




"""simple docstring"""







import argparse
import os

# New Code #
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
from accelerate.utils import find_executable_batch_size


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to combine both the gradient accumulation
# and automatic batch size finder utilities of Accelerate to perfrom
# automatic gradient accumulation
#
# 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)
#
# 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
#
########################################################################

SCREAMING_SNAKE_CASE_      =					32





def     __snake_case   (							_lowercase ,_lowercase = 16						):


				"""simple docstring"""







				UpperCamelCase										=  AutoTokenizer.from_pretrained('''bert-base-cased'''						)
				UpperCamelCase										=  load_dataset('''glue''' ,'''mrpc'''						)

				def tokenize_function(_lowercase						):
								# max_length=None => use the model max length (it's actually the default)
								UpperCamelCase										=  tokenizer(examples['''sentence1'''] ,examples['''sentence2'''] ,truncation=_lowercase ,max_length=_lowercase						)
								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():
								UpperCamelCase										=  datasets.map(
								    _lowercase ,batched=_lowercase ,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
				UpperCamelCase										=  tokenized_datasets.rename_column('''label''' ,'''labels'''						)

				def collate_fn(_lowercase						):
								# When using mixed precision we want round multiples of 8/16
								if accelerator.mixed_precision == "fp8":
												UpperCamelCase										=  16
								elif accelerator.mixed_precision != "no":
												UpperCamelCase										=  8
								else:
												UpperCamelCase										=  None

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

				# Instantiate dataloaders.
				UpperCamelCase										=  DataLoader(
				    tokenized_datasets['''train'''] ,shuffle=_lowercase ,collate_fn=_lowercase ,batch_size=_lowercase						)
				UpperCamelCase										=  DataLoader(
				    tokenized_datasets['''validation'''] ,shuffle=_lowercase ,collate_fn=_lowercase ,batch_size=_lowercase						)

				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

		SCREAMING_SNAKE_CASE_      =					mocked_dataloaders  # noqa: F811





def     __snake_case   (							_lowercase ,_lowercase						):


				"""simple docstring"""







				if os.environ.get('''TESTING_MOCKED_DATALOADERS''' ,_lowercase						) == "1":
								UpperCamelCase										=  2
				# Initialize accelerator
				UpperCamelCase										=  Accelerator(cpu=args.cpu ,mixed_precision=args.mixed_precision						)
				# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
				UpperCamelCase										=  config["lr"]
				UpperCamelCase										=  int(config['''num_epochs''']						)
				UpperCamelCase										=  int(config['''seed''']						)
				UpperCamelCase										=  int(config['''batch_size''']						)

				UpperCamelCase										=  evaluate.load('''glue''' ,'''mrpc'''						)

				# New Code #
				# We use the `find_executable_batch_size` decorator, passing in the desired observed batch size
				# to train on. If a CUDA OOM error occurs, it will retry this loop cutting the batch size in
				# half each time. From this, we can calculate the number of gradient accumulation steps needed
				# and modify the Accelerator object as a result
				@find_executable_batch_size(starting_batch_size=int(_lowercase						)						)
				def inner_training_loop(_lowercase						):
								# Since we need to modify the outside accelerator object, we need to bring it
								# to the local scope
								nonlocal accelerator

								# We can calculate the number of gradient accumulation steps based on the current
								# batch size vs the starting batch size
								UpperCamelCase										=  observed_batch_size // batch_size

								# And then set it in the Accelerator directly:
								UpperCamelCase										=  num_gradient_accumulation_steps

								# Next we need to free all of the stored model references in the Accelerator each time
								accelerator.free_memory()

								# And set the seed so our results are reproducable each reset
								set_seed(_lowercase						)

								# Instantiate the model (we build the model here so that the seed also control new weights initialization)
								UpperCamelCase										=  AutoModelForSequenceClassification.from_pretrained('''bert-base-cased''' ,return_dict=_lowercase						)

								# 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).
								UpperCamelCase										=  model.to(accelerator.device						)

