

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 	__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  Dict  ,      lowerCAmelCase:  List[Any]  ,      lowerCAmelCase:  int  ,      lowerCAmelCase:  Dict )   ->       Any:
				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 	__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  Any  ,      lowerCAmelCase:  List[Any]  ,      lowerCAmelCase:  List[Any]  ,      lowerCAmelCase:  Optional[int]  ,      lowerCAmelCase:  int=True )   ->       Optional[int]:
				model.train()
				_UpperCAmelCase       :		str 		=    model(lowerCAmelCase )
				_UpperCAmelCase       :		Tuple 		=    F.mse_loss(lowerCAmelCase  ,      target.to(output.device ) )
				if not do_backward:
								loss /= accelerator.gradient_accumulation_steps
								loss.backward()
				else:
								accelerator.backward(lowerCAmelCase )

def 	__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  int  ,      lowerCAmelCase:  Tuple=False )   ->       Union[str, Any]:
				set_seed(42 )
				_UpperCAmelCase       :		Dict 		=    RegressionModel()
				_UpperCAmelCase       :		Any 		=    deepcopy(lowerCAmelCase )
				_UpperCAmelCase       :		str 		=    RegressionDataset(length=80 )
				_UpperCAmelCase       :		Optional[int] 		=    DataLoader(lowerCAmelCase  ,      batch_size=16 )
				model.to(accelerator.device )
				if sched:
								_UpperCAmelCase       :		Dict 		=    AdamW(params=model.parameters()  ,      lr=1E-3 )
								_UpperCAmelCase       :		Optional[Any] 		=    AdamW(params=ddp_model.parameters()  ,      lr=1E-3 )
								_UpperCAmelCase       :		Union[str, Any] 		=    LambdaLR(lowerCAmelCase  ,      lr_lambda=lambda lowerCAmelCase : epoch**0.65 )
								_UpperCAmelCase       :		Optional[Any] 		=    LambdaLR(lowerCAmelCase  ,      lr_lambda=lambda lowerCAmelCase : epoch**0.65 )
				# Make a copy of `model`
				if sched:
								_UpperCAmelCase       :		Union[str, Any] 		=    accelerator.prepare(lowerCAmelCase  ,      lowerCAmelCase  ,      lowerCAmelCase  ,      lowerCAmelCase )
				else:
								_UpperCAmelCase       :		int 		=    accelerator.prepare(lowerCAmelCase  ,      lowerCAmelCase )
				if sched:
								return (model, opt, sched, dataloader, ddp_model, ddp_opt, ddp_sched)
				return model, ddp_model, dataloader

def 	__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  Optional[int] )   ->       str:
				# Test when on a single CPU or GPU that the context manager does nothing
				_UpperCAmelCase       :		Union[str, Any] 		=    get_training_setup(lowerCAmelCase )
				# Use a single batch
				_UpperCAmelCase       :		Any 		=    next(iter(lowerCAmelCase ) ).values()
				for iteration in range(3 ):
								# Gather the distributed inputs and targs for the base model
								_UpperCAmelCase       :		List[str] 		=    accelerator.gather((ddp_input, ddp_target) )
								_UpperCAmelCase       :		Optional[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 )

								# 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 )
								_UpperCAmelCase       :		Dict 		=    ddp_input[torch.randperm(len(lowerCAmelCase ) )]

def 	__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  List[Any] )   ->       Dict:
				# Test on distributed setup that context manager behaves properly
				_UpperCAmelCase       :		List[str] 		=    get_training_setup(lowerCAmelCase )
				# Use a single batch
				_UpperCAmelCase       :		Union[str, Any] 		=    next(iter(lowerCAmelCase ) ).values()
				for iteration in range(3 ):
								# Gather the distributed inputs and targs for the base model
								_UpperCAmelCase       :		Optional[int] 		=    accelerator.gather((ddp_input, ddp_target) )
								_UpperCAmelCase       :		Dict 		=    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 )
								_UpperCAmelCase       :		Tuple 		=    ddp_input[torch.randperm(len(lowerCAmelCase ) )]

