



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 the experiment tracking capability,
# 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_		 =					1_6
UpperCAmelCase_		 =					3_2

def 	SCREAMING_SNAKE_CASE_							(	_snake_case	:Accelerator	,   _snake_case	:int = 16				)  ->     Optional[Any]:
   _A      =       AutoTokenizer.from_pretrained('''bert-base-cased'''				)
   _A      =       load_dataset('''glue'''	,   '''mrpc'''				)

   def tokenize_function(_snake_case	:Optional[int]				):
      # max_length=None => use the model max length (it's actually the default)
      _A      =       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      =       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      =       tokenized_datasets.rename_column('''label'''	,   '''labels'''				)

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

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

   # Instantiate dataloaders.
   _A      =       DataLoader(
       tokenized_datasets['''train''']	,   shuffle=_snake_case	,   collate_fn=_snake_case	,   batch_size=_snake_case				)
   _A      =       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

   UpperCAmelCase_		 =					mocked_dataloaders  # noqa: F811

def 	SCREAMING_SNAKE_CASE_							(	_snake_case	:List[str]	,   _snake_case	:str				)  ->     Optional[int]:
   # For testing only
   if os.environ.get('''TESTING_MOCKED_DATALOADERS'''	,   _snake_case				) == "1":
      _A      =       2
   # Initialize Accelerator

   # New Code #
   # We pass in "all" to `log_with` to grab all available trackers in the environment
   # Note: If using a custom `Tracker` class, should be passed in here such as:
   # >>> log_with = ["all", MyCustomTrackerClassInstance()]
   if args.with_tracking:
      _A      =       Accelerator(
          cpu=args.cpu	,   mixed_precision=args.mixed_precision	,   log_with='''all'''	,   project_dir=args.project_dir				)
   else:
      _A      =       Accelerator(cpu=args.cpu	,   mixed_precision=args.mixed_precision				)
   # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
   _A      =       config['''lr''']
   _A      =       int(config['''num_epochs''']				)
   _A      =       int(config['''seed''']				)
   _A      =       int(config['''batch_size''']				)
   set_seed(_snake_case				)

   _A  ,    _A      =       get_dataloaders(_snake_case	,   _snake_case				)
   _A      =       evaluate.load('''glue'''	,   '''mrpc'''				)

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

   # Instantiate the model (we build the model here so that the seed also control new weights initialization)
   _A      =       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      =       model.to(accelerator.device				)

   # Instantiate optimizer
   _A      =       AdamW(params=model.parameters()	,   lr=_snake_case				)

   # Instantiate scheduler
   _A      =       get_linear_schedule_with_warmup(
       optimizer=_snake_case	,   num_warmup_steps=100	,   num_training_steps=(len(_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      =       accelerator.prepare(
       _snake_case	,   _snake_case	,   _snake_case	,   _snake_case	,   _snake_case				)

   # New Code #
   # We need to initialize the trackers we use. Overall configurations can also be stored
   if args.with_tracking:
      _A      =       os.path.split(_snake_case				)[-1].split('''.'''				)[0]
      accelerator.init_trackers(_snake_case	,   _snake_case				)

   # Now we train the model
   for epoch in range(_snake_case				):
      model.train()
      # New Code #
      # For our tracking example, we will log the total loss of each epoch
      if args.with_tracking:
         _A      =       0
      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      =       model(**_snake_case				)
         _A      =       outputs.loss
         # New Code #
         if args.with_tracking:
            total_loss += loss.detach().float()
         _A      =       loss / gradient_accumulation_steps
         accelerator.backward(_snake_case				)
         if step % gradient_accumulation_steps == 0:
            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` (the default).
         batch.to(accelerator.device				)
         with torch.no_grad():
            _A      =       model(**_snake_case				)
         _A      =       outputs.logits.argmax(dim=-1				)
         _A  ,    _A      =       accelerator.gather_for_metrics((predictions, batch['''labels'''])				)
         metric.add_batch(
             predictions=_snake_case	,   references=_snake_case	,   )

      _A      =       metric.compute()
      # Use accelerator.print to print only on the main process.
      accelerator.print(F'''epoch {epoch}:'''	,   _snake_case				)

