




'''simple docstring'''





import shutil
import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, BatchEncoding, MBartTokenizer, MBartTokenizerFast, is_torch_available
from transformers.testing_utils import (
    get_tests_dir,
    nested_simplify,
    require_sentencepiece,
    require_tokenizers,
    require_torch,
)

from ...test_tokenization_common import TokenizerTesterMixin


_SCREAMING_SNAKE_CASE							:   List[Any]      		= get_tests_dir("fixtures/test_sentencepiece.model")


if is_torch_available():
		from transformers.models.mbart.modeling_mbart import shift_tokens_right

_SCREAMING_SNAKE_CASE							:   int      		= 25_0004
_SCREAMING_SNAKE_CASE							:   List[str]      		= 25_0020




@require_sentencepiece
@require_tokenizers
class  _snake_case		(	lowercase_						,						unittest.TestCase							):
			lowerCAmelCase_     :      str						       =    MBartTokenizer
			lowerCAmelCase_     :      List[Any]						       =    MBartTokenizerFast
			lowerCAmelCase_     :      Tuple						       =    True
			lowerCAmelCase_     :      Any						       =    True




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

										'''simple docstring'''




										super().setUp()

										# We have a SentencePiece fixture for testing
										snake_case_						=      MBartTokenizer(a__				,							keep_accents=a__							)
										tokenizer.save_pretrained(self.tmpdirname							)




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

										'''simple docstring'''




										snake_case_						=      MBartTokenizer(a__				,							keep_accents=a__							)

										snake_case_						=      tokenizer.tokenize("This is a test"							)
										self.assertListEqual(a__				,							["▁This", "▁is", "▁a", "▁t", "est"]							)

										self.assertListEqual(
										    tokenizer.convert_tokens_to_ids(a__							)				,							[value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]]				,							)

										snake_case_						=      tokenizer.tokenize("I was born in 92000, and this is falsé."							)
										self.assertListEqual(
										    a__				,							[
										        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",
										        ".",
										    ]				,							)
										snake_case_						=      tokenizer.convert_tokens_to_ids(a__							)
										self.assertListEqual(
										    a__				,							[
										        value + tokenizer.fairseq_offset
										        for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
										        #                                       ^ unk: 2 + 1 = 3                  unk: 2 + 1 = 3 ^
										    ]				,							)

										snake_case_						=      tokenizer.convert_ids_to_tokens(a__							)
										self.assertListEqual(
										    a__				,							[
										        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>",
										        ".",
										    ]				,							)




			def    lowerCAmelCase__				(					self							)				->				int:

										'''simple docstring'''




										if not self.test_slow_tokenizer:
																	# as we don't have a slow version, we can't compare the outputs between slow and fast versions
																	return

										snake_case_						=      (self.rust_tokenizer_class, "hf-internal-testing/tiny-random-mbart", {})
										for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
																	with self.subTest(F'{tokenizer.__class__.__name__} ({pretrained_name})'							):
																								snake_case_						=      self.rust_tokenizer_class.from_pretrained(a__				,							**a__							)
																								snake_case_						=      self.tokenizer_class.from_pretrained(a__				,							**a__							)

																								snake_case_						=      tempfile.mkdtemp()

																								snake_case_						=      tokenizer_r.save_pretrained(a__							)
																								snake_case_						=      tokenizer_p.save_pretrained(a__							)

																								# Checks it save with the same files + the tokenizer.json file for the fast one
																								self.assertTrue(any("tokenizer.json" in f for f in tokenizer_r_files							)							)
																								snake_case_						=      tuple(f for f in tokenizer_r_files if "tokenizer.json" not in f							)
																								self.assertSequenceEqual(a__				,							a__							)

																								# Checks everything loads correctly in the same way
																								snake_case_						=      tokenizer_r.from_pretrained(a__							)
																								snake_case_						=      tokenizer_p.from_pretrained(a__							)

																								# Check special tokens are set accordingly on Rust and Python
																								for key in tokenizer_pp.special_tokens_map:
																															self.assertTrue(hasattr(a__				,							a__							)							)
																															# self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
																															# self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))

																								shutil.rmtree(a__							)

																								# Save tokenizer rust, legacy_format=True
																								snake_case_						=      tempfile.mkdtemp()

																								snake_case_						=      tokenizer_r.save_pretrained(a__				,							legacy_format=a__							)
																								snake_case_						=      tokenizer_p.save_pretrained(a__							)