def 	__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  List[Any]=False  ,      lowerCAmelCase:  Optional[int]=False )   ->       Union[str, Any]:
				_UpperCAmelCase       :		str 		=    Accelerator(
				    split_batches=lowerCAmelCase  ,      dispatch_batches=lowerCAmelCase  ,      gradient_accumulation_steps=2 )
				# Test that context manager behaves properly
				_UpperCAmelCase       :		int 		=    get_training_setup(lowerCAmelCase )
				for iteration, batch in enumerate(lowerCAmelCase ):
								_UpperCAmelCase       :		List[str] 		=    batch.values()
								# Gather the distributed inputs and targs for the base model
								_UpperCAmelCase       :		Tuple 		=    accelerator.gather((ddp_input, ddp_target) )
								_UpperCAmelCase       :		Dict 		=    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 )
								_UpperCAmelCase       :		int 		=    ddp_input[torch.randperm(len(lowerCAmelCase ) )]
				GradientState._reset_state()

def 	__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  List[str]=False  ,      lowerCAmelCase:  Optional[Any]=False )   ->       Optional[int]:
				_UpperCAmelCase       :		Optional[int] 		=    Accelerator(
				    split_batches=lowerCAmelCase  ,      dispatch_batches=lowerCAmelCase  ,      gradient_accumulation_steps=2 )
				# Test that context manager behaves properly
				_UpperCAmelCase       :		Optional[Any] 		=    get_training_setup(lowerCAmelCase  ,      lowerCAmelCase )
				for iteration, batch in enumerate(lowerCAmelCase ):
								_UpperCAmelCase       :		Optional[int] 		=    batch.values()
								# Gather the distributed inputs and targs for the base model
								_UpperCAmelCase       :		int 		=    accelerator.gather((ddp_input, ddp_target) )
								_UpperCAmelCase       :		Dict 		=    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'
								_UpperCAmelCase       :		Tuple 		=    (((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 	__SCREAMING_SNAKE_CASE							(				)   ->       Any:
				_UpperCAmelCase       :		Optional[int] 		=    Accelerator()

				_UpperCAmelCase       :		Dict 		=    RegressionDataset(length=80 )
				_UpperCAmelCase       :		Optional[Any] 		=    DataLoader(lowerCAmelCase  ,      batch_size=16 )
				_UpperCAmelCase       :		Any 		=    RegressionDataset(length=96 )
				_UpperCAmelCase       :		Union[str, Any] 		=    DataLoader(lowerCAmelCase  ,      batch_size=16 )
				_UpperCAmelCase       :		List[Any] 		=    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 	__SCREAMING_SNAKE_CASE							(				)   ->       Dict:
				_UpperCAmelCase       :		Optional[int] 		=    Accelerator()
				_UpperCAmelCase       :		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 	__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  Dict )   ->       Union[str, Any]:
				# For xla_spawn (TPUs)
				main()


if __name__ == "__main__":
					main()





import unittest

from transformers import load_tool

from .test_tools_common import ToolTesterMixin


SCREAMING_SNAKE_CASE_        =      '\nHugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf originally as a company that developed a chatbot app targeted at teenagers.[2] After open-sourcing the model behind the chatbot, the company pivoted to focus on being a platform for machine learning.\n\nIn March 2021, Hugging Face raised $40 million in a Series B funding round.[3]\n\nOn April 28, 2021, the company launched the BigScience Research Workshop in collaboration with several other research groups to release an open large language model.[4] In 2022, the workshop concluded with the announcement of BLOOM, a multilingual large language model with 176 billion parameters.[5]\n'

class 				a  (	unittest.TestCase							, UpperCAmelCase				):


						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										_UpperCAmelCase       :		Tuple 		=    load_tool("text-question-answering"	)
										self.tool.setup()
										_UpperCAmelCase       :		str 		=    load_tool("text-question-answering"						,					remote=A_	)


						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										_UpperCAmelCase       :		List[str] 		=    self.tool(A_						,					"What did Hugging Face do in April 2021?"	)
										self.assertEqual(A_						,					"launched the BigScience Research Workshop"	)


						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										_UpperCAmelCase       :		Optional[Any] 		=    self.remote_tool(A_						,					"What did Hugging Face do in April 2021?"	)
										self.assertEqual(A_						,					"launched the BigScience Research Workshop"	)


						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										_UpperCAmelCase       :		int 		=    self.tool(text=A_						,					question="What did Hugging Face do in April 2021?"	)
										self.assertEqual(A_						,					"launched the BigScience Research Workshop"	)





						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										_UpperCAmelCase       :		List[Any] 		=    self.remote_tool(text=A_						,					question="What did Hugging Face do in April 2021?"	)
										self.assertEqual(A_						,					"launched the BigScience Research Workshop"	)