      # New Code #
      # To actually log, we call `Accelerator.log`
      # The values passed can be of `str`, `int`, `float` or `dict` of `str` to `float`/`int`
      if args.with_tracking:
         accelerator.log(
             {
                 '''accuracy''': eval_metric['''accuracy'''],
                 '''f1''': eval_metric['''f1'''],
                 '''train_loss''': total_loss.item() / len(_snake_case				),
                 '''epoch''': epoch,
             }	,   step=_snake_case	,   )

    # New Code #
    # When a run is finished, you should call `accelerator.end_training()`
    # to close all of the open trackers
   if args.with_tracking:
      accelerator.end_training()

def 	SCREAMING_SNAKE_CASE_							(	)  ->     List[Any]:
   _A      =       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.'''				)
   parser.add_argument(
       '''--with_tracking'''	,   action='''store_true'''	,   help='''Whether to load in all available experiment trackers from the environment and use them for logging.'''	,   )
   parser.add_argument(
       '''--project_dir'''	,   type=_snake_case	,   default='''logs'''	,   help='''Location on where to store experiment tracking logs` and relevent project information'''	,   )
   _A      =       parser.parse_args()
   _A      =       {'''lr''': 2E-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
   training_function(_snake_case	,   _snake_case				)


if __name__ == "__main__":
   main()








import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
   from PIL import Image

   from transformers import AlignProcessor, EfficientNetImageProcessor


@require_vision
class 	lowerCamelCase__	(       unittest.TestCase):



 """simple docstring"""

 def     snake_case_      (    self					:					Tuple      )   ->	Optional[int]:
    _A      =       tempfile.mkdtemp()

    _A      =       [
        '''[UNK]''',
        '''[CLS]''',
        '''[SEP]''',
        '''[PAD]''',
        '''[MASK]''',
        '''want''',
        '''##want''',
        '''##ed''',
        '''wa''',
        '''un''',
        '''runn''',
        '''##ing''',
        ''',''',
        '''low''',
        '''lowest''',
    ]
    _A      =       os.path.join(self.tmpdirname			,						VOCAB_FILES_NAMES['''vocab_file''']      )
    with open(self.vocab_file			,						'''w'''			,						encoding='''utf-8'''      ) as vocab_writer:
       vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens]      )      )

    _A      =       {
        '''do_resize''': True,
        '''size''': 20,
        '''do_center_crop''': True,
        '''crop_size''': 18,
        '''do_normalize''': True,
        '''image_mean''': [0.4814_5466, 0.457_8275, 0.4082_1073],
        '''image_std''': [0.2686_2954, 0.2613_0258, 0.2757_7711],
    }
    _A      =       os.path.join(self.tmpdirname			,						__lowerCAmelCase      )
    with open(self.image_processor_file			,						'''w'''			,						encoding='''utf-8'''      ) as fp:
       json.dump(__lowerCAmelCase			,						__lowerCAmelCase      )

 def     snake_case_      (    self					:					Dict			,						**__lowerCAmelCase					:					int      )   ->	Optional[int]:
    return BertTokenizer.from_pretrained(self.tmpdirname			,						**__lowerCAmelCase      )

 def     snake_case_      (    self					:					str			,						**__lowerCAmelCase					:					Optional[Any]      )   ->	Tuple:
    return BertTokenizerFast.from_pretrained(self.tmpdirname			,						**__lowerCAmelCase      )

 def     snake_case_      (    self					:					Tuple			,						**__lowerCAmelCase					:					str      )   ->	Union[str, Any]:
    return EfficientNetImageProcessor.from_pretrained(self.tmpdirname			,						**__lowerCAmelCase      )

 def     snake_case_      (    self					:					Optional[Any]      )   ->	Optional[int]:
    shutil.rmtree(self.tmpdirname      )

 def     snake_case_      (    self					:					int      )   ->	Optional[Any]:
    _A      =       [np.random.randint(2_55			,						size=(3, 30, 4_00)			,						dtype=np.uinta      )]
    _A      =       [Image.fromarray(np.moveaxis(__lowerCAmelCase			,						0			,						-1      )      ) for x in image_inputs]
    return image_inputs

 def     snake_case_      (    self					:					Dict      )   ->	List[str]:
    _A      =       self.get_tokenizer()
    _A      =       self.get_rust_tokenizer()
    _A      =       self.get_image_processor()

    _A      =       AlignProcessor(tokenizer=__lowerCAmelCase			,						image_processor=__lowerCAmelCase      )
    processor_slow.save_pretrained(self.tmpdirname      )
    _A      =       AlignProcessor.from_pretrained(self.tmpdirname			,						use_fast=__lowerCAmelCase      )