								# Instantiate optimizer
								UpperCamelCase										=  AdamW(params=model.parameters() ,lr=_lowercase						)
								UpperCamelCase										=  get_dataloaders(_lowercase ,_lowercase						)

								# Instantiate scheduler
								UpperCamelCase										=  get_linear_schedule_with_warmup(
								    optimizer=_lowercase ,num_warmup_steps=100 ,num_training_steps=(len(_lowercase						) * num_epochs) ,)

								# 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.
								UpperCamelCase										=  accelerator.prepare(
								    _lowercase ,_lowercase ,_lowercase ,_lowercase ,_lowercase						)

								# Now we train the model
								for epoch in range(_lowercase						):
												model.train()
												for step, batch in enumerate(_lowercase						):
																# And perform gradient accumulation
																with accelerator.accumulate(_lowercase						):
																				# We could avoid this line since we set the accelerator with `device_placement=True`.
																				batch.to(accelerator.device						)
																				UpperCamelCase										=  model(**_lowercase						)
																				UpperCamelCase										=  outputs.loss
																				accelerator.backward(_lowercase						)
																				optimizer.step()
																				lr_scheduler.step()
																				optimizer.zero_grad()

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

												UpperCamelCase										=  metric.compute()
												# Use accelerator.print to print only on the main process.
												accelerator.print(f'epoch {epoch}:' ,_lowercase						)

    # New Code #
    # And call it at the end with no arguments
    # Note: You could also refactor this outside of your training loop function
				inner_training_loop()





def     __snake_case   (							):


				"""simple docstring"""







				UpperCamelCase										=  argparse.ArgumentParser(description='''Simple example of training script.'''						)
				parser.add_argument(
				    '''--mixed_precision''' ,type=_lowercase ,default=_lowercase ,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.'''						)
				UpperCamelCase										=  parser.parse_args()
				# New Code #
				# We modify the starting batch size to be an observed batch size of 256, to guarentee an initial CUDA OOM
				UpperCamelCase										=  {"lr": 2e-5, "num_epochs": 3, "seed": 42, "batch_size": 256}
				training_function(_lowercase ,_lowercase						)


if __name__ == "__main__":
		main()





"""simple docstring"""







import unittest

from transformers import ErnieMTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


SCREAMING_SNAKE_CASE_      =					get_tests_dir('fixtures/spiece.model')

@require_sentencepiece
@require_tokenizers
class 							snake_case_ (				lowerCamelCase_  ,   unittest.TestCase ):







   """simple docstring"""




   A_										=					ErnieMTokenizer
   A_										=					False
   A_										=					True
   A_										=					False
   A_										=					False







   def      UpperCAmelCase__ (		self)							->      List[str]:
       super().setUp()

       # We have a SentencePiece fixture for testing
       UpperCamelCase										=  ErnieMTokenizer(lowerCamelCase_					,				unk_token='''<unk>'''					,				pad_token='''<pad>''')
       tokenizer.save_pretrained(self.tmpdirname)







   def      UpperCAmelCase__ (		self					,				lowerCamelCase_)							->      Optional[Any]:
       UpperCamelCase										=  '''this is a test'''
       UpperCamelCase										=  '''this is a test'''
       return input_text, output_text







   def      UpperCAmelCase__ (		self)							->      Optional[Any]:
       UpperCamelCase										=  '''<pad>'''
       UpperCamelCase										=  0

       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)							->      Optional[Any]:
       UpperCamelCase										=  list(self.get_tokenizer().get_vocab().keys())

       self.assertEqual(vocab_keys[0]					,				'''<pad>''')
       self.assertEqual(vocab_keys[1]					,				'''<unk>''')
       self.assertEqual(vocab_keys[-1]					,				'''▁eloquent''')
       self.assertEqual(len(lowerCamelCase_)					,				3_0_0_0_0)







   def      UpperCAmelCase__ (		self)							->      int:
       self.assertEqual(self.get_tokenizer().vocab_size					,				3_0_0_0_0)







   def      UpperCAmelCase__ (		self)							->      List[Any]:
       if not self.test_rust_tokenizer:
           return

