






'''simple docstring'''
import argparse
import json
import os

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler


a_					= 1_6
a_					= 3_2





def  _a(  UpperCamelCase__				:						Accelerator,	UpperCamelCase__				:						int = 1_6,	UpperCamelCase__				:						str = "bert-base-cased"							):




      '''simple docstring'''






      SCREAMING_SNAKE_CASE__     :				Any									=AutoTokenizer.from_pretrained(UpperCamelCase__							)
      SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=load_dataset('''glue''',	'''mrpc'''							)

      def tokenize_function(UpperCamelCase__				:						Union[str, Any]							):
            # max_length=None => use the model max length (it's actually the default)
            SCREAMING_SNAKE_CASE__     :				int									=tokenizer(examples['''sentence1'''],	examples['''sentence2'''],	truncation=UpperCamelCase__,	max_length=UpperCamelCase__							)
            return outputs

      # Apply the method we just defined to all the examples in all the splits of the dataset
      SCREAMING_SNAKE_CASE__     :				str									=datasets.map(
          UpperCamelCase__,	batched=UpperCamelCase__,	remove_columns=['''idx''', '''sentence1''', '''sentence2'''],	load_from_cache_file=UpperCamelCase__							)

      # We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
      # transformers library
      SCREAMING_SNAKE_CASE__     :				Tuple									=tokenized_datasets.rename_column('''label''',	'''labels'''							)

      def collate_fn(UpperCamelCase__				:						List[Any]							):
            # On TPU it's best to pad everything to the same length or training will be very slow.
            if accelerator.distributed_type == DistributedType.TPU:
                  return tokenizer.pad(UpperCamelCase__,	padding='''max_length''',	max_length=1_2_8,	return_tensors='''pt'''							)
            return tokenizer.pad(UpperCamelCase__,	padding='''longest''',	return_tensors='''pt'''							)

      # Instantiate dataloaders.
      SCREAMING_SNAKE_CASE__     :				Any									=DataLoader(
          tokenized_datasets['''train'''],	shuffle=UpperCamelCase__,	collate_fn=UpperCamelCase__,	batch_size=UpperCamelCase__							)
      SCREAMING_SNAKE_CASE__     :				Optional[Any]									=DataLoader(
          tokenized_datasets['''validation'''],	shuffle=UpperCamelCase__,	collate_fn=UpperCamelCase__,	batch_size=UpperCamelCase__							)

      return train_dataloader, eval_dataloader





def  _a(  UpperCamelCase__				:						Tuple,	UpperCamelCase__				:						Optional[Any]							):




      '''simple docstring'''






      SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=Accelerator()

      # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
      SCREAMING_SNAKE_CASE__     :				Any									=config['''lr''']
      SCREAMING_SNAKE_CASE__     :				int									=int(config['''num_epochs''']							)
      SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=int(config['''seed''']							)
      SCREAMING_SNAKE_CASE__     :				str									=int(config['''batch_size''']							)
      SCREAMING_SNAKE_CASE__     :				List[Any]									=args.model_name_or_path

      set_seed(UpperCamelCase__							)
      SCREAMING_SNAKE_CASE__		,	SCREAMING_SNAKE_CASE__     :				Tuple									=get_dataloaders(UpperCamelCase__,	UpperCamelCase__,	UpperCamelCase__							)

      # Instantiate the model (we build the model here so that the seed also control new weights initialization)
      SCREAMING_SNAKE_CASE__     :				Any									=AutoModelForSequenceClassification.from_pretrained(UpperCamelCase__,	return_dict=UpperCamelCase__							)

      # Instantiate optimizer
      SCREAMING_SNAKE_CASE__     :				List[Any]									=(
          AdamW
          if accelerator.state.deepspeed_plugin is None
          or '''optimizer''' not in accelerator.state.deepspeed_plugin.deepspeed_config
          else DummyOptim
      )
      SCREAMING_SNAKE_CASE__     :				int									=optimizer_cls(params=model.parameters(),	lr=UpperCamelCase__							)

      if accelerator.state.deepspeed_plugin is not None:
            SCREAMING_SNAKE_CASE__     :				Tuple									=accelerator.state.deepspeed_plugin.deepspeed_config[
                '''gradient_accumulation_steps'''
            ]
      else:
            SCREAMING_SNAKE_CASE__     :				Optional[int]									=1
      SCREAMING_SNAKE_CASE__     :				Any									=(len(UpperCamelCase__							) * num_epochs) // gradient_accumulation_steps

