






import json
import os
import re
import shutil
import tempfile
import unittest
from typing import Tuple

from transformers import AddedToken, BatchEncoding, ByTaTokenizer
from transformers.utils import cached_property, is_tf_available, is_torch_available

from ...test_tokenization_common import TokenizerTesterMixin


if is_torch_available():
				A        =			'''pt'''
elif is_tf_available():
				A        =			'''tf'''
else:
				A        =			'''jax'''


class 							__a							(  __A ,	unittest.TestCase						):





		'''simple docstring'''



		UpperCAmelCase__      :				Union[str, Any]				    =						ByTaTokenizer
		UpperCAmelCase__      :				str				    =						False


		def   __snake_case			(						self       ):
						super().setUp()
						SCREAMING_SNAKE_CASE_   :	List[str]													=   ByTaTokenizer()
						tokenizer.save_pretrained(self.tmpdirname       )


		@cached_property
		def   __snake_case			(						self       ):
						return ByTaTokenizer.from_pretrained('google/byt5-small'       )


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


		def   __snake_case			(						self ,    UpperCamelCase__ ,    UpperCamelCase__=False ,    UpperCamelCase__=20 ,    UpperCamelCase__=5       ):
						# XXX The default common tokenizer tests assume that every ID is decodable on its own.
						# This assumption is invalid for ByT5 because single bytes might not be
						# valid utf-8 (byte 128 for instance).
						# Here we're overriding the smallest possible method to provide
						# a clean sequence without making the same assumption.

						SCREAMING_SNAKE_CASE_   :	str													=   []
						for i in range(len(UpperCamelCase__       )       ):
										try:
														SCREAMING_SNAKE_CASE_   :	int													=   tokenizer.decode([i] ,    clean_up_tokenization_spaces=UpperCamelCase__       )
										except UnicodeDecodeError:
														pass
										toks.append((i, tok)       )

						SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   list(filter(lambda UpperCamelCase__       : re.match(R'^[ a-zA-Z]+$' ,    t[1]       ) ,    UpperCamelCase__       )       )
						SCREAMING_SNAKE_CASE_   :	Any													=   list(filter(lambda UpperCamelCase__       : [t[0]] == tokenizer.encode(t[1] ,    add_special_tokens=UpperCamelCase__       ) ,    UpperCamelCase__       )       )
						if max_length is not None and len(UpperCamelCase__       ) > max_length:
										SCREAMING_SNAKE_CASE_   :	Tuple													=   toks[:max_length]
						if min_length is not None and len(UpperCamelCase__       ) < min_length and len(UpperCamelCase__       ) > 0:
										while len(UpperCamelCase__       ) < min_length:
														SCREAMING_SNAKE_CASE_   :	Dict													=   toks + toks
        # toks_str = [t[1] for t in toks]
						SCREAMING_SNAKE_CASE_   :	Any													=   [t[0] for t in toks]

						# Ensure consistency
						SCREAMING_SNAKE_CASE_   :	Tuple													=   tokenizer.decode(UpperCamelCase__ ,    clean_up_tokenization_spaces=UpperCamelCase__       )
						if " " not in output_txt and len(UpperCamelCase__       ) > 1:
										SCREAMING_SNAKE_CASE_   :	Optional[int]													=   (
										    tokenizer.decode([toks_ids[0]] ,    clean_up_tokenization_spaces=UpperCamelCase__       )
										    + ' '
										    + tokenizer.decode(toks_ids[1:] ,    clean_up_tokenization_spaces=UpperCamelCase__       )
										)
						if with_prefix_space:
										SCREAMING_SNAKE_CASE_   :	str													=   ' ' + output_txt
						SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   tokenizer.encode(UpperCamelCase__ ,    add_special_tokens=UpperCamelCase__       )
						return output_txt, output_ids


		def   __snake_case			(						self       ):
						SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   self.ta_base_tokenizer
						SCREAMING_SNAKE_CASE_   :	Any													=   tokenizer(['hi</s>', 'I went to the gym</s>', '</s>']       )
						SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   tokenizer(['hi', 'I went to the gym', '']       )
						self.assertListEqual(batch_with_eos_added['input_ids'] ,    batch_without_eos_added['input_ids']       )


		def   __snake_case			(						self       ):
						SCREAMING_SNAKE_CASE_   :	List[str]													=   self.ta_base_tokenizer
						SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   'Unicode €.'
						SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   tokenizer(UpperCamelCase__       )
						SCREAMING_SNAKE_CASE_   :	Optional[int]													=   [88, 113, 108, 102, 114, 103, 104, 35, 229, 133, 175, 49, 1]
						self.assertEqual(encoded['input_ids'] ,    UpperCamelCase__       )

