






import tempfile
import unittest
from pathlib import Path
from shutil import copyfile

from transformers import MaMaaaTokenizer, is_torch_available
from transformers.testing_utils import (
    get_tests_dir,
    nested_simplify,
    require_sentencepiece,
    require_tokenizers,
    require_torch,
    slow,
)
from transformers.utils import is_sentencepiece_available


if is_sentencepiece_available():
				from transformers.models.mam_aaa.tokenization_mam_aaa import VOCAB_FILES_NAMES, save_json

from ...test_tokenization_common import TokenizerTesterMixin


if is_sentencepiece_available():
				A        =			get_tests_dir('fixtures/test_sentencepiece.model')


if is_torch_available():
				from transformers.models.mam_aaa.modeling_mam_aaa import shift_tokens_right

A        =			12_8022
A        =			12_8028


@require_sentencepiece
class 							__a							(  __A ,	unittest.TestCase						):





		'''simple docstring'''



		UpperCAmelCase__      :				Dict				    =						MaMaaaTokenizer
		UpperCAmelCase__      :				List[Any]				    =						False
		UpperCAmelCase__      :				Tuple				    =						False
		UpperCAmelCase__      :				Union[str, Any]				    =						True


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

						SCREAMING_SNAKE_CASE_   :	Dict													=   ['</s>', '<unk>', '▁This', '▁is', '▁a', '▁t', 'est', '\u0120', '<pad>']
						SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   dict(zip(UpperCamelCase__ ,    range(len(UpperCamelCase__       )       )       )       )
						SCREAMING_SNAKE_CASE_   :	List[Any]													=   Path(self.tmpdirname       )
						save_json(UpperCamelCase__ ,    save_dir / VOCAB_FILES_NAMES['vocab_file']       )
						if not (save_dir / VOCAB_FILES_NAMES["spm_file"]).exists():
										copyfile(UpperCamelCase__ ,    save_dir / VOCAB_FILES_NAMES['spm_file']       )

						SCREAMING_SNAKE_CASE_   :	Any													=   MaMaaaTokenizer.from_pretrained(self.tmpdirname       )
						tokenizer.save_pretrained(self.tmpdirname       )


		def   __snake_case			(						self ,    **UpperCamelCase__       ):
						return MaMaaaTokenizer.from_pretrained(self.tmpdirname ,    **UpperCamelCase__       )


		def   __snake_case			(						self ,    UpperCamelCase__       ):
						return (
						    "This is a test",
						    "This is a test",
						)


		def   __snake_case			(						self       ):
						SCREAMING_SNAKE_CASE_   :	List[str]													=   '</s>'
						SCREAMING_SNAKE_CASE_   :	Dict													=   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       ):
						SCREAMING_SNAKE_CASE_   :	List[Any]													=   self.get_tokenizer()
						SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   list(tokenizer.get_vocab().keys()       )

						self.assertEqual(vocab_keys[0] ,    '</s>'       )
						self.assertEqual(vocab_keys[1] ,    '<unk>'       )
						self.assertEqual(vocab_keys[-1] ,    '<s>'       )
						self.assertEqual(len(UpperCamelCase__       ) ,    tokenizer.vocab_size + len(tokenizer.get_added_vocab()       )       )


		@unittest.skip('Skip this test while all models are still to be uploaded.'       )
		def   __snake_case			(						self       ):
						pass


		def   __snake_case			(						self       ):
						SCREAMING_SNAKE_CASE_   :	int													=   self.get_tokenizer()

						SCREAMING_SNAKE_CASE_   :	Optional[int]													=   tokenizer.tokenize('This is a test'       )
						self.assertListEqual(UpperCamelCase__ ,    ['▁This', '▁is', '▁a', '▁t', 'est']       )

						self.assertListEqual(
						    tokenizer.convert_tokens_to_ids(UpperCamelCase__       ) ,    [2, 3, 4, 5, 6] ,    )

						SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   tokenizer.convert_ids_to_tokens([2, 3, 4, 5, 6]       )
						self.assertListEqual(UpperCamelCase__ ,    ['▁This', '▁is', '▁a', '▁t', 'est']       )

						SCREAMING_SNAKE_CASE_   :	Any													=   tokenizer.convert_tokens_to_string(UpperCamelCase__       )
						self.assertEqual(UpperCamelCase__ ,    'This is a test'       )






