'''simple docstring'''




import json
import os
import unittest

from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES, BioGptTokenizer
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin





class     __magic_name__						(    _UpperCAmelCase,     unittest.TestCase):
			UpperCamelCase__				=       BioGptTokenizer
			UpperCamelCase__				=       False







			def 	SCREAMING_SNAKE_CASE_     (	self				:  Union[str, Any]    ):
										super().setUp()

										# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
										lowercase_					:			List[Any]										=       [
										    """l""",
										    """o""",
										    """w""",
										    """e""",
										    """r""",
										    """s""",
										    """t""",
										    """i""",
										    """d""",
										    """n""",
										    """w</w>""",
										    """r</w>""",
										    """t</w>""",
										    """lo""",
										    """low""",
										    """er</w>""",
										    """low</w>""",
										    """lowest</w>""",
										    """newer</w>""",
										    """wider</w>""",
										    """<unk>""",
										]
										lowercase_					:			Dict										=       dict(zip(lowercase_				,			range(len(lowercase_    )    )    )    )
										lowercase_					:			List[Any]										=       ["""l o 123""", """lo w 1456""", """e r</w> 1789""", """"""]

										lowercase_					:			Optional[int]										=       os.path.join(self.tmpdirname				,			VOCAB_FILES_NAMES["""vocab_file"""]    )
										lowercase_					:			Dict										=       os.path.join(self.tmpdirname				,			VOCAB_FILES_NAMES["""merges_file"""]    )
										with open(self.vocab_file				,			"""w"""    ) as fp:
																	fp.write(json.dumps(lowercase_    )    )
										with open(self.merges_file				,			"""w"""    ) as fp:
																	fp.write("""\n""".join(lowercase_    )    )







			def 	SCREAMING_SNAKE_CASE_     (	self				:  Any				,			lowercase_				:  int    ):
										lowercase_					:			Optional[Any]										=       """lower newer"""
										lowercase_					:			Optional[Any]										=       """lower newer"""
										return input_text, output_text







			def 	SCREAMING_SNAKE_CASE_     (	self				:  Optional[int]    ):
										lowercase_					:			List[str]										=       BioGptTokenizer(self.vocab_file				,			self.merges_file    )

										lowercase_					:			List[Any]										=       """lower"""
										lowercase_					:			List[str]										=       ["""low""", """er</w>"""]
										lowercase_					:			Optional[Any]										=       tokenizer.tokenize(lowercase_    )
										self.assertListEqual(lowercase_				,			lowercase_    )

										lowercase_					:			int										=       tokens + ["""<unk>"""]
										lowercase_					:			Tuple										=       [14, 15, 20]
										self.assertListEqual(tokenizer.convert_tokens_to_ids(lowercase_    )				,			lowercase_    )







			@slow
			def 	SCREAMING_SNAKE_CASE_     (	self				:  Union[str, Any]    ):
										lowercase_					:			Optional[int]										=       BioGptTokenizer.from_pretrained("""microsoft/biogpt"""    )

										lowercase_					:			List[Any]										=       tokenizer.encode("""sequence builders"""				,			add_special_tokens=lowercase_    )
										lowercase_					:			int										=       tokenizer.encode("""multi-sequence build"""				,			add_special_tokens=lowercase_    )

										lowercase_					:			Optional[int]										=       tokenizer.build_inputs_with_special_tokens(lowercase_    )
										lowercase_					:			Tuple										=       tokenizer.build_inputs_with_special_tokens(lowercase_				,			lowercase_    )

										self.assertTrue(encoded_sentence == [2] + text    )
										self.assertTrue(encoded_pair == [2] + text + [2] + text_a    )






'''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     __magic_name__						(    _UpperCAmelCase):







			def __init__(	self				:  Optional[Any]				,			lowercase_				:  str    ):
										lowercase_					:			int										=       data







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




def 							lowerCamelCase	(      UpperCAmelCase__       :					Any=True )			->      Any:
							lowercase_					:			Optional[int]										=       Accelerator(even_batches=UpperCAmelCase__ )
							assert accelerator.num_processes == 2, "this script expects that two GPUs are available"
							return accelerator




def 							lowerCamelCase	(      UpperCAmelCase__       :					Accelerator      ,      UpperCAmelCase__       :					int      ,      UpperCAmelCase__       :					int      ,      UpperCAmelCase__       :					bool = False )			->      Optional[Any]:
							if iterable:
														lowercase_					:			Dict										=       DummyIterableDataset(torch.as_tensor(range(UpperCAmelCase__ ) ) )
							else:
														lowercase_					:			Union[str, Any]										=       TensorDataset(torch.as_tensor(range(UpperCAmelCase__ ) ) )

