


import pickle
import shutil
import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, XLMRobertaTokenizer, XLMRobertaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


_SCREAMING_SNAKE_CASE      :			List[str]      				=   get_tests_dir("fixtures/test_sentencepiece.model")

@require_sentencepiece
@require_tokenizers
class   A__ (   _UpperCamelCase	, unittest.TestCase     ):







				"""simple docstring"""

				__magic_name__        =		XLMRobertaTokenizer
				__magic_name__        =		XLMRobertaTokenizerFast
				__magic_name__        =		True
				__magic_name__        =		True
				def 		a_      (	self   ):
									super().setUp()

									# We have a SentencePiece fixture for testing
									snake_case					=    XLMRobertaTokenizer(_UpperCAmelCase      , keep_accents=_UpperCAmelCase   )
									tokenizer.save_pretrained(self.tmpdirname   )
				def 		a_      (	self   ):
									snake_case					=    '<pad>'
									snake_case					=    1

									self.assertEqual(self.get_tokenizer()._convert_token_to_id(_UpperCAmelCase   )      , _UpperCAmelCase   )
									self.assertEqual(self.get_tokenizer()._convert_id_to_token(_UpperCAmelCase   )      , _UpperCAmelCase   )
				def 		a_      (	self   ):
									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(_UpperCAmelCase   )      , 1_0_0_2   )
				def 		a_      (	self   ):
									self.assertEqual(self.get_tokenizer().vocab_size      , 1_0_0_2   )
				def 		a_      (	self   ):
									snake_case					=    XLMRobertaTokenizer(_UpperCAmelCase      , keep_accents=_UpperCAmelCase   )

									snake_case					=    tokenizer.tokenize('''This is a test'''   )
									self.assertListEqual(_UpperCAmelCase      , ['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est''']   )

									self.assertListEqual(
									    tokenizer.convert_tokens_to_ids(_UpperCAmelCase   )      , [value + tokenizer.fairseq_offset for value in [2_8_5, 4_6, 1_0, 1_7_0, 3_8_2]]      , )

									snake_case					=    tokenizer.tokenize('''I was born in 92000, and this is falsé.'''   )
									self.assertListEqual(
									    _UpperCAmelCase      , [
									        SPIECE_UNDERLINE + '''I''',
									        SPIECE_UNDERLINE + '''was''',
									        SPIECE_UNDERLINE + '''b''',
									        '''or''',
									        '''n''',
									        SPIECE_UNDERLINE + '''in''',
									        SPIECE_UNDERLINE + '''''',
									        '''9''',
									        '''2''',
									        '''0''',
									        '''0''',
									        '''0''',
									        ''',''',
									        SPIECE_UNDERLINE + '''and''',
									        SPIECE_UNDERLINE + '''this''',
									        SPIECE_UNDERLINE + '''is''',
									        SPIECE_UNDERLINE + '''f''',
									        '''al''',
									        '''s''',
									        '''é''',
									        '''.''',
									    ]      , )
									snake_case					=    tokenizer.convert_tokens_to_ids(_UpperCAmelCase   )
									self.assertListEqual(
									    _UpperCAmelCase      , [
									        value + tokenizer.fairseq_offset
									        for value in [8, 2_1, 8_4, 5_5, 2_4, 1_9, 7, 2, 6_0_2, 3_4_7, 3_4_7, 3_4_7, 3, 1_2, 6_6, 4_6, 7_2, 8_0, 6, 2, 4]
									        #                                       ^ unk: 2 + 1 = 3                  unk: 2 + 1 = 3 ^
									    ]      , )

