






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      SCREAMING_SNAKE_CASE					( snake_case,  snake_case,  snake_case,  snake_case,  snake_case ):







				"""simple docstring"""






				with open(_lowerCamelCase ) as metadata_file:
								__magic_name__		:Union[str, Any]        =      json.load(_lowerCamelCase )
				__magic_name__		:List[str]        =      LukeConfig(use_entity_aware_attention=_lowerCamelCase,  **metadata['''model_config'''] )

				# Load in the weights from the checkpoint_path
				__magic_name__		:List[str]        =      torch.load(_lowerCamelCase,  map_location='''cpu''' )

				# Load the entity vocab file
				__magic_name__		:Dict        =      load_entity_vocab(_lowerCamelCase )

				__magic_name__		:Tuple        =      RobertaTokenizer.from_pretrained(metadata['''model_config''']['''bert_model_name'''] )

				# Add special tokens to the token vocabulary for downstream tasks
				__magic_name__		:Optional[Any]        =      AddedToken('''<ent>''',  lstrip=_lowerCamelCase,  rstrip=_lowerCamelCase )
				__magic_name__		:Optional[int]        =      AddedToken('''<ent2>''',  lstrip=_lowerCamelCase,  rstrip=_lowerCamelCase )
				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(_lowerCamelCase )
				with open(os.path.join(_lowerCamelCase,  LukeTokenizer.vocab_files_names['''entity_vocab_file'''] ),  '''w''' ) as f:
								json.dump(_lowerCamelCase,  _lowerCamelCase )

				__magic_name__		:Optional[Any]        =      LukeTokenizer.from_pretrained(_lowerCamelCase )

				# Initialize the embeddings of the special tokens
				__magic_name__		:Union[str, Any]        =      state_dict['embeddings.word_embeddings.weight']
				__magic_name__		:Optional[int]        =      word_emb[tokenizer.convert_tokens_to_ids(['''@'''] )[0]].unsqueeze(0 )
				__magic_name__		:Dict        =      word_emb[tokenizer.convert_tokens_to_ids(['''#'''] )[0]].unsqueeze(0 )
				__magic_name__		:Tuple        =      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"]:
												__magic_name__		:Union[str, Any]        =      f'''encoder.layer.{layer_index}.attention.self.'''
												__magic_name__		:Dict        =      state_dict[prefix + matrix_name]
												__magic_name__		:Union[str, Any]        =      state_dict[prefix + matrix_name]
												__magic_name__		:Union[str, Any]        =      state_dict[prefix + matrix_name]

    # Initialize the embedding of the [MASK2] entity using that of the [MASK] entity for downstream tasks
				__magic_name__		:List[Any]        =      state_dict['entity_embeddings.entity_embeddings.weight']
				__magic_name__		:Tuple        =      entity_emb[entity_vocab['[MASK]']]

				__magic_name__		:Union[str, Any]        =      LukeModel(config=_lowerCamelCase ).eval()

				__magic_name__		:Any        =      model.load_state_dict(_lowerCamelCase,  strict=_lowerCamelCase )
				if not (len(_lowerCamelCase ) == 1 and missing_keys[0] == "embeddings.position_ids"):
								raise ValueError(f'''Missing keys {", ".join(_lowerCamelCase )}. 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
				__magic_name__		:Any        =      LukeTokenizer.from_pretrained(_lowerCamelCase,  task='''entity_classification''' )

				__magic_name__		:Tuple        =      (
				    '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 .'
				)
				__magic_name__		:Optional[Any]        =      (3_9, 4_2)
				__magic_name__		:Union[str, Any]        =      tokenizer(_lowerCamelCase,  entity_spans=[span],  add_prefix_space=_lowerCamelCase,  return_tensors='''pt''' )

				__magic_name__		:int        =      model(**_lowerCamelCase )

				# Verify word hidden states
				if model_size == "large":
								__magic_name__		:Optional[Any]        =      torch.Size((1, 4_2, 1_0_2_4) )
								__magic_name__		:Tuple        =      torch.tensor(
								    [[0.0133, 0.0865, 0.0095], [0.3093, -0.2576, -0.7418], [-0.1720, -0.2117, -0.2869]] )
				else:  # base
								__magic_name__		:str        =      torch.Size((1, 4_2, 7_6_8) )
								__magic_name__		:Optional[Any]        =      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],  _lowerCamelCase,  atol=1E-4 ):
								raise ValueError

				# Verify entity hidden states
				if model_size == "large":
								__magic_name__		:Optional[Any]        =      torch.Size((1, 1, 1_0_2_4) )
								__magic_name__		:List[Any]        =      torch.tensor([[0.0466, -0.0106, -0.0179]] )
				else:  # base
								__magic_name__		:Tuple        =      torch.Size((1, 1, 7_6_8) )
								__magic_name__		:Optional[Any]        =      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],  _lowerCamelCase,  atol=1E-4 ):
								raise ValueError

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

def      SCREAMING_SNAKE_CASE					( snake_case ):







				"""simple docstring"""






