




"""simple docstring"""






# Copyright 2021 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 warnings
from typing import List
from unittest.mock import Mock

import torch
from torch.utils.data import DataLoader, IterableDataset, TensorDataset

from accelerate.accelerator import Accelerator
from accelerate.utils.dataclasses import DistributedType






class lowerCAmelCase__	(     lowercase						):






			'''simple docstring'''



			def __init__(  self  ,      lowercase  ):
							_lowerCamelCase     :			Dict    =    data






			def __iter__(  self  ):
							for element in self.data:
											yield element







def      _snake_case			(				lowercase__=True		):
				_lowerCamelCase     :			Any    =    Accelerator(even_batches=lowercase__		)
				assert accelerator.num_processes == 2, "this script expects that two GPUs are available"
				return accelerator







def      _snake_case			(				lowercase__							,    lowercase__							,    lowercase__							,    lowercase__ = False		):
				if iterable:
								_lowerCamelCase     :			List[Any]    =    DummyIterableDataset(torch.as_tensor(range(lowercase__		)		)		)
				else:
								_lowerCamelCase     :			List[str]    =    TensorDataset(torch.as_tensor(range(lowercase__		)		)		)

				_lowerCamelCase     :			Any    =    DataLoader(lowercase__							,    batch_size=lowercase__		)
				_lowerCamelCase     :			str    =    accelerator.prepare(lowercase__		)

				return dl







def      _snake_case			(				lowercase__							,    lowercase__							,    lowercase__							,    lowercase__							,    lowercase__							,    ):
				_lowerCamelCase     :			int    =    create_dataloader(accelerator=lowercase__							,    dataset_size=lowercase__							,    batch_size=lowercase__		)

				_lowerCamelCase     :			Optional[Any]    =    [len(batch[0]		) for batch in dl]

				if accelerator.process_index == 0:
								assert batch_sizes == process_0_expected_batch_sizes
				elif accelerator.process_index == 1:
								assert batch_sizes == process_1_expected_batch_sizes







def      _snake_case			(				):
				_lowerCamelCase     :			int    =    create_accelerator()

				# without padding, we would expect a different number of batches
				verify_dataloader_batch_sizes(
				    lowercase__							,    dataset_size=3							,    batch_size=1							,    process_0_expected_batch_sizes=[1, 1]							,    process_1_expected_batch_sizes=[1, 1]							,    )

				# without padding, we would expect the same number of batches, but different sizes
				verify_dataloader_batch_sizes(
				    lowercase__							,    dataset_size=7							,    batch_size=2							,    process_0_expected_batch_sizes=[2, 2]							,    process_1_expected_batch_sizes=[2, 2]							,    )







def      _snake_case			(				):
				_lowerCamelCase     :			Optional[int]    =    create_accelerator(even_batches=lowercase__		)

				verify_dataloader_batch_sizes(
				    lowercase__							,    dataset_size=3							,    batch_size=1							,    process_0_expected_batch_sizes=[1, 1]							,    process_1_expected_batch_sizes=[1]							,    )

				verify_dataloader_batch_sizes(
				    lowercase__							,    dataset_size=7							,    batch_size=2							,    process_0_expected_batch_sizes=[2, 2]							,    process_1_expected_batch_sizes=[2, 1]							,    )







def      _snake_case			(				):
				_lowerCamelCase     :			int    =    create_accelerator(even_batches=lowercase__		)

				_lowerCamelCase     :			str    =    torch.nn.Linear(1							,    1		)
				_lowerCamelCase     :			Dict    =    accelerator.prepare(lowercase__		)

				_lowerCamelCase     :			Optional[Any]    =    create_dataloader(lowercase__							,    dataset_size=3							,    batch_size=1		)

