


"""simple docstring"""



import argparse
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


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to properly calculate the metrics on the
# validation dataset when in a distributed system, and builds off the
# `nlp_example.py` script.
#
# 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)
#
# To help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# 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
#
########################################################################


__snake_case       =       16
__snake_case       =       32







def     __lowerCAmelCase		(		lowercase    :    Accelerator		,				lowercase    :    int = 16					)      ->		int:


    """simple docstring"""





    snake_case       : Any    =       AutoTokenizer.from_pretrained("bert-base-cased"					)
    snake_case       : Union[str, Any]    =       load_dataset("glue"		,				"mrpc"					)

    def tokenize_function(lowercase    :    Tuple					):
        # max_length=None => use the model max length (it's actually the default)
        snake_case       : Any    =       tokenizer(examples["sentence1"]		,				examples["sentence2"]		,				truncation=lowercase		,				max_length=lowercase					)
        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():
        snake_case       : Optional[Any]    =       datasets.map(
            lowercase		,				batched=lowercase		,				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
    snake_case       : int    =       tokenized_datasets.rename_column("label"		,				"labels"					)

    def collate_fn(lowercase    :    List[Any]					):
        # On TPU it's best to pad everything to the same length or training will be very slow.
        snake_case       : str    =       128 if accelerator.distributed_type == DistributedType.TPU else None
        # When using mixed precision we want round multiples of 8/16
        if accelerator.mixed_precision == "fp8":
            snake_case       : Tuple    =       16
        elif accelerator.mixed_precision != "no":
            snake_case       : Optional[Any]    =       8
        else:
            snake_case       : Optional[int]    =       None

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

    # Instantiate dataloaders.
    snake_case       : Optional[int]    =       DataLoader(
        tokenized_datasets["train"]		,				shuffle=lowercase		,				collate_fn=lowercase		,				batch_size=lowercase					)
    snake_case       : Any    =       DataLoader(
        tokenized_datasets["validation"]		,				shuffle=lowercase		,				collate_fn=lowercase		,				batch_size=lowercase					)

    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

  __snake_case       =       mocked_dataloaders  # noqa: F811







def     __lowerCAmelCase		(		lowercase    :    Union[str, Any]		,				lowercase    :    Any					)      ->		Tuple:


    """simple docstring"""





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

    snake_case       : Dict    =       evaluate.load("glue"		,				"mrpc"					)

    # If the batch size is too big we use gradient accumulation
    snake_case       : List[str]    =       1
    if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
        snake_case       : Any    =       batch_size // MAX_GPU_BATCH_SIZE
        snake_case       : str    =       MAX_GPU_BATCH_SIZE

    set_seed(lowercase					)
    snake_case   ,snake_case       : int    =       get_dataloaders(lowercase		,				lowercase					)
    # Instantiate the model (we build the model here so that the seed also control new weights initialization)
    snake_case       : Tuple    =       AutoModelForSequenceClassification.from_pretrained("bert-base-cased"		,				return_dict=lowercase					)

    # 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).
    snake_case       : str    =       model.to(accelerator.device					)

    # Instantiate optimizer
    snake_case       : Any    =       AdamW(params=model.parameters()		,				lr=lowercase					)

    # Instantiate scheduler
    snake_case       : int    =       get_linear_schedule_with_warmup(
        optimizer=lowercase		,				num_warmup_steps=100		,				num_training_steps=(len(lowercase					) * num_epochs) // gradient_accumulation_steps		,				)

    # 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.
    snake_case   ,snake_case   ,snake_case   ,snake_case   ,snake_case       : List[str]    =       accelerator.prepare(
        lowercase		,				lowercase		,				lowercase		,				lowercase		,				lowercase					)