		@slow
		def   __snake_case			(						self       ):
						# fmt: off
						SCREAMING_SNAKE_CASE_   :	str													=   {'input_ids': [[128022, 110108, 397, 11, 38272, 2247, 124811, 285, 18105, 1586, 207, 7, 39534, 4428, 397, 1019, 18105, 1586, 207, 7, 41337, 16786, 241, 7, 20214, 17, 125690, 10398, 7, 44378, 58069, 68342, 7798, 7343, 11, 299, 33310, 4, 158, 37350, 94077, 4569, 299, 33310, 90, 4, 52840, 290, 4, 31270, 112, 299, 682, 4, 52840, 39953, 14079, 193, 52519, 90894, 17894, 120697, 11, 40445, 551, 17, 1019, 52519, 90894, 17756, 963, 11, 40445, 480, 17, 9792, 1120, 5173, 1393, 6240, 16786, 241, 120996, 28, 1245, 1393, 118240, 11123, 1019, 93612, 2691, 10618, 98058, 120409, 1928, 279, 4, 40683, 367, 178, 207, 1019, 103, 103121, 506, 65296, 5, 2], [128022, 21217, 367, 117, 125450, 128, 719, 7, 7308, 40, 93612, 12669, 1116, 16704, 71, 17785, 3699, 15592, 35, 144, 9584, 241, 11943, 713, 950, 799, 2247, 88427, 150, 149, 118813, 120706, 1019, 106906, 81518, 28, 1224, 22799, 397, 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], [128022, 1658, 123311, 5155, 5578, 4722, 279, 14947, 2366, 1120, 1197, 14, 1348, 9232, 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, 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, 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], [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, 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/m2m100_418M' ,    revision='c168bae485c864188cf9aa0e4108b0b6934dc91e' ,    )






@require_torch
@require_sentencepiece
@require_tokenizers
class 							__a							(  unittest.TestCase						):





		'''simple docstring'''



		UpperCAmelCase__      :				Optional[Any]				    =						"""facebook/m2m100_418M"""
		UpperCAmelCase__      :				Optional[int]				    =						[
		    """In my opinion, there are two levels of response from the French government.""",
		    """NSA Affair Emphasizes Complete Lack of Debate on Intelligence""",
		]
		UpperCAmelCase__      :				Optional[Any]				    =						[
		    """Selon moi, il y a deux niveaux de réponse de la part du gouvernement français.""",
		    """L'affaire NSA souligne l'absence totale de débat sur le renseignement""",
		]

		# fmt: off
		UpperCAmelCase__      :				Optional[Any]				    =						[EN_CODE, 593, 1949, 115781, 4, 71586, 4234, 60633, 126233, 432, 123808, 15592, 1197, 117132, 120618, 5, 2]


		@classmethod
		def   __snake_case			(						cls       ):
						SCREAMING_SNAKE_CASE_   :	MaMaaaTokenizer													=   MaMaaaTokenizer.from_pretrained(
						    cls.checkpoint_name ,    src_lang='en' ,    tgt_lang='fr'       )
						SCREAMING_SNAKE_CASE_   :	Any													=   1
						return cls


		def   __snake_case			(						self       ):
						self.assertEqual(self.tokenizer.get_lang_id('ar'       ) ,    128006       )
						self.assertEqual(self.tokenizer.get_lang_id('en'       ) ,    128022       )
						self.assertEqual(self.tokenizer.get_lang_id('ro'       ) ,    128076       )
						self.assertEqual(self.tokenizer.get_lang_id('mr'       ) ,    128063       )


		def   __snake_case			(						self       ):
						SCREAMING_SNAKE_CASE_   :	Any													=   self.tokenizer.get_vocab()
						self.assertEqual(len(UpperCamelCase__       ) ,    self.tokenizer.vocab_size       )
						self.assertEqual(vocab['<unk>'] ,    3       )
						self.assertIn(self.tokenizer.get_lang_token('en'       ) ,    UpperCamelCase__       )


