





import argparse
import json
import math
import os
import time
import traceback
import zipfile
from collections import Counter

import requests






def 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				Dict       , UpperCamelCase__:				Optional[Any]=None     ):
		SCREAMING_SNAKE_CASE__								=   None
		if token is not None:
				SCREAMING_SNAKE_CASE__								=   {"""Accept""": """application/vnd.github+json""", """Authorization""": f'''Bearer {token}'''}

		SCREAMING_SNAKE_CASE__								=   f'''https://api.github.com/repos/huggingface/transformers/actions/runs/{workflow_run_id}/jobs?per_page=100'''
		SCREAMING_SNAKE_CASE__								=   requests.get(UpperCamelCase__       , headers=UpperCamelCase__     ).json()
		SCREAMING_SNAKE_CASE__								=   {}

		try:
				job_links.update({job["""name"""]: job["""html_url"""] for job in result["""jobs"""]}     )
				SCREAMING_SNAKE_CASE__								=   math.ceil((result["""total_count"""] - 100) / 100     )

				for i in range(UpperCamelCase__     ):
						SCREAMING_SNAKE_CASE__								=   requests.get(url + f'''&page={i + 2}'''       , headers=UpperCamelCase__     ).json()
						job_links.update({job["""name"""]: job["""html_url"""] for job in result["""jobs"""]}     )

				return job_links
		except Exception:
				print(f'''Unknown error, could not fetch links:\n{traceback.format_exc()}'''     )

		return {}






def 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				Dict       , UpperCamelCase__:				str=None     ):
		SCREAMING_SNAKE_CASE__								=   None
		if token is not None:
				SCREAMING_SNAKE_CASE__								=   {"""Accept""": """application/vnd.github+json""", """Authorization""": f'''Bearer {token}'''}

		SCREAMING_SNAKE_CASE__								=   f'''https://api.github.com/repos/huggingface/transformers/actions/runs/{worflow_run_id}/artifacts?per_page=100'''
		SCREAMING_SNAKE_CASE__								=   requests.get(UpperCamelCase__       , headers=UpperCamelCase__     ).json()
		SCREAMING_SNAKE_CASE__								=   {}

		try:
				artifacts.update({artifact["""name"""]: artifact["""archive_download_url"""] for artifact in result["""artifacts"""]}     )
				SCREAMING_SNAKE_CASE__								=   math.ceil((result["""total_count"""] - 100) / 100     )

				for i in range(UpperCamelCase__     ):
						SCREAMING_SNAKE_CASE__								=   requests.get(url + f'''&page={i + 2}'''       , headers=UpperCamelCase__     ).json()
						artifacts.update({artifact["""name"""]: artifact["""archive_download_url"""] for artifact in result["""artifacts"""]}     )

				return artifacts
		except Exception:
				print(f'''Unknown error, could not fetch links:\n{traceback.format_exc()}'''     )

		return {}






def 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				Any       , UpperCamelCase__:				List[Any]       , UpperCamelCase__:				Tuple       , UpperCamelCase__:				Optional[Any]     ):
		SCREAMING_SNAKE_CASE__								=   None
		if token is not None:
				SCREAMING_SNAKE_CASE__								=   {"""Accept""": """application/vnd.github+json""", """Authorization""": f'''Bearer {token}'''}

		SCREAMING_SNAKE_CASE__								=   requests.get(UpperCamelCase__       , headers=UpperCamelCase__       , allow_redirects=UpperCamelCase__     )
		SCREAMING_SNAKE_CASE__								=   result.headers["""Location"""]
		SCREAMING_SNAKE_CASE__								=   requests.get(UpperCamelCase__       , allow_redirects=UpperCamelCase__     )
		SCREAMING_SNAKE_CASE__								=   os.path.join(UpperCamelCase__       , f'''{artifact_name}.zip'''     )
		with open(UpperCamelCase__       , """wb"""     ) as fp:
				fp.write(response.content     )






def 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				int       , UpperCamelCase__:				Any=None     ):
		SCREAMING_SNAKE_CASE__								=   []
		SCREAMING_SNAKE_CASE__								=   []
		SCREAMING_SNAKE_CASE__								=   None