    # Now we train the model
    for epoch in range(lowercase					):
        model.train()
        for step, batch in enumerate(lowercase					):
            # We could avoid this line since we set the accelerator with `device_placement=True`.
            batch.to(accelerator.device					)
            snake_case       : Optional[int]    =       model(**lowercase					)
            snake_case       : int    =       outputs.loss
            snake_case       : List[Any]    =       loss / gradient_accumulation_steps
            accelerator.backward(lowercase					)
            if step % gradient_accumulation_steps == 0:
                optimizer.step()
                lr_scheduler.step()
                optimizer.zero_grad()

        model.eval()
        snake_case       : List[str]    =       0
        for step, batch in enumerate(lowercase					):
            # We could avoid this line since we set the accelerator with `device_placement=True`.
            batch.to(accelerator.device					)
            with torch.no_grad():
                snake_case       : List[Any]    =       model(**lowercase					)
            snake_case       : List[Any]    =       outputs.logits.argmax(dim=-1					)
            snake_case   ,snake_case       : Any    =       accelerator.gather((predictions, batch["labels"])					)
            # New Code #
            # First we check if it's a distributed system
            if accelerator.use_distributed:
                # Then see if we're on the last batch of our eval dataloader
                if step == len(lowercase					) - 1:
                    # Last batch needs to be truncated on distributed systems as it contains additional samples
                    snake_case       : Union[str, Any]    =       predictions[: len(eval_dataloader.dataset					) - samples_seen]
                    snake_case       : List[Any]    =       references[: len(eval_dataloader.dataset					) - samples_seen]
                else:
                    # Otherwise we add the number of samples seen
                    samples_seen += references.shape[0]
            # All of this can be avoided if you use `Accelerator.gather_for_metrics` instead of `Accelerator.gather`:
            # accelerator.gather_for_metrics((predictions, batch["labels"]))
            metric.add_batch(
                predictions=lowercase		,				references=lowercase		,				)

        snake_case       : int    =       metric.compute()
        # Use accelerator.print to print only on the main process.
        accelerator.print(F'epoch {epoch}:'		,				lowercase					)







def     __lowerCAmelCase		(		)      ->		Dict:


    """simple docstring"""





    snake_case       : List[str]    =       argparse.ArgumentParser(description="Simple example of training script."					)
    parser.add_argument(
        "--mixed_precision"		,				type=lowercase		,				default=lowercase		,				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."					)
    snake_case       : str    =       parser.parse_args()
    snake_case       : Tuple    =       {"lr": 2e-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
    training_function(lowercase		,				lowercase					)


if __name__ == "__main__":
  main()









"""simple docstring"""



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

import regex as re

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


__snake_case       =       logging.get_logger(__name__)


__snake_case       =       {"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt"""}

__snake_case       =       {
    """vocab_file""": {
        """allenai/longformer-base-4096""": """https://huggingface.co/allenai/longformer-base-4096/resolve/main/vocab.json""",
        """allenai/longformer-large-4096""": (
            """https://huggingface.co/allenai/longformer-large-4096/resolve/main/vocab.json"""
        ),
        """allenai/longformer-large-4096-finetuned-triviaqa""": (
            """https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/vocab.json"""
        ),
        """allenai/longformer-base-4096-extra.pos.embd.only""": (
            """https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/vocab.json"""
        ),
        """allenai/longformer-large-4096-extra.pos.embd.only""": (
            """https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/vocab.json"""
        ),
    },
    """merges_file""": {
        """allenai/longformer-base-4096""": """https://huggingface.co/allenai/longformer-base-4096/resolve/main/merges.txt""",
        """allenai/longformer-large-4096""": (
            """https://huggingface.co/allenai/longformer-large-4096/resolve/main/merges.txt"""
        ),
        """allenai/longformer-large-4096-finetuned-triviaqa""": (
            """https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/merges.txt"""
        ),
        """allenai/longformer-base-4096-extra.pos.embd.only""": (
            """https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/merges.txt"""
        ),
        """allenai/longformer-large-4096-extra.pos.embd.only""": (
            """https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/merges.txt"""
        ),
    },
}

