repo_description: 🤗 Transformers: the model-definition framework for state-of-the-art machine learning models in text, vision, audio, and multimodal models, for both inference and training. 
repo_topics: audio deep-learning deepseek gemma glm hacktoberfest llm machine-learning model-hub natural-language-processing nlp pretrained-models python pytorch pytorch-transformers qwen speech-recognition transformer vlm
code: # Copyright 2020 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 dataclasses
  import json
  import os
  import sys
  import types
  from argparse import ArgumentDefaultsHelpFormatter, ArgumentParser, ArgumentTypeError
  from collections.abc import Callable, Iterable
  from copy import copy
  from enum import Enum
  from inspect import isclass
  from pathlib import Path
  from typing import Any, Literal, NewType, Union, get_type_hints
  
  import yaml
  
  
  DataClass = NewType("DataClass", Any)
  DataClassType = NewType("DataClassType", Any)
  
  
  # From https://stackoverflow.com/questions/15008758/parsing-boolean-values-with-argparse
  def string_to_bool(v):
      if isinstance(v, bool):
          return v
      if v.lower() in ("yes", "true", "t", "y", "1"):
          return True
      elif v.lower() in ("no", "false", "f", "n", "0"):
          return False
      else:
          raise ArgumentTypeError(
              f"Truthy value expected: got {v} but expected one of yes/no, true/false, t/f, y/n, 1/0 (case insensitive)."
          )
  
  
  def make_choice_type_function(choices: list) -> Callable[[str], Any]:
      """
      Creates a mapping function from each choices string representation to the actual value. Used to support multiple
      value types for a single argument.
  
      Args:
          choices (list): List of choices.
  
      Returns:
          Callable[[str], Any]: Mapping function from string representation to actual value for each choice.
      """
      str_to_choice = {str(choice): choice for choice in choices}
      return lambda arg: str_to_choice.get(arg, arg)
  
  
  def HfArg(
      *,
      aliases: str | list[str] | None = None,
      help: str | None = None,
      default: Any = dataclasses.MISSING,
      default_factory: Callable[[], Any] = dataclasses.MISSING,
      metadata: dict | None = None,
      **kwargs,
  ) -> dataclasses.Field:
      """Argument helper enabling a concise syntax to create dataclass fields for parsing with `HfArgumentParser`.
  
      Example comparing the use of `HfArg` and `dataclasses.field`:
      ```
      @dataclass
      class Args:
          regular_arg: str = dataclasses.field(default="Huggingface", metadata={"aliases": ["--example", "-e"], "help": "This syntax could be better!"})
          hf_arg: str = HfArg(default="Huggingface", aliases=["--example", "-e"], help="What a nice syntax!")
      ```
  
      Args:
          aliases (Union[str, list[str]], optional):
              Single string or list of strings of aliases to pass on to argparse, e.g. `aliases=["--example", "-e"]`.
              Defaults to None.
          help (str, optional): Help string to pass on to argparse that can be displayed with --help. Defaults to None.
          default (Any, optional):
              Default value for the argument. If not default or default_factory is specified, the argument is required.
              Defaults to dataclasses.MISSING.
          default_factory (Callable[[], Any], optional):
              The default_factory is a 0-argument function called to initialize a field's value. It is useful to provide
              default values for mutable types, e.g. lists: `default_factory=list`. Mutually exclusive with `default=`.
              Defaults to dataclasses.MISSING.
          metadata (dict, optional): Further metadata to pass on to `dataclasses.field`. Defaults to None.
  
      Returns:
          Field: A `dataclasses.Field` with the desired properties.
      """
      if metadata is None:
          # Important, don't use as default param in function signature because dict is mutable and shared across function calls
          metadata = {}
      if aliases is not None:
          metadata["aliases"] = aliases
      if help is not None:
          metadata["help"] = help
  
      return dataclasses.field(metadata=metadata, default=default, default_factory=default_factory, **kwargs)
  
  
  class HfArgumentParser(ArgumentParser):
      """
      This subclass of `argparse.ArgumentParser` uses type hints on dataclasses to generate arguments.
  
      The class is designed to play well with the native argparse. In particular, you can add more (non-dataclass backed)
      arguments to the parser after initialization and you'll get the output back after parsing as an additional
      namespace. Optional: To create sub argument groups use the `_argument_group_name` attribute in the dataclass.
  
