


"""simple docstring"""




import unittest

from transformers import BarthezTokenizer, BarthezTokenizerFast, BatchEncoding
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow

from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
@require_sentencepiece
@slow  # see https://github.com/huggingface/transformers/issues/11457
class 					lowerCAmelCase	(					lowerCamelCase_	,    unittest.TestCase    ):




		'''simple docstring'''







		SCREAMING_SNAKE_CASE_						:     Optional[Any]								=     BarthezTokenizer
		SCREAMING_SNAKE_CASE_						:     Dict								=     BarthezTokenizerFast
		SCREAMING_SNAKE_CASE_						:     List[Any]								=     True
		SCREAMING_SNAKE_CASE_						:     Union[str, Any]								=     True







		def 							__A						(	self				)     ->							Dict:
								super().setUp()

								SCREAMING_SNAKE_CASE													=					BarthezTokenizerFast.from_pretrained('moussaKam/mbarthez'				)
								tokenizer.save_pretrained(self.tmpdirname				)
								tokenizer.save_pretrained(self.tmpdirname						,       legacy_format=lowerCAmelCase__				)
								SCREAMING_SNAKE_CASE													=					tokenizer







		def 							__A						(	self				)     ->							Any:
								SCREAMING_SNAKE_CASE													=					'<pad>'
								SCREAMING_SNAKE_CASE													=					1

								self.assertEqual(self.get_tokenizer()._convert_token_to_id(lowerCAmelCase__				)						,       lowerCAmelCase__				)
								self.assertEqual(self.get_tokenizer()._convert_id_to_token(lowerCAmelCase__				)						,       lowerCAmelCase__				)







		def 							__A						(	self				)     ->							Dict:
								SCREAMING_SNAKE_CASE													=					list(self.get_tokenizer().get_vocab().keys()				)

								self.assertEqual(vocab_keys[0]						,       '<s>'				)
								self.assertEqual(vocab_keys[1]						,       '<pad>'				)
								self.assertEqual(vocab_keys[-1]						,       '<mask>'				)
								self.assertEqual(len(lowerCAmelCase__				)						,       101_122				)







		def 							__A						(	self				)     ->							List[str]:
								self.assertEqual(self.get_tokenizer().vocab_size						,       101_122				)







		@require_torch
		def 							__A						(	self				)     ->							str:
								SCREAMING_SNAKE_CASE													=					['A long paragraph for summarization.', 'Another paragraph for summarization.']
								SCREAMING_SNAKE_CASE													=					[0, 57, 3_018, 70_307, 91, 2]

								SCREAMING_SNAKE_CASE													=					self.tokenizer(
								    lowerCAmelCase__						,       max_length=len(lowerCAmelCase__				)						,       padding=lowerCAmelCase__						,       truncation=lowerCAmelCase__						,       return_tensors='pt'				)
								self.assertIsInstance(lowerCAmelCase__						,       lowerCAmelCase__				)

								self.assertEqual((2, 6)						,       batch.input_ids.shape				)
								self.assertEqual((2, 6)						,       batch.attention_mask.shape				)
								SCREAMING_SNAKE_CASE													=					batch.input_ids.tolist()[0]
								self.assertListEqual(lowerCAmelCase__						,       lowerCAmelCase__				)







		def 							__A						(	self				)     ->							Union[str, Any]:
								if not self.test_rust_tokenizer:
														return

								SCREAMING_SNAKE_CASE													=					self.get_tokenizer()
								SCREAMING_SNAKE_CASE													=					self.get_rust_tokenizer()

								SCREAMING_SNAKE_CASE													=					'I was born in 92000, and this is falsé.'

