





"""simple docstring"""




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

from accelerate.utils.dataclasses import DistributedType


class  __UpperCamelCase       :






							def __init__(     self   ,      lowerCAmelCase__=2   ,      lowerCAmelCase__=3   ,      lowerCAmelCase__=64   ,      lowerCAmelCase__=None   )			->				Optional[Any]:
								a   :	int			=       np.random.default_rng(lowercase_   )
								a   :	List[Any]			=       length
								a   :	Optional[Any]			=       rng.normal(size=(length,)   ).astype(np.floataa   )
								a   :	Tuple			=       a * self.x + b + rng.normal(scale=0.1   ,      size=(length,)   ).astype(np.floataa   )






							def __len__(     self   )			->				List[Any]:
								return self.length






							def __getitem__(     self   ,      lowerCAmelCase__   )			->				int:
								return {"x": self.x[i], "y": self.y[i]}


class  __UpperCamelCase       (		torch.nn.Module		):






							def __init__(     self   ,      lowerCAmelCase__=0   ,      lowerCAmelCase__=0   ,      lowerCAmelCase__=False   )			->				Optional[Any]:
								super().__init__()
								a   :	Optional[int]			=       torch.nn.Parameter(torch.tensor([2, 3]   ).float()   )
								a   :	List[Any]			=       torch.nn.Parameter(torch.tensor([2, 3]   ).float()   )
								a   :	List[Any]			=       True






							def 					__a     (     self   ,      lowerCAmelCase__=None   )			->				Union[str, Any]:
								if self.first_batch:
									print(f"""Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}"""   )
									a   :	List[Any]			=       False
								return x * self.a[0] + self.b[0]


class  __UpperCamelCase       (		torch.nn.Module		):






							def __init__(     self   ,      lowerCAmelCase__=0   ,      lowerCAmelCase__=0   ,      lowerCAmelCase__=False   )			->				List[Any]:
								super().__init__()
								a   :	str			=       torch.nn.Parameter(torch.tensor(lowercase_   ).float()   )
								a   :	List[str]			=       torch.nn.Parameter(torch.tensor(lowercase_   ).float()   )
								a   :	str			=       True






							def 					__a     (     self   ,      lowerCAmelCase__=None   )			->				Union[str, Any]:
								if self.first_batch:
									print(f"""Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}"""   )
									a   :	str			=       False
								return x * self.a + self.b






def    _SCREAMING_SNAKE_CASE	(			_lowercase		:					Dict					, _lowercase		:					Dict = 16      )				->Dict:






	'''simple docstring'''






	from datasets import load_dataset
	from transformers import AutoTokenizer

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

	a   :	List[Any]			=       {v: i for i, v in enumerate(__SCREAMING_SNAKE_CASE      )}

	def tokenize_function(_lowercase		:					int      ):
		# max_length=None => use the model max length (it's actually the default)
		a   :	str			=       tokenizer(
		    examples["sentence1"]					, examples["sentence2"]					, truncation=__SCREAMING_SNAKE_CASE					, max_length=__SCREAMING_SNAKE_CASE					, padding="max_length"      )
		if "label" in examples:
			a   :	Optional[int]			=       [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   :	str			=       datasets.map(
	    __SCREAMING_SNAKE_CASE					, batched=__SCREAMING_SNAKE_CASE					, remove_columns=["sentence1", "sentence2", "label"]					, )

	def collate_fn(_lowercase		:					Any      ):
		# 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(__SCREAMING_SNAKE_CASE					, padding="max_length"					, max_length=128					, return_tensors="pt"      )
		return tokenizer.pad(__SCREAMING_SNAKE_CASE					, padding="longest"					, return_tensors="pt"      )

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

	return train_dataloader, eval_dataloader









"""simple docstring"""




# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import argparse
import os
import subprocess

from packaging.version import Version, parse

from accelerate.commands.config.config_args import default_config_file, load_config_from_file


a		:				Optional[int]  			=    '''Run commands across TPU VMs for initial setup before running `accelerate launch`.'''






def    _SCREAMING_SNAKE_CASE	(			_lowercase		:					Any=None      )				->Optional[Any]:






	'''simple docstring'''






	if subparsers is not None:
		a   :	int			=       subparsers.add_parser("tpu-config"					, description=_description      )
	else:
		a   :	List[Any]			=       argparse.ArgumentParser("Accelerate tpu-config command"					, description=_description      )
	# Core arguments
	a   :	Dict			=       parser.add_argument_group(
	    "Config Arguments"					, "Arguments that can be configured through `accelerate config`."      )
	config_args.add_argument(
	    "--config_file"					, type=_lowercase					, default=_lowercase					, help="Path to the config file to use for accelerate."					, )
	config_args.add_argument(
	    "--tpu_name"					, default=_lowercase					, help="The name of the TPU to use. If not specified, will use the TPU specified in the config file."					, )
	config_args.add_argument(
	    "--tpu_zone"					, default=_lowercase					, help="The zone of the TPU to use. If not specified, will use the zone specified in the config file."					, )
	a   :	Any			=       parser.add_argument_group("TPU Arguments"					, "Arguments for options ran inside the TPU."      )
	pod_args.add_argument(
	    "--use_alpha"					, action="store_true"					, help="Whether to use `gcloud alpha` when running the TPU training script instead of `gcloud`."					, )
	pod_args.add_argument(
	    "--command_file"					, default=_lowercase					, help="The path to the file containing the commands to run on the pod on startup."					, )
	pod_args.add_argument(
	    "--command"					, action="append"					, nargs="+"					, help="A command to run on the pod. Can be passed multiple times."					, )
	pod_args.add_argument(
	    "--install_accelerate"					, action="store_true"					, help="Whether to install accelerate on the pod. Defaults to False."					, )
	pod_args.add_argument(
	    "--accelerate_version"					, default="latest"					, help="The version of accelerate to install on the pod. If not specified, will use the latest pypi version. Specify 'dev' to install from GitHub."					, )
	pod_args.add_argument(
	    "--debug"					, action="store_true"					, help="If set, will print the command that would be run instead of running it."      )

	if subparsers is not None:
		parser.set_defaults(func=_lowercase      )
	return parser






def    _SCREAMING_SNAKE_CASE	(			_lowercase		:					Any      )				->Tuple:






	'''simple docstring'''






	a   :	Union[str, Any]			=       None

	# Get the default from the config file if it exists.
	if args.config_file is not None or os.path.isfile(_lowercase      ):
		a   :	Optional[Any]			=       load_config_from_file(args.config_file      )
		if not args.command_file and defaults.command_file is not None and not args.command:
			a   :	int			=       defaults.command_file
		if not args.command and defaults.commands is not None:
			a   :	Union[str, Any]			=       defaults.commands
		if not args.tpu_name:
			a   :	int			=       defaults.tpu_name
		if not args.tpu_zone:
			a   :	Union[str, Any]			=       defaults.tpu_zone
	if args.accelerate_version == "dev":
		a   :	int			=       "git+https://github.com/huggingface/accelerate.git"
	elif args.accelerate_version == "latest":
		a   :	Optional[Any]			=       "accelerate -U"
	elif isinstance(parse(args.accelerate_version      )					, _lowercase      ):
		a   :	Optional[Any]			=       F"""accelerate=={args.accelerate_version}"""

	if not args.command_file and not args.command:
		raise ValueError("You must specify either a command file or a command to run on the pod."      )

	if args.command_file:
		with open(args.command_file					, "r"      ) as f:
			a   :	int			=       [f.read().splitlines()]

    # To turn list of lists into list of strings
	if isinstance(args.command[0]					, _lowercase      ):
		a   :	Union[str, Any]			=       [line for cmd in args.command for line in cmd]
	# Default to the shared folder and install accelerate
	a   :	Tuple			=       ["cd /usr/share"]
	if args.install_accelerate:
		new_cmd += [F"""pip install {args.accelerate_version}"""]
	new_cmd += args.command
	a   :	List[Any]			=       "; ".join(_lowercase      )

	# Then send it to gcloud
	# Eventually try to use google-api-core to do this instead of subprocess
	a   :	str			=       ["gcloud"]
	if args.use_alpha:
		cmd += ["alpha"]
	cmd += [
	    "compute",
	    "tpus",
	    "tpu-vm",
	    "ssh",
	    args.tpu_name,
	    "--zone",
	    args.tpu_zone,
	    "--command",
	    args.command,
	    "--worker",
	    "all",
	]
	if args.debug:
		print(F"""Running {' '.join(_lowercase      )}"""      )
		return
	subprocess.run(_lowercase      )
	print("Successfully setup pod."      )







def    _SCREAMING_SNAKE_CASE	(			)				->Tuple:






	'''simple docstring'''






	a   :	List[Any]			=       tpu_command_parser()
	a   :	Optional[int]			=       parser.parse_args()

	tpu_command_launcher(_lowercase      )


