


import unittest
from pathlib import Path
from tempfile import TemporaryDirectory

from transformers import AutoConfig, TFAutoModel, is_tensorflow_text_available, is_tf_available
from transformers.models.bert.tokenization_bert import BertTokenizer
from transformers.testing_utils import require_tensorflow_text, require_tf, slow


if is_tf_available():
	import tensorflow as tf

if is_tensorflow_text_available():
	from transformers.models.bert import TFBertTokenizer


A			:						Union[str, Any]        =  ['bert-base-uncased', 'bert-base-cased']
A			:						str        =  'hf-internal-testing/tiny-bert-tf-only'

if is_tf_available():



	class 				A       (			tf.keras.Model				):
						'''simple docstring'''






						def __init__(self       :		str ,   _UpperCAmelCase       :		List[Any]						)		->  Any:




								"""simple docstring"""


								super().__init__()
								lowercase__	     =						tokenizer
								lowercase__	     =						AutoConfig.from_pretrained(_UpperCAmelCase						)
								lowercase__	     =						TFAutoModel.from_config(_UpperCAmelCase						)


						def     lowerCamelCase__	(self       :		Optional[Any] ,   _UpperCAmelCase       :		Optional[int]						)		->  Union[str, Any]:




								"""simple docstring"""


								lowercase__	     =						self.tokenizer(_UpperCAmelCase						)
								lowercase__	     =						self.bert(**_UpperCAmelCase						)
								return out["pooler_output"]



@require_tf
@require_tensorflow_text
class 				A       (			unittest.TestCase				):
					'''simple docstring'''






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




							"""simple docstring"""


							super().setUp()

							lowercase__	     =						[
							    BertTokenizer.from_pretrained(_UpperCAmelCase						) for checkpoint in (TOKENIZER_CHECKPOINTS * 2)
							]  # repeat for when fast_bert_tokenizer=false
							lowercase__	     =						[TFBertTokenizer.from_pretrained(_UpperCAmelCase						) for checkpoint in TOKENIZER_CHECKPOINTS] + [
							    TFBertTokenizer.from_pretrained(_UpperCAmelCase ,   use_fast_bert_tokenizer=_UpperCAmelCase						)
							    for checkpoint in TOKENIZER_CHECKPOINTS
							]
							assert len(self.tokenizers						) == len(self.tf_tokenizers						)

							lowercase__	     =						[
							    """This is a straightforward English test sentence.""",
							    """This one has some weird characters\rto\nsee\r\nif  those\u00E9break things.""",
							    """Now we're going to add some Chinese: 一 二 三 一二三""",
							    """And some much more rare Chinese: 齉 堃 齉堃""",
							    """Je vais aussi écrire en français pour tester les accents""",
							    """Classical Irish also has some unusual characters, so in they go: Gaelaċ, ꝼ""",
							]
							lowercase__	     =						list(zip(self.test_sentences ,   self.test_sentences[::-1]						)						)

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




							"""simple docstring"""


							for tokenizer, tf_tokenizer in zip(self.tokenizers ,   self.tf_tokenizers						):
									for test_inputs in (self.test_sentences, self.paired_sentences):
											lowercase__	     =						tokenizer(_UpperCAmelCase ,   return_tensors="""tf""" ,   padding="""longest"""						)
											lowercase__	     =						tf_tokenizer(_UpperCAmelCase						)

											for key in python_outputs.keys():
													self.assertTrue(tf.reduce_all(python_outputs[key].shape == tf_outputs[key].shape						)						)
													self.assertTrue(tf.reduce_all(tf.cast(python_outputs[key] ,   tf.intaa						) == tf_outputs[key]						)						)

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




							"""simple docstring"""


							for tf_tokenizer in self.tf_tokenizers:
									lowercase__	     =						tf_tokenizer(self.paired_sentences						)
									lowercase__	     =						tf_tokenizer(
									    text=[sentence[0] for sentence in self.paired_sentences] ,   text_pair=[sentence[1] for sentence in self.paired_sentences] ,   )
									for key in merged_outputs.keys():
											self.assertTrue(tf.reduce_all(tf.cast(merged_outputs[key] ,   tf.intaa						) == separated_outputs[key]						)						)

					@slow
					def     lowerCamelCase__	(self       :		Optional[Any]						)		->  Optional[Any]:




							"""simple docstring"""


							for tf_tokenizer in self.tf_tokenizers:
									lowercase__	     =						tf.function(_UpperCAmelCase						)
									for test_inputs in (self.test_sentences, self.paired_sentences):
											lowercase__	     =						tf.constant(_UpperCAmelCase						)
											lowercase__	     =						compiled_tokenizer(_UpperCAmelCase						)
											lowercase__	     =						tf_tokenizer(_UpperCAmelCase						)

											for key in eager_outputs.keys():
													self.assertTrue(tf.reduce_all(eager_outputs[key] == compiled_outputs[key]						)						)


