Is transformers safe?

passages = pickle.load(passages_file)

self.index_id_to_db_id = pickle.load(metadata_file)

wrapped_encoder = Seq2SeqLMEncoderExportableModule(self.encoder).to(self.full_model.device).eval()

Seq2SeqLMDecoderExportableModuleWithStaticCache(
                model=self.full_model,
                max_static_cache_length=self.generation_config.cache_config.get("max_cache_len"),
                batch_size=self.generation_config.cach …

self.semantic_model = AutoModel.from_config(config.semantic_model_config).eval()

self.semantic_model = AutoModel.from_config(config.semantic_model_config).eval()

cmdclass={"deps_table_update": DepsTableUpdateCommand},

subprocess.run(
        ["ruff", "check", new_module_folder, tests_folder, model_init_file, "--fix"],
        cwd=repo_path,
        stdout=subprocess.DEVNULL,
    )

subprocess.run(
        ["ruff", "format", new_module_folder, tests_folder, model_init_file],
        cwd=repo_path,
        stdout=subprocess.DEVNULL,
    )

subprocess.run(
        ["python", "utils/check_doc_toc.py", "--fix_and_overwrite"], cwd=repo_path, stdout=subprocess.DEVNULL
    )

subprocess.run(["python", "utils/sort_auto_mappings.py"], cwd=repo_path, stdout=subprocess.DEVNULL)

subprocess.run(
        ["python", "utils/modular_model_converter.py", new_lowercase_name], cwd=repo_path, stdout=subprocess.DEVNULL
    )

ffmpeg_process = subprocess.Popen(ffmpeg_command, stdin=subprocess.PIPE, stdout=subprocess.PIPE)

with subprocess.Popen(ffmpeg_command, stdin=subprocess.PIPE, stdout=subprocess.PIPE) as ffmpeg_process:

with subprocess.Popen(ffmpeg_command, stdout=subprocess.PIPE, bufsize=bufsize) as ffmpeg_process:

ffmpeg_devices = subprocess.run(command, text=True, stderr=subprocess.PIPE, encoding="utf-8")

subprocess.check_output(["ninja", "--version"])

long_description=open("README.md", "r", encoding="utf-8").read(),

with open(audio, "rb") as audio_file:

with open(file_name, "r", encoding="utf-8") as f:

autofile = (repo_path / "src" / "transformers" / "models" / "auto" / "auto_mappings.py").read_text()

file = (repo_path / "src" / "transformers" / "models" / "auto" / filename).read_text()

with open(toc_file, "r", encoding="utf-8") as f:

with open(module_name, "r", encoding="utf-8") as file:

with open(original_test_path, "r", encoding="utf-8") as f:

with open(examples_path, encoding="utf-8") as f:

with open(json_file, encoding="utf-8") as reader:

with open(model, "rb") as f:

with open(self.original_tokenizer.vocab_file, "rb") as f:

with open(vocab_file, "rb") as f:

with open(vocab_file, "rb") as f:

with open(vocab_file, "rb") as f:

with open(
            os.path.join(data_dir, self.train_file if filename is None else filename), "r", encoding="utf-8"
        ) as reader:

with open(
            os.path.join(data_dir, self.dev_file if filename is None else filename), "r", encoding="utf-8"
        ) as reader:

with open(input_file, "r", encoding="utf-8-sig") as f:

with open(module_file, encoding="utf-8") as f:

with open(filename, encoding="utf-8") as f:

module_hash: str = hashlib.sha256(b"".join(bytes(f) + f.read_bytes() for f in module_files)).hexdigest()

source_files_hash.update(file_path.read_bytes())

with open(requirements, "r", encoding="utf-8") as f:

with open(json_path, encoding="utf-8") as f:

with open(json_file, encoding="utf-8") as reader:

shutil.rmtree(stale_egg_info)

with open(target, "w", encoding="utf-8", newline="\n") as f:

with open(file_name, "w", encoding="utf-8") as f:

with open(new_module_folder / f"modular_{new_lowercase_name}.py", "w", encoding="utf-8") as f:

with open(new_module_folder / "__init__.py", "w", encoding="utf-8") as f:

with open(tests_folder / "__init__.py", "w", encoding="utf-8"):

with open(tests_folder / filename, "w", encoding="utf-8") as f:

with open(
        repo_path / "docs" / "source" / "en" / "model_doc" / f"{new_lowercase_name}.md", "w", encoding="utf-8"
    ) as f:

with open(filename, "w", encoding="utf-8") as f:

with open(json_file_path, "w", encoding="utf-8") as writer:

os.remove(file_name)

with open(output_prediction_file, "w", encoding="utf-8") as writer:

with open(output_nbest_file, "w", encoding="utf-8") as writer:

with open(output_null_log_odds_file, "w", encoding="utf-8") as writer:

with open(output_prediction_file, "w", encoding="utf-8") as writer:

with open(output_nbest_file, "w", encoding="utf-8") as writer:

with open(output_null_log_odds_file, "w", encoding="utf-8") as writer:

with open(json_file_path, "w", encoding="utf-8") as f:

shutil.copyfile(resolved_module_file, submodule_path / module_file)

shutil.copyfile(source_file, target_path)

shutil.copyfile(resolved_module_file, submodule_path / module_file)

shutil.copyfile(object_file, dest_file)

shutil.copyfile(needed_file, dest_file)

with open(json_path, "w", encoding="utf-8") as f:

with open(json_file_path, "w", encoding="utf-8") as writer:

import requests

response = requests.post(
            urljoin(get_service_root_url(self.base_url) + "/", "load_model"), json={"model": model}, stream=True
        )

from .requests import RequestState, RequestStatus

from .requests import RequestState, RequestStatus, get_device_and_memory_breakdown, logger

logger.info(f"Paged cache initialized: {self.max_batch_tokens = }, {self.num_sectors = }, {mb_per_sector = }")

logger.debug(f"Found a prefix match of {prefix_len} tokens for request {request_id}")

logger.debug(f"Evicting {len(evicted_blocks)} cached blocks from allocator {allocator.index}")

_, total, reserved, allocated = get_device_and_memory_breakdown()

logger.info(f"Memory available for cache allocation: {available_memory // 1024**2} MB")

from .requests import FutureRequestState, logger

logger.warning(f"Processor {class_name} might not be supported by CB.")

logger.warning(f"Processor {class_name} isn't supported by CB. Dropping it.")

logger.warning(f"Processor {class_name} isn't supported by CB. Kept it because {drop_unsupported = }.")

logger.warning(
            f"Ignored logit_processor_kwargs: {problematic_keys}. {self.supported_keys = } and {self.ignored_keys = }"
        )

from .requests import GenerationOutput, RequestState, RequestStatus, logger

logger.info(
            f"Rank {global_rank} requested background thread to stop with {status = }. Now {self._local_status = }"
        )

logger.error(f"A fatal error was already recorded, ignoring later error: {error}")

logger.error(f"Error processing new request: {e}", exc_info=True)

logger.warning(
                    f"{msg} Switching from {original_attn_impl} to {target_implem}. If you need to use eager or sdpa, "
                    "set `auto_switch_to_flash=False` in the continuous batching config." …

logger.info(f"{msg} Consider using a flash `attn_implementation` when loading the model.")

logger.warning("Manager thread is already running.")

logger.warning("\nBatch processor was not initialized.")

logger.warning(msg)

logger.info("Continuous batching manager will be kept for next session.")

logger.warning(f"Generation thread did not exit after join timeout ({timeout}).")