				__magic_name__		:str        =      {}
				with open(_lowerCamelCase,  '''r''',  encoding='''utf-8''' ) as f:
								for index, line in enumerate(_lowerCamelCase ):
												__magic_name__		:List[str]        =      line.rstrip().split('''\t''' )
												__magic_name__		:Any        =      index

				return entity_vocab


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE__      :			Tuple							=		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."""
					)
					SCREAMING_SNAKE_CASE__      :			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,
					)














import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional

import datasets
import numpy as np
import tensorflow as tf

from transformers import (
    AutoConfig,
    AutoTokenizer,
    EvalPrediction,
    HfArgumentParser,
    PreTrainedTokenizer,
    TFAutoModelForSequenceClassification,
    TFTrainer,
    TFTrainingArguments,
)
from transformers.utils import logging as hf_logging


hf_logging.set_verbosity_info()
hf_logging.enable_default_handler()
hf_logging.enable_explicit_format()

def      __lowercase					( snake_case,  snake_case,  snake_case,  snake_case,  snake_case,  snake_case = None,  ):







				"""simple docstring"""






				__magic_name__		:List[Any]        =      {}

				if train_file is not None:
								__magic_name__		:List[Any]        =      [train_file]
				if eval_file is not None:
								__magic_name__		:int        =      [eval_file]
				if test_file is not None:
								__magic_name__		:Any        =      [test_file]

				__magic_name__		:Optional[Any]        =      datasets.load_dataset('''csv''',  data_files=snake_case )
				__magic_name__		:List[Any]        =      list(ds[list(files.keys() )[0]].features.keys() )
				__magic_name__		:Optional[int]        =      features_name.pop(snake_case )
				__magic_name__		:List[str]        =      list(set(ds[list(files.keys() )[0]][label_name] ) )
				__magic_name__		:List[Any]        =      {label: i for i, label in enumerate(snake_case )}
				__magic_name__		:Optional[Any]        =      tokenizer.model_input_names
				__magic_name__		:Any        =      {}

				if len(snake_case ) == 1:
								for k in files.keys():
												__magic_name__		:Union[str, Any]        =      ds[k].map(
												    lambda snake_case : tokenizer.batch_encode_plus(
												        example[features_name[0]],  truncation=snake_case,  max_length=snake_case,  padding='''max_length''' ),  batched=snake_case,  )
				elif len(snake_case ) == 2:
								for k in files.keys():
												__magic_name__		:Optional[Any]        =      ds[k].map(
												    lambda snake_case : tokenizer.batch_encode_plus(
												        (example[features_name[0]], example[features_name[1]]),  truncation=snake_case,  max_length=snake_case,  padding='''max_length''',  ),  batched=snake_case,  )

				def gen_train():
								for ex in transformed_ds[datasets.Split.TRAIN]:
												__magic_name__		:Union[str, Any]        =      {k: v for k, v in ex.items() if k in input_names}
												__magic_name__		:int        =      labelaid[ex[label_name]]
												yield (d, label)

				def gen_val():
								for ex in transformed_ds[datasets.Split.VALIDATION]:
												__magic_name__		:Optional[Any]        =      {k: v for k, v in ex.items() if k in input_names}
												__magic_name__		:Dict        =      labelaid[ex[label_name]]
												yield (d, label)

				def gen_test():
								for ex in transformed_ds[datasets.Split.TEST]:
												__magic_name__		:Any        =      {k: v for k, v in ex.items() if k in input_names}
												__magic_name__		:List[Any]        =      labelaid[ex[label_name]]
												yield (d, label)

				__magic_name__		:Optional[Any]        =      (
				    tf.data.Dataset.from_generator(
				        snake_case,  ({k: tf.intaa for k in input_names}, tf.intaa),  ({k: tf.TensorShape([None] ) for k in input_names}, tf.TensorShape([] )),  )
				    if datasets.Split.TRAIN in transformed_ds
				    else None
				)

				if train_ds is not None:
								__magic_name__		:Optional[int]        =      train_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TRAIN] ) ) )

				__magic_name__		:Any        =      (
				    tf.data.Dataset.from_generator(
				        snake_case,  ({k: tf.intaa for k in input_names}, tf.intaa),  ({k: tf.TensorShape([None] ) for k in input_names}, tf.TensorShape([] )),  )
				    if datasets.Split.VALIDATION in transformed_ds
				    else None
				)

				if val_ds is not None:
								__magic_name__		:str        =      val_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.VALIDATION] ) ) )

