
'''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
# and perform 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)
#
# 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_											=						16
a_											=						32

def 						UpperCamelCase_     (	__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE = 1_6       ):
    """simple docstring"""






    snake_case_ :    List[Any]     			=						AutoTokenizer.from_pretrained("bert-base-cased"       )
    snake_case_ :    List[Any]     			=						load_dataset("glue",		"mrpc"       )

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

    def collate_fn(__SCREAMING_SNAKE_CASE       ):
        # On TPU it's best to pad everything to the same length or training will be very slow.
        snake_case_ :    int     			=						1_2_8 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_ :    str     			=						1_6
        elif accelerator.mixed_precision != "no":
            snake_case_ :    str     			=						8
        else:
            snake_case_ :    int     			=						None

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

    # Instantiate dataloaders.
    snake_case_ :    Tuple     			=						DataLoader(
        tokenized_datasets["train"],		shuffle=__SCREAMING_SNAKE_CASE,		collate_fn=__SCREAMING_SNAKE_CASE,		batch_size=__SCREAMING_SNAKE_CASE       )
    snake_case_ :    Optional[int]     			=						DataLoader(
        tokenized_datasets["validation"],		shuffle=__SCREAMING_SNAKE_CASE,		collate_fn=__SCREAMING_SNAKE_CASE,		batch_size=__SCREAMING_SNAKE_CASE       )

    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_											=						mocked_dataloaders  # noqa: F811

def 						UpperCamelCase_     (	__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       ):
    """simple docstring"""






    if os.environ.get("TESTING_MOCKED_DATALOADERS",		__SCREAMING_SNAKE_CASE       ) == "1":
        snake_case_ :    List[str]     			=						2
    # New Code #
    snake_case_ :    Optional[int]     			=						int(args.gradient_accumulation_steps       )
    # Initialize accelerator
    snake_case_ :    Optional[int]     			=						Accelerator(
        cpu=args.cpu,		mixed_precision=args.mixed_precision,		gradient_accumulation_steps=__SCREAMING_SNAKE_CASE       )
    if accelerator.distributed_type == DistributedType.TPU and gradient_accumulation_steps > 1:
        raise NotImplementedError(
            "Gradient accumulation on TPUs is currently not supported. Pass `gradient_accumulation_steps=1`"       )
    # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
    snake_case_ :    Optional[int]     			=						config["lr"]
    snake_case_ :    Union[str, Any]     			=						int(config["num_epochs"]       )
    snake_case_ :    Union[str, Any]     			=						int(config["seed"]       )
    snake_case_ :    Any     			=						int(config["batch_size"]       )

    snake_case_ :    int     			=						evaluate.load("glue",		"mrpc"       )

    set_seed(__SCREAMING_SNAKE_CASE       )
    snake_case_			, snake_case_ :    str     			=						get_dataloaders(__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       )
    # Instantiate the model (we build the model here so that the seed also control new weights initialization)
    snake_case_ :    Dict     			=						AutoModelForSequenceClassification.from_pretrained("bert-base-cased",		return_dict=__SCREAMING_SNAKE_CASE       )

    # 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_ :    Tuple     			=						model.to(accelerator.device       )

    # Instantiate optimizer
    snake_case_ :    List[str]     			=						AdamW(params=model.parameters(),		lr=__SCREAMING_SNAKE_CASE       )

    # Instantiate scheduler
    snake_case_ :    List[Any]     			=						get_linear_schedule_with_warmup(
        optimizer=__SCREAMING_SNAKE_CASE,		num_warmup_steps=1_0_0,		num_training_steps=(len(__SCREAMING_SNAKE_CASE       ) * 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.
    snake_case_			, snake_case_			, snake_case_			, snake_case_			, snake_case_ :    Union[str, Any]     			=						accelerator.prepare(
        __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       )

    # Now we train the model
    for epoch in range(__SCREAMING_SNAKE_CASE       ):
        model.train()
        for step, batch in enumerate(__SCREAMING_SNAKE_CASE       ):
            # We could avoid this line since we set the accelerator with `device_placement=True`.
            batch.to(accelerator.device       )
            # New code #
            # We use the new `accumulate` context manager to perform gradient accumulation
            # We also currently do not support TPUs nor advise it as bugs were found on the XLA side when running our tests.
            with accelerator.accumulate(__SCREAMING_SNAKE_CASE       ):
                snake_case_ :    Optional[Any]     			=						model(**__SCREAMING_SNAKE_CASE       )
                snake_case_ :    str     			=						output.loss
                accelerator.backward(__SCREAMING_SNAKE_CASE       )
                optimizer.step()
                lr_scheduler.step()
                optimizer.zero_grad()

        model.eval()
        for step, batch in enumerate(__SCREAMING_SNAKE_CASE       ):
            # We could avoid this line since we set the accelerator with `device_placement=True`.
            batch.to(accelerator.device       )
            with torch.no_grad():
                snake_case_ :    int     			=						model(**__SCREAMING_SNAKE_CASE       )
            snake_case_ :    int     			=						outputs.logits.argmax(dim=-1       )
            snake_case_			, snake_case_ :    Dict     			=						accelerator.gather_for_metrics((predictions, batch["labels"])       )
            metric.add_batch(
                predictions=__SCREAMING_SNAKE_CASE,		references=__SCREAMING_SNAKE_CASE,		)

        snake_case_ :    List[str]     			=						metric.compute()
        # Use accelerator.print to print only on the main process.
        accelerator.print(f'epoch {epoch}:',		__SCREAMING_SNAKE_CASE       )

def 						UpperCamelCase_     (	):
    """simple docstring"""