      # Instantiate scheduler
      if (
          accelerator.state.deepspeed_plugin is None
          or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
      ):
            SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=get_linear_schedule_with_warmup(
                optimizer=UpperCamelCase__,	num_warmup_steps=0,	num_training_steps=UpperCamelCase__,	)
      else:
            SCREAMING_SNAKE_CASE__     :				List[str]									=DummyScheduler(UpperCamelCase__,	total_num_steps=UpperCamelCase__,	warmup_num_steps=0							)

      # Prepare everything
      # There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
      # prepare method.
      SCREAMING_SNAKE_CASE__		,	SCREAMING_SNAKE_CASE__		,	SCREAMING_SNAKE_CASE__		,	SCREAMING_SNAKE_CASE__		,	SCREAMING_SNAKE_CASE__     :				List[Any]									=accelerator.prepare(
          UpperCamelCase__,	UpperCamelCase__,	UpperCamelCase__,	UpperCamelCase__,	UpperCamelCase__							)

      # We need to keep track of how many total steps we have iterated over
      SCREAMING_SNAKE_CASE__     :				int									=0
      # We also need to keep track of the stating epoch so files are named properly
      SCREAMING_SNAKE_CASE__     :				Optional[Any]									=0

      # Now we train the model
      SCREAMING_SNAKE_CASE__     :				List[Any]									=evaluate.load('''glue''',	'''mrpc'''							)
      SCREAMING_SNAKE_CASE__     :				Optional[int]									=0
      SCREAMING_SNAKE_CASE__     :				Dict									={}
      for epoch in range(UpperCamelCase__,	UpperCamelCase__							):
            model.train()
            for step, batch in enumerate(UpperCamelCase__							):
                  SCREAMING_SNAKE_CASE__     :				List[str]									=model(**UpperCamelCase__							)
                  SCREAMING_SNAKE_CASE__     :				Optional[Any]									=outputs.loss
                  SCREAMING_SNAKE_CASE__     :				Any									=loss / gradient_accumulation_steps
                  accelerator.backward(UpperCamelCase__							)
                  if step % gradient_accumulation_steps == 0:
                        optimizer.step()
                        lr_scheduler.step()
                        optimizer.zero_grad()

                  overall_step += 1

            model.eval()
            SCREAMING_SNAKE_CASE__     :				Any									=0
            for step, batch in enumerate(UpperCamelCase__							):
                  # We could avoid this line since we set the accelerator with `device_placement=True`.
                  batch.to(accelerator.device							)
                  with torch.no_grad():
                        SCREAMING_SNAKE_CASE__     :				Tuple									=model(**UpperCamelCase__							)
                  SCREAMING_SNAKE_CASE__     :				Optional[int]									=outputs.logits.argmax(dim=-1							)
                  # It is slightly faster to call this once, than multiple times
                  SCREAMING_SNAKE_CASE__		,	SCREAMING_SNAKE_CASE__     :				str									=accelerator.gather(
                      (predictions, batch['''labels'''])							)  # If we are in a multiprocess environment, the last batch has duplicates
                  if accelerator.use_distributed:
                        if step == len(UpperCamelCase__							) - 1:
                              SCREAMING_SNAKE_CASE__     :				Any									=predictions[: len(eval_dataloader.dataset							) - samples_seen]
                              SCREAMING_SNAKE_CASE__     :				Optional[Any]									=references[: len(eval_dataloader.dataset							) - samples_seen]
                        else:
                              samples_seen += references.shape[0]
                  metric.add_batch(
                      predictions=UpperCamelCase__,	references=UpperCamelCase__,	)

            SCREAMING_SNAKE_CASE__     :				List[str]									=metric.compute()
            # Use accelerator.print to print only on the main process.
            accelerator.print(f"epoch {epoch}:",	UpperCamelCase__							)
            SCREAMING_SNAKE_CASE__     :				List[Any]									=eval_metric['''accuracy''']

            if best_performance < eval_metric["accuracy"]:
                  SCREAMING_SNAKE_CASE__     :				Dict									=eval_metric['''accuracy''']

      if args.performance_lower_bound is not None:
            assert (
                args.performance_lower_bound <= best_performance
            ), f"Best performance metric {best_performance} is lower than the lower bound {args.performance_lower_bound}"

      accelerator.wait_for_everyone()
      if accelerator.is_main_process:
            with open(os.path.join(args.output_dir,	'''all_results.json'''							),	'''w'''							) as f:
                  json.dump(UpperCamelCase__,	UpperCamelCase__							)





def  _a(  ):




