_classes, pool_type=global_pool)
-
- def get_classifier(self):
- return self.last_linear
-
- def reset_classifier(self, num_classes, global_pool='avg'):
- self.num_classes = num_classes
- self.global_pool, self.last_linear = create_classifier(
- self.num_features, self.num_classes, pool_type=global_pool)
-
- def forward_features(self, x):
- x_conv0 = self.conv0(x)
-
- x_stem_0 = self.cell_stem_0(x_conv0)
- x_stem_1 = self.cell_stem_1(x_conv0, x_stem_0)
-
- x_cell_0 = self.cell_0(x_stem_1, x_stem_0)
- x_cell_1 = self.cell_1(x_cell_0, x_stem_1)
- x_cell_2 = self.cell_2(x_cell_1, x_cell_0)
- x_cell_3 = self.cell_3(x_cell_2, x_cell_1)
- x_cell_4 = self.cell_4(x_cell_3, x_cell_2)
- x_cell_5 = self.cell_5(x_cell_4, x_cell_3)
-
- x_reduction_cell_0 = self.reduction_cell_0(x_cell_5, x_cell_4)
- x_cell_6 = self.cell_6(x_reduction_cell_0, x_cell_4)
- x_cell_7 = self.cell_7(x_cell_6, x_reduction_cell_0)
- x_cell_8 = self.cell_8(x_cell_7, x_cell_6)
- x_cell_9 = self.cell_9(x_cell_8, x_cell_7)
- x_cell_10 = self.cell_10(x_cell_9, x_cell_8)
- x_cell_11 = self.cell_11(x_cell_10, x_cell_9)
-
- x_reduction_cell_1 = self.reduction_cell_1(x_cell_11, x_cell_10)
- x_cell_12 = self.cell_12(x_reduction_cell_1, x_cell_10)
- x_cell_13 = self.cell_13(x_cell_12, x_reduction_cell_1)
- x_cell_14 = self.cell_14(x_cell_13, x_cell_12)
- x_cell_15 = self.cell_15(x_cell_14, x_cell_13)
- x_cell_16 = self.cell_16(x_cell_15, x_cell_14)
- x_cell_17 = self.cell_17(x_cell_16, x_cell_15)
- x = self.act(x_cell_17)
- return x
-
- def forward(self, x):
- x = self.forward_features(x)
- x = self.global_pool(x)
- if self.drop_rate > 0:
- x = F.dropout(x, self.drop_rate, training=self.training)
- x = self.last_linear(x)
- return x
-
-
-def _create_nasnet(variant, pretrained=False, **kwargs):
- return build_model_with_cfg(
- NASNetALarge, variant, pretrained,
- default_cfg=default_cfgs[variant],
- feature_cfg=dict(feature_cls='hook', no_rewrite=True), # not possible to re-write this model
- **kwargs)
-
-
-@register_model
-def nasnetalarge(pretrained=False, **kwargs):
- """NASNet-A large model architecture.
- """
- model_kwargs = dict(pad_type='same', **kwargs)
- return _create_nasnet('nasnetalarge', pretrained, **model_kwargs)
diff --git a/spaces/coraKong/WorldSimulation/plugins/CharacterCreationPlugin.py b/spaces/coraKong/WorldSimulation/plugins/CharacterCreationPlugin.py
deleted file mode 100644
index 280a9a293942dbea39caaaa3a781c0955122fbe0..0000000000000000000000000000000000000000
--- a/spaces/coraKong/WorldSimulation/plugins/CharacterCreationPlugin.py
+++ /dev/null
@@ -1,22 +0,0 @@
-import random
-from Character import Character
-from utils import get_random_name
-
-class CharacterCreationPlugin:
- def __init__(self, special_constitution_ratio=None, spiritual_roots_ratio=None):
- self.special_constitution_ratio = special_constitution_ratio
- self.spiritual_roots_ratio = spiritual_roots_ratio
-
- def create_character(self):
- # 根据 special_constitution_ratio 随机确定这个角色拥有哪些特殊体质
- special_constitution = [1 if random.random() < ratio else 0 for ratio in self.special_constitution_ratio]
-
- # 根据 spiritual_roots_ratio 随机确定这个角色拥有哪些灵根
- spiritual_roots = [1 if random.random() < ratio else 0 for ratio in self.spiritual_roots_ratio]
-
- character = Character(get_random_name(), random.choice(["男", "女"]), special_constitution, spiritual_roots)
- return character
-
- def set_parameters(self, special_constitution_ratio, spiritual_roots_ratio):
- self.special_constitution_ratio = special_constitution_ratio
- self.spiritual_roots_ratio = spiritual_roots_ratio
\ No newline at end of file
diff --git a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/oneformer/detectron2/modeling/backbone/backbone.py b/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/oneformer/detectron2/modeling/backbone/backbone.py
deleted file mode 100644
index 04f3c3c009d972bcab46eaeab33a8bfcc05b726c..0000000000000000000000000000000000000000
--- a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/oneformer/detectron2/modeling/backbone/backbone.py
+++ /dev/null
@@ -1,74 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-from abc import ABCMeta, abstractmethod
-from typing import Dict
-import torch.nn as nn
-
-from annotator.oneformer.detectron2.layers import ShapeSpec
-
-__all__ = ["Backbone"]
-
-
-class Backbone(nn.Module, metaclass=ABCMeta):
- """
- Abstract base class for network backbones.
- """
-
- def __init__(self):
- """
- The `__init__` method of any subclass can specify its own set of arguments.
- """
- super().__init__()
-
- @abstractmethod
- def forward(self):
- """
- Subclasses must override this method, but adhere to the same return type.
-
- Returns:
- dict[str->Tensor]: mapping from feature name (e.g., "res2") to tensor
- """
- pass
-
- @property
- def size_divisibility(self) -> int:
- """
- Some backbones require the input height and width to be divisible by a
- specific integer. This is typically true for encoder / decoder type networks
- with lateral connection (e.g., FPN) for which feature maps need to match
- dimension in the "bottom up" and "top down" paths. Set to 0 if no specific
- input size divisibility is required.
- """
- return 0
-
- @property
- def padding_constraints(self) -> Dict[str, int]:
- """
- This property is a generalization of size_divisibility. Some backbones and training
- recipes require specific padding constraints, such as enforcing divisibility by a specific
- integer (e.g., FPN) or padding to a square (e.g., ViTDet with large-scale jitter
- in :paper:vitdet). `padding_constraints` contains these optional items like:
- {
- "size_divisibility": int,
- "square_size": int,
- # Future options are possible
- }
- `size_divisibility` will read from here if presented and `square_size` indicates the
- square padding size if `square_size` > 0.
-
- TODO: use type of Dict[str, int] to avoid torchscipt issues. The type of padding_constraints
- could be generalized as TypedDict (Python 3.8+) to support more types in the future.
- """
- return {}
-
- def output_shape(self):
- """
- Returns:
- dict[str->ShapeSpec]
- """
- # this is a backward-compatible default
- return {
- name: ShapeSpec(
- channels=self._out_feature_channels[name], stride=self._out_feature_strides[name]
- )
- for name in self._out_features
- }
diff --git a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/oneformer/oneformer/modeling/pixel_decoder/ops/functions/__init__.py b/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/oneformer/oneformer/modeling/pixel_decoder/ops/functions/__init__.py
deleted file mode 100644
index 2b06b5ac538b63bdb9a6c82e4635b95bb5491d5b..0000000000000000000000000000000000000000
--- a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/oneformer/oneformer/modeling/pixel_decoder/ops/functions/__init__.py
+++ /dev/null
@@ -1,13 +0,0 @@
-# ------------------------------------------------------------------------------------------------
-# Deformable DETR
-# Copyright (c) 2020 SenseTime. All Rights Reserved.
-# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
-# ------------------------------------------------------------------------------------------------
-# Modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/tree/pytorch_1.0.0
-# ------------------------------------------------------------------------------------------------
-
-# Copyright (c) Facebook, Inc. and its affiliates.
-# Modified by Bowen Cheng from https://github.com/fundamentalvision/Deformable-DETR
-
-from .ms_deform_attn_func import MSDeformAttnFunction
-
diff --git a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/uniformer/mmcv/visualization/image.py b/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/uniformer/mmcv/visualization/image.py
deleted file mode 100644
index 61a56c75b67f593c298408462c63c0468be8e276..0000000000000000000000000000000000000000
--- a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/uniformer/mmcv/visualization/image.py
+++ /dev/null
@@ -1,152 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import cv2
-import numpy as np
-
-from annotator.uniformer.mmcv.image import imread, imwrite
-from .color import color_val
-
-
-def imshow(img, win_name='', wait_time=0):
- """Show an image.
-
- Args:
- img (str or ndarray): The image to be displayed.
- win_name (str): The window name.
- wait_time (int): Value of waitKey param.
- """
- cv2.imshow(win_name, imread(img))
- if wait_time == 0: # prevent from hanging if windows was closed
- while True:
- ret = cv2.waitKey(1)
-
- closed = cv2.getWindowProperty(win_name, cv2.WND_PROP_VISIBLE) < 1
- # if user closed window or if some key pressed
- if closed or ret != -1:
- break
- else:
- ret = cv2.waitKey(wait_time)
-
-
-def imshow_bboxes(img,
- bboxes,
- colors='green',
- top_k=-1,
- thickness=1,
- show=True,
- win_name='',
- wait_time=0,
- out_file=None):
- """Draw bboxes on an image.
-
- Args:
- img (str or ndarray): The image to be displayed.
- bboxes (list or ndarray): A list of ndarray of shape (k, 4).
- colors (list[str or tuple or Color]): A list of colors.
- top_k (int): Plot the first k bboxes only if set positive.
- thickness (int): Thickness of lines.
- show (bool): Whether to show the image.
- win_name (str): The window name.
- wait_time (int): Value of waitKey param.
- out_file (str, optional): The filename to write the image.
-
- Returns:
- ndarray: The image with bboxes drawn on it.
- """
- img = imread(img)
- img = np.ascontiguousarray(img)
-
- if isinstance(bboxes, np.ndarray):
- bboxes = [bboxes]
- if not isinstance(colors, list):
- colors = [colors for _ in range(len(bboxes))]
- colors = [color_val(c) for c in colors]
- assert len(bboxes) == len(colors)
-
- for i, _bboxes in enumerate(bboxes):
- _bboxes = _bboxes.astype(np.int32)
- if top_k <= 0:
- _top_k = _bboxes.shape[0]
- else:
- _top_k = min(top_k, _bboxes.shape[0])
- for j in range(_top_k):
- left_top = (_bboxes[j, 0], _bboxes[j, 1])
- right_bottom = (_bboxes[j, 2], _bboxes[j, 3])
- cv2.rectangle(
- img, left_top, right_bottom, colors[i], thickness=thickness)
-
- if show:
- imshow(img, win_name, wait_time)
- if out_file is not None:
- imwrite(img, out_file)
- return img
-
-
-def imshow_det_bboxes(img,
- bboxes,
- labels,
- class_names=None,
- score_thr=0,
- bbox_color='green',
- text_color='green',
- thickness=1,
- font_scale=0.5,
- show=True,
- win_name='',
- wait_time=0,
- out_file=None):
- """Draw bboxes and class labels (with scores) on an image.
-
- Args:
- img (str or ndarray): The image to be displayed.
- bboxes (ndarray): Bounding boxes (with scores), shaped (n, 4) or
- (n, 5).
- labels (ndarray): Labels of bboxes.
- class_names (list[str]): Names of each classes.
- score_thr (float): Minimum score of bboxes to be shown.
- bbox_color (str or tuple or :obj:`Color`): Color of bbox lines.
- text_color (str or tuple or :obj:`Color`): Color of texts.
- thickness (int): Thickness of lines.
- font_scale (float): Font scales of texts.
- show (bool): Whether to show the image.
- win_name (str): The window name.
- wait_time (int): Value of waitKey param.
- out_file (str or None): The filename to write the image.
-
- Returns:
- ndarray: The image with bboxes drawn on it.
- """
- assert bboxes.ndim == 2
- assert labels.ndim == 1
- assert bboxes.shape[0] == labels.shape[0]
- assert bboxes.shape[1] == 4 or bboxes.shape[1] == 5
- img = imread(img)
- img = np.ascontiguousarray(img)
-
- if score_thr > 0:
- assert bboxes.shape[1] == 5
- scores = bboxes[:, -1]
- inds = scores > score_thr
- bboxes = bboxes[inds, :]
- labels = labels[inds]
-
- bbox_color = color_val(bbox_color)
- text_color = color_val(text_color)
-
- for bbox, label in zip(bboxes, labels):
- bbox_int = bbox.astype(np.int32)
- left_top = (bbox_int[0], bbox_int[1])
- right_bottom = (bbox_int[2], bbox_int[3])
- cv2.rectangle(
- img, left_top, right_bottom, bbox_color, thickness=thickness)
- label_text = class_names[
- label] if class_names is not None else f'cls {label}'
- if len(bbox) > 4:
- label_text += f'|{bbox[-1]:.02f}'
- cv2.putText(img, label_text, (bbox_int[0], bbox_int[1] - 2),
- cv2.FONT_HERSHEY_COMPLEX, font_scale, text_color)
-
- if show:
- imshow(img, win_name, wait_time)
- if out_file is not None:
- imwrite(img, out_file)
- return img
diff --git a/spaces/crashedice/signify/SOURCE/yolo_files/utils/loss.py b/spaces/crashedice/signify/SOURCE/yolo_files/utils/loss.py
deleted file mode 100644
index c577ca6a19de76599b7343603795c2672099c1b5..0000000000000000000000000000000000000000
--- a/spaces/crashedice/signify/SOURCE/yolo_files/utils/loss.py
+++ /dev/null
@@ -1,216 +0,0 @@
-# Loss functions
-
-import torch
-import torch.nn as nn
-
-from SOURCE.yolo_files.utils.general import bbox_iou
-from SOURCE.yolo_files.utils.torch_utils import is_parallel
-
-
-def smooth_BCE(eps=0.1): # https://github.com/ultralytics/yolov3/issues/238#issuecomment-598028441
- # return positive, negative label smoothing BCE targets
- return 1.0 - 0.5 * eps, 0.5 * eps
-
-
-class BCEBlurWithLogitsLoss(nn.Module):
- # BCEwithLogitLoss() with reduced missing label effects.