																								# Checks it save with the same files
																								self.assertSequenceEqual(a__				,							a__							)

																								# Checks everything loads correctly in the same way
																								snake_case_						=      tokenizer_r.from_pretrained(a__							)
																								snake_case_						=      tokenizer_p.from_pretrained(a__							)

																								# Check special tokens are set accordingly on Rust and Python
																								for key in tokenizer_pp.special_tokens_map:
																															self.assertTrue(hasattr(a__				,							a__							)							)

																								shutil.rmtree(a__							)

																								# Save tokenizer rust, legacy_format=False
																								snake_case_						=      tempfile.mkdtemp()

																								snake_case_						=      tokenizer_r.save_pretrained(a__				,							legacy_format=a__							)
																								snake_case_						=      tokenizer_p.save_pretrained(a__							)

																								# Checks it saved the tokenizer.json file
																								self.assertTrue(any("tokenizer.json" in f for f in tokenizer_r_files							)							)

																								# Checks everything loads correctly in the same way
																								snake_case_						=      tokenizer_r.from_pretrained(a__							)
																								snake_case_						=      tokenizer_p.from_pretrained(a__							)

																								# Check special tokens are set accordingly on Rust and Python
																								for key in tokenizer_pp.special_tokens_map:
																															self.assertTrue(hasattr(a__				,							a__							)							)

																								shutil.rmtree(a__							)




@require_torch
@require_sentencepiece
@require_tokenizers
class  _snake_case		(	unittest.TestCase							):
			lowerCAmelCase_     :      Optional[Any]						       =    "facebook/mbart-large-en-ro"
			lowerCAmelCase_     :      List[Any]						       =    [
			    " UN Chief Says There Is No Military Solution in Syria",
			    " Secretary-General Ban Ki-moon says his response to Russia's stepped up military support for Syria is that \"there is no military solution\" to the nearly five-year conflict and more weapons will only worsen the violence and misery for millions of people.",
			]
			lowerCAmelCase_     :      Any						       =    [
			    "Şeful ONU declară că nu există o soluţie militară în Siria",
			    "Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al Rusiei"
			    " pentru Siria este că \"nu există o soluţie militară\" la conflictul de aproape cinci ani şi că noi arme nu vor"
			    " face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.",
			]
			lowerCAmelCase_     :      Optional[Any]						       =    [8274, 12_7873, 2_5916, 7, 8622, 2071, 438, 6_7485, 53, 18_7895, 23, 5_1712, 2, EN_CODE]




			@classmethod
			def    lowerCAmelCase__				(					cls							)				->				int:

										'''simple docstring'''




										snake_case_						=      MBartTokenizer.from_pretrained(
										    cls.checkpoint_name				,							src_lang="en_XX"				,							tgt_lang="ro_RO"							)
										snake_case_						=      1
										return cls




			def    lowerCAmelCase__				(					self							)				->				Tuple:

										'''simple docstring'''




										self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["ar_AR"]				,							250_001							)
										self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["en_EN"]				,							250_004							)
										self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["ro_RO"]				,							250_020							)




			def    lowerCAmelCase__				(					self							)				->				Optional[Any]:

										'''simple docstring'''




										snake_case_						=      self.tokenizer.batch_encode_plus(self.src_text							).input_ids[0]
										self.assertListEqual(self.expected_src_tokens				,							a__							)




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

										'''simple docstring'''




										self.assertIn(a__				,							self.tokenizer.all_special_ids							)
										snake_case_						=      [RO_CODE, 884, 9_019, 96, 9, 916, 86_792, 36, 18_743, 15_596, 5, 2]
										snake_case_						=      self.tokenizer.decode(a__				,							skip_special_tokens=a__							)
										snake_case_						=      self.tokenizer.decode(generated_ids[1:]				,							skip_special_tokens=a__							)
										self.assertEqual(a__				,							a__							)
										self.assertNotIn(self.tokenizer.eos_token				,							a__							)




			def    lowerCAmelCase__				(					self							)				->				Dict:

										'''simple docstring'''




										snake_case_						=      ["this is gunna be a long sentence " * 20]
										assert isinstance(src_text[0]				,							a__							)
										snake_case_						=      10
										snake_case_						=      self.tokenizer(a__				,							max_length=a__				,							truncation=a__							).input_ids[0]
										self.assertEqual(ids[-2]				,							2							)
										self.assertEqual(ids[-1]				,							a__							)
										self.assertEqual(len(a__							)				,							a__							)




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

										'''simple docstring'''




										self.assertListEqual(self.tokenizer.convert_tokens_to_ids(["<mask>", "ar_AR"]							)				,							[250_026, 250_001]							)




			def    lowerCAmelCase__				(					self							)				->				Optional[Any]:

