




"""simple docstring"""


import argparse
import os
import re


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_dummies.py
A_						=				'''src/diffusers'''

# Matches is_xxx_available()
A_						=				re.compile(r'''is\_([a-z_]*)_available\(\)''')
# Matches from xxx import bla
A_						=				re.compile(r'''\s+from\s+\S*\s+import\s+([^\(\s].*)\n''')


A_						=				'''
{0} = None
'''

A_						=				'''
class {0}(metaclass=DummyObject):
    _backends = {1}

    def __init__(self, *args, **kwargs):
        requires_backends(self, {1})

    @classmethod
    def from_config(cls, *args, **kwargs):
        requires_backends(cls, {1})

    @classmethod
    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, {1})
'''


A_						=				'''
def {0}(*args, **kwargs):
    requires_backends({0}, {1})
'''


def 					UpperCAmelCase__       (snake_case__							:					str    ):
  """simple docstring"""

  _snake_case							:		Union[str, Any]								=  _re_backend.findall(__snake_case    )
  if len(__snake_case    ) == 0:
    return None

  return "_and_".join(__snake_case    )


def 					UpperCAmelCase__       ():
  """simple docstring"""

  with open(os.path.join(__snake_case		,   """__init__.py"""    )		,   """r"""		,   encoding="""utf-8"""		,   newline="""\n"""    ) as f:
    _snake_case							:		Tuple								=  f.readlines()

  # Get to the point we do the actual imports for type checking
  _snake_case							:		int								=  0
  _snake_case							:		Optional[int]								=  {}
  # Go through the end of the file
  while line_index < len(__snake_case    ):
    # If the line contains is_backend_available, we grab all objects associated with the `else` block
    _snake_case							:		Tuple								=  find_backend(lines[line_index]    )
    if backend is not None:
      while not lines[line_index].startswith("""else:"""    ):
        line_index += 1
      line_index += 1
      _snake_case							:		Dict								=  []
      # Until we unindent, add backend objects to the list
      while line_index < len(__snake_case    ) and len(lines[line_index]    ) > 1:
        _snake_case							:		Any								=  lines[line_index]
        _snake_case							:		Dict								=  _re_single_line_import.search(__snake_case    )
        if single_line_import_search is not None:
          objects.extend(single_line_import_search.groups()[0].split(""", """    )    )
        elif line.startswith(""" """ * 8    ):
          objects.append(line[8:-2]    )
        line_index += 1

      if len(__snake_case    ) > 0:
        _snake_case							:		str								=  objects
    else:
      line_index += 1

  return backend_specific_objects


def 					UpperCAmelCase__       (snake_case__							:					Tuple		,   snake_case__							:					str    ):
  """simple docstring"""

  if name.isupper():
    return DUMMY_CONSTANT.format(__snake_case    )
  elif name.islower():
    return DUMMY_FUNCTION.format(__snake_case		,   __snake_case    )
  else:
    return DUMMY_CLASS.format(__snake_case		,   __snake_case    )


def 					UpperCAmelCase__       (snake_case__							:					List[str]=None    ):
  """simple docstring"""

  if backend_specific_objects is None:
    _snake_case							:		Optional[int]								=  read_init()
  # For special correspondence backend to module name as used in the function requires_modulename
  _snake_case							:		Any								=  {}

  for backend, objects in backend_specific_objects.items():
    _snake_case							:		Dict								=  """[""" + """, """.join(F"\"{b}\"" for b in backend.split("""_and_"""    )    ) + """]"""
    _snake_case							:		int								=  """# This file is autogenerated by the command `make fix-copies`, do not edit.\n"""
    dummy_file += "from ..utils import DummyObject, requires_backends\n\n"
    dummy_file += "\n".join([create_dummy_object(__snake_case		,   __snake_case    ) for o in objects]    )
    _snake_case							:		int								=  dummy_file

  return dummy_files


def 					UpperCAmelCase__       (snake_case__							:					int=False    ):
  """simple docstring"""

  _snake_case							:		Tuple								=  create_dummy_files()
  # For special correspondence backend to shortcut as used in utils/dummy_xxx_objects.py
  _snake_case							:		Optional[Any]								=  {"""torch""": """pt"""}

  # Locate actual dummy modules and read their content.
  _snake_case							:		Union[str, Any]								=  os.path.join(__snake_case		,   """utils"""    )
  _snake_case							:		Optional[Any]								=  {
      backend: os.path.join(__snake_case		,   F"dummy_{short_names.get(__snake_case		,   __snake_case    )}_objects.py"    )
      for backend in dummy_files.keys()
  }