      Args:
          dataclass_types (`DataClassType` or `Iterable[DataClassType]`, *optional*):
              Dataclass type, or list of dataclass types for which we will "fill" instances with the parsed args.
          kwargs (`dict[str, Any]`, *optional*):
              Passed to `argparse.ArgumentParser()` in the regular way.
      """
  
      dataclass_types: Iterable[DataClassType]
  
      def __init__(self, dataclass_types: DataClassType | Iterable[DataClassType] | None = None, **kwargs):
          # Make sure dataclass_types is an iterable
          if dataclass_types is None:
              dataclass_types = []
          elif not isinstance(dataclass_types, Iterable):
              dataclass_types = [dataclass_types]
  
          # To make the default appear when using --help
          if "formatter_class" not in kwargs:
              kwargs["formatter_class"] = ArgumentDefaultsHelpFormatter
          super().__init__(**kwargs)
          if dataclasses.is_dataclass(dataclass_types):
              dataclass_types = [dataclass_types]
          self.dataclass_types = list(dataclass_types)
          for dtype in self.dataclass_types:
              self._add_dataclass_arguments(dtype)
  
      @staticmethod
      def _parse_dataclass_field(parser: ArgumentParser, field: dataclasses.Field):
          # Long-option strings are conventionlly separated by hyphens rather
          # than underscores, e.g., "--long-format" rather than "--long_format".
          # Argparse converts hyphens to underscores so that the destination
          # string is a valid attribute name. Hf_argparser should do the same.
          long_options = [f"--{field.name}"]
          if "_" in field.name:
              long_options.append(f"--{field.name.replace('_', '-')}")
  
          kwargs = field.metadata.copy()
          # field.metadata is not used at all by Data Classes,
          # it is provided as a third-party extension mechanism.
          if isinstance(field.type, str):
              raise RuntimeError(
                  "Unresolved type detected, which should have been done with the help of "
                  "`typing.get_type_hints` method by default"
              )
  
          aliases = kwargs.pop("aliases", [])
          if isinstance(aliases, str):
              aliases = [aliases]
  
          origin_type = getattr(field.type, "__origin__", field.type)
          if origin_type is Union or (hasattr(types, "UnionType") and isinstance(origin_type, types.UnionType)):
              if str not in field.type.__args__ and (
                  len(field.type.__args__) != 2 or type(None) not in field.type.__args__
              ):
                  raise ValueError(
                      "Only `Union[X, NoneType]` (i.e., `Optional[X]`) is allowed for `Union` because"
                      " the argument parser only supports one type per argument."
                      f" Problem encountered in field '{field.name}'."
                  )
              if type(None) not in field.type.__args__:
                  # filter `str` in Union
                  field.type = field.type.__args__[0] if field.type.__args__[1] is str else field.type.__args__[1]
                  origin_type = getattr(field.type, "__origin__", field.type)
              elif bool not in field.type.__args__:
                  # filter `NoneType` in Union (except for `Union[bool, NoneType]`)
                  field.type = (
                      field.type.__args__[0] if isinstance(None, field.type.__args__[1]) else field.type.__args__[1]
                  )
                  origin_type = getattr(field.type, "__origin__", field.type)
  
          # A variable to store kwargs for a boolean field, if needed
          # so that we can init a `no_*` complement argument (see below)
          bool_kwargs = {}
          if origin_type is Literal or (isinstance(field.type, type) and issubclass(field.type, Enum)):
              if origin_type is Literal:
                  kwargs["choices"] = field.type.__args__
              else:
                  kwargs["choices"] = [x.value for x in field.type]
  
              kwargs["type"] = make_choice_type_function(kwargs["choices"])
  
              if field.default is not dataclasses.MISSING:
                  kwargs["default"] = field.default
              else:
                  kwargs["required"] = True
          elif field.type is bool or field.type == bool | None:
              # Copy the correct kwargs to use to instantiate a `no_*` complement argument below.
              # We do not initialize it here because the `no_*` alternative must be instantiated after the real argument
              bool_kwargs = copy(kwargs)
  
