'''simple docstring'''






import tempfile
import unittest
from pathlib import Path
from shutil import copyfile

from transformers import BatchEncoding, MarianTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import is_sentencepiece_available, is_tf_available, is_torch_available


if is_sentencepiece_available():
     from transformers.models.marian.tokenization_marian import VOCAB_FILES_NAMES, save_json

from ...test_tokenization_common import TokenizerTesterMixin


__a										=					get_tests_dir('fixtures/test_sentencepiece.model')

__a										=					{'''target_lang''': '''fi''', '''source_lang''': '''en'''}
__a										=					'''>>zh<<'''
__a										=					'''Helsinki-NLP/'''

if is_torch_available():
     __a										=					'''pt'''
elif is_tf_available():
     __a										=					'''tf'''
else:
     __a										=					'''jax'''





@require_sentencepiece
class     A__							(       lowerCamelCase_					,		unittest.TestCase   ):





   """simple docstring"""





   UpperCamelCase_       :						List[Any]    				=     MarianTokenizer
   UpperCamelCase_       :						int    				=     False
   UpperCamelCase_       :						List[str]    				=     True




   def 			_lowerCAmelCase		( self     :		Optional[Any]			)       ->    Optional[Any]:







      """simple docstring"""




      super().setUp()
      _UpperCAmelCase							:	str							=   ["</s>", "<unk>", "▁This", "▁is", "▁a", "▁t", "est", "\u0120", "<pad>"]
      _UpperCAmelCase							:	int							=   dict(zip(_lowercase						,     range(len(_lowercase			)			)			)			)
      _UpperCAmelCase							:	List[str]							=   Path(self.tmpdirname			)
      save_json(_lowercase						,     save_dir / VOCAB_FILES_NAMES["vocab"]			)
      save_json(_lowercase						,     save_dir / VOCAB_FILES_NAMES["tokenizer_config_file"]			)
      if not (save_dir / VOCAB_FILES_NAMES["source_spm"]).exists():
         copyfile(_lowercase						,     save_dir / VOCAB_FILES_NAMES["source_spm"]			)
         copyfile(_lowercase						,     save_dir / VOCAB_FILES_NAMES["target_spm"]			)

      _UpperCAmelCase							:	List[Any]							=   MarianTokenizer.from_pretrained(self.tmpdirname			)
      tokenizer.save_pretrained(self.tmpdirname			)




   def 			_lowerCAmelCase		( self     :		Optional[Any]						,     **lowerCAmelCase__     :		Optional[Any]			)       ->    List[Any]:







      """simple docstring"""




      return MarianTokenizer.from_pretrained(self.tmpdirname						,     **_lowercase			)




   def 			_lowerCAmelCase		( self     :		Optional[Any]						,     lowerCAmelCase__     :		int			)       ->    Any:







      """simple docstring"""




      return (
          "This is a test",
          "This is a test",
      )




   def 			_lowerCAmelCase		( self     :		Optional[Any]			)       ->    Union[str, Any]:







      """simple docstring"""




      _UpperCAmelCase							:	Union[str, Any]							=   "</s>"
      _UpperCAmelCase							:	str							=   0

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




   def 			_lowerCAmelCase		( self     :		Dict			)       ->    Optional[int]:







      """simple docstring"""




      _UpperCAmelCase							:	Optional[Any]							=   list(self.get_tokenizer().get_vocab().keys()			)

      self.assertEqual(vocab_keys[0]						,     "</s>"			)
      self.assertEqual(vocab_keys[1]						,     "<unk>"			)
      self.assertEqual(vocab_keys[-1]						,     "<pad>"			)
      self.assertEqual(len(_lowercase			)						,     9			)




   def 			_lowerCAmelCase		( self     :		Any			)       ->    Dict:







      """simple docstring"""




      self.assertEqual(self.get_tokenizer().vocab_size						,     9			)




   def 			_lowerCAmelCase		( self     :		int			)       ->    Optional[int]:







      """simple docstring"""




