mirror of
https://github.com/ggerganov/llama.cpp.git
synced 2024-10-29 22:20:15 +01:00
fa79495bb4
* llama : fix mpt and olmo pre-tokenizer * llama : pre-tokenize non-special user-defined tokens first * llama : fix detection of control-like user-defined tokens * convert_hf : identify which user-defined tokens are control tokens Only used in _set_vocab_gpt2() for now. * convert_hf : identify more added control tokens for SPM tokenziers This makes Gemma and Gemma-2 tokenize pretty much EVERYTHING correctly, including HTML tags and consecutive spaces, but it unfortunately requires model re-conversion. There seems to be a weird behavior of the HF tokenizer for Gemma, which prefers to use the 16-space token over more lengthy space tokens, while using the SentencePiece tokenizer does not do this. (the implementation in llama.cpp has the same behavior as SentencePiece) * llama : fix wrong pre-tokenization of byte tokens * llama : fix Viking pre-tokenizer regex The order was previously wrong, which caused errors in some tests. * llama : fix command-r detokenization * convert_hf : reduce usages of the UNKNOWN token type * llama : add UNKNOWN tokens in the special tokens cache * convert_hf : reduce usages of UNKNOWN for InternLM2 This makes the changes from #8321 more consistent with the other changes made here. * test-tokenizer-random : reduce potential confilcts with #8379 * test-tokenizer-random : add a failing edge case for falcon
567 lines
22 KiB
Python
567 lines
22 KiB
Python
# Test libllama tokenizer == AutoTokenizer.
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# Brute force random words/text generation.
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#
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# Sample usage:
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#
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# python3 tests/test-tokenizer-random.py ./models/ggml-vocab-llama-bpe.gguf ./models/tokenizers/llama-bpe
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#
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from __future__ import annotations
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import time
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import logging
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import argparse
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import subprocess
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import random
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import unicodedata
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from pathlib import Path
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from typing import Any, Iterator, cast
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from typing_extensions import Buffer
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import cffi
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from transformers import AutoTokenizer, PreTrainedTokenizer
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logger = logging.getLogger("test-tokenizer-random")
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class LibLlama:
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DEFAULT_PATH_LLAMA_H = "./include/llama.h"
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DEFAULT_PATH_INCLUDES = ["./ggml/include/", "./include/"]
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DEFAULT_PATH_LIBLLAMA = "./build/src/libllama.so" # CMakeLists.txt: BUILD_SHARED_LIBS ON
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def __init__(self, path_llama_h: str | None = None, path_includes: list[str] = [], path_libllama: str | None = None):
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path_llama_h = path_llama_h or self.DEFAULT_PATH_LLAMA_H
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path_includes = path_includes or self.DEFAULT_PATH_INCLUDES
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path_libllama = path_libllama or self.DEFAULT_PATH_LIBLLAMA
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(self.ffi, self.lib) = self._load_libllama_cffi(path_llama_h, path_includes, path_libllama)
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self.lib.llama_backend_init()
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def _load_libllama_cffi(self, path_llama_h: str, path_includes: list[str], path_libllama: str) -> tuple[cffi.FFI, Any]:
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cmd = ["gcc", "-O0", "-E", "-P", "-D__restrict=", "-D__attribute__(x)=", "-D__asm__(x)="]
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cmd += ["-I" + path for path in path_includes] + [path_llama_h]
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res = subprocess.run(cmd, stdout=subprocess.PIPE)
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assert (res.returncode == 0)
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source = res.stdout.decode()
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ffi = cffi.FFI()
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if True: # workarounds for pycparser
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source = "typedef struct { } __builtin_va_list;" + "\n" + source
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source = source.replace("sizeof (int)", str(ffi.sizeof("int")))
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source = source.replace("sizeof (void *)", str(ffi.sizeof("void*")))
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source = source.replace("sizeof (size_t)", str(ffi.sizeof("size_t")))
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source = source.replace("sizeof(int32_t)", str(ffi.sizeof("int32_t")))
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ffi.cdef(source, override=True)
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lib = ffi.dlopen(path_libllama)
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return (ffi, lib)
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def model_default_params(self, **kwargs):
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mparams = self.lib.llama_model_default_params()
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for k, v in kwargs.items():
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setattr(mparams, k, v)
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return mparams
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def context_default_params(self, **kwargs):
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cparams = self.lib.llama_context_default_params()
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for k, v in kwargs.items():
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setattr(cparams, k, v)
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return cparams
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class LibLlamaModel:
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def __init__(self, libllama: LibLlama, path_model: str, mparams={}, cparams={}):
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self.lib: Any = libllama.lib
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self.ffi = libllama.ffi
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if isinstance(mparams, dict):
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mparams = libllama.model_default_params(**mparams)
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self.model = self.lib.llama_load_model_from_file(path_model.encode(), mparams)
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if not self.model:
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raise RuntimeError("error: failed to load model '%s'" % path_model)
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if isinstance(cparams, dict):
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cparams = libllama.context_default_params(**cparams)
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self.ctx = self.lib.llama_new_context_with_model(self.model, cparams)
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if not self.ctx:
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raise RuntimeError("error: failed to create context for model '%s'" % path_model)
