text stringlengths 1 1.02k | class_index int64 0 10.8k | source stringlengths 85 188 |
|---|---|---|
# This might happen when running inside of a pipeline, where the task is already initialized
# from outside of Hugging Face
if self._clearml.Task.running_locally() and self._clearml.Task.current_task():
self._clearml_task = self._clearml.Task.current_task()
... | 10,580 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/integrations/integration_utils.py |
output_uri=True,
)
self._log_model = os.getenv("CLEARML_LOG_MODEL", "TRUE").upper() in ENV_VARS_TRUE_VALUES.union(
{"TRUE"}
)
ClearMLCallback._task_created_in_callback = True
logger.info("ClearML ... | 10,580 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/integrations/integration_utils.py |
suffixed_hparams_section = ClearMLCallback._hparams_section + ClearMLCallback.log_suffix
ignore_hparams_config_section = suffixed_hparams_section + "/" + ClearMLCallback._ignore_hparams_overrides
if self._clearml.Task.running_locally():
self._copy_training_args_as_hparams(args, s... | 10,580 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/integrations/integration_utils.py |
else:
self._copy_training_args_as_hparams(
args, ClearMLCallback._hparams_section + ClearMLCallback.log_suffix
) | 10,580 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/integrations/integration_utils.py |
if getattr(model, "config", None) is not None:
ignore_model_config_section = (
suffixed_hparams_section + "/" + ClearMLCallback._ignoge_model_config_overrides
)
configuration_object_description = ClearMLCallback._model_config_description.format(
... | 10,580 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/integrations/integration_utils.py |
+ "when running remotely. Otherwise, the overrides will be applied when running remotely"
),
)
self._clearml_task.set_configuration_object(
name=ClearMLCallback._model_config_section + ClearMLCallback.log_suffix,
... | 10,580 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/integrations/integration_utils.py |
name=ClearMLCallback._model_config_section + ClearMLCallback.log_suffix,
config_dict=model.config.to_dict(),
description=configuration_object_description,
) | 10,580 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/integrations/integration_utils.py |
def on_train_begin(self, args, state, control, model=None, tokenizer=None, **kwargs):
if self._clearml is None:
return
self._checkpoints_saved = []
if state.is_hyper_param_search:
self._initialized = False
if not self._initialized:
self.setup(args, sta... | 10,580 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/integrations/integration_utils.py |
def on_log(self, args, state, control, model=None, tokenizer=None, logs=None, **kwargs):
if self._clearml is None:
return
if not self._initialized:
self.setup(args, state, model, tokenizer, **kwargs)
if state.is_world_process_zero:
eval_prefix = "eval_"
... | 10,580 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/integrations/integration_utils.py |
elif k.startswith(eval_prefix):
self._clearml_task.get_logger().report_scalar(
title="eval" + ClearMLCallback.log_suffix,
series=k[eval_prefix_len:],
value=v,
iteration=state.global_st... | 10,580 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/integrations/integration_utils.py |
iteration=state.global_step,
)
else:
logger.warning(
"Trainer is attempting to log a value of "
f'"{v}" of type {type(v)} for key "{k}" as a scalar. '
"This invocation of ClearML logger's ... | 10,580 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/integrations/integration_utils.py |
def on_save(self, args, state, control, **kwargs):
if self._log_model and self._clearml_task and state.is_world_process_zero:
ckpt_dir = f"checkpoint-{state.global_step}"
artifact_path = os.path.join(args.output_dir, ckpt_dir)
name = ckpt_dir + ClearMLCallback.log_suffix
... | 10,580 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/integrations/integration_utils.py |
self._clearml.model.Model.remove(
self._checkpoints_saved[0],
delete_weights_file=True,
force=True,
raise_on_errors=True,
)
except Exception as e:
logger.warning(
... | 10,580 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/integrations/integration_utils.py |
def _copy_training_args_as_hparams(self, training_args, prefix):
as_dict = {
field.name: getattr(training_args, field.name)
for field in fields(training_args)
if field.init and not field.name.endswith("_token")
}
flat_dict = {str(k): v for k, v in self._clearm... | 10,580 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/integrations/integration_utils.py |
class FlyteCallback(TrainerCallback):
"""A [`TrainerCallback`] that sends the logs to [Flyte](https://flyte.org/).