      '''simple docstring'''






      SCREAMING_SNAKE_CASE__     :				int									=argparse.ArgumentParser(description='''Simple example of training script tracking peak GPU memory usage.'''							)
      parser.add_argument(
          '''--model_name_or_path''',	type=UpperCamelCase__,	default='''bert-base-cased''',	help='''Path to pretrained model or model identifier from huggingface.co/models.''',	required=UpperCamelCase__,	)
      parser.add_argument(
          '''--output_dir''',	type=UpperCamelCase__,	default='''.''',	help='''Optional save directory where all checkpoint folders will be stored. Default is the current working directory.''',	)
      parser.add_argument(
          '''--performance_lower_bound''',	type=UpperCamelCase__,	default=UpperCamelCase__,	help='''Optional lower bound for the performance metric. If set, the training will throw error when the performance metric drops below this value.''',	)
      parser.add_argument(
          '''--num_epochs''',	type=UpperCamelCase__,	default=3,	help='''Number of train epochs.''',	)
      SCREAMING_SNAKE_CASE__     :				Any									=parser.parse_args()
      SCREAMING_SNAKE_CASE__     :				str									={'''lr''': 2e-5, '''num_epochs''': args.num_epochs, '''seed''': 4_2, '''batch_size''': 1_6}
      training_function(UpperCamelCase__,	UpperCamelCase__							)


if __name__ == "__main__":
  main()







'''simple docstring'''
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  _a(  UpperCamelCase__				:						str,	UpperCamelCase__				:						Tuple,	UpperCamelCase__				:						Any,	UpperCamelCase__				:						List[str],	UpperCamelCase__				:						Union[str, Any]							):




      '''simple docstring'''






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

      # Load in the weights from the checkpoint_path
      SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=torch.load(UpperCamelCase__,	map_location='''cpu'''							)['''module''']

      # Load the entity vocab file
      SCREAMING_SNAKE_CASE__     :				List[str]									=load_original_entity_vocab(UpperCamelCase__							)
      # add an entry for [MASK2]
      SCREAMING_SNAKE_CASE__     :				Optional[int]									=max(entity_vocab.values()							) + 1
      config.entity_vocab_size += 1

      SCREAMING_SNAKE_CASE__     :				Optional[int]									=XLMRobertaTokenizer.from_pretrained(metadata['''model_config''']['''bert_model_name''']							)

      # Add special tokens to the token vocabulary for downstream tasks
      SCREAMING_SNAKE_CASE__     :				List[Any]									=AddedToken('''<ent>''',	lstrip=UpperCamelCase__,	rstrip=UpperCamelCase__							)
      SCREAMING_SNAKE_CASE__     :				Any									=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:
            SCREAMING_SNAKE_CASE__     :				Optional[Any]									=json.load(UpperCamelCase__							)
      SCREAMING_SNAKE_CASE__     :				int									='''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__							)

      SCREAMING_SNAKE_CASE__     :				Optional[Any]									=MLukeTokenizer.from_pretrained(UpperCamelCase__							)

      # Initialize the embeddings of the special tokens
      SCREAMING_SNAKE_CASE__     :				str									=tokenizer.convert_tokens_to_ids(['''@''']							)[0]
      SCREAMING_SNAKE_CASE__     :				Optional[int]									=tokenizer.convert_tokens_to_ids(['''#''']							)[0]