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n_tokens_max = self.lib.llama_n_ctx(self.ctx)
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self.token_ids = self.ffi.new("llama_token[]", n_tokens_max)
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self.text_buff = self.ffi.new("uint8_t[]", 1024)
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def free(self):
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if self.ctx:
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self.lib.llama_free(self.ctx)
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if self.model:
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self.lib.llama_free_model(self.model)
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self.ctx = None
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self.model = None
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self.lib = None
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def tokenize(self, text: str, add_special: bool = False, parse_special: bool = False) -> list[int]:
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encoded_text: bytes = text.encode("utf-8")
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num = self.lib.llama_tokenize(self.model, encoded_text, len(encoded_text), self.token_ids, len(self.token_ids), add_special, parse_special)
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while num < 0 and len(self.token_ids) < (16 << 20):
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self.token_ids = self.ffi.new("llama_token[]", -2 * num)
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num = self.lib.llama_tokenize(self.model, encoded_text, len(encoded_text), self.token_ids, len(self.token_ids), add_special, parse_special)
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return list(self.token_ids[0:num])
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def detokenize(self, ids: list[int], remove_special: bool = False, unparse_special: bool = False) -> str:
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if len(self.token_ids) < len(ids):
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self.token_ids = self.ffi.new("llama_token[]", 2 * len(ids))
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for i, id in enumerate(ids):
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self.token_ids[i] = id
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num = self.lib.llama_detokenize(self.model, self.token_ids, len(ids), self.text_buff, len(self.text_buff), remove_special, unparse_special)
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while num < 0 and len(self.text_buff) < (16 << 20):
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self.text_buff = self.ffi.new("uint8_t[]", -2 * num)
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num = self.lib.llama_detokenize(self.model, self.token_ids, len(ids), self.text_buff, len(self.text_buff), remove_special, unparse_special)
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return str(cast(Buffer, self.ffi.buffer(self.text_buff, num)), encoding="utf-8", errors="replace") # replace errors with '\uFFFD'
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class Tokenizer:
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def encode(self, text: str) -> list[int]:
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raise NotImplementedError
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def decode(self, ids: list[int]) -> str:
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raise NotImplementedError
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class TokenizerGroundtruth (Tokenizer):
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def __init__(self, dir_tokenizer: str):
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self.model: PreTrainedTokenizer = AutoTokenizer.from_pretrained(dir_tokenizer)
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# guess BOS and EOS
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ids = self.encode("a")
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assert 1 <= len(ids) <= 3
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add_bos_token = len(ids) > 1 and self.model.bos_token_id == ids[0]
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add_eos_token = len(ids) > 1 and self.model.eos_token_id == ids[-1]
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self.add_bos_token = getattr(self.model, "add_bos_token", add_bos_token)
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self.add_eos_token = getattr(self.model, "add_eos_token", add_eos_token)
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# build vocab
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tokens = list(self.model.get_vocab().values())
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self.vocab = self.model.batch_decode(tokens, skip_special_tokens=True)
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self.vocab = list(sorted(self.vocab))
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# tokens and lists
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self.special_tokens = list(self.model.all_special_tokens)
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self.added_tokens = self.model.batch_decode(self.model.added_tokens_encoder.values(), skip_special_tokens=False)
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self.bos_token = self.model.bos_token
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self.eos_token = self.model.eos_token
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def encode(self, text: str) -> list[int]:
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return self.model.encode(text, add_special_tokens=True)
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def decode(self, ids: list[int]) -> str:
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return self.model.decode(ids, skip_special_tokens=False)
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class TokenizerLlamaCpp (Tokenizer):
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libllama: LibLlama | None = None
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def __init__(self, vocab_file: str):
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if not self.libllama:
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self.libllama = LibLlama()
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self.model = LibLlamaModel(self.libllama, vocab_file, mparams=dict(vocab_only=True), cparams=dict(n_ctx=4096))
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def encode(self, text: str) -> list[int]:
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return self.model.tokenize(text, add_special=True, parse_special=True)
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def decode(self, ids: list[int]) -> str:
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return self.model.detokenize(ids, remove_special=False, unparse_special=True)
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def generator_custom_text() -> Iterator[str]:
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"""General tests"""
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yield from [
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"",
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" ",
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" ",
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" ",
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"\t",
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"\n",
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"\n\n",
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"\n\n\n",
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"\t\n",
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"Hello world",
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" Hello world",
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"Hello World",
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" Hello World",
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" Hello World!",
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"Hello, world!",
