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

lowerCamelCase__          =		16
lowerCamelCase__          =		32
def  lowerCAmelCase__			(  SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_ = 16     )				->		Dict:
			lowerCAmelCase__      :      Optional[int]         =		AutoTokenizer.from_pretrained('bert-base-cased'     )
			lowerCAmelCase__      :      Tuple         =		load_dataset('glue'       ,							'mrpc'     )

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

			def collate_fn(SCREAMING_SNAKE_CASE_     ):
						# On TPU it's best to pad everything to the same length or training will be very slow.
						lowerCAmelCase__      :      Union[str, Any]         =		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":
									lowerCAmelCase__      :      Tuple         =		16
						elif accelerator.mixed_precision != "no":
									lowerCAmelCase__      :      str         =		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__      :      str         =		DataLoader(
			    tokenized_datasets['train']       ,							shuffle=SCREAMING_SNAKE_CASE_       ,							collate_fn=SCREAMING_SNAKE_CASE_       ,							batch_size=SCREAMING_SNAKE_CASE_     )
			lowerCAmelCase__      :      Dict         =		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

					lowerCamelCase__          =		mocked_dataloaders  # noqa: F811
def  lowerCAmelCase__			(  SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_     )				->		Dict:
			# For testing only
			if os.environ.get('TESTING_MOCKED_DATALOADERS'       ,							SCREAMING_SNAKE_CASE_     ) == "1":
						lowerCAmelCase__      :      List[str]         =		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:
						lowerCAmelCase__      :      Optional[Any]         =		Accelerator(
						    cpu=args.cpu       ,							mixed_precision=args.mixed_precision       ,							log_with='all'       ,							project_dir=args.project_dir     )
			else:
						lowerCAmelCase__      :      Any         =		Accelerator(cpu=args.cpu       ,							mixed_precision=args.mixed_precision     )
			# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
			lowerCAmelCase__      :      Tuple         =		config['lr']
			lowerCAmelCase__      :      Union[str, Any]         =		int(config['num_epochs']     )
			lowerCAmelCase__      :      Optional[Any]         =		int(config['seed']     )
			lowerCAmelCase__      :      Optional[Any]         =		int(config['batch_size']     )
			set_seed(SCREAMING_SNAKE_CASE_     )

			lowerCAmelCase__      ,				lowerCAmelCase__      :      List[Any]         =		get_dataloaders(SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_     )
			lowerCAmelCase__      :      str         =		evaluate.load('glue'       ,							'mrpc'     )

			# If the batch size is too big we use gradient accumulation
			lowerCAmelCase__      :      Dict         =		1
			if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
						lowerCAmelCase__      :      str         =		batch_size // MAX_GPU_BATCH_SIZE
						lowerCAmelCase__      :      Optional[int]         =		MAX_GPU_BATCH_SIZE

			# Instantiate the model (we build the model here so that the seed also control new weights initialization)
			lowerCAmelCase__      :      Any         =		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__      :      Tuple         =		model.to(accelerator.device     )

			# Instantiate optimizer
			lowerCAmelCase__      :      Tuple         =		AdamW(params=model.parameters()       ,							lr=SCREAMING_SNAKE_CASE_     )

			# Instantiate scheduler
			lowerCAmelCase__      :      List[str]         =		get_linear_schedule_with_warmup(
			    optimizer=SCREAMING_SNAKE_CASE_       ,							num_warmup_steps=100       ,							num_training_steps=(len(SCREAMING_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.
			lowerCAmelCase__      ,				lowerCAmelCase__      ,				lowerCAmelCase__      ,				lowerCAmelCase__      ,				lowerCAmelCase__      :      List[Any]         =		accelerator.prepare(
			    SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_     )

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

			# Now we train the model
			for epoch in range(SCREAMING_SNAKE_CASE_     ):
						model.train()
						# New Code #
						# For our tracking example, we will log the total loss of each epoch
						if args.with_tracking:
									lowerCAmelCase__      :      Tuple         =		0
						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     )
									lowerCAmelCase__      :      Union[str, Any]         =		model(**SCREAMING_SNAKE_CASE_     )
									lowerCAmelCase__      :      Optional[int]         =		outputs.loss
									# New Code #
									if args.with_tracking:
												total_loss += loss.detach().float()
									lowerCAmelCase__      :      Union[str, Any]         =		loss / gradient_accumulation_steps
									accelerator.backward(SCREAMING_SNAKE_CASE_     )
									if step % gradient_accumulation_steps == 0:
												optimizer.step()
												lr_scheduler.step()
												optimizer.zero_grad()

