old_contents: # Copyright (c) Facebook, Inc. and its affiliates.
  #
  # This source code is licensed under the MIT license found in the
  # LICENSE file in the root directory of this source tree.
  
  import functools
  import importlib
  
  from fairseq.hub_utils import (  # noqa; noqa
      BPEHubInterface as bpe,
      TokenizerHubInterface as tokenizer,
  )
  from fairseq.models import MODEL_REGISTRY  # noqa
  
  
  dependencies = [
      "dataclasses",
      "hydra",
      "numpy",
      "regex",
      "requests",
      "torch",
  ]
  
  
  # Check for required dependencies and raise a RuntimeError if any are missing.
  missing_deps = []
  for dep in dependencies:
      try:
          importlib.import_module(dep)
      except ImportError:
          # Hack: the hydra package is provided under the "hydra-core" name in
          # pypi. We don't want the user mistakenly calling `pip install hydra`
          # since that will install an unrelated package.
          if dep == "hydra":
              dep = "hydra-core"
          missing_deps.append(dep)
  if len(missing_deps) > 0:
      raise RuntimeError("Missing dependencies: {}".format(", ".join(missing_deps)))
  
  
  # torch.hub doesn't build Cython components, so if they are not found then try
  # to build them here
  try:
      import fairseq.data.token_block_utils_fast  # noqa
  except ImportError:
      try:
          import cython  # noqa
          import os
          from setuptools import sandbox
  
          sandbox.run_setup(
              os.path.join(os.path.dirname(__file__), "setup.py"),
              ["build_ext", "--inplace"],
          )
      except ImportError:
          print(
              "Unable to build Cython components. Please make sure Cython is "
              "installed if the torch.hub model you are loading depends on it."
          )
  
  
  # automatically expose models defined in FairseqModel::hub_models
  for _model_type, _cls in MODEL_REGISTRY.items():
      for model_name in _cls.hub_models().keys():
          globals()[model_name] = functools.partial(
              _cls.from_pretrained,
              model_name,
          )
  
new_contents: # Copyright (c) Facebook, Inc. and its affiliates.
  #
  # This source code is licensed under the MIT license found in the
  # LICENSE file in the root directory of this source tree.
  
  import functools
  import importlib
  
  from fairseq.hub_utils import (  # noqa; noqa
      BPEHubInterface as bpe,
      TokenizerHubInterface as tokenizer,
  )
  from fairseq.models import MODEL_REGISTRY  # noqa
  
  
  dependencies = [
      "dataclasses",
      "hydra",
      "numpy",
      "omegaconf",
      "regex",
      "requests",
      "torch",
  ]
  
  
  # Check for required dependencies and raise a RuntimeError if any are missing.
  missing_deps = []
  for dep in dependencies:
      try:
          importlib.import_module(dep)
      except ImportError:
          # Hack: the hydra package is provided under the "hydra-core" name in
          # pypi. We don't want the user mistakenly calling `pip install hydra`
          # since that will install an unrelated package.
          if dep == "hydra":
              dep = "hydra-core"
          missing_deps.append(dep)
  if len(missing_deps) > 0:
      raise RuntimeError("Missing dependencies: {}".format(", ".join(missing_deps)))
  
  
  # torch.hub doesn't build Cython components, so if they are not found then try
  # to build them here
  try:
      import fairseq.data.token_block_utils_fast  # noqa
  except ImportError:
      try:
          import cython  # noqa
          import os
          from setuptools import sandbox
  
          sandbox.run_setup(
              os.path.join(os.path.dirname(__file__), "setup.py"),
              ["build_ext", "--inplace"],
          )
      except ImportError:
          print(
              "Unable to build Cython components. Please make sure Cython is "
              "installed if the torch.hub model you are loading depends on it."
          )
  
  
  # automatically expose models defined in FairseqModel::hub_models
  for _model_type, _cls in MODEL_REGISTRY.items():
      for model_name in _cls.hub_models().keys():
          globals()[model_name] = functools.partial(
              _cls.from_pretrained,
              model_name,
          )
  
subject: Add omegaconf dependency to hubconf.py (fixes #3093) (#3102)