






"""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
#
########################################################################


UpperCAmelCase_   : int   			= 16
UpperCAmelCase_   : Tuple   			= 32



def 					_A			(__a    ,				__a = 16							) ->       str:
    """simple docstring"""






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

    def tokenize_function(__a							):
        # max_length=None => use the model max length (it's actually the default)
        SCREAMING_SNAKE_CASE_      :  Any    						=		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
    # starting with the main process first:
    with accelerator.main_process_first():
        SCREAMING_SNAKE_CASE_      :  Any    						=		datasets.map(
            __a    ,				batched=__a    ,				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
    SCREAMING_SNAKE_CASE_      :  Tuple    						=		tokenized_datasets.rename_column('''label'''    ,				'''labels'''							)

    def collate_fn(__a							):
        # On TPU it's best to pad everything to the same length or training will be very slow.
        SCREAMING_SNAKE_CASE_      :  Dict    						=		1_28 if accelerator.distributed_type == DistributedType.TPU else None
        # When using mixed precision we want round multiples of 8/16
        if accelerator.mixed_precision == "fp8":
            SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		16
        elif accelerator.mixed_precision != "no":
            SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		8
        else:
            SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		None

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

    # Instantiate dataloaders.
    SCREAMING_SNAKE_CASE_      :  List[str]    						=		DataLoader(
        tokenized_datasets['''train''']    ,				shuffle=__a    ,				collate_fn=__a    ,				batch_size=__a							)
    SCREAMING_SNAKE_CASE_      :  int    						=		DataLoader(
        tokenized_datasets['''validation''']    ,				shuffle=__a    ,				collate_fn=__a    ,				batch_size=__a							)

    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

  UpperCAmelCase_   : Dict   			= mocked_dataloaders  # noqa: F811



def 					_A			(__a    ,				__a							) ->       List[str]:
    """simple docstring"""






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

    SCREAMING_SNAKE_CASE_      :  str    						=		evaluate.load('''glue'''    ,				'''mrpc'''							)

    # If the batch size is too big we use gradient accumulation
    SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		1
    if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
        SCREAMING_SNAKE_CASE_      :  Dict    						=		batch_size // MAX_GPU_BATCH_SIZE
        SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		MAX_GPU_BATCH_SIZE

    set_seed(__a							)
    SCREAMING_SNAKE_CASE_      :  Any    						=		get_dataloaders(__a    ,				__a							)
    # Instantiate the model (we build the model here so that the seed also control new weights initialization)
    SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		AutoModelForSequenceClassification.from_pretrained('''bert-base-cased'''    ,				return_dict=__a							)

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

    # Instantiate optimizer
    SCREAMING_SNAKE_CASE_      :  Dict    						=		AdamW(params=model.parameters()    ,				lr=__a							)

    # Instantiate scheduler
    SCREAMING_SNAKE_CASE_      :  int    						=		get_linear_schedule_with_warmup(
        optimizer=__a    ,				num_warmup_steps=1_00    ,				num_training_steps=(len(__a							) * 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.
    SCREAMING_SNAKE_CASE_      :  int    						=		accelerator.prepare(
        __a    ,				__a    ,				__a    ,				__a    ,				__a							)

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

        model.eval()
        SCREAMING_SNAKE_CASE_      :  Any    						=		0
        for step, batch in enumerate(__a							):
            # We could avoid this line since we set the accelerator with `device_placement=True`.
            batch.to(accelerator.device							)
            with torch.no_grad():
                SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		model(**__a							)
            SCREAMING_SNAKE_CASE_      :  Any    						=		outputs.logits.argmax(dim=-1							)
            SCREAMING_SNAKE_CASE_      :  int    						=		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(__a							) - 1:
                    # Last batch needs to be truncated on distributed systems as it contains additional samples
                    SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		predictions[: len(eval_dataloader.dataset							) - samples_seen]
                    SCREAMING_SNAKE_CASE_      :  Union[str, 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=__a    ,				references=__a    ,				)

        SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		metric.compute()
        # Use accelerator.print to print only on the main process.
        accelerator.print(f'epoch {epoch}:'    ,				__a							)



def 					_A			() ->       Tuple:
    """simple docstring"""






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


if __name__ == "__main__":
  main()







"""simple docstring"""
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


A:   List[str]								=		{
    "configuration_clipseg": [
        "CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "CLIPSegConfig",
        "CLIPSegTextConfig",
        "CLIPSegVisionConfig",
    ],
    "processing_clipseg": ["CLIPSegProcessor"],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A:   Optional[int]								=		[
          "CLIPSEG_PRETRAINED_MODEL_ARCHIVE_LIST",
          "CLIPSegModel",
          "CLIPSegPreTrainedModel",
          "CLIPSegTextModel",
          "CLIPSegVisionModel",
          "CLIPSegForImageSegmentation",
      ]

if TYPE_CHECKING:
      from .configuration_clipseg import (
          CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP,
          CLIPSegConfig,
          CLIPSegTextConfig,
          CLIPSegVisionConfig,
      )
      from .processing_clipseg import CLIPSegProcessor

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_clipseg import (
                CLIPSEG_PRETRAINED_MODEL_ARCHIVE_LIST,
                CLIPSegForImageSegmentation,
                CLIPSegModel,
                CLIPSegPreTrainedModel,
                CLIPSegTextModel,
                CLIPSegVisionModel,
            )

else:
      import sys

      A:   Tuple								=		_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
