

"""simple docstring"""





import collections
import importlib.util
import os
import re
from pathlib import Path


__UpperCamelCase    : Tuple				 =						'''src/transformers'''


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

def     __SCREAMING_SNAKE_CASE    ( A_					):
	if _re_test_backend.search(SCREAMING_SNAKE_CASE_					) is None:
		return None
	lowerCAmelCase__      :		List[Any]    							=      [b[0] for b in _re_backend.findall(SCREAMING_SNAKE_CASE_					)]
	backends.sort()
	return "_and_".join(SCREAMING_SNAKE_CASE_					)

def     __SCREAMING_SNAKE_CASE    ( A_					):
	with open(SCREAMING_SNAKE_CASE_		,	'''r'''		,	encoding='''utf-8'''		,	newline='''\n'''					) as f:
		lowerCAmelCase__      :		List[str]    							=      f.readlines()

	lowerCAmelCase__      :		str    							=      0
	while line_index < len(SCREAMING_SNAKE_CASE_					) and not lines[line_index].startswith('''_import_structure = {'''					):
		line_index += 1

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

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

	lowerCAmelCase__      :		Optional[int]    							=      {'''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.
		lowerCAmelCase__      :		str    							=      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:
			lowerCAmelCase__      :		Tuple    							=      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

			lowerCAmelCase__      :		Optional[int]    							=      []
			# Until we unindent, add backend objects to the list
			while len(lines[line_index]					) <= 1 or lines[line_index].startswith(''' ''' * 4					):
				lowerCAmelCase__      :		Dict    							=      lines[line_index]
				if _re_import_struct_add_one.search(SCREAMING_SNAKE_CASE_					) is not None:
					objects.append(_re_import_struct_add_one.search(SCREAMING_SNAKE_CASE_					).groups()[0]					)
				elif _re_import_struct_add_many.search(SCREAMING_SNAKE_CASE_					) is not None:
					lowerCAmelCase__      :		int    							=      _re_import_struct_add_many.search(SCREAMING_SNAKE_CASE_					).groups()[0].split(''', '''					)
					lowerCAmelCase__      :		List[str]    							=      [obj[1:-1] for obj in imports if len(SCREAMING_SNAKE_CASE_					) > 0]
					objects.extend(SCREAMING_SNAKE_CASE_					)
				elif _re_between_brackets.search(SCREAMING_SNAKE_CASE_					) is not None:
					lowerCAmelCase__      :		int    							=      _re_between_brackets.search(SCREAMING_SNAKE_CASE_					).groups()[0].split(''', '''					)
					lowerCAmelCase__      :		int    							=      [obj[1:-1] for obj in imports if len(SCREAMING_SNAKE_CASE_					) > 0]
					objects.extend(SCREAMING_SNAKE_CASE_					)
				elif _re_quote_object.search(SCREAMING_SNAKE_CASE_					) is not None:
					objects.append(_re_quote_object.search(SCREAMING_SNAKE_CASE_					).groups()[0]					)
				elif line.startswith(''' ''' * 8 + '''\"'''					):
					objects.append(line[9:-3]					)
				elif line.startswith(''' ''' * 12 + '''\"'''					):
					objects.append(line[13:-3]					)
				line_index += 1

			lowerCAmelCase__      :		List[Any]    							=      objects
		else:
			line_index += 1

    # At this stage we are in the TYPE_CHECKING part, first grab the objects without a specific backend
	lowerCAmelCase__      :		Optional[int]    							=      []
	while (
	    line_index < len(SCREAMING_SNAKE_CASE_					)
	    and find_backend(lines[line_index]					) is None
	    and not lines[line_index].startswith('''else'''					)
	):
		lowerCAmelCase__      :		Dict    							=      lines[line_index]
		lowerCAmelCase__      :		Tuple    							=      _re_import.search(SCREAMING_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

	lowerCAmelCase__      :		str    							=      {'''none''': objects}
	# Let's continue with backend-specific objects
	while line_index < len(SCREAMING_SNAKE_CASE_					):
		# If the line is an if is_backend_available, we grab all objects associated.
		lowerCAmelCase__      :		Any    							=      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:
			lowerCAmelCase__      :		Tuple    							=      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

			lowerCAmelCase__      :		Tuple    							=      []
			# Until we unindent, add backend objects to the list
			while len(lines[line_index]					) <= 1 or lines[line_index].startswith(''' ''' * 8					):
				lowerCAmelCase__      :		Optional[int]    							=      lines[line_index]
				lowerCAmelCase__      :		Dict    							=      _re_import.search(SCREAMING_SNAKE_CASE_					)
				if single_line_import_search is not None:
					objects.extend(single_line_import_search.groups()[0].split(''', '''					)					)
				elif line.startswith(''' ''' * 12					):
					objects.append(line[12:-2]					)
				line_index += 1

			lowerCAmelCase__      :		List[Any]    							=      objects
		else:
			line_index += 1

	return import_dict_objects, type_hint_objects

def     __SCREAMING_SNAKE_CASE    ( A_		,	A_					):

	def find_duplicates(A_					):
		return [k for k, v in collections.Counter(SCREAMING_SNAKE_CASE_					).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!"]