		def   __snake_case			(						self       ):
						SCREAMING_SNAKE_CASE_   :	str													=   'en'
						SCREAMING_SNAKE_CASE_   :	str													=   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       )
						# fmt: off
						SCREAMING_SNAKE_CASE_   :	Any													=   [FR_CODE, 5364, 82, 8642, 4, 294, 47, 8, 14028, 136, 3286, 9706, 6, 90797, 6, 144012, 162, 88128, 30061, 5, 2]
						# fmt: on
						SCREAMING_SNAKE_CASE_   :	str													=   self.tokenizer.decode(UpperCamelCase__ ,    skip_special_tokens=UpperCamelCase__       )
						SCREAMING_SNAKE_CASE_   :	Dict													=   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       ):
						SCREAMING_SNAKE_CASE_   :	Any													=   tempfile.mkdtemp()
						SCREAMING_SNAKE_CASE_   :	List[str]													=   self.tokenizer.lang_token_to_id
						self.tokenizer.save_pretrained(UpperCamelCase__       )
						SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   MaMaaaTokenizer.from_pretrained(UpperCamelCase__       )
						self.assertDictEqual(new_tok.lang_token_to_id ,    UpperCamelCase__       )


		@require_torch
		def   __snake_case			(						self       ):
						SCREAMING_SNAKE_CASE_   :	int													=   'en'
						SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   'fr'

						SCREAMING_SNAKE_CASE_   :	List[Any]													=   self.tokenizer(self.src_text ,    text_target=self.tgt_text ,    padding=UpperCamelCase__ ,    return_tensors='pt'       )

						SCREAMING_SNAKE_CASE_   :	int													=   shift_tokens_right(
						    batch['labels'] ,    self.tokenizer.pad_token_id ,    self.tokenizer.eos_token_id       )

						for k in batch:
										SCREAMING_SNAKE_CASE_   :	Tuple													=   batch[k].tolist()
						# batch = {k: v.tolist() for k,v in batch.items()}
						# fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4
						# batch.decoder_inputs_ids[0][0] ==
						assert batch.input_ids[1][0] == EN_CODE
						assert batch.input_ids[1][-1] == 2
						assert batch.labels[1][0] == FR_CODE
						assert batch.labels[1][-1] == 2
						assert batch.decoder_input_ids[1][:2] == [2, FR_CODE]


		@require_torch
		def   __snake_case			(						self       ):
						SCREAMING_SNAKE_CASE_   :	str													=   'mr'
						self.assertListEqual(self.tokenizer.prefix_tokens ,    [self.tokenizer.get_lang_id('mr'       )]       )
						self.assertListEqual(self.tokenizer.suffix_tokens ,    [self.tokenizer.eos_token_id]       )

						SCREAMING_SNAKE_CASE_   :	str													=   'zh'
						self.assertListEqual(self.tokenizer.prefix_tokens ,    [self.tokenizer.get_lang_id('zh'       )]       )
						self.assertListEqual(self.tokenizer.suffix_tokens ,    [self.tokenizer.eos_token_id]       )


		@require_torch
		def   __snake_case			(						self       ):
						SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   'mr'
						self.tokenizer._switch_to_target_mode()
						self.assertListEqual(self.tokenizer.prefix_tokens ,    [self.tokenizer.get_lang_id('mr'       )]       )
						self.assertListEqual(self.tokenizer.suffix_tokens ,    [self.tokenizer.eos_token_id]       )
						self.tokenizer._switch_to_input_mode()
						self.assertListEqual(self.tokenizer.prefix_tokens ,    [self.tokenizer.get_lang_id(self.tokenizer.src_lang       )]       )

						SCREAMING_SNAKE_CASE_   :	str													=   'zh'
						self.tokenizer._switch_to_target_mode()
						self.assertListEqual(self.tokenizer.prefix_tokens ,    [self.tokenizer.get_lang_id('zh'       )]       )
						self.assertListEqual(self.tokenizer.suffix_tokens ,    [self.tokenizer.eos_token_id]       )
						self.tokenizer._switch_to_input_mode()
						self.assertListEqual(self.tokenizer.prefix_tokens ,    [self.tokenizer.get_lang_id(self.tokenizer.src_lang       )]       )






