




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       _a (		SCREAMING_SNAKE_CASE_				:							Union[str, Any]=2 , SCREAMING_SNAKE_CASE_				:							Optional[Any]=True , SCREAMING_SNAKE_CASE_				:							List[Any]=False	):
					__lowerCAmelCase							  =			Accelerator(step_scheduler_with_optimizer=SCREAMING_SNAKE_CASE_ , split_batches=SCREAMING_SNAKE_CASE_	)
					__lowerCAmelCase							  =			torch.nn.Linear(2 , 4	)
					__lowerCAmelCase							  =			torch.optim.AdamW(model.parameters() , lr=1.0	)
					__lowerCAmelCase							  =			torch.optim.lr_scheduler.OneCycleLR(SCREAMING_SNAKE_CASE_ , max_lr=0.01 , steps_per_epoch=2 , epochs=1	)
					__lowerCAmelCase  ,				__lowerCAmelCase  ,				__lowerCAmelCase							  =			accelerator.prepare(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_	)

					# 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       _a (		SCREAMING_SNAKE_CASE_				:							int=2 , SCREAMING_SNAKE_CASE_				:							Any=True , SCREAMING_SNAKE_CASE_				:							Any=False	):
					__lowerCAmelCase							  =			Accelerator(step_scheduler_with_optimizer=SCREAMING_SNAKE_CASE_ , split_batches=SCREAMING_SNAKE_CASE_	)
					__lowerCAmelCase							  =			torch.nn.Linear(2 , 4	)
					__lowerCAmelCase							  =			torch.optim.AdamW(model.parameters() , lr=1.0	)
					__lowerCAmelCase							  =			torch.optim.lr_scheduler.LambdaLR(SCREAMING_SNAKE_CASE_ , lr_lambda=lambda SCREAMING_SNAKE_CASE_	: 1 - n / 10	)
					__lowerCAmelCase  ,				__lowerCAmelCase  ,				__lowerCAmelCase							  =			accelerator.prepare(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_	)

					# Optimizer has stepped
					__lowerCAmelCase							  =			False
					scheduler.step()
					__lowerCAmelCase							  =			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
					__lowerCAmelCase							  =			True
					scheduler.step()
					if not step_scheduler_with_optimizer:
										__lowerCAmelCase							  =			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       _a (		SCREAMING_SNAKE_CASE_				:							int = 2	):
					from transformers import get_linear_schedule_with_warmup

					__lowerCAmelCase							  =			[1, 2, 4]
					for num_steps in steps:
										__lowerCAmelCase							  =			GradientAccumulationPlugin(num_steps=SCREAMING_SNAKE_CASE_ , adjust_scheduler=num_steps > 1	)
										__lowerCAmelCase							  =			Accelerator(gradient_accumulation_plugin=SCREAMING_SNAKE_CASE_	)
										__lowerCAmelCase							  =			torch.nn.Linear(2 , 4	)
										__lowerCAmelCase							  =			torch.optim.AdamW(model.parameters() , lr=10.0	)
										__lowerCAmelCase							  =			get_linear_schedule_with_warmup(optimizer=SCREAMING_SNAKE_CASE_ , num_warmup_steps=0 , num_training_steps=20	)

										__lowerCAmelCase  ,				__lowerCAmelCase  ,				__lowerCAmelCase							  =			accelerator.prepare(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_	)

										for i in range(10 * num_steps	):
															with accelerator.accumulate(SCREAMING_SNAKE_CASE_	):
																				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      a__       (   unittest.TestCase ):






						def 							__SCREAMING_SNAKE_CASE(  self     ):




											"""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 							__SCREAMING_SNAKE_CASE(  self     ):




											"""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 							__SCREAMING_SNAKE_CASE(  self     ):




											"""simple docstring"""



											debug_launcher(partial(_A						,     num_processes=1						,     step_scheduler_with_optimizer=_A     )						,     num_processes=1     )






						def 							__SCREAMING_SNAKE_CASE(  self     ):




											"""simple docstring"""



											debug_launcher(partial(_A						,     num_processes=1						,     step_scheduler_with_optimizer=_A     )						,     num_processes=1     )






						def 							__SCREAMING_SNAKE_CASE(  self     ):




											"""simple docstring"""



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






						def 							__SCREAMING_SNAKE_CASE(  self     ):




											"""simple docstring"""



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






						def 							__SCREAMING_SNAKE_CASE(  self     ):




											"""simple docstring"""



											AcceleratorState._reset_state(_A     )
											debug_launcher(partial(_A						,     step_scheduler_with_optimizer=_A     )     )






						def 							__SCREAMING_SNAKE_CASE(  self     ):




											"""simple docstring"""



											AcceleratorState._reset_state(_A     )
											debug_launcher(partial(_A						,     step_scheduler_with_optimizer=_A     )     )






						@require_huggingface_suite
						def 							__SCREAMING_SNAKE_CASE(  self     ):




											"""simple docstring"""



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










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,
)


UpperCamelCase__            =     {
    """configuration_marian""": ["""MARIAN_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MarianConfig""", """MarianOnnxConfig"""],
}

try:
					if not is_sentencepiece_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCamelCase__            =     ["""MarianTokenizer"""]

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCamelCase__            =     [
					    """MARIAN_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """MarianForCausalLM""",
					    """MarianModel""",
					    """MarianMTModel""",
					    """MarianPreTrainedModel""",
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCamelCase__            =     ["""TFMarianModel""", """TFMarianMTModel""", """TFMarianPreTrainedModel"""]

try:
					if not is_flax_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCamelCase__            =     ["""FlaxMarianModel""", """FlaxMarianMTModel""", """FlaxMarianPreTrainedModel"""]

if TYPE_CHECKING:
					from .configuration_marian import MARIAN_PRETRAINED_CONFIG_ARCHIVE_MAP, MarianConfig, MarianOnnxConfig

					try:
										if not is_sentencepiece_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_marian import MarianTokenizer

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_marian import (
										    MARIAN_PRETRAINED_MODEL_ARCHIVE_LIST,
										    MarianForCausalLM,
										    MarianModel,
										    MarianMTModel,
										    MarianPreTrainedModel,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_marian import TFMarianModel, TFMarianMTModel, TFMarianPreTrainedModel

					try:
										if not is_flax_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_flax_marian import FlaxMarianModel, FlaxMarianMTModel, FlaxMarianPreTrainedModel

else:
					import sys

					UpperCamelCase__            =     _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