	lowerCAmelCase__      :		Tuple    							=      []
	for key in import_dict_objects.keys():
		lowerCAmelCase__      :		Any    							=      find_duplicates(import_dict_objects[key]					)
		if duplicate_imports:
			errors.append(f'Duplicate _import_structure definitions for: {duplicate_imports}'					)
		lowerCAmelCase__      :		Tuple    							=      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]					)					):
			lowerCAmelCase__      :		Optional[int]    							=      '''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     __SCREAMING_SNAKE_CASE    ( ):
	lowerCAmelCase__      :		str    							=      []
	for root, _, files in os.walk(SCREAMING_SNAKE_CASE_					):
		if "__init__.py" in files:
			lowerCAmelCase__      :		Optional[int]    							=      os.path.join(SCREAMING_SNAKE_CASE_		,	'''__init__.py'''					)
			lowerCAmelCase__      :		Union[str, Any]    							=      parse_init(SCREAMING_SNAKE_CASE_					)
			if objects is not None:
				lowerCAmelCase__      :		Union[str, Any]    							=      analyze_results(*SCREAMING_SNAKE_CASE_					)
				if len(SCREAMING_SNAKE_CASE_					) > 0:
					lowerCAmelCase__      :		Tuple    							=      f'Problem in {fname}, both halves do not define the same objects.\n{errors[0]}'
					failures.append('''\n'''.join(SCREAMING_SNAKE_CASE_					)					)
	if len(SCREAMING_SNAKE_CASE_					) > 0:
		raise ValueError('''\n\n'''.join(SCREAMING_SNAKE_CASE_					)					)

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


__UpperCamelCase    : Any				 =						[
    '''convert_pytorch_checkpoint_to_tf2''',
    '''modeling_flax_pytorch_utils''',
]

def     __SCREAMING_SNAKE_CASE    ( ):
	# This is to make sure the transformers module imported is the one in the repo.
	lowerCAmelCase__      :		Optional[Any]    							=      importlib.util.spec_from_file_location(
	    '''transformers'''		,	os.path.join(SCREAMING_SNAKE_CASE_		,	'''__init__.py'''					)		,	submodule_search_locations=[PATH_TO_TRANSFORMERS]		,	)
	lowerCAmelCase__      :		Tuple    							=      spec.loader.load_module()

	lowerCAmelCase__      :		Dict    							=      [
	    module
	    for module in get_transformers_submodules()
	    if module not in IGNORE_SUBMODULES and module not in transformers._import_structure.keys()
	]
	if len(SCREAMING_SNAKE_CASE_					) > 0:
		lowerCAmelCase__      :		Union[str, Any]    							=      '''\n'''.join(f'- {module}' for module in module_not_registered					)
		raise ValueError(
		    '''The following submodules are not properly registered 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 os
from pathlib import Path

import fairseq
import torch
from packaging import version
from torch import nn

from transformers import (
    BartConfig,
    BartForConditionalGeneration,
    BartForSequenceClassification,
    BartModel,
    BartTokenizer,
)
from transformers.utils import logging


A		: int								=     ['''bart.large''', '''bart.large.mnli''', '''bart.large.cnn''', '''bart_xsum/model.pt''']
A		: Any								=     {'''bart.large''': BartModel, '''bart.large.mnli''': BartForSequenceClassification}
if version.parse(fairseq.__version__) < version.parse('''0.9.0'''):
		raise Exception('''requires fairseq >= 0.9.0''')


logging.set_verbosity_info()
A		: Optional[int]								=     logging.get_logger(__name__)

A		: int								=     ''' Hello world! cécé herlolip'''

A		: List[Any]								=     [
    ('''model.classification_heads.mnli.dense.weight''', '''classification_head.dense.weight'''),
    ('''model.classification_heads.mnli.dense.bias''', '''classification_head.dense.bias'''),
    ('''model.classification_heads.mnli.out_proj.weight''', '''classification_head.out_proj.weight'''),
    ('''model.classification_heads.mnli.out_proj.bias''', '''classification_head.out_proj.bias'''),
]




def 	UpperCamelCase__  (     SCREAMING_SNAKE_CASE_	:	Union[str, Any]       )  ->     Tuple:
						_lowercase					       =					[
						    """encoder.version""",
						    """decoder.version""",
						    """model.encoder.version""",
						    """model.decoder.version""",
						    """_float_tensor""",
						]
						for k in ignore_keys:
												state_dict.pop(SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_       )




def 	UpperCamelCase__  (     SCREAMING_SNAKE_CASE_	:	Union[str, Any]		,   SCREAMING_SNAKE_CASE_	:	Optional[int]		,   SCREAMING_SNAKE_CASE_	:	List[Any]       )  ->     List[str]:
						_lowercase					       =					dct.pop(SCREAMING_SNAKE_CASE_       )
						_lowercase					       =					val




def 	UpperCamelCase__  (     SCREAMING_SNAKE_CASE_	:	Any       )  ->     Any:
						_lowercase					       =					torch.load(SCREAMING_SNAKE_CASE_		,   map_location="""cpu"""       )
						_lowercase					       =					torch.hub.load("""pytorch/fairseq"""		,   """bart.large.cnn"""       ).eval()
						hub_interface.model.load_state_dict(sd["""model"""]       )
						return hub_interface




def 	UpperCamelCase__  (     SCREAMING_SNAKE_CASE_	:	List[str]       )  ->     Optional[int]:
						_lowercase       ,						_lowercase					       =					emb.weight.shape
						_lowercase					       =					nn.Linear(SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_		,   bias=SCREAMING_SNAKE_CASE_       )
						_lowercase					       =					emb.weight.data
						return lin_layer