- def __init__(self, alpha=0.05):
- super(BCEBlurWithLogitsLoss, self).__init__()
- self.loss_fcn = nn.BCEWithLogitsLoss(reduction='none') # must be nn.BCEWithLogitsLoss()
- self.alpha = alpha
-
- def forward(self, pred, true):
- loss = self.loss_fcn(pred, true)
- pred = torch.sigmoid(pred) # prob from logits
- dx = pred - true # reduce only missing label effects
- # dx = (pred - true).abs() # reduce missing label and false label effects
- alpha_factor = 1 - torch.exp((dx - 1) / (self.alpha + 1e-4))
- loss *= alpha_factor
- return loss.mean()
-
-
-class FocalLoss(nn.Module):
- # Wraps focal loss around existing loss_fcn(), i.e. criteria = FocalLoss(nn.BCEWithLogitsLoss(), gamma=1.5)
- def __init__(self, loss_fcn, gamma=1.5, alpha=0.25):
- super(FocalLoss, self).__init__()
- self.loss_fcn = loss_fcn # must be nn.BCEWithLogitsLoss()
- self.gamma = gamma
- self.alpha = alpha
- self.reduction = loss_fcn.reduction
- self.loss_fcn.reduction = 'none' # required to apply FL to each element
-
- def forward(self, pred, true):
- loss = self.loss_fcn(pred, true)
- # p_t = torch.exp(-loss)
- # loss *= self.alpha * (1.000001 - p_t) ** self.gamma # non-zero power for gradient stability
-
- # TF implementation https://github.com/tensorflow/addons/blob/v0.7.1/tensorflow_addons/losses/focal_loss.py
- pred_prob = torch.sigmoid(pred) # prob from logits
- p_t = true * pred_prob + (1 - true) * (1 - pred_prob)
- alpha_factor = true * self.alpha + (1 - true) * (1 - self.alpha)
- modulating_factor = (1.0 - p_t) ** self.gamma
- loss *= alpha_factor * modulating_factor
-
- if self.reduction == 'mean':
- return loss.mean()
- elif self.reduction == 'sum':
- return loss.sum()
- else: # 'none'
- return loss
-
-
-class QFocalLoss(nn.Module):
- # Wraps Quality focal loss around existing loss_fcn(), i.e. criteria = FocalLoss(nn.BCEWithLogitsLoss(), gamma=1.5)
- def __init__(self, loss_fcn, gamma=1.5, alpha=0.25):
- super(QFocalLoss, self).__init__()
- self.loss_fcn = loss_fcn # must be nn.BCEWithLogitsLoss()
- self.gamma = gamma
- self.alpha = alpha
- self.reduction = loss_fcn.reduction
- self.loss_fcn.reduction = 'none' # required to apply FL to each element
-
- def forward(self, pred, true):
- loss = self.loss_fcn(pred, true)
-
- pred_prob = torch.sigmoid(pred) # prob from logits
- alpha_factor = true * self.alpha + (1 - true) * (1 - self.alpha)
- modulating_factor = torch.abs(true - pred_prob) ** self.gamma
- loss *= alpha_factor * modulating_factor
-
- if self.reduction == 'mean':
- return loss.mean()
- elif self.reduction == 'sum':
- return loss.sum()
- else: # 'none'
- return loss
-
-
-class ComputeLoss:
- # Compute losses
- def __init__(self, model, autobalance=False):
- super(ComputeLoss, self).__init__()
- device = next(model.parameters()).device # get model device
- h = model.hyp # hyperparameters
-
- # Define criteria
- BCEcls = nn.BCEWithLogitsLoss(pos_weight=torch.tensor([h['cls_pw']], device=device))
- BCEobj = nn.BCEWithLogitsLoss(pos_weight=torch.tensor([h['obj_pw']], device=device))
-
- # Class label smoothing https://arxiv.org/pdf/1902.04103.pdf eqn 3
- self.cp, self.cn = smooth_BCE(eps=h.get('label_smoothing', 0.0)) # positive, negative BCE targets
-
- # Focal loss
- g = h['fl_gamma'] # focal loss gamma
- if g > 0:
- BCEcls, BCEobj = FocalLoss(BCEcls, g), FocalLoss(BCEobj, g)
-
- det = model.module.model[-1] if is_parallel(model) else model.model[-1] # Detect() module
- self.balance = {3: [4.0, 1.0, 0.4]}.get(det.nl, [4.0, 1.0, 0.25, 0.06, .02]) # P3-P7
- self.ssi = list(det.stride).index(16) if autobalance else 0 # stride 16 index
- self.BCEcls, self.BCEobj, self.gr, self.hyp, self.autobalance = BCEcls, BCEobj, model.gr, h, autobalance
- for k in 'na', 'nc', 'nl', 'anchors':
- setattr(self, k, getattr(det, k))
-
- def __call__(self, p, targets): # predictions, targets, model
- device = targets.device
- lcls, lbox, lobj = torch.zeros(1, device=device), torch.zeros(1, device=device), torch.zeros(1, device=device)
- tcls, tbox, indices, anchors = self.build_targets(p, targets) # targets
-
- # Losses
- for i, pi in enumerate(p): # layer index, layer predictions
- b, a, gj, gi = indices[i] # image, anchor, gridy, gridx
- tobj = torch.zeros_like(pi[..., 0], device=device) # target obj
-
- n = b.shape[0] # number of targets
- if n:
- ps = pi[b, a, gj, gi] # prediction subset corresponding to targets
-
- # Regression
- pxy = ps[:, :2].sigmoid() * 2. - 0.5
- pwh = (ps[:, 2:4].sigmoid() * 2) ** 2 * anchors[i]
- pbox = torch.cat((pxy, pwh), 1) # predicted box
- iou = bbox_iou(pbox.T, tbox[i], x1y1x2y2=False, CIoU=True) # iou(prediction, target)
- lbox += (1.0 - iou).mean() # iou loss
-
- # Objectness
- tobj[b, a, gj, gi] = (1.0 - self.gr) + self.gr * iou.detach().clamp(0).type(tobj.dtype) # iou ratio
-
- # Classification
- if self.nc > 1: # cls loss (only if multiple classes)
- t = torch.full_like(ps[:, 5:], self.cn, device=device) # targets
- t[range(n), tcls[i]] = self.cp
- lcls += self.BCEcls(ps[:, 5:], t) # BCE
-
- # Append targets to text file
- # with open('targets.txt', 'a') as file:
- # [file.write('%11.5g ' * 4 % tuple(x) + '\n') for x in torch.cat((txy[i], twh[i]), 1)]
-
- obji = self.BCEobj(pi[..., 4], tobj)
- lobj += obji * self.balance[i] # obj loss
- if self.autobalance:
- self.balance[i] = self.balance[i] * 0.9999 + 0.0001 / obji.detach().item()
-
- if self.autobalance:
- self.balance = [x / self.balance[self.ssi] for x in self.balance]
- lbox *= self.hyp['box']
- lobj *= self.hyp['obj']
- lcls *= self.hyp['cls']
- bs = tobj.shape[0] # batch size
-
- loss = lbox + lobj + lcls
- return loss * bs, torch.cat((lbox, lobj, lcls, loss)).detach()
-
- def build_targets(self, p, targets):
- # Build targets for compute_loss(), input targets(image,class,x,y,w,h)
- na, nt = self.na, targets.shape[0] # number of anchors, targets
- tcls, tbox, indices, anch = [], [], [], []
- gain = torch.ones(7, device=targets.device) # normalized to gridspace gain
- ai = torch.arange(na, device=targets.device).float().view(na, 1).repeat(1, nt) # same as .repeat_interleave(nt)
- targets = torch.cat((targets.repeat(na, 1, 1), ai[:, :, None]), 2) # append anchor indices
-
- g = 0.5 # bias
- off = torch.tensor([[0, 0],
- [1, 0], [0, 1], [-1, 0], [0, -1], # j,k,l,m
- # [1, 1], [1, -1], [-1, 1], [-1, -1], # jk,jm,lk,lm
- ], device=targets.device).float() * g # offsets
-
- for i in range(self.nl):
- anchors = self.anchors[i]
- gain[2:6] = torch.tensor(p[i].shape)[[3, 2, 3, 2]] # xyxy gain
-
- # Match targets to anchors
- t = targets * gain
- if nt:
- # Matches
- r = t[:, :, 4:6] / anchors[:, None] # wh ratio
- j = torch.max(r, 1. / r).max(2)[0] < self.hyp['anchor_t'] # compare
- # j = wh_iou(anchors, t[:, 4:6]) > model.hyp['iou_t'] # iou(3,n)=wh_iou(anchors(3,2), gwh(n,2))
- t = t[j] # filter
-
- # Offsets
- gxy = t[:, 2:4] # grid xy
- gxi = gain[[2, 3]] - gxy # inverse
- j, k = ((gxy % 1. < g) & (gxy > 1.)).T
- l, m = ((gxi % 1. < g) & (gxi > 1.)).T
- j = torch.stack((torch.ones_like(j), j, k, l, m))
- t = t.repeat((5, 1, 1))[j]
- offsets = (torch.zeros_like(gxy)[None] + off[:, None])[j]
- else:
- t = targets[0]
- offsets = 0
-
- # Define
- b, c = t[:, :2].long().T # image, class
- gxy = t[:, 2:4] # grid xy
- gwh = t[:, 4:6] # grid wh
- gij = (gxy - offsets).long()
- gi, gj = gij.T # grid xy indices
-
- # Append
- a = t[:, 6].long() # anchor indices
- indices.append((b, a, gj.clamp_(0, gain[3] - 1), gi.clamp_(0, gain[2] - 1))) # image, anchor, grid indices
- tbox.append(torch.cat((gxy - gij, gwh), 1)) # box
- anch.append(anchors[a]) # anchors
- tcls.append(c) # class
-
- return tcls, tbox, indices, anch
diff --git a/spaces/csuer/vits/text/ngu_dialect.py b/spaces/csuer/vits/text/ngu_dialect.py
deleted file mode 100644
index ce3e12bbf0469426872eed5f681985d3e1be9b26..0000000000000000000000000000000000000000
--- a/spaces/csuer/vits/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/daarumadx/bot/src/transform/opencv/mask.py b/spaces/daarumadx/bot/src/transform/opencv/mask.py
deleted file mode 100644
index 16cb257b102efb52fca7dc88af3d1e11efed448e..0000000000000000000000000000000000000000
--- a/spaces/daarumadx/bot/src/transform/opencv/mask.py
+++ /dev/null
@@ -1,182 +0,0 @@
-"""OpenCV Mask Transforms."""
-
-import cv2
-import numpy as np
-
-from transform.opencv import ImageTransformOpenCV
-from transform.opencv.bodypart.extract import extract_annotations
-from transform.gan.generator import tensor2im
-
-
-class MaskImageTransformOpenCV(ImageTransformOpenCV):
- """Mask Image Transform OpenCV."""
-
- def __init__(self, input_index=(-2, -1)):
- """
- Mask Image Transform OpenCV constructor.
-
- :param input_index: index where to take the inputs (default is (-2,-1)
- for the two previous transformation)
- :param args: args parameter to run the image transformation (default use Conf.args)
- """
- super().__init__(input_index=input_index)
-
-
-class MaskToMaskref(MaskImageTransformOpenCV):
- """Mask & Correct -> MaskRef [OPENCV]."""
-
- def _execute(self, *args):
- """
- Create mask ref.
-
- :param args: <[RGB,RGB]>image correct, image mask
- :return: image
- """
- # Create a total green image
- green = np.zeros(args[0].shape, np.uint8)
- green[:, :, :] = (0, 255, 0) # (B, G, R)
-
- # Define the green color filter
- f1 = np.asarray([0, 250, 0]) # green color filter
- f2 = np.asarray([10, 255, 10])
-
- # From mask, extrapolate only the green mask
- green_mask = cv2.inRange(args[1], f1, f2) # green is 0
-
- # (OPTIONAL) Apply dilate and open to mask
- kernel = np.ones((5, 5), np.uint8) # Try change it?
- green_mask = cv2.dilate(green_mask, kernel, iterations=1)
- # green_mask = cv2.morphologyEx(green_mask, cv2.MORPH_OPEN, kernel)
-
- # Create an inverted mask
- green_mask_inv = cv2.bitwise_not(green_mask)
-
- # Cut correct and green image, using the green_mask & green_mask_inv
- res1 = cv2.bitwise_and(args[0], args[0], mask=green_mask_inv)
- res2 = cv2.bitwise_and(green, green, mask=green_mask)
-
- # Compone:
- return cv2.add(res1, res2)
-
-
-class MaskdetToMaskfin(MaskImageTransformOpenCV):
- """Maskdet -> Maskfin [OPENCV]."""
-
- def __init__(self, input_index=(-2, -1)):
- """
- Maskdet To Maskfin constructor.
-
- :param input_index: index where to take the inputs (default is (-2,-1)
- for the two previous transformation)
- :param args: args parameter to run the image transformation (default use Conf.args)
- """
- super().__init__(input_index=input_index)
-
- def _setup(self, *args):
- self.__aur_size = self._args["prefs"]["aursize"]
- self.__nip_size = self._args["prefs"]["nipsize"]
- self.__tit_size = self._args["prefs"]["titsize"]
- self.__vag_size = self._args["prefs"]["vagsize"]
- self.__hair_size = self._args["prefs"]["hairsize"]
-
- def _execute(self, *args):
- """
- Create maskfin.
