



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
#
########################################################################

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

def   _lowerCAmelCase   (						A__						,  A__ = 16	):
      lowercase__     =  AutoTokenizer.from_pretrained('bert-base-cased'	)
      lowercase__     =  load_dataset('glue'						,  'mrpc'	)

      def tokenize_function(A__	):
            # max_length=None => use the model max length (it's actually the default)
            lowercase__     =  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():
            lowercase__     =  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
      lowercase__     =  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.
            lowercase__     =  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":
                  lowercase__     =  16
            elif accelerator.mixed_precision != "no":
                  lowercase__     =  8
            else:
                  lowercase__     =  None

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

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

       a__      :				Any   	=			mocked_dataloaders  # noqa: F811

def   _lowerCAmelCase   (						A__						,  A__	):
      # For testing only
      if os.environ.get('TESTING_MOCKED_DATALOADERS'						,  A__	) == "1":
            lowercase__     =  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:
            lowercase__     =  Accelerator(
                cpu=args.cpu						,  mixed_precision=args.mixed_precision						,  log_with='all'						,  project_dir=args.project_dir	)
      else:
            lowercase__     =  Accelerator(cpu=args.cpu						,  mixed_precision=args.mixed_precision	)
      # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
      lowercase__     =  config['lr']
      lowercase__     =  int(config['num_epochs']	)
      lowercase__     =  int(config['seed']	)
      lowercase__     =  int(config['batch_size']	)
      set_seed(A__	)

      lowercase__,	lowercase__     =  get_dataloaders(A__						,  A__	)
      lowercase__     =  evaluate.load('glue'						,  'mrpc'	)

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

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

      # Instantiate optimizer
      lowercase__     =  AdamW(params=model.parameters()						,  lr=A__	)

      # Instantiate scheduler
      lowercase__     =  get_linear_schedule_with_warmup(
          optimizer=A__						,  num_warmup_steps=100						,  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.
      lowercase__,	lowercase__,	lowercase__,	lowercase__,	lowercase__     =  accelerator.prepare(
          A__						,  A__						,  A__						,  A__						,  A__	)

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

      # Now we train the model
      for epoch in range(A__	):
            model.train()
            # New Code #
            # For our tracking example, we will log the total loss of each epoch
            if args.with_tracking:
                  lowercase__     =  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	)
                  lowercase__     =  model(**A__	)
                  lowercase__     =  outputs.loss
                  # New Code #
                  if args.with_tracking:
                        total_loss += loss.detach().float()
                  lowercase__     =  loss / gradient_accumulation_steps
                  accelerator.backward(A__	)
                  if step % gradient_accumulation_steps == 0:
                        optimizer.step()
                        lr_scheduler.step()
                        optimizer.zero_grad()

            model.eval()
            for step, batch in enumerate(A__	):
                  # We could avoid this line since we set the accelerator with `device_placement=True` (the default).
                  batch.to(accelerator.device	)
                  with torch.no_grad():
                        lowercase__     =  model(**A__	)
                  lowercase__     =  outputs.logits.argmax(dim=-1	)
                  lowercase__,	lowercase__     =  accelerator.gather_for_metrics((predictions, batch['labels'])	)
                  metric.add_batch(
                      predictions=A__						,  references=A__						,  )

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

            # 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(A__	),
                          'epoch': epoch,
                      }						,  step=A__						,  )

    # 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   _lowerCAmelCase   (						):
      lowercase__     =  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.'	)
      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=A__						,  default='logs'						,  help='Location on where to store experiment tracking logs` and relevent project information'						,  )
      lowercase__     =  parser.parse_args()
      lowercase__     =  {'lr': 2E-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
      training_function(A__						,  A__	)


if __name__ == "__main__":
       main()





import pickle
import shutil
import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, XGLMTokenizer, XGLMTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


a__      :				Any   	=			get_tests_dir("fixtures/test_sentencepiece.model")



@require_sentencepiece
@require_tokenizers
class       UpperCAmelCase__(							lowerCamelCase     ,							unittest.TestCase	):

   '''simple docstring'''




   A    :  str       				=    XGLMTokenizer
   A    :  List[Any]       				=    XGLMTokenizerFast
   A    :  int       				=    True
   A    :  Optional[Any]       				=    True


   def 			UpperCAmelCase		(						self  :			Optional[int])	->				Optional[Any]:




         """simple docstring"""

         super().setUp()

         # We have a SentencePiece fixture for testing
         lowercase__     =  XGLMTokenizer(lowerCAmelCase			,				keep_accents=lowerCAmelCase)
         tokenizer.save_pretrained(self.tmpdirname)


