






import argparse
from pathlib import Path

import torch

from transformers import OPTConfig, OPTModel
from transformers.utils import logging


logging.set_verbosity_info()
UpperCAmelCase_ :						Any      =      logging.get_logger(__name__)

def    SCREAMING_SNAKE_CASE_							(							__magic_name__					:  Tuple					)		->			Any:






	"""simple docstring"""



	UpperCamelCase     :Optional[int]          =	torch.load(__magic_name__	,			map_location="""cpu"""					)
	if "model" in sd.keys():
		UpperCamelCase     :Any          =	torch.load(__magic_name__	,			map_location="""cpu"""					)["""model"""]

	# pop unnecessary weights
	UpperCamelCase     :str          =	[
	    """decoder.version""",
	    """decoder.output_projection.weight""",
	]
	for key in keys_to_delete:
		if key in sd:
			sd.pop(__magic_name__					)

	UpperCamelCase     :Dict          =	{
	    """decoder.project_in_dim.weight""": """decoder.project_in.weight""",
	    """decoder.project_out_dim.weight""": """decoder.project_out.weight""",
	    """decoder.layer_norm.weight""": """decoder.final_layer_norm.weight""",
	    """decoder.layer_norm.bias""": """decoder.final_layer_norm.bias""",
	}
	for old_key, new_key in keys_to_rename.items():
		if old_key in sd:
			UpperCamelCase     :Optional[Any]          =	sd.pop(__magic_name__					)

	UpperCamelCase     :Optional[int]          =	list(sd.keys()					)
	for key in keys:
		if ".qkv_proj." in key:
			UpperCamelCase     :List[str]          =	sd[key]
			# We split QKV in separate Q,K,V

			UpperCamelCase     :List[str]          =	key.replace(""".qkv_proj."""	,			""".q_proj."""					)
			UpperCamelCase     :Optional[int]          =	key.replace(""".qkv_proj."""	,			""".k_proj."""					)
			UpperCamelCase     :Any          =	key.replace(""".qkv_proj."""	,			""".v_proj."""					)

			UpperCamelCase     :str          =	value.shape[0]
			assert depth % 3 == 0
			# `SequeuceParallelTransformerBlock` has QKV weight is separated in K,V,Q despite the naming:
			# https://cs.github.com/facebookresearch/metaseq/blob/51871bd73cd04c038f239ea2a26db1d7f6b37927/metaseq/modules/sequence_parallel_transformer_layer.py#L97
			UpperCamelCase     ,      UpperCamelCase     ,      UpperCamelCase     :Any          =	torch.split(__magic_name__	,			depth // 3	,			dim=0					)

			UpperCamelCase     :List[Any]          =	q
			UpperCamelCase     :Tuple          =	k
			UpperCamelCase     :Optional[Any]          =	v
			del sd[key]

	return sd

@torch.no_grad()
def    SCREAMING_SNAKE_CASE_							(							__magic_name__					:  List[Any]	,			__magic_name__					:  Any	,			__magic_name__					:  Union[str, Any]=None					)		->			Any:






	"""simple docstring"""



	UpperCamelCase     :Optional[Any]          =	load_checkpoint(__magic_name__					)

	if config is not None:
		UpperCamelCase     :Optional[Any]          =	OPTConfig.from_pretrained(__magic_name__					)
	else:
		UpperCamelCase     :List[Any]          =	OPTConfig()

	UpperCamelCase     :Union[str, Any]          =	OPTModel(__magic_name__					).half().eval()
	model.load_state_dict(__magic_name__					)

	# Check results
	Path(__magic_name__					).mkdir(exist_ok=__magic_name__					)
	model.save_pretrained(__magic_name__					)


if __name__ == "__main__":
	UpperCAmelCase_ :						str      =      argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    '''--fairseq_path''',
	    type=str,
	    help=(
	        '''path to fairseq checkpoint in correct format. You can find all checkpoints in the correct format here:'''
	        ''' https://huggingface.co/models?other=opt_metasq'''
	    ),
	)
	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='''Define HF config.''')
	UpperCAmelCase_ :						Dict      =      parser.parse_args()
	convert_opt_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, config=args.hf_config)








import html

from ...feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from ...utils import is_bsa_available, logging, requires_backends


if is_bsa_available():
	import bsa
	from bsa import BeautifulSoup


UpperCAmelCase_ :						Any      =      logging.get_logger(__name__)

class     _SCREAMING_SNAKE_CASE       (			_a      ):





	def __init__(		self				:					Optional[int] ,  **__lowerCamelCase				:					Optional[int]	):
		requires_backends(self ,  ["""bs4"""]	)
		super().__init__(**__lowerCamelCase	)