									snake_case					=    tokenizer.convert_ids_to_tokens(_UpperCAmelCase   )
									self.assertListEqual(
									    _UpperCAmelCase      , [
									        SPIECE_UNDERLINE + '''I''',
									        SPIECE_UNDERLINE + '''was''',
									        SPIECE_UNDERLINE + '''b''',
									        '''or''',
									        '''n''',
									        SPIECE_UNDERLINE + '''in''',
									        SPIECE_UNDERLINE + '''''',
									        '''<unk>''',
									        '''2''',
									        '''0''',
									        '''0''',
									        '''0''',
									        ''',''',
									        SPIECE_UNDERLINE + '''and''',
									        SPIECE_UNDERLINE + '''this''',
									        SPIECE_UNDERLINE + '''is''',
									        SPIECE_UNDERLINE + '''f''',
									        '''al''',
									        '''s''',
									        '''<unk>''',
									        '''.''',
									    ]      , )
				def 		a_      (	self   ):
									if not self.test_slow_tokenizer:
														# as we don't have a slow version, we can't compare the outputs between slow and fast versions
														return

									snake_case					=    (self.rust_tokenizer_class, 'hf-internal-testing/tiny-xlm-roberta', {})
									for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
														with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''   ):
																			snake_case					=    self.rust_tokenizer_class.from_pretrained(_UpperCAmelCase      , **_UpperCAmelCase   )
																			snake_case					=    self.tokenizer_class.from_pretrained(_UpperCAmelCase      , **_UpperCAmelCase   )

																			snake_case					=    tempfile.mkdtemp()

																			snake_case					=    tokenizer_r.save_pretrained(_UpperCAmelCase   )
																			snake_case					=    tokenizer_p.save_pretrained(_UpperCAmelCase   )

																			# Checks it save with the same files + the tokenizer.json file for the fast one
																			self.assertTrue(any('''tokenizer.json''' in f for f in tokenizer_r_files   )   )
																			snake_case					=    tuple(f for f in tokenizer_r_files if '''tokenizer.json''' not in f   )
																			self.assertSequenceEqual(_UpperCAmelCase      , _UpperCAmelCase   )

																			# Checks everything loads correctly in the same way
																			snake_case					=    tokenizer_r.from_pretrained(_UpperCAmelCase   )
																			snake_case					=    tokenizer_p.from_pretrained(_UpperCAmelCase   )

																			# Check special tokens are set accordingly on Rust and Python
																			for key in tokenizer_pp.special_tokens_map:
																								self.assertTrue(hasattr(_UpperCAmelCase      , _UpperCAmelCase   )   )
																								# self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
																								# self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))

																			shutil.rmtree(_UpperCAmelCase   )

																			# Save tokenizer rust, legacy_format=True
																			snake_case					=    tempfile.mkdtemp()

																			snake_case					=    tokenizer_r.save_pretrained(_UpperCAmelCase      , legacy_format=_UpperCAmelCase   )
																			snake_case					=    tokenizer_p.save_pretrained(_UpperCAmelCase   )

																			# Checks it save with the same files
																			self.assertSequenceEqual(_UpperCAmelCase      , _UpperCAmelCase   )

																			# Checks everything loads correctly in the same way
																			snake_case					=    tokenizer_r.from_pretrained(_UpperCAmelCase   )
																			snake_case					=    tokenizer_p.from_pretrained(_UpperCAmelCase   )

																			# Check special tokens are set accordingly on Rust and Python
																			for key in tokenizer_pp.special_tokens_map:
																								self.assertTrue(hasattr(_UpperCAmelCase      , _UpperCAmelCase   )   )

																			shutil.rmtree(_UpperCAmelCase   )

																			# Save tokenizer rust, legacy_format=False
																			snake_case					=    tempfile.mkdtemp()

																			snake_case					=    tokenizer_r.save_pretrained(_UpperCAmelCase      , legacy_format=_UpperCAmelCase   )
																			snake_case					=    tokenizer_p.save_pretrained(_UpperCAmelCase   )

																			# Checks it saved the tokenizer.json file
																			self.assertTrue(any('''tokenizer.json''' in f for f in tokenizer_r_files   )   )

