
"""simple docstring"""




import sys
from pathlib import Path


lowerCAmelCase__							=				Path(__file__).resolve().parents[3] / 'src'
sys.path.insert(1, str(git_repo_path))

import dataclasses  # noqa
import io  # noqa
import itertools  # noqa
import json  # noqa
import os  # noqa
import unittest  # noqa
from copy import deepcopy  # noqa

from parameterized import parameterized  # noqa
from transformers import TrainingArguments, is_torch_available  # noqa
from transformers.deepspeed import is_deepspeed_available  # noqa
from transformers.file_utils import WEIGHTS_NAME  # noqa
from transformers.testing_utils import (  # noqa
    CaptureLogger,
    ExtendSysPath,
    TestCasePlus,
    execute_subprocess_async,
    get_gpu_count,
    mockenv_context,
    require_deepspeed,
    require_torch_gpu,
    require_torch_multi_gpu,
    slow,
)
from transformers.trainer_utils import set_seed  # noqa


set_seed(42)

lowerCAmelCase__							=				{'base': 'patrickvonplaten/wav2vec2_tiny_random', 'robust': 'patrickvonplaten/wav2vec2_tiny_random_robust'}

lowerCAmelCase__							=				'zero2'
lowerCAmelCase__							=				'zero3'
lowerCAmelCase__							=				[ZEROa, ZEROa]



def        _lowerCamelCase ( __a,							__a,							__a    ):
		# customize the test name generator function as we want both params to appear in the sub-test
		# name, as by default it shows only the first param
		SCREAMING_SNAKE_CASE_						 = parameterized.to_safe_name('''_'''.join(str(__a    ) for x in param.args    )    )
		return F'{func.__name__}_{param_based_name}'


# Cartesian-product of zero stages with models to test
lowerCAmelCase__							=				list(itertools.product(stages, models.keys()))





@slow
@require_deepspeed
@require_torch_gpu
class  snake_case     (							__lowercase   ):

							@parameterized.expand(SCREAMING_SNAKE_CASE_       ,       name_func=SCREAMING_SNAKE_CASE_    )
							def _lowercase					(self       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    ):

									"""simple docstring"""







									self.run_and_check(
									    stage=SCREAMING_SNAKE_CASE_       ,       model=SCREAMING_SNAKE_CASE_       ,       distributed=SCREAMING_SNAKE_CASE_       ,       fpaa=SCREAMING_SNAKE_CASE_       ,       )

							@require_torch_multi_gpu
							@parameterized.expand(SCREAMING_SNAKE_CASE_       ,       name_func=SCREAMING_SNAKE_CASE_    )
							def _lowercase					(self       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    ):

									"""simple docstring"""







									self.run_and_check(
									    stage=SCREAMING_SNAKE_CASE_       ,       model=SCREAMING_SNAKE_CASE_       ,       distributed=SCREAMING_SNAKE_CASE_       ,       fpaa=SCREAMING_SNAKE_CASE_       ,       )

							@parameterized.expand(SCREAMING_SNAKE_CASE_       ,       name_func=SCREAMING_SNAKE_CASE_    )
							def _lowercase					(self       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    ):

									"""simple docstring"""







									self.run_and_check(
									    stage=SCREAMING_SNAKE_CASE_       ,       model=SCREAMING_SNAKE_CASE_       ,       distributed=SCREAMING_SNAKE_CASE_       ,       fpaa=SCREAMING_SNAKE_CASE_       ,       )

							@require_torch_multi_gpu
							@parameterized.expand(SCREAMING_SNAKE_CASE_       ,       name_func=SCREAMING_SNAKE_CASE_    )
							def _lowercase					(self       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    ):

									"""simple docstring"""







									self.run_and_check(
									    stage=SCREAMING_SNAKE_CASE_       ,       model=SCREAMING_SNAKE_CASE_       ,       distributed=SCREAMING_SNAKE_CASE_       ,       fpaa=SCREAMING_SNAKE_CASE_       ,       )

							def _lowercase					(self       ,       SCREAMING_SNAKE_CASE_    ):

