import shutil
import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, BatchEncoding, MBartaaTokenizer, MBartaaTokenizerFast, is_torch_available
from transformers.testing_utils import (
    get_tests_dir,
    nested_simplify,
    require_sentencepiece,
    require_tokenizers,
    require_torch,
    slow,
)

from ...test_tokenization_common import TokenizerTesterMixin


UpperCAmelCase__  :     Optional[Any]   			=get_tests_dir('''fixtures/test_sentencepiece.model''')

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

UpperCAmelCase__  :     Any   			=25_00_04
UpperCAmelCase__  :     Any   			=25_00_20

@require_sentencepiece
@require_tokenizers
class  __A       (  a      ,   unittest.TestCase      ):
				__A								=			MBartaaTokenizer
				__A								=			MBartaaTokenizerFast
				__A								=			True
				__A								=			True


				def _snake_case    (						self							):
								super().setUp()

								# We have a SentencePiece fixture for testing
								lowerCamelCase			=MBartaaTokenizer(UpperCAmelCase_				,	src_lang="""en_XX"""				,	tgt_lang="""ro_RO"""				,	keep_accents=UpperCAmelCase_							)
								tokenizer.save_pretrained(self.tmpdirname							)


				def _snake_case    (						self							):
								lowerCamelCase			="""<s>"""
								lowerCamelCase			=0

								self.assertEqual(self.get_tokenizer()._convert_token_to_id(UpperCAmelCase_							)				,	UpperCAmelCase_							)
								self.assertEqual(self.get_tokenizer()._convert_id_to_token(UpperCAmelCase_							)				,	UpperCAmelCase_							)


				def _snake_case    (						self							):
								lowerCamelCase			=list(self.get_tokenizer().get_vocab().keys()							)

								self.assertEqual(vocab_keys[0]				,	"""<s>"""							)
								self.assertEqual(vocab_keys[1]				,	"""<pad>"""							)
								self.assertEqual(vocab_keys[-1]				,	"""<mask>"""							)
								self.assertEqual(len(UpperCAmelCase_							)				,	1054							)


				def _snake_case    (						self							):
								self.assertEqual(self.get_tokenizer().vocab_size				,	1054							)


				def _snake_case    (						self							):
								lowerCamelCase			=MBartaaTokenizer(UpperCAmelCase_				,	src_lang="""en_XX"""				,	tgt_lang="""ro_RO"""				,	keep_accents=UpperCAmelCase_							)

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

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

								lowerCamelCase			=tokenizer.tokenize("""I was born in 92000, and this is falsé."""							)
								self.assertListEqual(
								    UpperCAmelCase_				,	[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""", """."""]				,	)
								lowerCamelCase			=tokenizer.convert_tokens_to_ids(UpperCAmelCase_							)
								self.assertListEqual(
								    UpperCAmelCase_				,	[
								        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]
								    ]				,	)

								lowerCamelCase			=tokenizer.convert_ids_to_tokens(UpperCAmelCase_							)
								self.assertListEqual(
								    UpperCAmelCase_				,	[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>""", """."""]				,	)


				@slow
				def _snake_case    (						self							):
								# fmt: off
								lowerCamelCase			={"""input_ids""": [[250004, 11062, 82772, 7, 15, 82772, 538, 51529, 237, 17198, 1290, 206, 9, 215175, 1314, 136, 17198, 1290, 206, 9, 56359, 42, 122009, 9, 16466, 16, 87344, 4537, 9, 4717, 78381, 6, 159958, 7, 15, 24480, 618, 4, 527, 22693, 5428, 4, 2777, 24480, 9874, 4, 43523, 594, 4, 803, 18392, 33189, 18, 4, 43523, 24447, 12399, 100, 24955, 83658, 9626, 144057, 15, 839, 22335, 16, 136, 24955, 83658, 83479, 15, 39102, 724, 16, 678, 645, 2789, 1328, 4589, 42, 122009, 115774, 23, 805, 1328, 46876, 7, 136, 53894, 1940, 42227, 41159, 17721, 823, 425, 4, 27512, 98722, 206, 136, 5531, 4970, 919, 17336, 5, 2], [250004, 20080, 618, 83, 82775, 47, 479, 9, 1517, 73, 53894, 333, 80581, 110117, 18811, 5256, 1295, 51, 152526, 297, 7986, 390, 124416, 538, 35431, 214, 98, 15044, 25737, 136, 7108, 43701, 23, 756, 135355, 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], [250004, 581, 63773, 119455, 6, 147797, 88203, 7, 645, 70, 21, 3285, 10269, 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]], """attention_mask""": [[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, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
								# fmt: on

