

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
from accelerate.local_sgd import LocalSGD


########################################################################
# This is a fully working simple example to use Accelerate
# with LocalSGD, which is a method to synchronize model
# parameters every K batches. It is different, but complementary
# to 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		       =   1_6
_a		       =   3_2







def _a (       SCREAMING_SNAKE_CASE		:			Union[str, Any]    ,					SCREAMING_SNAKE_CASE		:			Dict = 16		)    ->    Union[str, Any]:

 """simple docstring"""






 __lowerCAmelCase:   Union[str, Any]   	=      AutoTokenizer.from_pretrained('bert-base-cased'		)
 __lowerCAmelCase:   Any   	=      load_dataset('glue'    ,					'mrpc'		)

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

 def collate_fn(SCREAMING_SNAKE_CASE		:			Optional[int]		):
  # On TPU it's best to pad everything to the same length or training will be very slow.
  __lowerCAmelCase:   int   	=      1_28 if accelerator.distributed_type == DistributedType.TPU else None
  # When using mixed precision we want round multiples of 8/16
  if accelerator.mixed_precision == "fp8":
   __lowerCAmelCase:   Optional[int]   	=      16
  elif accelerator.mixed_precision != "no":
   __lowerCAmelCase:   List[Any]   	=      8
  else:
   __lowerCAmelCase:   Dict   	=      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.
 __lowerCAmelCase:   Union[str, Any]   	=      DataLoader(
     tokenized_datasets['train']    ,					shuffle=SCREAMING_SNAKE_CASE    ,					collate_fn=SCREAMING_SNAKE_CASE    ,					batch_size=SCREAMING_SNAKE_CASE		)
 __lowerCAmelCase:   Optional[Any]   	=      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

  _a		       =   mocked_dataloaders  # noqa: F811







def _a (       SCREAMING_SNAKE_CASE		:			Optional[Any]    ,					SCREAMING_SNAKE_CASE		:			int		)    ->    List[Any]:

 """simple docstring"""






 if os.environ.get('TESTING_MOCKED_DATALOADERS'    ,					SCREAMING_SNAKE_CASE		) == "1":
  __lowerCAmelCase:   List[Any]   	=      2
 # New Code #
 __lowerCAmelCase:   Union[str, Any]   	=      int(args.gradient_accumulation_steps		)
 __lowerCAmelCase:   List[str]   	=      int(args.local_sgd_steps		)
 # Initialize accelerator
 __lowerCAmelCase:   Tuple   	=      Accelerator(
     cpu=args.cpu    ,					mixed_precision=args.mixed_precision    ,					gradient_accumulation_steps=SCREAMING_SNAKE_CASE		)
 if accelerator.distributed_type not in [DistributedType.NO, DistributedType.MULTI_CPU, DistributedType.MULTI_GPU]:
  raise NotImplementedError('LocalSGD is supported only for CPUs and GPUs (no DeepSpeed or MegatronLM)'		)
 # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
 __lowerCAmelCase:   Any   	=      config["""lr"""]
 __lowerCAmelCase:   List[str]   	=      int(config['num_epochs']		)
 __lowerCAmelCase:   Any   	=      int(config['seed']		)
 __lowerCAmelCase:   Dict   	=      int(config['batch_size']		)

 __lowerCAmelCase:   Union[str, Any]   	=      evaluate.load('glue'    ,					'mrpc'		)

 set_seed(SCREAMING_SNAKE_CASE		)
 __lowerCAmelCase:   Any   	=      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)
 __lowerCAmelCase:   int   	=      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).
 __lowerCAmelCase:   List[Any]   	=      model.to(accelerator.device		)

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

 # Instantiate scheduler
 __lowerCAmelCase:   int   	=      get_linear_schedule_with_warmup(
     optimizer=SCREAMING_SNAKE_CASE    ,					num_warmup_steps=1_00    ,					num_training_steps=(len(SCREAMING_SNAKE_CASE		) * 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.
 __lowerCAmelCase:   List[str]   	=      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()
  with LocalSGD(
      accelerator=SCREAMING_SNAKE_CASE    ,					model=SCREAMING_SNAKE_CASE    ,					local_sgd_steps=SCREAMING_SNAKE_CASE    ,					enabled=local_sgd_steps is not None		) as local_sgd:
   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		)
    # 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(SCREAMING_SNAKE_CASE		):
     __lowerCAmelCase:   str   	=      model(**SCREAMING_SNAKE_CASE		)
     __lowerCAmelCase:   int   	=      output.loss
     accelerator.backward(SCREAMING_SNAKE_CASE		)
     optimizer.step()
     lr_scheduler.step()
     optimizer.zero_grad()
     # LocalSGD-specific line
     local_sgd.step()

  model.eval()
  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():
    __lowerCAmelCase:   Optional[Any]   	=      model(**SCREAMING_SNAKE_CASE		)
   __lowerCAmelCase:   Dict   	=      outputs.logits.argmax(dim=-1		)
   __lowerCAmelCase:   List[Any]   	=      accelerator.gather_for_metrics((predictions, batch['labels'])		)
   metric.add_batch(
       predictions=SCREAMING_SNAKE_CASE    ,					references=SCREAMING_SNAKE_CASE    ,					)

