






"""simple docstring"""



import argparse
import json
import os

import torch

from transformers import LukeConfig, LukeModel, LukeTokenizer, RobertaTokenizer
from transformers.tokenization_utils_base import AddedToken




@torch.no_grad()
def      _lowerCAmelCase		(							UpperCAmelCase__       :					List[Any],						UpperCAmelCase__       :					Any,						UpperCAmelCase__       :					str,						UpperCAmelCase__       :					Optional[int],						UpperCAmelCase__       :					int      )		->Optional[int]:
						# Load configuration defined in the metadata file
						with open(UpperCAmelCase__      ) as metadata_file:
												A__						:  List[str]		  =  json.load(UpperCAmelCase__      )
						A__						:  Optional[int]		  =  LukeConfig(use_entity_aware_attention=UpperCAmelCase__,						**metadata["""model_config"""]      )

						# Load in the weights from the checkpoint_path
						A__						:  Optional[Any]		  =  torch.load(UpperCAmelCase__,						map_location="""cpu"""      )

						# Load the entity vocab file
						A__						:  int		  =  load_entity_vocab(UpperCAmelCase__      )

						A__						:  str		  =  RobertaTokenizer.from_pretrained(metadata["""model_config"""]["""bert_model_name"""]      )

						# Add special tokens to the token vocabulary for downstream tasks
						A__						:  Optional[Any]		  =  AddedToken("""<ent>""",						lstrip=UpperCAmelCase__,						rstrip=UpperCAmelCase__      )
						A__						:  int		  =  AddedToken("""<ent2>""",						lstrip=UpperCAmelCase__,						rstrip=UpperCAmelCase__      )
						tokenizer.add_special_tokens({"""additional_special_tokens""": [entity_token_a, entity_token_a]}      )
						config.vocab_size += 2

						print(f'Saving tokenizer to {pytorch_dump_folder_path}'      )
						tokenizer.save_pretrained(UpperCAmelCase__      )
						with open(os.path.join(UpperCAmelCase__,						LukeTokenizer.vocab_files_names["""entity_vocab_file"""]      ),						"""w"""      ) as f:
												json.dump(UpperCAmelCase__,						UpperCAmelCase__      )

						A__						:  Optional[int]		  =  LukeTokenizer.from_pretrained(UpperCAmelCase__      )

						# Initialize the embeddings of the special tokens
						A__						:  Optional[Any]		  =  state_dict["""embeddings.word_embeddings.weight"""]
						A__						:  Optional[Any]		  =  word_emb[tokenizer.convert_tokens_to_ids(["""@"""]      )[0]].unsqueeze(0      )
						A__						:  List[Any]		  =  word_emb[tokenizer.convert_tokens_to_ids(["""#"""]      )[0]].unsqueeze(0      )
						A__						:  int		  =  torch.cat([word_emb, ent_emb, enta_emb]      )

						# Initialize the query layers of the entity-aware self-attention mechanism
						for layer_index in range(config.num_hidden_layers      ):
												for matrix_name in ["query.weight", "query.bias"]:
																		A__						:  Optional[Any]		  =  f'encoder.layer.{layer_index}.attention.self.'
																		A__						:  Union[str, Any]		  =  state_dict[prefix + matrix_name]
																		A__						:  Tuple		  =  state_dict[prefix + matrix_name]
																		A__						:  Union[str, Any]		  =  state_dict[prefix + matrix_name]

    # Initialize the embedding of the [MASK2] entity using that of the [MASK] entity for downstream tasks
						A__						:  str		  =  state_dict["""entity_embeddings.entity_embeddings.weight"""]
						A__						:  str		  =  entity_emb[entity_vocab["""[MASK]"""]]

						A__						:  Tuple		  =  LukeModel(config=UpperCAmelCase__      ).eval()

						A__  ,  A__						:  Optional[Any]		  =  model.load_state_dict(UpperCAmelCase__,						strict=UpperCAmelCase__      )
						if not (len(UpperCAmelCase__      ) == 1 and missing_keys[0] == "embeddings.position_ids"):
												raise ValueError(f'Missing keys {", ".join(UpperCAmelCase__      )}. Expected only missing embeddings.position_ids'      )
						if not (all(key.startswith("""entity_predictions"""      ) or key.startswith("""lm_head"""      ) for key in unexpected_keys      )):
												raise ValueError(
												    """Unexpected keys"""
												    f' {", ".join([key for key in unexpected_keys if not (key.startswith("entity_predictions"      ) or key.startswith("lm_head"      ))]      )}'      )

						# Check outputs
						A__						:  Union[str, Any]		  =  LukeTokenizer.from_pretrained(UpperCAmelCase__,						task="""entity_classification"""      )

						A__						:  Dict		  =  (
						    """Top seed Ana Ivanovic said on Thursday she could hardly believe her luck as a fortuitous netcord helped the"""
						    """ new world number one avoid a humiliating second- round exit at Wimbledon ."""
						)
						A__						:  Tuple		  =  (3_9, 4_2)
						A__						:  List[Any]		  =  tokenizer(UpperCAmelCase__,						entity_spans=[span],						add_prefix_space=UpperCAmelCase__,						return_tensors="""pt"""      )

						A__						:  Optional[int]		  =  model(**UpperCAmelCase__      )