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" Hello, world!",
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" this is 🦙.cpp",
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"w048 7tuijk dsdfhu",
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"нещо на Български",
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"កាន់តែពិសេសអាចខលចេញ",
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"🚀 (normal) 😶🌫️ (multiple emojis concatenated) ✅ (only emoji that has its own token)",
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"Hello",
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" Hello",
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" Hello",
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" Hello",
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" Hello",
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" Hello\n Hello",
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" (",
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"\n =",
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"' era",
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"Hello, y'all! How are you 😁 ?我想在apple工作1314151天～",
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"3",
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"33",
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"333",
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"3333",
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"33333",
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"333333",
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"3333333",
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"33333333",
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"333333333",
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]
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def generator_custom_text_edge_cases() -> Iterator[str]:
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"""Edge cases found while debugging"""
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yield from [
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'\x1f-a', # unicode_ranges_control, {0x00001C, 0x00001F}
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'¼-a', # unicode_ranges_digit, 0x00BC
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'½-a', # unicode_ranges_digit, 0x00BD
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'¾-a', # unicode_ranges_digit, 0x00BE
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'a 〇b', # unicode_ranges_digit, 0x3007
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'Ⅵ-a', # unicode_ranges_digit, {0x00002150, 0x0000218F} // Number Forms
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'\uFEFF//', # unicode_ranges_control, 0xFEFF (BOM)
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'Cửa Việt', # llama-3, ignore_merges = true
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'<s>a', # Phi-3 fail
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'<unk><|endoftext|><s>', # Phi-3 fail
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'a\na', # bert fail
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'"`', # falcon
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' \u2e4e', # falcon
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'\n\x0b ', # falcon
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'a\xa0\xa0\x00b', # jina-v2-es
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'one <mask>', # jina-v2-es <mask> lstrip=true
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'a </s> b', # rstrip phi-3
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'a <mask> b', # lstrip jina-v2
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'\xa0aC', # deepseek
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'\u2029 \uA3E4', # deepseek-llm
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"a ?",
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'å', # mpt
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'\U000ac517', # utf-8 encode error, falcon
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'\U000522f4', # utf-8 encode error, starcoder
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"<s><s><unk><s>a<s>b<s>c<unk>d<unk></s>",
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"<s> <s> <unk><s>a<s>b<s>c<unk>d<unk></s>",
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]
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def generator_vocab_words(tokenizer: TokenizerGroundtruth) -> Iterator[str]:
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"""Brute force check all vocab words"""
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yield from tokenizer.vocab
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def generator_ascii_lr_strip() -> Iterator[str]:
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WHITESPACES = ["", " ", " "]
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CHARACTERS = list(chr(i) for i in range(1, 0x80)) + [""]
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for char1 in CHARACTERS:
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for char2 in CHARACTERS:
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for lstrip in WHITESPACES:
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for rstrip in WHITESPACES:
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yield lstrip + char1 + char2 + rstrip
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yield lstrip + char1 + rstrip + char2
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yield char1 + lstrip + char2 + rstrip
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def generator_apostrophe() -> Iterator[str]:
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WHITESPACES = ["", " ", " "]
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CHARACTERS = list(chr(i) for i in range(1, 0x80)) + [""]
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for char1 in CHARACTERS:
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for char2 in CHARACTERS:
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for lstrip in WHITESPACES:
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for rstrip in WHITESPACES:
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yield char1 + lstrip + "'" + rstrip + char2
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yield char1 + char2 + lstrip + "'" + rstrip + "z"
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yield "a" + lstrip + "'" + rstrip + char1 + char2
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def generator_added_lr_strip(tokenizer: TokenizerGroundtruth) -> Iterator[str]:
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WHITESPACES = ["", " ", " ", "\n", "\r\n", "\n\n", "\t", "\t\t"]
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all_tokens = list(sorted(set(tokenizer.special_tokens + tokenizer.added_tokens)))
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for token in all_tokens:
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for lstrip in WHITESPACES:
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for rstrip in WHITESPACES:
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yield lstrip + token + rstrip
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yield "a" + lstrip + token + rstrip
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yield lstrip + token + rstrip + "z"
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yield "a" + lstrip + token + rstrip + "z"
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def generator_random_added_tokens(tokenizer: TokenizerGroundtruth, iterations=100) -> Iterator[str]:
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separations = [" ", "\n", "\t", "-", "!", "one", "1", "<s>", "</s>"]
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all_tokens = list(sorted(set(tokenizer.special_tokens + tokenizer.added_tokens + separations)))
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rand = random.Random()
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for m in range(iterations):
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rand.seed(m)
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words = rand.choices(all_tokens, k=500)