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




							"""simple docstring"""


							for tf_tokenizer in self.tf_tokenizers:
									lowercase__	     =						ModelToSave(tokenizer=_UpperCAmelCase						)
									lowercase__	     =						tf.convert_to_tensor(self.test_sentences						)
									lowercase__	     =						model(_UpperCAmelCase						)  # Build model with some sample inputs
									with TemporaryDirectory() as tempdir:
											lowercase__	     =						Path(_UpperCAmelCase						) / """saved.model"""
											model.save(_UpperCAmelCase						)
											lowercase__	     =						tf.keras.models.load_model(_UpperCAmelCase						)
									lowercase__	     =						loaded_model(_UpperCAmelCase						)
									# We may see small differences because the loaded model is compiled, so we need an epsilon for the test
									self.assertLessEqual(tf.reduce_max(tf.abs(out - loaded_output						)						) ,   1E-5						)





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
# and perform 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)
#
# 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]        =  1_6
A			:						str        =  3_2






def 					UpperCamelCase   (      __magic_name__	:				Accelerator       ,       __magic_name__	:				int = 16	)  ->    List[str]:
		"""simple docstring"""




		lowercase__	     =						AutoTokenizer.from_pretrained("""bert-base-cased"""	)
		lowercase__	     =						load_dataset("""glue"""       ,       """mrpc"""	)

		def tokenize_function(__magic_name__	:				int	):
				# max_length=None => use the model max length (it's actually the default)
				lowercase__	     =						tokenizer(examples["""sentence1"""]       ,       examples["""sentence2"""]       ,       truncation=__magic_name__       ,       max_length=__magic_name__	)
				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(
				    __magic_name__       ,       batched=__magic_name__       ,       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(__magic_name__	:				Optional[int]	):
				# 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(
				    __magic_name__       ,       padding="""longest"""       ,       max_length=__magic_name__       ,       pad_to_multiple_of=__magic_name__       ,       return_tensors="""pt"""       ,       )

		# Instantiate dataloaders.
		lowercase__	     =						DataLoader(
		    tokenized_datasets["""train"""]       ,       shuffle=__magic_name__       ,       collate_fn=__magic_name__       ,       batch_size=__magic_name__	)
		lowercase__	     =						DataLoader(
		    tokenized_datasets["""validation"""]       ,       shuffle=__magic_name__       ,       collate_fn=__magic_name__       ,       batch_size=__magic_name__	)

		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			:						Union[str, Any]        =  mocked_dataloaders  # noqa: F811






def 					UpperCamelCase   (      __magic_name__	:				Optional[Any]       ,       __magic_name__	:				List[Any]	)  ->    Dict:
		"""simple docstring"""




		if os.environ.get("""TESTING_MOCKED_DATALOADERS"""       ,       __magic_name__	) == "1":
				lowercase__	     =						2
		# New Code #
		lowercase__	     =						int(args.gradient_accumulation_steps	)
		# Initialize accelerator
		lowercase__	     =						Accelerator(
		    cpu=args.cpu       ,       mixed_precision=args.mixed_precision       ,       gradient_accumulation_steps=__magic_name__	)
		if accelerator.distributed_type == DistributedType.TPU and gradient_accumulation_steps > 1:
				raise NotImplementedError(
				    """Gradient accumulation on TPUs is currently not supported. Pass `gradient_accumulation_steps=1`"""	)
		# 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"""]	)

		lowercase__	     =						evaluate.load("""glue"""       ,       """mrpc"""	)

		set_seed(__magic_name__	)
		lowercase__     ,      lowercase__	     =						get_dataloaders(__magic_name__       ,       __magic_name__	)
		# 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=__magic_name__	)

		# 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=__magic_name__	)

		# Instantiate scheduler
		lowercase__	     =						get_linear_schedule_with_warmup(
		    optimizer=__magic_name__       ,       num_warmup_steps=100       ,       num_training_steps=(len(__magic_name__	) * 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.
		lowercase__     ,      lowercase__     ,      lowercase__     ,      lowercase__     ,      lowercase__	     =						accelerator.prepare(
		    __magic_name__       ,       __magic_name__       ,       __magic_name__       ,       __magic_name__       ,       __magic_name__	)

		# Now we train the model
		for epoch in range(__magic_name__	):
				model.train()
				for step, batch in enumerate(__magic_name__	):
						# We could avoid this line since we set the accelerator with `device_placement=True`.
						batch.to(accelerator.device	)
						# New code #
						# We use the new `accumulate` context manager to perform gradient accumulation
						# We also currently do not support TPUs nor advise it as bugs were found on the XLA side when running our tests.
						with accelerator.accumulate(__magic_name__	):
								lowercase__	     =						model(**__magic_name__	)
								lowercase__	     =						output.loss
								accelerator.backward(__magic_name__	)
								optimizer.step()
								lr_scheduler.step()
								optimizer.zero_grad()

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

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






def 					UpperCamelCase   (      )  ->    Optional[Any]:
		"""simple docstring"""




		lowercase__	     =						argparse.ArgumentParser(description="""Simple example of training script."""	)
		parser.add_argument(
		    """--mixed_precision"""       ,       type=__magic_name__       ,       default=__magic_name__       ,       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."""       ,       )
		# New Code #
		parser.add_argument(
		    """--gradient_accumulation_steps"""       ,       type=__magic_name__       ,       default=1       ,       help="""The number of minibatches to be ran before gradients are accumulated."""       ,       )
		parser.add_argument("""--cpu"""       ,       action="""store_true"""       ,       help="""If passed, will train on the CPU."""	)
		lowercase__	     =						parser.parse_args()
		lowercase__	     =						{"""lr""": 2E-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
		training_function(__magic_name__       ,       __magic_name__	)


if __name__ == "__main__":
	main()

