



import numpy as np
import torch
from torch.utils.data import DataLoader

from accelerate.utils.dataclasses import DistributedType

class        snake_case__    :







						"""simple docstring"""




						def __init__(       self ,       __lowercase=2 ,       __lowercase=3 ,       __lowercase=6_4 ,       __lowercase=None  ) ->				str:
									"""simple docstring"""




									a__       :						Union[str, Any]							=  np.random.default_rng(__lowercase  )
									a__       :						List[str]							=  length
									a__       :						Optional[Any]							=  rng.normal(size=(length,)  ).astype(np.floataa  )
									a__       :						Union[str, Any]							=  a * self.x + b + rng.normal(scale=0.1 ,       size=(length,)  ).astype(np.floataa  )

						def __len__(       self  ) ->				int:
									"""simple docstring"""




									return self.length

						def __getitem__(       self ,       __lowercase  ) ->				List[str]:
									"""simple docstring"""




									return {"x": self.x[i], "y": self.y[i]}




class        snake_case__    (torch.nn.Module		):







						"""simple docstring"""




						def __init__(       self ,       __lowercase=0 ,       __lowercase=0 ,       __lowercase=False  ) ->				List[str]:
									"""simple docstring"""




									super().__init__()
									a__       :						List[str]							=  torch.nn.Parameter(torch.tensor([2, 3]  ).float()  )
									a__       :						List[Any]							=  torch.nn.Parameter(torch.tensor([2, 3]  ).float()  )
									a__       :						Tuple							=  True

						def 							SCREAMING_SNAKE_CASE__(       self ,       __lowercase=None  ) ->				Any:
									"""simple docstring"""




									if self.first_batch:
												print(F'''Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}'''  )
												a__       :						Optional[int]							=  False
									return x * self.a[0] + self.b[0]




class        snake_case__    (torch.nn.Module		):







						"""simple docstring"""




						def __init__(       self ,       __lowercase=0 ,       __lowercase=0 ,       __lowercase=False  ) ->				Union[str, Any]:
									"""simple docstring"""




									super().__init__()
									a__       :						Tuple							=  torch.nn.Parameter(torch.tensor(__lowercase  ).float()  )
									a__       :						List[Any]							=  torch.nn.Parameter(torch.tensor(__lowercase  ).float()  )
									a__       :						List[str]							=  True

						def 							SCREAMING_SNAKE_CASE__(       self ,       __lowercase=None  ) ->				int:
									"""simple docstring"""




									if self.first_batch:
												print(F'''Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}'''  )
												a__       :						Union[str, Any]							=  False
									return x * self.a + self.b




def      lowerCAmelCase_					(  _lowercase    :       Union[str, Any]						,       _lowercase    :       int = 16)							->	int:







			"""simple docstring"""
			from datasets import load_dataset
			from transformers import AutoTokenizer

			a__       :						int							=  AutoTokenizer.from_pretrained("""bert-base-cased""")
			a__       :						Dict							=  {"""train""": """tests/test_samples/MRPC/train.csv""", """validation""": """tests/test_samples/MRPC/dev.csv"""}
			a__       :						List[str]							=  load_dataset("""csv"""						,       data_files=_lowercase)
			a__       :						str							=  datasets["""train"""].unique("""label""")

			a__       :						Any							=  {v: i for i, v in enumerate(_lowercase)}

			def tokenize_function(_lowercase    :       Dict):
						# max_length=None => use the model max length (it's actually the default)
						a__       :						str							=  tokenizer(
						    examples["""sentence1"""]						,       examples["""sentence2"""]						,       truncation=_lowercase						,       max_length=_lowercase						,       padding="""max_length""")
						if "label" in examples:
									a__       :						Any							=  [label_to_id[l] for l in examples["""label"""]]
						return outputs

			# Apply the method we just defined to all the examples in all the splits of the dataset
			a__       :						Dict							=  datasets.map(
			    _lowercase						,       batched=_lowercase						,       remove_columns=["""sentence1""", """sentence2""", """label"""]						,       )

			def collate_fn(_lowercase    :       Optional[int]):
						# On TPU it's best to pad everything to the same length or training will be very slow.
						if accelerator.distributed_type == DistributedType.TPU:
									return tokenizer.pad(_lowercase						,       padding="""max_length"""						,       max_length=128						,       return_tensors="""pt""")
						return tokenizer.pad(_lowercase						,       padding="""longest"""						,       return_tensors="""pt""")

