

import argparse
import os

# New Code #
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
from accelerate.utils import find_executable_batch_size


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to combine both the gradient accumulation
# and automatic batch size finder utilities of Accelerate to perfrom
# automatic gradient accumulation
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################

__a						:Tuple            = 32

def 			__snake_case	(   __UpperCamelCase  :   Accelerator							,__UpperCamelCase  :   int = 16						):



      """simple docstring"""

      A_	     =							AutoTokenizer.from_pretrained("bert-base-cased"						)
      A_	     =							load_dataset("glue"							,"mrpc"						)

      def tokenize_function(__UpperCamelCase  :   int						):
            # max_length=None => use the model max length (it's actually the default)
            A_	     =							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
      # starting with the main process first:
      with accelerator.main_process_first():
            A_	     =							datasets.map(
                __UpperCamelCase							,batched=__UpperCamelCase							,remove_columns=["idx", "sentence1", "sentence2"]							,)

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

      def collate_fn(__UpperCamelCase  :   int						):
            # When using mixed precision we want round multiples of 8/16
            if accelerator.mixed_precision == "fp8":
                  A_	     =							16
            elif accelerator.mixed_precision != "no":
                  A_	     =							8
            else:
                  A_	     =							None

            return tokenizer.pad(
                __UpperCamelCase							,padding="longest"							,pad_to_multiple_of=__UpperCamelCase							,return_tensors="pt"							,)

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

      return train_dataloader, eval_dataloader


# For testing only
if os.environ.get('TESTING_MOCKED_DATALOADERS', None) == "1":
      from accelerate.test_utils.training import mocked_dataloaders

      __a						:Tuple            = mocked_dataloaders  # noqa: F811

def 			__snake_case	(   __UpperCamelCase  :   Optional[Any]							,__UpperCamelCase  :   int						):



      """simple docstring"""

      if os.environ.get("TESTING_MOCKED_DATALOADERS"							,__UpperCamelCase						) == "1":
            A_	     =							2
      # Initialize accelerator
      A_	     =							Accelerator(cpu=args.cpu							,mixed_precision=args.mixed_precision						)
      # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
      A_	     =							config["lr"]
      A_	     =							int(config["num_epochs"]						)
      A_	     =							int(config["seed"]						)
      A_	     =							int(config["batch_size"]						)

      A_	     =							evaluate.load("glue"							,"mrpc"						)

      # New Code #
      # We use the `find_executable_batch_size` decorator, passing in the desired observed batch size
      # to train on. If a CUDA OOM error occurs, it will retry this loop cutting the batch size in
      # half each time. From this, we can calculate the number of gradient accumulation steps needed
      # and modify the Accelerator object as a result
      @find_executable_batch_size(starting_batch_size=int(__UpperCamelCase						)						)
      def inner_training_loop(__UpperCamelCase  :   Optional[int]						):
            # Since we need to modify the outside accelerator object, we need to bring it
            # to the local scope
            nonlocal accelerator

            # We can calculate the number of gradient accumulation steps based on the current
            # batch size vs the starting batch size
            A_	     =							observed_batch_size // batch_size

            # And then set it in the Accelerator directly:
            A_	     =							num_gradient_accumulation_steps

            # Next we need to free all of the stored model references in the Accelerator each time
            accelerator.free_memory()

            # And set the seed so our results are reproducable each reset
            set_seed(__UpperCamelCase						)

            # Instantiate the model (we build the model here so that the seed also control new weights initialization)
            A_	     =							AutoModelForSequenceClassification.from_pretrained("bert-base-cased"							,return_dict=__UpperCamelCase						)

            # We could avoid this line since the accelerator is set with `device_placement=True` (default value).
            # Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
            # creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
            A_	     =							model.to(accelerator.device						)

            # Instantiate optimizer
            A_	     =							AdamW(params=model.parameters()							,lr=__UpperCamelCase						)
            A_     ,    A_	     =							get_dataloaders(__UpperCamelCase							,__UpperCamelCase						)

            # Instantiate scheduler
            A_	     =							get_linear_schedule_with_warmup(
                optimizer=__UpperCamelCase							,num_warmup_steps=100							,num_training_steps=(len(__UpperCamelCase						) * num_epochs)							,)