      _UpperCAmelCase							:	str							=   MarianTokenizer.from_pretrained(F"""{ORG_NAME}opus-mt-en-de"""			)
      _UpperCAmelCase							:	int							=   en_de_tokenizer(["I am a small frog"]						,     return_tensors=_lowercase			)
      self.assertIsInstance(_lowercase						,     _lowercase			)
      _UpperCAmelCase							:	Union[str, Any]							=   [3_8, 1_2_1, 1_4, 6_9_7, 3_8_8_4_8, 0]
      self.assertListEqual(_lowercase						,     batch.input_ids[0]			)

      _UpperCAmelCase							:	int							=   tempfile.mkdtemp()
      en_de_tokenizer.save_pretrained(_lowercase			)
      _UpperCAmelCase							:	List[Any]							=   [x.name for x in Path(_lowercase			).glob("*"			)]
      self.assertIn("source.spm"						,     _lowercase			)
      MarianTokenizer.from_pretrained(_lowercase			)




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







      """simple docstring"""




      _UpperCAmelCase							:	str							=   self.get_tokenizer()

      _UpperCAmelCase							:	Optional[int]							=   tok(
          ["I am a small frog" * 1_0_0_0, "I am a small frog"]						,     padding=_lowercase						,     truncation=_lowercase						,     return_tensors=_lowercase			)
      self.assertIsInstance(_lowercase						,     _lowercase			)
      self.assertEqual(batch.input_ids.shape						,     (2, 5_1_2)			)




   def 			_lowerCAmelCase		( self     :		List[Any]			)       ->    str:







      """simple docstring"""




      _UpperCAmelCase							:	str							=   self.get_tokenizer()
      _UpperCAmelCase							:	str							=   tok(["I am a tiny frog", "I am a small frog"]						,     padding=_lowercase						,     return_tensors=_lowercase			)
      self.assertIsInstance(_lowercase						,     _lowercase			)
      self.assertEqual(batch_smaller.input_ids.shape						,     (2, 1_0)			)




   @slow
   def 			_lowerCAmelCase		( self     :		Tuple			)       ->    Optional[Any]:







      """simple docstring"""




      _UpperCAmelCase							:	Union[str, Any]							=   {"input_ids": [[4_3_4_9_5, 4_6_2, 2_0, 4_2_1_6_4, 1_3_6_9, 5_2, 4_6_4, 1_3_2, 1_7_0_3, 4_9_2, 1_3, 7_4_9_1, 3_8_9_9_9, 6, 8, 4_6_4, 1_3_2, 1_7_0_3, 4_9_2, 1_3, 4_6_6_9, 3_7_8_6_7, 1_3, 7_5_2_5, 2_7, 1_5_9_3, 9_8_8, 1_3, 3_3_9_7_2, 7_0_2_9, 6, 2_0, 8_2_5_1, 3_8_3, 2, 2_7_0, 5_8_6_6, 3_7_8_8, 2, 2_3_5_3, 8_2_5_1, 1_2_3_3_8, 2, 1_3_9_5_8, 3_8_7, 2, 3_6_2_9, 6_9_5_3, 1_8_8, 2_9_0_0, 2, 1_3_9_5_8, 8_0_1_1, 1_1_5_0_1, 2_3, 8_4_6_0, 4_0_7_3, 3_4_0_0_9, 2_0, 4_3_5, 1_1_4_3_9, 2_7, 8, 8_4_6_0, 4_0_7_3, 6_0_0_4, 2_0, 9_9_8_8, 3_7_5, 2_7, 3_3, 2_6_6, 1_9_4_5, 1_0_7_6, 1_3_5_0, 3_7_8_6_7, 3_2_8_8, 5, 5_7_7, 1_0_7_6, 4_3_7_4, 8, 5_0_8_2, 5, 2_6_4_5_3, 2_5_7, 5_5_6, 4_0_3, 2, 2_4_2, 1_3_2, 3_8_3, 3_1_6, 4_9_2, 8, 1_0_7_6_7, 6, 3_1_6, 3_0_4, 4_2_3_9, 3, 0], [1_4_8, 1_5_7_2_2, 1_9, 1_8_3_9, 1_2, 1_3_5_0, 1_3, 2_2_3_2_7, 5_0_8_2, 5_4_1_8, 4_7_5_6_7, 3_5_9_3_8, 5_9, 3_1_8, 1_9_5_5_2, 1_0_8, 2_1_8_3, 5_4, 1_4_9_7_6, 4_8_3_5, 3_2, 5_4_7, 1_1_1_4, 8, 3_1_5, 2_4_1_7, 5, 9_2, 1_9_0_8_8, 3, 0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0], [3_6, 6_3_9_5, 1_2_5_7_0, 3_9_1_4_7, 1_1_5_9_7, 6, 2_6_6, 4, 4_5_4_0_5, 7_2_9_6, 3, 0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0]], "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, 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, 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, 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, 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, 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, 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, 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, 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, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
      # fmt: on