    snake_case_ :    Tuple     			=						argparse.ArgumentParser(description="Simple example of training script."       )
    parser.add_argument(
        "--mixed_precision",		type=__SCREAMING_SNAKE_CASE,		default=__SCREAMING_SNAKE_CASE,		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.",		)
    # New Code #
    parser.add_argument(
        "--gradient_accumulation_steps",		type=__SCREAMING_SNAKE_CASE,		default=1,		help="The number of minibatches to be ran before gradients are accumulated.",		)
    parser.add_argument("--cpu",		action="store_true",		help="If passed, will train on the CPU."       )
    snake_case_ :    List[str]     			=						parser.parse_args()
    snake_case_ :    Union[str, Any]     			=						{"lr": 2E-5, "num_epochs": 3, "seed": 4_2, "batch_size": 1_6}
    training_function(__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       )


if __name__ == "__main__":
  main()






'''simple docstring'''
import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
  from .tokenization_pegasus import PegasusTokenizer
else:
  a_											=						None


a_											=						logging.get_logger(__name__)


a_											=						"▁"

a_											=						{"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"}

a_											=						{
    "vocab_file": {"google/pegasus-xsum": "https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model"},
    "tokenizer_file": {
        "google/pegasus-xsum": "https://huggingface.co/google/pegasus-xsum/resolve/main/tokenizer.json"
    },
}

a_											=						{
    "google/pegasus-xsum": 512,
}






class    UpperCAmelCase_				(	snake_case__					):
 UpperCAmelCase_		    =						VOCAB_FILES_NAMES
 UpperCAmelCase_		    =						PRETRAINED_VOCAB_FILES_MAP
 UpperCAmelCase_		    =						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 UpperCAmelCase_		    =						PegasusTokenizer
 UpperCAmelCase_		    =						["""input_ids""", """attention_mask"""]





 def __init__(    self ,       lowercase_=None ,       lowercase_=None ,       lowercase_="<pad>" ,       lowercase_="</s>" ,       lowercase_="<unk>" ,       lowercase_="<mask_2>" ,       lowercase_="<mask_1>" ,       lowercase_=None ,       lowercase_=1_03 ,       **lowercase_ ,       ):
     snake_case_ :    List[Any]     			=						offset

     if additional_special_tokens is not None:
         if not isinstance(lowercase_ ,       lowercase_):
             raise TypeError(
                 F'additional_special_tokens should be of type {type(lowercase_)}, but is'
                 F' {type(lowercase_)}')

         snake_case_ :    Tuple     			=						(
             ([mask_token_sent] + additional_special_tokens)
             if mask_token_sent not in additional_special_tokens and mask_token_sent is not None
             else additional_special_tokens
         )
         # fill additional tokens with ..., <unk_token_102> in case not all additional tokens are already taken
         additional_special_tokens_extended += [
             F'<unk_{i}>' for i in range(len(lowercase_) ,       self.offset - 1)
         ]

         if len(set(lowercase_)) != len(lowercase_):
             raise ValueError(
                 "Please make sure that the provided additional_special_tokens do not contain an incorrectly"
                 F' shifted list of <unk_x> tokens. Found {additional_special_tokens_extended}.')
         snake_case_ :    Optional[Any]     			=						additional_special_tokens_extended
     else:
         snake_case_ :    Tuple     			=						[mask_token_sent] if mask_token_sent is not None else []
         additional_special_tokens += [F'<unk_{i}>' for i in range(2 ,       self.offset)]

     super().__init__(
         lowercase_ ,       tokenizer_file=lowercase_ ,       pad_token=lowercase_ ,       eos_token=lowercase_ ,       unk_token=lowercase_ ,       mask_token=lowercase_ ,       mask_token_sent=lowercase_ ,       offset=lowercase_ ,       additional_special_tokens=lowercase_ ,       **lowercase_ ,       )
     snake_case_ :    int     			=						vocab_file
     snake_case_ :    str     			=						False if not self.vocab_file else True





 def        snake_case__		(    self ,       lowercase_):
     snake_case_ :    Union[str, Any]     			=						set(self.all_special_ids)  # call it once instead of inside list comp
     all_special_ids.remove(self.unk_token_id)  # <unk> is only sometimes special

     if all_special_ids != set(range(len(self.additional_special_tokens) + 3)):
         raise ValueError(
             "There should be 3 special tokens: mask_token, pad_token, and eos_token +"
             F' {len(self.additional_special_tokens)} additional_special_tokens, but got {all_special_ids}')

     return [1 if x in all_special_ids else 0 for x in seq]





 def        snake_case__		(    self ,       lowercase_ ,       lowercase_ = None ,       lowercase_ = False):
     if already_has_special_tokens:
         return self._special_token_mask(lowercase_)
     elif token_ids_a is None:
         return self._special_token_mask(lowercase_) + [1]
     else:
         return self._special_token_mask(token_ids_a + token_ids_a) + [1]





 def        snake_case__		(    self ,       lowercase_ ,       lowercase_=None):
     if token_ids_a is None:
         return token_ids_a + [self.eos_token_id]
     # We don't expect to process pairs, but leave the pair logic for API consistency
     return token_ids_a + token_ids_a + [self.eos_token_id]





 def        snake_case__		(    self ,       lowercase_ ,       lowercase_ = None):
     if not self.can_save_slow_tokenizer:
         raise ValueError(
             "Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
             "tokenizer.")

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

     if os.path.abspath(self.vocab_file) != os.path.abspath(lowercase_):
         copyfile(self.vocab_file ,       lowercase_)

     return (out_vocab_file,)