-
- steps:
- 1. Extract annotation
- 1.a: Filter by color
- 1.b: Find ellipses
- 1.c: Filter out ellipses by max size, and max total numbers
- 1.d: Detect Problems
- 1.e: Resolve the problems, or discard the transformation
- 2. With the body list, draw maskfin, using maskref
-
- :param args: <[RGB, RGB]> maskref image, maskdet image
- :return: image
- """
- def to_int(a, b):
- return int(round(a * float(b)))
-
- enable_pubes = (self.__hair_size > 0)
-
- # Create a total green image, in which draw details ellipses
- details = np.zeros(args[0].shape, np.uint8)
- details[:, :, :] = (0, 255, 0) # (B, G, R)
-
- # Extract body part features:
- bodypart_list = extract_annotations(args[1], enable_pubes)
-
- # Check if the list is not empty:
- if bodypart_list:
-
- self.__draw_bodypart_details(bodypart_list, details, to_int)
-
- # Define the green color filter
- f1 = np.asarray([0, 250, 0]) # green color filter
- f2 = np.asarray([10, 255, 10])
-
- # From maskref, extrapolate only the green mask
- green_mask = cv2.bitwise_not(cv2.inRange(args[0], f1, f2)) # green is 0
-
- # Create an inverted mask
- green_mask_inv = cv2.bitwise_not(green_mask)
-
- # Cut maskref and detail image, using the green_mask & green_mask_inv
- res1 = cv2.bitwise_and(args[0], args[0], mask=green_mask)
- res2 = cv2.bitwise_and(details, details, mask=green_mask_inv)
-
- # Compone:
- maskfin = cv2.add(res1, res2)
- return maskfin
-
- def __draw_bodypart_details(self, bodypart_list, details, to_int):
- # Draw body part in details image:
- for obj in bodypart_list:
-
- if obj.w < obj.h:
- a_max = int(obj.h / 2) # asse maggiore
- a_min = int(obj.w / 2) # asse minore
- angle = 0 # angle
- else:
- a_max = int(obj.w / 2)
- a_min = int(obj.h / 2)
- angle = 90
-
- x = int(obj.x)
- y = int(obj.y)
-
- aurmax = to_int(self.__aur_size, a_max)
- aurmin = to_int(self.__aur_size, a_min)
- nipmax = to_int(self.__nip_size, a_max)
- nipmin = to_int(self.__nip_size, a_min)
- titmax = to_int(self.__tit_size, a_max)
- titmin = to_int(self.__tit_size, a_min)
- vagmax = to_int(self.__vag_size, a_max)
- vagmin = to_int(self.__vag_size, a_min)
- hairmax = to_int(self.__hair_size, a_max)
- hairmin = to_int(self.__hair_size, a_min)
-
- self.__draw_ellipse(a_max, a_min, angle, aurmax, aurmin, details, hairmax, hairmin, nipmax, nipmin, obj,
- titmax, titmin, vagmax, vagmin, x, y)
-
- @staticmethod
- def __draw_ellipse(a_max, a_min, angle, aurmax, aurmin, details, hairmax, hairmin, nipmax, nipmin, obj,
- titmax, titmin, vagmax, vagmin, x, y):
- # Draw ellipse
- if obj.name == "tit":
- cv2.ellipse(details, (x, y), (titmax, titmin), angle, 0, 360, (0, 205, 0), -1) # (0,0,0,50)
- elif obj.name == "aur":
- cv2.ellipse(details, (x, y), (aurmax, aurmin), angle, 0, 360, (0, 0, 255), -1) # red
- elif obj.name == "nip":
- cv2.ellipse(details, (x, y), (nipmax, nipmin), angle, 0, 360, (255, 255, 255), -1) # white
- elif obj.name == "belly":
- cv2.ellipse(details, (x, y), (a_max, a_min), angle, 0, 360, (255, 0, 255), -1) # purple
- elif obj.name == "vag":
- cv2.ellipse(details, (x, y), (vagmax, vagmin), angle, 0, 360, (255, 0, 0), -1) # blue
- elif obj.name == "hair":
- xmin = x - hairmax
- ymin = y - hairmin
- xmax = x + hairmax
- ymax = y + hairmax
- cv2.rectangle(details, (xmin, ymin), (xmax, ymax), (100, 100, 100), -1)
diff --git a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/aiofiles/os.py b/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/aiofiles/os.py
deleted file mode 100644
index 29bc748fa91a6d3de6ec42842416de6af7134f5c..0000000000000000000000000000000000000000
--- a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/aiofiles/os.py
+++ /dev/null
@@ -1,51 +0,0 @@
-"""Async executor versions of file functions from the os module."""
-import os
-
-from . import ospath as path
-from .ospath import wrap
-
-__all__ = [
- "path",
- "stat",
- "statvfs",
- "rename",
- "renames",
- "replace",
- "remove",
- "unlink",
- "mkdir",
- "makedirs",
- "rmdir",
- "removedirs",
- "link",
- "symlink",
- "readlink",
- "listdir",
- "scandir",
- "access",
- "sendfile",
- "wrap",
-]
-
-
-stat = wrap(os.stat)
-rename = wrap(os.rename)
-renames = wrap(os.renames)
-replace = wrap(os.replace)
-remove = wrap(os.remove)
-unlink = wrap(os.unlink)
-mkdir = wrap(os.mkdir)
-makedirs = wrap(os.makedirs)
-rmdir = wrap(os.rmdir)
-removedirs = wrap(os.removedirs)
-link = wrap(os.link)
-symlink = wrap(os.symlink)
-readlink = wrap(os.readlink)
-listdir = wrap(os.listdir)
-scandir = wrap(os.scandir)
-access = wrap(os.access)
-
-if hasattr(os, "sendfile"):
- sendfile = wrap(os.sendfile)
-if hasattr(os, "statvfs"):
- statvfs = wrap(os.statvfs)
diff --git a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/gradio/templates/cdn/assets/index-320faa81.js b/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/gradio/templates/cdn/assets/index-320faa81.js
deleted file mode 100644
index e292b1e2242e82aec3fcc2bbedf871391d21f82c..0000000000000000000000000000000000000000
--- a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/gradio/templates/cdn/assets/index-320faa81.js
+++ /dev/null
@@ -1,2 +0,0 @@
-import{S as s,e,s as o}from"./index-9e76ffee.js";class n extends s{constructor(t){super(),e(this,t,null,null,o,{})}}const c=n,r=["static"];export{c as Component,r as modes};
-//# sourceMappingURL=index-320faa81.js.map
diff --git a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/huggingface_hub/repository.py b/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/huggingface_hub/repository.py
deleted file mode 100644
index 757995870b202b1a18a83df61e55cacdd7b21439..0000000000000000000000000000000000000000
--- a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/huggingface_hub/repository.py
+++ /dev/null
@@ -1,1461 +0,0 @@
-import atexit
-import os
-import re
-import subprocess
-import threading
-import time
-from contextlib import contextmanager
-from pathlib import Path
-from typing import Callable, Dict, Iterator, List, Optional, Tuple, Union
-from urllib.parse import urlparse
-
-from huggingface_hub.constants import REPO_TYPES_URL_PREFIXES, REPOCARD_NAME
-from huggingface_hub.repocard import metadata_load, metadata_save
-
-from .hf_api import HfApi, repo_type_and_id_from_hf_id
-from .lfs import LFS_MULTIPART_UPLOAD_COMMAND
-from .utils import (
- HfFolder,
- SoftTemporaryDirectory,
- logging,
- run_subprocess,
- tqdm,
- validate_hf_hub_args,
-)
-from .utils._typing import TypedDict
-
-
-logger = logging.get_logger(__name__)
-
-
-class CommandInProgress:
- """
- Utility to follow commands launched asynchronously.
- """
-
- def __init__(
- self,
- title: str,
- is_done_method: Callable,
- status_method: Callable,
- process: subprocess.Popen,
- post_method: Optional[Callable] = None,
- ):
- self.title = title
- self._is_done = is_done_method
- self._status = status_method
- self._process = process
- self._stderr = ""
- self._stdout = ""
- self._post_method = post_method
-
- @property
- def is_done(self) -> bool:
- """
- Whether the process is done.
- """
- result = self._is_done()
-
- if result and self._post_method is not None:
- self._post_method()
- self._post_method = None
-
- return result
-
- @property
- def status(self) -> int:
- """
- The exit code/status of the current action. Will return `0` if the
- command has completed successfully, and a number between 1 and 255 if
- the process errored-out.
-
- Will return -1 if the command is still ongoing.
- """
- return self._status()
-
- @property
- def failed(self) -> bool:
- """
- Whether the process errored-out.
- """
- return self.status > 0
-
- @property
- def stderr(self) -> str:
- """
- The current output message on the standard error.
- """
- if self._process.stderr is not None:
- self._stderr += self._process.stderr.read()
- return self._stderr
-
- @property
- def stdout(self) -> str:
- """
- The current output message on the standard output.
- """
- if self._process.stdout is not None:
- self._stdout += self._process.stdout.read()
- return self._stdout
-
- def __repr__(self):
- status = self.status
-
- if status == -1:
- status = "running"
-
- return (
- f"[{self.title} command, status code: {status},"
- f" {'in progress.' if not self.is_done else 'finished.'} PID:"
- f" {self._process.pid}]"
- )
-
-
-def is_git_repo(folder: Union[str, Path]) -> bool:
- """
- Check if the folder is the root or part of a git repository
-
- Args:
- folder (`str`):
- The folder in which to run the command.
-
- Returns:
- `bool`: `True` if the repository is part of a repository, `False`
- otherwise.
- """
- folder_exists = os.path.exists(os.path.join(folder, ".git"))
- git_branch = subprocess.run("git branch".split(), cwd=folder, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
- return folder_exists and git_branch.returncode == 0
-
-
-def is_local_clone(folder: Union[str, Path], remote_url: str) -> bool:
- """
- Check if the folder is a local clone of the remote_url
-
- Args:
- folder (`str` or `Path`):
- The folder in which to run the command.
- remote_url (`str`):
- The url of a git repository.
-
- Returns:
- `bool`: `True` if the repository is a local clone of the remote
- repository specified, `False` otherwise.
- """
- if not is_git_repo(folder):
- return False
-
- remotes = run_subprocess("git remote -v", folder).stdout
-
- # Remove token for the test with remotes.
- remote_url = re.sub(r"https://.*@", "https://", remote_url)
- remotes = [re.sub(r"https://.*@", "https://", remote) for remote in remotes.split()]
- return remote_url in remotes
-
-
-def is_tracked_with_lfs(filename: Union[str, Path]) -> bool:
- """
- Check if the file passed is tracked with git-lfs.
-
- Args:
- filename (`str` or `Path`):
- The filename to check.
-
- Returns:
- `bool`: `True` if the file passed is tracked with git-lfs, `False`
- otherwise.
- """
- folder = Path(filename).parent
- filename = Path(filename).name
-
- try:
- p = run_subprocess("git check-attr -a".split() + [filename], folder)
- attributes = p.stdout.strip()
- except subprocess.CalledProcessError as exc:
- if not is_git_repo(folder):
- return False
- else:
- raise OSError(exc.stderr)
-
- if len(attributes) == 0:
- return False
-
- found_lfs_tag = {"diff": False, "merge": False, "filter": False}
-
- for attribute in attributes.split("\n"):
- for tag in found_lfs_tag.keys():
- if tag in attribute and "lfs" in attribute:
- found_lfs_tag[tag] = True
-
- return all(found_lfs_tag.values())
-
-
-def is_git_ignored(filename: Union[str, Path]) -> bool:
- """
- Check if file is git-ignored. Supports nested .gitignore files.
-
- Args:
- filename (`str` or `Path`):
- The filename to check.
-
- Returns:
- `bool`: `True` if the file passed is ignored by `git`, `False`
- otherwise.
- """
- folder = Path(filename).parent
- filename = Path(filename).name
-
- try:
- p = run_subprocess("git check-ignore".split() + [filename], folder, check=False)
- # Will return exit code 1 if not gitignored
- is_ignored = not bool(p.returncode)
- except subprocess.CalledProcessError as exc:
- raise OSError(exc.stderr)
-
- return is_ignored
-
-
-def is_binary_file(filename: Union[str, Path]) -> bool:
- """
- Check if file is a binary file.
-
- Args:
- filename (`str` or `Path`):
- The filename to check.
-
- Returns:
- `bool`: `True` if the file passed is a binary file, `False` otherwise.
- """
- try:
- with open(filename, "rb") as f:
- content = f.read(10 * (1024**2)) # Read a maximum of 10MB
-
- # Code sample taken from the following stack overflow thread
- # https://stackoverflow.com/questions/898669/how-can-i-detect-if-a-file-is-binary-non-text-in-python/7392391#7392391
- text_chars = bytearray({7, 8, 9, 10, 12, 13, 27} | set(range(0x20, 0x100)) - {0x7F})
- return bool(content.translate(None, text_chars))
- except UnicodeDecodeError:
- return True
-
-
-def files_to_be_staged(pattern: str = ".", folder: Union[str, Path, None] = None) -> List[str]:
- """
- Returns a list of filenames that are to be staged.
-
- Args:
- pattern (`str` or `Path`):
- The pattern of filenames to check. Put `.` to get all files.
- folder (`str` or `Path`):
- The folder in which to run the command.
-
- Returns:
- `List[str]`: List of files that are to be staged.
- """
- try:
- p = run_subprocess("git ls-files --exclude-standard -mo".split() + [pattern], folder)
- if len(p.stdout.strip()):
- files = p.stdout.strip().split("\n")
- else:
- files = []
- except subprocess.CalledProcessError as exc:
- raise EnvironmentError(exc.stderr)
-
- return files
-
-
-def is_tracked_upstream(folder: Union[str, Path]) -> bool:
- """
- Check if the current checked-out branch is tracked upstream.
-
- Args:
- folder (`str` or `Path`):
- The folder in which to run the command.
-
- Returns:
- `bool`: `True` if the current checked-out branch is tracked upstream,
- `False` otherwise.