      self.tokenizer_integration_test_util(
          expected_encoding=_lowercase						,     model_name="Helsinki-NLP/opus-mt-en-de"						,     revision="1a8c2263da11e68e50938f97e10cd57820bd504c"						,     decode_kwargs={"use_source_tokenizer": True}						,     )




   def 			_lowerCAmelCase		( self     :		Optional[int]			)       ->    Optional[Any]:







      """simple docstring"""




      _UpperCAmelCase							:	Optional[int]							=   MarianTokenizer.from_pretrained("hf-internal-testing/test-marian-two-vocabs"			)

      _UpperCAmelCase							:	int							=   "Tämä on testi"
      _UpperCAmelCase							:	Optional[int]							=   "This is a test"

      _UpperCAmelCase							:	Dict							=   [7_6, 7, 2_0_4_7, 2]
      _UpperCAmelCase							:	str							=   [6_9, 1_2, 1_1, 9_4_0, 2]

      _UpperCAmelCase							:	List[str]							=   tokenizer(_lowercase			).input_ids
      self.assertListEqual(_lowercase						,     _lowercase			)

      _UpperCAmelCase							:	Tuple							=   tokenizer(text_target=_lowercase			).input_ids
      self.assertListEqual(_lowercase						,     _lowercase			)

      _UpperCAmelCase							:	str							=   tokenizer.decode(_lowercase						,     skip_special_tokens=_lowercase			)
      self.assertEqual(_lowercase						,     _lowercase			)
import unittest

from transformers import GPTNeoXJapaneseConfig, is_torch_available
from transformers.models.gpt_neox_japanese.tokenization_gpt_neox_japanese import GPTNeoXJapaneseTokenizer
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
   import torch

   from transformers import GPTNeoXJapaneseForCausalLM, GPTNeoXJapaneseModel







class __snake_case				:




   def __init__(    self			:				Optional[int]       ,	_lowercase			:				Union[str, Any]       ,	_lowercase			:				List[Any]=13       ,	_lowercase			:				List[Any]=7       ,	_lowercase			:				Optional[int]=True       ,	_lowercase			:				str=True       ,	_lowercase			:				str=True       ,	_lowercase			:				List[str]=True       ,	_lowercase			:				List[str]=99       ,	_lowercase			:				List[str]=32       ,	_lowercase			:				str=5       ,	_lowercase			:				str=4       ,	_lowercase			:				str=4       ,	_lowercase			:				Union[str, Any]="gelu"       ,	_lowercase			:				str=0.0       ,	_lowercase			:				Union[str, Any]=0.1       ,	_lowercase			:				List[str]=True       ,	_lowercase			:				Union[str, Any]=5_12       ,	_lowercase			:				List[str]=16       ,	_lowercase			:				Dict=2       ,	_lowercase			:				int=0.02       ,	_lowercase			:				Any=3       ,	_lowercase			:				int=4       ,	_lowercase			:				List[str]=None       ,	):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      parent
     SCREAMING_SNAKE_CASE__										=      batch_size
     SCREAMING_SNAKE_CASE__										=      seq_length
     SCREAMING_SNAKE_CASE__										=      is_training
     SCREAMING_SNAKE_CASE__										=      use_input_mask
     SCREAMING_SNAKE_CASE__										=      use_token_type_ids
     SCREAMING_SNAKE_CASE__										=      use_labels
     SCREAMING_SNAKE_CASE__										=      vocab_size
     SCREAMING_SNAKE_CASE__										=      hidden_size
     SCREAMING_SNAKE_CASE__										=      num_hidden_layers
     SCREAMING_SNAKE_CASE__										=      num_attention_heads
     SCREAMING_SNAKE_CASE__										=      intermediate_multiple_size
     SCREAMING_SNAKE_CASE__										=      hidden_act
     SCREAMING_SNAKE_CASE__										=      hidden_dropout
     SCREAMING_SNAKE_CASE__										=      attention_dropout
     SCREAMING_SNAKE_CASE__										=      weight_tying
     SCREAMING_SNAKE_CASE__										=      max_position_embeddings
     SCREAMING_SNAKE_CASE__										=      type_vocab_size
     SCREAMING_SNAKE_CASE__										=      type_sequence_label_size
     SCREAMING_SNAKE_CASE__										=      initializer_range
     SCREAMING_SNAKE_CASE__										=      num_labels
     SCREAMING_SNAKE_CASE__										=      num_choices
     SCREAMING_SNAKE_CASE__										=      scope




   def 	__a						(    self			:				List[Any]      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      ids_tensor([self.batch_size, self.seq_length]       ,	self.vocab_size      )

     SCREAMING_SNAKE_CASE__										=      None
     if self.use_input_mask:
       SCREAMING_SNAKE_CASE__										=      random_attention_mask([self.batch_size, self.seq_length]      )

     SCREAMING_SNAKE_CASE__										=      None
     if self.use_labels:
       SCREAMING_SNAKE_CASE__										=      ids_tensor([self.batch_size, self.seq_length]       ,	self.num_labels      )

     SCREAMING_SNAKE_CASE__										=      self.get_config()

     return config, input_ids, input_mask, token_labels




   def 	__a						(    self			:				Optional[int]      ):



     """simple docstring"""






     return GPTNeoXJapaneseConfig(
         vocab_size=self.vocab_size       ,	hidden_size=self.hidden_size       ,	num_hidden_layers=self.num_hidden_layers       ,	num_attention_heads=self.num_attention_heads       ,	intermediate_multiple_size=self.intermediate_multiple_size       ,	hidden_act=self.hidden_act       ,	hidden_dropout=self.hidden_dropout       ,	attention_dropout=self.attention_dropout       ,	weight_tying=self.weight_tying       ,	max_position_embeddings=self.max_position_embeddings       ,	type_vocab_size=self.type_vocab_size       ,	is_decoder=_lowercase       ,	initializer_range=self.initializer_range       ,	)




   def 	__a						(    self			:				Optional[int]      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__							,     SCREAMING_SNAKE_CASE__							,     SCREAMING_SNAKE_CASE__							,     SCREAMING_SNAKE_CASE__										=      self.prepare_config_and_inputs()

     SCREAMING_SNAKE_CASE__										=      True

     return config, input_ids, input_mask, token_labels




   def 	__a						(    self			:				List[Any]       ,	_lowercase			:				Optional[Any]       ,	_lowercase			:				int       ,	_lowercase			:				List[Any]      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      GPTNeoXJapaneseModel(config=_lowercase      )
     model.to(_lowercase      )
     model.eval()
     SCREAMING_SNAKE_CASE__										=      model(_lowercase       ,	attention_mask=_lowercase      )
     SCREAMING_SNAKE_CASE__										=      model(_lowercase      )
     self.parent.assertEqual(result.last_hidden_state.shape       ,	(self.batch_size, self.seq_length, self.hidden_size)      )