		@require_torch
		def   __snake_case			(						self       ):
						SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   self.tokenizer._build_translation_inputs('A test' ,    return_tensors='pt' ,    src_lang='en' ,    tgt_lang='ar'       )

						self.assertEqual(
						    nested_simplify(UpperCamelCase__       ) ,    {
						        # en_XX, A, test, EOS
						        'input_ids': [[128022, 58, 4183, 2]],
						        'attention_mask': [[1, 1, 1, 1]],
						        # ar_AR
						        'forced_bos_token_id': 128006,
						    } ,    )







from copy import deepcopy

import torch
import torch.nn.functional as F
from torch.optim import AdamW
from torch.optim.lr_scheduler import LambdaLR
from torch.utils.data import DataLoader

from accelerate.accelerator import Accelerator
from accelerate.state import GradientState
from accelerate.test_utils import RegressionDataset, RegressionModel
from accelerate.utils import DistributedType, is_torch_version, set_seed



def 					_lowerCamelCase(			lowerCAmelCase__				:		int    ,    lowerCAmelCase__				:		str    ,    lowerCAmelCase__				:		List[str]    ,    lowerCAmelCase__				:		Optional[int]      ):





				'''simple docstring'''







				for param, grad_param in zip(model_a.parameters()    ,    model_b.parameters()      ):
								if not param.requires_grad:
												continue
								if not did_step:
												# Grads should not be in sync
												assert (
												    torch.allclose(param.grad    ,    grad_param.grad      ) is False
												), F'''Gradients in sync when they should not be at iteration {iteration}:\nmodel_a grad ({param.grad}) == model_b grad ({grad_param.grad})'''
								else:
												# Grads should be in sync
												assert (
												    torch.allclose(param.grad    ,    grad_param.grad      ) is True
												), F'''Gradients not in sync when they should be at iteration {iteration}:\nmodel_a grad ({param.grad}) != model_b grad ({grad_param.grad})'''



def 					_lowerCamelCase(			lowerCAmelCase__				:		Dict    ,    lowerCAmelCase__				:		List[Any]    ,    lowerCAmelCase__				:		Optional[Any]    ,    lowerCAmelCase__				:		Optional[int]    ,    lowerCAmelCase__				:		Optional[Any]=True      ):





				'''simple docstring'''







				model.train()
				SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   model(lowerCAmelCase__      )
				SCREAMING_SNAKE_CASE_   :	List[Any]													=   F.mse_loss(lowerCAmelCase__    ,    target.to(output.device      )      )
				if not do_backward:
								loss /= accelerator.gradient_accumulation_steps
								loss.backward()
				else:
								accelerator.backward(lowerCAmelCase__      )



def 					_lowerCamelCase(			lowerCAmelCase__				:		Union[str, Any]    ,    lowerCAmelCase__				:		List[str]=False      ):





				'''simple docstring'''







				set_seed(42      )
				SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   RegressionModel()
				SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   deepcopy(lowerCAmelCase__      )
				SCREAMING_SNAKE_CASE_   :	Tuple													=   RegressionDataset(length=80      )
				SCREAMING_SNAKE_CASE_   :	str													=   DataLoader(lowerCAmelCase__    ,    batch_size=16      )
				model.to(accelerator.device      )
				if sched:
								SCREAMING_SNAKE_CASE_   :	Dict													=   AdamW(params=model.parameters()    ,    lr=1E-3      )
								SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   AdamW(params=ddp_model.parameters()    ,    lr=1E-3      )
								SCREAMING_SNAKE_CASE_   :	List[str]													=   LambdaLR(lowerCAmelCase__    ,    lr_lambda=lambda lowerCAmelCase__      : epoch**0.65      )
								SCREAMING_SNAKE_CASE_   :	Tuple													=   LambdaLR(lowerCAmelCase__    ,    lr_lambda=lambda lowerCAmelCase__      : epoch**0.65      )
				# Make a copy of `model`
				if sched:
								SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_   :	Optional[int]													=   accelerator.prepare(lowerCAmelCase__    ,    lowerCAmelCase__    ,    lowerCAmelCase__    ,    lowerCAmelCase__      )
				else:
								SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_   :	List[str]													=   accelerator.prepare(lowerCAmelCase__    ,    lowerCAmelCase__      )
				if sched:
								return (model, opt, sched, dataloader, ddp_model, ddp_opt, ddp_sched)
				return model, ddp_model, dataloader



def 					_lowerCamelCase(			lowerCAmelCase__				:		int      ):





				'''simple docstring'''







				SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   get_training_setup(lowerCAmelCase__      )
				# Use a single batch
				SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   next(iter(lowerCAmelCase__      )      ).values()
				for iteration in range(3      ):
								# Gather the distributed inputs and targs for the base model
								SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_   :	int													=   accelerator.gather((ddp_input, ddp_target)      )
								SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_   :	Any													=   input.to(accelerator.device      ), target.to(accelerator.device      )
								# Perform our initial ground truth step in non "DDP"
								step_model(lowerCAmelCase__    ,    lowerCAmelCase__    ,    lowerCAmelCase__    ,    lowerCAmelCase__      )
								# Do "gradient accumulation" (noop)
								if iteration % 2 == 0:
												# Accumulate grads locally
												with accelerator.no_sync(lowerCAmelCase__      ):
																step_model(lowerCAmelCase__    ,    lowerCAmelCase__    ,    lowerCAmelCase__    ,    lowerCAmelCase__      )
								else:
												# Sync grads
												step_model(lowerCAmelCase__    ,    lowerCAmelCase__    ,    lowerCAmelCase__    ,    lowerCAmelCase__      )

								# Since `no_sync` is a noop, `ddp_model` and `model` grads should always be in sync
								check_model_parameters(lowerCAmelCase__    ,    lowerCAmelCase__    ,    lowerCAmelCase__    ,    lowerCAmelCase__      )
								for param, ddp_param in zip(model.parameters()    ,    ddp_model.parameters()      ):
												if not param.requires_grad:
																continue
												assert torch.allclose(
												    param.grad    ,    ddp_param.grad      ), F'''Gradients not in sync when they should be:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})'''

								# Shuffle ddp_input on each iteration
								torch.manual_seed(1337 + iteration      )
								SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   ddp_input[torch.randperm(len(lowerCAmelCase__      )      )]



def 					_lowerCamelCase(			lowerCAmelCase__				:		Any      ):





				'''simple docstring'''







				SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_   :	str													=   get_training_setup(lowerCAmelCase__      )
				# Use a single batch
				SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_   :	Dict													=   next(iter(lowerCAmelCase__      )      ).values()
				for iteration in range(3      ):
								# Gather the distributed inputs and targs for the base model
								SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   accelerator.gather((ddp_input, ddp_target)      )
								SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_   :	int													=   input.to(accelerator.device      ), target.to(accelerator.device      )
								# Perform our initial ground truth step in non "DDP"
								step_model(lowerCAmelCase__    ,    lowerCAmelCase__    ,    lowerCAmelCase__    ,    lowerCAmelCase__      )
								# Do "gradient accumulation" (noop)
								if iteration % 2 == 0:
												# Accumulate grads locally
												with accelerator.no_sync(lowerCAmelCase__      ):
																step_model(lowerCAmelCase__    ,    lowerCAmelCase__    ,    lowerCAmelCase__    ,    lowerCAmelCase__      )
								else:
												# Sync grads
												step_model(lowerCAmelCase__    ,    lowerCAmelCase__    ,    lowerCAmelCase__    ,    lowerCAmelCase__      )

								# DDP model and model should only be in sync when not (iteration % 2 == 0)
								for param, ddp_param in zip(model.parameters()    ,    ddp_model.parameters()      ):
												if not param.requires_grad:
																continue
												if iteration % 2 == 0:
																# Grads should not be in sync
																assert (
																    torch.allclose(param.grad    ,    ddp_param.grad      ) is False
																), F'''Gradients in sync when they should not be:\nModel grad ({param.grad}) == DDP grad ({ddp_param.grad})'''
												else:
																# Grads should be in sync
																assert (
																    torch.allclose(param.grad    ,    ddp_param.grad      ) is True
																), F'''Gradients not in sync when they should be:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})'''

        # Shuffle ddp_input on each iteration
								torch.manual_seed(1337 + iteration      )
								SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   ddp_input[torch.randperm(len(lowerCAmelCase__      )      )]



def 					_lowerCamelCase(			lowerCAmelCase__				:		List[Any]=False    ,    lowerCAmelCase__				:		Union[str, Any]=False      ):





				'''simple docstring'''