- """
- try:
- run_subprocess("git rev-parse --symbolic-full-name --abbrev-ref @{u}", folder)
- return True
- except subprocess.CalledProcessError as exc:
- if "HEAD" in exc.stderr:
- raise OSError("No branch checked out")
-
- return False
-
-
-def commits_to_push(folder: Union[str, Path], upstream: Optional[str] = None) -> int:
- """
- Check the number of commits that would be pushed upstream
-
- Args:
- folder (`str` or `Path`):
- The folder in which to run the command.
- upstream (`str`, *optional*):
- The name of the upstream repository with which the comparison should be
- made.
-
- Returns:
- `int`: Number of commits that would be pushed upstream were a `git
- push` to proceed.
- """
- try:
- result = run_subprocess(f"git cherry -v {upstream or ''}", folder)
- return len(result.stdout.split("\n")) - 1
- except subprocess.CalledProcessError as exc:
- raise EnvironmentError(exc.stderr)
-
-
-class PbarT(TypedDict):
- # Used to store an opened progress bar in `_lfs_log_progress`
- bar: tqdm
- past_bytes: int
-
-
-@contextmanager
-def _lfs_log_progress():
- """
- This is a context manager that will log the Git LFS progress of cleaning,
- smudging, pulling and pushing.
- """
-
- if logger.getEffectiveLevel() >= logging.ERROR:
- try:
- yield
- except Exception:
- pass
- return
-
- def output_progress(stopping_event: threading.Event):
- """
- To be launched as a separate thread with an event meaning it should stop
- the tail.
- """
- # Key is tuple(state, filename), value is a dict(tqdm bar and a previous value)
- pbars: Dict[Tuple[str, str], PbarT] = {}
-
- def close_pbars():
- for pbar in pbars.values():
- pbar["bar"].update(pbar["bar"].total - pbar["past_bytes"])
- pbar["bar"].refresh()
- pbar["bar"].close()
-
- def tail_file(filename) -> Iterator[str]:
- """
- Creates a generator to be iterated through, which will return each
- line one by one. Will stop tailing the file if the stopping_event is
- set.
- """
- with open(filename, "r") as file:
- current_line = ""
- while True:
- if stopping_event.is_set():
- close_pbars()
- break
-
- line_bit = file.readline()
- if line_bit is not None and not len(line_bit.strip()) == 0:
- current_line += line_bit
- if current_line.endswith("\n"):
- yield current_line
- current_line = ""
- else:
- time.sleep(1)
-
- # If the file isn't created yet, wait for a few seconds before trying again.
- # Can be interrupted with the stopping_event.
- while not os.path.exists(os.environ["GIT_LFS_PROGRESS"]):
- if stopping_event.is_set():
- close_pbars()
- return
-
- time.sleep(2)
-
- for line in tail_file(os.environ["GIT_LFS_PROGRESS"]):
- try:
- state, file_progress, byte_progress, filename = line.split()
- except ValueError as error:
- # Try/except to ease debugging. See https://github.com/huggingface/huggingface_hub/issues/1373.
- raise ValueError(f"Cannot unpack LFS progress line:\n{line}") from error
- description = f"{state.capitalize()} file {filename}"
-
- current_bytes, total_bytes = byte_progress.split("/")
- current_bytes_int = int(current_bytes)
- total_bytes_int = int(total_bytes)
-
- pbar = pbars.get((state, filename))
- if pbar is None:
- # Initialize progress bar
- pbars[(state, filename)] = {
- "bar": tqdm(
- desc=description,
- initial=current_bytes_int,
- total=total_bytes_int,
- unit="B",
- unit_scale=True,
- unit_divisor=1024,
- ),
- "past_bytes": int(current_bytes),
- }
- else:
- # Update progress bar
- pbar["bar"].update(current_bytes_int - pbar["past_bytes"])
- pbar["past_bytes"] = current_bytes_int
-
- current_lfs_progress_value = os.environ.get("GIT_LFS_PROGRESS", "")
-
- with SoftTemporaryDirectory() as tmpdir:
- os.environ["GIT_LFS_PROGRESS"] = os.path.join(tmpdir, "lfs_progress")
- logger.debug(f"Following progress in {os.environ['GIT_LFS_PROGRESS']}")
-
- exit_event = threading.Event()
- x = threading.Thread(target=output_progress, args=(exit_event,), daemon=True)
- x.start()
-
- try:
- yield
- finally:
- exit_event.set()
- x.join()
-
- os.environ["GIT_LFS_PROGRESS"] = current_lfs_progress_value
-
-
-class Repository:
- """
- Helper class to wrap the git and git-lfs commands.
-
- The aim is to facilitate interacting with huggingface.co hosted model or
- dataset repos, though not a lot here (if any) is actually specific to
- huggingface.co.
- """
-
- command_queue: List[CommandInProgress]
-
- @validate_hf_hub_args
- def __init__(
- self,
- local_dir: Union[str, Path],
- clone_from: Optional[str] = None,
- repo_type: Optional[str] = None,
- token: Union[bool, str] = True,
- git_user: Optional[str] = None,
- git_email: Optional[str] = None,
- revision: Optional[str] = None,
- skip_lfs_files: bool = False,
- client: Optional[HfApi] = None,
- ):
- """
- Instantiate a local clone of a git repo.
-
- If `clone_from` is set, the repo will be cloned from an existing remote repository.
- If the remote repo does not exist, a `EnvironmentError` exception will be thrown.
- Please create the remote repo first using [`create_repo`].
-
- `Repository` uses the local git credentials by default. If explicitly set, the `token`
- or the `git_user`/`git_email` pair will be used instead.
-
- Args:
- local_dir (`str` or `Path`):
- path (e.g. `'my_trained_model/'`) to the local directory, where
- the `Repository` will be initialized.
- clone_from (`str`, *optional*):
- Either a repository url or `repo_id`.
- Example:
- - `"https://huggingface.co/philschmid/playground-tests"`
- - `"philschmid/playground-tests"`
- repo_type (`str`, *optional*):
- To set when cloning a repo from a repo_id. Default is model.
- token (`bool` or `str`, *optional*):
- A valid authentication token (see https://huggingface.co/settings/token).
- If `None` or `True` and machine is logged in (through `huggingface-cli login`
- or [`~huggingface_hub.login`]), token will be retrieved from the cache.
- If `False`, token is not sent in the request header.
- git_user (`str`, *optional*):
- will override the `git config user.name` for committing and
- pushing files to the hub.
- git_email (`str`, *optional*):
- will override the `git config user.email` for committing and
- pushing files to the hub.
- revision (`str`, *optional*):
- Revision to checkout after initializing the repository. If the
- revision doesn't exist, a branch will be created with that
- revision name from the default branch's current HEAD.
- skip_lfs_files (`bool`, *optional*, defaults to `False`):
- whether to skip git-LFS files or not.
- client (`HfApi`, *optional*):
- Instance of [`HfApi`] to use when calling the HF Hub API. A new
- instance will be created if this is left to `None`.
-
- Raises:
- - [`EnvironmentError`](https://docs.python.org/3/library/exceptions.html#EnvironmentError)
- if the remote repository set in `clone_from` does not exist.
- """
- if isinstance(local_dir, Path):
- local_dir = str(local_dir)
- os.makedirs(local_dir, exist_ok=True)
- self.local_dir = os.path.join(os.getcwd(), local_dir)
- self._repo_type = repo_type
- self.command_queue = []
- self.skip_lfs_files = skip_lfs_files
- self.client = client if client is not None else HfApi()
-
- self.check_git_versions()
-
- if isinstance(token, str):
- self.huggingface_token: Optional[str] = token
- elif token is False:
- self.huggingface_token = None
- else:
- # if `True` -> explicit use of the cached token
- # if `None` -> implicit use of the cached token
- self.huggingface_token = HfFolder.get_token()
-
- if clone_from is not None:
- self.clone_from(repo_url=clone_from)
- else:
- if is_git_repo(self.local_dir):
- logger.debug("[Repository] is a valid git repo")
- else:
- raise ValueError("If not specifying `clone_from`, you need to pass Repository a valid git clone.")
-
- if self.huggingface_token is not None and (git_email is None or git_user is None):
- user = self.client.whoami(self.huggingface_token)
-
- if git_email is None:
- git_email = user["email"]
-
- if git_user is None:
- git_user = user["fullname"]
-
- if git_user is not None or git_email is not None:
- self.git_config_username_and_email(git_user, git_email)
-
- self.lfs_enable_largefiles()
- self.git_credential_helper_store()
-
- if revision is not None:
- self.git_checkout(revision, create_branch_ok=True)
-
- # This ensures that all commands exit before exiting the Python runtime.
- # This will ensure all pushes register on the hub, even if other errors happen in subsequent operations.
- atexit.register(self.wait_for_commands)
-
- @property
- def current_branch(self) -> str:
- """
- Returns the current checked out branch.
-
- Returns:
- `str`: Current checked out branch.
- """
- try:
- result = run_subprocess("git rev-parse --abbrev-ref HEAD", self.local_dir).stdout.strip()
- except subprocess.CalledProcessError as exc:
- raise EnvironmentError(exc.stderr)
-
- return result
-
- def check_git_versions(self):
- """
- Checks that `git` and `git-lfs` can be run.
-
- Raises:
- - [`EnvironmentError`](https://docs.python.org/3/library/exceptions.html#EnvironmentError)
- if `git` or `git-lfs` are not installed.
- """
- try:
- git_version = run_subprocess("git --version", self.local_dir).stdout.strip()
- except FileNotFoundError:
- raise EnvironmentError("Looks like you do not have git installed, please install.")
-
- try:
- lfs_version = run_subprocess("git-lfs --version", self.local_dir).stdout.strip()
- except FileNotFoundError:
- raise EnvironmentError(
- "Looks like you do not have git-lfs installed, please install."
- " You can install from https://git-lfs.github.com/."
- " Then run `git lfs install` (you only have to do this once)."
- )
- logger.info(git_version + "\n" + lfs_version)
-
- @validate_hf_hub_args
- def clone_from(self, repo_url: str, token: Union[bool, str, None] = None):
- """
- Clone from a remote. If the folder already exists, will try to clone the
- repository within it.
-
- If this folder is a git repository with linked history, will try to
- update the repository.
-
- Args:
- repo_url (`str`):
- The URL from which to clone the repository
- token (`Union[str, bool]`, *optional*):
- Whether to use the authentication token. It can be:
- - a string which is the token itself
- - `False`, which would not use the authentication token
- - `True`, which would fetch the authentication token from the
- local folder and use it (you should be logged in for this to
- work).
- - `None`, which would retrieve the value of
- `self.huggingface_token`.
-
-
-
- Raises the following error:
-
- - [`ValueError`](https://docs.python.org/3/library/exceptions.html#ValueError)
- if an organization token (starts with "api_org") is passed. Use must use
- your own personal access token (see https://hf.co/settings/tokens).
-
- - [`EnvironmentError`](https://docs.python.org/3/library/exceptions.html#EnvironmentError)
- if you are trying to clone the repository in a non-empty folder, or if the
- `git` operations raise errors.
-
-
- """
- token = (
- token # str -> use it
- if isinstance(token, str)
- else (
- None # `False` -> explicit no token
- if token is False
- else self.huggingface_token # `None` or `True` -> use default
- )
- )
- if token is not None and token.startswith("api_org"):
- raise ValueError(
- "You must use your personal access token, not an Organization token"
- " (see https://hf.co/settings/tokens)."
- )
-
- hub_url = self.client.endpoint
- if hub_url in repo_url or ("http" not in repo_url and len(repo_url.split("/")) <= 2):
- repo_type, namespace, repo_name = repo_type_and_id_from_hf_id(repo_url, hub_url=hub_url)
- repo_id = f"{namespace}/{repo_name}" if namespace is not None else repo_name
-
- if repo_type is not None:
- self._repo_type = repo_type
-
- repo_url = hub_url + "/"
-
- if self._repo_type in REPO_TYPES_URL_PREFIXES:
- repo_url += REPO_TYPES_URL_PREFIXES[self._repo_type]
-
- if token is not None:
- # Add token in git url when provided
- scheme = urlparse(repo_url).scheme
- repo_url = repo_url.replace(f"{scheme}://", f"{scheme}://user:{token}@")
-
- repo_url += repo_id
-
- # For error messages, it's cleaner to show the repo url without the token.
- clean_repo_url = re.sub(r"(https?)://.*@", r"\1://", repo_url)
- try:
- run_subprocess("git lfs install", self.local_dir)
-
- # checks if repository is initialized in a empty repository or in one with files
- if len(os.listdir(self.local_dir)) == 0:
- logger.warning(f"Cloning {clean_repo_url} into local empty directory.")
-
- with _lfs_log_progress():
- env = os.environ.copy()
-
- if self.skip_lfs_files:
- env.update({"GIT_LFS_SKIP_SMUDGE": "1"})
-
- run_subprocess(
- # 'git lfs clone' is deprecated (will display a warning in the terminal)
- # but we still use it as it provides a nicer UX when downloading large
- # files (shows progress).
- f"{'git clone' if self.skip_lfs_files else 'git lfs clone'} {repo_url} .",
- self.local_dir,
- env=env,
- )
- else:
- # Check if the folder is the root of a git repository
- if not is_git_repo(self.local_dir):
- raise EnvironmentError(
- "Tried to clone a repository in a non-empty folder that isn't"
- f" a git repository ('{self.local_dir}'). If you really want to"
- f" do this, do it manually:\n cd {self.local_dir} && git init"
- " && git remote add origin && git pull origin main\n or clone"
- " repo to a new folder and move your existing files there"
- " afterwards."
- )
-
- if is_local_clone(self.local_dir, repo_url):
- logger.warning(
- f"{self.local_dir} is already a clone of {clean_repo_url}."
- " Make sure you pull the latest changes with"
- " `repo.git_pull()`."