              # Hack because type=bool in argparse does not behave as we want.
              kwargs["type"] = string_to_bool
              if field.type is bool or (field.default is not None and field.default is not dataclasses.MISSING):
                  # Default value is False if we have no default when of type bool.
                  default = False if field.default is dataclasses.MISSING else field.default
                  # This is the value that will get picked if we don't include --{field.name} in any way
                  kwargs["default"] = default
                  # This tells argparse we accept 0 or 1 value after --{field.name}
                  kwargs["nargs"] = "?"
                  # This is the value that will get picked if we do --{field.name} (without value)
                  kwargs["const"] = True
          elif isclass(origin_type) and issubclass(origin_type, list):
              kwargs["type"] = field.type.__args__[0]
              kwargs["nargs"] = "+"
              if field.default_factory is not dataclasses.MISSING:
                  kwargs["default"] = field.default_factory()
              elif field.default is dataclasses.MISSING:
                  kwargs["required"] = True
          else:
              kwargs["type"] = field.type
              if field.default is not dataclasses.MISSING:
                  kwargs["default"] = field.default
              elif field.default_factory is not dataclasses.MISSING:
                  kwargs["default"] = field.default_factory()
              else:
                  kwargs["required"] = True
          parser.add_argument(*long_options, *aliases, **kwargs)
  
          # Add a complement `no_*` argument for a boolean field AFTER the initial field has already been added.
          # Order is important for arguments with the same destination!
          # We use a copy of earlier kwargs because the original kwargs have changed a lot before reaching down
          # here and we do not need those changes/additional keys.
          if field.default is True and (field.type is bool or field.type == bool | None):
              bool_kwargs["default"] = False
              parser.add_argument(
                  f"--no_{field.name}",
                  f"--no-{field.name.replace('_', '-')}",
                  action="store_false",
                  dest=field.name,
                  **bool_kwargs,
              )
  
      def _add_dataclass_arguments(self, dtype: DataClassType):
          if hasattr(dtype, "_argument_group_name"):
              parser = self.add_argument_group(dtype._argument_group_name)
          else:
              parser = self
  
          try:
              type_hints: dict[str, type] = get_type_hints(dtype)
          except NameError:
              raise RuntimeError(
                  f"Type resolution failed for {dtype}. Try declaring the class in global scope or "
                  "removing line of `from __future__ import annotations` which opts in Postponed "
                  "Evaluation of Annotations (PEP 563)"
              )
  
          for field in dataclasses.fields(dtype):
              if not field.init:
                  continue
              field.type = type_hints[field.name]
              self._parse_dataclass_field(parser, field)
  
      def parse_args_into_dataclasses(
          self,
          args=None,
          return_remaining_strings=False,
          look_for_args_file=True,
          args_filename=None,
          args_file_flag=None,
      ) -> tuple[DataClass, ...]:
          """
          Parse command-line args into instances of the specified dataclass types.
  
          This relies on argparse's `ArgumentParser.parse_known_args`. See the doc at:
          docs.python.org/3/library/argparse.html#argparse.ArgumentParser.parse_args
  
          Args:
              args:
                  List of strings to parse. The default is taken from sys.argv. (same as argparse.ArgumentParser)
              return_remaining_strings:
                  If true, also return a list of remaining argument strings.
              look_for_args_file:
                  If true, will look for a ".args" file with the same base name as the entry point script for this
                  process, and will append its potential content to the command line args.
              args_filename:
                  If not None, will uses this file instead of the ".args" file specified in the previous argument.
              args_file_flag:
                  If not None, will look for a file in the command-line args specified with this flag. The flag can be
                  specified multiple times and precedence is determined by the order (last one wins).
  
          Returns:
              Tuple consisting of:
  
                  - the dataclass instances in the same order as they were passed to the initializer.abspath
                  - if applicable, an additional namespace for more (non-dataclass backed) arguments added to the parser
                    after initialization.
                  - The potential list of remaining argument strings. (same as argparse.ArgumentParser.parse_known_args)
          """
  
          if args_file_flag or args_filename or (look_for_args_file and len(sys.argv)):
              args_files = []
  
              if args_filename:
                  args_files.append(Path(args_filename))
              elif look_for_args_file and len(sys.argv):
                  args_files.append(Path(sys.argv[0]).with_suffix(".args"))
  
              # args files specified via command line flag should overwrite default args files so we add them last
              if args_file_flag:
                  # Create special parser just to extract the args_file_flag values
                  args_file_parser = ArgumentParser()
                  args_file_parser.add_argument(args_file_flag, type=str, action="append")
  