NOTE: This callback only works within a Flyte task.
Args:
save_log_history (`bool`, *optional*, defaults to `True`):
When set to True, the training logs are saved as a Flyt... | 10,581 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/integrations/integration_utils.py |
def __init__(self, save_log_history: bool = True, sync_checkpoints: bool = True):
super().__init__()
if not is_flytekit_available():
raise ImportError("FlyteCallback requires flytekit to be installed. Run `pip install flytekit`.")
if not is_flyte_deck_standard_available() or not is_... | 10,581 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/integrations/integration_utils.py |
def on_save(self, args, state, control, **kwargs):
if self.sync_checkpoints and state.is_world_process_zero:
ckpt_dir = f"checkpoint-{state.global_step}"
artifact_path = os.path.join(args.output_dir, ckpt_dir)
logger.info(f"Syncing checkpoint in {ckpt_dir} to Flyte. This may... | 10,581 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/integrations/integration_utils.py |
class DVCLiveCallback(TrainerCallback):
"""
A [`TrainerCallback`] that sends the logs to [DVCLive](https://www.dvc.org/doc/dvclive).
Use the environment variables below in `setup` to configure the integration. To customize this callback beyond
those environment variables, see [here](https://dvc.org/doc... | 10,582 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/integrations/integration_utils.py |
def __init__(
self,
live: Optional[Any] = None,
log_model: Optional[Union[Literal["all"], bool]] = None,
**kwargs,
):
if not is_dvclive_available():
raise RuntimeError("DVCLiveCallback requires dvclive to be installed. Run `pip install dvclive`.")
from dvc... | 10,582 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/integrations/integration_utils.py |
def setup(self, args, state, model):
"""
Setup the optional DVCLive integration. To customize this callback beyond the environment variables below, see
[here](https://dvc.org/doc/dvclive/ml-frameworks/huggingface).
Environment:
- **HF_DVCLIVE_LOG_MODEL** (`str`, *optional*):
... | 10,582 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/integrations/integration_utils.py |
def on_log(self, args, state, control, model=None, logs=None, **kwargs):
if not self._initialized:
self.setup(args, state, model)
if state.is_world_process_zero:
from dvclive.plots import Metric
from dvclive.utils import standardize_metric_name
for key, v... | 10,582 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/integrations/integration_utils.py |
def on_save(self, args, state, control, **kwargs):
if self._log_model == "all" and self._initialized and state.is_world_process_zero:
self.live.log_artifact(args.output_dir)
def on_train_end(self, args, state, control, **kwargs):
if self._initialized and state.is_world_process_zero:
... | 10,582 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/integrations/integration_utils.py |
class UserCommands(BaseTransformersCLICommand):
@staticmethod
def register_subcommand(parser: ArgumentParser):
login_parser = parser.add_parser("login", help="Log in using the same credentials as on huggingface.co")
login_parser.set_defaults(func=lambda args: LoginCommand(args))
whoami_p... | 10,583 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/commands/user.py |
# new system: git-based repo system
repo_parser = parser.add_parser(
"repo",
help="Deprecated: use `huggingface-cli` instead. Commands to interact with your huggingface.co repos.",
)
repo_subparsers = repo_parser.add_subparsers(
help="Deprecated: use `huggingf... | 10,583 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/commands/user.py |
repo_create_parser.set_defaults(func=lambda args: RepoCreateCommand(args)) | 10,583 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/commands/user.py |
class ANSI:
"""
Helper for en.wikipedia.org/wiki/ANSI_escape_code
"""
_bold = "\u001b[1m"
_red = "\u001b[31m"
_gray = "\u001b[90m"
_reset = "\u001b[0m"
@classmethod
def bold(cls, s):
return f"{cls._bold}{s}{cls._reset}"
@classmethod
def red(cls, s):
return ... | 10,584 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/commands/user.py |
class BaseUserCommand:
def __init__(self, args):
self.args = args | 10,585 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/commands/user.py |
class LoginCommand(BaseUserCommand):
def run(self):
print(
ANSI.red(
"ERROR! `huggingface-cli login` uses an outdated login mechanism "
"that is not compatible with the Hugging Face Hub backend anymore. "
"Please use `huggingface-cli login instead.... | 10,586 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/commands/user.py |
class WhoamiCommand(BaseUserCommand):
def run(self):
print(
ANSI.red(
"WARNING! `transformers-cli whoami` is deprecated and will be removed in v5. Please use "
"`huggingface-cli whoami` instead."
