



"""simple docstring"""

import argparse
import json
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
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler


lowercase__					:						Optional[int]												=			16
lowercase__					:						Any												=			32




def __lowercase    (							_a	,    _a = 16	,    _a = "bert-base-cased"					):
    snake_case_				:  Tuple       =			AutoTokenizer.from_pretrained(_a					)
    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)
        snake_case_				:  Tuple       =			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
    snake_case_				:  int       =			datasets.map(
        _a	,    batched=_a	,    remove_columns=['''idx''', '''sentence1''', '''sentence2''']	,    load_from_cache_file=_a					)

    # We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
    # transformers library
    snake_case_				:  Any       =			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.
        if accelerator.distributed_type == DistributedType.TPU:
            return tokenizer.pad(_a	,    padding='''max_length'''	,    max_length=128	,    return_tensors='''pt'''					)
        return tokenizer.pad(_a	,    padding='''longest'''	,    return_tensors='''pt'''					)

    # Instantiate dataloaders.
    snake_case_				:  List[Any]       =			DataLoader(
        tokenized_datasets['''train''']	,    shuffle=_a	,    collate_fn=_a	,    batch_size=_a					)
    snake_case_				:  Any       =			DataLoader(
        tokenized_datasets['''validation''']	,    shuffle=_a	,    collate_fn=_a	,    batch_size=_a					)

    return train_dataloader, eval_dataloader




def __lowercase    (							_a	,    _a	,    _a	,    _a					):
    model.eval()
    snake_case_				:  Union[str, 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():
            snake_case_				:  Dict       =			model(**_a					)
        snake_case_				:  Dict       =			outputs.logits.argmax(dim=-1					)
        # It is slightly faster to call this once, than multiple times
        snake_case_,			snake_case_				:  str       =			accelerator.gather(
            (predictions, batch['''labels'''])					)  # If we are in a multiprocess environment, the last batch has duplicates
        if accelerator.use_distributed:
            if step == len(_a					) - 1:
                snake_case_				:  str       =			predictions[: len(eval_dataloader.dataset					) - samples_seen]
                snake_case_				:  Any       =			references[: len(eval_dataloader.dataset					) - samples_seen]
            else:
                samples_seen += references.shape[0]
        metric.add_batch(
            predictions=_a	,    references=_a	,    )

    snake_case_				:  str       =			metric.compute()
    return eval_metric["accuracy"]




def __lowercase    (							_a	,    _a					):
    # Initialize accelerator
    snake_case_				:  List[str]       =			Accelerator()

    # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
    snake_case_				:  str       =			config['''lr''']
    snake_case_				:  Any       =			int(config['''num_epochs''']					)
    snake_case_				:  Any       =			int(config['''seed''']					)
    snake_case_				:  Any       =			int(config['''batch_size''']					)
    snake_case_				:  Optional[int]       =			args.model_name_or_path

    set_seed(_a					)
    snake_case_,			snake_case_				:  Union[str, Any]       =			get_dataloaders(_a	,    _a	,    _a					)

    # Instantiate the model (we build the model here so that the seed also control new weights initialization)
    snake_case_				:  str       =			AutoModelForSequenceClassification.from_pretrained(_a	,    return_dict=_a					)

    # Instantiate optimizer
    snake_case_				:  Optional[Any]       =			(
        AdamW
        if accelerator.state.deepspeed_plugin is None
        or '''optimizer''' not in accelerator.state.deepspeed_plugin.deepspeed_config
        else DummyOptim
    )
    snake_case_				:  Optional[int]       =			optimizer_cls(params=model.parameters()	,    lr=_a					)

    if accelerator.state.deepspeed_plugin is not None:
        snake_case_				:  Optional[int]       =			accelerator.state.deepspeed_plugin.deepspeed_config[
            '''gradient_accumulation_steps'''
        ]
    else:
        snake_case_				:  Dict       =			1
    snake_case_				:  Tuple       =			(len(_a					) * num_epochs) // gradient_accumulation_steps

    # Instantiate scheduler
    if (
        accelerator.state.deepspeed_plugin is None
        or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
    ):
        snake_case_				:  List[Any]       =			get_linear_schedule_with_warmup(
            optimizer=_a	,    num_warmup_steps=0	,    num_training_steps=_a	,    )
    else:
        snake_case_				:  Any       =			DummyScheduler(_a	,    total_num_steps=_a	,    warmup_num_steps=0					)