						# decoding
						SCREAMING_SNAKE_CASE_   :	int													=   tokenizer.decode(UpperCamelCase__       )
						self.assertEqual(UpperCamelCase__ ,    'Unicode €.</s>'       )

						SCREAMING_SNAKE_CASE_   :	List[str]													=   tokenizer('e è é ê ë'       )
						SCREAMING_SNAKE_CASE_   :	Any													=   [104, 35, 198, 171, 35, 198, 172, 35, 198, 173, 35, 198, 174, 1]
						self.assertEqual(encoded['input_ids'] ,    UpperCamelCase__       )
						# decoding
						SCREAMING_SNAKE_CASE_   :	Tuple													=   tokenizer.decode(UpperCamelCase__       )
						self.assertEqual(UpperCamelCase__ ,    'e è é ê ë</s>'       )

						# encode/decode, but with `encode` instead of `__call__`
						self.assertEqual(tokenizer.decode(tokenizer.encode('e è é ê ë'       )       ) ,    'e è é ê ë</s>'       )


		def   __snake_case			(						self       ):
						SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   self.ta_base_tokenizer
						SCREAMING_SNAKE_CASE_   :	int													=   ['A long paragraph for summarization.', 'Another paragraph for summarization.']
						# fmt: off
						SCREAMING_SNAKE_CASE_   :	Any													=   [68, 35, 111, 114, 113, 106, 35, 115, 100, 117, 100, 106, 117, 100, 115, 107, 35, 105, 114, 117, 35, 118, 120, 112, 112, 100, 117, 108, 125, 100, 119, 108, 114, 113, 49, 1, 0]
						# fmt: on
						SCREAMING_SNAKE_CASE_   :	Optional[int]													=   tokenizer(UpperCamelCase__ ,    padding=UpperCamelCase__ ,    return_tensors=UpperCamelCase__       )
						self.assertIsInstance(UpperCamelCase__ ,    UpperCamelCase__       )

						if FRAMEWORK != "jax":
										SCREAMING_SNAKE_CASE_   :	List[str]													=   list(batch.input_ids.numpy()[0]       )
						else:
										SCREAMING_SNAKE_CASE_   :	str													=   list(batch.input_ids.tolist()[0]       )

						self.assertListEqual(UpperCamelCase__ ,    UpperCamelCase__       )

						self.assertEqual((2, 37) ,    batch.input_ids.shape       )
						self.assertEqual((2, 37) ,    batch.attention_mask.shape       )


		def   __snake_case			(						self       ):
						SCREAMING_SNAKE_CASE_   :	List[Any]													=   self.ta_base_tokenizer
						SCREAMING_SNAKE_CASE_   :	Optional[int]													=   ['A long paragraph for summarization.', 'Another paragraph for summarization.']
						SCREAMING_SNAKE_CASE_   :	Any													=   tokenizer(UpperCamelCase__ ,    padding=UpperCamelCase__ ,    return_tensors=UpperCamelCase__       )
						# check if input_ids are returned and no decoder_input_ids
						self.assertIn('input_ids' ,    UpperCamelCase__       )
						self.assertIn('attention_mask' ,    UpperCamelCase__       )
						self.assertNotIn('decoder_input_ids' ,    UpperCamelCase__       )
						self.assertNotIn('decoder_attention_mask' ,    UpperCamelCase__       )