																			# Checks everything loads correctly in the same way
																			snake_case					=    tokenizer_r.from_pretrained(_UpperCAmelCase   )
																			snake_case					=    tokenizer_p.from_pretrained(_UpperCAmelCase   )

																			# Check special tokens are set accordingly on Rust and Python
																			for key in tokenizer_pp.special_tokens_map:
																								self.assertTrue(hasattr(_UpperCAmelCase      , _UpperCAmelCase   )   )

																			shutil.rmtree(_UpperCAmelCase   )
				@cached_property
				def 		a_      (	self   ):
									return XLMRobertaTokenizer.from_pretrained('''xlm-roberta-base'''   )
				def 		a_      (	self   ):
									with tempfile.NamedTemporaryFile() as f:
														shutil.copyfile(_UpperCAmelCase      , f.name   )
														snake_case					=    XLMRobertaTokenizer(f.name      , keep_accents=_UpperCAmelCase   )
														snake_case					=    pickle.dumps(_UpperCAmelCase   )
									pickle.loads(_UpperCAmelCase   )
				def 		a_      (	self   ):
									if not self.test_rust_tokenizer:
														return

									snake_case					=    self.get_tokenizer()
									snake_case					=    self.get_rust_tokenizer()

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

									snake_case					=    tokenizer.tokenize(_UpperCAmelCase   )
									snake_case					=    rust_tokenizer.tokenize(_UpperCAmelCase   )
									self.assertListEqual(_UpperCAmelCase      , _UpperCAmelCase   )

									snake_case					=    tokenizer.encode(_UpperCAmelCase      , add_special_tokens=_UpperCAmelCase   )
									snake_case					=    rust_tokenizer.encode(_UpperCAmelCase      , add_special_tokens=_UpperCAmelCase   )
									self.assertListEqual(_UpperCAmelCase      , _UpperCAmelCase   )

									snake_case					=    self.get_rust_tokenizer()
									snake_case					=    tokenizer.encode(_UpperCAmelCase   )
									snake_case					=    rust_tokenizer.encode(_UpperCAmelCase   )
									self.assertListEqual(_UpperCAmelCase      , _UpperCAmelCase   )
				@slow
				def 		a_      (	self   ):
									snake_case					=    'Hello World!'
									snake_case					=    [0, 3_5_3_7_8, 6_6_6_1, 3_8, 2]
									# xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')  # xlmr.large has same tokenizer
									# xlmr.eval()
									# xlmr.encode(symbols)

									self.assertListEqual(_UpperCAmelCase      , self.big_tokenizer.encode(_UpperCAmelCase   )   )
				@slow
				def 		a_      (	self   ):
									snake_case					=    (
									    'This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) " [ ] ! : - . Also we will'
									    ' add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth'
									)
									snake_case					=    [
									    0,
									    3_2_9_3,
									    8_3,
									    1_0,
									    4_5_5_2,
									    4_9_8_9,
									    7_9_8_6,
									    6_7_8,
									    1_0,
									    5_9_1_5,
									    1_1_1,
									    1_7_9_4_5_9,
									    1_2_4_8_5_0,
									    4,
									    6_0_4_4,
									    2_3_7,
									    1_2,
									    6,
									    5,
									    6,
									    4,
									    6_7_8_0,
									    7_0_5,
									    1_5,
									    1_3_8_8,
									    4_4,
									    3_7_8,
									    1_0_1_1_4,
									    7_1_1,
									    1_5_2,
									    2_0,
									    6,
									    5,
									    2_2_3_7_6,
									    6_4_2,
									    1_2_2_1,
									    1_5_1_9_0,
									    3_4_1_5_3,
									    4_5_0,
									    5_6_0_8,
									    9_5_9,
									    1_1_1_9,
									    5_7_7_0_2,
									    1_3_6,
									    1_8_6,
									    4_7,
									    1_0_9_8,
									    2_9_3_6_7,
									    4_7,
									    # 4426, # What fairseq tokenizes from "<unk>": "_<"
									    # 3678, # What fairseq tokenizes from "<unk>": "unk"
									    # 2740, # What fairseq tokenizes from "<unk>": ">"
									    3,  # What we tokenize from "<unk>": "<unk>"
									    6,  # Residue from the tokenization: an extra sentencepiece underline
									    4,
									    6_0_4_4,
									    2_3_7,
									    6_2_8_4,
									    5_0_9_0_1,
									    5_2_8,
									    3_1,
									    9_0,
									    3_4,
									    9_2_7,
									    2,
									]
									# xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')  # xlmr.large has same tokenizer
									# xlmr.eval()
									# xlmr.encode(symbols)