		with zipfile.ZipFile(UpperCamelCase__     ) as z:
				for filename in z.namelist():
						if not os.path.isdir(UpperCamelCase__     ):
								# read the file
								if filename in ["failures_line.txt", "summary_short.txt", "job_name.txt"]:
										with z.open(UpperCamelCase__     ) as f:
												for line in f:
														SCREAMING_SNAKE_CASE__								=   line.decode("""UTF-8"""     ).strip()
														if filename == "failures_line.txt":
																try:
																		# `error_line` is the place where `error` occurs
																		SCREAMING_SNAKE_CASE__								=   line[: line.index(""": """     )]
																		SCREAMING_SNAKE_CASE__								=   line[line.index(""": """     ) + len(""": """     ) :]
																		errors.append([error_line, error]     )
																except Exception:
																		# skip un-related lines
																		pass
														elif filename == "summary_short.txt" and line.startswith("""FAILED """     ):
																# `test` is the test method that failed
																SCREAMING_SNAKE_CASE__								=   line[len("""FAILED """     ) :]
																failed_tests.append(UpperCamelCase__     )
														elif filename == "job_name.txt":
																SCREAMING_SNAKE_CASE__								=   line

		if len(UpperCamelCase__     ) != len(UpperCamelCase__     ):
				raise ValueError(
				    f'''`errors` and `failed_tests` should have the same number of elements. Got {len(UpperCamelCase__     )} for `errors` '''
				    f'''and {len(UpperCamelCase__     )} for `failed_tests` instead. The test reports in {artifact_zip_path} have some'''
				    """ problem."""     )

		SCREAMING_SNAKE_CASE__								=   None
		if job_name and job_links:
				SCREAMING_SNAKE_CASE__								=   job_links.get(UpperCamelCase__       , UpperCamelCase__     )

		# A list with elements of the form (line of error, error, failed test)
		SCREAMING_SNAKE_CASE__								=   [x + [y] + [job_link] for x, y in zip(UpperCamelCase__       , UpperCamelCase__     )]

		return result






def 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				Tuple       , UpperCamelCase__:				Any=None     ):
		SCREAMING_SNAKE_CASE__								=   []

		SCREAMING_SNAKE_CASE__								=   [os.path.join(UpperCamelCase__       , UpperCamelCase__     ) for p in os.listdir(UpperCamelCase__     ) if p.endswith(""".zip"""     )]
		for p in paths:
				errors.extend(get_errors_from_single_artifact(UpperCamelCase__       , job_links=UpperCamelCase__     )     )

		return errors






def 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				int       , UpperCamelCase__:				str=None     ):
		SCREAMING_SNAKE_CASE__								=   Counter()
		counter.update([x[1] for x in logs]     )
		SCREAMING_SNAKE_CASE__								=   counter.most_common()
		SCREAMING_SNAKE_CASE__								=   {}
		for error, count in counts:
				if error_filter is None or error not in error_filter:
						SCREAMING_SNAKE_CASE__								=   {"""count""": count, """failed_tests""": [(x[2], x[0]) for x in logs if x[1] == error]}

		SCREAMING_SNAKE_CASE__								=   dict(sorted(r.items()       , key=lambda UpperCamelCase__     : item[1]["count"]       , reverse=UpperCamelCase__     )     )
		return r






def 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				Tuple     ):
		SCREAMING_SNAKE_CASE__								=   test.split("""::"""     )[0]
		if test.startswith("""tests/models/"""     ):
				SCREAMING_SNAKE_CASE__								=   test.split("""/"""     )[2]
		else:
				SCREAMING_SNAKE_CASE__								=   None

		return test






def 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				Tuple       , UpperCamelCase__:				Any=None     ):
		SCREAMING_SNAKE_CASE__								=   [(x[0], x[1], get_model(x[2]     )) for x in logs]
		SCREAMING_SNAKE_CASE__								=   [x for x in logs if x[2] is not None]
		SCREAMING_SNAKE_CASE__								=   {x[2] for x in logs}

		SCREAMING_SNAKE_CASE__								=   {}
		for test in tests:
				SCREAMING_SNAKE_CASE__								=   Counter()
				# count by errors in `test`
				counter.update([x[1] for x in logs if x[2] == test]     )
				SCREAMING_SNAKE_CASE__								=   counter.most_common()
				SCREAMING_SNAKE_CASE__								=   {error: count for error, count in counts if (error_filter is None or error not in error_filter)}
				SCREAMING_SNAKE_CASE__								=   sum(error_counts.values()     )
				if n_errors > 0:
						SCREAMING_SNAKE_CASE__								=   {"""count""": n_errors, """errors""": error_counts}

