





'''simple docstring'''







import json
import os
import unittest

from transformers.models.gptsan_japanese.tokenization_gptsan_japanese import (
    VOCAB_FILES_NAMES,
    GPTSanJapaneseTokenizer,
)
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class 				__SCREAMING_SNAKE_CASE  (lowerCamelCase_       ,							unittest.TestCase      ):






				"""simple docstring"""







				__a            =GPTSanJapaneseTokenizer
				__a            =False
				__a            ={'do_clean_text': False, 'add_prefix_space': False}




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

								# fmt: off
								_a								= ["こん", "こんに", "にちは", "ばんは", "世界,㔺界", "、", "。", "<BR>", "<SP>", "<TAB>", "<URL>", "<EMAIL>", "<TEL>", "<DATE>", "<PRICE>", "<BLOCK>", "<KIGOU>", "<U2000U2BFF>", "<|emoji1|>", "<unk>", "<|bagoftoken|>", "<|endoftext|>"]
								# fmt: on
								_a								= {"emoji": {"\ud83d\ude00": "<|emoji1|>"}, "emoji_inv": {"<|emoji1|>": "\ud83d\ude00"}}  # 😀
								_a								= {"unk_token": "<unk>"}

								_a								= os.path.join(self.tmpdirname					,				VOCAB_FILES_NAMES["vocab_file"]					)
								_a								= os.path.join(self.tmpdirname					,				VOCAB_FILES_NAMES["emoji_file"]					)
								with open(self.vocab_file					,				"w"					,				encoding="utf-8"					) as vocab_writer:
												vocab_writer.write("".join([x + "\n" for x in vocab_tokens]					)					)
								with open(self.emoji_file					,				"w"					) as emoji_writer:
												emoji_writer.write(json.dumps(__a					)					)




				def   UpperCamelCase__   (  self     :					Dict					,				**__a     :					str					):
								kwargs.update(self.special_tokens_map					)
								return GPTSanJapaneseTokenizer.from_pretrained(self.tmpdirname					,				**__a					)




				def   UpperCamelCase__   (  self     :					str					,				__a     :					Any					):
								_a								= "こんにちは、世界。 \nこんばんは、㔺界。😀"
								_a								= "こんにちは、世界。 \nこんばんは、世界。😀"
								return input_text, output_text




				def   UpperCamelCase__   (  self     :					Optional[Any]					,				__a     :					List[str]					):
								_a	,					_a								= self.get_input_output_texts(__a					)
								_a								= tokenizer.encode(__a					,				add_special_tokens=__a					)
								_a								= tokenizer.decode(__a					,				clean_up_tokenization_spaces=__a					)
								return text, ids




				def   UpperCamelCase__   (  self     :					Any					):
								pass  # TODO add if relevant




				def   UpperCamelCase__   (  self     :					Tuple					):
								pass  # TODO add if relevant




				def   UpperCamelCase__   (  self     :					str					):
								pass  # TODO add if relevant




				def   UpperCamelCase__   (  self     :					Tuple					):
								_a								= self.get_tokenizer()

								# Testing tokenization
								_a								= "こんにちは、世界。　こんばんは、㔺界。"
								_a								= ["こん", "にちは", "、", "世界", "。", "<SP>", "こん", "ばんは", "、", "㔺界", "。"]
								_a								= tokenizer.tokenize(__a					)
								self.assertListEqual(__a					,				__a					)

								# Testing conversion to ids without special tokens
								_a								= [0, 2, 5, 4, 6, 8, 0, 3, 5, 4, 6]
								_a								= tokenizer.convert_tokens_to_ids(__a					)
								self.assertListEqual(__a					,				__a					)

								# Testing conversion to ids with special tokens
								_a								= tokens + [tokenizer.unk_token]
								_a								= [0, 2, 5, 4, 6, 8, 0, 3, 5, 4, 6, 19]
								_a								= tokenizer.convert_tokens_to_ids(__a					)
								self.assertListEqual(__a					,				__a					)




				def   UpperCamelCase__   (  self     :					Optional[int]					):
								_a								= self.get_tokenizer()

								# Testing tokenization
								_a								= "こんにちは、<|bagoftoken|>世界。こんばんは、<|bagoftoken|>㔺界。"
								_a								= "こんにちは、、、、世界。こんばんは、、、、世界。"
								_a								= tokenizer.encode(__a					)
								_a								= tokenizer.decode(__a					)
								self.assertEqual(__a					,				__a					)




				@slow
				def   UpperCamelCase__   (  self     :					List[Any]					):
								_a								= self.tokenizer_class.from_pretrained("Tanrei/GPTSAN-japanese"					)

								# Testing tokenization
								_a								= "こんにちは、世界。"
								_a								= "こんばんは、㔺界。😀"
								_a								= "こんにちは、世界。こんばんは、世界。😀"
								_a								= tokenizer.encode(prefix_text + input_text					)
								_a								= tokenizer.encode(""					,				prefix_text=prefix_text + input_text					)
								_a								= tokenizer.encode(__a					,				prefix_text=__a					)
								_a								= tokenizer.decode(__a					)
								_a								= tokenizer.decode(__a					)
								_a								= tokenizer.decode(__a					)
								self.assertEqual(__a					,				__a					)
								self.assertEqual(__a					,				__a					)
								self.assertEqual(__a					,				__a					)




				@slow
				def   UpperCamelCase__   (  self     :					Any					):
								_a								= self.tokenizer_class.from_pretrained("Tanrei/GPTSAN-japanese"					)

