

# 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 _UpperCamelCase							(							__A   ):
			'''simple docstring'''







			def __init__(			self							: List[str]      ,      a							: Tuple	)							->       str:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       data






			def __iter__(			self							: int	)							->       Optional[Any]:

				"""simple docstring"""

				for element in self.data:
					yield element

def  lowerCamelCase__ (					_a=True):
	SCREAMING_SNAKE_CASE   :      Tuple				     =       Accelerator(even_batches=_a)
	assert accelerator.num_processes == 2, "this script expects that two GPUs are available"
	return accelerator

def  lowerCamelCase__ (					_a							,			_a							,			_a							,			_a = False):
	if iterable:
		SCREAMING_SNAKE_CASE   :      List[Any]				     =       DummyIterableDataset(torch.as_tensor(range(_a)))
	else:
		SCREAMING_SNAKE_CASE   :      Dict				     =       TensorDataset(torch.as_tensor(range(_a)))

	SCREAMING_SNAKE_CASE   :      List[Any]				     =       DataLoader(_a							,			batch_size=_a)
	SCREAMING_SNAKE_CASE   :      Optional[int]				     =       accelerator.prepare(_a)

	return dl

def  lowerCamelCase__ (					_a							,			_a							,			_a							,			_a							,			_a							,			):
	SCREAMING_SNAKE_CASE   :      List[str]				     =       create_dataloader(accelerator=_a							,			dataset_size=_a							,			batch_size=_a)

	SCREAMING_SNAKE_CASE   :      Optional[int]				     =       [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  lowerCamelCase__ (					):
	SCREAMING_SNAKE_CASE   :      int				     =       create_accelerator()

	# without padding, we would expect a different number of batches
	verify_dataloader_batch_sizes(
	    _a							,			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(
	    _a							,			dataset_size=7							,			batch_size=2							,			process_0_expected_batch_sizes=[2, 2]							,			process_1_expected_batch_sizes=[2, 2]							,			)

def  lowerCamelCase__ (					):
	SCREAMING_SNAKE_CASE   :      List[str]				     =       create_accelerator(even_batches=_a)

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

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

def  lowerCamelCase__ (					):
	SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       create_accelerator(even_batches=_a)

	SCREAMING_SNAKE_CASE   :      str				     =       torch.nn.Linear(1							,			1)
	SCREAMING_SNAKE_CASE   :      Tuple				     =       accelerator.prepare(_a)

	SCREAMING_SNAKE_CASE   :      List[str]				     =       create_dataloader(_a							,			dataset_size=3							,			batch_size=1)

	SCREAMING_SNAKE_CASE   :      int				     =       []
	with accelerator.join_uneven_inputs([ddp_model]):
		for batch_idx, batch in enumerate(_a):
			SCREAMING_SNAKE_CASE   :      Optional[int]				     =       ddp_model(batch[0].float())
			SCREAMING_SNAKE_CASE   :      List[Any]				     =       output.sum()
			loss.backward()
			batch_idxs.append(_a)

	accelerator.wait_for_everyone()

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

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

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

def  lowerCamelCase__ (					):
	SCREAMING_SNAKE_CASE   :      List[Any]				     =       True
	SCREAMING_SNAKE_CASE   :      str				     =       False
	SCREAMING_SNAKE_CASE   :      int				     =       create_accelerator(even_batches=_a)
	SCREAMING_SNAKE_CASE   :      Optional[int]				     =       torch.nn.Linear(1							,			1)
	SCREAMING_SNAKE_CASE   :      List[Any]				     =       accelerator.prepare(_a)
	SCREAMING_SNAKE_CASE   :      str				     =       create_dataloader(_a							,			dataset_size=3							,			batch_size=1)
	SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       create_dataloader(_a							,			dataset_size=3							,			batch_size=1)

	with accelerator.join_uneven_inputs([ddp_model]							,			even_batches=_a):
		SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       train_dl.batch_sampler.even_batches
		SCREAMING_SNAKE_CASE   :      Dict				     =       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  lowerCamelCase__ (					):
	SCREAMING_SNAKE_CASE   :      List[Any]				     =       True
	SCREAMING_SNAKE_CASE   :      List[Any]				     =       False
	SCREAMING_SNAKE_CASE   :      Any				     =       create_accelerator(even_batches=_a)
	SCREAMING_SNAKE_CASE   :      Tuple				     =       torch.nn.Linear(1							,			1)
	SCREAMING_SNAKE_CASE   :      str				     =       accelerator.prepare(_a)
	create_dataloader(_a							,			dataset_size=3							,			batch_size=1							,			iterable=_a)
	SCREAMING_SNAKE_CASE   :      List[str]				     =       create_dataloader(_a							,			dataset_size=3							,			batch_size=1)

	with warnings.catch_warnings():
		warnings.filterwarnings("ignore")
		try:
			with accelerator.join_uneven_inputs([ddp_model]							,			even_batches=_a):
				SCREAMING_SNAKE_CASE   :      Union[str, 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  lowerCamelCase__ (					):
	SCREAMING_SNAKE_CASE   :      List[str]				     =       create_accelerator()
	SCREAMING_SNAKE_CASE   :      List[str]				     =       torch.nn.Linear(1							,			1)
	SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       accelerator.prepare(_a)
	create_dataloader(_a							,			dataset_size=3							,			batch_size=1							,			iterable=_a)

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

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

def  lowerCamelCase__ (					):
	SCREAMING_SNAKE_CASE   :      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")
	SCREAMING_SNAKE_CASE   :      str				     =       accelerator.state.distributed_type
	SCREAMING_SNAKE_CASE   :      List[Any]				     =       DistributedType.FSDP
	test_join_raises_warning_for_non_ddp_distributed(_a)
	SCREAMING_SNAKE_CASE   :      int				     =       original_state


if __name__ == "__main__":
			main()


import os
import tempfile
import unittest

from transformers.models.marian.convert_marian_tatoeba_to_pytorch import DEFAULT_REPO, TatoebaConverter
from transformers.testing_utils import slow
from transformers.utils import cached_property






@unittest.skipUnless(os.path.exists(__A   )							,	'Tatoeba directory does not exist.'   )
class _UpperCamelCase							(							unittest.TestCase   ):
			'''simple docstring'''







			@cached_property
			def 							__UpperCamelCase (			self							: Any	)							->       str:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       tempfile.mkdtemp()
				return TatoebaConverter(save_dir=a	)
			@slow
			def 							__UpperCamelCase (			self							: List[Any]	)							->       int:

				"""simple docstring"""

				self.resolver.convert_models(["heb-eng"]	)






			@slow
			def 							__UpperCamelCase (			self							: Optional[int]	)							->       Dict:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE     ,SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       self.resolver.write_model_card("opus-mt-he-en"      ,      dry_run=a	)
				assert mmeta["long_pair"] == "heb-eng"