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if words and words[0] == tokenizer.bos_token: # skip spam warning of double BOS
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while len(words) > 1 and words[1] == tokenizer.bos_token: # leave one starting BOS
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words.pop(0)
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if tokenizer.add_bos_token: # drop all starting BOS
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words.pop(0)
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if words and words[-1] == tokenizer.eos_token: # skip spam warning of double EOS
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while len(words) > 1 and words[-2] == tokenizer.eos_token: # leave one trailing EOS
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words.pop(-1)
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if tokenizer.add_bos_token: # drop all trailing EOS
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words.pop(-1)
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yield "".join(words)
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def generator_random_chars(iterations=100) -> Iterator[str]:
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"""Brute force random text with simple characters"""
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NUM_WORDS = 400
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WHITESPACES = list(" " * 20 + "\n" * 5 + "\r\n" * 5 + "\t" * 5)
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CHARS = list(sorted(set("""
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ABCDEFGHIJKLMNOPQRSTUVWXYZ
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abcdefghijklmnopqrstuvwxyz
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ÁÉÍÓÚÀÈÌÒÙÂÊÎÔÛÄËÏÖÜ
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áéíóúàèìòùâêîôûäëïöü
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.-,*/-+ª!"·$%&/()=?¿[]{}<>\\|@#~½¬~;:_
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""")))
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rand = random.Random()
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for m in range(iterations):
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rand.seed(m)
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text = []
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for _ in range(NUM_WORDS):
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k = rand.randint(1, 7)
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word = rand.choices(CHARS, k=k)
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word.append(rand.choice(WHITESPACES))
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text.append("".join(word))
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yield "".join(text)
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def generator_unicodes() -> Iterator[str]:
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"""Iterate unicode characters"""
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MAX_CODEPOINTS = 0x30000 # 0x110000
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def _valid(cpt):
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if cpt >= 0x30000: # unassigned and supplementary
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return False
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# if cpt == 0x2029: # deepseek-llm
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# return False
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if unicodedata.category(chr(cpt)) in ("Cn", "Cs", "Co"): # undefined, surrogates, private
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return False
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return True
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characters = [chr(cpt) for cpt in range(0, MAX_CODEPOINTS) if _valid(cpt)]
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yield from characters
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def generator_random_unicodes(iterations=100) -> Iterator[str]:
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"""Brute force random text with unicode characters"""
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NUM_WORDS = 200
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WHITESPACES = list(" " * 20 + "\n" * 5 + "\r\n" * 5 + "\t" * 5)
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characters = list(generator_unicodes())
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rand = random.Random()
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for m in range(iterations):
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rand.seed(m)
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text = []
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for _ in range(NUM_WORDS):
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k = rand.randint(1, 7)
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word = rand.choices(characters, k=k)
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word.append(rand.choice(WHITESPACES))
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text.append("".join(word))
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yield "".join(text)
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def generator_random_vocab_chars(tokenizer: TokenizerGroundtruth, iterations=100) -> Iterator[str]:
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"""Brute force random text with vocab characters"""
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vocab_chars = set()
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for word in tokenizer.vocab:
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vocab_chars.update(word)
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vocab_chars = list(sorted(vocab_chars))
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rand = random.Random()
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for m in range(iterations):
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rand.seed(m)
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text = rand.choices(vocab_chars, k=1024)
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yield "".join(text)
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def generator_random_vocab_words(tokenizer: TokenizerGroundtruth, iterations=100) -> Iterator[str]:
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"""Brute force random text from vocab words"""
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vocab = [w.strip() for w in tokenizer.vocab]
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yield from vocab
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rand = random.Random()
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for m in range(iterations):
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rand.seed(m)
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text = []
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num_words = rand.randint(300, 400)
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for i in range(num_words):
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k = rand.randint(1, 3)
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words = rand.choices(vocab, k=k)
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sep = rand.choice(" \n\r\t")
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text.append("".join(words) + sep)
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yield "".join(text)
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def compare_tokenizers(tokenizer1: TokenizerGroundtruth, tokenizer2: TokenizerLlamaCpp, generator: Iterator[str]):
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def find_first_mismatch(ids1: list[int] | str, ids2: list[int] | str):
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||
for i, (a, b) in enumerate(zip(ids1, ids2)):
|
||
if a != b:
|
||
return i
|
||
if len(ids1) == len(ids2):
|
||
return -1
|
||
return min(len(ids1), len(ids2))
|
||
|
||
def check_detokenizer(text: str, text1: str, text2: str) -> bool:
|
||
if text1 == text2: # equal to TokenizerGroundtruth?
