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_=2							,	lowerCamelCase_=True							,	lowerCamelCase_=False  ):



			"""simple docstring"""





			_snake_case              =      Accelerator(step_scheduler_with_optimizer=lowerCamelCase_							,	split_batches=lowerCamelCase_  )
			_snake_case              =      torch.nn.Linear(2							,	4  )
			_snake_case              =      torch.optim.AdamW(model.parameters()							,	lr=1.0  )
			_snake_case              =      torch.optim.lr_scheduler.OneCycleLR(lowerCamelCase_							,	max_lr=0.0_1							,	steps_per_epoch=2							,	epochs=1  )
			_snake_case       , _snake_case       , _snake_case              =      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_=2							,	lowerCamelCase_=True							,	lowerCamelCase_=False  ):



			"""simple docstring"""





			_snake_case              =      Accelerator(step_scheduler_with_optimizer=lowerCamelCase_							,	split_batches=lowerCamelCase_  )
			_snake_case              =      torch.nn.Linear(2							,	4  )
			_snake_case              =      torch.optim.AdamW(model.parameters()							,	lr=1.0  )
			_snake_case              =      torch.optim.lr_scheduler.LambdaLR(lowerCamelCase_							,	lr_lambda=lambda lowerCamelCase_  : 1 - n / 10  )
			_snake_case       , _snake_case       , _snake_case              =      accelerator.prepare(lowerCamelCase_							,	lowerCamelCase_							,	lowerCamelCase_  )

			# Optimizer has stepped
			_snake_case              =      False
			scheduler.step()
			_snake_case              =      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
			_snake_case              =      True
			scheduler.step()
			if not step_scheduler_with_optimizer:
						_snake_case              =      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_ = 2  ):



			"""simple docstring"""





			from transformers import get_linear_schedule_with_warmup

			_snake_case              =      [1, 2, 4]
			for num_steps in steps:
						_snake_case              =      GradientAccumulationPlugin(num_steps=lowerCamelCase_							,	adjust_scheduler=num_steps > 1  )
						_snake_case              =      Accelerator(gradient_accumulation_plugin=lowerCamelCase_  )
						_snake_case              =      torch.nn.Linear(2							,	4  )
						_snake_case              =      torch.optim.AdamW(model.parameters()							,	lr=1_0.0  )
						_snake_case              =      get_linear_schedule_with_warmup(optimizer=lowerCamelCase_							,	num_warmup_steps=0							,	num_training_steps=20  )

						_snake_case       , _snake_case       , _snake_case              =      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 	_SCREAMING_SNAKE_CASE							(     unittest.TestCase							):






		"""simple docstring"""







		def 				_a				(			self    :				Dict   ):






					"""simple docstring"""
					debug_launcher(partial(__SCREAMING_SNAKE_CASE  ,					num_processes=1   )  ,					num_processes=1   )
					debug_launcher(partial(__SCREAMING_SNAKE_CASE  ,					num_processes=1  ,					split_batches=__SCREAMING_SNAKE_CASE   )  ,					num_processes=1   )



		def 				_a				(			self    :				List[Any]   ):






					"""simple docstring"""
					debug_launcher(partial(__SCREAMING_SNAKE_CASE  ,					num_processes=1   )  ,					num_processes=1   )
					debug_launcher(partial(__SCREAMING_SNAKE_CASE  ,					num_processes=1  ,					split_batches=__SCREAMING_SNAKE_CASE   )  ,					num_processes=1   )



		def 				_a				(			self    :				Optional[Any]   ):






					"""simple docstring"""
					debug_launcher(partial(__SCREAMING_SNAKE_CASE  ,					num_processes=1  ,					step_scheduler_with_optimizer=__SCREAMING_SNAKE_CASE   )  ,					num_processes=1   )



		def 				_a				(			self    :				Optional[int]   ):






					"""simple docstring"""
					debug_launcher(partial(__SCREAMING_SNAKE_CASE  ,					num_processes=1  ,					step_scheduler_with_optimizer=__SCREAMING_SNAKE_CASE   )  ,					num_processes=1   )



		def 				_a				(			self    :				List[str]   ):