								self.tokenizer_integration_test_util(
								    expected_encoding=UpperCAmelCase_				,	model_name="""facebook/mbart-large-50"""				,	revision="""d3913889c59cd5c9e456b269c376325eabad57e2"""				,	)


				def _snake_case    (						self							):
								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

								lowerCamelCase			=(self.rust_tokenizer_class, """hf-internal-testing/tiny-random-mbart50""", {})
								for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
												with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""							):
																lowerCamelCase			=self.rust_tokenizer_class.from_pretrained(UpperCAmelCase_				,	**UpperCAmelCase_							)
																lowerCamelCase			=self.tokenizer_class.from_pretrained(UpperCAmelCase_				,	**UpperCAmelCase_							)

																lowerCamelCase			=tempfile.mkdtemp()

																lowerCamelCase			=tokenizer_r.save_pretrained(UpperCAmelCase_							)
																lowerCamelCase			=tokenizer_p.save_pretrained(UpperCAmelCase_							)

																# 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							)							)
																lowerCamelCase			=tuple(f for f in tokenizer_r_files if """tokenizer.json""" not in f							)
																self.assertSequenceEqual(UpperCAmelCase_				,	UpperCAmelCase_							)

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

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

																shutil.rmtree(UpperCAmelCase_							)

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

																lowerCamelCase			=tokenizer_r.save_pretrained(UpperCAmelCase_				,	legacy_format=UpperCAmelCase_							)
																lowerCamelCase			=tokenizer_p.save_pretrained(UpperCAmelCase_							)

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

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

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

																shutil.rmtree(UpperCAmelCase_							)

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

																lowerCamelCase			=tokenizer_r.save_pretrained(UpperCAmelCase_				,	legacy_format=UpperCAmelCase_							)
																lowerCamelCase			=tokenizer_p.save_pretrained(UpperCAmelCase_							)

																# 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
																lowerCamelCase			=tokenizer_r.from_pretrained(UpperCAmelCase_							)
																lowerCamelCase			=tokenizer_p.from_pretrained(UpperCAmelCase_							)

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

																shutil.rmtree(UpperCAmelCase_							)




@require_torch
@require_sentencepiece
@require_tokenizers
class  __A       (  unittest.TestCase      ):
				__A								=			"""facebook/mbart-large-50-one-to-many-mmt"""
				__A								=			[
				    """ 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.""",
				]
				__A								=			[
				    """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.""",
				]
				__A								=			[EN_CODE, 82_74, 12_78_73, 2_59_16, 7, 86_22, 20_71, 4_38, 6_74_85, 53, 18_78_95, 23, 5_17_12, 2]


				@classmethod
				def _snake_case    (						cls							):
								lowerCamelCase			=MBartaaTokenizer.from_pretrained(
								    cls.checkpoint_name				,	src_lang="""en_XX"""				,	tgt_lang="""ro_RO"""							)
								lowerCamelCase			=1
								return cls


				def _snake_case    (						self							):
								self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["""ar_AR"""]				,	250001							)
								self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["""en_EN"""]				,	250004							)
								self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["""ro_RO"""]				,	250020							)
								self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["""mr_IN"""]				,	250038							)


				def _snake_case    (						self							):
								lowerCamelCase			=self.tokenizer.batch_encode_plus(self.src_text							).input_ids[0]
								self.assertListEqual(self.expected_src_tokens				,	UpperCAmelCase_							)


				def _snake_case    (						self							):
								self.assertIn(UpperCAmelCase_				,	self.tokenizer.all_special_ids							)
								lowerCamelCase			=[RO_CODE, 884, 9019, 96, 9, 916, 86792, 36, 18743, 15596, 5, 2]
								lowerCamelCase			=self.tokenizer.decode(UpperCAmelCase_				,	skip_special_tokens=UpperCAmelCase_							)
								lowerCamelCase			=self.tokenizer.decode(generated_ids[1:]				,	skip_special_tokens=UpperCAmelCase_							)
								self.assertEqual(UpperCAmelCase_				,	UpperCAmelCase_							)
								self.assertNotIn(self.tokenizer.eos_token				,	UpperCAmelCase_							)