									self.assertListEqual(_UpperCAmelCase      , self.big_tokenizer.encode(_UpperCAmelCase   )   )




				@slow
				def 		a_      (	self   ):
									# fmt: off
									snake_case					=    {'input_ids': [[0, 1_1_0_6_2, 8_2_7_7_2, 7, 1_5, 8_2_7_7_2, 5_3_8, 5_1_5_2_9, 2_3_7, 1_7_1_9_8, 1_2_9_0, 2_0_6, 9, 2_1_5_1_7_5, 1_3_1_4, 1_3_6, 1_7_1_9_8, 1_2_9_0, 2_0_6, 9, 5_6_3_5_9, 4_2, 1_2_2_0_0_9, 9, 1_6_4_6_6, 1_6, 8_7_3_4_4, 4_5_3_7, 9, 4_7_1_7, 7_8_3_8_1, 6, 1_5_9_9_5_8, 7, 1_5, 2_4_4_8_0, 6_1_8, 4, 5_2_7, 2_2_6_9_3, 5_4_2_8, 4, 2_7_7_7, 2_4_4_8_0, 9_8_7_4, 4, 4_3_5_2_3, 5_9_4, 4, 8_0_3, 1_8_3_9_2, 3_3_1_8_9, 1_8, 4, 4_3_5_2_3, 2_4_4_4_7, 1_2_3_9_9, 1_0_0, 2_4_9_5_5, 8_3_6_5_8, 9_6_2_6, 1_4_4_0_5_7, 1_5, 8_3_9, 2_2_3_3_5, 1_6, 1_3_6, 2_4_9_5_5, 8_3_6_5_8, 8_3_4_7_9, 1_5, 3_9_1_0_2, 7_2_4, 1_6, 6_7_8, 6_4_5, 2_7_8_9, 1_3_2_8, 4_5_8_9, 4_2, 1_2_2_0_0_9, 1_1_5_7_7_4, 2_3, 8_0_5, 1_3_2_8, 4_6_8_7_6, 7, 1_3_6, 5_3_8_9_4, 1_9_4_0, 4_2_2_2_7, 4_1_1_5_9, 1_7_7_2_1, 8_2_3, 4_2_5, 4, 2_7_5_1_2, 9_8_7_2_2, 2_0_6, 1_3_6, 5_5_3_1, 4_9_7_0, 9_1_9, 1_7_3_3_6, 5, 2], [0, 2_0_0_8_0, 6_1_8, 8_3, 8_2_7_7_5, 4_7, 4_7_9, 9, 1_5_1_7, 7_3, 5_3_8_9_4, 3_3_3, 8_0_5_8_1, 1_1_0_1_1_7, 1_8_8_1_1, 5_2_5_6, 1_2_9_5, 5_1, 1_5_2_5_2_6, 2_9_7, 7_9_8_6, 3_9_0, 1_2_4_4_1_6, 5_3_8, 3_5_4_3_1, 2_1_4, 9_8, 1_5_0_4_4, 2_5_7_3_7, 1_3_6, 7_1_0_8, 4_3_7_0_1, 2_3, 7_5_6, 1_3_5_3_5_5, 7, 5, 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, 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, 5_8_1, 6_3_7_7_3, 1_1_9_4_5_5, 6, 1_4_7_7_9_7, 8_8_2_0_3, 7, 6_4_5, 7_0, 2_1, 3_2_8_5, 1_0_2_6_9, 5, 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, 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]], '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, 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], [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, 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=_UpperCAmelCase      , model_name='''xlm-roberta-base'''      , revision='''d9d8a8ea5eb94b1c6654ae9249df7793cd2933d3'''      , )