						model.eval()
						for step, batch in enumerate(SCREAMING_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():
												lowerCAmelCase__      :      List[Any]         =		model(**SCREAMING_SNAKE_CASE_     )
									lowerCAmelCase__      :      List[str]         =		outputs.logits.argmax(dim=-1     )
									lowerCAmelCase__      ,				lowerCAmelCase__      :      List[Any]         =		accelerator.gather_for_metrics((predictions, batch['labels'])     )
									metric.add_batch(
									    predictions=SCREAMING_SNAKE_CASE_       ,							references=SCREAMING_SNAKE_CASE_       ,							)

						lowerCAmelCase__      :      int         =		metric.compute()
						# Use accelerator.print to print only on the main process.
						accelerator.print(F'''epoch {epoch}:'''       ,							SCREAMING_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(SCREAMING_SNAKE_CASE_     ),
									        'epoch': epoch,
									    }       ,							step=SCREAMING_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  lowerCAmelCase__			(  )				->		List[str]:
			lowerCAmelCase__      :      List[str]         =		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.'       ,							)
			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=SCREAMING_SNAKE_CASE_       ,							default='logs'       ,							help='Location on where to store experiment tracking logs` and relevent project information'       ,							)
			lowerCAmelCase__      :      List[Any]         =		parser.parse_args()
			lowerCAmelCase__      :      List[Any]         =		{'lr': 2e-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
			training_function(SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_     )


if __name__ == "__main__":
					main()

import json
import os
import unittest

from transformers import BatchEncoding, MvpTokenizer, MvpTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin, filter_roberta_detectors







@require_tokenizers
class      A__							(     __magic_name__						,   unittest.TestCase	):
				lowercase			    =  MvpTokenizer
				lowercase			    =  MvpTokenizerFast
				lowercase			    =  True
				lowercase			    =  filter_roberta_detectors






				def 							_lowerCamelCase		(						self					:      int   ):






							'''simple docstring'''







							super().setUp()
							lowerCAmelCase__      :      Dict         =		[
							    'l',
							    'o',
							    'w',
							    'e',
							    'r',
							    's',
							    't',
							    'i',
							    'd',
							    'n',
							    '\u0120',
							    '\u0120l',
							    '\u0120n',
							    '\u0120lo',
							    '\u0120low',
							    'er',
							    '\u0120lowest',
							    '\u0120newer',
							    '\u0120wider',
							    '<unk>',
							]
							lowerCAmelCase__      :      Any         =		dict(zip(a    ,     range(len(a   )   )   )   )
							lowerCAmelCase__      :      Union[str, Any]         =		['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
							lowerCAmelCase__      :      Any         =		{'unk_token': '<unk>'}

							lowerCAmelCase__      :      int         =		os.path.join(self.tmpdirname    ,     VOCAB_FILES_NAMES['vocab_file']   )
							lowerCAmelCase__      :      Optional[int]         =		os.path.join(self.tmpdirname    ,     VOCAB_FILES_NAMES['merges_file']   )
							with open(self.vocab_file    ,     'w'    ,     encoding='utf-8'   ) as fp:
										fp.write(json.dumps(a   ) + '\n'   )
							with open(self.merges_file    ,     'w'    ,     encoding='utf-8'   ) as fp:
										fp.write('\n'.join(a   )   )






				def 							_lowerCamelCase		(						self					:      str    ,     **a					:      Tuple   ):






							'''simple docstring'''







							kwargs.update(self.special_tokens_map   )
							return self.tokenizer_class.from_pretrained(self.tmpdirname    ,     **a   )






				def 							_lowerCamelCase		(						self					:      Dict    ,     **a					:      Optional[int]   ):






							'''simple docstring'''







							kwargs.update(self.special_tokens_map   )
							return self.rust_tokenizer_class.from_pretrained(self.tmpdirname    ,     **a   )






				def 							_lowerCamelCase		(						self					:      Tuple    ,     a					:      Dict   ):






							'''simple docstring'''







							return "lower newer", "lower newer"






				@cached_property
				def 							_lowerCamelCase		(						self					:      List[str]   ):






							'''simple docstring'''







							return MvpTokenizer.from_pretrained('RUCAIBox/mvp'   )






				@cached_property
				def 							_lowerCamelCase		(						self					:      Dict   ):






							'''simple docstring'''







							return MvpTokenizerFast.from_pretrained('RUCAIBox/mvp'   )






				@require_torch
				def 							_lowerCamelCase		(						self					:      Optional[int]   ):






							'''simple docstring'''







							lowerCAmelCase__      :      List[Any]         =		['A long paragraph for summarization.', 'Another paragraph for summarization.']
							lowerCAmelCase__      :      List[Any]         =		[0, 250, 251, 17_818, 13, 39_186, 1_938, 4, 2]

							for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
										lowerCAmelCase__      :      int         =		tokenizer(a    ,     max_length=len(a   )    ,     padding=a    ,     return_tensors='pt'   )
										self.assertIsInstance(a    ,     a   )

										self.assertEqual((2, 9)    ,     batch.input_ids.shape   )
										self.assertEqual((2, 9)    ,     batch.attention_mask.shape   )
										lowerCAmelCase__      :      List[Any]         =		batch.input_ids.tolist()[0]
										self.assertListEqual(a    ,     a   )
										# Test that special tokens are reset