										'''simple docstring'''




										snake_case_						=      tempfile.mkdtemp()
										snake_case_						=      self.tokenizer.fairseq_tokens_to_ids
										self.tokenizer.save_pretrained(a__							)
										snake_case_						=      MBartTokenizer.from_pretrained(a__							)
										self.assertDictEqual(new_tok.fairseq_tokens_to_ids				,							a__							)




			@require_torch
			def    lowerCAmelCase__				(					self							)				->				Any:

										'''simple docstring'''




										snake_case_						=      self.tokenizer(self.src_text				,							text_target=self.tgt_text				,							padding=a__				,							return_tensors="pt"							)
										snake_case_						=      shift_tokens_right(batch["labels"]				,							self.tokenizer.pad_token_id							)

										# fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4
										assert batch.input_ids[1][-2:].tolist() == [2, EN_CODE]
										assert batch.decoder_input_ids[1][0].tolist() == RO_CODE
										assert batch.decoder_input_ids[1][-1] == 2
										assert batch.labels[1][-2:].tolist() == [2, RO_CODE]




			@require_torch
			def    lowerCAmelCase__				(					self							)				->				Union[str, Any]:

										'''simple docstring'''




										snake_case_						=      self.tokenizer(
										    self.src_text				,							text_target=self.tgt_text				,							padding=a__				,							truncation=a__				,							max_length=len(self.expected_src_tokens							)				,							return_tensors="pt"				,							)

										snake_case_						=      shift_tokens_right(batch["labels"]				,							self.tokenizer.pad_token_id							)

										self.assertIsInstance(a__				,							a__							)

										self.assertEqual((2, 14)				,							batch.input_ids.shape							)
										self.assertEqual((2, 14)				,							batch.attention_mask.shape							)
										snake_case_						=      batch.input_ids.tolist()[0]
										self.assertListEqual(self.expected_src_tokens				,							a__							)
										self.assertEqual(2				,							batch.decoder_input_ids[0, -1]							)  # EOS
										# Test that special tokens are reset
										self.assertEqual(self.tokenizer.prefix_tokens				,							[]							)
										self.assertEqual(self.tokenizer.suffix_tokens				,							[self.tokenizer.eos_token_id, EN_CODE]							)




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

										'''simple docstring'''




										snake_case_						=      self.tokenizer(self.src_text				,							padding=a__				,							truncation=a__				,							max_length=3				,							return_tensors="pt"							)
										snake_case_						=      self.tokenizer(
										    text_target=self.tgt_text				,							padding=a__				,							truncation=a__				,							max_length=10				,							return_tensors="pt"							)
										snake_case_						=      targets["input_ids"]
										snake_case_						=      shift_tokens_right(a__				,							self.tokenizer.pad_token_id							)

										self.assertEqual(batch.input_ids.shape[1]				,							3							)
										self.assertEqual(batch.decoder_input_ids.shape[1]				,							10							)




			@require_torch
			def    lowerCAmelCase__				(					self							)				->				List[Any]:

										'''simple docstring'''




										snake_case_						=      self.tokenizer._build_translation_inputs(
										    "A test"				,							return_tensors="pt"				,							src_lang="en_XX"				,							tgt_lang="ar_AR"							)

										self.assertEqual(
										    nested_simplify(a__							)				,							{
										        # A, test, EOS, en_XX
										        "input_ids": [[62, 3_034, 2, 250_004]],
										        "attention_mask": [[1, 1, 1, 1]],
										        # ar_AR
										        "forced_bos_token_id": 250_001,
										    }				,							)







import argparse

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


lowerCAmelCase							:  Optional[Any]   	=						16
lowerCAmelCase							:  List[str]   	=						32







def 			A_      (    _UpperCAmelCase				,	_UpperCAmelCase = 16    ):
						SCREAMING_SNAKE_CASE_: Tuple  		=       AutoTokenizer.from_pretrained("bert-base-cased"    )
						SCREAMING_SNAKE_CASE_: List[Any]  		=       load_dataset("glue"				,	"mrpc"    )

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

						def collate_fn(_UpperCAmelCase    ):
												# On TPU it's best to pad everything to the same length or training will be very slow.
												SCREAMING_SNAKE_CASE_: List[str]  		=       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":
																		SCREAMING_SNAKE_CASE_: List[Any]  		=       16
												elif accelerator.mixed_precision != "no":
																		SCREAMING_SNAKE_CASE_: Optional[Any]  		=       8
												else:
																		SCREAMING_SNAKE_CASE_: List[str]  		=       None

												return tokenizer.pad(
												    _UpperCAmelCase				,	padding="longest"				,	max_length=_UpperCAmelCase				,	pad_to_multiple_of=_UpperCAmelCase				,	return_tensors="pt"				,	)