            # 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.
            A_     ,    A_     ,    A_     ,    A_     ,    A_	     =							accelerator.prepare(
                __UpperCamelCase							,__UpperCamelCase							,__UpperCamelCase							,__UpperCamelCase							,__UpperCamelCase						)

            # Now we train the model
            for epoch in range(__UpperCamelCase						):
                  model.train()
                  for step, batch in enumerate(__UpperCamelCase						):
                        # And perform gradient accumulation
                        with accelerator.accumulate(__UpperCamelCase						):
                              # We could avoid this line since we set the accelerator with `device_placement=True`.
                              batch.to(accelerator.device						)
                              A_	     =							model(**__UpperCamelCase						)
                              A_	     =							outputs.loss
                              accelerator.backward(__UpperCamelCase						)
                              optimizer.step()
                              lr_scheduler.step()
                              optimizer.zero_grad()

                  model.eval()
                  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():
                              A_	     =							model(**__UpperCamelCase						)
                        A_	     =							outputs.logits.argmax(dim=-1						)
                        A_     ,    A_	     =							accelerator.gather_for_metrics((predictions, batch["labels"])						)
                        metric.add_batch(
                            predictions=__UpperCamelCase							,references=__UpperCamelCase							,)

                  A_	     =							metric.compute()
                  # Use accelerator.print to print only on the main process.
                  accelerator.print(f'''epoch {epoch}:'''							,__UpperCamelCase						)

    # New Code #
    # And call it at the end with no arguments
    # Note: You could also refactor this outside of your training loop function
      inner_training_loop()

def 			__snake_case	(   ):



      """simple docstring"""

      A_	     =							argparse.ArgumentParser(description="Simple example of training script."						)
      parser.add_argument(
          "--mixed_precision"							,type=__UpperCamelCase							,default=__UpperCamelCase							,choices=["no", "fp16", "bf16", "fp8"]							,help="Whether to use mixed precision. Choose"
          "between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."
          "and an Nvidia Ampere GPU."							,)
      parser.add_argument("--cpu"							,action="store_true"							,help="If passed, will train on the CPU."						)
      A_	     =							parser.parse_args()
      # New Code #
      # We modify the starting batch size to be an observed batch size of 256, to guarentee an initial CUDA OOM
      A_	     =							{"lr": 2E-5, "num_epochs": 3, "seed": 42, "batch_size": 256}
      training_function(__UpperCamelCase							,__UpperCamelCase						)


if __name__ == "__main__":
      main()









import json
import os
from typing import List, Optional, Tuple

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


__a						:Optional[Any]            = logging.get_logger(__name__)

__a						:Union[str, Any]            = {
    'vocab_file': 'vocab.json',
    'merges_file': 'merges.txt',
}

__a						:Union[str, Any]            = {
    'vocab_file': {
        'microsoft/biogpt': 'https://huggingface.co/microsoft/biogpt/resolve/main/vocab.json',
    },
    'merges_file': {'microsoft/biogpt': 'https://huggingface.co/microsoft/biogpt/resolve/main/merges.txt'},
}

__a						:str            = {
    'microsoft/biogpt': 1024,
}

def 			__snake_case	(   __UpperCamelCase  :   Any						):



      """simple docstring"""

      A_	     =							set()
      A_	     =							word[0]
      for char in word[1:]:
            pairs.add((prev_char, char)						)
            A_	     =							char
      return pairs



class      _a				( snake_case_							):




      """simple docstring"""

      _lowerCamelCase					: Tuple					=					VOCAB_FILES_NAMES
      _lowerCamelCase					: Any					=					PRETRAINED_VOCAB_FILES_MAP
      _lowerCamelCase					: str					=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      _lowerCamelCase					: List[Any]					=					['input_ids', 'attention_mask']





      def __init__(      self       :							Union[str, Any]	, UpperCAmelCase       :							Union[str, Any]	, UpperCAmelCase       :							Optional[int]	, UpperCAmelCase       :							List[Any]="<unk>"	, UpperCAmelCase       :							Dict="<s>"	, UpperCAmelCase       :							Tuple="</s>"	, UpperCAmelCase       :							Optional[int]="</s>"	, UpperCAmelCase       :							int="<pad>"	, **UpperCAmelCase       :							List[str]	, ):
            super().__init__(
                bos_token=UpperCAmelCase	, eos_token=UpperCAmelCase	, sep_token=UpperCAmelCase	, unk_token=UpperCAmelCase	, pad_token=UpperCAmelCase	, **UpperCAmelCase	, )

            try:
                  import sacremoses
            except ImportError:
                  raise ImportError(
                      "You need to install sacremoses to use BioGptTokenizer. "
                      "See https://pypi.org/project/sacremoses/ for installation."  )