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




         """simple docstring"""

         lowercase__     =  '<pad>'
         lowercase__     =  1

         self.assertEqual(self.get_tokenizer()._convert_token_to_id(lowerCAmelCase)			,				lowerCAmelCase)
         self.assertEqual(self.get_tokenizer()._convert_id_to_token(lowerCAmelCase)			,				lowerCAmelCase)


   def 			UpperCAmelCase		(						self  :			str)	->				List[str]:




         """simple docstring"""

         lowercase__     =  list(self.get_tokenizer().get_vocab().keys())

         self.assertEqual(vocab_keys[0]			,				'<s>')
         self.assertEqual(vocab_keys[1]			,				'<pad>')
         self.assertEqual(len(lowerCAmelCase)			,				10_08)


   def 			UpperCAmelCase		(						self  :			List[str])	->				str:




         """simple docstring"""

         self.assertEqual(self.get_tokenizer().vocab_size			,				10_08)


   def 			UpperCAmelCase		(						self  :			Optional[Any])	->				List[str]:




         """simple docstring"""

         lowercase__     =  XGLMTokenizer(lowerCAmelCase			,				keep_accents=lowerCAmelCase)

         lowercase__     =  tokenizer.tokenize('This is a test')
         self.assertListEqual(lowerCAmelCase			,				['▁This', '▁is', '▁a', '▁t', 'est'])

         self.assertListEqual(
             tokenizer.convert_tokens_to_ids(lowerCAmelCase)			,				[value + tokenizer.fairseq_offset for value in [2_85, 46, 10, 1_70, 3_82]]			,				)

         lowercase__     =  tokenizer.tokenize('I was born in 92000, and this is falsé.')
         self.assertListEqual(
             lowerCAmelCase			,				[
                 SPIECE_UNDERLINE + 'I',
                 SPIECE_UNDERLINE + 'was',
                 SPIECE_UNDERLINE + 'b',
                 'or',
                 'n',
                 SPIECE_UNDERLINE + 'in',
                 SPIECE_UNDERLINE + '',
                 '9',
                 '2',
                 '0',
                 '0',
                 '0',
                 ',',
                 SPIECE_UNDERLINE + 'and',
                 SPIECE_UNDERLINE + 'this',
                 SPIECE_UNDERLINE + 'is',
                 SPIECE_UNDERLINE + 'f',
                 'al',
                 's',
                 'é',
                 '.',
             ]			,				)
         lowercase__     =  tokenizer.convert_tokens_to_ids(lowerCAmelCase)
         self.assertListEqual(
             lowerCAmelCase			,				[
                 value + tokenizer.fairseq_offset
                 for value in [8, 21, 84, 55, 24, 19, 7, 2, 6_02, 3_47, 3_47, 3_47, 3, 12, 66, 46, 72, 80, 6, 2, 4]
             ]			,				)

         lowercase__     =  tokenizer.convert_ids_to_tokens(lowerCAmelCase)
         self.assertListEqual(
             lowerCAmelCase			,				[
                 SPIECE_UNDERLINE + 'I',
                 SPIECE_UNDERLINE + 'was',
                 SPIECE_UNDERLINE + 'b',
                 'or',
                 'n',
                 SPIECE_UNDERLINE + 'in',
                 SPIECE_UNDERLINE + '',
                 '<unk>',
                 '2',
                 '0',
                 '0',
                 '0',
                 ',',
                 SPIECE_UNDERLINE + 'and',
                 SPIECE_UNDERLINE + 'this',
                 SPIECE_UNDERLINE + 'is',
                 SPIECE_UNDERLINE + 'f',
                 'al',
                 's',
                 '<unk>',
                 '.',
             ]			,				)


   @cached_property
   def 			UpperCAmelCase		(						self  :			int)	->				Dict:




         """simple docstring"""

         return XGLMTokenizer.from_pretrained('facebook/xglm-564M')


   def 			UpperCAmelCase		(						self  :			Optional[int])	->				Dict:




         """simple docstring"""

         with tempfile.NamedTemporaryFile() as f:
               shutil.copyfile(lowerCAmelCase			,				f.name)
               lowercase__     =  XGLMTokenizer(f.name			,				keep_accents=lowerCAmelCase)
               lowercase__     =  pickle.dumps(lowerCAmelCase)
         pickle.loads(lowerCAmelCase)


   def 			UpperCAmelCase		(						self  :			Optional[Any])	->				str:




         """simple docstring"""

         if not self.test_rust_tokenizer:
               return

         lowercase__     =  self.get_tokenizer()
         lowercase__     =  self.get_rust_tokenizer()

         lowercase__     =  'I was born in 92000, and this is falsé.'

