



# 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


__snake_case     :Dict			       ='Run commands across TPU VMs for initial setup before running `accelerate launch`.'






def  lowerCamelCase_	(      lowerCAmelCase__	:	Tuple=None				)       ->							List[Any]:

							'''simple docstring'''






							if subparsers is not None:
														A      						=    subparsers.add_parser('tpu-config'			,	description=_description				)
							else:
														A      						=    argparse.ArgumentParser('Accelerate tpu-config command'			,	description=_description				)
							# Core arguments
							A      						=    parser.add_argument_group(
							    'Config Arguments'			,	'Arguments that can be configured through `accelerate config`.'				)
							config_args.add_argument(
							    '--config_file'			,	type=lowerCAmelCase__			,	default=lowerCAmelCase__			,	help='Path to the config file to use for accelerate.'			,	)
							config_args.add_argument(
							    '--tpu_name'			,	default=lowerCAmelCase__			,	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=lowerCAmelCase__			,	help='The zone of the TPU to use. If not specified, will use the zone specified in the config file.'			,	)
							A      						=    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=lowerCAmelCase__			,	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=lowerCAmelCase__				)
							return parser






def  lowerCamelCase_	(      lowerCAmelCase__	:	Optional[Any]				)       ->							List[str]:

							'''simple docstring'''






							A      						=    None

							# Get the default from the config file if it exists.
							if args.config_file is not None or os.path.isfile(lowerCAmelCase__				):
														A      						=    load_config_from_file(args.config_file				)
														if not args.command_file and defaults.command_file is not None and not args.command:
																					A      						=    defaults.command_file
														if not args.command and defaults.commands is not None:
																					A      						=    defaults.commands
														if not args.tpu_name:
																					A      						=    defaults.tpu_name
														if not args.tpu_zone:
																					A      						=    defaults.tpu_zone
							if args.accelerate_version == "dev":
														A      						=    'git+https://github.com/huggingface/accelerate.git'
							elif args.accelerate_version == "latest":
														A      						=    'accelerate -U'
							elif isinstance(parse(args.accelerate_version				)			,	lowerCAmelCase__				):
														A      						=    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      						=    [f.read().splitlines()]

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

							# Then send it to gcloud
							# Eventually try to use google-api-core to do this instead of subprocess
							A      						=    ['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(lowerCAmelCase__				)}'''				)
														return
							subprocess.run(lowerCAmelCase__				)
							print('Successfully setup pod.'				)






def  lowerCamelCase_	(      )       ->							List[Any]:

							'''simple docstring'''






							A      						=    tpu_command_parser()
							A      						=    parser.parse_args()

							tpu_command_launcher(lowerCAmelCase__				)




from typing import List

import numpy as np






def  lowerCamelCase_	(      lowerCAmelCase__	:	dict				)       ->							int:

							'''simple docstring'''






							A      						=    {key: len(lowerCAmelCase__				) for key, value in gen_kwargs.items() if isinstance(lowerCAmelCase__			,	lowerCAmelCase__				)}
							if len(set(lists_lengths.values()				)				) > 1:
														raise RuntimeError(
														    (
														        'Sharding is ambiguous for this dataset: '
														        + 'we found several data sources lists of different lengths, and we don\'t know over which list we should parallelize:\n'
														        + '\n'.join(F'''\t- key {key} has length {length}''' for key, length in lists_lengths.items()				)
														        + '\nTo fix this, check the \'gen_kwargs\' and make sure to use lists only for data sources, '
														        + 'and use tuples otherwise. In the end there should only be one single list, or several lists with the same length.'
														    )				)
							A      						=    max(lists_lengths.values()			,	default=0				)
							return max(1			,	lowerCAmelCase__				)






def  lowerCamelCase_	(      lowerCAmelCase__	:	int			,	lowerCAmelCase__	:	int				)       ->							List[range]:

							'''simple docstring'''






							A      						=    []
							for group_idx in range(lowerCAmelCase__				):
														A      						=    num_shards // max_num_jobs + (group_idx < (num_shards % max_num_jobs))
														if num_shards_to_add == 0:
																					break
														A      						=    shards_indices_per_group[-1].stop if shards_indices_per_group else 0
														A      						=    range(lowerCAmelCase__			,	start + num_shards_to_add				)
														shards_indices_per_group.append(lowerCAmelCase__				)
							return shards_indices_per_group






def  lowerCamelCase_	(      lowerCAmelCase__	:	dict			,	lowerCAmelCase__	:	int				)       ->							List[dict]:

							'''simple docstring'''






							A      						=    _number_of_shards_in_gen_kwargs(lowerCAmelCase__				)
							if num_shards == 1:
														return [dict(lowerCAmelCase__				)]
							else:
														A      						=    _distribute_shards(num_shards=lowerCAmelCase__			,	max_num_jobs=lowerCAmelCase__				)
														return [
														    {
														        key: [value[shard_idx] for shard_idx in shard_indices_per_group[group_idx]]
														        if isinstance(lowerCAmelCase__			,	lowerCAmelCase__				)
														        else value
														        for key, value in gen_kwargs.items()
														    }
														    for group_idx in range(len(lowerCAmelCase__				)				)
														]






def  lowerCamelCase_	(      lowerCAmelCase__	:	List[dict]				)       ->							dict:

							'''simple docstring'''






							return {
							    key: [value for gen_kwargs in gen_kwargs_list for value in gen_kwargs[key]]
							    if isinstance(gen_kwargs_list[0][key]			,	lowerCAmelCase__				)
							    else gen_kwargs_list[0][key]
							    for key in gen_kwargs_list[0]
							}






def  lowerCamelCase_	(      lowerCAmelCase__	:	np.random.Generator			,	lowerCAmelCase__	:	dict				)       ->							dict:

							'''simple docstring'''






							A      						=    {len(lowerCAmelCase__				) for value in gen_kwargs.values() if isinstance(lowerCAmelCase__			,	lowerCAmelCase__				)}
							A      						=    {}
							for size in list_sizes:
														A      						=    list(range(lowerCAmelCase__				)				)
														rng.shuffle(indices_per_size[size]				)
							# Now let's copy the gen_kwargs and shuffle the lists based on their sizes
							A      						=    dict(lowerCAmelCase__				)
							for key, value in shuffled_kwargs.items():
														if isinstance(lowerCAmelCase__			,	lowerCAmelCase__				):
																					A      						=    [value[i] for i in indices_per_size[len(lowerCAmelCase__				)]]
							return shuffled_kwargs