


'''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


lowerCAmelCase    :     Dict					=						16
lowerCAmelCase    :     Optional[int]					=						32






def 			lowercase				(_A   ,						_A = 1_6   ,						_A = "bert-base-cased"					):







       """simple docstring"""







       _lowerCAmelCase    :							Optional[int]   			=       AutoTokenizer.from_pretrained(_A					)
       _lowerCAmelCase    :							Tuple   			=       load_dataset('glue'   ,						'mrpc'					)

       def tokenize_function(_A					):
              # max_length=None => use the model max length (it's actually the default)
              _lowerCAmelCase    :							int   			=       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
       _lowerCAmelCase    :							List[Any]   			=       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
       _lowerCAmelCase    :							List[str]   			=       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=1_2_8   ,						return_tensors='pt'					)
              return tokenizer.pad(_A   ,						padding='longest'   ,						return_tensors='pt'					)

       # Instantiate dataloaders.
       _lowerCAmelCase    :							Tuple   			=       DataLoader(
           tokenized_datasets['train']   ,						shuffle=_A   ,						collate_fn=_A   ,						batch_size=_A					)
       _lowerCAmelCase    :							str   			=       DataLoader(
           tokenized_datasets['validation']   ,						shuffle=_A   ,						collate_fn=_A   ,						batch_size=_A					)

       return train_dataloader, eval_dataloader






def 			lowercase				(_A   ,						_A   ,						_A   ,						_A					):







       """simple docstring"""







       model.eval()
       _lowerCAmelCase    :							List[str]   			=       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():
                     _lowerCAmelCase    :							Any   			=       model(**_A					)
              _lowerCAmelCase    :							List[str]   			=       outputs.logits.argmax(dim=-1					)
              # It is slightly faster to call this once, than multiple times
              _lowerCAmelCase			,     _lowerCAmelCase    :							List[Any]   			=       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:
                            _lowerCAmelCase    :							List[Any]   			=       predictions[: len(eval_dataloader.dataset					) - samples_seen]
                            _lowerCAmelCase    :							int   			=       references[: len(eval_dataloader.dataset					) - samples_seen]
                     else:
                            samples_seen += references.shape[0]
              metric.add_batch(
                  predictions=_A   ,						references=_A   ,						)

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






def 			lowercase				(_A   ,						_A					):







       """simple docstring"""







       _lowerCAmelCase    :							Optional[Any]   			=       Accelerator()

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

       set_seed(_A					)
       _lowerCAmelCase			,     _lowerCAmelCase    :							Optional[Any]   			=       get_dataloaders(_A   ,						_A   ,						_A					)

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

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

       if accelerator.state.deepspeed_plugin is not None:
              _lowerCAmelCase    :							str   			=       accelerator.state.deepspeed_plugin.deepspeed_config[
                  'gradient_accumulation_steps'
              ]
       else:
              _lowerCAmelCase    :							str   			=       1
       _lowerCAmelCase    :							List[Any]   			=       (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
       ):
              _lowerCAmelCase    :							str   			=       get_linear_schedule_with_warmup(
                  optimizer=_A   ,						num_warmup_steps=0   ,						num_training_steps=_A   ,						)
       else:
              _lowerCAmelCase    :							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.
       _lowerCAmelCase			,     _lowerCAmelCase			,     _lowerCAmelCase			,     _lowerCAmelCase			,     _lowerCAmelCase    :							Optional[Any]   			=       accelerator.prepare(
           _A   ,						_A   ,						_A   ,						_A   ,						_A					)

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

       if args.partial_train_epoch is not None:
              _lowerCAmelCase    :							Any   			=       args.partial_train_epoch

       if args.resume_from_checkpoint:
              accelerator.load_state(args.resume_from_checkpoint					)
              _lowerCAmelCase    :							str   			=       args.resume_from_checkpoint.split('epoch_'					)[1]
              _lowerCAmelCase    :							str   			=       ''
              for char in epoch_string:
                     if char.isdigit():
                            state_epoch_num += char
                     else:
                            break
              _lowerCAmelCase    :							Any   			=       int(_A					) + 1
              _lowerCAmelCase    :							str   			=       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:
                     _lowerCAmelCase    :							Tuple   			=       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
       _lowerCAmelCase    :							Tuple   			=       {}
       for epoch in range(_A   ,						_A					):
              model.train()
              for step, batch in enumerate(_A					):
                     _lowerCAmelCase    :							Any   			=       model(**_A					)
                     _lowerCAmelCase    :							str   			=       outputs.loss
                     _lowerCAmelCase    :							Any   			=       loss / gradient_accumulation_steps
                     accelerator.backward(_A					)
                     if step % gradient_accumulation_steps == 0:
                            optimizer.step()
                            lr_scheduler.step()
                            optimizer.zero_grad()

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







       """simple docstring"""







       _lowerCAmelCase    :							List[str]   			=       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.'   ,						)
       _lowerCAmelCase    :							int   			=       parser.parse_args()
       _lowerCAmelCase    :							List[Any]   			=       {'lr': 2E-5, 'num_epochs': args.num_epochs, 'seed': 4_2, 'batch_size': 1_6}

       training_function(_A   ,						_A					)


if __name__ == "__main__":
       main()





'''simple docstring'''







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCAmelCase    :     Any					=						{
    """configuration_x_clip""": [
        """XCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """XCLIPConfig""",
        """XCLIPTextConfig""",
        """XCLIPVisionConfig""",
    ],
    """processing_x_clip""": ["""XCLIPProcessor"""],
}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCAmelCase    :     Optional[Any]					=						[
           """XCLIP_PRETRAINED_MODEL_ARCHIVE_LIST""",
           """XCLIPModel""",
           """XCLIPPreTrainedModel""",
           """XCLIPTextModel""",
           """XCLIPVisionModel""",
       ]

if TYPE_CHECKING:
       from .configuration_x_clip import (
           XCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
           XCLIPConfig,
           XCLIPTextConfig,
           XCLIPVisionConfig,
       )
       from .processing_x_clip import XCLIPProcessor

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_x_clip import (
                  XCLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
                  XCLIPModel,
                  XCLIPPreTrainedModel,
                  XCLIPTextModel,
                  XCLIPVisionModel,
              )

else:
       import sys

       lowerCAmelCase    :     Dict					=						_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