									"""simple docstring"""







									pass

							def _lowercase					(self       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_ = 10       ,       SCREAMING_SNAKE_CASE_ = True       ,       SCREAMING_SNAKE_CASE_ = True       ,       SCREAMING_SNAKE_CASE_ = True       ,       ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = models[model]

									SCREAMING_SNAKE_CASE_						 = self.run_trainer(
									    stage=SCREAMING_SNAKE_CASE_       ,       model_name=SCREAMING_SNAKE_CASE_       ,       eval_steps=SCREAMING_SNAKE_CASE_       ,       num_train_epochs=1       ,       distributed=SCREAMING_SNAKE_CASE_       ,       fpaa=SCREAMING_SNAKE_CASE_       ,       )

									self.do_checks(SCREAMING_SNAKE_CASE_    )

									return output_dir

							def _lowercase					(self       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_ = 10       ,       SCREAMING_SNAKE_CASE_ = 1       ,       SCREAMING_SNAKE_CASE_ = True       ,       SCREAMING_SNAKE_CASE_ = True       ,       ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = self.get_auto_remove_tmp_dir('''./xxx'''       ,       after=SCREAMING_SNAKE_CASE_    )
									SCREAMING_SNAKE_CASE_						 = f'\n            --model_name_or_path {model_name}\n            --dataset_name hf-internal-testing/librispeech_asr_dummy\n            --dataset_config_name clean\n            --train_split_name validation\n            --validation_split_name validation\n            --output_dir {output_dir}\n            --num_train_epochs {str(SCREAMING_SNAKE_CASE_    )}\n            --per_device_train_batch_size 2\n            --per_device_eval_batch_size 2\n            --evaluation_strategy steps\n            --learning_rate 5e-4\n            --warmup_steps 8\n            --orthography timit\n            --preprocessing_num_workers 1\n            --group_by_length\n            --freeze_feature_extractor\n            --report_to none\n            --save_steps 0\n            --eval_steps {eval_steps}\n            --report_to none\n        '.split()

									if fpaa:
											args.extend(['''--fp16''']    )

									# currently ds_config_wav2vec2_zero.json requires "zero_optimization.find_unused_parameters": true,
									# hence the separate config files
									SCREAMING_SNAKE_CASE_						 = f'--deepspeed {self.test_file_dir_str}/ds_config_wav2vec2_{stage}.json'.split()
									SCREAMING_SNAKE_CASE_						 = [f'{self.examples_dir_str}/research_projects/wav2vec2/run_asr.py']
									SCREAMING_SNAKE_CASE_						 = self.get_launcher(SCREAMING_SNAKE_CASE_    )

									SCREAMING_SNAKE_CASE_						 = launcher + script + args + ds_args
									# keep for quick debug
									# print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] +cmd)); die
									execute_subprocess_async(SCREAMING_SNAKE_CASE_       ,       env=self.get_env()    )

									return output_dir







							def _lowercase					(self       ,       SCREAMING_SNAKE_CASE_=False    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = min(2       ,       get_gpu_count()    ) if distributed else 1
									return f'deepspeed --num_nodes 1 --num_gpus {num_gpus}'.split()


"""simple docstring"""




import os
import unittest

from transformers.models.transfo_xl.tokenization_transfo_xl import VOCAB_FILES_NAMES, TransfoXLTokenizer

from ...test_tokenization_common import TokenizerTesterMixin





class  snake_case     (							__lowercase       ,			unittest.TestCase   ):
							UpperCAmelCase__						     =      TransfoXLTokenizer
							UpperCAmelCase__						     =      False
							UpperCAmelCase__						     =      False

							def _lowercase					(self    ):

									"""simple docstring"""







									super().setUp()