       UpperCamelCase										=  self.get_tokenizer()
       UpperCamelCase										=  self.get_rust_tokenizer()

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

       UpperCamelCase										=  tokenizer.tokenize(lowerCamelCase_)
       UpperCamelCase										=  rust_tokenizer.tokenize(lowerCamelCase_)
       self.assertListEqual(lowerCamelCase_					,				lowerCamelCase_)

       UpperCamelCase										=  tokenizer.encode(lowerCamelCase_					,				add_special_tokens=lowerCamelCase_)
       UpperCamelCase										=  rust_tokenizer.encode(lowerCamelCase_					,				add_special_tokens=lowerCamelCase_)
       self.assertListEqual(lowerCamelCase_					,				lowerCamelCase_)

       UpperCamelCase										=  self.get_rust_tokenizer()
       UpperCamelCase										=  tokenizer.encode(lowerCamelCase_)
       UpperCamelCase										=  rust_tokenizer.encode(lowerCamelCase_)
       self.assertListEqual(lowerCamelCase_					,				lowerCamelCase_)







   def      UpperCAmelCase__ (		self)							->      List[Any]:
       UpperCamelCase										=  ErnieMTokenizer(lowerCamelCase_					,				do_lower_case=lowerCamelCase_					,				unk_token='''<unk>'''					,				pad_token='''<pad>''')

       UpperCamelCase										=  tokenizer.tokenize('''This is a test''')
       self.assertListEqual(lowerCamelCase_					,				['''▁this''', '''▁is''', '''▁a''', '''▁test'''])

       self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCamelCase_)					,				[4_8, 2_5, 2_1, 1_2_8_9])

       UpperCamelCase										=  tokenizer.tokenize('''I was born in 92000, and this is falsé.''')
       # ErnieMTokenizer(paddlenlp implementation) outputs '9' instead of '_9' so to mimic that '_9' is changed to '9'
       self.assertListEqual(
           lowerCamelCase_					,				['''▁i''', '''▁was''', '''▁born''', '''▁in''', '''9''', '''2000''', ''',''', '''▁and''', '''▁this''', '''▁is''', '''▁fal''', '''s''', '''é''', '''.'''])
       UpperCamelCase										=  tokenizer.convert_tokens_to_ids(lowerCamelCase_)
       self.assertListEqual(lowerCamelCase_					,				[3_1, 2_3, 3_8_6, 1_9, 5_1_8, 3_0_5_0, 1_5, 1_7, 4_8, 2_5, 8_2_5_6, 1_8, 1, 9])

       UpperCamelCase										=  tokenizer.convert_ids_to_tokens(lowerCamelCase_)
       self.assertListEqual(
           lowerCamelCase_					,				['''▁i''', '''▁was''', '''▁born''', '''▁in''', '''9''', '''2000''', ''',''', '''▁and''', '''▁this''', '''▁is''', '''▁fal''', '''s''', '''<unk>''', '''.''']					,				)







   def      UpperCAmelCase__ (		self)							->      Dict:
       UpperCamelCase										=  ErnieMTokenizer(lowerCamelCase_					,				unk_token='''<unk>'''					,				pad_token='''<pad>''')

       UpperCamelCase										=  tokenizer.encode('''sequence builders''')
       UpperCamelCase										=  tokenizer.encode('''multi-sequence build''')

       UpperCamelCase										=  tokenizer.build_inputs_with_special_tokens(lowerCamelCase_)
       UpperCamelCase										=  tokenizer.build_inputs_with_special_tokens(lowerCamelCase_					,				lowerCamelCase_)

       assert encoded_sentence == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]
       assert encoded_pair == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + [
           tokenizer.sep_token_id
       ] + text_a + [tokenizer.sep_token_id]