- )
- else:
- output = run_subprocess("git remote get-url origin", self.local_dir, check=False)
-
- error_msg = (
- f"Tried to clone {clean_repo_url} in an unrelated git"
- " repository.\nIf you believe this is an error, please add"
- f" a remote with the following URL: {clean_repo_url}."
- )
- if output.returncode == 0:
- clean_local_remote_url = re.sub(r"https://.*@", "https://", output.stdout)
- error_msg += f"\nLocal path has its origin defined as: {clean_local_remote_url}"
- raise EnvironmentError(error_msg)
-
- except subprocess.CalledProcessError as exc:
- raise EnvironmentError(exc.stderr)
-
- def git_config_username_and_email(self, git_user: Optional[str] = None, git_email: Optional[str] = None):
- """
- Sets git username and email (only in the current repo).
-
- Args:
- git_user (`str`, *optional*):
- The username to register through `git`.
- git_email (`str`, *optional*):
- The email to register through `git`.
- """
- try:
- if git_user is not None:
- run_subprocess("git config user.name".split() + [git_user], self.local_dir)
-
- if git_email is not None:
- run_subprocess(f"git config user.email {git_email}".split(), self.local_dir)
- except subprocess.CalledProcessError as exc:
- raise EnvironmentError(exc.stderr)
-
- def git_credential_helper_store(self):
- """
- Sets the git credential helper to `store`
- """
- try:
- run_subprocess("git config credential.helper store", self.local_dir)
- except subprocess.CalledProcessError as exc:
- raise EnvironmentError(exc.stderr)
-
- def git_head_hash(self) -> str:
- """
- Get commit sha on top of HEAD.
-
- Returns:
- `str`: The current checked out commit SHA.
- """
- try:
- p = run_subprocess("git rev-parse HEAD", self.local_dir)
- return p.stdout.strip()
- except subprocess.CalledProcessError as exc:
- raise EnvironmentError(exc.stderr)
-
- def git_remote_url(self) -> str:
- """
- Get URL to origin remote.
-
- Returns:
- `str`: The URL of the `origin` remote.
- """
- try:
- p = run_subprocess("git config --get remote.origin.url", self.local_dir)
- url = p.stdout.strip()
- # Strip basic auth info.
- return re.sub(r"https://.*@", "https://", url)
- except subprocess.CalledProcessError as exc:
- raise EnvironmentError(exc.stderr)
-
- def git_head_commit_url(self) -> str:
- """
- Get URL to last commit on HEAD. We assume it's been pushed, and the url
- scheme is the same one as for GitHub or HuggingFace.
-
- Returns:
- `str`: The URL to the current checked-out commit.
- """
- sha = self.git_head_hash()
- url = self.git_remote_url()
- if url.endswith("/"):
- url = url[:-1]
- return f"{url}/commit/{sha}"
-
- def list_deleted_files(self) -> List[str]:
- """
- Returns a list of the files that are deleted in the working directory or
- index.
-
- Returns:
- `List[str]`: A list of files that have been deleted in the working
- directory or index.
- """
- try:
- git_status = run_subprocess("git status -s", self.local_dir).stdout.strip()
- except subprocess.CalledProcessError as exc:
- raise EnvironmentError(exc.stderr)
-
- if len(git_status) == 0:
- return []
-
- # Receives a status like the following
- # D .gitignore
- # D new_file.json
- # AD new_file1.json
- # ?? new_file2.json
- # ?? new_file4.json
-
- # Strip each line of whitespaces
- modified_files_statuses = [status.strip() for status in git_status.split("\n")]
-
- # Only keep files that are deleted using the D prefix
- deleted_files_statuses = [status for status in modified_files_statuses if "D" in status.split()[0]]
-
- # Remove the D prefix and strip to keep only the relevant filename
- deleted_files = [status.split()[-1].strip() for status in deleted_files_statuses]
-
- return deleted_files
-
- def lfs_track(self, patterns: Union[str, List[str]], filename: bool = False):
- """
- Tell git-lfs to track files according to a pattern.
-
- Setting the `filename` argument to `True` will treat the arguments as
- literal filenames, not as patterns. Any special glob characters in the
- filename will be escaped when writing to the `.gitattributes` file.
-
- Args:
- patterns (`Union[str, List[str]]`):
- The pattern, or list of patterns, to track with git-lfs.
- filename (`bool`, *optional*, defaults to `False`):
- Whether to use the patterns as literal filenames.
- """
- if isinstance(patterns, str):
- patterns = [patterns]
- try:
- for pattern in patterns:
- run_subprocess(
- f"git lfs track {'--filename' if filename else ''} {pattern}",
- self.local_dir,
- )
- except subprocess.CalledProcessError as exc:
- raise EnvironmentError(exc.stderr)
-
- def lfs_untrack(self, patterns: Union[str, List[str]]):
- """
- Tell git-lfs to untrack those files.
-
- Args:
- patterns (`Union[str, List[str]]`):
- The pattern, or list of patterns, to untrack with git-lfs.
- """
- if isinstance(patterns, str):
- patterns = [patterns]
- try:
- for pattern in patterns:
- run_subprocess("git lfs untrack".split() + [pattern], self.local_dir)
- except subprocess.CalledProcessError as exc:
- raise EnvironmentError(exc.stderr)
-
- def lfs_enable_largefiles(self):
- """
- HF-specific. This enables upload support of files >5GB.
- """
- try:
- lfs_config = "git config lfs.customtransfer.multipart"
- run_subprocess(f"{lfs_config}.path huggingface-cli", self.local_dir)
- run_subprocess(
- f"{lfs_config}.args {LFS_MULTIPART_UPLOAD_COMMAND}",
- self.local_dir,
- )
- except subprocess.CalledProcessError as exc:
- raise EnvironmentError(exc.stderr)
-
- def auto_track_binary_files(self, pattern: str = ".") -> List[str]:
- """
- Automatically track binary files with git-lfs.
-
- Args:
- pattern (`str`, *optional*, defaults to "."):
- The pattern with which to track files that are binary.
-
- Returns:
- `List[str]`: List of filenames that are now tracked due to being
- binary files
- """
- files_to_be_tracked_with_lfs = []
-
- deleted_files = self.list_deleted_files()
-
- for filename in files_to_be_staged(pattern, folder=self.local_dir):
- if filename in deleted_files:
- continue
-
- path_to_file = os.path.join(os.getcwd(), self.local_dir, filename)
-
- if not (is_tracked_with_lfs(path_to_file) or is_git_ignored(path_to_file)):
- size_in_mb = os.path.getsize(path_to_file) / (1024 * 1024)
-
- if size_in_mb >= 10:
- logger.warning(
- "Parsing a large file to check if binary or not. Tracking large"
- " files using `repository.auto_track_large_files` is"
- " recommended so as to not load the full file in memory."
- )
-
- is_binary = is_binary_file(path_to_file)
-
- if is_binary:
- self.lfs_track(filename)
- files_to_be_tracked_with_lfs.append(filename)
-
- # Cleanup the .gitattributes if files were deleted
- self.lfs_untrack(deleted_files)
-
- return files_to_be_tracked_with_lfs
-
- def auto_track_large_files(self, pattern: str = ".") -> List[str]:
- """
- Automatically track large files (files that weigh more than 10MBs) with
- git-lfs.
-
- Args:
- pattern (`str`, *optional*, defaults to "."):
- The pattern with which to track files that are above 10MBs.
-
- Returns:
- `List[str]`: List of filenames that are now tracked due to their
- size.
- """
- files_to_be_tracked_with_lfs = []
-
- deleted_files = self.list_deleted_files()
-
- for filename in files_to_be_staged(pattern, folder=self.local_dir):
- if filename in deleted_files:
- continue
-
- path_to_file = os.path.join(os.getcwd(), self.local_dir, filename)
- size_in_mb = os.path.getsize(path_to_file) / (1024 * 1024)
-
- if size_in_mb >= 10 and not is_tracked_with_lfs(path_to_file) and not is_git_ignored(path_to_file):
- self.lfs_track(filename)
- files_to_be_tracked_with_lfs.append(filename)
-
- # Cleanup the .gitattributes if files were deleted
- self.lfs_untrack(deleted_files)
-
- return files_to_be_tracked_with_lfs
-
- def lfs_prune(self, recent=False):
- """
- git lfs prune
-
- Args:
- recent (`bool`, *optional*, defaults to `False`):
- Whether to prune files even if they were referenced by recent
- commits. See the following
- [link](https://github.com/git-lfs/git-lfs/blob/f3d43f0428a84fc4f1e5405b76b5a73ec2437e65/docs/man/git-lfs-prune.1.ronn#recent-files)
- for more information.
- """
- try:
- with _lfs_log_progress():
- result = run_subprocess(f"git lfs prune {'--recent' if recent else ''}", self.local_dir)
- logger.info(result.stdout)
- except subprocess.CalledProcessError as exc:
- raise EnvironmentError(exc.stderr)
-
- def git_pull(self, rebase: bool = False, lfs: bool = False):
- """
- git pull
-
- Args:
- rebase (`bool`, *optional*, defaults to `False`):
- Whether to rebase the current branch on top of the upstream
- branch after fetching.
- lfs (`bool`, *optional*, defaults to `False`):
- Whether to fetch the LFS files too. This option only changes the
- behavior when a repository was cloned without fetching the LFS
- files; calling `repo.git_pull(lfs=True)` will then fetch the LFS
- file from the remote repository.
- """
- command = "git pull" if not lfs else "git lfs pull"
- if rebase:
- command += " --rebase"
- try:
- with _lfs_log_progress():
- result = run_subprocess(command, self.local_dir)
- logger.info(result.stdout)
- except subprocess.CalledProcessError as exc:
- raise EnvironmentError(exc.stderr)
-
- def git_add(self, pattern: str = ".", auto_lfs_track: bool = False):
- """
- git add
-
- Setting the `auto_lfs_track` parameter to `True` will automatically
- track files that are larger than 10MB with `git-lfs`.
-
- Args:
- pattern (`str`, *optional*, defaults to "."):
- The pattern with which to add files to staging.
- auto_lfs_track (`bool`, *optional*, defaults to `False`):
- Whether to automatically track large and binary files with
- git-lfs. Any file over 10MB in size, or in binary format, will
- be automatically tracked.
- """
- if auto_lfs_track:
- # Track files according to their size (>=10MB)
- tracked_files = self.auto_track_large_files(pattern)
-
- # Read the remaining files and track them if they're binary
- tracked_files.extend(self.auto_track_binary_files(pattern))
-
- if tracked_files:
- logger.warning(
- f"Adding files tracked by Git LFS: {tracked_files}. This may take a"
- " bit of time if the files are large."
- )
-
- try:
- result = run_subprocess("git add -v".split() + [pattern], self.local_dir)
- logger.info(f"Adding to index:\n{result.stdout}\n")
- except subprocess.CalledProcessError as exc:
- raise EnvironmentError(exc.stderr)
-
- def git_commit(self, commit_message: str = "commit files to HF hub"):
- """
- git commit
-
- Args:
- commit_message (`str`, *optional*, defaults to "commit files to HF hub"):
- The message attributed to the commit.
- """
- try:
- result = run_subprocess("git commit -v -m".split() + [commit_message], self.local_dir)
- logger.info(f"Committed:\n{result.stdout}\n")
- except subprocess.CalledProcessError as exc:
- if len(exc.stderr) > 0:
- raise EnvironmentError(exc.stderr)
- else:
- raise EnvironmentError(exc.stdout)
-
- def git_push(
- self,
- upstream: Optional[str] = None,
- blocking: bool = True,
- auto_lfs_prune: bool = False,
- ) -> Union[str, Tuple[str, CommandInProgress]]:
- """
- git push
-
- If used without setting `blocking`, will return url to commit on remote
- repo. If used with `blocking=True`, will return a tuple containing the
- url to commit and the command object to follow for information about the
- process.
-
- Args:
- upstream (`str`, *optional*):
- Upstream to which this should push. If not specified, will push
- to the lastly defined upstream or to the default one (`origin
- main`).
- blocking (`bool`, *optional*, defaults to `True`):
- Whether the function should return only when the push has
- finished. Setting this to `False` will return an
- `CommandInProgress` object which has an `is_done` property. This
- property will be set to `True` when the push is finished.
- auto_lfs_prune (`bool`, *optional*, defaults to `False`):
- Whether to automatically prune files once they have been pushed
- to the remote.
- """
- command = "git push"
-
- if upstream:
- command += f" --set-upstream {upstream}"
-
- number_of_commits = commits_to_push(self.local_dir, upstream)
-
- if number_of_commits > 1:
- logger.warning(f"Several commits ({number_of_commits}) will be pushed upstream.")
- if blocking:
- logger.warning("The progress bars may be unreliable.")
-
- try:
- with _lfs_log_progress():
- process = subprocess.Popen(
- command.split(),
- stderr=subprocess.PIPE,
- stdout=subprocess.PIPE,
- encoding="utf-8",
- cwd=self.local_dir,
- )
-
- if blocking:
- stdout, stderr = process.communicate()
- return_code = process.poll()
- process.kill()
-
- if len(stderr):
- logger.warning(stderr)
-
- if return_code:
- raise subprocess.CalledProcessError(return_code, process.args, output=stdout, stderr=stderr)
-
- except subprocess.CalledProcessError as exc:
- raise EnvironmentError(exc.stderr)
-
- if not blocking:
-
- def status_method():
- status = process.poll()
- if status is None:
- return -1
- else:
- return status
-
- command_in_progress = CommandInProgress(
- "push",
- is_done_method=lambda: process.poll() is not None,
- status_method=status_method,
- process=process,
- post_method=self.lfs_prune if auto_lfs_prune else None,
- )
-
- self.command_queue.append(command_in_progress)
-
- return self.git_head_commit_url(), command_in_progress
-
- if auto_lfs_prune:
- self.lfs_prune()
-
- return self.git_head_commit_url()
-
- def git_checkout(self, revision: str, create_branch_ok: bool = False):
- """
- git checkout a given revision
-
- Specifying `create_branch_ok` to `True` will create the branch to the
- given revision if that revision doesn't exist.
