


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  _snake_case       (					A			,   A			,   A			,   A	)						->   Tuple:
							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  _snake_case       (					A			,   A			,   A			,   A			,   A=True	)						->   Optional[Any]:
							model.train()
							lowerCAmelCase__	       =     model(A	)
							lowerCAmelCase__	       =     F.mse_loss(A			,   target.to(output.device	)	)
							if not do_backward:
														loss /= accelerator.gradient_accumulation_steps
														loss.backward()
							else:
														accelerator.backward(A	)


def  _snake_case       (					A			,   A=False	)						->   Tuple:
							set_seed(42	)
							lowerCAmelCase__	       =     RegressionModel()
							lowerCAmelCase__	       =     deepcopy(A	)
							lowerCAmelCase__	       =     RegressionDataset(length=80	)
							lowerCAmelCase__	       =     DataLoader(A			,   batch_size=16	)
							model.to(accelerator.device	)
							if sched:
														lowerCAmelCase__	       =     AdamW(params=model.parameters()			,   lr=1E-3	)
														lowerCAmelCase__	       =     AdamW(params=ddp_model.parameters()			,   lr=1E-3	)
														lowerCAmelCase__	       =     LambdaLR(A			,   lr_lambda=lambda A	: epoch**0.65	)
														lowerCAmelCase__	       =     LambdaLR(A			,   lr_lambda=lambda A	: epoch**0.65	)
							# Make a copy of `model`
							if sched:
														lowerCAmelCase__       , lowerCAmelCase__       , lowerCAmelCase__       , lowerCAmelCase__	       =     accelerator.prepare(A			,   A			,   A			,   A	)
							else:
														lowerCAmelCase__       , lowerCAmelCase__	       =     accelerator.prepare(A			,   A	)
							if sched:
														return (model, opt, sched, dataloader, ddp_model, ddp_opt, ddp_sched)
							return model, ddp_model, dataloader


def  _snake_case       (					A	)						->   int:
							# Test when on a single CPU or GPU that the context manager does nothing
							lowerCAmelCase__       , lowerCAmelCase__       , lowerCAmelCase__	       =     get_training_setup(A	)
							# Use a single batch
							lowerCAmelCase__       , lowerCAmelCase__	       =     next(iter(A	)	).values()
							for iteration in range(3	):
														# Gather the distributed inputs and targs for the base model
														lowerCAmelCase__       , lowerCAmelCase__	       =     accelerator.gather((ddp_input, ddp_target)	)
														lowerCAmelCase__       , lowerCAmelCase__	       =     input.to(accelerator.device	), target.to(accelerator.device	)
														# Perform our initial ground truth step in non "DDP"
														step_model(A			,   A			,   A			,   A	)
														# Do "gradient accumulation" (noop)
														if iteration % 2 == 0:
																					# Accumulate grads locally
																					with accelerator.no_sync(A	):
																												step_model(A			,   A			,   A			,   A	)
														else:
																					# Sync grads
																					step_model(A			,   A			,   A			,   A	)

														# Since `no_sync` is a noop, `ddp_model` and `model` grads should always be in sync
														check_model_parameters(A			,   A			,   A			,   A	)
														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	)
														lowerCAmelCase__	       =     ddp_input[torch.randperm(len(A	)	)]


def  _snake_case       (					A	)						->   int:
							# Test on distributed setup that context manager behaves properly
							lowerCAmelCase__       , lowerCAmelCase__       , lowerCAmelCase__	       =     get_training_setup(A	)
							# Use a single batch
							lowerCAmelCase__       , lowerCAmelCase__	       =     next(iter(A	)	).values()
							for iteration in range(3	):
														# Gather the distributed inputs and targs for the base model
														lowerCAmelCase__       , lowerCAmelCase__	       =     accelerator.gather((ddp_input, ddp_target)	)
														lowerCAmelCase__       , lowerCAmelCase__	       =     input.to(accelerator.device	), target.to(accelerator.device	)
														# Perform our initial ground truth step in non "DDP"
														step_model(A			,   A			,   A			,   A	)
														# Do "gradient accumulation" (noop)
														if iteration % 2 == 0:
																					# Accumulate grads locally
																					with accelerator.no_sync(A	):
																												step_model(A			,   A			,   A			,   A	)
														else:
																					# Sync grads
																					step_model(A			,   A			,   A			,   A	)