|
||
return True
|
||
# equal to source text?
|
||
if tokenizer1.add_bos_token: # remove BOS
|
||
if text2.startswith(tokenizer1.bos_token):
|
||
text2 = text2[len(tokenizer1.bos_token):]
|
||
if tokenizer1.add_eos_token: # remove EOS
|
||
if text2.endswith(tokenizer1.eos_token):
|
||
text2 = text2[:-len(tokenizer1.eos_token)]
|
||
return text == text2
|
||
|
||
t_encode1 = 0
|
||
t_encode2 = 0
|
||
t_decode1 = 0
|
||
t_decode2 = 0
|
||
t_start = time.perf_counter()
|
||
encode_errors = 0
|
||
decode_errors = 0
|
||
MAX_ERRORS = 10
|
||
|
||
logger.info("%s: %s" % (generator.__qualname__, "ini"))
|
||
for text in generator:
|
||
# print(repr(text), text.encode())
|
||
# print(repr(text), hex(ord(text[0])), text.encode())
|
||
t0 = time.perf_counter()
|
||
ids1 = tokenizer1.encode(text)
|
||
t1 = time.perf_counter()
|
||
ids2 = tokenizer2.encode(text)
|
||
t2 = time.perf_counter()
|
||
text1 = tokenizer1.decode(ids1)
|
||
t3 = time.perf_counter()
|
||
text2 = tokenizer2.decode(ids1)
|
||
t4 = time.perf_counter()
|
||
t_encode1 += t1 - t0
|
||
t_encode2 += t2 - t1
|
||
t_decode1 += t3 - t2
|
||
t_decode2 += t4 - t3
|
||
if encode_errors < MAX_ERRORS and ids1 != ids2:
|
||
i = find_first_mismatch(ids1, ids2)
|
||
ids1 = list(ids1)[max(0, i - 2) : i + 5 + 1]
|
||
ids2 = list(ids2)[max(0, i - 2) : i + 5 + 1]
|
||
logger.error(" Expected: " + str(ids1))
|
||
logger.error(" Result: " + str(ids2))
|
||
encode_errors += 1
|
||
logger.error(f" {encode_errors=}")
|
||
if decode_errors < MAX_ERRORS and not check_detokenizer(text, text1, text2):
|
||
i = find_first_mismatch(text1, text2)
|
||
text1 = list(text1[max(0, i - 2) : i + 5 + 1])
|
||
text2 = list(text2[max(0, i - 2) : i + 5 + 1])
|
||
logger.error(" Expected: " + " ".join(hex(ord(x)) for x in text1))
|
||
logger.error(" Result: " + " ".join(hex(ord(x)) for x in text2))
|
||
decode_errors += 1
|
||
logger.error(f" {decode_errors=}")
|
||
if encode_errors >= MAX_ERRORS and decode_errors >= MAX_ERRORS:
|
||
logger.error(f" EXIT: {encode_errors=} {decode_errors=}")
|
||
# raise Exception()
|
||
break
|
||
|
||
t_total = time.perf_counter() - t_start
|
||
logger.info(f"{generator.__qualname__}: end, {t_encode1=:.3f} {t_encode2=:.3f} {t_decode1=:.3f} {t_decode2=:.3f} {t_total=:.3f}")
|
||
|
||
|
||
def main(argv: list[str] | None = None):
|
||
parser = argparse.ArgumentParser()
|
||
parser.add_argument("vocab_file", type=str, help="path to vocab 'gguf' file")
|
||
parser.add_argument("dir_tokenizer", type=str, help="directory containing 'tokenizer.model' file")
|
||
parser.add_argument("--verbose", action="store_true", help="increase output verbosity")
|
||
args = parser.parse_args(argv)
|
||
|
||