-
- Args:
- revision (`str`):
- The revision to checkout.
- create_branch_ok (`str`, *optional*, defaults to `False`):
- Whether creating a branch named with the `revision` passed at
- the current checked-out reference if `revision` isn't an
- existing revision is allowed.
- """
- try:
- result = run_subprocess(f"git checkout {revision}", self.local_dir)
- logger.warning(f"Checked out {revision} from {self.current_branch}.")
- logger.warning(result.stdout)
- except subprocess.CalledProcessError as exc:
- if not create_branch_ok:
- raise EnvironmentError(exc.stderr)
- else:
- try:
- result = run_subprocess(f"git checkout -b {revision}", self.local_dir)
- logger.warning(
- f"Revision `{revision}` does not exist. Created and checked out branch `{revision}`."
- )
- logger.warning(result.stdout)
- except subprocess.CalledProcessError as exc:
- raise EnvironmentError(exc.stderr)
-
- def tag_exists(self, tag_name: str, remote: Optional[str] = None) -> bool:
- """
- Check if a tag exists or not.
-
- Args:
- tag_name (`str`):
- The name of the tag to check.
- remote (`str`, *optional*):
- Whether to check if the tag exists on a remote. This parameter
- should be the identifier of the remote.
-
- Returns:
- `bool`: Whether the tag exists.
- """
- if remote:
- try:
- result = run_subprocess(f"git ls-remote origin refs/tags/{tag_name}", self.local_dir).stdout.strip()
- except subprocess.CalledProcessError as exc:
- raise EnvironmentError(exc.stderr)
-
- return len(result) != 0
- else:
- try:
- git_tags = run_subprocess("git tag", self.local_dir).stdout.strip()
- except subprocess.CalledProcessError as exc:
- raise EnvironmentError(exc.stderr)
-
- git_tags = git_tags.split("\n")
- return tag_name in git_tags
-
- def delete_tag(self, tag_name: str, remote: Optional[str] = None) -> bool:
- """
- Delete a tag, both local and remote, if it exists
-
- Args:
- tag_name (`str`):
- The tag name to delete.
- remote (`str`, *optional*):
- The remote on which to delete the tag.
-
- Returns:
- `bool`: `True` if deleted, `False` if the tag didn't exist.
- If remote is not passed, will just be updated locally
- """
- delete_locally = True
- delete_remotely = True
-
- if not self.tag_exists(tag_name):
- delete_locally = False
-
- if not self.tag_exists(tag_name, remote=remote):
- delete_remotely = False
-
- if delete_locally:
- try:
- run_subprocess(["git", "tag", "-d", tag_name], self.local_dir).stdout.strip()
- except subprocess.CalledProcessError as exc:
- raise EnvironmentError(exc.stderr)
-
- if remote and delete_remotely:
- try:
- run_subprocess(f"git push {remote} --delete {tag_name}", self.local_dir).stdout.strip()
- except subprocess.CalledProcessError as exc:
- raise EnvironmentError(exc.stderr)
-
- return True
-
- def add_tag(self, tag_name: str, message: Optional[str] = None, remote: Optional[str] = None):
- """
- Add a tag at the current head and push it
-
- If remote is None, will just be updated locally
-
- If no message is provided, the tag will be lightweight. if a message is
- provided, the tag will be annotated.
-
- Args:
- tag_name (`str`):
- The name of the tag to be added.
- message (`str`, *optional*):
- The message that accompanies the tag. The tag will turn into an
- annotated tag if a message is passed.
- remote (`str`, *optional*):
- The remote on which to add the tag.
- """
- if message:
- tag_args = ["git", "tag", "-a", tag_name, "-m", message]
- else:
- tag_args = ["git", "tag", tag_name]
-
- try:
- run_subprocess(tag_args, self.local_dir).stdout.strip()
- except subprocess.CalledProcessError as exc:
- raise EnvironmentError(exc.stderr)
-
- if remote:
- try:
- run_subprocess(f"git push {remote} {tag_name}", self.local_dir).stdout.strip()
- except subprocess.CalledProcessError as exc:
- raise EnvironmentError(exc.stderr)
-
- def is_repo_clean(self) -> bool:
- """
- Return whether or not the git status is clean or not
-
- Returns:
- `bool`: `True` if the git status is clean, `False` otherwise.
- """
- try:
- git_status = run_subprocess("git status --porcelain", self.local_dir).stdout.strip()
- except subprocess.CalledProcessError as exc:
- raise EnvironmentError(exc.stderr)
-
- return len(git_status) == 0
-
- def push_to_hub(
- self,
- commit_message: str = "commit files to HF hub",
- blocking: bool = True,
- clean_ok: bool = True,
- auto_lfs_prune: bool = False,
- ) -> Union[None, str, Tuple[str, CommandInProgress]]:
- """
- Helper to add, commit, and push files to remote repository on the
- HuggingFace Hub. Will automatically track large files (>10MB).
-
- Args:
- commit_message (`str`):
- Message to use for the commit.
- blocking (`bool`, *optional*, defaults to `True`):
- Whether the function should return only when the `git push` has
- finished.
- clean_ok (`bool`, *optional*, defaults to `True`):
- If True, this function will return None if the repo is
- untouched. Default behavior is to fail because the git command
- fails.
- auto_lfs_prune (`bool`, *optional*, defaults to `False`):
- Whether to automatically prune files once they have been pushed
- to the remote.
- """
- if clean_ok and self.is_repo_clean():
- logger.info("Repo currently clean. Ignoring push_to_hub")
- return None
- self.git_add(auto_lfs_track=True)
- self.git_commit(commit_message)
- return self.git_push(
- upstream=f"origin {self.current_branch}",
- blocking=blocking,
- auto_lfs_prune=auto_lfs_prune,
- )
-
- @contextmanager
- def commit(
- self,
- commit_message: str,
- branch: Optional[str] = None,
- track_large_files: bool = True,
- blocking: bool = True,
- auto_lfs_prune: bool = False,
- ):
- """
- Context manager utility to handle committing to a repository. This
- automatically tracks large files (>10Mb) with git-lfs. Set the
- `track_large_files` argument to `False` if you wish to ignore that
- behavior.
-
- Args:
- commit_message (`str`):
- Message to use for the commit.
- branch (`str`, *optional*):
- The branch on which the commit will appear. This branch will be
- checked-out before any operation.
- track_large_files (`bool`, *optional*, defaults to `True`):
- Whether to automatically track large files or not. Will do so by
- default.
- blocking (`bool`, *optional*, defaults to `True`):
- Whether the function should return only when the `git push` has
- finished.
- auto_lfs_prune (`bool`, defaults to `True`):
- Whether to automatically prune files once they have been pushed
- to the remote.
-
- Examples:
-
- ```python
- >>> with Repository(
- ... "text-files",
- ... clone_from="/text-files",
- ... token=True,
- >>> ).commit("My first file :)"):
- ... with open("file.txt", "w+") as f:
- ... f.write(json.dumps({"hey": 8}))
-
- >>> import torch
-
- >>> model = torch.nn.Transformer()
- >>> with Repository(
- ... "torch-model",
- ... clone_from="/torch-model",
- ... token=True,
- >>> ).commit("My cool model :)"):
- ... torch.save(model.state_dict(), "model.pt")
- ```
-
- """
-
- files_to_stage = files_to_be_staged(".", folder=self.local_dir)
-
- if len(files_to_stage):
- if len(files_to_stage) > 5:
- files_in_msg = str(files_to_stage[:5])[:-1] + ", ...]"
-
- logger.error(
- "There exists some updated files in the local repository that are not"
- f" committed: {files_in_msg}. This may lead to errors if checking out"
- " a branch. These files and their modifications will be added to the"
- " current commit."
- )
-
- if branch is not None:
- self.git_checkout(branch, create_branch_ok=True)
-
- if is_tracked_upstream(self.local_dir):
- logger.warning("Pulling changes ...")
- self.git_pull(rebase=True)
- else:
- logger.warning(f"The current branch has no upstream branch. Will push to 'origin {self.current_branch}'")
-
- current_working_directory = os.getcwd()
- os.chdir(os.path.join(current_working_directory, self.local_dir))
-
- try:
- yield self
- finally:
- self.git_add(auto_lfs_track=track_large_files)
-
- try:
- self.git_commit(commit_message)
- except OSError as e:
- # If no changes are detected, there is nothing to commit.
- if "nothing to commit" not in str(e):
- raise e
-
- try:
- self.git_push(
- upstream=f"origin {self.current_branch}",
- blocking=blocking,
- auto_lfs_prune=auto_lfs_prune,
- )
- except OSError as e:
- # If no changes are detected, there is nothing to commit.
- if "could not read Username" in str(e):
- raise OSError("Couldn't authenticate user for push. Did you set `token` to `True`?") from e
- else:
- raise e
-
- os.chdir(current_working_directory)
-
- def repocard_metadata_load(self) -> Optional[Dict]:
- filepath = os.path.join(self.local_dir, REPOCARD_NAME)
- if os.path.isfile(filepath):
- return metadata_load(filepath)
- return None
-
- def repocard_metadata_save(self, data: Dict) -> None:
- return metadata_save(os.path.join(self.local_dir, REPOCARD_NAME), data)
-
- @property
- def commands_failed(self):
- """
- Returns the asynchronous commands that failed.
- """
- return [c for c in self.command_queue if c.status > 0]
-
- @property
- def commands_in_progress(self):
- """
- Returns the asynchronous commands that are currently in progress.
- """
- return [c for c in self.command_queue if not c.is_done]
-
- def wait_for_commands(self):
- """
- Blocking method: blocks all subsequent execution until all commands have
- been processed.
- """
- index = 0
- for command_failed in self.commands_failed:
- logger.error(f"The {command_failed.title} command with PID {command_failed._process.pid} failed.")
- logger.error(command_failed.stderr)
-
- while self.commands_in_progress:
- if index % 10 == 0:
- logger.warning(
- f"Waiting for the following commands to finish before shutting down: {self.commands_in_progress}."
- )
-
- index += 1
-
- time.sleep(1)
diff --git a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/markdown_it/rules_inline/balance_pairs.py b/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/markdown_it/rules_inline/balance_pairs.py
deleted file mode 100644
index bbb2101c7e1614dde2323d3a8a42b388f354789e..0000000000000000000000000000000000000000
--- a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/markdown_it/rules_inline/balance_pairs.py
+++ /dev/null
@@ -1,137 +0,0 @@
-"""Balance paired characters (*, _, etc) in inline tokens."""
-from __future__ import annotations
-
-from .state_inline import Delimiter, StateInline
-
-
-def processDelimiters(state: StateInline, delimiters: list[Delimiter]) -> None:
- """For each opening emphasis-like marker find a matching closing one."""
- if not delimiters:
- return
-
- openersBottom = {}
- maximum = len(delimiters)
-
- # headerIdx is the first delimiter of the current (where closer is) delimiter run
- headerIdx = 0
- lastTokenIdx = -2 # needs any value lower than -1
- jumps: list[int] = []
- closerIdx = 0
- while closerIdx < maximum:
- closer = delimiters[closerIdx]
-
- jumps.append(0)
-
- # markers belong to same delimiter run if:
- # - they have adjacent tokens
- # - AND markers are the same
- #
- if (
- delimiters[headerIdx].marker != closer.marker
- or lastTokenIdx != closer.token - 1
- ):
- headerIdx = closerIdx
- lastTokenIdx = closer.token
-
- # Length is only used for emphasis-specific "rule of 3",
- # if it's not defined (in strikethrough or 3rd party plugins),
- # we can default it to 0 to disable those checks.
- #
- closer.length = closer.length or 0
-
- if not closer.close:
- closerIdx += 1
- continue
-
- # Previously calculated lower bounds (previous fails)
- # for each marker, each delimiter length modulo 3,
- # and for whether this closer can be an opener;
- # https://github.com/commonmark/cmark/commit/34250e12ccebdc6372b8b49c44fab57c72443460
- if closer.marker not in openersBottom:
- openersBottom[closer.marker] = [-1, -1, -1, -1, -1, -1]
-
- minOpenerIdx = openersBottom[closer.marker][
- (3 if closer.open else 0) + (closer.length % 3)
- ]
-
- openerIdx = headerIdx - jumps[headerIdx] - 1
-
- newMinOpenerIdx = openerIdx
-
- while openerIdx > minOpenerIdx:
- opener = delimiters[openerIdx]
-
- if opener.marker != closer.marker:
- openerIdx -= jumps[openerIdx] + 1
- continue
-
- if opener.open and opener.end < 0:
- isOddMatch = False
-
- # from spec:
- #
- # If one of the delimiters can both open and close emphasis, then the
- # sum of the lengths of the delimiter runs containing the opening and
- # closing delimiters must not be a multiple of 3 unless both lengths
- # are multiples of 3.
- #
- if (
- (opener.close or closer.open)
- and ((opener.length + closer.length) % 3 == 0)
- and (opener.length % 3 != 0 or closer.length % 3 != 0)
- ):
- isOddMatch = True
-
- if not isOddMatch:
- # If previous delimiter cannot be an opener, we can safely skip
- # the entire sequence in future checks. This is required to make
- # sure algorithm has linear complexity (see *_*_*_*_*_... case).
- #
- if openerIdx > 0 and not delimiters[openerIdx - 1].open:
- lastJump = jumps[openerIdx - 1] + 1
- else:
- lastJump = 0
-
- jumps[closerIdx] = closerIdx - openerIdx + lastJump
- jumps[openerIdx] = lastJump
-
- closer.open = False
- opener.end = closerIdx
- opener.close = False
- newMinOpenerIdx = -1
-
- # treat next token as start of run,
- # it optimizes skips in **<...>**a**<...>** pathological case
- lastTokenIdx = -2
-
- break
-
- openerIdx -= jumps[openerIdx] + 1
-
- if newMinOpenerIdx != -1:
- # If match for this delimiter run failed, we want to set lower bound for
- # future lookups. This is required to make sure algorithm has linear
- # complexity.