						# Instantiate dataloaders.
						SCREAMING_SNAKE_CASE_: Dict  		=       DataLoader(
						    tokenized_datasets["train"]				,	shuffle=_UpperCAmelCase				,	collate_fn=_UpperCAmelCase				,	batch_size=_UpperCAmelCase				,	drop_last=_UpperCAmelCase    )
						SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       DataLoader(
						    tokenized_datasets["validation"]				,	shuffle=_UpperCAmelCase				,	collate_fn=_UpperCAmelCase				,	batch_size=_UpperCAmelCase				,	drop_last=(accelerator.mixed_precision == "fp8")				,	)

						return train_dataloader, eval_dataloader







def 			A_      (    _UpperCAmelCase				,	_UpperCAmelCase    ):
						# Initialize accelerator
						SCREAMING_SNAKE_CASE_: str  		=       Accelerator(cpu=args.cpu				,	mixed_precision=args.mixed_precision    )
						# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
						SCREAMING_SNAKE_CASE_: int  		=       config["lr"]
						SCREAMING_SNAKE_CASE_: Any  		=       int(config["num_epochs"]    )
						SCREAMING_SNAKE_CASE_: Optional[int]  		=       int(config["seed"]    )
						SCREAMING_SNAKE_CASE_: List[Any]  		=       int(config["batch_size"]    )

						SCREAMING_SNAKE_CASE_: List[str]  		=       evaluate.load("glue"				,	"mrpc"    )

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

						set_seed(_UpperCAmelCase    )
						SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: str  		=       get_dataloaders(_UpperCAmelCase				,	_UpperCAmelCase    )
						# Instantiate the model (we build the model here so that the seed also control new weights initialization)
						SCREAMING_SNAKE_CASE_: List[Any]  		=       AutoModelForSequenceClassification.from_pretrained("bert-base-cased"				,	return_dict=_UpperCAmelCase    )

						# 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).
						SCREAMING_SNAKE_CASE_: Tuple  		=       model.to(accelerator.device    )
						# Instantiate optimizer
						SCREAMING_SNAKE_CASE_: Optional[int]  		=       AdamW(params=model.parameters()				,	lr=_UpperCAmelCase    )

						# Instantiate scheduler
						SCREAMING_SNAKE_CASE_: Optional[int]  		=       get_linear_schedule_with_warmup(
						    optimizer=_UpperCAmelCase				,	num_warmup_steps=1_00				,	num_training_steps=(len(_UpperCAmelCase    ) * 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.

						SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: Optional[int]  		=       accelerator.prepare(
						    _UpperCAmelCase				,	_UpperCAmelCase				,	_UpperCAmelCase				,	_UpperCAmelCase				,	_UpperCAmelCase    )

						# Now we train the model
						for epoch in range(_UpperCAmelCase    ):
												model.train()
												for step, batch in enumerate(_UpperCAmelCase    ):
																		# We could avoid this line since we set the accelerator with `device_placement=True`.
																		batch.to(accelerator.device    )
																		SCREAMING_SNAKE_CASE_: Tuple  		=       model(**_UpperCAmelCase    )
																		SCREAMING_SNAKE_CASE_: Optional[int]  		=       outputs.loss
																		SCREAMING_SNAKE_CASE_: Tuple  		=       loss / gradient_accumulation_steps
																		accelerator.backward(_UpperCAmelCase    )
																		if step % gradient_accumulation_steps == 0:
																								optimizer.step()
																								lr_scheduler.step()
																								optimizer.zero_grad()

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

												SCREAMING_SNAKE_CASE_: List[Any]  		=       metric.compute()
												# Use accelerator.print to print only on the main process.
												accelerator.print(f"epoch {epoch}:"				,	_UpperCAmelCase    )







def 			A_      (    ):
						SCREAMING_SNAKE_CASE_: Any  		=       argparse.ArgumentParser(description="Simple example of training script."    )
						parser.add_argument(
						    "--mixed_precision"				,	type=_UpperCAmelCase				,	default=_UpperCAmelCase				,	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."    )
						SCREAMING_SNAKE_CASE_: Optional[Any]  		=       parser.parse_args()
						SCREAMING_SNAKE_CASE_: Optional[int]  		=       {"lr": 2e-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
						training_function(_UpperCAmelCase				,	_UpperCAmelCase    )


if __name__ == "__main__":
						main()






