

import os
from shutil import copyfile
from typing import List, Optional, Tuple

from tokenizers import processors

from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
       from .tokenization_nllb import NllbTokenizer
else:
       snake_case_			  = None


snake_case_			  = logging.get_logger(__name__)


snake_case_			  = {"vocab_file": "sentencepiece.bpe.model", "tokenizer_file": "tokenizer.json"}

snake_case_			  = {
    "vocab_file": {
        "facebook/nllb-200-distilled-600M": (
            "https://huggingface.co/facebook/nllb-200-distilled-600M/resolve/main/sentencepiece.bpe.model"
        ),
    },
    "tokenizer_file": {
        "facebook/nllb-200-distilled-600M": (
            "https://huggingface.co/facebook/nllb-200-distilled-600M/resolve/main/tokenizer.json"
        ),
    },
}

snake_case_			  = {
    "facebook/nllb-large-en-ro": 1_024,
    "facebook/nllb-200-distilled-600M": 1_024,
}

# fmt: off
snake_case_			  = ["ace_Arab", "ace_Latn", "acm_Arab", "acq_Arab", "aeb_Arab", "afr_Latn", "ajp_Arab", "aka_Latn", "amh_Ethi", "apc_Arab", "arb_Arab", "ars_Arab", "ary_Arab", "arz_Arab", "asm_Beng", "ast_Latn", "awa_Deva", "ayr_Latn", "azb_Arab", "azj_Latn", "bak_Cyrl", "bam_Latn", "ban_Latn", "bel_Cyrl", "bem_Latn", "ben_Beng", "bho_Deva", "bjn_Arab", "bjn_Latn", "bod_Tibt", "bos_Latn", "bug_Latn", "bul_Cyrl", "cat_Latn", "ceb_Latn", "ces_Latn", "cjk_Latn", "ckb_Arab", "crh_Latn", "cym_Latn", "dan_Latn", "deu_Latn", "dik_Latn", "dyu_Latn", "dzo_Tibt", "ell_Grek", "eng_Latn", "epo_Latn", "est_Latn", "eus_Latn", "ewe_Latn", "fao_Latn", "pes_Arab", "fij_Latn", "fin_Latn", "fon_Latn", "fra_Latn", "fur_Latn", "fuv_Latn", "gla_Latn", "gle_Latn", "glg_Latn", "grn_Latn", "guj_Gujr", "hat_Latn", "hau_Latn", "heb_Hebr", "hin_Deva", "hne_Deva", "hrv_Latn", "hun_Latn", "hye_Armn", "ibo_Latn", "ilo_Latn", "ind_Latn", "isl_Latn", "ita_Latn", "jav_Latn", "jpn_Jpan", "kab_Latn", "kac_Latn", "kam_Latn", "kan_Knda", "kas_Arab", "kas_Deva", "kat_Geor", "knc_Arab", "knc_Latn", "kaz_Cyrl", "kbp_Latn", "kea_Latn", "khm_Khmr", "kik_Latn", "kin_Latn", "kir_Cyrl", "kmb_Latn", "kon_Latn", "kor_Hang", "kmr_Latn", "lao_Laoo", "lvs_Latn", "lij_Latn", "lim_Latn", "lin_Latn", "lit_Latn", "lmo_Latn", "ltg_Latn", "ltz_Latn", "lua_Latn", "lug_Latn", "luo_Latn", "lus_Latn", "mag_Deva", "mai_Deva", "mal_Mlym", "mar_Deva", "min_Latn", "mkd_Cyrl", "plt_Latn", "mlt_Latn", "mni_Beng", "khk_Cyrl", "mos_Latn", "mri_Latn", "zsm_Latn", "mya_Mymr", "nld_Latn", "nno_Latn", "nob_Latn", "npi_Deva", "nso_Latn", "nus_Latn", "nya_Latn", "oci_Latn", "gaz_Latn", "ory_Orya", "pag_Latn", "pan_Guru", "pap_Latn", "pol_Latn", "por_Latn", "prs_Arab", "pbt_Arab", "quy_Latn", "ron_Latn", "run_Latn", "rus_Cyrl", "sag_Latn", "san_Deva", "sat_Beng", "scn_Latn", "shn_Mymr", "sin_Sinh", "slk_Latn", "slv_Latn", "smo_Latn", "sna_Latn", "snd_Arab", "som_Latn", "sot_Latn", "spa_Latn", "als_Latn", "srd_Latn", "srp_Cyrl", "ssw_Latn", "sun_Latn", "swe_Latn", "swh_Latn", "szl_Latn", "tam_Taml", "tat_Cyrl", "tel_Telu", "tgk_Cyrl", "tgl_Latn", "tha_Thai", "tir_Ethi", "taq_Latn", "taq_Tfng", "tpi_Latn", "tsn_Latn", "tso_Latn", "tuk_Latn", "tum_Latn", "tur_Latn", "twi_Latn", "tzm_Tfng", "uig_Arab", "ukr_Cyrl", "umb_Latn", "urd_Arab", "uzn_Latn", "vec_Latn", "vie_Latn", "war_Latn", "wol_Latn", "xho_Latn", "ydd_Hebr", "yor_Latn", "yue_Hant", "zho_Hans", "zho_Hant", "zul_Latn"]