				SCREAMING_SNAKE_CASE_   :	Optional[int]													=   Accelerator(
				    split_batches=lowerCAmelCase__    ,    dispatch_batches=lowerCAmelCase__    ,    gradient_accumulation_steps=2      )
				# Test that context manager behaves properly
				SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   get_training_setup(lowerCAmelCase__      )
				for iteration, batch in enumerate(lowerCAmelCase__      ):
								SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_   :	Any													=   batch.values()
								# Gather the distributed inputs and targs for the base model
								SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_   :	int													=   accelerator.gather((ddp_input, ddp_target)      )
								SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_   :	int													=   input.to(accelerator.device      ), target.to(accelerator.device      )
								# Perform our initial ground truth step in non "DDP"
								step_model(lowerCAmelCase__    ,    lowerCAmelCase__    ,    lowerCAmelCase__    ,    lowerCAmelCase__    ,    lowerCAmelCase__      )
								# Do "gradient accumulation" (noop)
								with accelerator.accumulate(lowerCAmelCase__      ):
												step_model(lowerCAmelCase__    ,    lowerCAmelCase__    ,    lowerCAmelCase__    ,    lowerCAmelCase__      )

								# DDP model and model should only be in sync when not (iteration % 2 == 0)
								for param, ddp_param in zip(model.parameters()    ,    ddp_model.parameters()      ):
												if not param.requires_grad:
																continue
												if ((iteration + 1) % 2 == 0) or (iteration == len(lowerCAmelCase__      ) - 1):
																# Grads should be in sync
																assert (
																    torch.allclose(param.grad    ,    ddp_param.grad      ) is True
																), F'''Gradients not in sync when they should be at iteration {iteration}:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})'''
												else:
																# Grads should not be in sync
																assert (
																    torch.allclose(param.grad    ,    ddp_param.grad      ) is False
																), F'''Gradients in sync when they should not be at iteration {iteration}:\nModel grad ({param.grad}) == DDP grad ({ddp_param.grad})'''

        # Shuffle ddp_input on each iteration
								torch.manual_seed(1337 + iteration      )
								SCREAMING_SNAKE_CASE_   :	str													=   ddp_input[torch.randperm(len(lowerCAmelCase__      )      )]
				GradientState._reset_state()



def 					_lowerCamelCase(			lowerCAmelCase__				:		int=False    ,    lowerCAmelCase__				:		List[str]=False      ):





				'''simple docstring'''







				SCREAMING_SNAKE_CASE_   :	str													=   Accelerator(
				    split_batches=lowerCAmelCase__    ,    dispatch_batches=lowerCAmelCase__    ,    gradient_accumulation_steps=2      )
				# Test that context manager behaves properly
				SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   get_training_setup(lowerCAmelCase__    ,    lowerCAmelCase__      )
				for iteration, batch in enumerate(lowerCAmelCase__      ):
								SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_   :	List[Any]													=   batch.values()
								# Gather the distributed inputs and targs for the base model
								SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   accelerator.gather((ddp_input, ddp_target)      )
								SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_   :	Tuple													=   input.to(accelerator.device      ), target.to(accelerator.device      )
								# Perform our initial ground truth step in non "DDP"
								model.train()
								ddp_model.train()
								step_model(lowerCAmelCase__    ,    lowerCAmelCase__    ,    lowerCAmelCase__    ,    lowerCAmelCase__    ,    lowerCAmelCase__      )
								opt.step()

								if ((iteration + 1) % 2 == 0) or ((iteration + 1) == len(lowerCAmelCase__      )):
												if split_batches:
																sched.step()
												else:
																for _ in range(accelerator.num_processes      ):
																				sched.step()
								opt.zero_grad()
								# Perform gradient accumulation under wrapper
								with accelerator.accumulate(lowerCAmelCase__      ):
												step_model(lowerCAmelCase__    ,    lowerCAmelCase__    ,    lowerCAmelCase__    ,    lowerCAmelCase__      )
												ddp_opt.step()
												ddp_sched.step()
												ddp_opt.zero_grad()

								# Learning rates should be the same
								assert (
								    opt.param_groups[0]["lr"] == ddp_opt.param_groups[0]["lr"]
								), F'''Learning rates found in each optimizer did not align\nopt: {opt.param_groups[0]['lr']}\nDDP opt: {ddp_opt.param_groups[0]['lr']}\n'''
								SCREAMING_SNAKE_CASE_   :	Optional[int]													=   (((iteration + 1) % 2) == 0) or ((iteration + 1) == len(lowerCAmelCase__      ))
								if accelerator.num_processes > 1:
												check_model_parameters(lowerCAmelCase__    ,    lowerCAmelCase__    ,    lowerCAmelCase__    ,    lowerCAmelCase__      )
								# Shuffle ddp_input on each iteration
								torch.manual_seed(1337 + iteration      )
				GradientState._reset_state()



def 					_lowerCamelCase(			):