@torch.no_grad()
def 	UpperCamelCase__  (     SCREAMING_SNAKE_CASE_	:	Any		,   SCREAMING_SNAKE_CASE_	:	Tuple		,   SCREAMING_SNAKE_CASE_	:	int=None       )  ->     int:
						if not os.path.exists(SCREAMING_SNAKE_CASE_       ):
												_lowercase					       =					torch.hub.load("""pytorch/fairseq"""		,   SCREAMING_SNAKE_CASE_       ).eval()
						else:
												_lowercase					       =					load_xsum_checkpoint(SCREAMING_SNAKE_CASE_       )

						bart.model.upgrade_state_dict(bart.model.state_dict()       )
						if hf_checkpoint_name is None:
												_lowercase					       =					checkpoint_path.replace("""."""		,   """-"""       )
						_lowercase					       =					BartConfig.from_pretrained(SCREAMING_SNAKE_CASE_       )
						_lowercase					       =					bart.encode(SCREAMING_SNAKE_CASE_       ).unsqueeze(0       )
						_lowercase					       =					BartTokenizer.from_pretrained(SCREAMING_SNAKE_CASE_       ).encode(SCREAMING_SNAKE_CASE_		,   return_tensors="""pt"""       ).unsqueeze(0       )
						if not torch.eq(SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_       ).all():
												raise ValueError(
												    f"""converted tokenizer and pretrained tokenizer returned different output: {tokens} != {tokensa}"""       )

						if checkpoint_path == "bart.large.mnli":
												_lowercase					       =					bart.state_dict()
												remove_ignore_keys_(SCREAMING_SNAKE_CASE_       )
												_lowercase					       =					state_dict["""model.decoder.embed_tokens.weight"""]
												for src, dest in mnli_rename_keys:
																		rename_key(SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_       )
												_lowercase					       =					BartForSequenceClassification(SCREAMING_SNAKE_CASE_       ).eval()
												model.load_state_dict(SCREAMING_SNAKE_CASE_       )
												_lowercase					       =					bart.predict("""mnli"""		,   SCREAMING_SNAKE_CASE_		,   return_logits=SCREAMING_SNAKE_CASE_       )
												_lowercase					       =					model(SCREAMING_SNAKE_CASE_       )[0]  # logits
						else:  # no classification heads to worry about
												_lowercase					       =					bart.model.state_dict()
												remove_ignore_keys_(SCREAMING_SNAKE_CASE_       )
												_lowercase					       =					state_dict["""decoder.embed_tokens.weight"""]
												_lowercase					       =					bart.extract_features(SCREAMING_SNAKE_CASE_       )
												if hf_checkpoint_name == "facebook/bart-large":
																		_lowercase					       =					BartModel(SCREAMING_SNAKE_CASE_       ).eval()
																		model.load_state_dict(SCREAMING_SNAKE_CASE_       )
																		_lowercase					       =					model(SCREAMING_SNAKE_CASE_       ).model[0]
												else:
																		_lowercase					       =					BartForConditionalGeneration(SCREAMING_SNAKE_CASE_       ).eval()  # an existing summarization ckpt
																		model.model.load_state_dict(SCREAMING_SNAKE_CASE_       )
																		if hasattr(SCREAMING_SNAKE_CASE_		,   """lm_head"""       ):
																								_lowercase					       =					make_linear_from_emb(model.model.shared       )
																		_lowercase					       =					model.model(SCREAMING_SNAKE_CASE_       )[0]

    # Check results
						if fairseq_output.shape != new_model_outputs.shape:
												raise ValueError(
												    f"""`fairseq_output` shape and `new_model_output` shape are different: {fairseq_output.shape=}, {new_model_outputs.shape}"""       )
						if (fairseq_output != new_model_outputs).any().item():
												raise ValueError("""Some values in `fairseq_output` are different from `new_model_outputs`"""       )
						Path(SCREAMING_SNAKE_CASE_       ).mkdir(exist_ok=SCREAMING_SNAKE_CASE_       )
						model.save_pretrained(SCREAMING_SNAKE_CASE_       )


if __name__ == "__main__":
		A		: List[Any]								=     argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '''fairseq_path''', type=str, help='''bart.large, bart.large.cnn or a path to a model.pt on local filesystem.'''
		)
		parser.add_argument('''pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
		parser.add_argument(
		    '''--hf_config''', default=None, type=str, help='''Which huggingface architecture to use: bart-large-xsum'''
		)
		A		: Any								=     parser.parse_args()
		convert_bart_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, hf_checkpoint_name=args.hf_config)