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


				def _snake_case    (						self							):
								self.assertListEqual(self.tokenizer.convert_tokens_to_ids(["""<mask>""", """ar_AR"""]							)				,	[250053, 250001]							)


				def _snake_case    (						self							):
								lowerCamelCase			=tempfile.mkdtemp()
								lowerCamelCase			=self.tokenizer.fairseq_tokens_to_ids
								self.tokenizer.save_pretrained(UpperCAmelCase_							)
								lowerCamelCase			=MBartaaTokenizer.from_pretrained(UpperCAmelCase_							)
								self.assertDictEqual(new_tok.fairseq_tokens_to_ids				,	UpperCAmelCase_							)


				@require_torch
				def _snake_case    (						self							):
								lowerCamelCase			=self.tokenizer(self.src_text				,	text_target=self.tgt_text				,	padding=UpperCAmelCase_				,	return_tensors="""pt"""							)
								lowerCamelCase			=shift_tokens_right(batch["""labels"""]				,	self.tokenizer.pad_token_id							)

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


				@require_torch
				def _snake_case    (						self							):
								lowerCamelCase			=self.tokenizer(
								    self.src_text				,	text_target=self.tgt_text				,	padding=UpperCAmelCase_				,	truncation=UpperCAmelCase_				,	max_length=len(self.expected_src_tokens							)				,	return_tensors="""pt"""				,	)
								lowerCamelCase			=shift_tokens_right(batch["""labels"""]				,	self.tokenizer.pad_token_id							)

								self.assertIsInstance(UpperCAmelCase_				,	UpperCAmelCase_							)

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


				def _snake_case    (						self							):
								lowerCamelCase			=self.tokenizer(self.src_text				,	padding=UpperCAmelCase_				,	truncation=UpperCAmelCase_				,	max_length=3				,	return_tensors="""pt"""							)
								lowerCamelCase			=self.tokenizer(
								    text_target=self.tgt_text				,	padding=UpperCAmelCase_				,	truncation=UpperCAmelCase_				,	max_length=10				,	return_tensors="""pt"""							)
								lowerCamelCase			=targets["""input_ids"""]
								lowerCamelCase			=shift_tokens_right(UpperCAmelCase_				,	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 _snake_case    (						self							):
								lowerCamelCase			=self.tokenizer._build_translation_inputs(
								    """A test"""				,	return_tensors="""pt"""				,	src_lang="""en_XX"""				,	tgt_lang="""ar_AR"""							)

								self.assertEqual(
								    nested_simplify(UpperCAmelCase_							)				,	{
								        # en_XX, A, test, EOS
								        """input_ids""": [[250004, 62, 3034, 2]],
								        """attention_mask""": [[1, 1, 1, 1]],
								        # ar_AR
								        """forced_bos_token_id""": 250001,
								    }				,	)












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


UpperCAmelCase__  :     Union[str, Any]   			=16
UpperCAmelCase__  :     Any   			=32





def        _lowercase   (  _UpperCAmelCase     , _UpperCAmelCase = 16			)  ->					int:
				lowerCamelCase			=AutoTokenizer.from_pretrained("""bert-base-cased"""			)
				lowerCamelCase			=load_dataset("""glue"""     , """mrpc"""			)

				def tokenize_function(_UpperCAmelCase			):
								# max_length=None => use the model max length (it's actually the default)
								lowerCamelCase			=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():
								lowerCamelCase			=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
				lowerCamelCase			=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.
								lowerCamelCase			=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			=16
								elif accelerator.mixed_precision != "no":
												lowerCamelCase			=8
								else:
												lowerCamelCase			=None