				'''simple docstring'''







				SCREAMING_SNAKE_CASE_   :	Optional[int]													=   Accelerator()

				SCREAMING_SNAKE_CASE_   :	int													=   RegressionDataset(length=80      )
				SCREAMING_SNAKE_CASE_   :	str													=   DataLoader(lowerCAmelCase__    ,    batch_size=16      )
				SCREAMING_SNAKE_CASE_   :	Dict													=   RegressionDataset(length=96      )
				SCREAMING_SNAKE_CASE_   :	Tuple													=   DataLoader(lowerCAmelCase__    ,    batch_size=16      )
				SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_   :	Dict													=   accelerator.prepare(lowerCAmelCase__    ,    lowerCAmelCase__      )
				assert accelerator.gradient_state.active_dataloader is None
				for iteration, _ in enumerate(lowerCAmelCase__      ):
								assert id(accelerator.gradient_state.active_dataloader      ) == id(lowerCAmelCase__      )
								if iteration < len(lowerCAmelCase__      ) - 1:
												assert not accelerator.gradient_state.end_of_dataloader
												if iteration == 1:
																for batch_num, _ in enumerate(lowerCAmelCase__      ):
																				assert id(accelerator.gradient_state.active_dataloader      ) == id(lowerCAmelCase__      )
																				if batch_num < len(lowerCAmelCase__      ) - 1:
																								assert not accelerator.gradient_state.end_of_dataloader
																				else:
																								assert accelerator.gradient_state.end_of_dataloader
								else:
												assert accelerator.gradient_state.end_of_dataloader
				assert accelerator.gradient_state.active_dataloader is None



def 					_lowerCamelCase(			):





				'''simple docstring'''







				SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   Accelerator()
				SCREAMING_SNAKE_CASE_   :	str													=   accelerator.state
				if state.local_process_index == 0:
								print('**Test `accumulate` gradient accumulation with dataloader break**'      )
				test_dataloader_break()
				if state.distributed_type == DistributedType.NO:
								if state.local_process_index == 0:
												print('**Test NOOP `no_sync` context manager**'      )
								test_noop_sync(lowerCAmelCase__      )
				if state.distributed_type in (DistributedType.MULTI_GPU, DistributedType.MULTI_CPU):
								if state.local_process_index == 0:
												print('**Test Distributed `no_sync` context manager**'      )
								test_distributed_sync(lowerCAmelCase__      )
				if state.distributed_type == DistributedType.MULTI_GPU:
								for split_batch in [True, False]:
												for dispatch_batches in [True, False]:
																if state.local_process_index == 0:
																				print(
																				    '**Test `accumulate` gradient accumulation, '    ,    F'''`split_batches={split_batch}` and `dispatch_batches={dispatch_batches}`**'''    ,    )
																test_gradient_accumulation(lowerCAmelCase__    ,    lowerCAmelCase__      )

    # Currently will break on torch 2.0 +, need to investigate why
				if is_torch_version('<'    ,    '2.0'      ) or state.distributed_type == DistributedType.NO:
								if state.local_process_index == 0:
												print(
												    '**Test `accumulate` gradient accumulation with optimizer and scheduler, '    ,    '`split_batches=False`, `dispatch_batches=False`**'    ,    )
								test_gradient_accumulation_with_opt_and_scheduler()
								if state.distributed_type == DistributedType.MULTI_GPU:
												for split_batch in [True, False]:
																for dispatch_batches in [True, False]:
																				if not split_batch and not dispatch_batches:
																								continue
																				if state.local_process_index == 0:
																								print(
																								    '**Test `accumulate` gradient accumulation with optimizer and scheduler, '    ,    F'''`split_batches={split_batch}` and `dispatch_batches={dispatch_batches}`**'''    ,    )
																				test_gradient_accumulation_with_opt_and_scheduler(lowerCAmelCase__    ,    lowerCAmelCase__      )



def 					_lowerCamelCase(			lowerCAmelCase__				:		Optional[int]      ):





				'''simple docstring'''







				main()


if __name__ == "__main__":
				main()