								SCREAMING_SNAKE_CASE													=					tokenizer.tokenize(lowerCAmelCase__				)
								SCREAMING_SNAKE_CASE													=					rust_tokenizer.tokenize(lowerCAmelCase__				)
								self.assertListEqual(lowerCAmelCase__						,       lowerCAmelCase__				)

								SCREAMING_SNAKE_CASE													=					tokenizer.encode(lowerCAmelCase__						,       add_special_tokens=lowerCAmelCase__				)
								SCREAMING_SNAKE_CASE													=					rust_tokenizer.encode(lowerCAmelCase__						,       add_special_tokens=lowerCAmelCase__				)
								self.assertListEqual(lowerCAmelCase__						,       lowerCAmelCase__				)

								SCREAMING_SNAKE_CASE													=					self.get_rust_tokenizer()
								SCREAMING_SNAKE_CASE													=					tokenizer.encode(lowerCAmelCase__				)
								SCREAMING_SNAKE_CASE													=					rust_tokenizer.encode(lowerCAmelCase__				)
								self.assertListEqual(lowerCAmelCase__						,       lowerCAmelCase__				)







		@slow
		def 							__A						(	self				)     ->							str:
								# fmt: off
								SCREAMING_SNAKE_CASE													=					{'input_ids': [[0, 490, 14_328, 4_507, 354, 47, 43_669, 95, 25, 78_117, 20_215, 19_779, 190, 22, 400, 4, 35_343, 80_310, 603, 86, 24_937, 105, 33_438, 94_762, 196, 39_642, 7, 15, 15_933, 173, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 10_534, 87, 25, 66, 3_358, 196, 55_289, 8, 82_961, 81, 2_204, 75_203, 7, 15, 763, 12_956, 216, 178, 14_328, 9_595, 1_377, 69_693, 7, 448, 71_021, 196, 18_106, 1_437, 13_974, 108, 9_083, 4, 49_315, 7, 39, 86, 1_326, 2_793, 46_333, 4, 448, 196, 74_588, 7, 49_315, 7, 39, 21, 822, 38_470, 74, 21, 66_723, 62_480, 8, 22_050, 5, 2]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}  # noqa: E501
								# fmt: on

								# moussaKam/mbarthez is a french model. So we also use french texts.
								SCREAMING_SNAKE_CASE													=					[
								    'Le transformeur est un modèle d\'apprentissage profond introduit en 2017, '
								    'utilisé principalement dans le domaine du traitement automatique des langues (TAL).',
								    'À l\'instar des réseaux de neurones récurrents (RNN), les transformeurs sont conçus '
								    'pour gérer des données séquentielles, telles que le langage naturel, pour des tâches '
								    'telles que la traduction et la synthèse de texte.',
								]

								self.tokenizer_integration_test_util(
								    expected_encoding=lowerCAmelCase__						,       model_name='moussaKam/mbarthez'						,       revision='c2e4ecbca5e3cd2c37fe1ac285ca4fbdf1366fb6'						,       sequences=lowerCAmelCase__						,       )






"""simple docstring"""




import ast
import os
import re
import shutil
import tempfile
import unittest
from unittest import mock

import torch

from accelerate.test_utils.examples import compare_against_test
from accelerate.test_utils.testing import TempDirTestCase, require_trackers, run_command, slow
from accelerate.utils import write_basic_config


# DataLoaders built from `test_samples/MRPC` for quick testing
# Should mock `{script_name}.get_dataloaders` via:
# @mock.patch("{script_name}.get_dataloaders", mocked_dataloaders)

__UpperCamelCase						=      [
    '''cross_validation.py''',
    '''gradient_accumulation.py''',
    '''local_sgd.py''',
    '''multi_process_metrics.py''',
    '''memory.py''',
    '''automatic_gradient_accumulation.py''',
    '''fsdp_with_peak_mem_tracking.py''',
    '''deepspeed_with_config_support.py''',
    '''megatron_lm_gpt_pretraining.py''',
]
class 					lowerCAmelCase	(					unittest.TestCase    ):