)
)
token = HfFolder.get_token()
... | 10,587 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/commands/user.py |
class LogoutCommand(BaseUserCommand):
def run(self):
print(
ANSI.red(
"ERROR! `transformers-cli logout` uses an outdated logout mechanism "
"that is not compatible with the Hugging Face Hub backend anymore. "
"Please use `huggingface-cli logout ins... | 10,588 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/commands/user.py |
class RepoCreateCommand(BaseUserCommand):
def run(self):
print(
ANSI.red(
"WARNING! Managing repositories through transformers-cli is deprecated. "
"Please use `huggingface-cli` instead."
)
)
token = HfFolder.get_token()
if toke... | 10,589 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/commands/user.py |
try:
stdout = subprocess.check_output(["git-lfs", "--version"]).decode("utf-8")
print(ANSI.gray(stdout.strip()))
except FileNotFoundError:
print(
ANSI.red(
"Looks like you do not have git-lfs installed, please install."
... | 10,589 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/commands/user.py |
if not self.args.yes:
choice = input("Proceed? [Y/n] ").lower()
if not (choice == "" or choice == "y" or choice == "yes"):
print("Abort")
exit()
try:
url = create_repo(repo_id=full_name, token=token)
except HTTPError as e:
p... | 10,589 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/commands/user.py |
class RunCommand(BaseTransformersCLICommand):
def __init__(self, nlp: Pipeline, reader: PipelineDataFormat):
self._nlp = nlp
self._reader = reader | 10,590 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/commands/run.py |
@staticmethod
def register_subcommand(parser: ArgumentParser):
run_parser = parser.add_parser("run", help="Run a pipeline through the CLI")
run_parser.add_argument("--task", choices=get_supported_tasks(), help="Task to run")
run_parser.add_argument("--input", type=str, help="Path to the file... | 10,590 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/commands/run.py |
)
run_parser.add_argument(
"--format",
type=str,
default="infer",
choices=PipelineDataFormat.SUPPORTED_FORMATS,
help="Input format to read from",
)
run_parser.add_argument(
"--device",
type=int,
defau... | 10,590 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/commands/run.py |
def run(self):
nlp, outputs = self._nlp, []
for entry in self._reader:
output = nlp(**entry) if self._reader.is_multi_columns else nlp(entry)
if isinstance(output, dict):
outputs.append(output)
else:
outputs += output
# Saving... | 10,590 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/commands/run.py |
class ModelPatterns:
"""
Holds the basic information about a new model for the add-new-model-like command. | 10,591 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/commands/add_new_model_like.py |
Args:
model_name (`str`): The model name.
checkpoint (`str`): The checkpoint to use for doc examples.
model_type (`str`, *optional*):
The model type, the identifier used internally in the library like `bert` or `xlm-roberta`. Will default to
`model_name` lowercased with s... | 10,591 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/commands/add_new_model_like.py |
uppercased with spaces and minuses replaced with underscores.
config_class (`str`, *optional*):
The tokenizer class associated with this model. Will default to `"{model_camel_cased}Config"`.