class       SCREAMING_SNAKE_CASE__					(__snake_case     ):
    __lowerCamelCase						:       str														=     VOCAB_FILES_NAMES
    __lowerCamelCase						:       List[Any]														=     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    __lowerCamelCase						:       Optional[Any]														=     PRETRAINED_VOCAB_FILES_MAP
    __lowerCamelCase						:       Any														=     ["""input_ids""", """attention_mask"""]
    __lowerCamelCase						:       Tuple														=     NllbTokenizer

    __lowerCamelCase						:       str														=     []
    __lowerCamelCase						:       List[str]														=     []





    def __init__(      self						,   a=None						,   a=None						,   a="<s>"						,   a="</s>"						,   a="</s>"						,   a="<s>"						,   a="<unk>"						,   a="<pad>"						,   a="<mask>"						,   a=None						,   a=None						,   a=None						,   a=False						,   **a						,   ):
         # Mask token behave like a normal word, i.e. include the space before it
         lowercase__    :   int      =       AddedToken(_lowerCAmelCase						,   lstrip=_lowerCAmelCase						,   rstrip=_lowerCAmelCase) if isinstance(_lowerCAmelCase						,   _lowerCAmelCase) else mask_token
         lowercase__    :   List[Any]      =       legacy_behaviour
         super().__init__(
             vocab_file=_lowerCAmelCase						,   tokenizer_file=_lowerCAmelCase						,   bos_token=_lowerCAmelCase						,   eos_token=_lowerCAmelCase						,   sep_token=_lowerCAmelCase						,   cls_token=_lowerCAmelCase						,   unk_token=_lowerCAmelCase						,   pad_token=_lowerCAmelCase						,   mask_token=_lowerCAmelCase						,   src_lang=_lowerCAmelCase						,   tgt_lang=_lowerCAmelCase						,   additional_special_tokens=_lowerCAmelCase						,   legacy_behaviour=_lowerCAmelCase						,   **_lowerCAmelCase						,   )

         lowercase__    :   int      =       vocab_file
         lowercase__    :   str      =       False if not self.vocab_file else True

         lowercase__    :   str      =       FAIRSEQ_LANGUAGE_CODES.copy()

         if additional_special_tokens is not None:
              # Only add those special tokens if they are not already there.
              _additional_special_tokens.extend(
                  [t for t in additional_special_tokens if t not in _additional_special_tokens])

         self.add_special_tokens({'additional_special_tokens': _additional_special_tokens})
         lowercase__    :   Optional[int]      =       {
             lang_code: self.convert_tokens_to_ids(_lowerCAmelCase) for lang_code in FAIRSEQ_LANGUAGE_CODES
         }

         lowercase__    :   List[str]      =       src_lang if src_lang is not None else "eng_Latn"
         lowercase__    :   Optional[Any]      =       self.convert_tokens_to_ids(self._src_lang)
         lowercase__    :   str      =       tgt_lang
         self.set_src_lang_special_tokens(self._src_lang)