		'''simple docstring'''







		def 							__A						(	self						,       lowerCAmelCase__						,       lowerCAmelCase__						,       lowerCAmelCase__ = None						,       lowerCAmelCase__ = None				)     ->							int:
								SCREAMING_SNAKE_CASE													=					None
								SCREAMING_SNAKE_CASE													=					os.path.abspath(os.path.join('examples'						,       'by_feature'				)				)
								SCREAMING_SNAKE_CASE													=					os.path.abspath('examples'				)
								for item in os.listdir(lowerCAmelCase__				):
														if item not in EXCLUDE_EXAMPLES:
																				SCREAMING_SNAKE_CASE													=					os.path.join(lowerCAmelCase__						,       lowerCAmelCase__				)
																				if os.path.isfile(lowerCAmelCase__				) and ".py" in item_path:
																										with self.subTest(
																										    tested_script=lowerCAmelCase__						,       feature_script=lowerCAmelCase__						,       tested_section='main()' if parser_only else 'training_function()'						,       ):
																																SCREAMING_SNAKE_CASE													=					compare_against_test(
																																    os.path.join(lowerCAmelCase__						,       lowerCAmelCase__				)						,       lowerCAmelCase__						,       lowerCAmelCase__						,       lowerCAmelCase__				)
																																SCREAMING_SNAKE_CASE													=					'\n'.join(lowerCAmelCase__				)
																																if special_strings is not None:
																																						for string in special_strings:
																																												SCREAMING_SNAKE_CASE													=					diff.replace(lowerCAmelCase__						,       ''				)
																																self.assertEqual(lowerCAmelCase__						,       ''				)







		def 							__A						(	self				)     ->							Optional[int]:
								self.one_complete_example('complete_nlp_example.py'						,       lowerCAmelCase__				)
								self.one_complete_example('complete_nlp_example.py'						,       lowerCAmelCase__				)







		def 							__A						(	self				)     ->							Optional[int]:
								SCREAMING_SNAKE_CASE													=					os.path.abspath(os.path.join('examples'						,       'cv_example.py'				)				)
								SCREAMING_SNAKE_CASE													=					[
								    ' ' * 16 + '{\n\n',
								    ' ' * 20 + '"accuracy": eval_metric["accuracy"],\n\n',
								    ' ' * 20 + '"f1": eval_metric["f1"],\n\n',
								    ' ' * 20 + '"train_loss": total_loss.item() / len(train_dataloader),\n\n',
								    ' ' * 20 + '"epoch": epoch,\n\n',
								    ' ' * 16 + '},\n\n',
								    ' ' * 16 + 'step=epoch,\n',
								    ' ' * 12,
								    ' ' * 8 + 'for step, batch in enumerate(active_dataloader):\n',
								]
								self.one_complete_example('complete_cv_example.py'						,       lowerCAmelCase__						,       lowerCAmelCase__						,       lowerCAmelCase__				)
								self.one_complete_example('complete_cv_example.py'						,       lowerCAmelCase__						,       lowerCAmelCase__						,       lowerCAmelCase__				)





@mock.patch.dict(os.environ	,    {"""TESTING_MOCKED_DATALOADERS""": """1"""}    )
class 					lowerCAmelCase	(					lowerCamelCase_    ):




		'''simple docstring'''







		SCREAMING_SNAKE_CASE_						:     List[str]								=     False







		@classmethod
		def 							__A						(	cls				)     ->							List[str]:
								super().setUpClass()
								SCREAMING_SNAKE_CASE													=					tempfile.mkdtemp()
								SCREAMING_SNAKE_CASE													=					os.path.join(cls._tmpdir						,       'default_config.yml'				)

								write_basic_config(save_location=cls.configPath				)
								SCREAMING_SNAKE_CASE													=					['accelerate', 'launch', '--config_file', cls.configPath]







