"""simple docstring"""


# This test is meant to be run in on an instance with TPUs like this:
#
#   python examples/pytorch/xla_spawn.py --num_cores=8 tests/test_trainer_tpu.py
#
# Replace 8 with the number of TPU cores you have.
#

import sys
from typing import Dict

from transformers import EvalPrediction, HfArgumentParser, TrainingArguments, is_torch_available
from transformers.utils import logging


a							=logging.get_logger(__name__)


if is_torch_available():
	import torch
	from torch import nn
	from torch.utils.data import Dataset

	from transformers import Trainer






	class    __UpperCAmelCase						(	__lowerCAmelCase	):

					def __init__(      self	, _lowerCamelCase = 101      ):
											lowerCamelCase__					=length

					def __len__(      self      ):
											return self.length






					def __getitem__(      self	, _lowerCamelCase      ):
											return i






	class    __UpperCAmelCase						:

					def __call__(      self	, _lowerCamelCase      ):
											return {"input_ids": torch.tensor(_lowerCamelCase      ), "labels": torch.tensor(_lowerCamelCase      )}






	class    __UpperCAmelCase						(	nn.Module	):

					def __init__(      self      ):
											super().__init__()
											# Add some (unused) params otherwise DDP will complain.
											lowerCamelCase__					=nn.Linear(120	, 80      )






					def 		_a	(      self	, _lowerCamelCase	, _lowerCamelCase=None      ):
											if labels is not None:
																	return torch.tensor(0.0	, device=input_ids.device      ), input_ids
											else:
																	return input_ids





def 			lowerCamelCase_ (					)							->					Union[str, Any]:




						'''simple docstring'''



						lowerCamelCase__					=HfArgumentParser((TrainingArguments,)				)
						sys.argv += ["--output_dir", "./examples"]
						lowerCamelCase__					=parser.parse_args_into_dataclasses()[0]

						logger.warning(
						    F'''Process rank: {training_args.local_rank}, device: {training_args.device}, '''
						    F'''tpu_num_cores: {training_args.tpu_num_cores}'''   ,    )

						# Essentially, what we want to verify in the distributed case is
						# that we get all samples back, in the right order.
						# (this is crucial for prediction for instance)
						for dataset_length in [1001, 256, 15]:
												lowerCamelCase__					=DummyDataset(__lowerCAmelCase				)

												def compute_metrics(__lowerCAmelCase				) -> Dict:
																		lowerCamelCase__					=list(range(len(__lowerCAmelCase				)				)				)
																		lowerCamelCase__					=p.predictions.tolist() == sequential and p.label_ids.tolist() == sequential
																		return {"success": success}

												lowerCamelCase__					=Trainer(
												    model=DummyModel()   ,    args=__lowerCAmelCase   ,    data_collator=DummyDataCollator()   ,    eval_dataset=__lowerCAmelCase   ,    compute_metrics=__lowerCAmelCase   ,    )
												lowerCamelCase__					=trainer.evaluate()
												logger.info(__lowerCAmelCase				)
												if metrics["eval_success"] is not True:
																		logger.error(__lowerCAmelCase				)
																		exit(1				)

												lowerCamelCase__					=trainer.predict(__lowerCAmelCase				)
												logger.info(p.metrics				)
												if p.metrics["test_success"] is not True:
																		logger.error(p.metrics				)
																		exit(1				)

												lowerCamelCase__					=2

												lowerCamelCase__					=trainer.evaluate()
												logger.info(__lowerCAmelCase				)
												if metrics["eval_success"] is not True:
																		logger.error(__lowerCAmelCase				)
																		exit(1				)

												lowerCamelCase__					=trainer.predict(__lowerCAmelCase				)
												logger.info(p.metrics				)
												if p.metrics["test_success"] is not True:
																		logger.error(p.metrics				)
																		exit(1				)

												lowerCamelCase__					=None

						logger.info("🔥 All distributed tests successful"				)





def 			lowerCamelCase_ (					__lowerCAmelCase				)							->					Tuple:




						'''simple docstring'''



						main()


if __name__ == "__main__":
	main()




'''simple docstring'''
import os
import tempfile
import unittest
from typing import List

from transformers.models.esm.tokenization_esm import VOCAB_FILES_NAMES, EsmTokenizer
from transformers.testing_utils import require_tokenizers
from transformers.tokenization_utils import PreTrainedTokenizer
from transformers.tokenization_utils_base import PreTrainedTokenizerBase