   @slow
   def      UpperCAmelCase__ (		self)							->      List[str]:
       # fmt: off
       UpperCamelCase										=  {'''input_ids''': [[0, 1_1_0_6_2, 8_2_7_7_2, 7, 1_5, 8_2_7_7_2, 5_3_8, 5_1_5_2_9, 2_3_7, 1_7_1_9_8, 1_2_9_0, 2_0_6, 9, 2_1_5_1_7_5, 1_3_1_4, 1_3_6, 1_7_1_9_8, 1_2_9_0, 2_0_6, 9, 5_6_3_5_9, 4_2, 1_2_2_0_0_9, 9, 1_6_4_6_6, 1_6, 8_7_3_4_4, 4_5_3_7, 9, 4_7_1_7, 7_8_3_8_1, 6, 1_5_9_9_5_8, 7, 1_5, 2_4_4_8_0, 6_1_8, 4, 5_2_7, 2_2_6_9_3, 9, 3_0_4, 4, 2_7_7_7, 2_4_4_8_0, 9_8_7_4, 4, 4_3_5_2_3, 5_9_4, 4, 8_0_3, 1_8_3_9_2, 3_3_1_8_9, 1_8, 4, 4_3_5_2_3, 2_4_4_4_7, 5, 5, 5, 1_6, 1_0_0, 2_4_9_5_5, 8_3_6_5_8, 9_6_2_6, 1_4_4_0_5_7, 1_5, 8_3_9, 2_2_3_3_5, 1_6, 1_3_6, 2_4_9_5_5, 8_3_6_5_8, 8_3_4_7_9, 1_5, 3_9_1_0_2, 7_2_4, 1_6, 6_7_8, 6_4_5, 6_4_6_0, 1_3_2_8, 4_5_8_9, 4_2, 1_2_2_0_0_9, 1_1_5_7_7_4, 2_3, 3_5_5_9, 1_3_2_8, 4_6_8_7_6, 7, 1_3_6, 5_3_8_9_4, 1_9_4_0, 4_2_2_2_7, 4_1_1_5_9, 1_7_7_2_1, 8_2_3, 4_2_5, 4, 2_7_5_1_2, 9_8_7_2_2, 2_0_6, 1_3_6, 5_5_3_1, 4_9_7_0, 9_1_9, 1_7_3_3_6, 5, 2], [0, 2_0_0_8_0, 6_1_8, 8_3, 8_2_7_7_5, 4_7, 4_7_9, 9, 1_5_1_7, 7_3, 5_3_8_9_4, 3_3_3, 8_0_5_8_1, 1_1_0_1_1_7, 1_8_8_1_1, 5_2_5_6, 1_2_9_5, 5_1, 1_5_2_5_2_6, 2_9_7, 7_9_8_6, 3_9_0, 1_2_4_4_1_6, 5_3_8, 3_5_4_3_1, 2_1_4, 9_8, 1_5_0_4_4, 2_5_7_3_7, 1_3_6, 7_1_0_8, 4_3_7_0_1, 2_3, 7_5_6, 1_3_5_3_5_5, 7, 5, 2, 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], [0, 5_8_1, 6_3_7_7_3, 1_1_9_4_5_5, 6, 1_4_7_7_9_7, 8_8_2_0_3, 7, 6_4_5, 7_0, 2_1, 3_2_8_5, 1_0_2_6_9, 5, 2, 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]]}
       # fmt: on

       self.tokenizer_integration_test_util(
           expected_encoding=lowerCamelCase_					,				model_name='''susnato/ernie-m-base_pytorch'''					,				sequences=[
               '''Transformers (formerly known as pytorch-transformers and pytorch-pretrained-bert) provides '''
               '''general-purpose architectures (BERT, GPT-2, RoBERTa, XLM, DistilBert, XLNet...) for Natural '''
               '''Language Understanding (NLU) and Natural Language Generation (NLG) with over32+ pretrained '''
               '''models in100+ languages and deep interoperability between Jax, PyTorch and TensorFlow.''',
               '''BERT is designed to pre-train deep bidirectional representations from unlabeled text by jointly '''
               '''conditioning on both left and right context in all layers.''',
               '''The quick brown fox jumps over the lazy dog.''',
           ]					,				)