- #
- # See details here:
- # https:#github.com/commonmark/cmark/issues/178#issuecomment-270417442
- #
- openersBottom[closer.marker][
- (3 if closer.open else 0) + ((closer.length or 0) % 3)
- ] = newMinOpenerIdx
-
- closerIdx += 1
-
-
-def link_pairs(state: StateInline) -> None:
- tokens_meta = state.tokens_meta
- maximum = len(state.tokens_meta)
-
- processDelimiters(state, state.delimiters)
-
- curr = 0
- while curr < maximum:
- curr_meta = tokens_meta[curr]
- if curr_meta and "delimiters" in curr_meta:
- processDelimiters(state, curr_meta["delimiters"])
- curr += 1
diff --git a/spaces/dcq/freegpt-webui/g4f/Provider/Providers/Bing.py b/spaces/dcq/freegpt-webui/g4f/Provider/Providers/Bing.py
deleted file mode 100644
index 87e04ac82293c7e22068af431ac407bdee435a1b..0000000000000000000000000000000000000000
--- a/spaces/dcq/freegpt-webui/g4f/Provider/Providers/Bing.py
+++ /dev/null
@@ -1,349 +0,0 @@
-import os
-import json
-import random
-import json
-import os
-import uuid
-import ssl
-import certifi
-import aiohttp
-import asyncio
-
-import requests
-from ...typing import sha256, Dict, get_type_hints
-
-url = 'https://bing.com/chat'
-model = ['gpt-4']
-supports_stream = True
-needs_auth = False
-
-ssl_context = ssl.create_default_context()
-ssl_context.load_verify_locations(certifi.where())
-
-
-class optionsSets:
- optionSet: dict = {
- 'tone': str,
- 'optionsSets': list
- }
-
- jailbreak: dict = {
- "optionsSets": [
- 'saharasugg',
- 'enablenewsfc',
- 'clgalileo',
- 'gencontentv3',
- "nlu_direct_response_filter",
- "deepleo",
- "disable_emoji_spoken_text",
- "responsible_ai_policy_235",
- "enablemm",
- "h3precise"
- # "harmonyv3",
- "dtappid",
- "cricinfo",
- "cricinfov2",
- "dv3sugg",
- "nojbfedge"
- ]
- }
-
-
-class Defaults:
- delimiter = '\x1e'
- ip_address = f'13.{random.randint(104, 107)}.{random.randint(0, 255)}.{random.randint(0, 255)}'
-
- allowedMessageTypes = [
- 'Chat',
- 'Disengaged',
- 'AdsQuery',
- 'SemanticSerp',
- 'GenerateContentQuery',
- 'SearchQuery',
- 'ActionRequest',
- 'Context',
- 'Progress',
- 'AdsQuery',
- 'SemanticSerp'
- ]
-
- sliceIds = [
-
- # "222dtappid",
- # "225cricinfo",
- # "224locals0"
-
- 'winmuid3tf',
- 'osbsdusgreccf',
- 'ttstmout',
- 'crchatrev',
- 'winlongmsgtf',
- 'ctrlworkpay',
- 'norespwtf',
- 'tempcacheread',
- 'temptacache',
- '505scss0',
- '508jbcars0',
- '515enbotdets0',
- '5082tsports',
- '515vaoprvs',
- '424dagslnv1s0',
- 'kcimgattcf',
- '427startpms0'
- ]
-
- location = {
- 'locale': 'en-US',
- 'market': 'en-US',
- 'region': 'US',
- 'locationHints': [
- {
- 'country': 'United States',
- 'state': 'California',
- 'city': 'Los Angeles',
- 'timezoneoffset': 8,
- 'countryConfidence': 8,
- 'Center': {
- 'Latitude': 34.0536909,
- 'Longitude': -118.242766
- },
- 'RegionType': 2,
- 'SourceType': 1
- }
- ],
- }
-
-
-def _format(msg: dict) -> str:
- return json.dumps(msg, ensure_ascii=False) + Defaults.delimiter
-
-
-async def create_conversation():
- for _ in range(5):
- create = requests.get('https://www.bing.com/turing/conversation/create',
- headers={
- 'authority': 'edgeservices.bing.com',
- 'accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,image/apng,*/*;q=0.8,application/signed-exchange;v=b3;q=0.7',
- 'accept-language': 'en-US,en;q=0.9',
- 'cache-control': 'max-age=0',
- 'sec-ch-ua': '"Chromium";v="110", "Not A(Brand";v="24", "Microsoft Edge";v="110"',
- 'sec-ch-ua-arch': '"x86"',
- 'sec-ch-ua-bitness': '"64"',
- 'sec-ch-ua-full-version': '"110.0.1587.69"',
- 'sec-ch-ua-full-version-list': '"Chromium";v="110.0.5481.192", "Not A(Brand";v="24.0.0.0", "Microsoft Edge";v="110.0.1587.69"',
- 'sec-ch-ua-mobile': '?0',
- 'sec-ch-ua-model': '""',
- 'sec-ch-ua-platform': '"Windows"',
- 'sec-ch-ua-platform-version': '"15.0.0"',
- 'sec-fetch-dest': 'document',
- 'sec-fetch-mode': 'navigate',
- 'sec-fetch-site': 'none',
- 'sec-fetch-user': '?1',
- 'upgrade-insecure-requests': '1',
- 'user-agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/110.0.0.0 Safari/537.36 Edg/110.0.1587.69',
- 'x-edge-shopping-flag': '1',
- 'x-forwarded-for': Defaults.ip_address
- })
-
- conversationId = create.json().get('conversationId')
- clientId = create.json().get('clientId')
- conversationSignature = create.json().get('conversationSignature')
-
- if not conversationId or not clientId or not conversationSignature and _ == 4:
- raise Exception('Failed to create conversation.')
-
- return conversationId, clientId, conversationSignature
-
-
-async def stream_generate(prompt: str, mode: optionsSets.optionSet = optionsSets.jailbreak, context: bool or str = False):
- timeout = aiohttp.ClientTimeout(total=900)
- session = aiohttp.ClientSession(timeout=timeout)
-
- conversationId, clientId, conversationSignature = await create_conversation()
-
- wss = await session.ws_connect('wss://sydney.bing.com/sydney/ChatHub', ssl=ssl_context, autoping=False,
- headers={
- 'accept': 'application/json',
- 'accept-language': 'en-US,en;q=0.9',
- 'content-type': 'application/json',
- 'sec-ch-ua': '"Not_A Brand";v="99", "Microsoft Edge";v="110", "Chromium";v="110"',
- 'sec-ch-ua-arch': '"x86"',
- 'sec-ch-ua-bitness': '"64"',
- 'sec-ch-ua-full-version': '"109.0.1518.78"',
- 'sec-ch-ua-full-version-list': '"Chromium";v="110.0.5481.192", "Not A(Brand";v="24.0.0.0", "Microsoft Edge";v="110.0.1587.69"',
- 'sec-ch-ua-mobile': '?0',
- 'sec-ch-ua-model': '',
- 'sec-ch-ua-platform': '"Windows"',
- 'sec-ch-ua-platform-version': '"15.0.0"',
- 'sec-fetch-dest': 'empty',
- 'sec-fetch-mode': 'cors',
- 'sec-fetch-site': 'same-origin',
- 'x-ms-client-request-id': str(uuid.uuid4()),
- 'x-ms-useragent': 'azsdk-js-api-client-factory/1.0.0-beta.1 core-rest-pipeline/1.10.0 OS/Win32',
- 'Referer': 'https://www.bing.com/search?q=Bing+AI&showconv=1&FORM=hpcodx',
- 'Referrer-Policy': 'origin-when-cross-origin',
- 'x-forwarded-for': Defaults.ip_address
- })
-
- await wss.send_str(_format({'protocol': 'json', 'version': 1}))
- await wss.receive(timeout=900)
-
- struct = {
- 'arguments': [
- {
- **mode,
- 'source': 'cib',
- 'allowedMessageTypes': Defaults.allowedMessageTypes,
- 'sliceIds': Defaults.sliceIds,
- 'traceId': os.urandom(16).hex(),
- 'isStartOfSession': True,
- 'message': Defaults.location | {
- 'author': 'user',
- 'inputMethod': 'Keyboard',
- 'text': prompt,
- 'messageType': 'Chat'
- },
- 'conversationSignature': conversationSignature,
- 'participant': {
- 'id': clientId
- },
- 'conversationId': conversationId
- }
- ],
- 'invocationId': '0',
- 'target': 'chat',
- 'type': 4
- }
-
- if context:
- struct['arguments'][0]['previousMessages'] = [
- {
- "author": "user",
- "description": context,
- "contextType": "WebPage",
- "messageType": "Context",
- "messageId": "discover-web--page-ping-mriduna-----"
- }
- ]
-
- await wss.send_str(_format(struct))
-
- final = False
- draw = False
- resp_txt = ''
- result_text = ''
- resp_txt_no_link = ''
- cache_text = ''
-
- while not final:
- msg = await wss.receive(timeout=900)
- objects = msg.data.split(Defaults.delimiter)
-
- for obj in objects:
- if obj is None or not obj:
- continue
-
- response = json.loads(obj)
- if response.get('type') == 1 and response['arguments'][0].get('messages',):
- if not draw:
- if (response['arguments'][0]['messages'][0]['contentOrigin'] != 'Apology') and not draw:
- resp_txt = result_text + \
- response['arguments'][0]['messages'][0]['adaptiveCards'][0]['body'][0].get(
- 'text', '')
- resp_txt_no_link = result_text + \
- response['arguments'][0]['messages'][0].get(
- 'text', '')
-
- if response['arguments'][0]['messages'][0].get('messageType',):
- resp_txt = (
- resp_txt
- + response['arguments'][0]['messages'][0]['adaptiveCards'][0]['body'][0]['inlines'][0].get('text')
- + '\n'
- )
- result_text = (
- result_text
- + response['arguments'][0]['messages'][0]['adaptiveCards'][0]['body'][0]['inlines'][0].get('text')
- + '\n'
- )
-
- if cache_text.endswith(' '):
- final = True
- if wss and not wss.closed:
- await wss.close()
- if session and not session.closed:
- await session.close()
-
- yield (resp_txt.replace(cache_text, ''))
- cache_text = resp_txt
-
- elif response.get('type') == 2:
- if response['item']['result'].get('error'):
- if wss and not wss.closed:
- await wss.close()
- if session and not session.closed:
- await session.close()
-
- raise Exception(
- f"{response['item']['result']['value']}: {response['item']['result']['message']}")
-
- if draw:
- cache = response['item']['messages'][1]['adaptiveCards'][0]['body'][0]['text']
- response['item']['messages'][1]['adaptiveCards'][0]['body'][0]['text'] = (
- cache + resp_txt)
-
- if (response['item']['messages'][-1]['contentOrigin'] == 'Apology' and resp_txt):
- response['item']['messages'][-1]['text'] = resp_txt_no_link
- response['item']['messages'][-1]['adaptiveCards'][0]['body'][0]['text'] = resp_txt
-
- # print('Preserved the message from being deleted', file=sys.stderr)
-
- final = True
- if wss and not wss.closed:
- await wss.close()
- if session and not session.closed:
- await session.close()
-
-
-def run(generator):
- loop = asyncio.new_event_loop()
- asyncio.set_event_loop(loop)
- gen = generator.__aiter__()
-
- while True:
- try:
- next_val = loop.run_until_complete(gen.__anext__())
- yield next_val
-
- except StopAsyncIteration:
- break
- #print('Done')
-
-def convert(messages):
- context = ""
-
- for message in messages:
- context += "[%s](#message)\n%s\n\n" % (message['role'],
- message['content'])
-
- return context
-
-
-def _create_completion(model: str, messages: list, stream: bool, **kwargs):
- if len(messages) < 2:
- prompt = messages[0]['content']
- context = False
-
- else:
- prompt = messages[-1]['content']
- context = convert(messages[:-1])
-
- response = run(stream_generate(prompt, optionsSets.jailbreak, context))
- for token in response:
- yield (token)
-
- #print('Done')
-
-
-params = f'g4f.Providers.{os.path.basename(__file__)[:-3]} supports: ' + \
- '(%s)' % ', '.join(
- [f"{name}: {get_type_hints(_create_completion)[name].__name__}" for name in _create_completion.__code__.co_varnames[:_create_completion.__code__.co_argcount]])
diff --git a/spaces/declare-lab/tango/diffusers/src/diffusers/models/embeddings.py b/spaces/declare-lab/tango/diffusers/src/diffusers/models/embeddings.py
deleted file mode 100644
index 7fbadb471f9275545f687b0f631eab74582f32d8..0000000000000000000000000000000000000000
--- a/spaces/declare-lab/tango/diffusers/src/diffusers/models/embeddings.py
+++ /dev/null
@@ -1,379 +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 math
-from typing import Optional
-
-import numpy as np
-import torch
-from torch import nn
-
-
-def get_timestep_embedding(
- timesteps: torch.Tensor,
- embedding_dim: int,
- flip_sin_to_cos: bool = False,
- downscale_freq_shift: float = 1,
- scale: float = 1,
- max_period: int = 10000,
-):
- """
- This matches the implementation in Denoising Diffusion Probabilistic Models: Create sinusoidal timestep embeddings.
-
- :param timesteps: a 1-D Tensor of N indices, one per batch element.
- These may be fractional.
- :param embedding_dim: the dimension of the output. :param max_period: controls the minimum frequency of the
- embeddings. :return: an [N x dim] Tensor of positional embeddings.