                  # Use only remaining args for further parsing (remove the args_file_flag)
                  cfg, args = args_file_parser.parse_known_args(args=args)
                  cmd_args_file_paths = vars(cfg).get(args_file_flag.lstrip("-"), None)
  
                  if cmd_args_file_paths:
                      args_files.extend([Path(p) for p in cmd_args_file_paths])
  
              file_args = []
              for args_file in args_files:
                  if args_file.exists():
                      file_args += args_file.read_text().split()
  
              # in case of duplicate arguments the last one has precedence
              # args specified via the command line should overwrite args from files, so we add them last
              args = file_args + args if args is not None else file_args + sys.argv[1:]
          namespace, remaining_args = self.parse_known_args(args=args)
          outputs = []
          for dtype in self.dataclass_types:
              keys = {f.name for f in dataclasses.fields(dtype) if f.init}
              inputs = {k: v for k, v in vars(namespace).items() if k in keys}
              for k in keys:
                  delattr(namespace, k)
              obj = dtype(**inputs)
              outputs.append(obj)
          if len(namespace.__dict__) > 0:
              # additional namespace.
              outputs.append(namespace)
          if return_remaining_strings:
              return (*outputs, remaining_args)
          else:
              if remaining_args:
                  raise ValueError(f"Some specified arguments are not used by the HfArgumentParser: {remaining_args}")
  
              return (*outputs,)
  
      def parse_dict(self, args: dict[str, Any], allow_extra_keys: bool = False) -> tuple[DataClass, ...]:
          """
          Alternative helper method that does not use `argparse` at all, instead uses a dict and populating the dataclass
          types.
  
          Args:
              args (`dict`):
                  dict containing config values
              allow_extra_keys (`bool`, *optional*, defaults to `False`):
                  Defaults to False. If False, will raise an exception if the dict contains keys that are not parsed.
  
          Returns:
              Tuple consisting of:
  
                  - the dataclass instances in the same order as they were passed to the initializer.
          """
          unused_keys = set(args.keys())
          outputs = []
          for dtype in self.dataclass_types:
              keys = {f.name for f in dataclasses.fields(dtype) if f.init}
              inputs = {k: v for k, v in args.items() if k in keys}
              unused_keys.difference_update(inputs.keys())
              obj = dtype(**inputs)
              outputs.append(obj)
          if not allow_extra_keys and unused_keys:
              raise ValueError(f"Some keys are not used by the HfArgumentParser: {sorted(unused_keys)}")
          return tuple(outputs)
  
      def parse_json_file(self, json_file: str | os.PathLike, allow_extra_keys: bool = False) -> tuple[DataClass, ...]:
          """
          Alternative helper method that does not use `argparse` at all, instead loading a json file and populating the
          dataclass types.
  
          Args:
              json_file (`str` or `os.PathLike`):
                  File name of the json file to parse
              allow_extra_keys (`bool`, *optional*, defaults to `False`):
                  Defaults to False. If False, will raise an exception if the json file contains keys that are not
                  parsed.
  
          Returns:
              Tuple consisting of:
  
                  - the dataclass instances in the same order as they were passed to the initializer.
          """
          with open(Path(json_file), encoding="utf-8") as open_json_file:
              data = json.loads(open_json_file.read())
          outputs = self.parse_dict(data, allow_extra_keys=allow_extra_keys)
          return tuple(outputs)
  
      def parse_yaml_file(self, yaml_file: str | os.PathLike, allow_extra_keys: bool = False) -> tuple[DataClass, ...]:
          """
          Alternative helper method that does not use `argparse` at all, instead loading a yaml file and populating the
          dataclass types.
  
          Args:
              yaml_file (`str` or `os.PathLike`):
                  File name of the yaml file to parse
              allow_extra_keys (`bool`, *optional*, defaults to `False`):
                  Defaults to False. If False, will raise an exception if the json file contains keys that are not
                  parsed.
  
          Returns:
              Tuple consisting of:
  
                  - the dataclass instances in the same order as they were passed to the initializer.
          """
          outputs = self.parse_dict(yaml.safe_load(Path(yaml_file).read_text()), allow_extra_keys=allow_extra_keys)
          return tuple(outputs)
  