





"""simple docstring"""




import collections
import os
import re
from pathlib import Path


A   : List[Any]			=				'''src/transformers'''


# Matches is_xxx_available()
A   : Any			=				re.compile(R'''is\_([a-z_]*)_available()''')
# Catches a one-line _import_struct = {xxx}
A   : Union[str, Any]			=				re.compile(R'''^_import_structure\s+=\s+\{([^\}]+)\}''')
# Catches a line with a key-values pattern: "bla": ["foo", "bar"]
A   : Tuple			=				re.compile(R'''\s+"\S*":\s+\[([^\]]*)\]''')
# Catches a line if not is_foo_available
A   : Any			=				re.compile(R'''^\s*if\s+not\s+is\_[a-z_]*\_available\(\)''')
# Catches a line _import_struct["bla"].append("foo")
A   : Tuple			=				re.compile(R'''^\s*_import_structure\["\S*"\]\.append\("(\S*)"\)''')
# Catches a line _import_struct["bla"].extend(["foo", "bar"]) or _import_struct["bla"] = ["foo", "bar"]
A   : Tuple			=				re.compile(R'''^\s*_import_structure\[\S*\](?:\.extend\(|\s*=\s+)\[([^\]]*)\]''')
# Catches a line with an object between quotes and a comma:     "MyModel",
A   : List[str]			=				re.compile(R'''^\s+"([^"]+)",''')
# Catches a line with objects between brackets only:    ["foo", "bar"],
A   : str			=				re.compile(R'''^\s+\[([^\]]+)\]''')
# Catches a line with from foo import bar, bla, boo
A   : Optional[Any]			=				re.compile(R'''\s+from\s+\S*\s+import\s+([^\(\s].*)\n''')
# Catches a line with try:
A   : Union[str, Any]			=				re.compile(R'''^\s*try:''')
# Catches a line with else:
A   : str			=				re.compile(R'''^\s*else:''')







def        __lowerCamelCase (							__a       :List[str]   )		->    Any:






		"""simple docstring"""

		if _re_test_backend.search(__a   ) is None:
				return None
		A__  				=       [b[0] for b in _re_backend.findall(__a   )]
		backends.sort()
		return "_and_".join(__a   )







def        __lowerCamelCase (							__a       :Tuple   )		->    List[Any]:






		"""simple docstring"""

		with open(__a   ,					"""r"""   ,					encoding="""utf-8"""   ,					newline="""\n"""   ) as f:
				A__  				=       f.readlines()

		A__  				=       0
		while line_index < len(__a   ) and not lines[line_index].startswith("""_import_structure = {"""   ):
				line_index += 1

		# If this is a traditional init, just return.
		if line_index >= len(__a   ):
				return None

		# First grab the objects without a specific backend in _import_structure
		A__  				=       []
		while not lines[line_index].startswith("""if TYPE_CHECKING"""   ) and find_backend(lines[line_index]   ) is None:
				A__  				=       lines[line_index]
				# If we have everything on a single line, let's deal with it.
				if _re_one_line_import_struct.search(__a   ):
						A__  				=       _re_one_line_import_struct.search(__a   ).groups()[0]
						A__  				=       re.findall(R"""\[([^\]]+)\]"""   ,					__a   )
						for imp in imports:
								objects.extend([obj[1:-1] for obj in imp.split(""", """   )]   )
						line_index += 1
						continue
				A__  				=       _re_import_struct_key_value.search(__a   )
				if single_line_import_search is not None:
						A__  				=       [obj[1:-1] for obj in single_line_import_search.groups()[0].split(""", """   ) if len(__a   ) > 0]
						objects.extend(__a   )
				elif line.startswith(""" """ * 8 + """\""""   ):
						objects.append(line[9:-3]   )
				line_index += 1

		A__  				=       {"""none""": objects}
		# Let's continue with backend-specific objects in _import_structure
		while not lines[line_index].startswith("""if TYPE_CHECKING"""   ):
				# If the line is an if not is_backend_available, we grab all objects associated.
				A__  				=       find_backend(lines[line_index]   )
				# Check if the backend declaration is inside a try block:
				if _re_try.search(lines[line_index - 1]   ) is None:
						A__  				=       None

				if backend is not None:
						line_index += 1

						# Scroll until we hit the else block of try-except-else
						while _re_else.search(lines[line_index]   ) is None:
								line_index += 1