				__magic_name__		:Optional[int]        =      (
				    tf.data.Dataset.from_generator(
				        snake_case,  ({k: tf.intaa for k in input_names}, tf.intaa),  ({k: tf.TensorShape([None] ) for k in input_names}, tf.TensorShape([] )),  )
				    if datasets.Split.TEST in transformed_ds
				    else None
				)

				if test_ds is not None:
								__magic_name__		:Dict        =      test_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TEST] ) ) )

				return train_ds, val_ds, test_ds, labelaid


SCREAMING_SNAKE_CASE__      :			List[Any]							=		logging.getLogger(__name__)
@dataclass
class 						lowerCamelCase_				:
				a__					       =  field(metadata={'''help''': '''Which column contains the label'''} )
				a__					       =  field(default=lowerCamelCase			,				metadata={'''help''': '''The path of the training file'''} )
				a__					       =  field(default=lowerCamelCase			,				metadata={'''help''': '''The path of the development file'''} )
				a__					       =  field(default=lowerCamelCase			,				metadata={'''help''': '''The path of the test file'''} )
				a__					       =  field(
				    default=1_28			,				metadata={
				        '''help''': (
				            '''The maximum total input sequence length after tokenization. Sequences longer '''
				            '''than this will be truncated, sequences shorter will be padded.'''
				        )
				    }			,				)
				a__					       =  field(
				    default=lowerCamelCase			,				metadata={'''help''': '''Overwrite the cached training and evaluation sets'''} )







@dataclass
class 						lowerCamelCase_				:
				a__					       =  field(
				    metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''} )
				a__					       =  field(
				    default=lowerCamelCase			,				metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''} )
				a__					       =  field(
				    default=lowerCamelCase			,				metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''} )
				a__					       =  field(default=lowerCamelCase			,				metadata={'''help''': '''Set this flag to use fast tokenization.'''} )
				# If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
				# or just modify its tokenizer_config.json.
				a__					       =  field(
				    default=lowerCamelCase			,				metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''}			,				)







def      __lowercase					( ):







				"""simple docstring"""






				__magic_name__		:str        =      HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments) )
				__magic_name__     ,				__magic_name__     ,				__magic_name__		:Any        =      parser.parse_args_into_dataclasses()

				if (
				    os.path.exists(training_args.output_dir )
				    and os.listdir(training_args.output_dir )
				    and training_args.do_train
				    and not training_args.overwrite_output_dir
				):
								raise ValueError(
								    f'''Output directory ({training_args.output_dir}) already exists and is not empty. Use'''
								    ''' --overwrite_output_dir to overcome.''' )

				# Setup logging
				logging.basicConfig(
				    format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''',  datefmt='''%m/%d/%Y %H:%M:%S''',  level=logging.INFO,  )
				logger.info(
				    f'''n_replicas: {training_args.n_replicas}, distributed training: {bool(training_args.n_replicas > 1 )}, '''
				    f'''16-bits training: {training_args.fpaa}''' )
				logger.info(f'''Training/evaluation parameters {training_args}''' )

				# Load pretrained model and tokenizer
				#
				# Distributed training:
				# The .from_pretrained methods guarantee that only one local process can concurrently
				# download model & vocab.

				__magic_name__		:Union[str, Any]        =      AutoTokenizer.from_pretrained(
				    model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path,  cache_dir=model_args.cache_dir,  )

				__magic_name__     ,				__magic_name__     ,				__magic_name__     ,				__magic_name__		:int        =      get_tfds(
				    train_file=data_args.train_file,  eval_file=data_args.dev_file,  test_file=data_args.test_file,  tokenizer=snake_case,  label_column_id=data_args.label_column_id,  max_seq_length=data_args.max_seq_length,  )

				__magic_name__		:Optional[Any]        =      AutoConfig.from_pretrained(
				    model_args.config_name if model_args.config_name else model_args.model_name_or_path,  num_labels=len(snake_case ),  labelaid=snake_case,  idalabel={id: label for label, id in labelaid.items()},  finetuning_task='''text-classification''',  cache_dir=model_args.cache_dir,  )

				with training_args.strategy.scope():
								__magic_name__		:Dict        =      TFAutoModelForSequenceClassification.from_pretrained(
								    model_args.model_name_or_path,  from_pt=bool('''.bin''' in model_args.model_name_or_path ),  config=snake_case,  cache_dir=model_args.cache_dir,  )

				def compute_metrics(snake_case ) -> Dict:
								__magic_name__		:str        =      np.argmax(p.predictions,  axis=1 )

								return {"acc": (preds == p.label_ids).mean()}

				# Initialize our Trainer
				__magic_name__		:str        =      TFTrainer(
				    model=snake_case,  args=snake_case,  train_dataset=snake_case,  eval_dataset=snake_case,  compute_metrics=snake_case,  )

				# Training
				if training_args.do_train:
								trainer.train()
								trainer.save_model()
								tokenizer.save_pretrained(training_args.output_dir )

				# Evaluation
				__magic_name__		:Optional[Any]        =      {}
				if training_args.do_eval:
								logger.info('''*** Evaluate ***''' )
								__magic_name__		:int        =      trainer.evaluate()
								__magic_name__		:Dict        =      os.path.join(training_args.output_dir,  '''eval_results.txt''' )

								with open(snake_case,  '''w''' ) as writer:
												logger.info('''***** Eval results *****''' )

												for key, value in result.items():
																logger.info(f'''  {key} = {value}''' )
																writer.write(f'''{key} = {value}\n''' )

												results.update(snake_case )

				return results


if __name__ == "__main__":
					main()