    _A      =       AlignProcessor(tokenizer=__lowerCAmelCase			,						image_processor=__lowerCAmelCase      )
    processor_fast.save_pretrained(self.tmpdirname      )
    _A      =       AlignProcessor.from_pretrained(self.tmpdirname      )

    self.assertEqual(processor_slow.tokenizer.get_vocab()			,						tokenizer_slow.get_vocab()      )
    self.assertEqual(processor_fast.tokenizer.get_vocab()			,						tokenizer_fast.get_vocab()      )
    self.assertEqual(tokenizer_slow.get_vocab()			,						tokenizer_fast.get_vocab()      )
    self.assertIsInstance(processor_slow.tokenizer			,						__lowerCAmelCase      )
    self.assertIsInstance(processor_fast.tokenizer			,						__lowerCAmelCase      )

    self.assertEqual(processor_slow.image_processor.to_json_string()			,						image_processor.to_json_string()      )
    self.assertEqual(processor_fast.image_processor.to_json_string()			,						image_processor.to_json_string()      )
    self.assertIsInstance(processor_slow.image_processor			,						__lowerCAmelCase      )
    self.assertIsInstance(processor_fast.image_processor			,						__lowerCAmelCase      )

 def     snake_case_      (    self					:					List[Any]      )   ->	List[str]:
    _A      =       AlignProcessor(tokenizer=self.get_tokenizer()			,						image_processor=self.get_image_processor()      )
    processor.save_pretrained(self.tmpdirname      )

    _A      =       self.get_tokenizer(bos_token='''(BOS)'''			,						eos_token='''(EOS)'''      )
    _A      =       self.get_image_processor(do_normalize=__lowerCAmelCase			,						padding_value=1.0      )

    _A      =       AlignProcessor.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     snake_case_      (    self					:					str      )   ->	List[Any]:
    _A      =       self.get_image_processor()
    _A      =       self.get_tokenizer()

    _A      =       AlignProcessor(tokenizer=__lowerCAmelCase			,						image_processor=__lowerCAmelCase      )

    _A      =       self.prepare_image_inputs()

    _A      =       image_processor(__lowerCAmelCase			,						return_tensors='''np'''      )
    _A      =       processor(images=__lowerCAmelCase			,						return_tensors='''np'''      )

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

 def     snake_case_      (    self					:					Union[str, Any]      )   ->	Dict:
    _A      =       self.get_image_processor()
    _A      =       self.get_tokenizer()

    _A      =       AlignProcessor(tokenizer=__lowerCAmelCase			,						image_processor=__lowerCAmelCase      )

    _A      =       '''lower newer'''

    _A      =       processor(text=__lowerCAmelCase      )

    _A      =       tokenizer(__lowerCAmelCase			,						padding='''max_length'''			,						max_length=64      )

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

 def     snake_case_      (    self					:					List[str]      )   ->	Any:
    _A      =       self.get_image_processor()
    _A      =       self.get_tokenizer()

    _A      =       AlignProcessor(tokenizer=__lowerCAmelCase			,						image_processor=__lowerCAmelCase      )

    _A      =       '''lower newer'''
    _A      =       self.prepare_image_inputs()

    _A      =       processor(text=__lowerCAmelCase			,						images=__lowerCAmelCase      )

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

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

 def     snake_case_      (    self					:					Optional[Any]      )   ->	str:
    _A      =       self.get_image_processor()
    _A      =       self.get_tokenizer()

    _A      =       AlignProcessor(tokenizer=__lowerCAmelCase			,						image_processor=__lowerCAmelCase      )

    _A      =       [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

    _A      =       processor.batch_decode(__lowerCAmelCase      )
    _A      =       tokenizer.batch_decode(__lowerCAmelCase      )

    self.assertListEqual(__lowerCAmelCase			,						__lowerCAmelCase      )

 def     snake_case_      (    self					:					str      )   ->	str:
    _A      =       self.get_image_processor()
    _A      =       self.get_tokenizer()

    _A      =       AlignProcessor(tokenizer=__lowerCAmelCase			,						image_processor=__lowerCAmelCase      )

    _A      =       '''lower newer'''
    _A      =       self.prepare_image_inputs()

    _A      =       processor(text=__lowerCAmelCase			,						images=__lowerCAmelCase      )

    self.assertListEqual(list(inputs.keys()      )			,						processor.model_input_names      )