__snake_case       =       {
    """allenai/longformer-base-4096""": 4096,
    """allenai/longformer-large-4096""": 4096,
    """allenai/longformer-large-4096-finetuned-triviaqa""": 4096,
    """allenai/longformer-base-4096-extra.pos.embd.only""": 4096,
    """allenai/longformer-large-4096-extra.pos.embd.only""": 4096,
}







@lru_cache()
# Copied from transformers.models.roberta.tokenization_roberta.bytes_to_unicode
def     __lowerCAmelCase		(		)      ->		str:


    """simple docstring"""





    snake_case       : int    =       (
        list(range(ord("!"					)		,				ord("~"					) + 1					)					) + list(range(ord("¡"					)		,				ord("¬"					) + 1					)					) + list(range(ord("®"					)		,				ord("ÿ"					) + 1					)					)
    )
    snake_case       : Any    =       bs[:]
    snake_case       : str    =       0
    for b in range(2**8					):
        if b not in bs:
            bs.append(lowercase					)
            cs.append(2**8 + n					)
            n += 1
    snake_case       : str    =       [chr(lowercase					) for n in cs]
    return dict(zip(lowercase		,				lowercase					)					)







def     __lowerCAmelCase		(		lowercase    :    int					)      ->		Tuple:


    """simple docstring"""





    snake_case       : Optional[Any]    =       set()
    snake_case       : Dict    =       word[0]
    for char in word[1:]:
        pairs.add((prev_char, char)					)
        snake_case       : Optional[int]    =       char
    return pairs







class _lowerCAmelCase							( snake_case_      ):
   __UpperCAmelCase					:			Dict				    =    VOCAB_FILES_NAMES
   __UpperCAmelCase					:			List[Any]				    =    PRETRAINED_VOCAB_FILES_MAP
   __UpperCAmelCase					:			str				    =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   __UpperCAmelCase					:			Dict				    =    ['''input_ids''', '''attention_mask''']







   def __init__(      self		,      UpperCamelCase__		,      UpperCamelCase__		,      UpperCamelCase__="replace"		,      UpperCamelCase__="<s>"		,      UpperCamelCase__="</s>"		,      UpperCamelCase__="</s>"		,      UpperCamelCase__="<s>"		,      UpperCamelCase__="<unk>"		,      UpperCamelCase__="<pad>"		,      UpperCamelCase__="<mask>"		,      UpperCamelCase__=False		,      **UpperCamelCase__		,      )      ->						Union[str, Any]:

       '''simple docstring'''




       snake_case       : Any    =       AddedToken(UpperCamelCase__		,      lstrip=UpperCamelCase__		,      rstrip=UpperCamelCase__		) if isinstance(UpperCamelCase__		,      UpperCamelCase__		) else bos_token
       snake_case       : int    =       AddedToken(UpperCamelCase__		,      lstrip=UpperCamelCase__		,      rstrip=UpperCamelCase__		) if isinstance(UpperCamelCase__		,      UpperCamelCase__		) else eos_token
       snake_case       : Dict    =       AddedToken(UpperCamelCase__		,      lstrip=UpperCamelCase__		,      rstrip=UpperCamelCase__		) if isinstance(UpperCamelCase__		,      UpperCamelCase__		) else sep_token
       snake_case       : Optional[int]    =       AddedToken(UpperCamelCase__		,      lstrip=UpperCamelCase__		,      rstrip=UpperCamelCase__		) if isinstance(UpperCamelCase__		,      UpperCamelCase__		) else cls_token
       snake_case       : Dict    =       AddedToken(UpperCamelCase__		,      lstrip=UpperCamelCase__		,      rstrip=UpperCamelCase__		) if isinstance(UpperCamelCase__		,      UpperCamelCase__		) else unk_token
       snake_case       : Tuple    =       AddedToken(UpperCamelCase__		,      lstrip=UpperCamelCase__		,      rstrip=UpperCamelCase__		) if isinstance(UpperCamelCase__		,      UpperCamelCase__		) else pad_token