         lowercase__     =  tokenizer.tokenize(lowerCAmelCase)
         lowercase__     =  rust_tokenizer.tokenize(lowerCAmelCase)
         self.assertListEqual(lowerCAmelCase			,				lowerCAmelCase)

         lowercase__     =  tokenizer.encode(lowerCAmelCase			,				add_special_tokens=lowerCAmelCase)
         lowercase__     =  rust_tokenizer.encode(lowerCAmelCase			,				add_special_tokens=lowerCAmelCase)
         self.assertListEqual(lowerCAmelCase			,				lowerCAmelCase)

         lowercase__     =  self.get_rust_tokenizer()
         lowercase__     =  tokenizer.encode(lowerCAmelCase)
         lowercase__     =  rust_tokenizer.encode(lowerCAmelCase)
         self.assertListEqual(lowerCAmelCase			,				lowerCAmelCase)


   @slow
   def 			UpperCAmelCase		(						self  :			List[str])	->				List[str]:




         """simple docstring"""

         lowercase__     =  'Hello World!'
         lowercase__     =  [2, 3_12_27, 44_47, 35]

         self.assertListEqual(lowerCAmelCase			,				self.big_tokenizer.encode(lowerCAmelCase))


   @slow
   def 			UpperCAmelCase		(						self  :			List[str])	->				Union[str, Any]:




         """simple docstring"""

         lowercase__     =  (
             'This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) " [ ] ! : - . Also we will'
             ' add words that should not exsist and be tokenized to unk, such as saoneuhaoesuth'
         )
         # fmt: off
         lowercase__     =  [2, 10_18, 67, 11, 19_88, 26_17, 56_31, 2_78, 11, 34_07, 48, 7_16_30, 2_80_85, 4, 32_34, 1_57, 13, 6, 5, 6, 4, 35_26, 7_68, 15, 6_59, 57, 2_98, 39_83, 8_64, 1_29, 21, 6, 5, 1_36_75, 3_77, 6_52, 75_80, 1_03_41, 1_55, 28_17, 4_22, 16_66, 7, 16_74, 53, 1_13, 20_22_77, 1_78_92, 33, 60, 87, 4, 32_34, 1_57, 61, 26_67, 5_23_76, 19, 88, 23, 7_35]
         # fmt: on

         self.assertListEqual(lowerCAmelCase			,				self.big_tokenizer.encode(lowerCAmelCase))



   @slow
   def 			UpperCAmelCase		(						self  :			str)	->				Dict:




         """simple docstring"""

         lowercase__     =  {
             'input_ids': [[2, 10_88_25, 11_63, 15, 8_80_10, 4_73, 1_58_98, 1_57, 1_36_72, 18_57, 3_12, 8, 23_80_21, 11_63, 53, 1_36_72, 18_57, 3_12, 8, 5_32_83, 18_23_96, 8, 1_85_66, 16, 3_67_33, 41_01, 8, 2_30, 24_40_17, 12_25_53, 7, 15, 13_25_97, 4, 2_93, 1_25_11, 76_10, 4, 34_14, 13_25_97, 9, 4, 3_23_61, 3_62, 4, 7_34, 2_85_12, 3_25_69, 18, 4, 3_23_61, 2_60_96, 1_49_82, 73, 1_87_15, 2_14_33, 23_52_61, 15, 4_92, 1_24_27, 16, 53, 1_87_15, 2_14_33, 6_54_54, 15, 2_36_59, 5_63, 16, 2_78, 5_97, 28_43, 5_95, 79_31, 18_23_96, 6_41_86, 22, 8_86, 5_95, 13_29_81, 53, 2_55_40, 34_49, 4_39_82, 3_99_01, 59_51, 8_78, 3_30, 4, 2_76_94, 8_02_69, 3_12, 53, 65_17, 1_17_80, 6_11, 2_04_08, 5], [2, 6, 13_25_97, 67, 4_28_97, 33, 5_92, 8, 16_37_29, 2_55_40, 3_61, 13_69_97, 10_95_14, 17_32_30, 7, 5_01, 60, 10_29_13, 1_96, 56_31, 2_35, 6_32_43, 4_73, 6, 23_17_57, 74, 52_77, 79_05, 53, 30_95, 3_73_17, 22, 4_54, 18_38_74, 5], [2, 2_68, 3_12_98, 4_65_30, 6, 13_29_35, 4_38_31, 7, 5_97, 32, 24, 36_88, 98_65, 5]],
             'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]
         }  # noqa: E501
         # fmt: on

         self.tokenizer_integration_test_util(
             expected_encoding=lowerCAmelCase			,				model_name='facebook/xglm-564M'			,				padding=lowerCAmelCase			,				)
