

import argparse
import json
import os
from collections import OrderedDict

import torch

from transformers import LukeConfig, LukeForMaskedLM, MLukeTokenizer, XLMRobertaTokenizer
from transformers.tokenization_utils_base import AddedToken





@torch.no_grad()
def 					__lowerCamelCase	( UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__      ):







							'''simple docstring'''






							with open(UpperCamelCase__      ) as metadata_file:
														snake_case_							=    json.load(UpperCamelCase__      )
							snake_case_							=    LukeConfig(use_entity_aware_attention=UpperCamelCase__   ,							**metadata['model_config']      )

							# Load in the weights from the checkpoint_path
							snake_case_							=    torch.load(UpperCamelCase__   ,							map_location='cpu'      )['module']

							# Load the entity vocab file
							snake_case_							=    load_original_entity_vocab(UpperCamelCase__      )
							# add an entry for [MASK2]
							snake_case_							=    max(entity_vocab.values()      ) + 1
							config.entity_vocab_size += 1

							snake_case_							=    XLMRobertaTokenizer.from_pretrained(metadata['model_config']['bert_model_name']      )

							# Add special tokens to the token vocabulary for downstream tasks
							snake_case_							=    AddedToken('<ent>'   ,							lstrip=UpperCamelCase__   ,							rstrip=UpperCamelCase__      )
							snake_case_							=    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__   ,							'tokenizer_config.json'      )   ,							'r'      ) as f:
														snake_case_							=    json.load(UpperCamelCase__      )
							snake_case_							=    'MLukeTokenizer'
							with open(os.path.join(UpperCamelCase__   ,							'tokenizer_config.json'      )   ,							'w'      ) as f:
														json.dump(UpperCamelCase__   ,							UpperCamelCase__      )

							with open(os.path.join(UpperCamelCase__   ,							MLukeTokenizer.vocab_files_names['entity_vocab_file']      )   ,							'w'      ) as f:
														json.dump(UpperCamelCase__   ,							UpperCamelCase__      )

							snake_case_							=    MLukeTokenizer.from_pretrained(UpperCamelCase__      )

							# Initialize the embeddings of the special tokens
							snake_case_							=    tokenizer.convert_tokens_to_ids(['@']      )[0]
							snake_case_							=    tokenizer.convert_tokens_to_ids(['#']      )[0]

							snake_case_							=    state_dict['embeddings.word_embeddings.weight']
							snake_case_							=    word_emb[ent_init_index].unsqueeze(0      )
							snake_case_							=    word_emb[enta_init_index].unsqueeze(0      )
							snake_case_							=    torch.cat([word_emb, ent_emb, enta_emb]      )
							# add special tokens for 'entity_predictions.bias'
							for bias_name in ["lm_head.decoder.bias", "lm_head.bias"]:
														snake_case_							=    state_dict[bias_name]
														snake_case_							=    decoder_bias[ent_init_index].unsqueeze(0      )
														snake_case_							=    decoder_bias[enta_init_index].unsqueeze(0      )
														snake_case_							=    torch.cat([decoder_bias, ent_decoder_bias, enta_decoder_bias]      )

							# 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"]:
																					snake_case_							=    F'''encoder.layer.{layer_index}.attention.self.'''
																					snake_case_							=    state_dict[prefix + matrix_name]
																					snake_case_							=    state_dict[prefix + matrix_name]
																					snake_case_							=    state_dict[prefix + matrix_name]

    # Initialize the embedding of the [MASK2] entity using that of the [MASK] entity for downstream tasks
							snake_case_							=    state_dict['entity_embeddings.entity_embeddings.weight']
							snake_case_							=    entity_emb[entity_vocab['[MASK]']].unsqueeze(0      )
							snake_case_							=    torch.cat([entity_emb, entity_mask_emb]      )
							# add [MASK2] for 'entity_predictions.bias'
							snake_case_							=    state_dict['entity_predictions.bias']
							snake_case_							=    entity_prediction_bias[entity_vocab['[MASK]']].unsqueeze(0      )
							snake_case_							=    torch.cat([entity_prediction_bias, entity_mask_bias]      )