		def   __snake_case			(						self       ):
						SCREAMING_SNAKE_CASE_   :	List[str]													=   self.ta_base_tokenizer
						SCREAMING_SNAKE_CASE_   :	str													=   [
						    'Summary of the text.',
						    'Another summary.',
						]
						SCREAMING_SNAKE_CASE_   :	Dict													=   tokenizer(
						    text_target=UpperCamelCase__ ,    max_length=32 ,    padding='max_length' ,    truncation=UpperCamelCase__ ,    return_tensors=UpperCamelCase__       )
						self.assertEqual(32 ,    targets['input_ids'].shape[1]       )


		def   __snake_case			(						self       ):
						SCREAMING_SNAKE_CASE_   :	Tuple													=   self.ta_base_tokenizer
						SCREAMING_SNAKE_CASE_   :	Optional[int]													=   ['A long paragraph for summarization. </s>']
						SCREAMING_SNAKE_CASE_   :	int													=   ['Summary of the text. </s>']
						# fmt: off
						SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   [68, 35, 111, 114, 113, 106, 35, 115, 100, 117, 100, 106, 117, 100, 115, 107, 35, 105, 114, 117, 35, 118, 120, 112, 112, 100, 117, 108, 125, 100, 119, 108, 114, 113, 49, 35, 1]
						SCREAMING_SNAKE_CASE_   :	str													=   [86, 120, 112, 112, 100, 117, 124, 35, 114, 105, 35, 119, 107, 104, 35, 119, 104, 123, 119, 49, 35, 1]
						# fmt: on

						SCREAMING_SNAKE_CASE_   :	Dict													=   tokenizer(UpperCamelCase__ ,    text_target=UpperCamelCase__       )

						self.assertEqual(UpperCamelCase__ ,    batch['input_ids'][0]       )
						self.assertEqual(UpperCamelCase__ ,    batch['labels'][0]       )


		def   __snake_case			(						self       ):
						# safety check on max_len default value so we are sure the test works
						SCREAMING_SNAKE_CASE_   :	Optional[int]													=   self.get_tokenizers()
						for tokenizer in tokenizers:
										with self.subTest(F'''{tokenizer.__class__.__name__}'''       ):
														self.assertNotEqual(tokenizer.model_max_length ,    42       )

        # Now let's start the test
						SCREAMING_SNAKE_CASE_   :	List[str]													=   self.get_tokenizers()
						for tokenizer in tokenizers:
										with self.subTest(F'''{tokenizer.__class__.__name__}'''       ):
														# Isolate this from the other tests because we save additional tokens/etc
														SCREAMING_SNAKE_CASE_   :	Tuple													=   tempfile.mkdtemp()

														SCREAMING_SNAKE_CASE_   :	Optional[int]													=   ' He is very happy, UNwant\u00E9d,running'
														SCREAMING_SNAKE_CASE_   :	int													=   tokenizer.encode(UpperCamelCase__ ,    add_special_tokens=UpperCamelCase__       )
														tokenizer.save_pretrained(UpperCamelCase__       )

														SCREAMING_SNAKE_CASE_   :	int													=   tokenizer.__class__.from_pretrained(UpperCamelCase__       )
														SCREAMING_SNAKE_CASE_   :	Dict													=   after_tokenizer.encode(UpperCamelCase__ ,    add_special_tokens=UpperCamelCase__       )
														self.assertListEqual(UpperCamelCase__ ,    UpperCamelCase__       )

														shutil.rmtree(UpperCamelCase__       )

						SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   self.get_tokenizers(model_max_length=42       )
						for tokenizer in tokenizers:
										with self.subTest(F'''{tokenizer.__class__.__name__}'''       ):
														# Isolate this from the other tests because we save additional tokens/etc
														SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   tempfile.mkdtemp()

														SCREAMING_SNAKE_CASE_   :	Dict													=   ' He is very happy, UNwant\u00E9d,running'
														tokenizer.add_tokens(['bim', 'bambam']       )
														SCREAMING_SNAKE_CASE_   :	List[str]													=   tokenizer.additional_special_tokens
														additional_special_tokens.append('new_additional_special_token'       )
														tokenizer.add_special_tokens({'additional_special_tokens': additional_special_tokens}       )
														SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   tokenizer.encode(UpperCamelCase__ ,    add_special_tokens=UpperCamelCase__       )
														tokenizer.save_pretrained(UpperCamelCase__       )

														SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   tokenizer.__class__.from_pretrained(UpperCamelCase__       )
														SCREAMING_SNAKE_CASE_   :	int													=   after_tokenizer.encode(UpperCamelCase__ ,    add_special_tokens=UpperCamelCase__       )
														self.assertListEqual(UpperCamelCase__ ,    UpperCamelCase__       )
														self.assertIn('new_additional_special_token' ,    after_tokenizer.additional_special_tokens       )
														self.assertEqual(after_tokenizer.model_max_length ,    42       )

														SCREAMING_SNAKE_CASE_   :	Any													=   tokenizer.__class__.from_pretrained(UpperCamelCase__ ,    model_max_length=43       )
														self.assertEqual(tokenizer.model_max_length ,    43       )

														shutil.rmtree(UpperCamelCase__       )


		def   __snake_case			(						self       ):
						SCREAMING_SNAKE_CASE_   :	Any													=   []
						if self.test_slow_tokenizer:
										tokenizer_list.append((self.tokenizer_class, self.get_tokenizer())       )

						if self.test_rust_tokenizer:
										tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer())       )

						for tokenizer_class, tokenizer_utils in tokenizer_list:
										with tempfile.TemporaryDirectory() as tmp_dir:
														tokenizer_utils.save_pretrained(UpperCamelCase__       )

														with open(os.path.join(UpperCamelCase__ ,    'special_tokens_map.json'       ) ,    encoding='utf-8'       ) as json_file:
																		SCREAMING_SNAKE_CASE_   :	Any													=   json.load(UpperCamelCase__       )

														with open(os.path.join(UpperCamelCase__ ,    'tokenizer_config.json'       ) ,    encoding='utf-8'       ) as json_file:
																		SCREAMING_SNAKE_CASE_   :	Optional[int]													=   json.load(UpperCamelCase__       )

														SCREAMING_SNAKE_CASE_   :	List[str]													=   [F'''<extra_id_{i}>''' for i in range(125       )]

														SCREAMING_SNAKE_CASE_   :	int													=   added_tokens_extra_ids + [
														    'an_additional_special_token'
														]
														SCREAMING_SNAKE_CASE_   :	List[Any]													=   added_tokens_extra_ids + [
														    'an_additional_special_token'
														]

														with open(os.path.join(UpperCamelCase__ ,    'special_tokens_map.json'       ) ,    'w' ,    encoding='utf-8'       ) as outfile:
																		json.dump(UpperCamelCase__ ,    UpperCamelCase__       )
														with open(os.path.join(UpperCamelCase__ ,    'tokenizer_config.json'       ) ,    'w' ,    encoding='utf-8'       ) as outfile:
																		json.dump(UpperCamelCase__ ,    UpperCamelCase__       )

														# the following checks allow us to verify that our test works as expected, i.e. that the tokenizer takes
														# into account the new value of additional_special_tokens given in the "tokenizer_config.json" and
														# "special_tokens_map.json" files
														SCREAMING_SNAKE_CASE_   :	Dict													=   tokenizer_class.from_pretrained(
														    UpperCamelCase__ ,    )
														self.assertIn(
														    'an_additional_special_token' ,    tokenizer_without_change_in_init.additional_special_tokens       )
														# self.assertIn("an_additional_special_token",tokenizer_without_change_in_init.get_vocab()) # ByT5Tokenization no vocab
														self.assertEqual(
														    ['an_additional_special_token'] ,    tokenizer_without_change_in_init.convert_ids_to_tokens(
														        tokenizer_without_change_in_init.convert_tokens_to_ids(['an_additional_special_token']       )       ) ,    )