tokenizer_class (`str`, *optional*):
The tokenizer class associated with this model (leav... | 10,591 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/commands/add_new_model_like.py |
model_name: str
checkpoint: str
model_type: Optional[str] = None
model_lower_cased: Optional[str] = None
model_camel_cased: Optional[str] = None
model_upper_cased: Optional[str] = None
config_class: Optional[str] = None
tokenizer_class: Optional[str] = None
image_processor_class: Optiona... | 10,591 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/commands/add_new_model_like.py |
def __post_init__(self):
if self.model_type is None:
self.model_type = self.model_name.lower().replace(" ", "-")
if self.model_lower_cased is None:
self.model_lower_cased = self.model_name.lower().replace(" ", "_").replace("-", "_")
if self.model_camel_cased is None:
... | 10,591 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/commands/add_new_model_like.py |
class AddNewModelLikeCommand(BaseTransformersCLICommand):
@staticmethod
def register_subcommand(parser: ArgumentParser):
add_new_model_like_parser = parser.add_parser("add-new-model-like")
add_new_model_like_parser.add_argument(
"--config_file", type=str, help="A file with all the in... | 10,592 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/commands/add_new_model_like.py |
def __init__(self, config_file=None, path_to_repo=None, *args):
if config_file is not None:
with open(config_file, "r", encoding="utf-8") as f:
config = json.load(f)
self.old_model_type = config["old_model_type"]
self.model_patterns = ModelPatterns(**config["n... | 10,592 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/commands/add_new_model_like.py |
REPO_PATH = Path(self.path_to_repo)
TRANSFORMERS_PATH = REPO_PATH / "src" / "transformers"
create_new_model_like(
model_type=self.old_model_type,
new_model_patterns=self.model_patterns,
add_copied_from=self.add_copied_from,
frameworks=self.frameworks,... | 10,592 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/commands/add_new_model_like.py |
class EnvironmentCommand(BaseTransformersCLICommand):
@staticmethod
def register_subcommand(parser: ArgumentParser):
download_parser = parser.add_parser("env")
download_parser.set_defaults(func=info_command_factory)
download_parser.add_argument(
"--accelerate-config_file",
... | 10,593 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/commands/env.py |
safetensors_version = f"{safetensors.__version__} but is ignored because of PyTorch version too old."
accelerate_version = "not installed"
accelerate_config = accelerate_config_str = "not found"
if is_accelerate_available():
import accelerate
from accelerate.commands.con... | 10,593 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/commands/env.py |
pt_version = "not installed"
pt_cuda_available = "NA"
if is_torch_available():
import torch
pt_version = torch.__version__
pt_cuda_available = torch.cuda.is_available()
pt_npu_available = is_torch_npu_available()
tf_version = "not installed"
... | 10,593 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/commands/env.py |
flax_version = flax.__version__
jax_version = jax.__version__
jaxlib_version = jaxlib.__version__
jax_backend = jax.lib.xla_bridge.get_backend().platform | 10,593 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/commands/env.py |
info = {
"`transformers` version": version,
"Platform": platform.platform(),
"Python version": platform.python_version(),
"Huggingface_hub version": huggingface_hub.__version__,
"Safetensors version": f"{safetensors_version}",
"Accelerate version":... | 10,593 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/commands/env.py |
info["GPU type"] = torch.cuda.get_device_name()
elif pt_npu_available:
info["Using NPU in script?"] = "<fill in>"
info["NPU type"] = torch.npu.get_device_name()
info["CANN version"] = torch.version.cann | 10,593 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/commands/env.py |
print("\nCopy-and-paste the text below in your GitHub issue and FILL OUT the two last points.\n")
print(self.format_dict(info))
return info
@staticmethod
def format_dict(d):
return "\n".join([f"- {prop}: {val}" for prop, val in d.items()]) + "\n" | 10,593 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/commands/env.py |
class ServeModelInfoResult(BaseModel):
"""
Expose model information
"""
infos: dict | 10,594 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/commands/serving.py |
class ServeTokenizeResult(BaseModel):
"""
Tokenize result model
"""
tokens: List[str]
tokens_ids: Optional[List[int]] | 10,595 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/commands/serving.py |
class ServeDeTokenizeResult(BaseModel):
"""
DeTokenize result model
"""
text: str | 10,596 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/commands/serving.py |
class ServeForwardResult(BaseModel):
"""
Forward result model
"""
output: Any | 10,597 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/commands/serving.py |
class ServeCommand(BaseTransformersCLICommand):
@staticmethod
def register_subcommand(parser: ArgumentParser):
"""
Register this command to argparse so it's available for the transformer-cli | 10,598 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/commands/serving.py |
Args:
parser: Root parser to register command-specific arguments
"""
serve_parser = parser.add_parser(
"serve", help="CLI tool to run inference requests through REST and GraphQL endpoints."
