


'''simple docstring'''




from typing import TYPE_CHECKING

from ..utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


__lowercase  :      str      		=				{
    """agents""": ["""Agent""", """AzureOpenAiAgent""", """HfAgent""", """LocalAgent""", """OpenAiAgent"""],
    """base""": ["""PipelineTool""", """RemoteTool""", """Tool""", """launch_gradio_demo""", """load_tool"""],
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__lowercase  :      int      		=				["""DocumentQuestionAnsweringTool"""]
						__lowercase  :      int      		=				["""ImageCaptioningTool"""]
						__lowercase  :      Dict      		=				["""ImageQuestionAnsweringTool"""]
						__lowercase  :      List[Any]      		=				["""ImageSegmentationTool"""]
						__lowercase  :      Optional[int]      		=				["""SpeechToTextTool"""]
						__lowercase  :      Tuple      		=				["""TextClassificationTool"""]
						__lowercase  :      str      		=				["""TextQuestionAnsweringTool"""]
						__lowercase  :      Optional[int]      		=				["""TextSummarizationTool"""]
						__lowercase  :      Dict      		=				["""TextToSpeechTool"""]
						__lowercase  :      List[Any]      		=				["""TranslationTool"""]

if TYPE_CHECKING:
						from .agents import Agent, AzureOpenAiAgent, HfAgent, LocalAgent, OpenAiAgent
						from .base import PipelineTool, RemoteTool, Tool, launch_gradio_demo, load_tool

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .document_question_answering import DocumentQuestionAnsweringTool
												from .image_captioning import ImageCaptioningTool
												from .image_question_answering import ImageQuestionAnsweringTool
												from .image_segmentation import ImageSegmentationTool
												from .speech_to_text import SpeechToTextTool
												from .text_classification import TextClassificationTool
												from .text_question_answering import TextQuestionAnsweringTool
												from .text_summarization import TextSummarizationTool
												from .text_to_speech import TextToSpeechTool
												from .translation import TranslationTool
else:
						import sys

						__lowercase  :      Dict      		=				_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)









'''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 							a_       (						lowercase=2							,						lowercase=True							,						lowercase=False    )			->Optional[Any]:






				'''simple docstring'''

				__lowerCamelCase		:      List[str]											=					Accelerator(step_scheduler_with_optimizer=lowercase							,						split_batches=lowercase    )
				__lowerCamelCase		:      Union[str, Any]											=					torch.nn.Linear(2							,						4    )
				__lowerCamelCase		:      str											=					torch.optim.AdamW(model.parameters()							,						lr=1.0    )
				__lowerCamelCase		:      List[str]											=					torch.optim.lr_scheduler.OneCycleLR(lowercase							,						max_lr=0.0_1							,						steps_per_epoch=2							,						epochs=1    )
				__lowerCamelCase						,					__lowerCamelCase						,					__lowerCamelCase		:      List[Any]											=					accelerator.prepare(lowercase							,						lowercase							,						lowercase    )

				# 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_       (						lowercase=2							,						lowercase=True							,						lowercase=False    )			->Dict:






				'''simple docstring'''

				__lowerCamelCase		:      Dict											=					Accelerator(step_scheduler_with_optimizer=lowercase							,						split_batches=lowercase    )
				__lowerCamelCase		:      Tuple											=					torch.nn.Linear(2							,						4    )
				__lowerCamelCase		:      Optional[Any]											=					torch.optim.AdamW(model.parameters()							,						lr=1.0    )
				__lowerCamelCase		:      Optional[Any]											=					torch.optim.lr_scheduler.LambdaLR(lowercase							,						lr_lambda=lambda lowercase    : 1 - n / 10    )
				__lowerCamelCase						,					__lowerCamelCase						,					__lowerCamelCase		:      Optional[int]											=					accelerator.prepare(lowercase							,						lowercase							,						lowercase    )

				# Optimizer has stepped
				__lowerCamelCase		:      List[Any]											=					False
				scheduler.step()
				__lowerCamelCase		:      List[Any]											=					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		:      Any											=					True
				scheduler.step()
				if not step_scheduler_with_optimizer:
								__lowerCamelCase		:      int											=					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_       (						lowercase = 2    )			->Dict:






				'''simple docstring'''

				from transformers import get_linear_schedule_with_warmup

				__lowerCamelCase		:      str											=					[1, 2, 4]
				for num_steps in steps:
								__lowerCamelCase		:      List[Any]											=					GradientAccumulationPlugin(num_steps=lowercase							,						adjust_scheduler=num_steps > 1    )
								__lowerCamelCase		:      Dict											=					Accelerator(gradient_accumulation_plugin=lowercase    )
								__lowerCamelCase		:      Any											=					torch.nn.Linear(2							,						4    )
								__lowerCamelCase		:      List[Any]											=					torch.optim.AdamW(model.parameters()							,						lr=1_0.0    )
								__lowerCamelCase		:      Optional[Any]											=					get_linear_schedule_with_warmup(optimizer=lowercase							,						num_warmup_steps=0							,						num_training_steps=20    )

								__lowerCamelCase						,					__lowerCamelCase						,					__lowerCamelCase		:      Optional[int]											=					accelerator.prepare(lowercase							,						lowercase							,						lowercase    )

								for i in range(10 * num_steps    ):
												with accelerator.accumulate(lowercase    ):
																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 _snake_case   ( unittest.TestCase   ):
							"""simple docstring"""





							def 		__SCREAMING_SNAKE_CASE(   self	)     ->Tuple:



											"""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	)     ->Tuple:



											"""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	)     ->Optional[Any]:



											"""simple docstring"""







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







							def 		__SCREAMING_SNAKE_CASE(   self	)     ->Dict:



											"""simple docstring"""







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







							def 		__SCREAMING_SNAKE_CASE(   self	)     ->Optional[int]:



											"""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	)     ->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 		__SCREAMING_SNAKE_CASE(   self	)     ->Any:



											"""simple docstring"""







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







							def 		__SCREAMING_SNAKE_CASE(   self	)     ->List[Any]:



											"""simple docstring"""







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







							@require_huggingface_suite
							def 		__SCREAMING_SNAKE_CASE(   self	)     ->Dict:



											"""simple docstring"""







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