					"""simple docstring"""
					AcceleratorState._reset_state(__SCREAMING_SNAKE_CASE   )
					debug_launcher(__SCREAMING_SNAKE_CASE   )
					debug_launcher(partial(__SCREAMING_SNAKE_CASE  ,					num_processes=1  ,					split_batches=__SCREAMING_SNAKE_CASE   )  ,					num_processes=1   )



		def 				_a				(			self    :				Tuple   ):






					"""simple docstring"""
					AcceleratorState._reset_state(__SCREAMING_SNAKE_CASE   )
					debug_launcher(__SCREAMING_SNAKE_CASE   )
					debug_launcher(partial(__SCREAMING_SNAKE_CASE  ,					num_processes=1  ,					split_batches=__SCREAMING_SNAKE_CASE   )  ,					num_processes=1   )



		def 				_a				(			self    :				str   ):






					"""simple docstring"""
					AcceleratorState._reset_state(__SCREAMING_SNAKE_CASE   )
					debug_launcher(partial(__SCREAMING_SNAKE_CASE  ,					step_scheduler_with_optimizer=__SCREAMING_SNAKE_CASE   )   )



		def 				_a				(			self    :				List[Any]   ):






					"""simple docstring"""
					AcceleratorState._reset_state(__SCREAMING_SNAKE_CASE   )
					debug_launcher(partial(__SCREAMING_SNAKE_CASE  ,					step_scheduler_with_optimizer=__SCREAMING_SNAKE_CASE   )   )







		@require_huggingface_suite
		def 				_a				(			self    :				int   ):






					"""simple docstring"""
					AcceleratorState._reset_state(__SCREAMING_SNAKE_CASE   )
					debug_launcher(partial(__SCREAMING_SNAKE_CASE  ,					num_processes=1   )   )
					debug_launcher(__SCREAMING_SNAKE_CASE   )


import unittest

from transformers import BlenderbotTokenizer, BlenderbotTokenizerFast
from transformers.utils import cached_property

class 	_SCREAMING_SNAKE_CASE							(     unittest.TestCase							):






		"""simple docstring"""







		@cached_property
		def 				_a				(			self    :				Tuple   ):






					"""simple docstring"""
					return BlenderbotTokenizer.from_pretrained("facebook/blenderbot-3B"   )



		@cached_property
		def 				_a				(			self    :				Tuple   ):






					"""simple docstring"""
					return BlenderbotTokenizerFast.from_pretrained("facebook/blenderbot-3B"   )



		def 				_a				(			self    :				Optional[Any]   ):






					"""simple docstring"""
					_snake_case              =      self.tokenizer_ab
					_snake_case              =      " I am a small frog."
					_snake_case              =      tok([src_text]  ,					padding=__SCREAMING_SNAKE_CASE  ,					truncation=__SCREAMING_SNAKE_CASE   )["input_ids"]
					_snake_case              =      tok.batch_decode(__SCREAMING_SNAKE_CASE  ,					skip_special_tokens=__SCREAMING_SNAKE_CASE  ,					clean_up_tokenization_spaces=__SCREAMING_SNAKE_CASE   )[0]
					assert src_text == decoded



		def 				_a				(			self    :				str   ):






					"""simple docstring"""
					_snake_case              =      self.rust_tokenizer_ab
					_snake_case              =      " I am a small frog."
					_snake_case              =      tok([src_text]  ,					padding=__SCREAMING_SNAKE_CASE  ,					truncation=__SCREAMING_SNAKE_CASE   )["input_ids"]
					_snake_case              =      tok.batch_decode(__SCREAMING_SNAKE_CASE  ,					skip_special_tokens=__SCREAMING_SNAKE_CASE  ,					clean_up_tokenization_spaces=__SCREAMING_SNAKE_CASE   )[0]
					assert src_text == decoded



		def 				_a				(			self    :				Optional[Any]   ):






					"""simple docstring"""
					assert self.tokenizer_ab.add_prefix_space
					assert self.tokenizer_ab([" Sam", "Sam"]   ).input_ids == [[5_502, 2], [5_502, 2]]







		def 				_a				(			self    :				str   ):






					"""simple docstring"""
					assert self.rust_tokenizer_ab.add_prefix_space
					assert self.rust_tokenizer_ab([" Sam", "Sam"]   ).input_ids == [[5_502, 2], [5_502, 2]]