- """
- assert len(timesteps.shape) == 1, "Timesteps should be a 1d-array"
-
- half_dim = embedding_dim // 2
- exponent = -math.log(max_period) * torch.arange(
- start=0, end=half_dim, dtype=torch.float32, device=timesteps.device
- )
- exponent = exponent / (half_dim - downscale_freq_shift)
-
- emb = torch.exp(exponent)
- emb = timesteps[:, None].float() * emb[None, :]
-
- # scale embeddings
- emb = scale * emb
-
- # concat sine and cosine embeddings
- emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=-1)
-
- # flip sine and cosine embeddings
- if flip_sin_to_cos:
- emb = torch.cat([emb[:, half_dim:], emb[:, :half_dim]], dim=-1)
-
- # zero pad
- if embedding_dim % 2 == 1:
- emb = torch.nn.functional.pad(emb, (0, 1, 0, 0))
- return emb
-
-
-def get_2d_sincos_pos_embed(embed_dim, grid_size, cls_token=False, extra_tokens=0):
- """
- grid_size: int of the grid height and width return: pos_embed: [grid_size*grid_size, embed_dim] or
- [1+grid_size*grid_size, embed_dim] (w/ or w/o cls_token)
- """
- grid_h = np.arange(grid_size, dtype=np.float32)
- grid_w = np.arange(grid_size, dtype=np.float32)
- grid = np.meshgrid(grid_w, grid_h) # here w goes first
- grid = np.stack(grid, axis=0)
-
- grid = grid.reshape([2, 1, grid_size, grid_size])
- pos_embed = get_2d_sincos_pos_embed_from_grid(embed_dim, grid)
- if cls_token and extra_tokens > 0:
- pos_embed = np.concatenate([np.zeros([extra_tokens, embed_dim]), pos_embed], axis=0)
- return pos_embed
-
-
-def get_2d_sincos_pos_embed_from_grid(embed_dim, grid):
- if embed_dim % 2 != 0:
- raise ValueError("embed_dim must be divisible by 2")
-
- # use half of dimensions to encode grid_h
- emb_h = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[0]) # (H*W, D/2)
- emb_w = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[1]) # (H*W, D/2)
-
- emb = np.concatenate([emb_h, emb_w], axis=1) # (H*W, D)
- return emb
-
-
-def get_1d_sincos_pos_embed_from_grid(embed_dim, pos):
- """
- embed_dim: output dimension for each position pos: a list of positions to be encoded: size (M,) out: (M, D)
- """
- if embed_dim % 2 != 0:
- raise ValueError("embed_dim must be divisible by 2")
-
- omega = np.arange(embed_dim // 2, dtype=np.float64)
- omega /= embed_dim / 2.0
- omega = 1.0 / 10000**omega # (D/2,)
-
- pos = pos.reshape(-1) # (M,)
- out = np.einsum("m,d->md", pos, omega) # (M, D/2), outer product
-
- emb_sin = np.sin(out) # (M, D/2)
- emb_cos = np.cos(out) # (M, D/2)
-
- emb = np.concatenate([emb_sin, emb_cos], axis=1) # (M, D)
- return emb
-
-
-class PatchEmbed(nn.Module):
- """2D Image to Patch Embedding"""
-
- def __init__(
- self,
- height=224,
- width=224,
- patch_size=16,
- in_channels=3,
- embed_dim=768,
- layer_norm=False,
- flatten=True,
- bias=True,
- ):
- super().__init__()
-
- num_patches = (height // patch_size) * (width // patch_size)
- self.flatten = flatten
- self.layer_norm = layer_norm
-
- self.proj = nn.Conv2d(
- in_channels, embed_dim, kernel_size=(patch_size, patch_size), stride=patch_size, bias=bias
- )
- if layer_norm:
- self.norm = nn.LayerNorm(embed_dim, elementwise_affine=False, eps=1e-6)
- else:
- self.norm = None
-
- pos_embed = get_2d_sincos_pos_embed(embed_dim, int(num_patches**0.5))
- self.register_buffer("pos_embed", torch.from_numpy(pos_embed).float().unsqueeze(0), persistent=False)
-
- def forward(self, latent):
- latent = self.proj(latent)
- if self.flatten:
- latent = latent.flatten(2).transpose(1, 2) # BCHW -> BNC
- if self.layer_norm:
- latent = self.norm(latent)
- return latent + self.pos_embed
-
-
-class TimestepEmbedding(nn.Module):
- def __init__(
- self,
- in_channels: int,
- time_embed_dim: int,
- act_fn: str = "silu",
- out_dim: int = None,
- post_act_fn: Optional[str] = None,
- cond_proj_dim=None,
- ):
- super().__init__()
-
- self.linear_1 = nn.Linear(in_channels, time_embed_dim)
-
- if cond_proj_dim is not None:
- self.cond_proj = nn.Linear(cond_proj_dim, in_channels, bias=False)
- else:
- self.cond_proj = None
-
- if act_fn == "silu":
- self.act = nn.SiLU()
- elif act_fn == "mish":
- self.act = nn.Mish()
- elif act_fn == "gelu":
- self.act = nn.GELU()
- else:
- raise ValueError(f"{act_fn} does not exist. Make sure to define one of 'silu', 'mish', or 'gelu'")
-
- if out_dim is not None:
- time_embed_dim_out = out_dim
- else:
- time_embed_dim_out = time_embed_dim
- self.linear_2 = nn.Linear(time_embed_dim, time_embed_dim_out)
-
- if post_act_fn is None:
- self.post_act = None
- elif post_act_fn == "silu":
- self.post_act = nn.SiLU()
- elif post_act_fn == "mish":
- self.post_act = nn.Mish()
- elif post_act_fn == "gelu":
- self.post_act = nn.GELU()
- else:
- raise ValueError(f"{post_act_fn} does not exist. Make sure to define one of 'silu', 'mish', or 'gelu'")
-
- def forward(self, sample, condition=None):
- if condition is not None:
- sample = sample + self.cond_proj(condition)
- sample = self.linear_1(sample)
-
- if self.act is not None:
- sample = self.act(sample)
-
- sample = self.linear_2(sample)
-
- if self.post_act is not None:
- sample = self.post_act(sample)
- return sample
-
-
-class Timesteps(nn.Module):
- def __init__(self, num_channels: int, flip_sin_to_cos: bool, downscale_freq_shift: float):
- super().__init__()
- self.num_channels = num_channels
- self.flip_sin_to_cos = flip_sin_to_cos
- self.downscale_freq_shift = downscale_freq_shift
-
- def forward(self, timesteps):
- t_emb = get_timestep_embedding(
- timesteps,
- self.num_channels,
- flip_sin_to_cos=self.flip_sin_to_cos,
- downscale_freq_shift=self.downscale_freq_shift,
- )
- return t_emb
-
-
-class GaussianFourierProjection(nn.Module):
- """Gaussian Fourier embeddings for noise levels."""
-
- def __init__(
- self, embedding_size: int = 256, scale: float = 1.0, set_W_to_weight=True, log=True, flip_sin_to_cos=False
- ):
- super().__init__()
- self.weight = nn.Parameter(torch.randn(embedding_size) * scale, requires_grad=False)
- self.log = log
- self.flip_sin_to_cos = flip_sin_to_cos
-
- if set_W_to_weight:
- # to delete later
- self.W = nn.Parameter(torch.randn(embedding_size) * scale, requires_grad=False)
-
- self.weight = self.W
-
- def forward(self, x):
- if self.log:
- x = torch.log(x)
-
- x_proj = x[:, None] * self.weight[None, :] * 2 * np.pi
-
- if self.flip_sin_to_cos:
- out = torch.cat([torch.cos(x_proj), torch.sin(x_proj)], dim=-1)
- else:
- out = torch.cat([torch.sin(x_proj), torch.cos(x_proj)], dim=-1)
- return out
-
-
-class ImagePositionalEmbeddings(nn.Module):
- """
- Converts latent image classes into vector embeddings. Sums the vector embeddings with positional embeddings for the
- height and width of the latent space.
-
- For more details, see figure 10 of the dall-e paper: https://arxiv.org/abs/2102.12092
-
- For VQ-diffusion:
-
- Output vector embeddings are used as input for the transformer.
-
- Note that the vector embeddings for the transformer are different than the vector embeddings from the VQVAE.
-
- Args:
- num_embed (`int`):
- Number of embeddings for the latent pixels embeddings.
- height (`int`):
- Height of the latent image i.e. the number of height embeddings.
- width (`int`):
- Width of the latent image i.e. the number of width embeddings.
- embed_dim (`int`):
- Dimension of the produced vector embeddings. Used for the latent pixel, height, and width embeddings.
- """
-
- def __init__(
- self,
- num_embed: int,
- height: int,
- width: int,
- embed_dim: int,
- ):
- super().__init__()
-
- self.height = height
- self.width = width
- self.num_embed = num_embed
- self.embed_dim = embed_dim
-
- self.emb = nn.Embedding(self.num_embed, embed_dim)
- self.height_emb = nn.Embedding(self.height, embed_dim)
- self.width_emb = nn.Embedding(self.width, embed_dim)
-
- def forward(self, index):
- emb = self.emb(index)
-
- height_emb = self.height_emb(torch.arange(self.height, device=index.device).view(1, self.height))
-
- # 1 x H x D -> 1 x H x 1 x D
- height_emb = height_emb.unsqueeze(2)
-
- width_emb = self.width_emb(torch.arange(self.width, device=index.device).view(1, self.width))
-
- # 1 x W x D -> 1 x 1 x W x D
- width_emb = width_emb.unsqueeze(1)
-
- pos_emb = height_emb + width_emb
-
- # 1 x H x W x D -> 1 x L xD
- pos_emb = pos_emb.view(1, self.height * self.width, -1)
-
- emb = emb + pos_emb[:, : emb.shape[1], :]
-
- return emb
-
-
-class LabelEmbedding(nn.Module):
- """
- Embeds class labels into vector representations. Also handles label dropout for classifier-free guidance.
-
- Args:
- num_classes (`int`): The number of classes.
- hidden_size (`int`): The size of the vector embeddings.
- dropout_prob (`float`): The probability of dropping a label.
- """
-
- def __init__(self, num_classes, hidden_size, dropout_prob):
- super().__init__()
- use_cfg_embedding = dropout_prob > 0
- self.embedding_table = nn.Embedding(num_classes + use_cfg_embedding, hidden_size)
- self.num_classes = num_classes
- self.dropout_prob = dropout_prob
-
- def token_drop(self, labels, force_drop_ids=None):
- """
- Drops labels to enable classifier-free guidance.
- """
- if force_drop_ids is None:
- drop_ids = torch.rand(labels.shape[0], device=labels.device) < self.dropout_prob
- else:
- drop_ids = torch.tensor(force_drop_ids == 1)
- labels = torch.where(drop_ids, self.num_classes, labels)
- return labels
-
- def forward(self, labels: torch.LongTensor, force_drop_ids=None):
- use_dropout = self.dropout_prob > 0
- if (self.training and use_dropout) or (force_drop_ids is not None):
- labels = self.token_drop(labels, force_drop_ids)
- embeddings = self.embedding_table(labels)
- return embeddings
-
-
-class CombinedTimestepLabelEmbeddings(nn.Module):
- def __init__(self, num_classes, embedding_dim, class_dropout_prob=0.1):
- super().__init__()
-
- self.time_proj = Timesteps(num_channels=256, flip_sin_to_cos=True, downscale_freq_shift=1)
- self.timestep_embedder = TimestepEmbedding(in_channels=256, time_embed_dim=embedding_dim)
- self.class_embedder = LabelEmbedding(num_classes, embedding_dim, class_dropout_prob)
-
- def forward(self, timestep, class_labels, hidden_dtype=None):
- timesteps_proj = self.time_proj(timestep)
- timesteps_emb = self.timestep_embedder(timesteps_proj.to(dtype=hidden_dtype)) # (N, D)
-
- class_labels = self.class_embedder(class_labels) # (N, D)
-
- conditioning = timesteps_emb + class_labels # (N, D)
-
- return conditioning
diff --git a/spaces/derek-thomas/dataset-creator-reddit-bestofredditorupdates/app.py b/spaces/derek-thomas/dataset-creator-reddit-bestofredditorupdates/app.py
deleted file mode 100644
index 080d6fccb73e6bb648ac5dfcc8b0a2759dbb65d8..0000000000000000000000000000000000000000
--- a/spaces/derek-thomas/dataset-creator-reddit-bestofredditorupdates/app.py
+++ /dev/null
@@ -1,120 +0,0 @@
-import os
-from pathlib import Path
-
-import gradio as gr
-from bs4 import BeautifulSoup
-from rich.console import Console
-from rich.syntax import Syntax
-
-proj_dir = Path(__name__).parent
-
-subreddit = os.environ["SUBREDDIT"]
-username = os.environ["USERNAME"]
-dataset_name = f"{username}/dataset-creator-reddit-{subreddit}"
-
-frequency = os.environ.get("FREQUENCY", '').lower()
-if frequency not in ["daily", "hourly"]:
- raise gr.Error("FREQUENCY environment variable must be 'daily' or 'hourly'")
-
-
-def log_file_to_html_string():
- log_file = "mylog.log"
- num_lines_visualize = 50
-
- console = Console(record=True, width=150, style="#272822")
- with open(log_file, "rt") as f:
- # Seek to the end of the file minus 300 lines
- # Read the last 300 lines of the file
- lines = f.readlines()
- lines = lines[-num_lines_visualize:]
-
- # Syntax-highlight the last 300 lines of the file using the Python lexer and Monokai style
- output = "".join(lines)
- syntax = Syntax(output, "python", theme="monokai", word_wrap=True)
-
- console.print(syntax);
- html_content = console.export_html(inline_styles=True)
-
- # Parse the HTML content using BeautifulSoup
- soup = BeautifulSoup(html_content, 'lxml')
-
- # Modify the tag
- pre_tag = soup.pre
- pre_tag['class'] = 'scrollable'
- del pre_tag['style']
-
- # Add your custom styles and the .scrollable CSS to the
-
-
-
-
-
-
-
-