	def 						_A (		self				:					List[str] ,  __lowerCamelCase				:					Any	):
		UpperCamelCase     :Optional[int]          =	[]
		UpperCamelCase     :List[str]          =	[]
		UpperCamelCase     :Union[str, Any]          =	element if element.name else element.parent
		for parent in child.parents:  # type: bs4.element.Tag
			UpperCamelCase     :Optional[Any]          =	parent.find_all(child.name ,  recursive=__lowerCamelCase	)
			xpath_tags.append(child.name	)
			xpath_subscripts.append(
			    0 if 1 == len(__lowerCamelCase	) else next(i for i, s in enumerate(__lowerCamelCase ,  1	) if s is child	)	)
			UpperCamelCase     :Any          =	parent
		xpath_tags.reverse()
		xpath_subscripts.reverse()
		return xpath_tags, xpath_subscripts





	def 						_A (		self				:					Any ,  __lowerCamelCase				:					Tuple	):
		UpperCamelCase     :Any          =	BeautifulSoup(__lowerCamelCase ,  """html.parser"""	)

		UpperCamelCase     :Union[str, Any]          =	[]
		UpperCamelCase     :Tuple          =	[]
		UpperCamelCase     :Tuple          =	[]

		for element in html_code.descendants:
			if type(__lowerCamelCase	) == bsa.element.NavigableString:
				if type(element.parent	) != bsa.element.Tag:
					continue

				UpperCamelCase     :Any          =	html.unescape(__lowerCamelCase	).strip()
				if not text_in_this_tag:
					continue

				all_doc_strings.append(__lowerCamelCase	)

				UpperCamelCase     ,      UpperCamelCase     :Optional[Any]          =	self.xpath_soup(__lowerCamelCase	)
				stringaxtag_seq.append(__lowerCamelCase	)
				stringaxsubs_seq.append(__lowerCamelCase	)

		if len(__lowerCamelCase	) != len(__lowerCamelCase	):
			raise ValueError("""Number of doc strings and xtags does not correspond"""	)
		if len(__lowerCamelCase	) != len(__lowerCamelCase	):
			raise ValueError("""Number of doc strings and xsubs does not correspond"""	)

		return all_doc_strings, stringaxtag_seq, stringaxsubs_seq





	def 						_A (		self				:					int ,  __lowerCamelCase				:					List[Any] ,  __lowerCamelCase				:					List[str]	):
		UpperCamelCase     :Tuple          =	""""""
		for tagname, subs in zip(__lowerCamelCase ,  __lowerCamelCase	):
			xpath += F"""/{tagname}"""
			if subs != 0:
				xpath += F"""[{subs}]"""
		return xpath





	def __call__(		self				:					Any ,  __lowerCamelCase				:					Dict	):
		UpperCamelCase     :Any          =	False

		# Check that strings has a valid type
		if isinstance(__lowerCamelCase ,  __lowerCamelCase	):
			UpperCamelCase     :List[Any]          =	True
		elif isinstance(__lowerCamelCase ,  (list, tuple)	):
			if len(__lowerCamelCase	) == 0 or isinstance(html_strings[0] ,  __lowerCamelCase	):
				UpperCamelCase     :Any          =	True

		if not valid_strings:
			raise ValueError(
			    """HTML strings must of type `str`, `List[str]` (batch of examples), """
			    F"""but is of type {type(__lowerCamelCase	)}."""	)

		UpperCamelCase     :str          =	bool(isinstance(__lowerCamelCase ,  (list, tuple)	) and (isinstance(html_strings[0] ,  __lowerCamelCase	))	)

		if not is_batched:
			UpperCamelCase     :Any          =	[html_strings]

		# Get nodes + xpaths
		UpperCamelCase     :Union[str, Any]          =	[]
		UpperCamelCase     :str          =	[]
		for html_string in html_strings:
			UpperCamelCase     ,      UpperCamelCase     ,      UpperCamelCase     :int          =	self.get_three_from_single(__lowerCamelCase	)
			nodes.append(__lowerCamelCase	)
			UpperCamelCase     :int          =	[]
			for node, tag_list, sub_list in zip(__lowerCamelCase ,  __lowerCamelCase ,  __lowerCamelCase	):
				UpperCamelCase     :str          =	self.construct_xpath(__lowerCamelCase ,  __lowerCamelCase	)
				xpath_strings.append(__lowerCamelCase	)
			xpaths.append(__lowerCamelCase	)

		# return as Dict
		UpperCamelCase     :Optional[int]          =	{"""nodes""": nodes, """xpaths""": xpaths}
		UpperCamelCase     :Any          =	BatchFeature(data=__lowerCamelCase ,  tensor_type=__lowerCamelCase	)

		return encoded_inputs
