# 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       lowercase__		(     UpperCamelCase_):


						def __init__( self			:       Tuple					,	UpperCamelCase__			:       Optional[int]		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Tuple           =							data



						def __iter__( self			:       Tuple		):





												'''simple docstring'''





												for element in self.data:
																		yield element






def    A			(					_lowercase=True		):
						SCREAMING_SNAKE_CASE							:					Dict           =							Accelerator(even_batches=_lowercase		)
						assert accelerator.num_processes == 2, "this script expects that two GPUs are available"
						return accelerator
def    A			(					_lowercase	,						_lowercase	,						_lowercase	,						_lowercase = False		):
						if iterable:
												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							DummyIterableDataset(torch.as_tensor(range(_lowercase		)		)		)
						else:
												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							TensorDataset(torch.as_tensor(range(_lowercase		)		)		)

						SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							DataLoader(_lowercase	,						batch_size=_lowercase		)
						SCREAMING_SNAKE_CASE							:					List[str]           =							accelerator.prepare(_lowercase		)

						return dl
def    A			(					_lowercase	,						_lowercase	,						_lowercase	,						_lowercase	,						_lowercase	,						):
						SCREAMING_SNAKE_CASE							:					Optional[int]           =							create_dataloader(accelerator=_lowercase	,						dataset_size=_lowercase	,						batch_size=_lowercase		)

						SCREAMING_SNAKE_CASE							:					List[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    A			(					):
						SCREAMING_SNAKE_CASE							:					Optional[Any]           =							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    A			(					):
						SCREAMING_SNAKE_CASE							:					Dict           =							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    A			(					):
						SCREAMING_SNAKE_CASE							:					str           =							create_accelerator(even_batches=_lowercase		)

						SCREAMING_SNAKE_CASE							:					Optional[Any]           =							torch.nn.Linear(1	,						1		)
						SCREAMING_SNAKE_CASE							:					Optional[int]           =							accelerator.prepare(_lowercase		)

						SCREAMING_SNAKE_CASE							:					Optional[int]           =							create_dataloader(_lowercase	,						dataset_size=3	,						batch_size=1		)

						SCREAMING_SNAKE_CASE							:					Tuple           =							[]
						with accelerator.join_uneven_inputs([ddp_model]		):
												for batch_idx, batch in enumerate(_lowercase		):
																		SCREAMING_SNAKE_CASE							:					int           =							ddp_model(batch[0].float()		)
																		SCREAMING_SNAKE_CASE							:					Any           =							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    A			(					_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    A			(					):
						SCREAMING_SNAKE_CASE							:					Optional[int]           =							True
						SCREAMING_SNAKE_CASE							:					Tuple           =							False
						SCREAMING_SNAKE_CASE							:					List[Any]           =							create_accelerator(even_batches=_lowercase		)
						SCREAMING_SNAKE_CASE							:					Optional[int]           =							torch.nn.Linear(1	,						1		)
						SCREAMING_SNAKE_CASE							:					List[str]           =							accelerator.prepare(_lowercase		)
						SCREAMING_SNAKE_CASE							:					Any           =							create_dataloader(_lowercase	,						dataset_size=3	,						batch_size=1		)
						SCREAMING_SNAKE_CASE							:					str           =							create_dataloader(_lowercase	,						dataset_size=3	,						batch_size=1		)