      SCREAMING_SNAKE_CASE__     :				Dict									=state_dict['''embeddings.word_embeddings.weight''']
      SCREAMING_SNAKE_CASE__     :				List[str]									=word_emb[ent_init_index].unsqueeze(0							)
      SCREAMING_SNAKE_CASE__     :				Tuple									=word_emb[enta_init_index].unsqueeze(0							)
      SCREAMING_SNAKE_CASE__     :				str									=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"]:
            SCREAMING_SNAKE_CASE__     :				Optional[Any]									=state_dict[bias_name]
            SCREAMING_SNAKE_CASE__     :				List[Any]									=decoder_bias[ent_init_index].unsqueeze(0							)
            SCREAMING_SNAKE_CASE__     :				str									=decoder_bias[enta_init_index].unsqueeze(0							)
            SCREAMING_SNAKE_CASE__     :				List[str]									=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"]:
                  SCREAMING_SNAKE_CASE__     :				Tuple									=f"encoder.layer.{layer_index}.attention.self."
                  SCREAMING_SNAKE_CASE__     :				Optional[int]									=state_dict[prefix + matrix_name]
                  SCREAMING_SNAKE_CASE__     :				Optional[int]									=state_dict[prefix + matrix_name]
                  SCREAMING_SNAKE_CASE__     :				List[Any]									=state_dict[prefix + matrix_name]

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

      SCREAMING_SNAKE_CASE__     :				int									=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'''							)
      SCREAMING_SNAKE_CASE__     :				Tuple									=OrderedDict()
      for key, value in state_dict.items():
            if not (key.startswith('''lm_head'''							) or key.startswith('''entity_predictions'''							)):
                  SCREAMING_SNAKE_CASE__     :				Optional[Any]									=state_dict[key]
            else:
                  SCREAMING_SNAKE_CASE__     :				Any									=state_dict[key]

      SCREAMING_SNAKE_CASE__		,	SCREAMING_SNAKE_CASE__     :				List[str]									=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
      SCREAMING_SNAKE_CASE__     :				Any									=MLukeTokenizer.from_pretrained(UpperCamelCase__,	task='''entity_classification'''							)

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

      SCREAMING_SNAKE_CASE__     :				List[Any]									=model(**UpperCamelCase__							)

      # Verify word hidden states
      if model_size == "large":
            raise NotImplementedError
      else:  # base
            SCREAMING_SNAKE_CASE__     :				str									=torch.Size((1, 3_3, 7_6_8)							)
            SCREAMING_SNAKE_CASE__     :				int									=torch.tensor([[0.0_8_9_2, 0.0_5_9_6, -0.2_8_1_9], [0.0_1_3_4, 0.1_1_9_9, 0.0_5_7_3], [-0.0_1_6_9, 0.0_9_2_7, 0.0_6_4_4]]							)

      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
            SCREAMING_SNAKE_CASE__     :				Any									=torch.Size((1, 1, 7_6_8)							)
            SCREAMING_SNAKE_CASE__     :				int									=torch.tensor([[-0.1_4_8_2, 0.0_6_0_9, 0.0_3_2_2]]							)

      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
      SCREAMING_SNAKE_CASE__     :				str									=MLukeTokenizer.from_pretrained(UpperCamelCase__							)
      SCREAMING_SNAKE_CASE__     :				Union[str, Any]									='''Tokyo is the capital of <mask>.'''
      SCREAMING_SNAKE_CASE__     :				Dict									=(2_4, 3_0)
      SCREAMING_SNAKE_CASE__     :				Optional[int]									=tokenizer(UpperCamelCase__,	entity_spans=[span],	return_tensors='''pt'''							)

      SCREAMING_SNAKE_CASE__     :				List[str]									=model(**UpperCamelCase__							)

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

      SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=outputs.entity_logits[0][0].argmax().item()
      SCREAMING_SNAKE_CASE__     :				Dict									=[
          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  _a(  UpperCamelCase__				:						Optional[Any]							):




      '''simple docstring'''






      SCREAMING_SNAKE_CASE__     :				List[Any]									=['''[MASK]''', '''[PAD]''', '''[UNK]''']

      SCREAMING_SNAKE_CASE__     :				List[str]									=[json.loads(UpperCamelCase__							) for line in open(UpperCamelCase__							)]

      SCREAMING_SNAKE_CASE__     :				Optional[int]									={}
      for entry in data:
            SCREAMING_SNAKE_CASE__     :				Tuple									=entry['''id''']
            for entity_name, language in entry["entities"]:
                  if entity_name in SPECIAL_TOKENS:
                        SCREAMING_SNAKE_CASE__     :				str									=entity_id
                        break
                  SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=f"{language}:{entity_name}"
                  SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=entity_id
      return new_mapping


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,
  )