





"""simple docstring"""



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, DistributedType
from accelerate.utils import find_executable_batch_size


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to ensure out-of-memory errors never
# interrupt training, 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)
#
# 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_			=      16
a_			=      32







def 		__lowercase					(       snake_case_  :  Accelerator    ,snake_case_  :  int = 16						)       ->Tuple:



    '''simple docstring'''







    __A   :						int									=					AutoTokenizer.from_pretrained('''bert-base-cased'''						)
    __A   :						List[Any]									=					load_dataset('''glue'''    ,'''mrpc'''						)

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

    def collate_fn(snake_case_  :  int						):
        # On TPU it's best to pad everything to the same length or training will be very slow.
        __A   :						List[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":
            __A   :						Any									=					16
        elif accelerator.mixed_precision != "no":
            __A   :						Dict									=					8
        else:
            __A   :						int									=					None

        return tokenizer.pad(
            snake_case_    ,padding='''longest'''    ,max_length=snake_case_    ,pad_to_multiple_of=snake_case_    ,return_tensors='''pt'''    ,)

    # Instantiate dataloaders.
    __A   :						Optional[Any]									=					DataLoader(
        tokenized_datasets['''train''']    ,shuffle=snake_case_    ,collate_fn=snake_case_    ,batch_size=snake_case_						)
    __A   :						Tuple									=					DataLoader(
        tokenized_datasets['''validation''']    ,shuffle=snake_case_    ,collate_fn=snake_case_    ,batch_size=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 		__lowercase					(       snake_case_  :  Optional[Any]    ,snake_case_  :  str						)       ->Any:



    '''simple docstring'''







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

    __A   :						Any									=					evaluate.load('''glue'''    ,'''mrpc'''						)

    # New Code #
    # We now can define an inner training loop function. It should take a batch size as the only parameter,
    # and build the dataloaders in there.
    # It also gets our decorator
    @find_executable_batch_size(starting_batch_size=snake_case_						)
    def inner_training_loop(snake_case_  :  int						):
        # And now just move everything below under this function
        # We need to bring in the Accelerator object from earlier
        nonlocal accelerator
        # And reset all of its attributes that could hold onto any memory:
        accelerator.free_memory()

        # Then we can declare the model, optimizer, and everything else:
        set_seed(snake_case_						)

        # Instantiate the model (we build the model here so that the seed also control new weights initialization)
        __A   :						Optional[int]									=					AutoModelForSequenceClassification.from_pretrained('''bert-base-cased'''    ,return_dict=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).
        __A   :						Tuple									=					model.to(accelerator.device						)

        # Instantiate optimizer
        __A   :						str									=					AdamW(params=model.parameters()    ,lr=snake_case_						)
        __A   :						Optional[int]									=					get_dataloaders(snake_case_    ,snake_case_						)

        # Instantiate scheduler
        __A   :						Tuple									=					get_linear_schedule_with_warmup(
            optimizer=snake_case_    ,num_warmup_steps=100    ,num_training_steps=(len(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.
        __A   :						List[str]									=					accelerator.prepare(
            snake_case_    ,snake_case_    ,snake_case_    ,snake_case_    ,snake_case_						)

        # Now we train the model
        for epoch in range(snake_case_						):
            model.train()
            for step, batch in enumerate(snake_case_						):
                # We could avoid this line since we set the accelerator with `device_placement=True`.
                batch.to(accelerator.device						)
                __A   :						Union[str, Any]									=					model(**snake_case_						)
                __A   :						Tuple									=					outputs.loss
                accelerator.backward(snake_case_						)
                optimizer.step()
                lr_scheduler.step()
                optimizer.zero_grad()

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

            __A   :						int									=					metric.compute()
            # Use accelerator.print to print only on the main process.
            accelerator.print(F"""epoch {epoch}:"""    ,snake_case_						)

    # 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 		__lowercase					(       )       ->List[str]:



    '''simple docstring'''







    __A   :						Dict									=					argparse.ArgumentParser(description='''Simple example of training script.'''						)
    parser.add_argument(
        '''--mixed_precision'''    ,type=snake_case_    ,default=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.'''    ,)
    parser.add_argument('''--cpu'''    ,action='''store_true'''    ,help='''If passed, will train on the CPU.'''						)
    __A   :						Optional[int]									=					parser.parse_args()
    __A   :						List[str]									=					{'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
    training_function(snake_case_    ,snake_case_						)


if __name__ == "__main__":
       main()










"""simple docstring"""



# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


a_			=      {
    """configuration_vivit""": ["""VIVIT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """VivitConfig"""],
}
try:
       if not is_vision_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a_			=      ["""VivitImageProcessor"""]


try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a_			=      [
           """VIVIT_PRETRAINED_MODEL_ARCHIVE_LIST""",
           """VivitModel""",
           """VivitPreTrainedModel""",
           """VivitForVideoClassification""",
       ]


if TYPE_CHECKING:
       from .configuration_vivit import VIVIT_PRETRAINED_CONFIG_ARCHIVE_MAP, VivitConfig

       try:
              if not is_vision_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .image_processing_vivit import VivitImageProcessor

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_vivit import (
                  VIVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  VivitForVideoClassification,
                  VivitModel,
                  VivitPreTrainedModel,
              )


else:
       import sys

       a_			=      _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