logging.basicConfig(level = logging.DEBUG if args.verbose else logging.INFO)
|
||
logger.info(f"VOCABFILE: '{args.vocab_file}'")
|
||
|
||
tokenizer1 = TokenizerGroundtruth(args.dir_tokenizer)
|
||
tokenizer2 = TokenizerLlamaCpp(args.vocab_file)
|
||
|
||
# compare_tokenizers(tokenizer1, tokenizer2, generator_custom_text())
|
||
# compare_tokenizers(tokenizer1, tokenizer2, generator_custom_text_edge_cases())
|
||
compare_tokenizers(tokenizer1, tokenizer2, generator_ascii_lr_strip())
|
||
compare_tokenizers(tokenizer1, tokenizer2, generator_apostrophe())
|
||
compare_tokenizers(tokenizer1, tokenizer2, generator_unicodes())
|
||
compare_tokenizers(tokenizer1, tokenizer2, generator_vocab_words(tokenizer1))
|
||
compare_tokenizers(tokenizer1, tokenizer2, generator_added_lr_strip(tokenizer1))
|
||
# compare_tokenizers(tokenizer1, tokenizer2, generator_random_added_tokens(tokenizer1, 10_000))
|
||
# compare_tokenizers(tokenizer1, tokenizer2, generator_random_chars(10_000))
|
||
# compare_tokenizers(tokenizer1, tokenizer2, generator_random_unicodes(10_000))
|
||
# compare_tokenizers(tokenizer1, tokenizer2, generator_random_vocab_chars(tokenizer1, 10_000))
|
||
# compare_tokenizers(tokenizer1, tokenizer2, generator_random_vocab_words(tokenizer1, 5_000))
|
||
|
||
tokenizer2.model.free()
|
||
|
||
|
||
if __name__ == "__main__":
|
||
# main()
|
||
|
||
if True:
|
||
logging.basicConfig(
|
||
level = logging.DEBUG,
|
||
format = "%(asctime)s.%(msecs)03d %(name)s %(levelname)s %(message)s",
|
||
datefmt = "%Y-%m-%d %H:%M:%S",
|
||
filename = logger.name + ".log",
|
||
filemode = "a"
|
||
)
|
||
logging.basicConfig(
|
||
level = logging.DEBUG,
|
||
format = "%(levelname)s %(message)s",
|
||
)
|
||
|
||
path_tokenizers = Path("./models/tokenizers/")
|
||
path_vocab_format = "./models/ggml-vocab-%s.gguf"
|
||
|
||
tokenizers = [
|
||
"llama-spm", # SPM
|
||
"phi-3", # SPM
|
||
"gemma", # SPM
|
||
"gemma-2", # SPM
|
||
"baichuan", # SPM
|
||
"bert-bge", # WPM
|
||
"jina-v2-en", # WPM
|
||
"llama-bpe", # BPE
|
||
"phi-2", # BPE
|
||
"deepseek-llm", # BPE
|
||
"deepseek-coder", # BPE
|
||
"falcon", # BPE
|
||
"mpt", # BPE
|
||
"starcoder", # BPE
|
||
"gpt-2", # BPE
|
||
"stablelm2", # BPE
|
||
"refact", # BPE
|
||
"qwen2", # BPE
|
||
"olmo", # BPE
|
||
"jina-v2-es", # BPE
|
||
"jina-v2-de", # BPE
|
||
"smaug-bpe", # BPE
|
||
"poro-chat", # BPE
|
||
"jina-v2-code", # BPE
|
||
"viking", # BPE
|
||
"jais", # BPE
|
||
]
|
||
|
||
logger.info("=" * 50)
|
||
for tokenizer in tokenizers:
|
||
logger.info("-" * 50)
|
||
logger.info(f"TOKENIZER: '{tokenizer}'")
|
||
vocab_file = Path(path_vocab_format % tokenizer)
|
||
dir_tokenizer = path_tokenizers / tokenizer
|
||
main([str(vocab_file), str(dir_tokenizer), "--verbose"])
|