)
serve_parser.add_argument(
"--task",
type=str,
... | 10,598 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/commands/serving.py |
serve_parser.add_argument("--tokenizer", type=str, help="Tokenizer name to use.")
serve_parser.add_argument(
"--device",
type=int,
default=-1,
help="Indicate the device to run onto, -1 indicates CPU, >= 0 indicates GPU (default: -1)",
)
serve_parse... | 10,598 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/commands/serving.py |
def __init__(self, pipeline: Pipeline, host: str, port: int, workers: int):
self._pipeline = pipeline
self.host = host
self.port = port
self.workers = workers | 10,598 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/commands/serving.py |
if not _serve_dependencies_installed:
raise RuntimeError(
"Using serve command requires FastAPI and uvicorn. "
'Please install transformers with [serving]: pip install "transformers[serving]". '
"Or install FastAPI and uvicorn separately."
)
... | 10,598 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/commands/serving.py |
APIRoute(
"/detokenize",
self.detokenize,
response_model=ServeDeTokenizeResult,
response_class=JSONResponse,
methods=["POST"],
),
APIRoute(
... | 10,598 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/commands/serving.py |
def run(self):
run(self._app, host=self.host, port=self.port, workers=self.workers)
def model_info(self):
return ServeModelInfoResult(infos=vars(self._pipeline.model.config))
def tokenize(self, text_input: str = Body(None, embed=True), return_ids: bool = Body(False, embed=True)):
"""
... | 10,598 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/commands/serving.py |
except Exception as e:
raise HTTPException(status_code=500, detail={"model": "", "error": str(e)})
def detokenize(
self,
tokens_ids: List[int] = Body(None, embed=True),
skip_special_tokens: bool = Body(False, embed=True),
cleanup_tokenization_spaces: bool = Body(True, em... | 10,598 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/commands/serving.py |
async def forward(self, inputs=Body(None, embed=True)):
"""
**inputs**: **attention_mask**: **tokens_type_ids**:
"""
# Check we don't have empty string
if len(inputs) == 0:
return ServeForwardResult(output=[], attention=[])
try:
# Forward through... | 10,598 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/commands/serving.py |
class DownloadCommand(BaseTransformersCLICommand):
@staticmethod
def register_subcommand(parser: ArgumentParser):
download_parser = parser.add_parser("download")
download_parser.add_argument(
"--cache-dir", type=str, default=None, help="Path to location to store the models"
)... | 10,599 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/commands/download.py |
def __init__(self, model: str, cache: str, force: bool, trust_remote_code: bool):
self._model = model
self._cache = cache
self._force = force
self._trust_remote_code = trust_remote_code
def run(self):
from ..models.auto import AutoModel, AutoTokenizer
AutoModel.from... | 10,599 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/commands/download.py |
class ConvertCommand(BaseTransformersCLICommand):
@staticmethod
def register_subcommand(parser: ArgumentParser):
"""
Register this command to argparse so it's available for the transformer-cli | 10,600 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/commands/convert.py |
Args:
parser: Root parser to register command-specific arguments
"""
train_parser = parser.add_parser(
"convert",
help="CLI tool to run convert model from original author checkpoints to Transformers PyTorch checkpoints.",
)
train_parser.add_argument("-... | 10,600 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/commands/convert.py |
train_parser.set_defaults(func=convert_command_factory) | 10,600 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/commands/convert.py |
def __init__(
self,
model_type: str,
tf_checkpoint: str,
pytorch_dump_output: str,
config: str,
finetuning_task_name: str,
*args,
):
self._logger = logging.get_logger("transformers-cli/converting")