   def 	__a						(    self			:				Dict       ,	_lowercase			:				int       ,	_lowercase			:				str       ,	_lowercase			:				Any      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      True
     SCREAMING_SNAKE_CASE__										=      GPTNeoXJapaneseModel(_lowercase      )
     model.to(_lowercase      )
     model.eval()
     SCREAMING_SNAKE_CASE__										=      model(_lowercase       ,	attention_mask=_lowercase      )
     self.parent.assertEqual(result.last_hidden_state.shape       ,	(self.batch_size, self.seq_length, self.hidden_size)      )




   def 	__a						(    self			:				Any       ,	_lowercase			:				Dict       ,	_lowercase			:				List[Any]       ,	_lowercase			:				Optional[Any]       ,	_lowercase			:				Dict      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      GPTNeoXJapaneseForCausalLM(config=_lowercase      )
     model.to(_lowercase      )
     model.eval()
     SCREAMING_SNAKE_CASE__										=      model(_lowercase       ,	attention_mask=_lowercase       ,	labels=_lowercase      )
     self.parent.assertEqual(result.logits.shape       ,	(self.batch_size, self.seq_length, self.vocab_size)      )




   def 	__a						(    self			:				int       ,	_lowercase			:				Dict       ,	_lowercase			:				Optional[Any]       ,	_lowercase			:				int      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      True
     SCREAMING_SNAKE_CASE__										=      GPTNeoXJapaneseForCausalLM(config=_lowercase      )
     model.to(_lowercase      )
     model.eval()

     # first forward pass
     SCREAMING_SNAKE_CASE__										=      model(_lowercase       ,	attention_mask=_lowercase       ,	use_cache=_lowercase      )
     SCREAMING_SNAKE_CASE__										=      outputs.past_key_values

     # create hypothetical multiple next token and extent to next_input_ids
     SCREAMING_SNAKE_CASE__										=      ids_tensor((self.batch_size, 3)       ,	config.vocab_size      )
     SCREAMING_SNAKE_CASE__										=      ids_tensor((self.batch_size, 3)       ,	vocab_size=2      )

     # append to next input_ids and
     SCREAMING_SNAKE_CASE__										=      torch.cat([input_ids, next_tokens]       ,	dim=-1      )
     SCREAMING_SNAKE_CASE__										=      torch.cat([input_mask, next_mask]       ,	dim=-1      )

     SCREAMING_SNAKE_CASE__										=      model(_lowercase       ,	attention_mask=_lowercase       ,	output_hidden_states=_lowercase      )
     SCREAMING_SNAKE_CASE__										=      output_from_no_past["""hidden_states"""][0]
     SCREAMING_SNAKE_CASE__										=      model(
         _lowercase       ,	attention_mask=_lowercase       ,	past_key_values=_lowercase       ,	output_hidden_states=_lowercase       ,	)["""hidden_states"""][0]

     # select random slice
     SCREAMING_SNAKE_CASE__										=      ids_tensor((1,)       ,	output_from_past.shape[-1]      ).item()
     SCREAMING_SNAKE_CASE__										=      output_from_no_past[:, -3:, random_slice_idx].detach()
     SCREAMING_SNAKE_CASE__										=      output_from_past[:, :, random_slice_idx].detach()

     self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]      )

     # test that outputs are equal for slice
     self.parent.assertTrue(torch.allclose(_lowercase       ,	_lowercase       ,	atol=1E-3      )      )




   def 	__a						(    self			:				str      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      self.prepare_config_and_inputs()
     SCREAMING_SNAKE_CASE__							,     SCREAMING_SNAKE_CASE__							,     SCREAMING_SNAKE_CASE__							,     SCREAMING_SNAKE_CASE__										=      config_and_inputs
     SCREAMING_SNAKE_CASE__										=      {"""input_ids""": input_ids, """attention_mask""": input_mask}
     return config, inputs_dict







