text: # 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 re
  import sys
  from argparse import ArgumentParser, ArgumentTypeError
  from enum import Enum
  from pathlib import Path
  from typing import Any, Iterable, List, NewType, Optional, Tuple, Union
  
  from .utils.logging import get_logger
  
  from sparsezoo import Zoo
  from sparsezoo.requests.base import ZOO_STUB_PREFIX
  
  
  logger = get_logger(__name__)
  
  
  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)."
          )
  
  
  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.
      """
  
      dataclass_types: Iterable[DataClassType]
  
      def __init__(self, dataclass_types: Union[DataClassType, Iterable[DataClassType]], **kwargs):
          """
          Args:
              dataclass_types:
                  Dataclass type, or list of dataclass types for which we will "fill" instances with the parsed args.
              kwargs:
                  (Optional) Passed to `argparse.ArgumentParser()` in the regular way.
          """
          super().__init__(**kwargs)
          if dataclasses.is_dataclass(dataclass_types):
              dataclass_types = [dataclass_types]
          self.dataclass_types = dataclass_types
          for dtype in self.dataclass_types:
              self._add_dataclass_arguments(dtype)
  
      def _add_dataclass_arguments(self, dtype: DataClassType):
          for field in dataclasses.fields(dtype):
              if not field.init:
                  continue
              field_name = f"--{field.name}"
              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 ImportError(
                      "This implementation is not compatible with Postponed Evaluation of Annotations (PEP 563),"
                      "which can be opted in from Python 3.7 with `from __future__ import annotations`."
                      "We will add compatibility when Python 3.9 is released."
                  )
              typestring = str(field.type)
              for prim_type in (int, float, str):
                  for collection in (List,):
                      if (
                          typestring == f"typing.Union[{collection[prim_type]}, NoneType]"
                          or typestring == f"typing.Optional[{collection[prim_type]}]"
                      ):
                          field.type = collection[prim_type]
                  if (
                      typestring == f"typing.Union[{prim_type.__name__}, NoneType]"
                      or typestring == f"typing.Optional[{prim_type.__name__}]"
                  ):
                      field.type = prim_type
  
              if isinstance(field.type, type) and issubclass(field.type, Enum):
                  kwargs["choices"] = [x.value for x in field.type]
                  kwargs["type"] = type(kwargs["choices"][0])
                  if field.default is not dataclasses.MISSING:
                      kwargs["default"] = field.default
                  else:
                      kwargs["required"] = True
              elif field.type is bool or field.type == Optional[bool]:
                  if field.default is True:
                      self.add_argument(f"--no_{field.name}", action="store_false", dest=field.name, **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 True if we have no default when of type bool.
                      default = True 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 (
                  hasattr(field.type, "__origin__") and re.search(r"^typing\.List\[(.*)\]$", str(field.type)) is not None
              ):
                  kwargs["nargs"] = "+"
                  kwargs["type"] = field.type.__args__[0]
                  assert all(
                      x == kwargs["type"] for x in field.type.__args__
                  ), f"{field.name} cannot be a List of mixed types"
                  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
              self.add_argument(field_name, **kwargs)
  
      def parse_args_into_dataclasses(
          self, args=None, return_remaining_strings=False, look_for_args_file=True, args_filename=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/3.7/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.
  
          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_filename or (look_for_args_file and len(sys.argv)):
              if args_filename:
                  args_file = Path(args_filename)
              else:
                  args_file = Path(sys.argv[0]).with_suffix(".args")
  
              if args_file.exists():
                  fargs = args_file.read_text().split()
                  args = fargs + args if args is not None else fargs + sys.argv[1:]
                  # in case of duplicate arguments the first one has precedence
                  # so we append rather than prepend.
          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 tuple(
                  *[_download_dataclass_zoo_stub_files(output) for output in outputs],
                  remaining_args,
              )
          else:
              if remaining_args:
                  raise ValueError(f"Some specified arguments are not used by the HfArgumentParser: {remaining_args}")
  
              return tuple(
                  [_download_dataclass_zoo_stub_files(output) for output in outputs]
              )
  
      def parse_json_file(self, json_file: str) -> Tuple[DataClass, ...]:
          """
          Alternative helper method that does not use `argparse` at all, instead loading a json file and populating the
          dataclass types.
          """
          data = json.loads(Path(json_file).read_text())
          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 data.items() if k in keys}
              obj = dtype(**inputs)
              outputs.append(obj)
          return tuple(
              [_download_dataclass_zoo_stub_files(output) for output in outputs]
          )
  
      def parse_dict(self, args: dict) -> Tuple[DataClass, ...]:
          """
          Alternative helper method that does not use `argparse` at all, instead uses a dict and populating the dataclass
          types.
          """
          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}
              obj = dtype(**inputs)
              outputs.append(obj)
          return tuple(
              [_download_dataclass_zoo_stub_files(output) for output in outputs]
          )
  
  
  def _download_dataclass_zoo_stub_files(data_class: DataClass):
      for name, val in data_class.__dict__.items():
          if not isinstance(val, str) or "recipe" in name or not val.startswith("zoo:"):
              continue
  
          logger.info(f"Downloading framework files for SparseZoo stub: {val}")
  
          zoo_model = Zoo.load_model_from_stub(val)
          framework_file_paths = zoo_model.download_framework_files()
          assert framework_file_paths, (
              "Unable to download any framework files for SparseZoo stub {val}"
          )
          framework_file_names = [os.path.basename(path) for path in framework_file_paths]
          if "pytorch_model.bin" not in framework_file_names or (
              "config.json" not in framework_file_names
          ):
              raise RuntimeError(
                  "Unable to find 'pytorch_model.bin' and 'config.json' in framework "
                  f"files downloaded from {val}. Found {framework_file_names}. Check "
                  "if the given stub is for a transformers repo model"
              )
          framework_dir_path = Path(framework_file_paths[0]).parent.absolute()
  
          logger.info(
              f"Overwriting argument {name} to downloaded {framework_dir_path}"
          )
  
          data_class.__dict__[name] = str(framework_dir_path)
  
      return data_class
  