			# Instantiate dataloaders.
			a__       :						List[str]							=  DataLoader(tokenized_datasets["""train"""]						,       shuffle=_lowercase						,       collate_fn=_lowercase						,       batch_size=2)
			a__       :						Any							=  DataLoader(tokenized_datasets["""validation"""]						,       shuffle=_lowercase						,       collate_fn=_lowercase						,       batch_size=1)

			return train_dataloader, eval_dataloader









import asyncio
import os
import shutil
import subprocess
import sys
import tempfile
import unittest
from distutils.util import strtobool
from functools import partial
from pathlib import Path
from typing import List, Union
from unittest import mock

import torch

from ..state import AcceleratorState, PartialState
from ..utils import (
    gather,
    is_bnb_available,
    is_comet_ml_available,
    is_datasets_available,
    is_deepspeed_available,
    is_mps_available,
    is_safetensors_available,
    is_tensorboard_available,
    is_torch_version,
    is_tpu_available,
    is_transformers_available,
    is_wandb_available,
    is_xpu_available,
)

def      lowerCAmelCase_					(  _lowercase    :       Union[str, Any]						,       _lowercase    :       Dict=False)							->	Any:







			"""simple docstring"""
			try:
						a__       :						str							=  os.environ[key]
			except KeyError:
						# KEY isn't set, default to `default`.
						a__       :						Optional[int]							=  default
			else:
						# KEY is set, convert it to True or False.
						try:
									a__       :						Optional[int]							=  strtobool(_lowercase)
						except ValueError:
									# More values are supported, but let's keep the message simple.
									raise ValueError(F'''If set, {key} must be yes or no.''')
			return _value


_lowercase		:				Dict      		=parse_flag_from_env("RUN_SLOW", default=False)

def      lowerCAmelCase_					(  _lowercase    :       Any)							->	str:







			"""simple docstring"""
			return unittest.skip("""Test was skipped""")(_lowercase)

def      lowerCAmelCase_					(  _lowercase    :       str)							->	List[str]:







			"""simple docstring"""
			return unittest.skipUnless(_run_slow_tests						,       """test is slow""")(_lowercase)

def      lowerCAmelCase_					(  _lowercase    :       List[Any])							->	Union[str, Any]:







			"""simple docstring"""
			return unittest.skipUnless(not torch.cuda.is_available()						,       """test requires only a CPU""")(_lowercase)

def      lowerCAmelCase_					(  _lowercase    :       List[Any])							->	List[Any]:







			"""simple docstring"""
			return unittest.skipUnless(torch.cuda.is_available()						,       """test requires a GPU""")(_lowercase)

def      lowerCAmelCase_					(  _lowercase    :       Optional[int])							->	Dict:







			"""simple docstring"""
			return unittest.skipUnless(is_xpu_available()						,       """test requires a XPU""")(_lowercase)

def      lowerCAmelCase_					(  _lowercase    :       Tuple)							->	List[str]:







			"""simple docstring"""
			return unittest.skipUnless(is_mps_available()						,       """test requires a `mps` backend support in `torch`""")(_lowercase)

def      lowerCAmelCase_					(  _lowercase    :       Dict)							->	Union[str, Any]:







			"""simple docstring"""
			return unittest.skipUnless(
			    is_transformers_available() and is_datasets_available()						,       """test requires the Hugging Face suite""")(_lowercase)

def      lowerCAmelCase_					(  _lowercase    :       Tuple)							->	Any:







			"""simple docstring"""
			return unittest.skipUnless(is_bnb_available()						,       """test requires the bitsandbytes library""")(_lowercase)

def      lowerCAmelCase_					(  _lowercase    :       Union[str, Any])							->	List[str]:







			"""simple docstring"""
			return unittest.skipUnless(is_tpu_available()						,       """test requires TPU""")(_lowercase)

def      lowerCAmelCase_					(  _lowercase    :       str)							->	int:







			"""simple docstring"""
			return unittest.skipUnless(torch.cuda.device_count() == 1						,       """test requires a GPU""")(_lowercase)

def      lowerCAmelCase_					(  _lowercase    :       Any)							->	List[str]:







			"""simple docstring"""
			return unittest.skipUnless(torch.xpu.device_count() == 1						,       """test requires a XPU""")(_lowercase)

def      lowerCAmelCase_					(  _lowercase    :       Union[str, Any])							->	Union[str, Any]:







			"""simple docstring"""
			return unittest.skipUnless(torch.cuda.device_count() > 1						,       """test requires multiple GPUs""")(_lowercase)

def      lowerCAmelCase_					(  _lowercase    :       int)							->	Tuple:







			"""simple docstring"""
			return unittest.skipUnless(torch.xpu.device_count() > 1						,       """test requires multiple XPUs""")(_lowercase)

def      lowerCAmelCase_					(  _lowercase    :       int)							->	Optional[Any]:







			"""simple docstring"""
			return unittest.skipUnless(is_safetensors_available()						,       """test requires safetensors""")(_lowercase)

def      lowerCAmelCase_					(  _lowercase    :       Optional[int])							->	List[str]:







			"""simple docstring"""
			return unittest.skipUnless(is_deepspeed_available()						,       """test requires DeepSpeed""")(_lowercase)

def      lowerCAmelCase_					(  _lowercase    :       Union[str, Any])							->	Optional[Any]:







			"""simple docstring"""
			return unittest.skipUnless(is_torch_version(""">="""						,       """1.12.0""")						,       """test requires torch version >= 1.12.0""")(_lowercase)

def      lowerCAmelCase_					(  _lowercase    :       Any=None						,       _lowercase    :       List[str]=None)							->	Dict:







			"""simple docstring"""
			if test_case is None:
						return partial(_lowercase						,       version=_lowercase)
			return unittest.skipUnless(is_torch_version(""">="""						,       _lowercase)						,       F'''test requires torch version >= {version}''')(_lowercase)

def      lowerCAmelCase_					(  _lowercase    :       Any)							->	int:







			"""simple docstring"""
			return unittest.skipUnless(is_tensorboard_available()						,       """test requires Tensorboard""")(_lowercase)

def      lowerCAmelCase_					(  _lowercase    :       str)							->	Union[str, Any]:







			"""simple docstring"""
			return unittest.skipUnless(is_wandb_available()						,       """test requires wandb""")(_lowercase)

def      lowerCAmelCase_					(  _lowercase    :       Optional[int])							->	Union[str, Any]:







			"""simple docstring"""
			return unittest.skipUnless(is_comet_ml_available()						,       """test requires comet_ml""")(_lowercase)


_lowercase		:				List[str]      		=(
    any([is_wandb_available(), is_tensorboard_available()]) and not is_comet_ml_available()
)

def      lowerCAmelCase_					(  _lowercase    :       Optional[int])							->	Union[str, Any]:







			"""simple docstring"""
			return unittest.skipUnless(
			    _atleast_one_tracker_available						,       """test requires at least one tracker to be available and for `comet_ml` to not be installed"""						,       )(_lowercase)

class        snake_case__    (unittest.TestCase		):







						"""simple docstring"""




						__lowerCAmelCase					:Optional[Any]      =					True

						@classmethod
						def 							SCREAMING_SNAKE_CASE__(       cls  ) ->				Optional[int]:
									"""simple docstring"""




									a__       :						Tuple							=  tempfile.mkdtemp()

						@classmethod
						def 							SCREAMING_SNAKE_CASE__(       cls  ) ->				Dict:
									"""simple docstring"""




									if os.path.exists(cls.tmpdir  ):
												shutil.rmtree(cls.tmpdir  )

						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				List[Any]:
									"""simple docstring"""




									if self.clear_on_setup:
												for path in Path(self.tmpdir  ).glob("""**/*"""  ):
															if path.is_file():
																		path.unlink()
															elif path.is_dir():
																		shutil.rmtree(__lowercase  )




class        snake_case__    (unittest.TestCase		):







						"""simple docstring"""




						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				Union[str, Any]:
									"""simple docstring"""




									super().tearDown()
									# Reset the state of the AcceleratorState singleton.
									AcceleratorState._reset_state()
									PartialState._reset_state()




class        snake_case__    (unittest.TestCase		):







						"""simple docstring"""




						def 							SCREAMING_SNAKE_CASE__(       self ,       __lowercase  ) ->				Union[str, Any]:
									"""simple docstring"""




									a__       :						Tuple							=  mocks if isinstance(__lowercase ,       (tuple, list)  ) else [mocks]
									for m in self.mocks:
												m.start()
												self.addCleanup(m.stop  )




def      lowerCAmelCase_					(  _lowercase    :       Optional[int])							->	List[Any]:







			"""simple docstring"""
			a__       :						Tuple							=  AcceleratorState()
			a__       :						List[str]							=  tensor[None].clone().to(state.device)
			a__       :						Any							=  gather(_lowercase).cpu()
			a__       :						Optional[Any]							=  tensor[0].cpu()
			for i in range(tensors.shape[0]):
						if not torch.equal(tensors[i]						,       _lowercase):
									return False
			return True

class        snake_case__    :