				_lowerCamelCase     :			Optional[Any]    =    []
				with accelerator.join_uneven_inputs([ddp_model]		):
								for batch_idx, batch in enumerate(lowercase__		):
												_lowerCamelCase     :			str    =    ddp_model(batch[0].float()		)
												_lowerCamelCase     :			str    =    output.sum()
												loss.backward()
												batch_idxs.append(lowercase__		)

				accelerator.wait_for_everyone()

				if accelerator.process_index == 0:
								assert batch_idxs == [0, 1]
				elif accelerator.process_index == 1:
								assert batch_idxs == [0]







def      _snake_case			(				lowercase__		):
				with warnings.catch_warnings(record=lowercase__		) as w:
								with accelerator.join_uneven_inputs([Mock()]		):
												pass

								assert issubclass(w[-1].category							,    lowercase__		)
								assert "only supported for multi-GPU" in str(w[-1].message		)







def      _snake_case			(				):
				_lowerCamelCase     :			Any    =    True
				_lowerCamelCase     :			Any    =    False
				_lowerCamelCase     :			List[str]    =    create_accelerator(even_batches=lowercase__		)
				_lowerCamelCase     :			Dict    =    torch.nn.Linear(1							,    1		)
				_lowerCamelCase     :			Union[str, Any]    =    accelerator.prepare(lowercase__		)
				_lowerCamelCase     :			Any    =    create_dataloader(lowercase__							,    dataset_size=3							,    batch_size=1		)
				_lowerCamelCase     :			Optional[Any]    =    create_dataloader(lowercase__							,    dataset_size=3							,    batch_size=1		)

				with accelerator.join_uneven_inputs([ddp_model]							,    even_batches=lowercase__		):
								_lowerCamelCase     :			Dict    =    train_dl.batch_sampler.even_batches
								_lowerCamelCase     :			Union[str, Any]    =    valid_dl.batch_sampler.even_batches

				assert train_dl_overridden_value == overridden_even_batches
				assert valid_dl_overridden_value == overridden_even_batches
				assert train_dl.batch_sampler.even_batches == default_even_batches
				assert valid_dl.batch_sampler.even_batches == default_even_batches







def      _snake_case			(				):
				_lowerCamelCase     :			Dict    =    True
				_lowerCamelCase     :			Tuple    =    False
				_lowerCamelCase     :			Dict    =    create_accelerator(even_batches=lowercase__		)
				_lowerCamelCase     :			Dict    =    torch.nn.Linear(1							,    1		)
				_lowerCamelCase     :			Union[str, Any]    =    accelerator.prepare(lowercase__		)
				create_dataloader(lowercase__							,    dataset_size=3							,    batch_size=1							,    iterable=lowercase__		)
				_lowerCamelCase     :			str    =    create_dataloader(lowercase__							,    dataset_size=3							,    batch_size=1		)

				with warnings.catch_warnings():
								warnings.filterwarnings('ignore'		)
								try:
												with accelerator.join_uneven_inputs([ddp_model]							,    even_batches=lowercase__		):
																_lowerCamelCase     :			Any    =    batch_dl.batch_sampler.even_batches
								except AttributeError:
												# ensure attribute error is not raised when processing iterable dl
												raise AssertionError

				assert batch_dl_overridden_value == overridden_even_batches
				assert batch_dl.batch_sampler.even_batches == default_even_batches







def      _snake_case			(				):
				_lowerCamelCase     :			List[Any]    =    create_accelerator()
				_lowerCamelCase     :			Any    =    torch.nn.Linear(1							,    1		)
				_lowerCamelCase     :			Any    =    accelerator.prepare(lowercase__		)
				create_dataloader(lowercase__							,    dataset_size=3							,    batch_size=1							,    iterable=lowercase__		)

				with warnings.catch_warnings(record=lowercase__		) as w:
								with accelerator.join_uneven_inputs([ddp_model]							,    even_batches=lowercase__		):
												pass

								assert issubclass(w[-1].category							,    lowercase__		)
								assert "only supported for map-style datasets" in str(w[-1].message		)







def      _snake_case			(				):
				_lowerCamelCase     :			Optional[int]    =    create_accelerator()

				accelerator.print('Test that even_batches variable ensures uniform batches across processes'		)
				test_default_ensures_even_batch_sizes()

				accelerator.print('Run tests with even_batches disabled'		)
				test_can_disable_even_batches()

				accelerator.print('Test joining uneven inputs'		)
				test_can_join_uneven_inputs()

				accelerator.print('Test overriding even_batches when joining uneven inputs'		)
				test_join_can_override_even_batches()

				accelerator.print('Test overriding even_batches for mixed dataloader types'		)
				test_join_can_override_for_mixed_type_dataloaders()