self._logger.info(f"Loading model {model_type... | 10,600 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/commands/convert.py |
convert_tf_checkpoint_to_pytorch(self._tf_checkpoint, self._config, self._pytorch_dump_output)
elif self._model_type == "bert":
try:
from ..models.bert.convert_bert_original_tf_checkpoint_to_pytorch import (
convert_tf_checkpoint_to_pytorch,
)
... | 10,600 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/commands/convert.py |
convert_tf_checkpoint_to_pytorch(self._tf_checkpoint, self._config, self._pytorch_dump_output)
elif self._model_type == "t5":
try:
from ..models.t5.convert_t5_original_tf_checkpoint_to_pytorch import convert_tf_checkpoint_to_pytorch
except ImportError:
rai... | 10,600 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/commands/convert.py |
convert_openai_checkpoint_to_pytorch(self._tf_checkpoint, self._config, self._pytorch_dump_output)
elif self._model_type == "gpt2":
try:
from ..models.gpt2.convert_gpt2_original_tf_checkpoint_to_pytorch import (
convert_gpt2_checkpoint_to_pytorch,
... | 10,600 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/commands/convert.py |
convert_xlnet_checkpoint_to_pytorch(
self._tf_checkpoint, self._config, self._pytorch_dump_output, self._finetuning_task_name
)
elif self._model_type == "xlm":
from ..models.xlm.convert_xlm_original_pytorch_checkpoint_to_pytorch import (
convert_xlm_checkp... | 10,600 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/commands/convert.py |
convert_rembert_tf_checkpoint_to_pytorch(self._tf_checkpoint, self._config, self._pytorch_dump_output)
else:
raise ValueError("--model_type should be selected in the list [bert, gpt, gpt2, t5, xlnet, xlm, lxmert]") | 10,600 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/commands/convert.py |
class BaseTransformersCLICommand(ABC):
@staticmethod
@abstractmethod
def register_subcommand(parser: ArgumentParser):
raise NotImplementedError()
@abstractmethod
def run(self):
raise NotImplementedError() | 10,601 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/commands/__init__.py |
class LfsCommands(BaseTransformersCLICommand):
"""
Implementation of a custom transfer agent for the transfer type "multipart" for git-lfs. This lets users upload
large files >5GB 🔥. Spec for LFS custom transfer agent is:
https://github.com/git-lfs/git-lfs/blob/master/docs/custom-transfers.md
This... | 10,602 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/commands/lfs.py |
@staticmethod
def register_subcommand(parser: ArgumentParser):
enable_parser = parser.add_parser(
"lfs-enable-largefiles",
help=(
"Deprecated: use `huggingface-cli` instead. Configure your repository to enable upload of files > 5GB."
),
)
e... | 10,602 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/commands/lfs.py |
class LfsEnableCommand:
def __init__(self, args):
self.args = args
def run(self):
warnings.warn(
"Managing repositories through transformers-cli is deprecated. Please use `huggingface-cli` instead."
)
local_path = os.path.abspath(self.args.path)
if not os.pat... | 10,603 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/commands/lfs.py |
class FileSlice(AbstractContextManager):
"""
File-like object that only reads a slice of a file
Inspired by stackoverflow.com/a/29838711/593036
"""
def __init__(self, filepath: str, seek_from: int, read_limit: int):
self.filepath = filepath
self.seek_from = seek_from
self.r... | 10,604 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/commands/lfs.py |
def __exit__(self, *args):
self.f.close() | 10,604 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/commands/lfs.py |
class LfsUploadCommand:
def __init__(self, args):
self.args = args
def run(self):
# Immediately after invoking a custom transfer process, git-lfs
# sends initiation data to the process over stdin.
# This tells the process useful information about the configuration.
init_... | 10,605 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/commands/lfs.py |