									SCREAMING_SNAKE_CASE_						 = [
									    '''<unk>''',
									    '''[CLS]''',
									    '''[SEP]''',
									    '''want''',
									    '''unwanted''',
									    '''wa''',
									    '''un''',
									    '''running''',
									    ''',''',
									    '''low''',
									    '''l''',
									]
									SCREAMING_SNAKE_CASE_						 = 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 _lowercase					(self       ,       **SCREAMING_SNAKE_CASE_    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = True
									return TransfoXLTokenizer.from_pretrained(self.tmpdirname       ,       **SCREAMING_SNAKE_CASE_    )

							def _lowercase					(self       ,       SCREAMING_SNAKE_CASE_    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = '''<unk> UNwanted , running'''
									SCREAMING_SNAKE_CASE_						 = '''<unk> unwanted, running'''
									return input_text, output_text

							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = TransfoXLTokenizer(vocab_file=self.vocab_file       ,       lower_case=SCREAMING_SNAKE_CASE_    )

									SCREAMING_SNAKE_CASE_						 = tokenizer.tokenize('''<unk> UNwanted , running'''    )
									self.assertListEqual(SCREAMING_SNAKE_CASE_       ,       ['''<unk>''', '''unwanted''', ''',''', '''running''']    )

									self.assertListEqual(tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE_    )       ,       [0, 4, 8, 7]    )

							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = TransfoXLTokenizer(lower_case=SCREAMING_SNAKE_CASE_    )

									self.assertListEqual(
									    tokenizer.tokenize(''' \tHeLLo ! how  \n Are yoU ?  '''    )       ,       ['''hello''', '''!''', '''how''', '''are''', '''you''', '''?''']    )

							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = TransfoXLTokenizer(lower_case=SCREAMING_SNAKE_CASE_    )

									self.assertListEqual(
									    tokenizer.tokenize(''' \tHeLLo ! how  \n Are yoU ?  '''    )       ,       ['''HeLLo''', '''!''', '''how''', '''Are''', '''yoU''', '''?''']    )

							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = TransfoXLTokenizer(lower_case=SCREAMING_SNAKE_CASE_    )
									SCREAMING_SNAKE_CASE_						 = '''Hello (bracket) and side-scrolled [and] Henry\'s $5,000 with 3.34 m. What\'s up!?'''
									SCREAMING_SNAKE_CASE_						 = [
									    '''Hello''',
									    '''(''',
									    '''bracket''',
									    ''')''',
									    '''and''',
									    '''side''',
									    '''@-@''',
									    '''scrolled''',
									    '''[''',
									    '''and''',
									    ''']''',
									    '''Henry''',
									    '''\'s''',
									    '''$''',
									    '''5''',
									    '''@,@''',
									    '''000''',
									    '''with''',
									    '''3''',
									    '''@.@''',
									    '''34''',
									    '''m''',
									    '''.''',
									    '''What''',
									    '''\'s''',
									    '''up''',
									    '''!''',
									    '''?''',
									]

									self.assertListEqual(tokenizer.tokenize(SCREAMING_SNAKE_CASE_    )       ,       SCREAMING_SNAKE_CASE_    )

									self.assertEqual(tokenizer.convert_tokens_to_string(SCREAMING_SNAKE_CASE_    )       ,       SCREAMING_SNAKE_CASE_    )







							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = self.get_tokenizer()
									SCREAMING_SNAKE_CASE_						 = len(SCREAMING_SNAKE_CASE_    )

									tokenizer.add_tokens(['''new1''', '''new2''']    )
									tokenizer.move_added_token('''new1'''       ,       1    )

									# Check that moved token is not copied (duplicate)
									self.assertEqual(len(SCREAMING_SNAKE_CASE_    )       ,       original_len + 2    )
									# Check that token is moved to specified id
									self.assertEqual(tokenizer.encode('''new1'''    )       ,       [1]    )
									self.assertEqual(tokenizer.decode([1]    )       ,       '''new1'''    )