






import json
import os
from datetime import date
from pathlib import Path

from tabulate import DataRow, TableFormat, tabulate


UpperCAmelCase_ :						List[Any]      =      TableFormat(
    lineabove=None,
    linebelowheader=None,
    linebetweenrows=None,
    linebelow=None,
    headerrow=DataRow('''''', '''|''', '''|'''),
    datarow=DataRow('''''', '''|''', '''|'''),
    padding=1,
    with_header_hide=None,
)


UpperCAmelCase_ :						List[str]      =      []
UpperCAmelCase_ :						Any      =      []

UpperCAmelCase_ :						Any      =      {'''type''': '''section''', '''text''': {'''type''': '''plain_text''', '''text''': '''No failed tests! 🤗''', '''emoji''': True}}

UpperCAmelCase_ :						Optional[int]      =      [
    {
        '''type''': '''header''',
        '''text''': {
            '''type''': '''plain_text''',
            '''text''': F'''🤗 Accelerate nightly {os.environ.get("TEST_TYPE", "")} test results''',
            '''emoji''': True,
        },
    }
]

UpperCAmelCase_ :						List[str]      =      0
for log in Path().glob('''*.log'''):
	UpperCAmelCase_ :						Optional[int]      =      0
	with open(log, '''r''') as f:
		for line in f:
			UpperCAmelCase_ :						Optional[int]      =      json.loads(line)
			if line.get('''nodeid''', '''''') != "":
				UpperCAmelCase_ :						List[str]      =      line['''nodeid''']
				if line.get('''duration''', None) is not None:
					UpperCAmelCase_ :						Optional[int]      =      F'''{line["duration"]:.4f}'''
					if line.get('''outcome''', '''''') == "failed":
						section_num_failed += 1
						failed.append([test, duration, log.name.split('''_''')[0]])
						total_num_failed += 1
	group_info.append([str(log), section_num_failed, failed])
	UpperCAmelCase_ :						Optional[Any]      =      []
	log.unlink()

UpperCAmelCase_ :						Optional[int]      =      ''''''
UpperCAmelCase_ :						Dict      =      []
if total_num_failed > 0:
	for name, num_failed, failed_tests in group_info:
		if num_failed > 0:
			if num_failed == 1:
				message += F"*{name[1:]}: {num_failed} failed test*\n"
			else:
				message += F"*{name[1:]}: {num_failed} failed tests*\n"
			UpperCAmelCase_ :						Tuple      =      []
			UpperCAmelCase_ :						Tuple      =      {}
			for test in failed_tests:
				UpperCAmelCase_ :						Optional[Any]      =      test[0].split('''::''')
				UpperCAmelCase_ :						str      =      data[0].split('''/''')[-1]
				if data[0] not in filesafailed:
					UpperCAmelCase_ :						str      =      [data[1:]]
				else:
					filesafailed[data[0]] += [data[1:]]
				failed_table.append(data)

			UpperCAmelCase_ :						List[Any]      =      [test[0] for test in failed_table]
			UpperCAmelCase_ :						Tuple      =      list(set(files))
			# Count number of instances in failed_tests
			UpperCAmelCase_ :						Tuple      =      []
			for file in individual_files:
				table.append([file, len(filesafailed[file])])

			UpperCAmelCase_ :						Dict      =      tabulate(
			    table,
			    headers=['''Test Location''', '''Num Failed'''],
			    tablefmt=hf_table_format,
			    stralign='''right''',
			)
			message += F"\n```\n{failed_table}\n```"
			all_filesafailed.append(filesafailed)
	if len(message) > 30_00:
		UpperCAmelCase_ :						str      =      '''Too many failed tests, please see the full report in the Action results.'''
		UpperCAmelCase_ :						str      =      len(err) + 10
		UpperCAmelCase_ :						Optional[Any]      =      message[: 30_00 - offset] + F'''\n...\n```\n{err}'''
	print(F'''### {message}''')
else:
	UpperCAmelCase_ :						Any      =      '''No failed tests! 🤗'''
	print(F'''## {message}''')
	payload.append(no_error_payload)

if os.environ.get('''TEST_TYPE''', '''''') != "":
	from slack_sdk import WebClient