														# 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	)
														lowerCAmelCase__	       =     ddp_input[torch.randperm(len(A	)	)]


def  _snake_case       (					A=False			,   A=False	)						->   Optional[Any]:
							lowerCAmelCase__	       =     Accelerator(
							    split_batches=A			,   dispatch_batches=A			,   gradient_accumulation_steps=2	)
							# Test that context manager behaves properly
							lowerCAmelCase__       , lowerCAmelCase__       , lowerCAmelCase__	       =     get_training_setup(A	)
							for iteration, batch in enumerate(A	):
														lowerCAmelCase__       , lowerCAmelCase__	       =     batch.values()
														# Gather the distributed inputs and targs for the base model
														lowerCAmelCase__       , lowerCAmelCase__	       =     accelerator.gather((ddp_input, ddp_target)	)
														lowerCAmelCase__       , lowerCAmelCase__	       =     input.to(accelerator.device	), target.to(accelerator.device	)
														# Perform our initial ground truth step in non "DDP"
														step_model(A			,   A			,   A			,   A			,   A	)
														# Do "gradient accumulation" (noop)
														with accelerator.accumulate(A	):
																					step_model(A			,   A			,   A			,   A	)

														# 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(A	) - 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	)
														lowerCAmelCase__	       =     ddp_input[torch.randperm(len(A	)	)]
							GradientState._reset_state()


def  _snake_case       (					A=False			,   A=False	)						->   List[str]:
							lowerCAmelCase__	       =     Accelerator(
							    split_batches=A			,   dispatch_batches=A			,   gradient_accumulation_steps=2	)
							# Test that context manager behaves properly
							lowerCAmelCase__       , lowerCAmelCase__       , lowerCAmelCase__       , lowerCAmelCase__       , lowerCAmelCase__       , lowerCAmelCase__       , lowerCAmelCase__	       =     get_training_setup(A			,   A	)
							for iteration, batch in enumerate(A	):
														lowerCAmelCase__       , lowerCAmelCase__	       =     batch.values()
														# Gather the distributed inputs and targs for the base model
														lowerCAmelCase__       , lowerCAmelCase__	       =     accelerator.gather((ddp_input, ddp_target)	)
														lowerCAmelCase__       , lowerCAmelCase__	       =     input.to(accelerator.device	), target.to(accelerator.device	)
														# Perform our initial ground truth step in non "DDP"
														model.train()
														ddp_model.train()
														step_model(A			,   A			,   A			,   A			,   A	)
														opt.step()

														if ((iteration + 1) % 2 == 0) or ((iteration + 1) == len(A	)):
																					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(A	):
																					step_model(A			,   A			,   A			,   A	)
																					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"""
														lowerCAmelCase__	       =     (((iteration + 1) % 2) == 0) or ((iteration + 1) == len(A	))
														if accelerator.num_processes > 1:
																					check_model_parameters(A			,   A			,   A			,   A	)
														# Shuffle ddp_input on each iteration
														torch.manual_seed(1337 + iteration	)
							GradientState._reset_state()


def  _snake_case       (					)						->   List[str]:
							lowerCAmelCase__	       =     Accelerator()

							lowerCAmelCase__	       =     RegressionDataset(length=80	)
							lowerCAmelCase__	       =     DataLoader(A			,   batch_size=16	)
							lowerCAmelCase__	       =     RegressionDataset(length=96	)
							lowerCAmelCase__	       =     DataLoader(A			,   batch_size=16	)
							lowerCAmelCase__       , lowerCAmelCase__	       =     accelerator.prepare(A			,   A	)
							assert accelerator.gradient_state.active_dataloader is None
							for iteration, _ in enumerate(A	):
														assert id(accelerator.gradient_state.active_dataloader	) == id(A	)
														if iteration < len(A	) - 1:
																					assert not accelerator.gradient_state.end_of_dataloader
																					if iteration == 1:
																												for batch_num, _ in enumerate(A	):
																																			assert id(accelerator.gradient_state.active_dataloader	) == id(A	)
																																			if batch_num < len(A	) - 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  _snake_case       (					)						->   List[Any]:
							lowerCAmelCase__	       =     Accelerator()
							lowerCAmelCase__	       =     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(A	)
							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(A	)
							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(A			,   A	)

