

"""simple docstring"""






import unittest
from functools import partial

import torch

from accelerate import Accelerator, debug_launcher
from accelerate.state import AcceleratorState, GradientState
from accelerate.test_utils import require_cpu, require_huggingface_suite
from accelerate.utils import GradientAccumulationPlugin
def       UpperCamelCase					(			_lowerCAmelCase     :						List[Any]=2,  _lowerCAmelCase     :						List[str]=True,  _lowerCAmelCase     :						List[str]=False )				->			Union[str, Any]:
	_UpperCAmelCase    :							List[Any]									=					Accelerator(step_scheduler_with_optimizer=_lowerCAmelCase,  split_batches=_lowerCAmelCase )
	_UpperCAmelCase    :							Optional[int]									=					torch.nn.Linear(2,  4 )
	_UpperCAmelCase    :							List[Any]									=					torch.optim.AdamW(model.parameters(),  lr=1.0 )
	_UpperCAmelCase    :							Optional[int]									=					torch.optim.lr_scheduler.OneCycleLR(_lowerCAmelCase,  max_lr=0.01,  steps_per_epoch=2,  epochs=1 )
	_UpperCAmelCase						,   _UpperCAmelCase						,   _UpperCAmelCase    :							Tuple									=					accelerator.prepare(_lowerCAmelCase,  _lowerCAmelCase,  _lowerCAmelCase )

	# Optimizer has stepped
	scheduler.step()
	if step_scheduler_with_optimizer or (num_processes == 1):
		assert (
		    scheduler.scheduler.last_epoch == num_processes
		), f'''Last Epoch ({scheduler.scheduler.last_epoch}) != Num Processes ({num_processes})'''
	else:
		assert (
		    scheduler.scheduler.last_epoch != num_processes
		), f'''Last Epoch ({scheduler.scheduler.last_epoch}) == Num Processes ({num_processes})'''
def       UpperCamelCase					(			_lowerCAmelCase     :						List[str]=2,  _lowerCAmelCase     :						Optional[int]=True,  _lowerCAmelCase     :						Optional[Any]=False )				->			str:
	_UpperCAmelCase    :							int									=					Accelerator(step_scheduler_with_optimizer=_lowerCAmelCase,  split_batches=_lowerCAmelCase )
	_UpperCAmelCase    :							Optional[int]									=					torch.nn.Linear(2,  4 )
	_UpperCAmelCase    :							Dict									=					torch.optim.AdamW(model.parameters(),  lr=1.0 )
	_UpperCAmelCase    :							Union[str, Any]									=					torch.optim.lr_scheduler.LambdaLR(_lowerCAmelCase,  lr_lambda=lambda _lowerCAmelCase : 1 - n / 10 )
	_UpperCAmelCase						,   _UpperCAmelCase						,   _UpperCAmelCase    :							Optional[int]									=					accelerator.prepare(_lowerCAmelCase,  _lowerCAmelCase,  _lowerCAmelCase )

	# Optimizer has stepped
	_UpperCAmelCase    :							Union[str, Any]									=					False
	scheduler.step()
	_UpperCAmelCase    :							List[str]									=					1 - (num_processes if (step_scheduler_with_optimizer and not split_batches) else 1) / 10
	assert (
	    scheduler.get_last_lr()[0] == expected_lr
	), f'''Wrong lr found at first step, expected {expected_lr}, got {scheduler.get_last_lr()[0]}'''

	# Optimizer has not stepped
	_UpperCAmelCase    :							Any									=					True
	scheduler.step()
	if not step_scheduler_with_optimizer:
		_UpperCAmelCase    :							str									=					1 - 2 / 10
	assert (
	    scheduler.get_last_lr()[0] == expected_lr
	), f'''Wrong lr found at second step, expected {expected_lr}, got {scheduler.get_last_lr()[0]}'''
def       UpperCamelCase					(			_lowerCAmelCase     :						int = 2 )				->			List[str]:
	from transformers import get_linear_schedule_with_warmup

	_UpperCAmelCase    :							Tuple									=					[1, 2, 4]
	for num_steps in steps:
		_UpperCAmelCase    :							int									=					GradientAccumulationPlugin(num_steps=_lowerCAmelCase,  adjust_scheduler=num_steps > 1 )
		_UpperCAmelCase    :							List[Any]									=					Accelerator(gradient_accumulation_plugin=_lowerCAmelCase )
		_UpperCAmelCase    :							List[str]									=					torch.nn.Linear(2,  4 )
		_UpperCAmelCase    :							int									=					torch.optim.AdamW(model.parameters(),  lr=10.0 )
		_UpperCAmelCase    :							List[str]									=					get_linear_schedule_with_warmup(optimizer=_lowerCAmelCase,  num_warmup_steps=0,  num_training_steps=20 )