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







def _a (       )    ->    Tuple:

 """simple docstring"""






 __lowerCAmelCase:   Optional[int]   	=      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.'    ,					)
 # New Code #
 parser.add_argument(
     '--gradient_accumulation_steps'    ,					type=SCREAMING_SNAKE_CASE    ,					default=1    ,					help='The number of minibatches to be ran before gradients are accumulated.'    ,					)
 parser.add_argument(
     '--local_sgd_steps'    ,					type=SCREAMING_SNAKE_CASE    ,					default=8    ,					help='Number of local SGD steps or None to disable local SGD'		)
 parser.add_argument('--cpu'    ,					action='store_true'    ,					help='If passed, will train on the CPU.'		)
 __lowerCAmelCase:   Tuple   	=      parser.parse_args()
 __lowerCAmelCase:   str   	=      {"""lr""": 2E-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
 training_function(SCREAMING_SNAKE_CASE    ,					SCREAMING_SNAKE_CASE		)


if __name__ == "__main__":
  main()













'''simple docstring'''







import json
import os
import shutil
import tempfile
import unittest

import numpy as np

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


if is_vision_available():
			from PIL import Image

			from transformers import VisionTextDualEncoderProcessor, ViTImageProcessor

@require_tokenizers
@require_vision
class _lowercase	(				unittest.TestCase		):




							def 		lowerCamelCase_				(							self:				Dict     ):
														lowerCamelCase__					:	int         =       tempfile.mkdtemp()

														# fmt: off
														lowerCamelCase__					:	int         =       ["""[UNK]""", """[CLS]""", """[SEP]""", """[PAD]""", """[MASK]""", """want""", """##want""", """##ed""", """wa""", """un""", """runn""", """##ing""", """,""", """low""", """lowest"""]
														# fmt: on
														lowerCamelCase__					:	Union[str, Any]         =       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]     )     )

														lowerCamelCase__					:	Tuple         =       {
														    """do_resize""": True,
														    """size""": {"""height""": 18, """width""": 18},
														    """do_normalize""": True,
														    """image_mean""": [0.5, 0.5, 0.5],
														    """image_std""": [0.5, 0.5, 0.5],
														}
														lowerCamelCase__					:	Tuple         =       os.path.join(self.tmpdirname			,							UpperCamelCase__     )
														with open(self.image_processor_file			,							"""w"""			,							encoding="""utf-8"""     ) as fp:
																					json.dump(UpperCamelCase__			,							UpperCamelCase__     )




							def 		lowerCamelCase_				(							self:				str			,							**UpperCamelCase__:				List[str]     ):
														return BertTokenizer.from_pretrained(self.tmpdirname			,							**UpperCamelCase__     )




							def 		lowerCamelCase_				(							self:				int			,							**UpperCamelCase__:				Tuple     ):
														return ViTImageProcessor.from_pretrained(self.tmpdirname			,							**UpperCamelCase__     )




							def 		lowerCamelCase_				(							self:				Optional[Any]     ):
														shutil.rmtree(self.tmpdirname     )




							def 		lowerCamelCase_				(							self:				Any     ):
														lowerCamelCase__					:	List[Any]         =       [np.random.randint(255			,							size=(3, 30, 400)			,							dtype=np.uinta     )]

														lowerCamelCase__					:	Tuple         =       [Image.fromarray(np.moveaxis(UpperCamelCase__			,							0			,							-1     )     ) for x in image_inputs]

														return image_inputs




							def 		lowerCamelCase_				(							self:				int     ):
														lowerCamelCase__					:	Optional[Any]         =       self.get_tokenizer()
														lowerCamelCase__					:	Dict         =       self.get_image_processor()

														lowerCamelCase__					:	Optional[Any]         =       VisionTextDualEncoderProcessor(tokenizer=UpperCamelCase__			,							image_processor=UpperCamelCase__     )

														processor.save_pretrained(self.tmpdirname     )
														lowerCamelCase__					:	int         =       VisionTextDualEncoderProcessor.from_pretrained(self.tmpdirname     )

														self.assertEqual(processor.tokenizer.get_vocab()			,							tokenizer.get_vocab()     )
														self.assertIsInstance(processor.tokenizer			,							(BertTokenizer, BertTokenizerFast)     )

														self.assertEqual(processor.image_processor.to_json_string()			,							image_processor.to_json_string()     )
														self.assertIsInstance(processor.image_processor			,							UpperCamelCase__     )