				accelerator.print('Test overriding even_batches raises a warning for iterable dataloaders'		)
				test_join_raises_warning_for_iterable_when_overriding_even_batches()

				accelerator.print('Test join with non DDP distributed raises warning'		)
				_lowerCamelCase     :			Dict    =    accelerator.state.distributed_type
				_lowerCamelCase     :			List[Any]    =    DistributedType.FSDP
				test_join_raises_warning_for_non_ddp_distributed(lowercase__		)
				_lowerCamelCase     :			Union[str, Any]    =    original_state


if __name__ == "__main__":
			main()





"""simple docstring"""






import datetime
import platform
import subprocess
from typing import Optional, Tuple, Union

import numpy as np







def      _snake_case			(				lowercase__							,    lowercase__		):
				_lowerCamelCase     :			Tuple    =    f'''{sampling_rate}'''
				_lowerCamelCase     :			str    =    '1'
				_lowerCamelCase     :			str    =    'f32le'
				_lowerCamelCase     :			Union[str, Any]    =    [
				    'ffmpeg',
				    '-i',
				    'pipe:0',
				    '-ac',
				    ac,
				    '-ar',
				    ar,
				    '-f',
				    format_for_conversion,
				    '-hide_banner',
				    '-loglevel',
				    'quiet',
				    'pipe:1',
				]

				try:
								with subprocess.Popen(lowercase__							,    stdin=subprocess.PIPE							,    stdout=subprocess.PIPE		) as ffmpeg_process:
												_lowerCamelCase     :			str    =    ffmpeg_process.communicate(lowercase__		)
				except FileNotFoundError as error:
								raise ValueError('ffmpeg was not found but is required to load audio files from filename'		) from error
				_lowerCamelCase     :			List[Any]    =    output_stream[0]
				_lowerCamelCase     :			Tuple    =    np.frombuffer(lowercase__							,    np.floataa		)
				if audio.shape[0] == 0:
								raise ValueError('Malformed soundfile'		)
				return audio







def      _snake_case			(				lowercase__							,    lowercase__							,    lowercase__ = "f32le"							,    ):
				_lowerCamelCase     :			Optional[Any]    =    f'''{sampling_rate}'''
				_lowerCamelCase     :			List[str]    =    '1'
				if format_for_conversion == "s16le":
								_lowerCamelCase     :			List[str]    =    2
				elif format_for_conversion == "f32le":
								_lowerCamelCase     :			List[Any]    =    4
				else:
								raise ValueError(f'''Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`'''		)

				_lowerCamelCase     :			Dict    =    platform.system()
				if system == "Linux":
								_lowerCamelCase     :			Optional[int]    =    'alsa'
								_lowerCamelCase     :			Optional[Any]    =    'default'
				elif system == "Darwin":
								_lowerCamelCase     :			Optional[int]    =    'avfoundation'
								_lowerCamelCase     :			Any    =    ':0'
				elif system == "Windows":
								_lowerCamelCase     :			Tuple    =    'dshow'
								_lowerCamelCase     :			Tuple    =    'default'

				_lowerCamelCase     :			Optional[int]    =    [
				    'ffmpeg',
				    '-f',
				    format_,
				    '-i',
				    input_,
				    '-ac',
				    ac,
				    '-ar',
				    ar,
				    '-f',
				    format_for_conversion,
				    '-fflags',
				    'nobuffer',
				    '-hide_banner',
				    '-loglevel',
				    'quiet',
				    'pipe:1',
				]
				_lowerCamelCase     :			Tuple    =    int(round(sampling_rate * chunk_length_s		)		) * size_of_sample
				_lowerCamelCase     :			List[Any]    =    _ffmpeg_stream(lowercase__							,    lowercase__		)
				for item in iterator:
								yield item







def      _snake_case			(				lowercase__							,    lowercase__							,    lowercase__ = None							,    lowercase__ = None							,    lowercase__ = "f32le"							,    ):
				if stream_chunk_s is not None:
								_lowerCamelCase     :			int    =    stream_chunk_s
				else:
								_lowerCamelCase     :			Optional[Any]    =    chunk_length_s