							lowercase_					:			Any										=       DataLoader(UpperCAmelCase__      ,      batch_size=UpperCAmelCase__ )
							lowercase_					:			Any										=       accelerator.prepare(UpperCAmelCase__ )

							return dl




def 							lowerCamelCase	(      UpperCAmelCase__       :					Accelerator      ,      UpperCAmelCase__       :					int      ,      UpperCAmelCase__       :					int      ,      UpperCAmelCase__       :					List[int]      ,      UpperCAmelCase__       :					List[int]      ,      )			->      int:
							lowercase_					:			List[str]										=       create_dataloader(accelerator=UpperCAmelCase__      ,      dataset_size=UpperCAmelCase__      ,      batch_size=UpperCAmelCase__ )

							lowercase_					:			Tuple										=       [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	(      )			->      int:
							lowercase_					:			Tuple										=       create_accelerator()

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




def 							lowerCamelCase	(      )			->      Optional[int]:
							lowercase_					:			Optional[int]										=       create_accelerator(even_batches=UpperCAmelCase__ )

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

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




def 							lowerCamelCase	(      )			->      List[str]:
							lowercase_					:			str										=       create_accelerator(even_batches=UpperCAmelCase__ )

							lowercase_					:			Dict										=       torch.nn.Linear(1      ,      1 )
							lowercase_					:			Optional[Any]										=       accelerator.prepare(UpperCAmelCase__ )

							lowercase_					:			List[Any]										=       create_dataloader(UpperCAmelCase__      ,      dataset_size=3      ,      batch_size=1 )

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

							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	(      UpperCAmelCase__       :					List[str] )			->      List[str]:
							with warnings.catch_warnings(record=UpperCAmelCase__ ) as w:
														with accelerator.join_uneven_inputs([Mock()] ):
																					pass

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




def 							lowerCamelCase	(      )			->      Any:
							lowercase_					:			str										=       True
							lowercase_					:			Tuple										=       False
							lowercase_					:			str										=       create_accelerator(even_batches=UpperCAmelCase__ )
							lowercase_					:			Union[str, Any]										=       torch.nn.Linear(1      ,      1 )
							lowercase_					:			Any										=       accelerator.prepare(UpperCAmelCase__ )
							lowercase_					:			Optional[int]										=       create_dataloader(UpperCAmelCase__      ,      dataset_size=3      ,      batch_size=1 )
							lowercase_					:			List[Any]										=       create_dataloader(UpperCAmelCase__      ,      dataset_size=3      ,      batch_size=1 )

							with accelerator.join_uneven_inputs([ddp_model]      ,      even_batches=UpperCAmelCase__ ):
														lowercase_					:			Union[str, Any]										=       train_dl.batch_sampler.even_batches
														lowercase_					:			List[str]										=       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	(      )			->      Dict:
							lowercase_					:			str										=       True
							lowercase_					:			Optional[Any]										=       False
							lowercase_					:			Union[str, Any]										=       create_accelerator(even_batches=UpperCAmelCase__ )
							lowercase_					:			Optional[int]										=       torch.nn.Linear(1      ,      1 )
							lowercase_					:			Optional[int]										=       accelerator.prepare(UpperCAmelCase__ )
							create_dataloader(UpperCAmelCase__      ,      dataset_size=3      ,      batch_size=1      ,      iterable=UpperCAmelCase__ )
							lowercase_					:			List[str]										=       create_dataloader(UpperCAmelCase__      ,      dataset_size=3      ,      batch_size=1 )

							with warnings.catch_warnings():
														warnings.filterwarnings("""ignore""" )
														try:
																					with accelerator.join_uneven_inputs([ddp_model]      ,      even_batches=UpperCAmelCase__ ):
																												lowercase_					:			Optional[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	(      )			->      List[Any]:
							lowercase_					:			Optional[Any]										=       create_accelerator()
							lowercase_					:			Optional[int]										=       torch.nn.Linear(1      ,      1 )
							lowercase_					:			List[Any]										=       accelerator.prepare(UpperCAmelCase__ )
							create_dataloader(UpperCAmelCase__      ,      dataset_size=3      ,      batch_size=1      ,      iterable=UpperCAmelCase__ )

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

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




def 							lowerCamelCase	(      )			->      List[str]:
							lowercase_					:			List[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""" )
							lowercase_					:			List[Any]										=       accelerator.state.distributed_type
							lowercase_					:			Union[str, Any]										=       DistributedType.FSDP
							test_join_raises_warning_for_non_ddp_distributed(UpperCAmelCase__ )
							lowercase_					:			str										=       original_state


if __name__ == "__main__":
						main()