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   A__ :







    """simple docstring"""

    def __init__(	self      , __snake_case      , __snake_case=1_3      , __snake_case=7      , __snake_case=True      , __snake_case=True      , __snake_case=True      , __snake_case=True      , __snake_case=9_9      , __snake_case=3_2      , __snake_case=5      , __snake_case=4      , __snake_case=4      , __snake_case="gelu"      , __snake_case=0.0      , __snake_case=0.1      , __snake_case=True      , __snake_case=5_1_2      , __snake_case=1_6      , __snake_case=2      , __snake_case=0.02      , __snake_case=3      , __snake_case=4      , __snake_case=None      , ):
         snake_case					=    parent
         snake_case					=    batch_size
         snake_case					=    seq_length
         snake_case					=    is_training
         snake_case					=    use_input_mask
         snake_case					=    use_token_type_ids
         snake_case					=    use_labels
         snake_case					=    vocab_size
         snake_case					=    hidden_size
         snake_case					=    num_hidden_layers
         snake_case					=    num_attention_heads
         snake_case					=    intermediate_multiple_size
         snake_case					=    hidden_act
         snake_case					=    hidden_dropout
         snake_case					=    attention_dropout
         snake_case					=    weight_tying
         snake_case					=    max_position_embeddings
         snake_case					=    type_vocab_size
         snake_case					=    type_sequence_label_size
         snake_case					=    initializer_range
         snake_case					=    num_labels
         snake_case					=    num_choices
         snake_case					=    scope
    def 		a_      (	self   ):
         snake_case					=    ids_tensor([self.batch_size, self.seq_length]      , self.vocab_size   )

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

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

         snake_case					=    self.get_config()

         return config, input_ids, input_mask, token_labels
    def 		a_      (	self   ):
         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=__snake_case      , initializer_range=self.initializer_range      , )
    def 		a_      (	self   ):
         snake_case			,  snake_case			,  snake_case			,  snake_case					=    self.prepare_config_and_inputs()

         snake_case					=    True

         return config, input_ids, input_mask, token_labels
    def 		a_      (	self      , __snake_case      , __snake_case      , __snake_case   ):
         snake_case					=    GPTNeoXJapaneseModel(config=__snake_case   )
         model.to(__snake_case   )
         model.eval()
         snake_case					=    model(__snake_case      , attention_mask=__snake_case   )
         snake_case					=    model(__snake_case   )
         self.parent.assertEqual(result.last_hidden_state.shape      , (self.batch_size, self.seq_length, self.hidden_size)   )
    def 		a_      (	self      , __snake_case      , __snake_case      , __snake_case   ):
         snake_case					=    True
         snake_case					=    GPTNeoXJapaneseModel(__snake_case   )
         model.to(__snake_case   )
         model.eval()
         snake_case					=    model(__snake_case      , attention_mask=__snake_case   )
         self.parent.assertEqual(result.last_hidden_state.shape      , (self.batch_size, self.seq_length, self.hidden_size)   )
    def 		a_      (	self      , __snake_case      , __snake_case      , __snake_case      , __snake_case   ):
         snake_case					=    GPTNeoXJapaneseForCausalLM(config=__snake_case   )
         model.to(__snake_case   )
         model.eval()
         snake_case					=    model(__snake_case      , attention_mask=__snake_case      , labels=__snake_case   )
         self.parent.assertEqual(result.logits.shape      , (self.batch_size, self.seq_length, self.vocab_size)   )
    def 		a_      (	self      , __snake_case      , __snake_case      , __snake_case   ):
         snake_case					=    True
         snake_case					=    GPTNeoXJapaneseForCausalLM(config=__snake_case   )
         model.to(__snake_case   )
         model.eval()