		SCREAMING_SNAKE_CASE__								=   dict(sorted(r.items()       , key=lambda UpperCamelCase__     : item[1]["count"]       , reverse=UpperCamelCase__     )     )
		return r






def 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				Tuple     ):
		SCREAMING_SNAKE_CASE__								=   """| no. | error | status |"""
		SCREAMING_SNAKE_CASE__								=   """|-:|:-|:-|"""
		SCREAMING_SNAKE_CASE__								=   [header, sep]
		for error in reduced_by_error:
				SCREAMING_SNAKE_CASE__								=   reduced_by_error[error]["""count"""]
				SCREAMING_SNAKE_CASE__								=   f'''| {count} | {error[:100]} |  |'''
				lines.append(UpperCamelCase__     )

		return "\n".join(UpperCamelCase__     )






def 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				Tuple     ):
		SCREAMING_SNAKE_CASE__								=   """| model | no. of errors | major error | count |"""
		SCREAMING_SNAKE_CASE__								=   """|-:|-:|-:|-:|"""
		SCREAMING_SNAKE_CASE__								=   [header, sep]
		for model in reduced_by_model:
				SCREAMING_SNAKE_CASE__								=   reduced_by_model[model]["""count"""]
				SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__								=   list(reduced_by_model[model]["""errors"""].items()     )[0]
				SCREAMING_SNAKE_CASE__								=   f'''| {model} | {count} | {error[:60]} | {_count} |'''
				lines.append(UpperCamelCase__     )

		return "\n".join(UpperCamelCase__     )


if __name__ == "__main__":
	_lowerCamelCase 					=      argparse.ArgumentParser()
	# Required parameters
	parser.add_argument('--workflow_run_id', type=str, required=True, help='A GitHub Actions workflow run id.')
	parser.add_argument(
	    '--output_dir',
	    type=str,
	    required=True,
	    help='Where to store the downloaded artifacts and other result files.',
	)
	parser.add_argument('--token', default=None, type=str, help='A token that has actions:read permission.')
	_lowerCamelCase 					=      parser.parse_args()

	os.makedirs(args.output_dir, exist_ok=True)

	_lowerCamelCase 					=      get_job_links(args.workflow_run_id, token=args.token)
	_lowerCamelCase 					=      {}
	# To deal with `workflow_call` event, where a job name is the combination of the job names in the caller and callee.
	# For example, `PyTorch 1.11 / Model tests (models/albert, single-gpu)`.
	if _job_links:
		for k, v in _job_links.items():
			# This is how GitHub actions combine job names.
			if " / " in k:
				_lowerCamelCase 					=      k.find(' / ')
				_lowerCamelCase 					=      k[index + len(' / ') :]
			_lowerCamelCase 					=      v
	with open(os.path.join(args.output_dir, 'job_links.json'), 'w', encoding='UTF-8') as fp:
		json.dump(job_links, fp, ensure_ascii=False, indent=4)

	_lowerCamelCase 					=      get_artifacts_links(args.workflow_run_id, token=args.token)
	with open(os.path.join(args.output_dir, 'artifacts.json'), 'w', encoding='UTF-8') as fp:
		json.dump(artifacts, fp, ensure_ascii=False, indent=4)

	for idx, (name, url) in enumerate(artifacts.items()):
		download_artifact(name, url, args.output_dir, args.token)
		# Be gentle to GitHub
		time.sleep(1)

	_lowerCamelCase 					=      get_all_errors(args.output_dir, job_links=job_links)

	# `e[1]` is the error
	_lowerCamelCase 					=      Counter()
	counter.update([e[1] for e in errors])

	# print the top 30 most common test errors
	_lowerCamelCase 					=      counter.most_common(30)
	for item in most_common:
		print(item)

	with open(os.path.join(args.output_dir, 'errors.json'), 'w', encoding='UTF-8') as fp:
		json.dump(errors, fp, ensure_ascii=False, indent=4)

	_lowerCamelCase 					=      reduce_by_error(errors)
	_lowerCamelCase 					=      reduce_by_model(errors)