		_UpperCAmelCase						,   _UpperCAmelCase						,   _UpperCAmelCase    :							Any									=					accelerator.prepare(_lowerCAmelCase,  _lowerCAmelCase,  _lowerCAmelCase )

		for i in range(10 * num_steps ):
			with accelerator.accumulate(_lowerCAmelCase ):
				optimizer.step()
				scheduler.step()

			if i == (10 * num_steps - 2):
				assert (
				    scheduler.get_last_lr()[0] != 0
				), f'''Wrong lr found at second-to-last step, expected non-zero, got {scheduler.get_last_lr()[0]}. num_steps: {num_steps}'''
		assert (
		    scheduler.get_last_lr()[0] == 0
		), f'''Wrong lr found at last step, expected 0, got {scheduler.get_last_lr()[0]}'''
		GradientState._reset_state()






@require_cpu
class     _UpperCAmelCase			(		unittest.TestCase):
			def 							__snake_case (		self     )    ->    int:






				'''simple docstring'''







				debug_launcher(partial(_A      ,		num_processes=1     )      ,		num_processes=1     )
				debug_launcher(partial(_A      ,		num_processes=1      ,		split_batches=_A     )      ,		num_processes=1     )
			def 							__snake_case (		self     )    ->    List[str]:






				'''simple docstring'''







				debug_launcher(partial(_A      ,		num_processes=1     )      ,		num_processes=1     )
				debug_launcher(partial(_A      ,		num_processes=1      ,		split_batches=_A     )      ,		num_processes=1     )
			def 							__snake_case (		self     )    ->    List[Any]:






				'''simple docstring'''







				debug_launcher(partial(_A      ,		num_processes=1      ,		step_scheduler_with_optimizer=_A     )      ,		num_processes=1     )
			def 							__snake_case (		self     )    ->    str:






				'''simple docstring'''







				debug_launcher(partial(_A      ,		num_processes=1      ,		step_scheduler_with_optimizer=_A     )      ,		num_processes=1     )
			def 							__snake_case (		self     )    ->    List[Any]:






				'''simple docstring'''







				AcceleratorState._reset_state(_A     )
				debug_launcher(_A     )
				debug_launcher(partial(_A      ,		num_processes=1      ,		split_batches=_A     )      ,		num_processes=1     )
			def 							__snake_case (		self     )    ->    Union[str, Any]:






				'''simple docstring'''







				AcceleratorState._reset_state(_A     )
				debug_launcher(_A     )
				debug_launcher(partial(_A      ,		num_processes=1      ,		split_batches=_A     )      ,		num_processes=1     )
			def 							__snake_case (		self     )    ->    Any:






				'''simple docstring'''







				AcceleratorState._reset_state(_A     )
				debug_launcher(partial(_A      ,		step_scheduler_with_optimizer=_A     )     )
			def 							__snake_case (		self     )    ->    Tuple:






				'''simple docstring'''







				AcceleratorState._reset_state(_A     )
				debug_launcher(partial(_A      ,		step_scheduler_with_optimizer=_A     )     )
			@require_huggingface_suite
			def 							__snake_case (		self     )    ->    List[Any]:






				'''simple docstring'''







				AcceleratorState._reset_state(_A     )
				debug_launcher(partial(_A      ,		num_processes=1     )     )
				debug_launcher(_A     )








"""simple docstring"""






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCamelCase__				:  Optional[int]													=  {'''configuration_t5''': ['''T5_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''T5Config''', '''T5OnnxConfig''']}

try:
						if not is_sentencepiece_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCamelCase__				:  Dict													=  ['''T5Tokenizer''']

try:
						if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCamelCase__				:  List[str]													=  ['''T5TokenizerFast''']

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCamelCase__				:  Optional[Any]													=  [
						    '''T5_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''T5EncoderModel''',
						    '''T5ForConditionalGeneration''',
						    '''T5Model''',
						    '''T5PreTrainedModel''',
						    '''load_tf_weights_in_t5''',
						    '''T5ForQuestionAnswering''',
						]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCamelCase__				:  int													=  [
						    '''TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''TFT5EncoderModel''',
						    '''TFT5ForConditionalGeneration''',
						    '''TFT5Model''',
						    '''TFT5PreTrainedModel''',
						]

try:
						if not is_flax_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCamelCase__				:  Any													=  [
						    '''FlaxT5EncoderModel''',
						    '''FlaxT5ForConditionalGeneration''',
						    '''FlaxT5Model''',
						    '''FlaxT5PreTrainedModel''',
						]


if TYPE_CHECKING:
						from .configuration_ta import T5_PRETRAINED_CONFIG_ARCHIVE_MAP, TaConfig, TaOnnxConfig

						try:
												if not is_sentencepiece_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_ta import TaTokenizer

						try:
												if not is_tokenizers_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_ta_fast import TaTokenizerFast

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_ta import (
												    T5_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TaEncoderModel,
												    TaForConditionalGeneration,
												    TaForQuestionAnswering,
												    TaModel,
												    TaPreTrainedModel,
												    load_tf_weights_in_ta,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_ta import (
												    TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TFTaEncoderModel,
												    TFTaForConditionalGeneration,
												    TFTaModel,
												    TFTaPreTrainedModel,
												)

						try:
												if not is_flax_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_flax_ta import (
												    FlaxTaEncoderModel,
												    FlaxTaForConditionalGeneration,
												    FlaxTaModel,
												    FlaxTaPreTrainedModel,
												)


else:
						import sys

						lowerCamelCase__				:  Dict													=  _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)