							snake_case_							=    LukeForMaskedLM(config=UpperCamelCase__      ).eval()

							state_dict.pop('entity_predictions.decoder.weight'      )
							state_dict.pop('lm_head.decoder.weight'      )
							state_dict.pop('lm_head.decoder.bias'      )
							snake_case_							=    OrderedDict()
							for key, value in state_dict.items():
														if not (key.startswith('lm_head'      ) or key.startswith('entity_predictions'      )):
																					snake_case_							=    state_dict[key]
														else:
																					snake_case_							=    state_dict[key]

							snake_case_				,    snake_case_							=    model.load_state_dict(UpperCamelCase__   ,							strict=UpperCamelCase__      )

							if set(UpperCamelCase__      ) != {"luke.embeddings.position_ids"}:
														raise ValueError(F'''Unexpected unexpected_keys: {unexpected_keys}'''      )
							if set(UpperCamelCase__      ) != {
							    "lm_head.decoder.weight",
							    "lm_head.decoder.bias",
							    "entity_predictions.decoder.weight",
							}:
														raise ValueError(F'''Unexpected missing_keys: {missing_keys}'''      )

							model.tie_weights()
							assert (model.luke.embeddings.word_embeddings.weight == model.lm_head.decoder.weight).all()
							assert (model.luke.entity_embeddings.entity_embeddings.weight == model.entity_predictions.decoder.weight).all()

							# Check outputs
							snake_case_							=    MLukeTokenizer.from_pretrained(UpperCamelCase__   ,							task='entity_classification'      )

							snake_case_							=    'ISO 639-3 uses the code fas for the dialects spoken across Iran and アフガニスタン (Afghanistan).'
							snake_case_							=    (0, 9)
							snake_case_							=    tokenizer(UpperCamelCase__   ,							entity_spans=[span]   ,							return_tensors='pt'      )

							snake_case_							=    model(**UpperCamelCase__      )

							# Verify word hidden states
							if model_size == "large":
														raise NotImplementedError
							else:  # base
														snake_case_							=    torch.Size((1, 33, 768)      )
														snake_case_							=    torch.tensor([[0.08_92, 0.05_96, -0.28_19], [0.01_34, 0.11_99, 0.05_73], [-0.01_69, 0.09_27, 0.06_44]]      )

							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":
														raise NotImplementedError
							else:  # base
														snake_case_							=    torch.Size((1, 1, 768)      )
														snake_case_							=    torch.tensor([[-0.14_82, 0.06_09, 0.03_22]]      )

							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

							# Verify masked word/entity prediction
							snake_case_							=    MLukeTokenizer.from_pretrained(UpperCamelCase__      )
							snake_case_							=    'Tokyo is the capital of <mask>.'
							snake_case_							=    (24, 30)
							snake_case_							=    tokenizer(UpperCamelCase__   ,							entity_spans=[span]   ,							return_tensors='pt'      )

							snake_case_							=    model(**UpperCamelCase__      )

							snake_case_							=    encoding['input_ids'][0].tolist()
							snake_case_							=    input_ids.index(tokenizer.convert_tokens_to_ids('<mask>'      )      )
							snake_case_							=    outputs.logits[0][mask_position_id].argmax(dim=-1      )
							assert "Japan" == tokenizer.decode(UpperCamelCase__      )

							snake_case_							=    outputs.entity_logits[0][0].argmax().item()
							snake_case_							=    [
							    entity for entity, entity_id in tokenizer.entity_vocab.items() if entity_id == predicted_entity_id
							]
							assert [e for e in multilingual_predicted_entities if e.startswith('en:'      )][0] == "en:Japan"

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





def 					__lowerCamelCase	( UpperCamelCase__      ):







