




"""simple docstring"""




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


lowerCAmelCase							:			Dict     							=							logging.get_logger(__name__)






class     __magic_name__							(       UpperCAmelCase__				):






				'''simple docstring'''





				def __init__(       self		, **_a ):





							"""simple docstring"""
							requires_backends(self		, ["""bs4"""] )
							super().__init__(**_a )





				def    _lowerCAmelCase				(       self		, _a ):





							"""simple docstring"""
							lowerCamelCase			      =					[]
							lowerCamelCase			      =					[]
							lowerCamelCase			      =					element if element.name else element.parent
							for parent in child.parents:  # type: bs4.element.Tag
										lowerCamelCase			      =					parent.find_all(child.name		, recursive=_a )
										xpath_tags.append(child.name )
										xpath_subscripts.append(
										    0 if 1 == len(_a ) else next(i for i, s in enumerate(_a		, 1 ) if s is child ) )
										lowerCamelCase			      =					parent
							xpath_tags.reverse()
							xpath_subscripts.reverse()
							return xpath_tags, xpath_subscripts





				def    _lowerCAmelCase				(       self		, _a ):





							"""simple docstring"""
							lowerCamelCase			      =					BeautifulSoup(_a		, """html.parser""" )

							lowerCamelCase			      =					[]
							lowerCamelCase			      =					[]
							lowerCamelCase			      =					[]

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

													lowerCamelCase			      =					html.unescape(_a ).strip()
													if not text_in_this_tag:
																continue

													all_doc_strings.append(_a )

													lowerCamelCase      ,   lowerCamelCase			      =					self.xpath_soup(_a )
													stringaxtag_seq.append(_a )
													stringaxsubs_seq.append(_a )

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

							return all_doc_strings, stringaxtag_seq, stringaxsubs_seq





				def    _lowerCAmelCase				(       self		, _a		, _a ):





							"""simple docstring"""
							lowerCamelCase			      =					""""""
							for tagname, subs in zip(_a		, _a ):
										xpath += f'/{tagname}'
										if subs != 0:
													xpath += f'[{subs}]'
							return xpath





				def __call__(       self		, _a ):





							"""simple docstring"""
							lowerCamelCase			      =					False

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

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

							lowerCamelCase			      =					bool(isinstance(_a		, (list, tuple) ) and (isinstance(html_strings[0]		, _a )) )

							if not is_batched:
										lowerCamelCase			      =					[html_strings]

							# Get nodes + xpaths
							lowerCamelCase			      =					[]
							lowerCamelCase			      =					[]
							for html_string in html_strings:
										lowerCamelCase      ,   lowerCamelCase      ,   lowerCamelCase			      =					self.get_three_from_single(_a )
										nodes.append(_a )
										lowerCamelCase			      =					[]
										for node, tag_list, sub_list in zip(_a		, _a		, _a ):
													lowerCamelCase			      =					self.construct_xpath(_a		, _a )
													xpath_strings.append(_a )
										xpaths.append(_a )

							# return as Dict
							lowerCamelCase			      =					{"""nodes""": nodes, """xpaths""": xpaths}
							lowerCamelCase			      =					BatchFeature(data=_a		, tensor_type=_a )

							return encoded_inputs









"""simple docstring"""




import argparse
from pathlib import Path

import torch

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


logging.set_verbosity_info()
lowerCAmelCase							:			Dict     							=							logging.get_logger(__name__)



def     a__ (				snake_case__				)						->					Dict:
			lowerCamelCase			      =					torch.load(snake_case__       , map_location="""cpu"""				)
			if "model" in sd.keys():
						lowerCamelCase			      =					torch.load(snake_case__       , map_location="""cpu"""				)["""model"""]

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

			lowerCamelCase			      =					{
			    """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:
									lowerCamelCase			      =					sd.pop(snake_case__				)

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

									lowerCamelCase			      =					key.replace(""".qkv_proj."""       , """.q_proj."""				)
									lowerCamelCase			      =					key.replace(""".qkv_proj."""       , """.k_proj."""				)
									lowerCamelCase			      =					key.replace(""".qkv_proj."""       , """.v_proj."""				)

									lowerCamelCase			      =					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
									lowerCamelCase      ,   lowerCamelCase      ,   lowerCamelCase			      =					torch.split(snake_case__       , depth // 3       , dim=0				)

									lowerCamelCase			      =					q
									lowerCamelCase			      =					k
									lowerCamelCase			      =					v
									del sd[key]

			return sd



@torch.no_grad()
def     a__ (				snake_case__       , snake_case__       , snake_case__=None				)						->					Tuple:
			lowerCamelCase			      =					load_checkpoint(snake_case__				)

			if config is not None:
						lowerCamelCase			      =					OPTConfig.from_pretrained(snake_case__				)
			else:
						lowerCamelCase			      =					OPTConfig()

			lowerCamelCase			      =					OPTModel(snake_case__				).half().eval()
			model.load_state_dict(snake_case__				)

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


if __name__ == "__main__":
					lowerCAmelCase							:			List[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.""")
					lowerCAmelCase							:			Optional[Any]     							=							parser.parse_args()
					convert_opt_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, config=args.hf_config)