						"""simple docstring"""




						def __init__(       self ,       __lowercase ,       __lowercase ,       __lowercase  ) ->				Any:
									"""simple docstring"""




									a__       :						Any							=  returncode
									a__       :						List[Any]							=  stdout
									a__       :						Any							=  stderr




async def      lowerCAmelCase_					(  _lowercase    :       str						,       _lowercase    :       str)							->	List[Any]:







			"""simple docstring"""
			while True:
						a__       :						str							=  await stream.readline()
						if line:
									callback(_lowercase)
						else:
									break

async def      lowerCAmelCase_					(  _lowercase    :       Any						,       _lowercase    :       Union[str, Any]=None						,       _lowercase    :       List[str]=None						,       _lowercase    :       Tuple=None						,       _lowercase    :       Optional[Any]=False						,       _lowercase    :       Dict=False)							->	_RunOutput:







			"""simple docstring"""
			if echo:
						print("""\nRunning: """						,       """ """.join(_lowercase))

			a__       :						int							=  await asyncio.create_subprocess_exec(
			    cmd[0]						,       *cmd[1:]						,       stdin=_lowercase						,       stdout=asyncio.subprocess.PIPE						,       stderr=asyncio.subprocess.PIPE						,       env=_lowercase						,       )

			# note: there is a warning for a possible deadlock when using `wait` with huge amounts of data in the pipe
			# https://docs.python.org/3/library/asyncio-subprocess.html#asyncio.asyncio.subprocess.Process.wait
			#
			# If it starts hanging, will need to switch to the following code. The problem is that no data
			# will be seen until it's done and if it hangs for example there will be no debug info.
			# out, err = await p.communicate()
			# return _RunOutput(p.returncode, out, err)

			a__       :						int							=  []
			a__       :						Optional[int]							=  []

			def tee(_lowercase    :       List[str]						,       _lowercase    :       Optional[int]						,       _lowercase    :       Any						,       _lowercase    :       Optional[Any]=""):
						a__       :						int							=  line.decode("""utf-8""").rstrip()
						sink.append(_lowercase)
						if not quiet:
									print(_lowercase						,       _lowercase						,       file=_lowercase)

    # XXX: the timeout doesn't seem to make any difference here
			await asyncio.wait(
			    [
			        asyncio.create_task(_read_stream(p.stdout						,       lambda _lowercase: tee(_lowercase						,       _lowercase						,       sys.stdout						,       label="""stdout:"""))),
			        asyncio.create_task(_read_stream(p.stderr						,       lambda _lowercase: tee(_lowercase						,       _lowercase						,       sys.stderr						,       label="""stderr:"""))),
			    ]						,       timeout=_lowercase						,       )
			return _RunOutput(await p.wait()						,       _lowercase						,       _lowercase)

def      lowerCAmelCase_					(  _lowercase    :       Optional[int]						,       _lowercase    :       Optional[int]=None						,       _lowercase    :       Tuple=None						,       _lowercase    :       Any=180						,       _lowercase    :       List[Any]=False						,       _lowercase    :       Dict=True)							->	_RunOutput:







			"""simple docstring"""
			a__       :						Any							=  asyncio.get_event_loop()
			a__       :						List[Any]							=  loop.run_until_complete(
			    _stream_subprocess(_lowercase						,       env=_lowercase						,       stdin=_lowercase						,       timeout=_lowercase						,       quiet=_lowercase						,       echo=_lowercase))

			a__       :						Optional[int]							=  """ """.join(_lowercase)
			if result.returncode > 0:
						a__       :						List[Any]							=  """\n""".join(result.stderr)
						raise RuntimeError(
						    F'''\'{cmd_str}\' failed with returncode {result.returncode}\n\n'''
						    F'''The combined stderr from workers follows:\n{stderr}''')

			return result

class        snake_case__    (A__		):







						"""simple docstring"""




						pass




def      lowerCAmelCase_					(  _lowercase    :       List[str]						,       _lowercase    :       Optional[int]=False)							->	Dict:







			"""simple docstring"""
			try:
						a__       :						List[Any]							=  subprocess.check_output(_lowercase						,       stderr=subprocess.STDOUT)
						if return_stdout:
									if hasattr(_lowercase						,       """decode"""):
												a__       :						Tuple							=  output.decode("""utf-8""")
									return output
			except subprocess.CalledProcessError as e:
						raise SubprocessCallException(
						    F'''Command `{' '.join(_lowercase)}` failed with the following error:\n\n{e.output.decode()}''') from e