  _snake_case							:		int								=  {}
  for backend, file_path in dummy_file_paths.items():
    if os.path.isfile(__snake_case    ):
      with open(__snake_case		,   """r"""		,   encoding="""utf-8"""		,   newline="""\n"""    ) as f:
        _snake_case							:		Union[str, Any]								=  f.read()
    else:
      _snake_case							:		int								=  """"""

  for backend in dummy_files.keys():
    if dummy_files[backend] != actual_dummies[backend]:
      if overwrite:
        print(
            F"Updating diffusers.utils.dummy_{short_names.get(__snake_case		,   __snake_case    )}_objects.py as the main "
            """__init__ has new objects."""    )
        with open(dummy_file_paths[backend]		,   """w"""		,   encoding="""utf-8"""		,   newline="""\n"""    ) as f:
          f.write(dummy_files[backend]    )
      else:
        raise ValueError(
            """The main __init__ has objects that are not present in """
            F"diffusers.utils.dummy_{short_names.get(__snake_case		,   __snake_case    )}_objects.py. Run `make fix-copies` "
            """to fix this."""    )


if __name__ == "__main__":
     A_						=				argparse.ArgumentParser()
     parser.add_argument('''--fix_and_overwrite''', action='''store_true''', help='''Whether to fix inconsistencies.''')
     A_						=				parser.parse_args()

     check_dummies(args.fix_and_overwrite)




"""simple docstring"""





import argparse

import torch

from transformers import BertForMaskedLM


if __name__ == "__main__":
				_a														=		argparse.ArgumentParser(
				    description=(
				        """Extraction some layers of the full BertForMaskedLM or RObertaForMaskedLM for Transfer Learned"""
				        """ Distillation"""
				    )
				)
				parser.add_argument("""--model_type""", default="""bert""", choices=["""bert"""])
				parser.add_argument("""--model_name""", default="""bert-base-uncased""", type=str)
				parser.add_argument("""--dump_checkpoint""", default="""serialization_dir/tf_bert-base-uncased_0247911.pth""", type=str)
				parser.add_argument("""--vocab_transform""", action="""store_true""")
				_a														=		parser.parse_args()

				if args.model_type == "bert":
								_a														=		BertForMaskedLM.from_pretrained(args.model_name)
								_a														=		"""bert"""
				else:
								raise ValueError("""args.model_type should be \"bert\".""")

				_a														=		model.state_dict()
				_a														=		{}

				for w in ["word_embeddings", "position_embeddings"]:
								_a														=		state_dict[F"""{prefix}.embeddings.{w}.weight"""]
				for w in ["weight", "bias"]:
								_a														=		state_dict[F"""{prefix}.embeddings.LayerNorm.{w}"""]

				_a														=		0
				for teacher_idx in [0, 2, 4, 7, 9, 11]:
								for w in ["weight", "bias"]:
												_a														=		state_dict[
												    F"""{prefix}.encoder.layer.{teacher_idx}.attention.self.query.{w}"""
												]
												_a														=		state_dict[
												    F"""{prefix}.encoder.layer.{teacher_idx}.attention.self.key.{w}"""
												]
												_a														=		state_dict[
												    F"""{prefix}.encoder.layer.{teacher_idx}.attention.self.value.{w}"""
												]

												_a														=		state_dict[
												    F"""{prefix}.encoder.layer.{teacher_idx}.attention.output.dense.{w}"""
												]
												_a														=		state_dict[
												    F"""{prefix}.encoder.layer.{teacher_idx}.attention.output.LayerNorm.{w}"""
												]

												_a														=		state_dict[
												    F"""{prefix}.encoder.layer.{teacher_idx}.intermediate.dense.{w}"""
												]
												_a														=		state_dict[
												    F"""{prefix}.encoder.layer.{teacher_idx}.output.dense.{w}"""
												]
												_a														=		state_dict[
												    F"""{prefix}.encoder.layer.{teacher_idx}.output.LayerNorm.{w}"""
												]
								std_idx += 1

				_a														=		state_dict["""cls.predictions.decoder.weight"""]
				_a														=		state_dict["""cls.predictions.bias"""]
				if args.vocab_transform:
								for w in ["weight", "bias"]:
												_a														=		state_dict[F"""cls.predictions.transform.dense.{w}"""]
												_a														=		state_dict[F"""cls.predictions.transform.LayerNorm.{w}"""]

				print(F"""N layers selected for distillation: {std_idx}""")
				print(F"""Number of params transferred for distillation: {len(compressed_sd.keys())}""")

				print(F"""Save transferred checkpoint to {args.dump_checkpoint}.""")
				torch.save(compressed_sd, args.dump_checkpoint)