				_lowerCamelCase     :			Optional[Any]    =    ffmpeg_microphone(lowercase__							,    lowercase__							,    format_for_conversion=lowercase__		)
				if format_for_conversion == "s16le":
								_lowerCamelCase     :			List[str]    =    np.intaa
								_lowerCamelCase     :			str    =    2
				elif format_for_conversion == "f32le":
								_lowerCamelCase     :			Any    =    np.floataa
								_lowerCamelCase     :			List[Any]    =    4
				else:
								raise ValueError(f'''Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`'''		)

				if stride_length_s is None:
								_lowerCamelCase     :			Union[str, Any]    =    chunk_length_s / 6
				_lowerCamelCase     :			Optional[int]    =    int(round(sampling_rate * chunk_length_s		)		) * size_of_sample
				if isinstance(lowercase__							,    (int, float)		):
								_lowerCamelCase     :			Any    =    [stride_length_s, stride_length_s]

				_lowerCamelCase     :			Tuple    =    int(round(sampling_rate * stride_length_s[0]		)		) * size_of_sample
				_lowerCamelCase     :			Optional[Any]    =    int(round(sampling_rate * stride_length_s[1]		)		) * size_of_sample
				_lowerCamelCase     :			List[Any]    =    datetime.datetime.now()
				_lowerCamelCase     :			Optional[int]    =    datetime.timedelta(seconds=lowercase__		)
				for item in chunk_bytes_iter(lowercase__							,    lowercase__							,    stride=(stride_left, stride_right)							,    stream=lowercase__		):
								# Put everything back in numpy scale
								_lowerCamelCase     :			List[Any]    =    np.frombuffer(item['raw']							,    dtype=lowercase__		)
								_lowerCamelCase     :			int    =    (
								    item['stride'][0] // size_of_sample,
								    item['stride'][1] // size_of_sample,
								)
								_lowerCamelCase     :			Optional[int]    =    sampling_rate
								audio_time += delta
								if datetime.datetime.now() > audio_time + 10 * delta:
												# We're late !! SKIP
												continue
								yield item







def      _snake_case			(				lowercase__							,    lowercase__							,    lowercase__							,    lowercase__ = False		):
				_lowerCamelCase     :			int    =    B''
				_lowerCamelCase, _lowerCamelCase     :			Dict    =    stride
				if stride_left + stride_right >= chunk_len:
								raise ValueError(
								    f'''Stride needs to be strictly smaller than chunk_len: ({stride_left}, {stride_right}) vs {chunk_len}'''		)
				_lowerCamelCase     :			str    =    0
				for raw in iterator:
								acc += raw
								if stream and len(lowercase__		) < chunk_len:
												_lowerCamelCase     :			Optional[int]    =    (_stride_left, 0)
												yield {"raw": acc[:chunk_len], "stride": stride, "partial": True}
								else:
												while len(lowercase__		) >= chunk_len:
																# We are flushing the accumulator
																_lowerCamelCase     :			str    =    (_stride_left, stride_right)
																_lowerCamelCase     :			str    =    {'raw': acc[:chunk_len], 'stride': stride}
																if stream:
																				_lowerCamelCase     :			List[Any]    =    False
																yield item
																_lowerCamelCase     :			Optional[Any]    =    stride_left
																_lowerCamelCase     :			str    =    acc[chunk_len - stride_left - stride_right :]
    # Last chunk
				if len(lowercase__		) > stride_left:
								_lowerCamelCase     :			Optional[Any]    =    {'raw': acc, 'stride': (_stride_left, 0)}
								if stream:
												_lowerCamelCase     :			Tuple    =    False
								yield item







def      _snake_case			(				lowercase__							,    lowercase__		):
				_lowerCamelCase     :			int    =    2**24  # 16Mo
				try:
								with subprocess.Popen(lowercase__							,    stdout=subprocess.PIPE							,    bufsize=lowercase__		) as ffmpeg_process:
												while True:
																_lowerCamelCase     :			Optional[Any]    =    ffmpeg_process.stdout.read(lowercase__		)
																if raw == b"":
																				break
																yield raw
				except FileNotFoundError as error:
								raise ValueError('ffmpeg was not found but is required to stream audio files from filename'		) from error