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

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

				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

							UpperCAmelCase__  :     Dict   			=mocked_dataloaders  # noqa: F811





def        _lowercase   (  _UpperCAmelCase     , _UpperCAmelCase			)  ->					Optional[int]:
				# For testing only
				if os.environ.get("""TESTING_MOCKED_DATALOADERS"""     , _UpperCAmelCase			) == "1":
								lowerCamelCase			=2
				# New Code #
				lowerCamelCase			=int(args.gradient_accumulation_steps			)
				lowerCamelCase			=int(args.local_sgd_steps			)
				# Initialize accelerator
				lowerCamelCase			=Accelerator(
				    cpu=args.cpu     , mixed_precision=args.mixed_precision     , gradient_accumulation_steps=_UpperCAmelCase			)
				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			=config["""lr"""]
				lowerCamelCase			=int(config["""num_epochs"""]			)
				lowerCamelCase			=int(config["""seed"""]			)
				lowerCamelCase			=int(config["""batch_size"""]			)

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

				set_seed(_UpperCAmelCase			)
				lowerCamelCase	,					lowerCamelCase			=get_dataloaders(_UpperCAmelCase     , _UpperCAmelCase			)
				# Instantiate the model (we build the model here so that the seed also control new weights initialization)
				lowerCamelCase			=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).
				lowerCamelCase			=model.to(accelerator.device			)

				# Instantiate optimizer
				lowerCamelCase			=AdamW(params=model.parameters()     , lr=_UpperCAmelCase			)

				# Instantiate scheduler
				lowerCamelCase			=get_linear_schedule_with_warmup(
				    optimizer=_UpperCAmelCase     , num_warmup_steps=1_00     , num_training_steps=(len(_UpperCAmelCase			) * 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	,					lowerCamelCase	,					lowerCamelCase	,					lowerCamelCase	,					lowerCamelCase			=accelerator.prepare(
				    _UpperCAmelCase     , _UpperCAmelCase     , _UpperCAmelCase     , _UpperCAmelCase     , _UpperCAmelCase			)

				# Now we train the model
				for epoch in range(_UpperCAmelCase			):
								model.train()
								with LocalSGD(
								    accelerator=_UpperCAmelCase     , model=_UpperCAmelCase     , local_sgd_steps=_UpperCAmelCase     , enabled=local_sgd_steps is not None			) as local_sgd:
												for step, batch in enumerate(_UpperCAmelCase			):
																# 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(_UpperCAmelCase			):
																				lowerCamelCase			=model(**_UpperCAmelCase			)
																				lowerCamelCase			=output.loss
																				accelerator.backward(_UpperCAmelCase			)
																				optimizer.step()
																				lr_scheduler.step()
																				optimizer.zero_grad()
																				# LocalSGD-specific line
																				local_sgd.step()

								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():
																lowerCamelCase			=model(**_UpperCAmelCase			)
												lowerCamelCase			=outputs.logits.argmax(dim=-1			)
												lowerCamelCase	,					lowerCamelCase			=accelerator.gather_for_metrics((predictions, batch["""labels"""])			)
												metric.add_batch(
												    predictions=_UpperCAmelCase     , references=_UpperCAmelCase     , )

								lowerCamelCase			=metric.compute()
								# Use accelerator.print to print only on the main process.
								accelerator.print(F"""epoch {epoch}:"""     , _UpperCAmelCase			)





def        _lowercase   (  )  ->					Any:
				lowerCamelCase			=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."""     , )
				# New Code #
				parser.add_argument(
				    """--gradient_accumulation_steps"""     , type=_UpperCAmelCase     , default=1     , help="""The number of minibatches to be ran before gradients are accumulated."""     , )
				parser.add_argument(
				    """--local_sgd_steps"""     , type=_UpperCAmelCase     , 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			=parser.parse_args()
				lowerCamelCase			={"""lr""": 2e-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
				training_function(_UpperCAmelCase     , _UpperCAmelCase			)


if __name__ == "__main__":
							main()