       # Mask token behave like a normal word, i.e. include the space before it
       snake_case       : Any    =       AddedToken(UpperCamelCase__		,      lstrip=UpperCamelCase__		,      rstrip=UpperCamelCase__		) if isinstance(UpperCamelCase__		,      UpperCamelCase__		) else mask_token

       super().__init__(
           errors=UpperCamelCase__		,      bos_token=UpperCamelCase__		,      eos_token=UpperCamelCase__		,      unk_token=UpperCamelCase__		,      sep_token=UpperCamelCase__		,      cls_token=UpperCamelCase__		,      pad_token=UpperCamelCase__		,      mask_token=UpperCamelCase__		,      add_prefix_space=UpperCamelCase__		,      **UpperCamelCase__		,      )

       with open(UpperCamelCase__		,      encoding="utf-8"		) as vocab_handle:
           snake_case       : Union[str, Any]    =       json.load(UpperCamelCase__		)
       snake_case       : List[Any]    =       {v: k for k, v in self.encoder.items()}
       snake_case       : Any    =       errors  # how to handle errors in decoding
       snake_case       : Optional[int]    =       bytes_to_unicode()
       snake_case       : Tuple    =       {v: k for k, v in self.byte_encoder.items()}
       with open(UpperCamelCase__		,      encoding="utf-8"		) as merges_handle:
           snake_case       : List[str]    =       merges_handle.read().split("\n"		)[1:-1]
       snake_case       : List[Any]    =       [tuple(merge.split()		) for merge in bpe_merges]
       snake_case       : Optional[Any]    =       dict(zip(UpperCamelCase__		,      range(len(UpperCamelCase__		)		)		)		)
       snake_case       : Optional[int]    =       {}
       snake_case       : Union[str, Any]    =       add_prefix_space

       # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
       snake_case       : Optional[Any]    =       re.compile(r"'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+"		)







   @property
   def 					lowerCamelCase						(      self		)      ->						Optional[Any]:

       '''simple docstring'''




       return len(self.encoder		)







   def 					lowerCamelCase						(      self		)      ->						str:

       '''simple docstring'''




       return dict(self.encoder		,      **self.added_tokens_encoder		)







   def 					lowerCamelCase						(      self		,      UpperCamelCase__		)      ->						Tuple:

       '''simple docstring'''




       if token in self.cache:
           return self.cache[token]
       snake_case       : Optional[Any]    =       tuple(UpperCamelCase__		)
       snake_case       : str    =       get_pairs(UpperCamelCase__		)

       if not pairs:
           return token

       while True:
           snake_case       : str    =       min(UpperCamelCase__		,      key=lambda UpperCamelCase__		: self.bpe_ranks.get(UpperCamelCase__		,      float("inf"		)		)		)
           if bigram not in self.bpe_ranks:
               break
           snake_case   ,snake_case       : List[Any]    =       bigram
           snake_case       : str    =       []
           snake_case       : Tuple    =       0
           while i < len(UpperCamelCase__		):
               try:
                   snake_case       : Union[str, Any]    =       word.index(UpperCamelCase__		,      UpperCamelCase__		)
               except ValueError:
                   new_word.extend(word[i:]		)
                   break
               else:
                   new_word.extend(word[i:j]		)
                   snake_case       : Dict    =       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
           snake_case       : Optional[Any]    =       tuple(UpperCamelCase__		)
           snake_case       : int    =       new_word
           if len(UpperCamelCase__		) == 1:
               break
           else:
               snake_case       : str    =       get_pairs(UpperCamelCase__		)
       snake_case       : List[Any]    =       " ".join(UpperCamelCase__		)
       snake_case       : List[Any]    =       word
       return word







   def 					lowerCamelCase						(      self		,      UpperCamelCase__		)      ->						List[Any]:

       '''simple docstring'''




       snake_case       : Optional[int]    =       []
       for token in re.findall(self.pat		,      UpperCamelCase__		):
           snake_case       : Any    =       "".join(
               self.byte_encoder[b] for b in token.encode("utf-8"		)		)  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
           bpe_tokens.extend(bpe_token for bpe_token in self.bpe(UpperCamelCase__		).split(" "		)		)
       return bpe_tokens







   def 					lowerCamelCase						(      self		,      UpperCamelCase__		)      ->						List[Any]:

       '''simple docstring'''




       return self.encoder.get(UpperCamelCase__		,      self.encoder.get(self.unk_token		)		)







   def 					lowerCamelCase						(      self		,      UpperCamelCase__		)      ->						List[str]:

       '''simple docstring'''




       return self.decoder.get(UpperCamelCase__		)







   def 					lowerCamelCase						(      self		,      UpperCamelCase__		)      ->						str:

       '''simple docstring'''




       snake_case       : int    =       "".join(UpperCamelCase__		)
       snake_case       : Dict    =       bytearray([self.byte_decoder[c] for c in text]		).decode("utf-8"		,      errors=self.errors		)
       return text







   def 					lowerCamelCase						(      self		,      UpperCamelCase__		,      UpperCamelCase__ = None		)      ->						Tuple[str]:

       '''simple docstring'''




       if not os.path.isdir(UpperCamelCase__		):
           logger.error(F'Vocabulary path ({save_directory}) should be a directory'		)
           return
       snake_case       : Dict    =       os.path.join(
           UpperCamelCase__		,      (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]		)
       snake_case       : List[Any]    =       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"		)

       snake_case       : List[Any]    =       0
       with open(UpperCamelCase__		,      "w"		,      encoding="utf-8"		) as writer:
           writer.write("#version: 0.2\n"		)
           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!"		)
                   snake_case       : Optional[Any]    =       token_index
               writer.write(" ".join(UpperCamelCase__		) + "\n"		)
               index += 1

       return vocab_file, merge_file







   def 					lowerCamelCase						(      self		,      UpperCamelCase__		,      UpperCamelCase__ = None		)      ->						List[int]:

       '''simple docstring'''




       if token_ids_a is None:
           return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
       snake_case       : Union[str, Any]    =       [self.cls_token_id]
       snake_case       : Optional[int]    =       [self.sep_token_id]
       return cls + token_ids_a + sep + sep + token_ids_a + sep







   def 					lowerCamelCase						(      self		,      UpperCamelCase__		,      UpperCamelCase__ = None		,      UpperCamelCase__ = False		)      ->						List[int]:

       '''simple docstring'''




       if already_has_special_tokens:
           return super().get_special_tokens_mask(
               token_ids_a=UpperCamelCase__		,      token_ids_a=UpperCamelCase__		,      already_has_special_tokens=UpperCamelCase__		)

       if token_ids_a is None:
           return [1] + ([0] * len(UpperCamelCase__		)) + [1]
       return [1] + ([0] * len(UpperCamelCase__		)) + [1, 1] + ([0] * len(UpperCamelCase__		)) + [1]







   def 					lowerCamelCase						(      self		,      UpperCamelCase__		,      UpperCamelCase__ = None		)      ->						List[int]:

       '''simple docstring'''




       snake_case       : Any    =       [self.sep_token_id]
       snake_case       : List[str]    =       [self.cls_token_id]

       if token_ids_a is None:
           return len(cls + token_ids_a + sep		) * [0]
       return len(cls + token_ids_a + sep + sep + token_ids_a + sep		) * [0]







   def 					lowerCamelCase						(      self		,      UpperCamelCase__		,      UpperCamelCase__=False		,      **UpperCamelCase__		)      ->						Any:

       '''simple docstring'''




       snake_case       : Optional[Any]    =       kwargs.pop("add_prefix_space"		,      self.add_prefix_space		)
       if (is_split_into_words or add_prefix_space) and (len(UpperCamelCase__		) > 0 and not text[0].isspace()):
           snake_case       : str    =       " " + text
       return (text, kwargs)





