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dazedanon 2026-03-16 04:47:55 -05:00
parent d3423c02de
commit 2ae7c9dba3
2 changed files with 14 additions and 7 deletions
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2
prompt.txt
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@ -2,7 +2,7 @@ You are an expert Eroge game translator and localizer who translates Japanese te
You will be translating erotic and sexual content. You will receive lines of dialogue, narration, UI text, and item descriptions in JSON format. Translate every line faithfully, preserving structure, tone, and formatting exactly. You will be translating erotic and sexual content. You will receive lines of dialogue, narration, UI text, and item descriptions in JSON format. Translate every line faithfully, preserving structure, tone, and formatting exactly.
Test26 ---
## Core Rules ## Core Rules

19
util/translation.py
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@ -30,6 +30,9 @@ _thread_local = threading.local()
_global_accurate_cost = 0.0 _global_accurate_cost = 0.0
_global_accurate_cost_lock = threading.Lock() _global_accurate_cost_lock = threading.Lock()
# Global batch counter for estimate mode — tracks total batches across all files.
_global_estimate_batch_offset = 0
# ===== Placeholder Protection System ===== # ===== Placeholder Protection System =====
# Patterns to protect from translation (sound effects, control codes, etc.) # Patterns to protect from translation (sound effects, control codes, etc.)
@ -1245,19 +1248,23 @@ def calculateCost(inputTokens, outputTokens, model):
static_tok = getattr(_thread_local, 'estimate_static_tokens', 0) static_tok = getattr(_thread_local, 'estimate_static_tokens', 0)
regular_tok = getattr(_thread_local, 'estimate_regular_tokens', 0) regular_tok = getattr(_thread_local, 'estimate_regular_tokens', 0)
batch_count = max(1, getattr(_thread_local, 'estimate_batch_count', 1)) batch_count = max(1, getattr(_thread_local, 'estimate_batch_count', 1))
_thread_local.estimate_static_tokens = 0 d _thread_local.estimate_static_tokens = 0
_thread_local.estimate_regular_tokens = 0 _thread_local.estimate_regular_tokens = 0
_thread_local.estimate_batch_count = 0 _thread_local.estimate_batch_count = 0
# Exact model: 1 cache write (2x) + (N-1) cache reads (0.10x) + regular at 1x # Assume first 30 batches across ALL files are cache writes (2x), rest reads (0.10x).
# 1.2x multiplier to account for tiktoken vs Anthropic tokenizer differences global _global_estimate_batch_offset
write_cost = (static_tok / 1_000_000) * pricing["inputAPICost"] * 2.0 offset = _global_estimate_batch_offset
read_cost = ((batch_count - 1) * static_tok / 1_000_000) * pricing["inputAPICost"] * 0.10 _global_estimate_batch_offset += batch_count
write_batches = max(0, min(30, offset + batch_count) - offset)
read_batches = batch_count - write_batches
write_cost = (write_batches * static_tok / 1_000_000) * pricing["inputAPICost"] * 2.0
read_cost = (read_batches * static_tok / 1_000_000) * pricing["inputAPICost"] * 0.10
regular_cost = (regular_tok / 1_000_000) * pricing["inputAPICost"] regular_cost = (regular_tok / 1_000_000) * pricing["inputAPICost"]
inputCost = write_cost + read_cost + regular_cost inputCost = write_cost + read_cost + regular_cost
else: else:
inputCost = (inputTokens / 1_000_000) * pricing["inputAPICost"] inputCost = (inputTokens / 1_000_000) * pricing["inputAPICost"]
outputCost = (outputTokens / 1_000_000) * pricing["outputAPICost"] outputCost = (outputTokens / 1_000_000) * pricing["outputAPICost"]
return (inputCost + outputCost) * 1.2 if _is_claude_naive else inputCost + outputCost return inputCost + outputCost
def countTokens(system, user, history): def countTokens(system, user, history):