								# Testing tokenization
								_a								= "こんにちは、世界。"
								_a								= "こんばんは、㔺界。😀"

								_a								= len(tokenizer.encode(__a					)					) - 2
								_a								= len(tokenizer.encode(__a					)					) - 2

								_a								= [1] + [0] * (len_prefix + len_text + 1)
								_a								= [1] * (len_prefix + len_text + 1) + [0]
								_a								= [1] + [1] * (len_prefix) + [0] * (len_text + 1)

								_a								= tokenizer(prefix_text + input_text					).token_type_ids
								_a								= tokenizer(""					,				prefix_text=prefix_text + input_text					).token_type_ids
								_a								= tokenizer(__a					,				prefix_text=__a					).token_type_ids
								self.assertListEqual(__a					,				__a					)
								self.assertListEqual(__a					,				__a					)
								self.assertListEqual(__a					,				__a					)




				@slow
				def   UpperCamelCase__   (  self     :					Dict					):
								_a								= self.tokenizer_class.from_pretrained("Tanrei/GPTSAN-japanese"					)

								_a								= tokenizer.encode("あンいワ"					)
								_a								= tokenizer.encode(""					,				prefix_text="あンいワ"					)
								_a								= tokenizer.encode("いワ"					,				prefix_text="あン"					)

								self.assertEqual(tokenizer.decode(__a					)					,				tokenizer.decode(__a					)					)
								self.assertEqual(tokenizer.decode(__a					)					,				tokenizer.decode(__a					)					)
								self.assertNotEqual(__a					,				__a					)
								self.assertNotEqual(__a					,				__a					)
								self.assertEqual(x_token_a[1]					,				x_token_a[-1]					)  # SEG token
								self.assertEqual(x_token_a[1]					,				x_token_a[3]					)  # SEG token




				@slow
				def   UpperCamelCase__   (  self     :					List[Any]					):
								_a								= self.tokenizer_class.from_pretrained("Tanrei/GPTSAN-japanese"					)

								_a								= [["武田信玄", "は、"], ["織田信長", "の配下の、"]]
								_a								= tokenizer(__a					,				padding=__a					)
								_a								= tokenizer.batch_encode_plus(__a					,				padding=__a					)

								# fmt: off
								_a								= [[3_59_93, 86_40, 2_59_48, 3_59_98, 3_06_47, 3_56_75, 3_59_99, 3_59_99], [3_59_93, 1_03_82, 98_68, 3_59_98, 3_06_46, 94_59, 3_06_46, 3_56_75]]
								_a								= [[1, 1, 1, 0, 0, 0, 0, 0], [1, 1, 1, 0, 0, 0, 0, 0]]
								_a								= [[1, 1, 1, 1, 1, 1, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1]]
								# fmt: on
								self.assertListEqual(x_token.input_ids					,				__a					)
								self.assertListEqual(x_token.token_type_ids					,				__a					)
								self.assertListEqual(x_token.attention_mask					,				__a					)
								self.assertListEqual(x_token_a.input_ids					,				__a					)
								self.assertListEqual(x_token_a.token_type_ids					,				__a					)
								self.assertListEqual(x_token_a.attention_mask					,				__a					)




				def   UpperCamelCase__   (  self     :					List[str]					):
								# Intentionally convert some words to accommodate character fluctuations unique to Japanese
								pass




				def   UpperCamelCase__   (  self     :					int					):
								# tokenizer has no padding token
								pass


# XXX: we want transformers master here - in the absense of conftest manipulating sys.path:
# hack it in for now:
import sys
from pathlib import Path


A     :	Optional[Any]    = 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(4_2)

A     :	List[str]    = {'base': 'patrickvonplaten/wav2vec2_tiny_random', 'robust': 'patrickvonplaten/wav2vec2_tiny_random_robust'}

A     :	Optional[int]    = 'zero2'
A     :	str    = 'zero3'
A     :	Tuple    = [ZEROa, ZEROa]


def     __lowerCAmelCase			(			a__						,    a__						,    a__  )       ->  Tuple:
				# 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
				__a										=    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
A     :	Union[str, Any]    = list(itertools.product(stages, models.keys()))







@slow
@require_deepspeed
@require_torch_gpu
class 			__A( a			):




				@parameterized.expand(_snake_case		,     name_func=_snake_case				)
				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case		,     _snake_case				)    ->      Any:


								'''simple docstring'''




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




				@require_torch_multi_gpu
				@parameterized.expand(_snake_case		,     name_func=_snake_case				)
				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case		,     _snake_case				)    ->      int:


								'''simple docstring'''




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




				@parameterized.expand(_snake_case		,     name_func=_snake_case				)
				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case		,     _snake_case				)    ->      str:


								'''simple docstring'''




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




				@require_torch_multi_gpu
				@parameterized.expand(_snake_case		,     name_func=_snake_case				)
				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case		,     _snake_case				)    ->      Optional[Any]:


								'''simple docstring'''




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




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case				)    ->      Tuple:


								'''simple docstring'''




								pass




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case		,     _snake_case		,     _snake_case = 10		,     _snake_case = True		,     _snake_case = True		,     _snake_case = True		,     )    ->      Any:


								'''simple docstring'''




								__a										=    models[model]

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

								self.do_checks(_snake_case				)

								return output_dir




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case		,     _snake_case		,     _snake_case = 10		,     _snake_case = 1		,     _snake_case = True		,     _snake_case = True		,     )    ->      Union[str, Any]:


								'''simple docstring'''




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

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

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

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

								return output_dir




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case=False				)    ->      List[str]:


								'''simple docstring'''




								__a										=    min(2		,     get_gpu_count()				) if distributed else 1
								return F"""deepspeed --num_nodes 1 --num_gpus {num_gpus}""".split()