            A_	     =							"en"
            A_	     =							sacremoses
            # cache of sm.MosesTokenizer instance
            A_	     =							{}
            A_	     =							{}
            with open(UpperCAmelCase	, encoding="utf-8"  ) as vocab_handle:
                  A_	     =							json.load(UpperCAmelCase  )
            A_	     =							{v: k for k, v in self.encoder.items()}
            with open(UpperCAmelCase	, encoding="utf-8"  ) as merges_handle:
                  A_	     =							merges_handle.read().split("\n"  )[:-1]
            A_	     =							[tuple(merge.split()[:2]  ) for merge in merges]
            A_	     =							dict(zip(UpperCAmelCase	, range(len(UpperCAmelCase  )  )  )  )
            A_	     =							{}





      @property
      def 		__A  (      self       :							int  ):
            return len(self.encoder  )





      def 		__A  (      self       :							Union[str, Any]  ):
            return dict(self.encoder	, **self.added_tokens_encoder  )





      def 		__A  (      self       :							Any	, UpperCAmelCase       :							Union[str, Any]	, UpperCAmelCase       :							Optional[Any]  ):
            if lang not in self.cache_moses_tokenizer:
                  A_	     =							self.sm.MosesTokenizer(lang=UpperCAmelCase  )
                  A_	     =							moses_tokenizer
            return self.cache_moses_tokenizer[lang].tokenize(
                UpperCAmelCase	, aggressive_dash_splits=UpperCAmelCase	, return_str=UpperCAmelCase	, escape=UpperCAmelCase  )





      def 		__A  (      self       :							List[Any]	, UpperCAmelCase       :							Tuple	, UpperCAmelCase       :							List[Any]  ):
            if lang not in self.cache_moses_detokenizer:
                  A_	     =							self.sm.MosesDetokenizer(lang=UpperCAmelCase  )
                  A_	     =							moses_detokenizer
            return self.cache_moses_detokenizer[lang].detokenize(UpperCAmelCase  )





      def 		__A  (      self       :							Optional[Any]	, UpperCAmelCase       :							int  ):
            A_	     =							tuple(token[:-1]  ) + (token[-1] + "</w>",)
            if token in self.cache:
                  return self.cache[token]
            A_	     =							get_pairs(UpperCAmelCase  )

            if not pairs:
                  return token + "</w>"

            while True:
                  A_	     =							min(UpperCAmelCase	, key=lambda UpperCAmelCase  : self.bpe_ranks.get(UpperCAmelCase	, float("inf"  )  )  )
                  if bigram not in self.bpe_ranks:
                        break
                  A_     ,    A_	     =							bigram
                  A_	     =							[]
                  A_	     =							0
                  while i < len(UpperCAmelCase  ):
                        try:
                              A_	     =							word.index(UpperCAmelCase	, UpperCAmelCase  )
                        except ValueError:
                              new_word.extend(word[i:]  )
                              break
                        else:
                              new_word.extend(word[i:j]  )
                              A_	     =							j

                        if word[i] == first and i < len(UpperCAmelCase  ) - 1 and word[i + 1] == second:
                              new_word.append(first + second  )
                              i += 2
                        else:
                              new_word.append(word[i]  )
                              i += 1
                  A_	     =							tuple(UpperCAmelCase  )
                  A_	     =							new_word
                  if len(UpperCAmelCase  ) == 1:
                        break
                  else:
                        A_	     =							get_pairs(UpperCAmelCase  )
            A_	     =							" ".join(UpperCAmelCase  )
            if word == "\n  </w>":
                  A_	     =							"\n</w>"
            A_	     =							word
            return word





      def 		__A  (      self       :							Optional[int]	, UpperCAmelCase       :							str	, UpperCAmelCase       :							Optional[Any]=False  ):
            if bypass_tokenizer:
                  A_	     =							text.split()
            else:
                  A_	     =							self.moses_tokenize(UpperCAmelCase	, self.lang  )

