




'''simple docstring'''

import argparse
import gc
import json
import os

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


SCREAMING_SNAKE_CASE__     =       1_6
SCREAMING_SNAKE_CASE__     =       3_2







def    lowercase__			(				__UpperCamelCase   )->			Union[str, Any]:
      return int(x / 2**20   )






class     a_						:

  def __enter__(				self				)  ->				Optional[Any]:



        """simple docstring"""






        gc.collect()
        torch.cuda.empty_cache()
        torch.cuda.reset_max_memory_allocated()  # reset the peak gauge to zero
        UpperCamelCase      = torch.cuda.memory_allocated()
        return self


  def __exit__(				self      ,		*_SCREAMING_SNAKE_CASE				)  ->				List[str]:



        """simple docstring"""






        gc.collect()
        torch.cuda.empty_cache()
        UpperCamelCase      = torch.cuda.memory_allocated()
        UpperCamelCase      = torch.cuda.max_memory_allocated()
        UpperCamelCase      = bamb(self.end - self.begin				)
        UpperCamelCase      = bamb(self.peak - self.begin				)
        # print(f"delta used/peak {self.used:4d}/{self.peaked:4d}")







def    lowercase__			(				__UpperCamelCase		,						__UpperCamelCase = 16		,						__UpperCamelCase = "bert-base-cased"		,						__UpperCamelCase = 320		,						__UpperCamelCase = 160		,						)->			Optional[int]:
      UpperCamelCase      = AutoTokenizer.from_pretrained(_a   )
      UpperCamelCase      = load_dataset(
          """glue"""		,						"""mrpc"""		,						split={"""train""": F"train[:{n_train}]", """validation""": F"validation[:{n_val}]"}   )

      def tokenize_function(__UpperCamelCase   ):
            # max_length=None => use the model max length (it's actually the default)
            UpperCamelCase      = tokenizer(examples["""sentence1"""]		,						examples["""sentence2"""]		,						truncation=_a		,						max_length=_a   )
            return outputs

      # Apply the method we just defined to all the examples in all the splits of the dataset
      UpperCamelCase      = datasets.map(
          _a		,						batched=_a		,						remove_columns=["""idx""", """sentence1""", """sentence2"""]		,						load_from_cache_file=_a   )

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

      def collate_fn(__UpperCamelCase   ):
            # 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(_a		,						padding="""max_length"""		,						max_length=128		,						return_tensors="""pt"""   )
            return tokenizer.pad(_a		,						padding="""longest"""		,						return_tensors="""pt"""   )

      # Instantiate dataloaders.
      UpperCamelCase      = DataLoader(
          tokenized_datasets["""train"""]		,						shuffle=_a		,						collate_fn=_a		,						batch_size=_a   )
      UpperCamelCase      = DataLoader(
          tokenized_datasets["""validation"""]		,						shuffle=_a		,						collate_fn=_a		,						batch_size=_a   )

      return train_dataloader, eval_dataloader







def    lowercase__			(				__UpperCamelCase		,						__UpperCamelCase   )->			Tuple:
      # Initialize accelerator
      UpperCamelCase      = Accelerator()

      # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
      UpperCamelCase      = config["lr"]
      UpperCamelCase      = int(config["""num_epochs"""]   )
      UpperCamelCase      = int(config["""seed"""]   )
      UpperCamelCase      = int(config["""batch_size"""]   )
      UpperCamelCase      = args.model_name_or_path

      set_seed(_a   )
      UpperCamelCase      = get_dataloaders(_a		,						_a		,						_a		,						args.n_train		,						args.n_val   )

      # Instantiate the model (we build the model here so that the seed also control new weights initialization)
      UpperCamelCase      = AutoModelForSequenceClassification.from_pretrained(_a		,						return_dict=_a   )

      # Instantiate optimizer
      UpperCamelCase      = (
          AdamW
          if accelerator.state.deepspeed_plugin is None
          or "optimizer" not in accelerator.state.deepspeed_plugin.deepspeed_config
          else DummyOptim
      )
      UpperCamelCase      = optimizer_cls(params=model.parameters()		,						lr=_a   )

      if accelerator.state.deepspeed_plugin is not None:
            UpperCamelCase      = accelerator.state.deepspeed_plugin.deepspeed_config[
                "gradient_accumulation_steps"
            ]
      else:
            UpperCamelCase      = 1
      UpperCamelCase      = (len(_a   ) * num_epochs) // gradient_accumulation_steps

      # Instantiate scheduler
      if (
          accelerator.state.deepspeed_plugin is None
          or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
      ):
            UpperCamelCase      = get_linear_schedule_with_warmup(
                optimizer=_a		,						num_warmup_steps=0		,						num_training_steps=_a		,						)
      else:
            UpperCamelCase      = DummyScheduler(_a		,						total_num_steps=_a		,						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.
      UpperCamelCase      = accelerator.prepare(
          _a		,						_a		,						_a		,						_a		,						_a   )