         # first forward pass
         snake_case					=    model(__snake_case      , attention_mask=__snake_case      , use_cache=__snake_case   )
         snake_case					=    outputs.past_key_values

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

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

         snake_case					=    model(__snake_case      , attention_mask=__snake_case      , output_hidden_states=__snake_case   )
         snake_case					=    output_from_no_past['''hidden_states'''][0]
         snake_case					=    model(
             __snake_case      , attention_mask=__snake_case      , past_key_values=__snake_case      , output_hidden_states=__snake_case      , )['''hidden_states'''][0]

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




    def 		a_      (	self   ):
         snake_case					=    self.prepare_config_and_inputs()
         snake_case			,  snake_case			,  snake_case			,  snake_case					=    config_and_inputs
         snake_case					=    {'''input_ids''': input_ids, '''attention_mask''': input_mask}
         return config, inputs_dict






@require_torch
class   A__ (   snake_case__	, snake_case__	, unittest.TestCase     ):







    """simple docstring"""

    __magic_name__        =		(GPTNeoXJapaneseModel, GPTNeoXJapaneseForCausalLM) if is_torch_available() else ()
    __magic_name__        =		(GPTNeoXJapaneseForCausalLM,) if is_torch_available() else ()
    __magic_name__        =		(
        {'feature-extraction': GPTNeoXJapaneseModel, 'text-generation': GPTNeoXJapaneseForCausalLM}
        if is_torch_available()
        else {}
    )
    __magic_name__        =		False
    __magic_name__        =		False
    __magic_name__        =		False
    __magic_name__        =		False
    def 		a_      (	self   ):
         snake_case					=    GPTNeoXJapaneseModelTester(self   )
         snake_case					=    ConfigTester(self      , config_class=__snake_case      , hidden_size=3_7   )
    def 		a_      (	self   ):
         self.config_tester.run_common_tests()
    def 		a_      (	self   ):
         snake_case			,  snake_case			,  snake_case			,  snake_case					=    self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(__snake_case      , __snake_case      , __snake_case   )
    def 		a_      (	self   ):
         snake_case			,  snake_case			,  snake_case			,  snake_case					=    self.model_tester.prepare_config_and_inputs_for_decoder()
         self.model_tester.create_and_check_model_as_decoder(__snake_case      , __snake_case      , __snake_case   )
    def 		a_      (	self   ):
         # This regression test was failing with PyTorch < 1.3
         snake_case			,  snake_case			,  snake_case			,  snake_case					=    self.model_tester.prepare_config_and_inputs_for_decoder()

         snake_case					=    None

         self.model_tester.create_and_check_model_as_decoder(__snake_case      , __snake_case      , __snake_case   )
    def 		a_      (	self   ):
         snake_case			,  snake_case			,  snake_case			,  snake_case					=    self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_decoder_model_past_large_inputs(__snake_case      , __snake_case      , __snake_case   )
    def 		a_      (	self   ):
         snake_case					=    self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_causal_lm(*__snake_case   )




    @slow
    def 		a_      (	self   ):
         snake_case					=    '''abeja/gpt-neox-japanese-2.7b'''

         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、''']

         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。''',
         ]

         snake_case					=    GPTNeoXJapaneseTokenizer.from_pretrained(__snake_case   )
         snake_case					=    GPTNeoXJapaneseForCausalLM.from_pretrained(__snake_case   )

         snake_case					=    []
         for prompt in prompts:
              snake_case					=    tokenizer(__snake_case      , return_tensors='''pt'''   ).input_ids
              snake_case					=    model.generate(__snake_case      , max_length=5_0   )
              snake_case					=    tokenizer.batch_decode(__snake_case      , skip_special_tokens=__snake_case   )
              predicted_outputs += generated_string
         self.assertListEqual(__snake_case      , __snake_case   )