            A_	     =							[]
            for token in text:
                  if token:
                        split_tokens.extend(list(self.bpe(UpperCAmelCase  ).split(" "  )  )  )

            return split_tokens





      def 		__A  (      self       :							Dict	, UpperCAmelCase       :							Union[str, Any]  ):
            return self.encoder.get(UpperCAmelCase	, self.encoder.get(self.unk_token  )  )





      def 		__A  (      self       :							Optional[Any]	, UpperCAmelCase       :							List[str]  ):
            return self.decoder.get(UpperCAmelCase	, self.unk_token  )





      def 		__A  (      self       :							Dict	, UpperCAmelCase       :							Any  ):
            A_	     =							[t.replace(" "	, ""  ).replace("</w>"	, " "  ) for t in tokens]
            A_	     =							"".join(UpperCAmelCase  ).split()
            # detokenize
            A_	     =							self.moses_detokenize(UpperCAmelCase	, self.lang  )
            return text





      def 		__A  (      self       :							List[str]	, UpperCAmelCase       :							List[int]	, UpperCAmelCase       :							Optional[List[int]] = None  ):
            if token_ids_a is None:
                  return [self.sep_token_id] + token_ids_a
            A_	     =							[self.sep_token_id]
            return sep + token_ids_a + sep + token_ids_a





      def 		__A  (      self       :							Any	, UpperCAmelCase       :							List[int]	, UpperCAmelCase       :							Optional[List[int]] = None	, UpperCAmelCase       :							bool = False  ):
            if already_has_special_tokens:
                  return super().get_special_tokens_mask(
                      token_ids_a=UpperCAmelCase	, token_ids_a=UpperCAmelCase	, already_has_special_tokens=UpperCAmelCase  )
            # no bos used in fairseq
            if token_ids_a is not None:
                  return [1] + ([0] * len(UpperCAmelCase  )) + [1] + ([0] * len(UpperCAmelCase  ))
            return [1] + ([0] * len(UpperCAmelCase  ))





      def 		__A  (      self       :							Optional[Any]	, UpperCAmelCase       :							List[int]	, UpperCAmelCase       :							Optional[List[int]] = None  ):
            A_	     =							[self.sep_token_id]

            # no bos used in fairseq
            if token_ids_a is None:
                  return len(token_ids_a + sep  ) * [0]
            return len(token_ids_a + sep  ) * [0] + len(token_ids_a + sep  ) * [1]





      def 		__A  (      self       :							Union[str, Any]	, UpperCAmelCase       :							str	, UpperCAmelCase       :							Optional[str] = None  ):
            if not os.path.isdir(UpperCAmelCase  ):
                  logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''  )
                  return
            A_	     =							os.path.join(
                UpperCAmelCase	, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]  )
            A_	     =							os.path.join(
                UpperCAmelCase	, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"]  )

            with open(UpperCAmelCase	, "w"	, encoding="utf-8"  ) as f:
                  f.write(json.dumps(self.encoder	, indent=2	, sort_keys=UpperCAmelCase	, ensure_ascii=UpperCAmelCase  ) + "\n"  )

            A_	     =							0
            with open(UpperCAmelCase	, "w"	, encoding="utf-8"  ) as writer:
                  for bpe_tokens, token_index in sorted(self.bpe_ranks.items()	, key=lambda UpperCAmelCase  : kv[1]  ):
                        if index != token_index:
                              logger.warning(
                                  f'''Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'''
                                  " Please check that the tokenizer is not corrupted!"  )
                              A_	     =							token_index
                        writer.write(" ".join(UpperCAmelCase  ) + "\n"  )
                        index += 1

            return vocab_file, merge_file





      def __getstate__(      self       :							Union[str, Any]  ):
            A_	     =							self.__dict__.copy()
            A_	     =							None
            return state







      def __setstate__(      self       :							List[Any]	, UpperCAmelCase       :							Dict  ):
            A_	     =							d

            try:
                  import sacremoses
            except ImportError:
                  raise ImportError(
                      "You need to install sacremoses to use XLMTokenizer. "
                      "See https://pypi.org/project/sacremoses/ for installation."  )

            A_	     =							sacremoses