						with accelerator.join_uneven_inputs([ddp_model]	,						even_batches=_lowercase		):
												SCREAMING_SNAKE_CASE							:					List[str]           =							train_dl.batch_sampler.even_batches
												SCREAMING_SNAKE_CASE							:					Tuple           =							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    A			(					):
						SCREAMING_SNAKE_CASE							:					Tuple           =							True
						SCREAMING_SNAKE_CASE							:					int           =							False
						SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							create_accelerator(even_batches=_lowercase		)
						SCREAMING_SNAKE_CASE							:					Optional[int]           =							torch.nn.Linear(1	,						1		)
						SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							accelerator.prepare(_lowercase		)
						create_dataloader(_lowercase	,						dataset_size=3	,						batch_size=1	,						iterable=_lowercase		)
						SCREAMING_SNAKE_CASE							:					Optional[Any]           =							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		):
																								SCREAMING_SNAKE_CASE							:					int           =							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    A			(					):
						SCREAMING_SNAKE_CASE							:					Tuple           =							create_accelerator()
						SCREAMING_SNAKE_CASE							:					Optional[Any]           =							torch.nn.Linear(1	,						1		)
						SCREAMING_SNAKE_CASE							:					List[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    A			(					):
						SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							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							:					Optional[Any]           =							accelerator.state.distributed_type
						SCREAMING_SNAKE_CASE							:					List[str]           =							DistributedType.FSDP
						test_join_raises_warning_for_non_ddp_distributed(_lowercase		)
						SCREAMING_SNAKE_CASE							:					List[str]           =							original_state


if __name__ == "__main__":
					main()






import os
import unittest

from transformers import FunnelTokenizer, FunnelTokenizerFast
from transformers.models.funnel.tokenization_funnel import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin





@require_tokenizers
class       lowercase__		(     UpperCamelCase_			,							unittest.TestCase):
						UpperCamelCase_  =	FunnelTokenizer
						UpperCamelCase_  =	FunnelTokenizerFast
						UpperCamelCase_  =	True
						UpperCamelCase_  =	True


						def  __A ( self			:       Union[str, Any]		):





												'''simple docstring'''





												super().setUp()

												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							[
												    '''<unk>''',
												    '''<cls>''',
												    '''<sep>''',
												    '''want''',
												    '''##want''',
												    '''##ed''',
												    '''wa''',
												    '''un''',
												    '''runn''',
												    '''##ing''',
												    ''',''',
												    '''low''',
												    '''lowest''',
												]
												SCREAMING_SNAKE_CASE							:					List[str]           =							os.path.join(self.tmpdirname					,	VOCAB_FILES_NAMES['''vocab_file''']		)
												with open(self.vocab_file					,	'''w'''					,	encoding='''utf-8'''		) as vocab_writer:
																		vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens]		)		)


						def  __A ( self			:       int					,	**UpperCamelCase__			:       List[Any]		):





												'''simple docstring'''





												return FunnelTokenizer.from_pretrained(self.tmpdirname					,	**UpperCamelCase__		)


						def  __A ( self			:       int					,	**UpperCamelCase__			:       List[Any]		):





												'''simple docstring'''





												return FunnelTokenizerFast.from_pretrained(self.tmpdirname					,	**UpperCamelCase__		)


						def  __A ( self			:       Any					,	UpperCamelCase__			:       List[str]		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							'''UNwant\u00E9d,running'''
												SCREAMING_SNAKE_CASE							:					int           =							'''unwanted, running'''
												return input_text, output_text


						def  __A ( self			:       Any		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					List[Any]           =							self.tokenizer_class(self.vocab_file		)

												SCREAMING_SNAKE_CASE							:					int           =							tokenizer.tokenize('''UNwant\u00E9d,running'''		)
												self.assertListEqual(UpperCamelCase__					,	['''un''', '''##want''', '''##ed''', ''',''', '''runn''', '''##ing''']		)
												self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCamelCase__		)					,	[7, 4, 5, 10, 8, 9]		)



						def  __A ( self			:       str		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Dict           =							self.get_tokenizers(do_lower_case=UpperCamelCase__		)
												for tokenizer in tokenizers:
																		SCREAMING_SNAKE_CASE							:					int           =							tokenizer('''UNwant\u00E9d,running'''		)
																		SCREAMING_SNAKE_CASE							:					Optional[Any]           =							len(inputs['''input_ids''']		) - 1
																		self.assertListEqual(inputs['''token_type_ids''']					,	[2] + [0] * sentence_len		)

																		SCREAMING_SNAKE_CASE							:					List[Any]           =							tokenizer('''UNwant\u00E9d,running'''					,	'''UNwant\u00E9d,running'''		)
																		self.assertListEqual(inputs['''token_type_ids''']					,	[2] + [0] * sentence_len + [1] * sentence_len		)