						# Verify word hidden states
						if model_size == "large":
												A__						:  Optional[int]		  =  torch.Size((1, 4_2, 1_0_2_4)      )
												A__						:  Union[str, Any]		  =  torch.tensor(
												    [[0.0133, 0.0865, 0.0095], [0.3093, -0.2576, -0.7418], [-0.1720, -0.2117, -0.2869]]      )
						else:  # base
												A__						:  List[str]		  =  torch.Size((1, 4_2, 7_6_8)      )
												A__						:  Dict		  =  torch.tensor([[0.0037, 0.1368, -0.0091], [0.1099, 0.3329, -0.1095], [0.0765, 0.5335, 0.1179]]      )

						if not (outputs.last_hidden_state.shape == expected_shape):
												raise ValueError(
												    f'Outputs.last_hidden_state.shape is {outputs.last_hidden_state.shape}, Expected shape is {expected_shape}'      )
						if not torch.allclose(outputs.last_hidden_state[0, :3, :3],						UpperCAmelCase__,						atol=1e-4      ):
												raise ValueError

						# Verify entity hidden states
						if model_size == "large":
												A__						:  Union[str, Any]		  =  torch.Size((1, 1, 1_0_2_4)      )
												A__						:  Any		  =  torch.tensor([[0.0466, -0.0106, -0.0179]]      )
						else:  # base
												A__						:  Optional[Any]		  =  torch.Size((1, 1, 7_6_8)      )
												A__						:  Optional[int]		  =  torch.tensor([[0.1457, 0.1044, 0.0174]]      )

						if not (outputs.entity_last_hidden_state.shape != expected_shape):
												raise ValueError(
												    f'Outputs.entity_last_hidden_state.shape is {outputs.entity_last_hidden_state.shape}, Expected shape is'
												    f' {expected_shape}'      )
						if not torch.allclose(outputs.entity_last_hidden_state[0, :3, :3],						UpperCAmelCase__,						atol=1e-4      ):
												raise ValueError

						# Finally, save our PyTorch model and tokenizer
						print("""Saving PyTorch model to {}""".format(UpperCAmelCase__      )      )
						model.save_pretrained(UpperCAmelCase__      )




def      _lowerCAmelCase		(							UpperCAmelCase__       :					Tuple      )		->Union[str, Any]:
						A__						:  int		  =  {}
						with open(UpperCAmelCase__,						"""r""",						encoding="""utf-8"""      ) as f:
												for index, line in enumerate(UpperCAmelCase__      ):
																		A__  ,  A__						:  List[str]		  =  line.rstrip().split("""\t"""      )
																		A__						:  int		  =  index

						return entity_vocab


if __name__ == "__main__":
						A_    =							argparse.ArgumentParser()
						# Required parameters
						parser.add_argument('''--checkpoint_path''', type=str, help='''Path to a pytorch_model.bin file.''')
						parser.add_argument(
						    '''--metadata_path''', default=None, type=str, help='''Path to a metadata.json file, defining the configuration.'''
						)
						parser.add_argument(
						    '''--entity_vocab_path''',
						    default=None,
						    type=str,
						    help='''Path to an entity_vocab.tsv file, containing the entity vocabulary.''',
						)
						parser.add_argument(
						    '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to where to dump the output PyTorch model.'''
						)
						parser.add_argument(
						    '''--model_size''', default='''base''', type=str, choices=['''base''', '''large'''], help='''Size of the model to be converted.'''
						)
						A_    =							parser.parse_args()
						convert_luke_checkpoint(
						    args.checkpoint_path,
						    args.metadata_path,
						    args.entity_vocab_path,
						    args.pytorch_dump_folder_path,
						    args.model_size,
						)














"""simple docstring"""



from __future__ import annotations






class 							__SCREAMING_SNAKE_CASE						:


							def __init__(			self      :						Any			,    snake_case      :						str			,    snake_case      :						str							):
													'''simple docstring'''





													A__  ,  A__						:  List[str]		  =  text, pattern
													A__  ,  A__						:  List[str]		  =  len(snake_case							), len(snake_case							)


							def 						_UpperCamelCase       (			self      :						str			,    snake_case      :						str							):
													'''simple docstring'''





													for i in range(self.patLen - 1			,    -1			,    -1							):
																			if char == self.pattern[i]:
																									return i
													return -1


							def 						_UpperCamelCase       (			self      :						Any			,    snake_case      :						int							):
													'''simple docstring'''





													for i in range(self.patLen - 1			,    -1			,    -1							):
																			if self.pattern[i] != self.text[current_pos + i]:
																									return current_pos + i
													return -1


							def 						_UpperCamelCase       (			self      :						Union[str, Any]							):
													'''simple docstring'''





													A__						:  str		  =  []
													for i in range(self.textLen - self.patLen + 1							):
																			A__						:  List[Any]		  =  self.mismatch_in_text(snake_case							)
																			if mismatch_index == -1:
																									positions.append(snake_case							)
																			else:
																									A__						:  Dict		  =  self.match_in_pattern(self.text[mismatch_index]							)
																									A__						:  Union[str, Any]		  =  (
																									    mismatch_index - match_index
																									)  # shifting index lgtm [py/multiple-definition]
													return positions




A_    =							'''ABAABA'''
A_    =							'''AB'''
A_    =							BoyerMooreSearch(text, pattern)
A_    =							bms.bad_character_heuristic()

if len(positions) == 0:
						print('''No match found''')
else:
						print('''Pattern found in following positions: ''')
						print(positions)