				@require_torch
				def 							_lowerCamelCase		(						self					:      str   ):






							'''simple docstring'''







							lowerCAmelCase__      :      List[Any]         =		['A long paragraph for summarization.', 'Another paragraph for summarization.']
							for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
										lowerCAmelCase__      :      Any         =		tokenizer(a    ,     padding=a    ,     return_tensors='pt'   )
										# check if input_ids are returned and no labels
										self.assertIn('input_ids'    ,     a   )
										self.assertIn('attention_mask'    ,     a   )
										self.assertNotIn('labels'    ,     a   )
										self.assertNotIn('decoder_attention_mask'    ,     a   )






				@require_torch
				def 							_lowerCamelCase		(						self					:      Optional[int]   ):






							'''simple docstring'''







							lowerCAmelCase__      :      Union[str, Any]         =		[
							    'Summary of the text.',
							    'Another summary.',
							]
							for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
										lowerCAmelCase__      :      Tuple         =		tokenizer(text_target=a    ,     max_length=32    ,     padding='max_length'    ,     return_tensors='pt'   )
										self.assertEqual(32    ,     targets['input_ids'].shape[1]   )






				@require_torch
				def 							_lowerCamelCase		(						self					:      Optional[int]   ):






							'''simple docstring'''







							for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
										lowerCAmelCase__      :      str         =		tokenizer(
										    ['I am a small frog' * 1_024, 'I am a small frog']    ,     padding=a    ,     truncation=a    ,     return_tensors='pt'   )
										self.assertIsInstance(a    ,     a   )
										self.assertEqual(batch.input_ids.shape    ,     (2, 1_024)   )






				@require_torch
				def 							_lowerCamelCase		(						self					:      Any   ):






							'''simple docstring'''







							lowerCAmelCase__      :      Union[str, Any]         =		['A long paragraph for summarization.']
							lowerCAmelCase__      :      Any         =		[
							    'Summary of the text.',
							]
							for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
										lowerCAmelCase__      :      List[Any]         =		tokenizer(a    ,     text_target=a    ,     return_tensors='pt'   )
										lowerCAmelCase__      :      Optional[int]         =		inputs['input_ids']
										lowerCAmelCase__      :      str         =		inputs['labels']
										self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item()   )
										self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item()   )
										self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item()   )
										self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item()   )






				def 							_lowerCamelCase		(						self					:      Any   ):






							'''simple docstring'''







							pass






				def 							_lowerCamelCase		(						self					:      Tuple   ):






							'''simple docstring'''







							for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
										with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''   ):
													lowerCAmelCase__      :      str         =		self.rust_tokenizer_class.from_pretrained(a    ,     **a   )
													lowerCAmelCase__      :      List[str]         =		self.tokenizer_class.from_pretrained(a    ,     **a   )
													lowerCAmelCase__      :      Optional[int]         =		'A, <mask> AllenNLP sentence.'
													lowerCAmelCase__      :      int         =		tokenizer_r.encode_plus(a    ,     add_special_tokens=a    ,     return_token_type_ids=a   )
													lowerCAmelCase__      :      Optional[int]         =		tokenizer_p.encode_plus(a    ,     add_special_tokens=a    ,     return_token_type_ids=a   )

													# token_type_ids should put 0 everywhere
													self.assertEqual(sum(tokens_r['token_type_ids']   )    ,     sum(tokens_p['token_type_ids']   )   )

													# attention_mask should put 1 everywhere, so sum over length should be 1
													self.assertEqual(
													    sum(tokens_r['attention_mask']   ) / len(tokens_r['attention_mask']   )    ,     sum(tokens_p['attention_mask']   ) / len(tokens_p['attention_mask']   )    ,     )

													lowerCAmelCase__      :      List[Any]         =		tokenizer_r.convert_ids_to_tokens(tokens_r['input_ids']   )
													lowerCAmelCase__      :      Dict         =		tokenizer_p.convert_ids_to_tokens(tokens_p['input_ids']   )

													# Rust correctly handles the space before the mask while python doesnt
													self.assertSequenceEqual(tokens_p['input_ids']    ,     [0, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 4, 2]   )
													self.assertSequenceEqual(tokens_r['input_ids']    ,     [0, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 4, 2]   )

													self.assertSequenceEqual(
													    a    ,     ['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>']   )
													self.assertSequenceEqual(
													    a    ,     ['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>']   )