						line_index += 1

						A__  				=       []
						# Until we unindent, add backend objects to the list
						while len(lines[line_index]   ) <= 1 or lines[line_index].startswith(""" """ * 4   ):
								A__  				=       lines[line_index]
								if _re_import_struct_add_one.search(__a   ) is not None:
										objects.append(_re_import_struct_add_one.search(__a   ).groups()[0]   )
								elif _re_import_struct_add_many.search(__a   ) is not None:
										A__  				=       _re_import_struct_add_many.search(__a   ).groups()[0].split(""", """   )
										A__  				=       [obj[1:-1] for obj in imports if len(__a   ) > 0]
										objects.extend(__a   )
								elif _re_between_brackets.search(__a   ) is not None:
										A__  				=       _re_between_brackets.search(__a   ).groups()[0].split(""", """   )
										A__  				=       [obj[1:-1] for obj in imports if len(__a   ) > 0]
										objects.extend(__a   )
								elif _re_quote_object.search(__a   ) is not None:
										objects.append(_re_quote_object.search(__a   ).groups()[0]   )
								elif line.startswith(""" """ * 8 + """\""""   ):
										objects.append(line[9:-3]   )
								elif line.startswith(""" """ * 1_2 + """\""""   ):
										objects.append(line[1_3:-3]   )
								line_index += 1

						A__  				=       objects
				else:
						line_index += 1

    # At this stage we are in the TYPE_CHECKING part, first grab the objects without a specific backend
		A__  				=       []
		while (
		    line_index < len(__a   )
		    and find_backend(lines[line_index]   ) is None
		    and not lines[line_index].startswith("""else"""   )
		):
				A__  				=       lines[line_index]
				A__  				=       _re_import.search(__a   )
				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

		A__  				=       {"""none""": objects}
		# Let's continue with backend-specific objects
		while line_index < len(__a   ):
				# If the line is an if is_backend_available, we grab all objects associated.
				A__  				=       find_backend(lines[line_index]   )
				# Check if the backend declaration is inside a try block:
				if _re_try.search(lines[line_index - 1]   ) is None:
						A__  				=       None

				if backend is not None:
						line_index += 1

						# Scroll until we hit the else block of try-except-else
						while _re_else.search(lines[line_index]   ) is None:
								line_index += 1

						line_index += 1

						A__  				=       []
						# Until we unindent, add backend objects to the list
						while len(lines[line_index]   ) <= 1 or lines[line_index].startswith(""" """ * 8   ):
								A__  				=       lines[line_index]
								A__  				=       _re_import.search(__a   )
								if single_line_import_search is not None:
										objects.extend(single_line_import_search.groups()[0].split(""", """   )   )
								elif line.startswith(""" """ * 1_2   ):
										objects.append(line[1_2:-2]   )
								line_index += 1

						A__  				=       objects
				else:
						line_index += 1

		return import_dict_objects, type_hint_objects







def        __lowerCamelCase (							__a       :Optional[int]   ,					__a       :str   )		->    Tuple:






		"""simple docstring"""

		def find_duplicates(__a       :List[Any]   ):
				return [k for k, v in collections.Counter(__a   ).items() if v > 1]

		if list(import_dict_objects.keys()   ) != list(type_hint_objects.keys()   ):
				return ["Both sides of the init do not have the same backends!"]

		A__  				=       []
		for key in import_dict_objects.keys():
				A__  				=       find_duplicates(import_dict_objects[key]   )
				if duplicate_imports:
						errors.append(F'Duplicate _import_structure definitions for: {duplicate_imports}'   )
				A__  				=       find_duplicates(type_hint_objects[key]   )
				if duplicate_type_hints:
						errors.append(F'Duplicate TYPE_CHECKING objects for: {duplicate_type_hints}'   )

				if sorted(set(import_dict_objects[key]   )   ) != sorted(set(type_hint_objects[key]   )   ):
						A__  				=       """base imports""" if key == """none""" else F'{key} backend'
						errors.append(F'Differences for {name}:'   )
						for a in type_hint_objects[key]:
								if a not in import_dict_objects[key]:
										errors.append(F'  {a} in TYPE_HINT but not in _import_structure.'   )
						for a in import_dict_objects[key]:
								if a not in type_hint_objects[key]:
										errors.append(F'  {a} in _import_structure but not in TYPE_HINT.'   )
		return errors







def        __lowerCamelCase (							)		->    Union[str, Any]:






		"""simple docstring"""

		A__  				=       []
		for root, _, files in os.walk(__a   ):
				if "__init__.py" in files:
						A__  				=       os.path.join(__a   ,					"""__init__.py"""   )
						A__  				=       parse_init(__a   )
						if objects is not None:
								A__  				=       analyze_results(*__a   )
								if len(__a   ) > 0:
										A__  				=       F'Problem in {fname}, both halves do not define the same objects.\n{errors[0]}'
										failures.append("""\n""".join(__a   )   )
		if len(__a   ) > 0:
				raise ValueError("""\n\n""".join(__a   )   )







def        __lowerCamelCase (							)		->    Tuple:






		"""simple docstring"""

		A__  				=       []
		for path, directories, files in os.walk(__a   ):
				for folder in directories:
						# Ignore private modules
						if folder.startswith("""_"""   ):
								directories.remove(__a   )
								continue
						# Ignore leftovers from branches (empty folders apart from pycache)
						if len(list((Path(__a   ) / folder).glob("""*.py"""   )   )   ) == 0:
								continue
						A__  				=       str((Path(__a   ) / folder).relative_to(__a   )   )
						A__  				=       short_path.replace(os.path.sep   ,					"""."""   )
						submodules.append(__a   )
				for fname in files:
						if fname == "__init__.py":
								continue
						A__  				=       str((Path(__a   ) / fname).relative_to(__a   )   )
						A__  				=       short_path.replace(""".py"""   ,					""""""   ).replace(os.path.sep   ,					"""."""   )
						if len(submodule.split("""."""   )   ) == 1:
								submodules.append(__a   )
		return submodules


A   : Optional[Any]			=				[
    '''convert_pytorch_checkpoint_to_tf2''',
    '''modeling_flax_pytorch_utils''',
    '''models.esm.openfold_utils''',
]







def        __lowerCamelCase (							)		->    Union[str, Any]:






		"""simple docstring"""

		from transformers.utils import direct_transformers_import

		A__  				=       direct_transformers_import(__a   )

		A__  				=       set(transformers._import_structure.keys()   )
		# This contains all the base keys of the _import_structure object defined in the init, but if the user is missing
		# some optional dependencies, they may not have all of them. Thus we read the init to read all additions and
		# (potentiall re-) add them.
		with open(os.path.join(__a   ,					"""__init__.py"""   )   ,					"""r"""   ) as f:
				A__  				=       f.read()
		import_structure_keys.update(set(re.findall(R"""import_structure\[\"([^\"]*)\"\]"""   ,					__a   )   )   )

		A__  				=       [
		    module
		    for module in get_transformers_submodules()
		    if module not in IGNORE_SUBMODULES and module not in import_structure_keys
		]

		if len(__a   ) > 0:
				A__  				=       """\n""".join(F'- {module}' for module in module_not_registered   )
				raise ValueError(
				    """The following submodules are not properly registed in the main init of Transformers:\n"""
				    F'{list_of_modules}\n'
				    """Make sure they appear somewhere in the keys of `_import_structure` with an empty list as value."""   )


if __name__ == "__main__":
					check_all_inits()
					check_submodules()










import argparse

import torch

from transformers import BertForMaskedLM


if __name__ == "__main__":
					A   : List[str]			=				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   : Tuple			=				parser.parse_args()

					if args.model_type == "bert":
										A   : Dict			=				BertForMaskedLM.from_pretrained(args.model_name)
										A   : List[str]			=				'''bert'''
					else:
										raise ValueError('''args.model_type should be "bert".''')

					A   : Optional[Any]			=				model.state_dict()
					A   : int			=				{}

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

					A   : Tuple			=				0
					for teacher_idx in [0, 2, 4, 7, 9, 1_1]:
										for w in ["weight", "bias"]:
															A   : Optional[Any]			=				state_dict[
															    F'''{prefix}.encoder.layer.{teacher_idx}.attention.self.query.{w}'''
															]
															A   : Optional[Any]			=				state_dict[
															    F'''{prefix}.encoder.layer.{teacher_idx}.attention.self.key.{w}'''
															]
															A   : Optional[Any]			=				state_dict[
															    F'''{prefix}.encoder.layer.{teacher_idx}.attention.self.value.{w}'''
															]

															A   : int			=				state_dict[
															    F'''{prefix}.encoder.layer.{teacher_idx}.attention.output.dense.{w}'''
															]
															A   : List[Any]			=				state_dict[
															    F'''{prefix}.encoder.layer.{teacher_idx}.attention.output.LayerNorm.{w}'''
															]

															A   : List[str]			=				state_dict[
															    F'''{prefix}.encoder.layer.{teacher_idx}.intermediate.dense.{w}'''
															]
															A   : Union[str, Any]			=				state_dict[
															    F'''{prefix}.encoder.layer.{teacher_idx}.output.dense.{w}'''
															]
															A   : List[str]			=				state_dict[
															    F'''{prefix}.encoder.layer.{teacher_idx}.output.LayerNorm.{w}'''
															]
										std_idx += 1

					A   : int			=				state_dict['''cls.predictions.decoder.weight''']
					A   : str			=				state_dict['''cls.predictions.bias''']
					if args.vocab_transform:
										for w in ["weight", "bias"]:
															A   : List[Any]			=				state_dict[F'''cls.predictions.transform.dense.{w}''']
															A   : List[str]			=				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)