							def 		lowerCamelCase_				(							self:				Tuple     ):
														lowerCamelCase__					:	Dict         =       VisionTextDualEncoderProcessor(
														    tokenizer=self.get_tokenizer()			,							image_processor=self.get_image_processor()     )
														processor.save_pretrained(self.tmpdirname     )

														lowerCamelCase__					:	int         =       self.get_tokenizer(bos_token="""(BOS)"""			,							eos_token="""(EOS)"""     )
														lowerCamelCase__					:	List[Any]         =       self.get_image_processor(do_normalize=UpperCamelCase__			,							padding_value=1.0     )

														lowerCamelCase__					:	Tuple         =       VisionTextDualEncoderProcessor.from_pretrained(
														    self.tmpdirname			,							bos_token="""(BOS)"""			,							eos_token="""(EOS)"""			,							do_normalize=UpperCamelCase__			,							padding_value=1.0     )

														self.assertEqual(processor.tokenizer.get_vocab()			,							tokenizer_add_kwargs.get_vocab()     )
														self.assertIsInstance(processor.tokenizer			,							(BertTokenizer, BertTokenizerFast)     )

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




							def 		lowerCamelCase_				(							self:				Union[str, Any]     ):
														lowerCamelCase__					:	Optional[Any]         =       self.get_image_processor()
														lowerCamelCase__					:	Union[str, Any]         =       self.get_tokenizer()

														lowerCamelCase__					:	Any         =       VisionTextDualEncoderProcessor(tokenizer=UpperCamelCase__			,							image_processor=UpperCamelCase__     )

														lowerCamelCase__					:	List[Any]         =       self.prepare_image_inputs()

														lowerCamelCase__					:	List[str]         =       image_processor(UpperCamelCase__			,							return_tensors="""np"""     )
														lowerCamelCase__					:	Optional[Any]         =       processor(images=UpperCamelCase__			,							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 		lowerCamelCase_				(							self:				Optional[int]     ):
														lowerCamelCase__					:	Any         =       self.get_image_processor()
														lowerCamelCase__					:	List[str]         =       self.get_tokenizer()

														lowerCamelCase__					:	List[Any]         =       VisionTextDualEncoderProcessor(tokenizer=UpperCamelCase__			,							image_processor=UpperCamelCase__     )

														lowerCamelCase__					:	List[Any]         =       """lower newer"""

														lowerCamelCase__					:	Union[str, Any]         =       processor(text=UpperCamelCase__     )

														lowerCamelCase__					:	Any         =       tokenizer(UpperCamelCase__     )

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




							def 		lowerCamelCase_				(							self:				Dict     ):
														lowerCamelCase__					:	Optional[Any]         =       self.get_image_processor()
														lowerCamelCase__					:	List[Any]         =       self.get_tokenizer()

														lowerCamelCase__					:	List[Any]         =       VisionTextDualEncoderProcessor(tokenizer=UpperCamelCase__			,							image_processor=UpperCamelCase__     )

														lowerCamelCase__					:	Any         =       """lower newer"""
														lowerCamelCase__					:	Dict         =       self.prepare_image_inputs()

														lowerCamelCase__					:	Tuple         =       processor(text=UpperCamelCase__			,							images=UpperCamelCase__     )

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

														# test if it raises when no input is passed
														with self.assertRaises(UpperCamelCase__     ):
																					processor()




							def 		lowerCamelCase_				(							self:				int     ):
														lowerCamelCase__					:	List[str]         =       self.get_image_processor()
														lowerCamelCase__					:	List[str]         =       self.get_tokenizer()

														lowerCamelCase__					:	int         =       VisionTextDualEncoderProcessor(tokenizer=UpperCamelCase__			,							image_processor=UpperCamelCase__     )

														lowerCamelCase__					:	Dict         =       [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

														lowerCamelCase__					:	Union[str, Any]         =       processor.batch_decode(UpperCamelCase__     )
														lowerCamelCase__					:	Dict         =       tokenizer.batch_decode(UpperCamelCase__     )

														self.assertListEqual(UpperCamelCase__			,							UpperCamelCase__     )




							def 		lowerCamelCase_				(							self:				List[str]     ):
														lowerCamelCase__					:	Any         =       self.get_image_processor()
														lowerCamelCase__					:	Union[str, Any]         =       self.get_tokenizer()

														lowerCamelCase__					:	int         =       VisionTextDualEncoderProcessor(tokenizer=UpperCamelCase__			,							image_processor=UpperCamelCase__     )

														lowerCamelCase__					:	Optional[Any]         =       """lower newer"""
														lowerCamelCase__					:	str         =       self.prepare_image_inputs()

														lowerCamelCase__					:	int         =       processor(text=UpperCamelCase__			,							images=UpperCamelCase__     )

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