		@classmethod
		def 							__A						(	cls				)     ->							Dict:
								super().tearDownClass()
								shutil.rmtree(cls._tmpdir				)







		def 							__A						(	self				)     ->							int:
								SCREAMING_SNAKE_CASE													=					F'\n        examples/by_feature/checkpointing.py\n        --checkpointing_steps epoch\n        --output_dir {self.tmpdir}\n        '.split()
								run_command(self._launch_args + testargs				)
								self.assertTrue(os.path.exists(os.path.join(self.tmpdir						,       'epoch_0'				)				)				)







		def 							__A						(	self				)     ->							List[Any]:
								SCREAMING_SNAKE_CASE													=					F'\n        examples/by_feature/checkpointing.py\n        --checkpointing_steps 1\n        --output_dir {self.tmpdir}\n        '.split()
								SCREAMING_SNAKE_CASE													=					run_command(self._launch_args + testargs				)
								self.assertTrue(os.path.exists(os.path.join(self.tmpdir						,       'step_2'				)				)				)







		def 							__A						(	self				)     ->							Union[str, Any]:
								SCREAMING_SNAKE_CASE													=					F'\n        examples/by_feature/checkpointing.py\n        --resume_from_checkpoint {os.path.join(self.tmpdir						,       "epoch_0"				)}\n        '.split()
								SCREAMING_SNAKE_CASE													=					run_command(self._launch_args + testargs						,       return_stdout=lowerCAmelCase__				)
								self.assertNotIn('epoch 0:'						,       lowerCAmelCase__				)
								self.assertIn('epoch 1:'						,       lowerCAmelCase__				)







		def 							__A						(	self				)     ->							int:
								SCREAMING_SNAKE_CASE													=					F'\n        examples/by_feature/checkpointing.py\n        --resume_from_checkpoint {os.path.join(self.tmpdir						,       "step_2"				)}\n        '.split()
								SCREAMING_SNAKE_CASE													=					run_command(self._launch_args + testargs						,       return_stdout=lowerCAmelCase__				)
								if torch.cuda.is_available():
														SCREAMING_SNAKE_CASE													=					torch.cuda.device_count()
								else:
														SCREAMING_SNAKE_CASE													=					1
								if num_processes > 1:
														self.assertNotIn('epoch 0:'						,       lowerCAmelCase__				)
														self.assertIn('epoch 1:'						,       lowerCAmelCase__				)
								else:
														self.assertIn('epoch 0:'						,       lowerCAmelCase__				)
														self.assertIn('epoch 1:'						,       lowerCAmelCase__				)







		@slow
		def 							__A						(	self				)     ->							Optional[Any]:
								SCREAMING_SNAKE_CASE													=					'\n        examples/by_feature/cross_validation.py\n        --num_folds 2\n        '.split()
								with mock.patch.dict(os.environ						,       {'TESTING_MOCKED_DATALOADERS': '0'}				):
														SCREAMING_SNAKE_CASE													=					run_command(self._launch_args + testargs						,       return_stdout=lowerCAmelCase__				)
														SCREAMING_SNAKE_CASE													=					re.findall('({.+})'						,       lowerCAmelCase__				)
														SCREAMING_SNAKE_CASE													=					[r for r in results if 'accuracy' in r][-1]
														SCREAMING_SNAKE_CASE													=					ast.literal_eval(lowerCAmelCase__				)
														self.assertGreaterEqual(results['accuracy']						,       0.75				)







		def 							__A						(	self				)     ->							str:
								SCREAMING_SNAKE_CASE													=					['examples/by_feature/multi_process_metrics.py']
								run_command(self._launch_args + testargs				)







		@require_trackers
		@mock.patch.dict(os.environ						,       {'WANDB_MODE': 'offline'}				)
		def 							__A						(	self				)     ->							Union[str, Any]:
								with tempfile.TemporaryDirectory() as tmpdir:
														SCREAMING_SNAKE_CASE													=					F'\n            examples/by_feature/tracking.py\n            --with_tracking\n            --project_dir {tmpdir}\n            '.split()
														run_command(self._launch_args + testargs				)
														self.assertTrue(os.path.exists(os.path.join(lowerCAmelCase__						,       'tracking'				)				)				)







		def 							__A						(	self				)     ->							Dict:
								SCREAMING_SNAKE_CASE													=					['examples/by_feature/gradient_accumulation.py']
								run_command(self._launch_args + testargs				)







		def 							__A						(	self				)     ->							List[Any]:
								SCREAMING_SNAKE_CASE													=					['examples/by_feature/local_sgd.py']
								run_command(self._launch_args + testargs				)