@require_tokenizers
class      snake_case__    (	unittest.TestCase ):







		"""simple docstring"""

		lowerCamelCase							=		EsmTokenizer



		def 	lowerCAmelCase							(	self							:       List[Any]	)		->       Any:






				"""simple docstring"""







				super().setUp()
				snake_case   :    Optional[int]			       =		tempfile.mkdtemp()
				# fmt: off
				snake_case   :    List[str]			       =		["<cls>", "<pad>", "<eos>", "<unk>", "L", "A", "G", "V", "S", "E", "R", "T", "I", "D", "P", "K", "Q", "N", "F", "Y", "M", "H", "W", "C", "X", "B", "U", "Z", "O", ".", "-", "<null_1>", "<mask>"]  # noqa: E501
				# fmt: on
				snake_case   :    List[Any]			       =		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 	lowerCAmelCase							(	self							:       str   ,     **UpperCamelCase__							:       Union[str, Any]	)		->       List[PreTrainedTokenizerBase]:






				"""simple docstring"""







				return [self.get_tokenizer(**UpperCamelCase__	)]



		def 	lowerCAmelCase							(	self							:       Union[str, Any]   ,     **UpperCamelCase__							:       Tuple	)		->       PreTrainedTokenizer:






				"""simple docstring"""







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



		def 	lowerCAmelCase							(	self							:       List[str]	)		->       Tuple:






				"""simple docstring"""







				snake_case   :    str			       =		self.tokenizer_class(self.vocab_file	)

				snake_case   :    Optional[int]			       =		tokenizer.tokenize('''LAGVS'''	)
				self.assertListEqual(UpperCamelCase__   ,     ['''L''', '''A''', '''G''', '''V''', '''S''']	)
				self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCamelCase__	)   ,     [4, 5, 6, 7, 8]	)



		def 	lowerCAmelCase							(	self							:       List[Any]	)		->       int:






				"""simple docstring"""







				snake_case   :    Dict			       =		self.tokenizer_class(self.vocab_file	)

				snake_case   :    List[Any]			       =		'''LAGVS'''
				self.assertListEqual(tokenizer.encode(UpperCamelCase__	)   ,     [0, 4, 5, 6, 7, 8, 2]	)



		def 	lowerCAmelCase							(	self							:       List[str]	)		->       int:






				"""simple docstring"""







				snake_case   :    Union[str, Any]			       =		self.tokenizer_class(self.vocab_file	)

				snake_case   :    int			       =		['''LAGVS''', '''WCB''']
				snake_case   :    List[str]			       =		tokenizer(UpperCamelCase__   ,     padding=UpperCamelCase__	)['''input_ids''']

				self.assertListEqual(UpperCamelCase__   ,     [[0, 4, 5, 6, 7, 8, 2], [0, 22, 23, 25, 2]]	)



		def 	lowerCAmelCase							(	self							:       int	)		->       List[str]:






				"""simple docstring"""







				snake_case   :    Dict			       =		self.tokenizer_class(self.vocab_file	)

				snake_case   :    List[str]			       =		['''LAGVS''', '''WCB''']
				snake_case   :    Any			       =		tokenizer(UpperCamelCase__   ,     padding=UpperCamelCase__	)['''input_ids''']

				self.assertListEqual(UpperCamelCase__   ,     [[0, 4, 5, 6, 7, 8, 2], [0, 22, 23, 25, 2, 1, 1]]	)



		def 	lowerCAmelCase							(	self							:       Any	)		->       int:






				"""simple docstring"""







				snake_case   :    List[str]			       =		self.get_tokenizers(fast=UpperCamelCase__	)
				for tokenizer in tokenizers:
						with self.subTest(f'{tokenizer.__class__.__name__}'	):
								snake_case   :    int			       =		'''<unk>'''
								snake_case   :    int			       =		'''<mask>'''

								snake_case   :    Any			       =		tokenizer.tokenize(UpperCamelCase__	)
								snake_case   :    Any			       =		tokenizer.tokenize(UpperCamelCase__	)

								self.assertEqual(len(UpperCamelCase__	)   ,     1	)
								self.assertEqual(len(UpperCamelCase__	)   ,     1	)
								self.assertEqual(token_a[0]   ,     UpperCamelCase__	)
								self.assertEqual(token_a[0]   ,     UpperCamelCase__	)


