


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__			:      int								=logging.get_logger(__name__)







class 	__lowercase    (__SCREAMING_SNAKE_CASE  ):





							"""simple docstring"""







							def __init__(      self			,						**lowerCAmelCase__       ):





										"""simple docstring"""




										requires_backends(self			,						['bs4']       )
										super().__init__(**lowerCAmelCase__       )






							def 					UpperCamelCase__     (      self			,						lowerCAmelCase__       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     str					=					[]
										SCREAMING_SNAKE_CASE_		:     Tuple					=					[]
										SCREAMING_SNAKE_CASE_		:     Tuple					=					element if element.name else element.parent
										for parent in child.parents:  # type: bs4.element.Tag
													SCREAMING_SNAKE_CASE_		:     Optional[int]					=					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       )       )
													SCREAMING_SNAKE_CASE_		:     Dict					=					parent
										xpath_tags.reverse()
										xpath_subscripts.reverse()
										return xpath_tags, xpath_subscripts






							def 					UpperCamelCase__     (      self			,						lowerCAmelCase__       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     int					=					BeautifulSoup(lowerCAmelCase__			,						'html.parser'       )

										SCREAMING_SNAKE_CASE_		:     Optional[int]					=					[]
										SCREAMING_SNAKE_CASE_		:     List[Any]					=					[]
										SCREAMING_SNAKE_CASE_		:     List[Any]					=					[]

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

																SCREAMING_SNAKE_CASE_		:     Any					=					html.unescape(lowerCAmelCase__       ).strip()
																if not text_in_this_tag:
																			continue

																all_doc_strings.append(lowerCAmelCase__       )

																SCREAMING_SNAKE_CASE_		,      SCREAMING_SNAKE_CASE_		:     List[str]					=					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 					UpperCamelCase__     (      self			,						lowerCAmelCase__			,						lowerCAmelCase__       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					''
										for tagname, subs in zip(lowerCAmelCase__			,						lowerCAmelCase__       ):
													xpath += F'''/{tagname}'''
													if subs != 0:
																xpath += F'''[{subs}]'''
										return xpath






							def __call__(      self			,						lowerCAmelCase__       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					False

										# Check that strings has a valid type
										if isinstance(lowerCAmelCase__			,						lowerCAmelCase__       ):
													SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					True
										elif isinstance(lowerCAmelCase__			,						(list, tuple)       ):
													if len(lowerCAmelCase__       ) == 0 or isinstance(html_strings[0]			,						lowerCAmelCase__       ):
																SCREAMING_SNAKE_CASE_		:     str					=					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__       )}.'''       )

										SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					bool(isinstance(lowerCAmelCase__			,						(list, tuple)       ) and (isinstance(html_strings[0]			,						lowerCAmelCase__       ))       )

										if not is_batched:
													SCREAMING_SNAKE_CASE_		:     List[str]					=					[html_strings]

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

										# return as Dict
										SCREAMING_SNAKE_CASE_		:     Dict					=					{'nodes': nodes, 'xpaths': xpaths}
										SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					BatchFeature(data=lowerCAmelCase__			,						tensor_type=lowerCAmelCase__       )

										return encoded_inputs









import argparse

import torch
from torch import nn

from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration






def 						a__			(				A__				):
			SCREAMING_SNAKE_CASE_		:     Any					=					[
			    'encoder.version',
			    'decoder.version',
			    'model.encoder.version',
			    'model.decoder.version',
			    'decoder.output_projection.weight',
			    '_float_tensor',
			    'encoder.embed_positions._float_tensor',
			    'decoder.embed_positions._float_tensor',
			]
			for k in ignore_keys:
						state_dict.pop(A__,      A__				)






def 						a__			(				A__				):
			SCREAMING_SNAKE_CASE_		,      SCREAMING_SNAKE_CASE_		:     List[Any]					=					emb.weight.shape
			SCREAMING_SNAKE_CASE_		:     List[Any]					=					nn.Linear(A__,      A__,      bias=A__				)
			SCREAMING_SNAKE_CASE_		:     Dict					=					emb.weight.data
			return lin_layer






def 						a__			(				A__				):
			SCREAMING_SNAKE_CASE_		:     List[Any]					=					torch.load(A__,      map_location='cpu'				)
			SCREAMING_SNAKE_CASE_		:     Tuple					=					mam_aaa['args'] or mam_aaa['cfg']['model']
			SCREAMING_SNAKE_CASE_		:     Any					=					mam_aaa['model']
			remove_ignore_keys_(A__				)
			SCREAMING_SNAKE_CASE_		:     List[str]					=					state_dict['encoder.embed_tokens.weight'].shape[0]

			SCREAMING_SNAKE_CASE_		:     Dict					=					MaMaaaConfig(
			    vocab_size=A__,      max_position_embeddings=1_0_2_4,      encoder_layers=args.encoder_layers,      decoder_layers=args.decoder_layers,      encoder_attention_heads=args.encoder_attention_heads,      decoder_attention_heads=args.decoder_attention_heads,      encoder_ffn_dim=args.encoder_ffn_embed_dim,      decoder_ffn_dim=args.decoder_ffn_embed_dim,      d_model=args.encoder_embed_dim,      encoder_layerdrop=args.encoder_layerdrop,      decoder_layerdrop=args.decoder_layerdrop,      dropout=args.dropout,      attention_dropout=args.attention_dropout,      activation_dropout=args.activation_dropout,      activation_function='relu',      )

			SCREAMING_SNAKE_CASE_		:     int					=					state_dict['decoder.embed_tokens.weight']
			SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					MaMaaaForConditionalGeneration(A__				)
			model.model.load_state_dict(A__,      strict=A__				)
			SCREAMING_SNAKE_CASE_		:     int					=					make_linear_from_emb(model.model.shared				)

			return model


if __name__ == "__main__":
				lowerCAmelCase__			:      Optional[int]								=argparse.ArgumentParser()
				# Required parameters
				parser.add_argument('fairseq_path', type=str, help='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.')
				lowerCAmelCase__			:      Optional[Any]								=parser.parse_args()
				lowerCAmelCase__			:      int								=convert_fairseq_mamaaa_checkpoint_from_disk(args.fairseq_pathß)
				model.save_pretrained(args.pytorch_dump_folder_path)





