





import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available


if is_vision_available():
     from PIL import Image

     from transformers import AutoProcessor, BertTokenizer, BlipImageProcessor, BlipProcessor, PreTrainedTokenizerFast







@require_vision
class 				__lowerCAmelCase       (    unittest.TestCase):







      def    _lowercase  (    self							)   ->						Optional[Any]:



        '''simple docstring'''




        a__	:					List[Any]										=tempfile.mkdtemp()

        a__	:					List[Any]										=BlipImageProcessor()
        a__	:					int										=BertTokenizer.from_pretrained("hf-internal-testing/tiny-random-BertModel"							)

        a__	:					Optional[Any]										=BlipProcessor(lowerCAmelCase__		,      lowerCAmelCase__							)

        processor.save_pretrained(self.tmpdirname							)







      def    _lowercase  (    self		,      **lowerCAmelCase__							)   ->						List[Any]:



        '''simple docstring'''




        return AutoProcessor.from_pretrained(self.tmpdirname		,      **lowerCAmelCase__							).tokenizer







      def    _lowercase  (    self		,      **lowerCAmelCase__							)   ->						List[Any]:



        '''simple docstring'''




        return AutoProcessor.from_pretrained(self.tmpdirname		,      **lowerCAmelCase__							).image_processor







      def    _lowercase  (    self							)   ->						Any:



        '''simple docstring'''




        shutil.rmtree(self.tmpdirname							)







      def    _lowercase  (    self							)   ->						Optional[Any]:



        '''simple docstring'''




        a__	:					Union[str, Any]										=[np.random.randint(2_5_5		,      size=(3, 3_0, 4_0_0)		,      dtype=np.uinta							)]

        a__	:					Union[str, Any]										=[Image.fromarray(np.moveaxis(lowerCAmelCase__		,      0		,      -1							)							) for x in image_inputs]

        return image_inputs







      def    _lowercase  (    self							)   ->						Union[str, Any]:



        '''simple docstring'''




        a__	:					List[str]										=BlipProcessor(tokenizer=self.get_tokenizer()		,      image_processor=self.get_image_processor()							)
        processor.save_pretrained(self.tmpdirname							)

        a__	:					List[str]										=self.get_tokenizer(bos_token="(BOS)"		,      eos_token="(EOS)"							)
        a__	:					Union[str, Any]										=self.get_image_processor(do_normalize=lowerCAmelCase__		,      padding_value=1.0							)

        a__	:					Optional[Any]										=BlipProcessor.from_pretrained(
            self.tmpdirname		,      bos_token="(BOS)"		,      eos_token="(EOS)"		,      do_normalize=lowerCAmelCase__		,      padding_value=1.0							)

        self.assertEqual(processor.tokenizer.get_vocab()		,      tokenizer_add_kwargs.get_vocab()							)
        self.assertIsInstance(processor.tokenizer		,      lowerCAmelCase__							)

        self.assertEqual(processor.image_processor.to_json_string()		,      image_processor_add_kwargs.to_json_string()							)
        self.assertIsInstance(processor.image_processor		,      lowerCAmelCase__							)







      def    _lowercase  (    self							)   ->						Union[str, Any]:



        '''simple docstring'''




        a__	:					str										=self.get_image_processor()
        a__	:					int										=self.get_tokenizer()

        a__	:					Optional[Any]										=BlipProcessor(tokenizer=lowerCAmelCase__		,      image_processor=lowerCAmelCase__							)

        a__	:					Dict										=self.prepare_image_inputs()

        a__	:					List[str]										=image_processor(lowerCAmelCase__		,      return_tensors="np"							)
        a__	:					Union[str, Any]										=processor(images=lowerCAmelCase__		,      return_tensors="np"							)

        for key in input_feat_extract.keys():
          self.assertAlmostEqual(input_feat_extract[key].sum()		,      input_processor[key].sum()		,      delta=1E-2							)







      def    _lowercase  (    self							)   ->						Optional[Any]:



        '''simple docstring'''




        a__	:					int										=self.get_image_processor()
        a__	:					int										=self.get_tokenizer()

        a__	:					Dict										=BlipProcessor(tokenizer=lowerCAmelCase__		,      image_processor=lowerCAmelCase__							)

        a__	:					Dict										="lower newer"

        a__	:					Any										=processor(text=lowerCAmelCase__							)

        a__	:					Optional[int]										=tokenizer(lowerCAmelCase__		,      return_token_type_ids=lowerCAmelCase__							)

        for key in encoded_tok.keys():
          self.assertListEqual(encoded_tok[key]		,      encoded_processor[key]							)







      def    _lowercase  (    self							)   ->						List[Any]:



        '''simple docstring'''




        a__	:					List[Any]										=self.get_image_processor()
        a__	:					Any										=self.get_tokenizer()

        a__	:					Dict										=BlipProcessor(tokenizer=lowerCAmelCase__		,      image_processor=lowerCAmelCase__							)

        a__	:					List[str]										="lower newer"
        a__	:					List[str]										=self.prepare_image_inputs()

        a__	:					Union[str, Any]										=processor(text=lowerCAmelCase__		,      images=lowerCAmelCase__							)

        self.assertListEqual(list(inputs.keys()							)		,      ["pixel_values", "input_ids", "attention_mask"]							)

        # test if it raises when no input is passed
        with pytest.raises(lowerCAmelCase__							):
          processor()







      def    _lowercase  (    self							)   ->						int:



        '''simple docstring'''




        a__	:					Optional[int]										=self.get_image_processor()
        a__	:					Tuple										=self.get_tokenizer()

        a__	:					Optional[Any]										=BlipProcessor(tokenizer=lowerCAmelCase__		,      image_processor=lowerCAmelCase__							)

        a__	:					Optional[Any]										=[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

        a__	:					List[str]										=processor.batch_decode(lowerCAmelCase__							)
        a__	:					Dict										=tokenizer.batch_decode(lowerCAmelCase__							)

        self.assertListEqual(lowerCAmelCase__		,      lowerCAmelCase__							)







      def    _lowercase  (    self							)   ->						Optional[int]:



        '''simple docstring'''




        a__	:					int										=self.get_image_processor()
        a__	:					str										=self.get_tokenizer()

        a__	:					Union[str, Any]										=BlipProcessor(tokenizer=lowerCAmelCase__		,      image_processor=lowerCAmelCase__							)

        a__	:					Optional[Any]										="lower newer"
        a__	:					int										=self.prepare_image_inputs()

        a__	:					Optional[int]										=processor(text=lowerCAmelCase__		,      images=lowerCAmelCase__							)

        # For now the processor supports only ['pixel_values', 'input_ids', 'attention_mask']
        self.assertListEqual(list(inputs.keys()							)		,      ["pixel_values", "input_ids", "attention_mask"]							)













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	:	Any		     =       16
UpperCAmelCase	:	str		     =       32
def      _A       (		SCREAMING_SNAKE_CASE     :   Accelerator       ,       SCREAMING_SNAKE_CASE     :   int = 16						):





  """simple docstring"""



  a__	:					int										=AutoTokenizer.from_pretrained("bert-base-cased"						)
  a__	:					List[str]										=load_dataset("glue"       ,       "mrpc"						)

  def tokenize_function(SCREAMING_SNAKE_CASE     :   List[Any]						):
    # max_length=None => use the model max length (it's actually the default)
    a__	:					int										=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():
    a__	:					Dict										=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
  a__	:					Dict										=tokenized_datasets.rename_column("label"       ,       "labels"						)

  def collate_fn(SCREAMING_SNAKE_CASE     :   str						):
    # On TPU it's best to pad everything to the same length or training will be very slow.
    a__	:					Optional[Any]										=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__	:					str										=16
    elif accelerator.mixed_precision != "no":
      a__	:					Union[str, Any]										=8
    else:
      a__	:					List[str]										=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.
  a__	:					Any										=DataLoader(
      tokenized_datasets["train"]       ,       shuffle=SCREAMING_SNAKE_CASE       ,       collate_fn=SCREAMING_SNAKE_CASE       ,       batch_size=SCREAMING_SNAKE_CASE						)
  a__	:					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

     UpperCAmelCase	:	str		     =       mocked_dataloaders  # noqa: F811
def      _A       (		SCREAMING_SNAKE_CASE     :   List[Any]       ,       SCREAMING_SNAKE_CASE     :   str						):





  """simple docstring"""



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

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

  # If the batch size is too big we use gradient accumulation
  a__	:					int										=1
  if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
    a__	:					Dict										=batch_size // MAX_GPU_BATCH_SIZE
    a__	:					Tuple										=MAX_GPU_BATCH_SIZE

  set_seed(SCREAMING_SNAKE_CASE						)
  a__					,							a__	:					Optional[int]										=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)
  a__	:					List[str]										=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).
  a__	:					List[str]										=model.to(accelerator.device						)

  # Instantiate optimizer
  a__	:					List[Any]										=AdamW(params=model.parameters()       ,       lr=SCREAMING_SNAKE_CASE						)

  # Instantiate scheduler
  a__	:					Optional[int]										=get_linear_schedule_with_warmup(
      optimizer=SCREAMING_SNAKE_CASE       ,       num_warmup_steps=100       ,       num_training_steps=(len(SCREAMING_SNAKE_CASE						) * 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.
  a__					,							a__					,							a__					,							a__					,							a__	:					Optional[int]										=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						)
      a__	:					Dict										=model(**SCREAMING_SNAKE_CASE						)
      a__	:					List[Any]										=outputs.loss
      a__	:					List[str]										=loss / gradient_accumulation_steps
      accelerator.backward(SCREAMING_SNAKE_CASE						)
      if step % gradient_accumulation_steps == 0:
        optimizer.step()
        lr_scheduler.step()
        optimizer.zero_grad()

    model.eval()
    a__	:					Optional[Any]										=0
    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():
        a__	:					Any										=model(**SCREAMING_SNAKE_CASE						)
      a__	:					str										=outputs.logits.argmax(dim=-1						)
      a__					,							a__	:					List[str]										=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(SCREAMING_SNAKE_CASE						) - 1:
          # Last batch needs to be truncated on distributed systems as it contains additional samples
          a__	:					Optional[Any]										=predictions[: len(eval_dataloader.dataset						) - samples_seen]
          a__	:					Dict										=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=SCREAMING_SNAKE_CASE       ,       references=SCREAMING_SNAKE_CASE       ,       )

    a__	:					Tuple										=metric.compute()
    # Use accelerator.print to print only on the main process.
    accelerator.print(f'''epoch {epoch}:'''       ,       SCREAMING_SNAKE_CASE						)
def      _A       (		):





  """simple docstring"""



  a__	:					List[str]										=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."       ,       )
  parser.add_argument("--cpu"       ,       action="store_true"       ,       help="If passed, will train on the CPU."						)
  a__	:					str										=parser.parse_args()
  a__	:					Optional[int]										={"lr": 2e-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
  training_function(SCREAMING_SNAKE_CASE       ,       SCREAMING_SNAKE_CASE						)


if __name__ == "__main__":
     main()