	UpperCAmelCase_ :						List[str]      =      WebClient(token=os.environ['''SLACK_API_TOKEN'''])
	if message != "No failed tests! 🤗":
		UpperCAmelCase_ :						Tuple      =      {
		    '''type''': '''section''',
		    '''text''': {
		        '''type''': '''mrkdwn''',
		        '''text''': message,
		    },
		}
		payload.append(md_report)
		UpperCAmelCase_ :						Dict      =      {
		    '''type''': '''section''',
		    '''text''': {
		        '''type''': '''mrkdwn''',
		        '''text''': '''*For more details:*''',
		    },
		    '''accessory''': {
		        '''type''': '''button''',
		        '''text''': {
		            '''type''': '''plain_text''',
		            '''text''': '''Check Action results''',
		            '''emoji''': True,
		        },
		        '''url''': F'''https://github.com/{os.environ["GITHUB_REPOSITORY"]}/actions/runs/{os.environ["GITHUB_RUN_ID"]}''',
		    },
		}
		payload.append(action_button)
		UpperCAmelCase_ :						str      =      {
		    '''type''': '''context''',
		    '''elements''': [
		        {
		            '''type''': '''plain_text''',
		            '''text''': F'''Nightly {os.environ.get("TEST_TYPE")} test results for {date.today()}''',
		        }
		    ],
		}
		payload.append(date_report)
	UpperCAmelCase_ :						Any      =      client.chat_postMessage(channel='''#accelerate-ci-daily''', text=message, blocks=payload)
	UpperCAmelCase_ :						Any      =      response.data['''ts''']
	for failed_file in all_filesafailed:
		for test_location, test_failures in failed_file.items():
			# Keep only the first instance of the test name
			UpperCAmelCase_ :						int      =      ''''''
			for i, row in enumerate(test_failures):
				if row[0] != test_class:
					UpperCAmelCase_ :						Optional[Any]      =      row[0]
				else:
					UpperCAmelCase_ :						List[Any]      =      ''''''

			UpperCAmelCase_ :						Tuple      =      {
			    '''type''': '''section''',
			    '''text''': {
			        '''type''': '''mrkdwn''',
			        '''text''': F'''Test location: {test_location}\n```\n{tabulate(test_failures, headers=["Class", "Test"], tablefmt=hf_table_format, stralign="right")}\n```''',
			    },
			}

			client.chat_postMessage(
			    channel='''#accelerate-ci-daily''',
			    thread_ts=ts,
			    blocks=[payload],
			)


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     _a					(						lowerCamelCase,	lowerCamelCase,	lowerCamelCase,	lowerCamelCase							):
 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     _a					(						lowerCamelCase,	lowerCamelCase,	lowerCamelCase,	lowerCamelCase,	lowerCamelCase=True							):
 model.train()
 lowerCamelCase		:	Dict							=      model(lowerCamelCase							)
 lowerCamelCase		:	Any							=      F.mse_loss(lowerCamelCase,	target.to(output.device							)							)
 if not do_backward:
  loss /= accelerator.gradient_accumulation_steps
  loss.backward()
 else:
  accelerator.backward(lowerCamelCase							)





def     _a					(						lowerCamelCase,	lowerCamelCase=False							):
 set_seed(42							)
 lowerCamelCase		:	Tuple							=      RegressionModel()
 lowerCamelCase		:	Any							=      deepcopy(lowerCamelCase							)
 lowerCamelCase		:	Any							=      RegressionDataset(length=80							)
 lowerCamelCase		:	Dict							=      DataLoader(lowerCamelCase,	batch_size=16							)
 model.to(accelerator.device							)
 if sched:
  lowerCamelCase		:	int							=      AdamW(params=model.parameters(),	lr=1e-3							)
  lowerCamelCase		:	Optional[Any]							=      AdamW(params=ddp_model.parameters(),	lr=1e-3							)
  lowerCamelCase		:	str							=      LambdaLR(lowerCamelCase,	lr_lambda=lambda lowerCamelCase							: epoch**0.6_5							)
  lowerCamelCase		:	Tuple							=      LambdaLR(lowerCamelCase,	lr_lambda=lambda lowerCamelCase							: epoch**0.6_5							)
 # Make a copy of `model`
 if sched:
  lowerCamelCase			,    lowerCamelCase			,    lowerCamelCase			,    lowerCamelCase		:	Optional[int]							=      accelerator.prepare(lowerCamelCase,	lowerCamelCase,	lowerCamelCase,	lowerCamelCase							)
 else:
  lowerCamelCase			,    lowerCamelCase		:	List[Any]							=      accelerator.prepare(lowerCamelCase,	lowerCamelCase							)
 if sched:
  return (model, opt, sched, dataloader, ddp_model, ddp_opt, ddp_sched)
 return model, ddp_model, dataloader





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





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





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





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

 lowerCamelCase		:	Optional[Any]							=      RegressionDataset(length=80							)
 lowerCamelCase		:	List[str]							=      DataLoader(lowerCamelCase,	batch_size=16							)
 lowerCamelCase		:	int							=      RegressionDataset(length=96							)
 lowerCamelCase		:	Optional[int]							=      DataLoader(lowerCamelCase,	batch_size=16							)
 lowerCamelCase			,    lowerCamelCase		:	Optional[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     _a					(						):
 lowerCamelCase		:	List[Any]							=      Accelerator()
 lowerCamelCase		:	int							=      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     _a					(						lowerCamelCase							):
 # For xla_spawn (TPUs)
 main()


if __name__ == "__main__":
      main()