	_lowerCamelCase 					=      make_github_table(reduced_by_error)
	_lowerCamelCase 					=      make_github_table_per_model(reduced_by_model)

	with open(os.path.join(args.output_dir, 'reduced_by_error.txt'), 'w', encoding='UTF-8') as fp:
		fp.write(sa)
	with open(os.path.join(args.output_dir, 'reduced_by_model.txt'), 'w', encoding='UTF-8') as fp:
		fp.write(sa)






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__				(							UpperCamelCase__:				List[Any]       , UpperCamelCase__:				str       , UpperCamelCase__:				Optional[Any]       , UpperCamelCase__:				Union[str, 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__				(							UpperCamelCase__:				Any       , UpperCamelCase__:				Optional[Any]       , UpperCamelCase__:				Any       , UpperCamelCase__:				List[str]       , UpperCamelCase__:				Tuple=True     ):
		model.train()
		SCREAMING_SNAKE_CASE__								=   model(UpperCamelCase__     )
		SCREAMING_SNAKE_CASE__								=   F.mse_loss(UpperCamelCase__       , target.to(output.device     )     )
		if not do_backward:
				loss /= accelerator.gradient_accumulation_steps
				loss.backward()
		else:
				accelerator.backward(UpperCamelCase__     )






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






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

				# Since `no_sync` is a noop, `ddp_model` and `model` grads should always be in sync
				check_model_parameters(UpperCamelCase__       , UpperCamelCase__       , UpperCamelCase__       , UpperCamelCase__     )
				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(1_337 + iteration     )
				SCREAMING_SNAKE_CASE__								=   ddp_input[torch.randperm(len(UpperCamelCase__     )     )]






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

				# 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(1_337 + iteration     )
				SCREAMING_SNAKE_CASE__								=   ddp_input[torch.randperm(len(UpperCamelCase__     )     )]






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

				# 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(UpperCamelCase__     ) - 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(1_337 + iteration     )
				SCREAMING_SNAKE_CASE__								=   ddp_input[torch.randperm(len(UpperCamelCase__     )     )]
		GradientState._reset_state()






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

				if ((iteration + 1) % 2 == 0) or ((iteration + 1) == len(UpperCamelCase__     )):
						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(UpperCamelCase__     ):
						step_model(UpperCamelCase__       , UpperCamelCase__       , UpperCamelCase__       , UpperCamelCase__     )
						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'''
				SCREAMING_SNAKE_CASE__								=   (((iteration + 1) % 2) == 0) or ((iteration + 1) == len(UpperCamelCase__     ))
				if accelerator.num_processes > 1:
						check_model_parameters(UpperCamelCase__       , UpperCamelCase__       , UpperCamelCase__       , UpperCamelCase__     )
				# Shuffle ddp_input on each iteration
				torch.manual_seed(1_337 + iteration     )
		GradientState._reset_state()






def 					SCREAMING_SNAKE_CASE__				(							):
		SCREAMING_SNAKE_CASE__								=   Accelerator()

		SCREAMING_SNAKE_CASE__								=   RegressionDataset(length=80     )
		SCREAMING_SNAKE_CASE__								=   DataLoader(UpperCamelCase__       , batch_size=16     )
		SCREAMING_SNAKE_CASE__								=   RegressionDataset(length=96     )
		SCREAMING_SNAKE_CASE__								=   DataLoader(UpperCamelCase__       , batch_size=16     )
		SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__								=   accelerator.prepare(UpperCamelCase__       , UpperCamelCase__     )
		assert accelerator.gradient_state.active_dataloader is None
		for iteration, _ in enumerate(UpperCamelCase__     ):
				assert id(accelerator.gradient_state.active_dataloader     ) == id(UpperCamelCase__     )
				if iteration < len(UpperCamelCase__     ) - 1:
						assert not accelerator.gradient_state.end_of_dataloader
						if iteration == 1:
								for batch_num, _ in enumerate(UpperCamelCase__     ):
										assert id(accelerator.gradient_state.active_dataloader     ) == id(UpperCamelCase__     )
										if batch_num < len(UpperCamelCase__     ) - 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__				(							):
		SCREAMING_SNAKE_CASE__								=   Accelerator()
		SCREAMING_SNAKE_CASE__								=   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(UpperCamelCase__     )
		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(UpperCamelCase__     )
		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(UpperCamelCase__       , UpperCamelCase__     )

    # 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(UpperCamelCase__       , UpperCamelCase__     )






def 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				Union[str, Any]     ):
		# For xla_spawn (TPUs)
		main()


if __name__ == "__main__":
	main()