      # We need to keep track of how many total steps we have iterated over
      UpperCamelCase      = 0
      # We also need to keep track of the stating epoch so files are named properly
      UpperCamelCase      = 0

      # Now we train the model
      UpperCamelCase      = {}
      for epoch in range(_a		,						_a   ):
            with TorchTracemalloc() as tracemalloc:
                  model.train()
                  for step, batch in enumerate(_a   ):
                        UpperCamelCase      = model(**_a   )
                        UpperCamelCase      = outputs.loss
                        UpperCamelCase      = loss / gradient_accumulation_steps
                        accelerator.backward(_a   )
                        if step % gradient_accumulation_steps == 0:
                              optimizer.step()
                              lr_scheduler.step()
                              optimizer.zero_grad()

                        overall_step += 1

        # Printing the GPU memory usage details such as allocated memory, peak memory, and total memory usage
            accelerator.print("""Memory before entering the train : {}""".format(bamb(tracemalloc.begin   )   )   )
            accelerator.print("""Memory consumed at the end of the train (end-begin): {}""".format(tracemalloc.used   )   )
            accelerator.print("""Peak Memory consumed during the train (max-begin): {}""".format(tracemalloc.peaked   )   )
            accelerator.print(
                """Total Peak Memory consumed during the train (max): {}""".format(
                    tracemalloc.peaked + bamb(tracemalloc.begin   )   )   )
            UpperCamelCase      = tracemalloc.peaked + bamb(tracemalloc.begin   )
            if args.peak_memory_upper_bound is not None:
                  assert (
                      train_total_peak_memory[F"epoch-{epoch}"] <= args.peak_memory_upper_bound
                  ), "Peak memory usage exceeded the upper bound"

      accelerator.wait_for_everyone()
      if accelerator.is_main_process:
            with open(os.path.join(args.output_dir		,						"""peak_memory_utilization.json"""   )		,						"""w"""   ) as f:
                  json.dump(_a		,						_a   )







def    lowercase__			(				)->			List[Any]:
      UpperCamelCase      = argparse.ArgumentParser(description="""Simple example of training script tracking peak GPU memory usage."""   )
      parser.add_argument(
          """--model_name_or_path"""		,						type=_a		,						default="""bert-base-cased"""		,						help="""Path to pretrained model or model identifier from huggingface.co/models."""		,						required=_a		,						)
      parser.add_argument(
          """--output_dir"""		,						type=_a		,						default="""."""		,						help="""Optional save directory where all checkpoint folders will be stored. Default is the current working directory."""		,						)
      parser.add_argument(
          """--peak_memory_upper_bound"""		,						type=_a		,						default=_a		,						help="""The upper bound of peak memory usage in MB. If set, the training will throw an error if the peak memory usage exceeds this value."""		,						)
      parser.add_argument(
          """--n_train"""		,						type=_a		,						default=320		,						help="""Number of training examples to use."""		,						)
      parser.add_argument(
          """--n_val"""		,						type=_a		,						default=160		,						help="""Number of validation examples to use."""		,						)
      parser.add_argument(
          """--num_epochs"""		,						type=_a		,						default=1		,						help="""Number of train epochs."""		,						)
      UpperCamelCase      = parser.parse_args()
      UpperCamelCase      = {"lr": 2E-5, "num_epochs": args.num_epochs, "seed": 42, "batch_size": 16}
      training_function(_a		,						_a   )


if __name__ == "__main__":
     main()








'''simple docstring'''

import os
from typing import List, Optional, Union

from ...tokenization_utils import PreTrainedTokenizer
from ...tokenization_utils_base import AddedToken
from ...utils import logging


SCREAMING_SNAKE_CASE__     =       logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__     =       {'vocab_file': 'vocab.txt'}

SCREAMING_SNAKE_CASE__     =       {
    'vocab_file': {
        'facebook/esm2_t6_8M_UR50D': 'https://huggingface.co/facebook/esm2_t6_8M_UR50D/resolve/main/vocab.txt',
        'facebook/esm2_t12_35M_UR50D': 'https://huggingface.co/facebook/esm2_t12_35M_UR50D/resolve/main/vocab.txt',
    },
}

SCREAMING_SNAKE_CASE__     =       {
    'facebook/esm2_t6_8M_UR50D': 1_0_2_4,
    'facebook/esm2_t12_35M_UR50D': 1_0_2_4,
}







def    lowercase__			(				__UpperCamelCase   )->			Any:
      with open(__UpperCamelCase		,						"""r"""   ) as f:
            UpperCamelCase      = f.read().splitlines()
            return [l.strip() for l in lines]






class     a_						(      lowerCamelCase    ):
  lowercase							=				VOCAB_FILES_NAMES
  lowercase							=				PRETRAINED_VOCAB_FILES_MAP
  lowercase							=				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  lowercase							=				["""input_ids""", """attention_mask"""]

  def __init__(				self      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE="<unk>"      ,		_SCREAMING_SNAKE_CASE="<cls>"      ,		_SCREAMING_SNAKE_CASE="<pad>"      ,		_SCREAMING_SNAKE_CASE="<mask>"      ,		_SCREAMING_SNAKE_CASE="<eos>"      ,		**_SCREAMING_SNAKE_CASE      ,		)  ->				Any:



        """simple docstring"""






        super().__init__(**_SCREAMING_SNAKE_CASE				)
        UpperCamelCase      = load_vocab_file(_SCREAMING_SNAKE_CASE				)
        UpperCamelCase      = dict(enumerate(self.all_tokens				)				)
        UpperCamelCase      = {tok: ind for ind, tok in enumerate(self.all_tokens				)}
        UpperCamelCase      = unk_token
        UpperCamelCase      = cls_token
        UpperCamelCase      = pad_token
        UpperCamelCase      = mask_token
        UpperCamelCase      = eos_token
        UpperCamelCase      = self.all_tokens
        self._create_trie(self.unique_no_split_tokens				)

  def   A__						(				self      ,		_SCREAMING_SNAKE_CASE				)  ->				str:



        """simple docstring"""






        return self._id_to_token.get(_SCREAMING_SNAKE_CASE      ,		self.unk_token				)

  def   A__						(				self      ,		_SCREAMING_SNAKE_CASE				)  ->				int:



        """simple docstring"""






        return self._token_to_id.get(_SCREAMING_SNAKE_CASE      ,		self._token_to_id.get(self.unk_token				)				)

  def   A__						(				self      ,		_SCREAMING_SNAKE_CASE      ,		**_SCREAMING_SNAKE_CASE				)  ->				List[str]:



        """simple docstring"""






        return text.split()

  def   A__						(				self      ,		_SCREAMING_SNAKE_CASE=False				)  ->				Dict:



        """simple docstring"""






        return len(self._id_to_token				)

  def   A__						(				self				)  ->				Tuple:



        """simple docstring"""






        return {token: i for i, token in enumerate(self.all_tokens				)}

  def   A__						(				self      ,		_SCREAMING_SNAKE_CASE				)  ->				int:



        """simple docstring"""






        return self._token_to_id.get(_SCREAMING_SNAKE_CASE      ,		self._token_to_id.get(self.unk_token				)				)

  def   A__						(				self      ,		_SCREAMING_SNAKE_CASE				)  ->				str:



        """simple docstring"""






        return self._id_to_token.get(_SCREAMING_SNAKE_CASE      ,		self.unk_token				)

  def   A__						(				self      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE = None				)  ->				List[int]:



        """simple docstring"""






        UpperCamelCase      = [self.cls_token_id]
        UpperCamelCase      = [self.eos_token_id]  # No sep token in ESM vocabulary
        if token_ids_a is None:
              if self.eos_token_id is None:
                    return cls + token_ids_a
              else:
                    return cls + token_ids_a + sep
        elif self.eos_token_id is None:
              raise ValueError("""Cannot tokenize multiple sequences when EOS token is not set!"""				)
        return cls + token_ids_a + sep + token_ids_a + sep  # Multiple inputs always have an EOS token

  def   A__						(				self      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE = None      ,		_SCREAMING_SNAKE_CASE = False				)  ->				List[int]:



        """simple docstring"""






        if already_has_special_tokens:
              if token_ids_a is not None:
                    raise ValueError(
                        """You should not supply a second sequence if the provided sequence of """
                        """ids is already formatted with special tokens for the model."""				)

              return [1 if token in self.all_special_ids else 0 for token in token_ids_a]
        UpperCamelCase      = [1] + ([0] * len(_SCREAMING_SNAKE_CASE				)) + [1]
        if token_ids_a is not None:
              mask += [0] * len(_SCREAMING_SNAKE_CASE				) + [1]
        return mask

  def   A__						(				self      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE				)  ->				Optional[int]:



        """simple docstring"""






        UpperCamelCase      = os.path.join(_SCREAMING_SNAKE_CASE      ,		(filename_prefix + """-""" if filename_prefix else """""") + """vocab.txt"""				)
        with open(_SCREAMING_SNAKE_CASE      ,		"""w"""				) as f:
              f.write("""\n""".join(self.all_tokens				)				)
        return (vocab_file,)

  @property
  def   A__						(				self				)  ->				int:



        """simple docstring"""






        return self.get_vocab_size(with_added_tokens=_SCREAMING_SNAKE_CASE				)


  def   A__						(				self      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE = False				)  ->				int:



        """simple docstring"""






        return super()._add_tokens(_SCREAMING_SNAKE_CASE      ,		special_tokens=_SCREAMING_SNAKE_CASE				)