@require_torch
class __snake_case				(						lowerCamelCase_   ,		lowerCamelCase_   ,		unittest.TestCase		):
   lowerCAmelCase_														=							(GPTNeoXJapaneseModel, GPTNeoXJapaneseForCausalLM) if is_torch_available() else ()
   lowerCAmelCase_														=							(GPTNeoXJapaneseForCausalLM,) if is_torch_available() else ()
   lowerCAmelCase_														=							(
       {"feature-extraction": GPTNeoXJapaneseModel, "text-generation": GPTNeoXJapaneseForCausalLM}
       if is_torch_available()
       else {}
   )
   lowerCAmelCase_														=							False
   lowerCAmelCase_														=							False
   lowerCAmelCase_														=							False
   lowerCAmelCase_														=							False




   def 	__a						(    self			:				List[str]      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      GPTNeoXJapaneseModelTester(self      )
     SCREAMING_SNAKE_CASE__										=      ConfigTester(self       ,	config_class=_lowercase       ,	hidden_size=37      )




   def 	__a						(    self			:				Tuple      ):



     """simple docstring"""






     self.config_tester.run_common_tests()




   def 	__a						(    self			:				Optional[int]      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__							,     SCREAMING_SNAKE_CASE__							,     SCREAMING_SNAKE_CASE__							,     SCREAMING_SNAKE_CASE__										=      self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model(_lowercase       ,	_lowercase       ,	_lowercase      )




   def 	__a						(    self			:				Union[str, Any]      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__							,     SCREAMING_SNAKE_CASE__							,     SCREAMING_SNAKE_CASE__							,     SCREAMING_SNAKE_CASE__										=      self.model_tester.prepare_config_and_inputs_for_decoder()
     self.model_tester.create_and_check_model_as_decoder(_lowercase       ,	_lowercase       ,	_lowercase      )




   def 	__a						(    self			:				str      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__							,     SCREAMING_SNAKE_CASE__							,     SCREAMING_SNAKE_CASE__							,     SCREAMING_SNAKE_CASE__										=      self.model_tester.prepare_config_and_inputs_for_decoder()

     SCREAMING_SNAKE_CASE__										=      None

     self.model_tester.create_and_check_model_as_decoder(_lowercase       ,	_lowercase       ,	_lowercase      )




   def 	__a						(    self			:				Union[str, Any]      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__							,     SCREAMING_SNAKE_CASE__							,     SCREAMING_SNAKE_CASE__							,     SCREAMING_SNAKE_CASE__										=      self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_decoder_model_past_large_inputs(_lowercase       ,	_lowercase       ,	_lowercase      )




   def 	__a						(    self			:				List[Any]      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_causal_lm(*_lowercase      )




   @slow
   def 	__a						(    self			:				Any      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      """abeja/gpt-neox-japanese-2.7b"""

     SCREAMING_SNAKE_CASE__										=      ["""データサイエンティストとは、""", """100年後に必要とされる会社は、""", """フルリモートの環境で働くために必要なことは、""", """国境の長いトンネルを抜けると""", """美味しい日本食といえば、"""]

     SCREAMING_SNAKE_CASE__										=      [
         """データサイエンティストとは、データを分析し、ビジネスに役立つ知見を導き出す専門家のことです。""",
         """100年後に必要とされる会社は、「人」が中心の会社です。""",
         """フルリモートの環境で働くために必要なことは、「自分の時間をコントロールする」ことです。""",
         """国境の長いトンネルを抜けると、そこは雪国だった。""",
         """美味しい日本食といえば、やっぱりお寿司ですよね。""",
     ]

     SCREAMING_SNAKE_CASE__										=      GPTNeoXJapaneseTokenizer.from_pretrained(_lowercase      )
     SCREAMING_SNAKE_CASE__										=      GPTNeoXJapaneseForCausalLM.from_pretrained(_lowercase      )

     SCREAMING_SNAKE_CASE__										=      []
     for prompt in prompts:
       SCREAMING_SNAKE_CASE__										=      tokenizer(_lowercase       ,	return_tensors="""pt"""      ).input_ids
       SCREAMING_SNAKE_CASE__										=      model.generate(_lowercase       ,	max_length=50      )
       SCREAMING_SNAKE_CASE__										=      tokenizer.batch_decode(_lowercase       ,	skip_special_tokens=_lowercase      )
       predicted_outputs += generated_string
     self.assertListEqual(_lowercase       ,	_lowercase      )