    # 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(A			,   A	)


def  _snake_case       (					A	)						->   Union[str, Any]:
							# For xla_spawn (TPUs)
							main()


if __name__ == "__main__":
		main()



'''simple docstring'''






from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


__UpperCAmelCase       =							logging.get_logger(__name__)

__UpperCAmelCase       =							{
    '''distilbert-base-uncased''': '''https://huggingface.co/distilbert-base-uncased/resolve/main/config.json''',
    '''distilbert-base-uncased-distilled-squad''': (
        '''https://huggingface.co/distilbert-base-uncased-distilled-squad/resolve/main/config.json'''
    ),
    '''distilbert-base-cased''': '''https://huggingface.co/distilbert-base-cased/resolve/main/config.json''',
    '''distilbert-base-cased-distilled-squad''': (
        '''https://huggingface.co/distilbert-base-cased-distilled-squad/resolve/main/config.json'''
    ),
    '''distilbert-base-german-cased''': '''https://huggingface.co/distilbert-base-german-cased/resolve/main/config.json''',
    '''distilbert-base-multilingual-cased''': (
        '''https://huggingface.co/distilbert-base-multilingual-cased/resolve/main/config.json'''
    ),
    '''distilbert-base-uncased-finetuned-sst-2-english''': (
        '''https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english/resolve/main/config.json'''
    ),
}




class 		a__    (							a__  ):





							'''simple docstring'''






							lowercase__      :       Optional[Any]     						=				"distilbert"
							lowercase__      :       Tuple     						=				{
							    "hidden_size": "dim",
							    "num_attention_heads": "n_heads",
							    "num_hidden_layers": "n_layers",
							}







							def __init__(		self ,   lowerCamelCase_=3_05_22 ,   lowerCamelCase_=5_12 ,   lowerCamelCase_=False ,   lowerCamelCase_=6 ,   lowerCamelCase_=12 ,   lowerCamelCase_=7_68 ,   lowerCamelCase_=4 * 7_68 ,   lowerCamelCase_=0.1 ,   lowerCamelCase_=0.1 ,   lowerCamelCase_="gelu" ,   lowerCamelCase_=0.02 ,   lowerCamelCase_=0.1 ,   lowerCamelCase_=0.2 ,   lowerCamelCase_=0 ,   **lowerCamelCase_ ,   )	->							Any:
														lowerCAmelCase__	       =     vocab_size
														lowerCAmelCase__	       =     max_position_embeddings
														lowerCAmelCase__	       =     sinusoidal_pos_embds
														lowerCAmelCase__	       =     n_layers
														lowerCAmelCase__	       =     n_heads
														lowerCAmelCase__	       =     dim
														lowerCAmelCase__	       =     hidden_dim
														lowerCAmelCase__	       =     dropout
														lowerCAmelCase__	       =     attention_dropout
														lowerCAmelCase__	       =     activation
														lowerCAmelCase__	       =     initializer_range
														lowerCAmelCase__	       =     qa_dropout
														lowerCAmelCase__	       =     seq_classif_dropout
														super().__init__(**lowerCamelCase_ ,   pad_token_id=lowerCamelCase_       )




class 		a__    (							a__  ):





							'''simple docstring'''






							@property
							def   __SCREAMING_SNAKE_CASE							(		self       )	->							Mapping[str, Mapping[int, str]]:
														if self.task == "multiple-choice":
																					lowerCAmelCase__	       =     {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
														else:
																					lowerCAmelCase__	       =     {0: '''batch''', 1: '''sequence'''}
														return OrderedDict(
														    [
														        ('''input_ids''', dynamic_axis),
														        ('''attention_mask''', dynamic_axis),
														    ]       )