    # 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(
        _a	,    _a	,    _a	,    _a	,    _a					)

    # We need to keep track of how many total steps we have iterated over
    snake_case_				:  Optional[Any]       =			0
    # We also need to keep track of the stating epoch so files are named properly
    snake_case_				:  Union[str, Any]       =			0
    snake_case_				:  str       =			evaluate.load('''glue'''	,    '''mrpc'''					)
    snake_case_				:  Union[str, Any]       =			num_epochs

    if args.partial_train_epoch is not None:
        snake_case_				:  Dict       =			args.partial_train_epoch

    if args.resume_from_checkpoint:
        accelerator.load_state(args.resume_from_checkpoint					)
        snake_case_				:  Tuple       =			args.resume_from_checkpoint.split('''epoch_'''					)[1]
        snake_case_				:  Optional[int]       =			''''''
        for char in epoch_string:
            if char.isdigit():
                state_epoch_num += char
            else:
                break
        snake_case_				:  Tuple       =			int(_a					) + 1
        snake_case_				:  Optional[int]       =			evaluation_loop(_a	,    _a	,    _a	,    _a					)
        accelerator.print('''resumed checkpoint performance:'''	,    _a					)
        accelerator.print('''resumed checkpoint\'s scheduler\'s lr:'''	,    lr_scheduler.get_lr()[0]					)
        accelerator.print('''resumed optimizers\'s lr:'''	,    optimizer.param_groups[0]['''lr''']					)
        with open(os.path.join(args.output_dir	,    f"state_{starting_epoch-1}.json"					)	,    '''r'''					) as f:
            snake_case_				:  List[str]       =			json.load(_a					)
            assert resumed_state["accuracy"] == accuracy, "Accuracy mismatch, loading from checkpoint failed"
            assert (
                resumed_state["lr"] == lr_scheduler.get_lr()[0]
            ), "Scheduler learning rate mismatch, loading from checkpoint failed"
            assert (
                resumed_state["optimizer_lr"] == optimizer.param_groups[0]["lr"]
            ), "Optimizer learning rate mismatch, loading from checkpoint failed"
            assert resumed_state["epoch"] == starting_epoch - 1, "Epoch mismatch, loading from checkpoint failed"
            return

    # Now we train the model
    snake_case_				:  Tuple       =			{}
    for epoch in range(_a	,    _a					):
        model.train()
        for step, batch in enumerate(_a					):
            snake_case_				:  int       =			model(**_a					)
            snake_case_				:  Optional[int]       =			outputs.loss
            snake_case_				:  List[str]       =			loss / gradient_accumulation_steps
            accelerator.backward(_a					)
            if step % gradient_accumulation_steps == 0:
                optimizer.step()
                lr_scheduler.step()
                optimizer.zero_grad()

            overall_step += 1
        snake_case_				:  int       =			f"epoch_{epoch}"
        snake_case_				:  Dict       =			os.path.join(args.output_dir	,    _a					)
        accelerator.save_state(_a					)
        snake_case_				:  Any       =			evaluation_loop(_a	,    _a	,    _a	,    _a					)
        snake_case_				:  Tuple       =			accuracy
        snake_case_				:  str       =			lr_scheduler.get_lr()[0]
        snake_case_				:  List[str]       =			optimizer.param_groups[0]['''lr''']
        snake_case_				:  List[Any]       =			epoch
        snake_case_				:  List[str]       =			overall_step
        accelerator.print(f"epoch {epoch}:"	,    _a					)

        accelerator.wait_for_everyone()
        if accelerator.is_main_process:
            with open(os.path.join(args.output_dir	,    f"state_{epoch}.json"					)	,    '''w'''					) as f:
                json.dump(_a	,    _a					)




def __lowercase    (							):
    snake_case_				:  int       =			argparse.ArgumentParser(description='''Simple example of training script tracking peak GPU memory usage.'''					)
    parser.add_argument(
        '''--model_name_or_path'''	,    type=_a	,    default='''bert-base-cased'''	,    help='''Path to pretrained model or model identifier from huggingface.co/models.'''	,    required=_a	,    )
    parser.add_argument(
        '''--output_dir'''	,    type=_a	,    default='''.'''	,    help='''Optional save directory where all checkpoint folders will be stored. Default is the current working directory.'''	,    )
    parser.add_argument(
        '''--resume_from_checkpoint'''	,    type=_a	,    default=_a	,    help='''If the training should continue from a checkpoint folder.'''	,    )
    parser.add_argument(
        '''--partial_train_epoch'''	,    type=_a	,    default=_a	,    help='''If passed, the training will stop after this number of epochs.'''	,    )
    parser.add_argument(
        '''--num_epochs'''	,    type=_a	,    default=2	,    help='''Number of train epochs.'''	,    )
    snake_case_				:  Any       =			parser.parse_args()
    snake_case_				:  Dict       =			{'''lr''': 2E-5, '''num_epochs''': args.num_epochs, '''seed''': 42, '''batch_size''': 16}

    training_function(_a	,    _a					)


if __name__ == "__main__":
     main()










"""simple docstring"""

from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union

import numpy as np
import PIL
from PIL import Image

from ...utils import BaseOutput, is_torch_available, is_transformers_available




@dataclass
class  _UpperCAmelCase (    lowerCAmelCase__):
 _lowerCAmelCase     :    Union[List[PIL.Image.Image], np.ndarray]
 _lowerCAmelCase     :    Optional[List[bool]]


if is_transformers_available() and is_torch_available():
     from .pipeline_semantic_stable_diffusion import SemanticStableDiffusionPipeline