							'''simple docstring'''






							snake_case_							=    ['[MASK]', '[PAD]', '[UNK]']

							snake_case_							=    [json.loads(UpperCamelCase__      ) for line in open(UpperCamelCase__      )]

							snake_case_							=    {}
							for entry in data:
														snake_case_							=    entry['id']
														for entity_name, language in entry["entities"]:
																					if entity_name in SPECIAL_TOKENS:
																												snake_case_							=    entity_id
																												break
																					snake_case_							=    F'''{language}:{entity_name}'''
																					snake_case_							=    entity_id
							return new_mapping


if __name__ == "__main__":
					_UpperCAmelCase		:   Optional[Any]										=       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."""
					)
					_UpperCAmelCase		:   List[Any]										=       parser.parse_args()
					convert_luke_checkpoint(
					    args.checkpoint_path,
					    args.metadata_path,
					    args.entity_vocab_path,
					    args.pytorch_dump_folder_path,
					    args.model_size,
					)







def 					__lowerCamelCase	( ):







							'''simple docstring'''






							return [list(range(1000 - i   ,							-1000 - i   ,							-1      )      ) for i in range(1000      )]


_UpperCAmelCase		:   Union[str, Any]										=       generate_large_matrix()
_UpperCAmelCase		:   Tuple										=       (
    [[4, 3, 2, -1], [3, 2, 1, -1], [1, 1, -1, -2], [-1, -1, -2, -3]],
    [[3, 2], [1, 0]],
    [[7, 7, 6]],
    [[7, 7, 6], [-1, -2, -3]],
    grid,
)





def 					__lowerCamelCase	( UpperCamelCase__      ):







							'''simple docstring'''






							assert all(row == sorted(UpperCamelCase__   ,							reverse=UpperCamelCase__      ) for row in grid      )
							assert all(list(UpperCamelCase__      ) == sorted(UpperCamelCase__   ,							reverse=UpperCamelCase__      ) for col in zip(*UpperCamelCase__      )      )





def 					__lowerCamelCase	( UpperCamelCase__      ):







							'''simple docstring'''






							snake_case_							=    0
							snake_case_							=    len(UpperCamelCase__      ) - 1

							# Edge cases such as no values or all numbers are negative.
							if not array or array[0] < 0:
														return 0

							while right + 1 > left:
														snake_case_							=    (left + right) // 2
														snake_case_							=    array[mid]

														# Num must be negative and the index must be greater than or equal to 0.
														if num < 0 and array[mid - 1] >= 0:
																					return mid

														if num >= 0:
																					snake_case_							=    mid + 1
														else:
																					snake_case_							=    mid - 1
    # No negative numbers so return the last index of the array + 1 which is the length.
							return len(UpperCamelCase__      )





def 					__lowerCamelCase	( UpperCamelCase__      ):







							'''simple docstring'''






							snake_case_							=    0
							snake_case_							=    len(grid[0]      )

							for i in range(len(UpperCamelCase__      )      ):
														snake_case_							=    find_negative_index(grid[i][:bound]      )
														total += bound
							return (len(UpperCamelCase__      ) * len(grid[0]      )) - total





def 					__lowerCamelCase	( UpperCamelCase__      ):







							'''simple docstring'''






							return len([number for row in grid for number in row if number < 0]      )





def 					__lowerCamelCase	( UpperCamelCase__      ):







							'''simple docstring'''






							snake_case_							=    0
							for row in grid:
														for i, number in enumerate(UpperCamelCase__      ):
																					if number < 0:
																												total += len(UpperCamelCase__      ) - i
																												break
							return total





def 					__lowerCamelCase	( ):







							'''simple docstring'''






							from timeit import timeit

							print('Running benchmarks'      )
							snake_case_							=    (
							    'from __main__ import count_negatives_binary_search, '
							    'count_negatives_brute_force, count_negatives_brute_force_with_break, grid'
							)
							for func in (
							    "count_negatives_binary_search",  # took 0.7727 seconds
							    "count_negatives_brute_force_with_break",  # took 4.6505 seconds
							    "count_negatives_brute_force",  # took 12.8160 seconds
							):
														snake_case_							=    timeit(F'''{func}(grid=grid)'''   ,							setup=UpperCamelCase__   ,							number=500      )
														print(F'''{func}() took {time:0.4f} seconds'''      )


if __name__ == "__main__":
					import doctest

					doctest.testmod()
					benchmark()