# After the initiation exchange, git-lfs will send any number of
# transfer requests to the stdin of the transfer process, in a serial sequence.
while True:
msg = read_msg()
if msg is None:
# When all transfers have been processed, git-lfs will send
... | 10,605 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/commands/lfs.py |
parts = []
for i, presigned_url in enumerate(presigned_urls):
with FileSlice(filepath, seek_from=i * chunk_size, read_limit=chunk_size) as data:
r = requests.put(presigned_url, data=data)
r.raise_for_status()
parts.append(
... | 10,605 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/commands/lfs.py |
# Not precise but that's ok. | 10,605 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/commands/lfs.py |
r = requests.post(
completion_url,
json={
"oid": oid,
"parts": parts,
},
)
r.raise_for_status()
write_msg({"event": "complete", "oid": oid}) | 10,605 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/commands/lfs.py |
class TrainCommand(BaseTransformersCLICommand):
@staticmethod
def register_subcommand(parser: ArgumentParser):
"""
Register this command to argparse so it's available for the transformer-cli
Args:
parser: Root parser to register command-specific arguments
"""
... | 10,606 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/commands/train.py |
train_parser.add_argument(
"--train_data",
type=str,
required=True,
help="path to train (and optionally evaluation) dataset as a csv with tab separated labels and sentences.",
)
train_parser.add_argument(
"--column_label", type=int, default=0, ... | 10,606 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/commands/train.py |
train_parser.add_argument("--validation_data", type=str, default="", help="path to validation dataset.")
train_parser.add_argument(
"--validation_split",
type=float,
default=0.1,
help="if validation dataset is not provided, fraction of train dataset to use as vali... | 10,606 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/commands/train.py |
train_parser.add_argument(
"--task", type=str, default="text_classification", help="Task to train the model on."
)
train_parser.add_argument(
"--model", type=str, default="google-bert/bert-base-uncased", help="Model's name or path to stored model."
)
train_parser.... | 10,606 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/commands/train.py |
os.makedirs(args.output, exist_ok=True)
self.output = args.output
self.column_label = args.column_label
self.column_text = args.column_text
self.column_id = args.column_id
self.logger.info(f"Loading {args.task} pipeline for {args.model}")
if args.task == "text_classific... | 10,606 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/commands/train.py |
self.logger.info(f"Loading dataset from {args.train_data}")
self.train_dataset = Processor.create_from_csv(
args.train_data,
column_label=args.column_label,
column_text=args.column_text,
column_id=args.column_id,
skip_first_row=args.skip_first_row,
... | 10,606 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/commands/train.py |
self.validation_split = args.validation_split
self.train_batch_size = args.train_batch_size
self.valid_batch_size = args.valid_batch_size
self.learning_rate = args.learning_rate
self.adam_epsilon = args.adam_epsilon
def run(self):
if self.framework == "tf":
retur... | 10,606 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/commands/train.py |
class DataCollatorMixin:
def __call__(self, features, return_tensors=None):
if return_tensors is None:
return_tensors = self.return_tensors
if return_tensors == "tf":
return self.tf_call(features)
elif return_tensors == "pt":
return self.torch_call(feature... | 10,607 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/data_collator.py |
class DefaultDataCollator(DataCollatorMixin):
"""
Very simple data collator that simply collates batches of dict-like objects and performs special handling for
potential keys named:
- `label`: handles a single value (int or float) per object
- `label_ids`: handles a list of values per objec... | 10,608 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/data_collator.py |
def __call__(self, features: List[Dict[str, Any]], return_tensors=None) -> Dict[str, Any]:
if return_tensors is None:
return_tensors = self.return_tensors
return default_data_collator(features, return_tensors) | 10,608 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/data_collator.py |
class DataCollatorWithPadding:
"""
Data collator that will dynamically pad the inputs received.
Args:
tokenizer ([`PreTrainedTokenizer`] or [`PreTrainedTokenizerFast`]):
The tokenizer used for encoding the data.
padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, d... | 10,609 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/data_collator.py |
- `True` or `'longest'` (default): Pad to the longest sequence in the batch (or no padding if only a single
sequence is provided).
- `'max_length'`: Pad to a maximum length specified with the argument `max_length` or to the maximum
acceptable input length for the model if that ar... | 10,609 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/data_collator.py |
This is especially useful to enable the use of Tensor Cores on NVIDIA hardware with compute capability >=
7.0 (Volta).
return_tensors (`str`, *optional*, defaults to `"pt"`):
The type of Tensor to return. Allowable values are "np", "pt" and "tf".
"""
tokenizer: PreTrainedTokeniz... | 10,609 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/data_collator.py |
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