														# Now we test that we can change the value of additional_special_tokens in the from_pretrained
														SCREAMING_SNAKE_CASE_   :	Tuple													=   added_tokens_extra_ids + [AddedToken('a_new_additional_special_token' ,    lstrip=UpperCamelCase__       )]
														SCREAMING_SNAKE_CASE_   :	Optional[int]													=   tokenizer_class.from_pretrained(
														    UpperCamelCase__ ,    additional_special_tokens=UpperCamelCase__ ,    )

														self.assertIn('a_new_additional_special_token' ,    tokenizer.additional_special_tokens       )
														self.assertEqual(
														    ['a_new_additional_special_token'] ,    tokenizer.convert_ids_to_tokens(
														        tokenizer.convert_tokens_to_ids(['a_new_additional_special_token']       )       ) ,    )


		def   __snake_case			(						self       ):
						SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   []
						if self.test_slow_tokenizer:
										tokenizer_list.append((self.tokenizer_class, self.get_tokenizer())       )

						if self.test_rust_tokenizer:
										tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer())       )

						for tokenizer_class, tokenizer_utils in tokenizer_list:
										with tempfile.TemporaryDirectory() as tmp_dir:
														tokenizer_utils.save_pretrained(UpperCamelCase__       )

														SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   tokenizer_class.from_pretrained(UpperCamelCase__       )

														self.assertTrue(tokenizer.decode([255]       ) == ''       )


		def   __snake_case			(						self       ):
						pass


		def   __snake_case			(						self       ):
						pass


		def   __snake_case			(						self       ):
						pass


		def   __snake_case			(						self       ):
						pass


		def   __snake_case			(						self       ):
						# The default common tokenizer tests uses invalid tokens for ByT5 that can only accept one-character strings
						# and special added tokens as tokens
						SCREAMING_SNAKE_CASE_   :	int													=   self.get_tokenizers(fast=UpperCamelCase__ ,    do_lower_case=UpperCamelCase__       )
						for tokenizer in tokenizers:
										with self.subTest(F'''{tokenizer.__class__.__name__}'''       ):
														SCREAMING_SNAKE_CASE_   :	Optional[int]													=   ['t', 'h', 'i', 's', ' ', 'i', 's', ' ', 'a', ' ', 't', 'e', 'x', 't', '</s>']
														SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   tokenizer.convert_tokens_to_string(UpperCamelCase__       )

														self.assertIsInstance(UpperCamelCase__ ,    UpperCamelCase__       )






		def   __snake_case			(						self       ):
						SCREAMING_SNAKE_CASE_   :	str													=   self.get_tokenizers()
						for tokenizer in tokenizers:
										with self.subTest(F'''{tokenizer.__class__.__name__}'''       ):
														SCREAMING_SNAKE_CASE_   :	Dict													=   [
														    'bos_token',
														    'eos_token',
														    'unk_token',
														    'sep_token',
														    'pad_token',
														    'cls_token',
														    'mask_token',
														]

														SCREAMING_SNAKE_CASE_   :	List[str]													=   0
														SCREAMING_SNAKE_CASE_   :	Dict													=   tokenizer.convert_ids_to_tokens(
														    UpperCamelCase__ ,    skip_special_tokens=UpperCamelCase__       )

														for attr in attributes_list:
																		setattr(UpperCamelCase__ ,    attr + '_id' ,    UpperCamelCase__       )
																		self.assertEqual(getattr(UpperCamelCase__ ,    UpperCamelCase__       ) ,    UpperCamelCase__       )
																		self.assertEqual(getattr(UpperCamelCase__ ,    attr + '_id'       ) ,    UpperCamelCase__       )

																		setattr(UpperCamelCase__ ,    attr + '_id' ,    UpperCamelCase__       )
																		self.assertEqual(getattr(UpperCamelCase__ ,    UpperCamelCase__       ) ,    UpperCamelCase__       )
																		self.assertEqual(getattr(UpperCamelCase__ ,    attr + '_id'       ) ,    UpperCamelCase__       )

														setattr(UpperCamelCase__ ,    'additional_special_tokens_ids' ,    []       )
														self.assertListEqual(getattr(UpperCamelCase__ ,    'additional_special_tokens'       ) ,    []       )
														self.assertListEqual(getattr(UpperCamelCase__ ,    'additional_special_tokens_ids'       ) ,    []       )

														setattr(UpperCamelCase__ ,    'additional_special_tokens_ids' ,    [token_id_to_test_setters]       )
														self.assertListEqual(getattr(UpperCamelCase__ ,    'additional_special_tokens'       ) ,    [token_to_test_setters]       )
														self.assertListEqual(getattr(UpperCamelCase__ ,    'additional_special_tokens_ids'       ) ,    [token_id_to_test_setters]       )







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()