    @property
    def       snake_case_				(      self):
         return self._src_lang





    @src_lang.setter
    def       snake_case_				(      self						,   a):
         lowercase__    :   int      =       new_src_lang
         self.set_src_lang_special_tokens(self._src_lang)





    def       snake_case_				(      self						,   a						,   a = None):
         if token_ids_a is None:
              return self.prefix_tokens + token_ids_a + self.suffix_tokens
         # We don't expect to process pairs, but leave the pair logic for API consistency
         return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens





    def       snake_case_				(      self						,   a						,   a = None):
         lowercase__    :   Optional[Any]      =       [self.sep_token_id]
         lowercase__    :   List[str]      =       [self.cls_token_id]

         if token_ids_a is None:
              return len(cls + token_ids_a + sep) * [0]
         return len(cls + token_ids_a + sep + sep + token_ids_a + sep) * [0]





    def       snake_case_				(      self						,   a						,   a						,   a						,   a						,   **a):
         if src_lang is None or tgt_lang is None:
              raise ValueError('Translation requires a `src_lang` and a `tgt_lang` for this model')
         lowercase__    :   Tuple      =       src_lang
         lowercase__    :   List[Any]      =       self(_lowerCAmelCase						,   add_special_tokens=_lowerCAmelCase						,   return_tensors=_lowerCAmelCase						,   **_lowerCAmelCase)
         lowercase__    :   int      =       self.convert_tokens_to_ids(_lowerCAmelCase)
         lowercase__    :   Tuple      =       tgt_lang_id
         return inputs





    def       snake_case_				(      self						,   a						,   a = "eng_Latn"						,   a = None						,   a = "fra_Latn"						,   **a						,   ):
         lowercase__    :   Union[str, Any]      =       src_lang
         lowercase__    :   Union[str, Any]      =       tgt_lang
         return super().prepare_seqaseq_batch(_lowerCAmelCase						,   _lowerCAmelCase						,   **_lowerCAmelCase)





    def       snake_case_				(      self):
         return self.set_src_lang_special_tokens(self.src_lang)





    def       snake_case_				(      self):
         return self.set_tgt_lang_special_tokens(self.tgt_lang)





    def       snake_case_				(      self						,   a):
         lowercase__    :   int      =       self.convert_tokens_to_ids(_lowerCAmelCase)

         if self.legacy_behaviour:
              lowercase__    :   Dict      =       []
              lowercase__    :   int      =       [self.eos_token_id, self.cur_lang_code]
         else:
              lowercase__    :   str      =       [self.cur_lang_code]
              lowercase__    :   Optional[int]      =       [self.eos_token_id]

         lowercase__    :   Tuple      =       self.convert_ids_to_tokens(self.prefix_tokens)
         lowercase__    :   Optional[Any]      =       self.convert_ids_to_tokens(self.suffix_tokens)

         lowercase__    :   Tuple      =       processors.TemplateProcessing(
             single=prefix_tokens_str + ['$A'] + suffix_tokens_str						,   pair=prefix_tokens_str + ['$A', '$B'] + suffix_tokens_str						,   special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str						,   self.prefix_tokens + self.suffix_tokens))						,   )





    def       snake_case_				(      self						,   a):
         lowercase__    :   List[str]      =       self.convert_tokens_to_ids(_lowerCAmelCase)
         if self.legacy_behaviour:
              lowercase__    :   Optional[Any]      =       []
              lowercase__    :   int      =       [self.eos_token_id, self.cur_lang_code]
         else:
              lowercase__    :   Optional[Any]      =       [self.cur_lang_code]
              lowercase__    :   List[Any]      =       [self.eos_token_id]

         lowercase__    :   List[Any]      =       self.convert_ids_to_tokens(self.prefix_tokens)
         lowercase__    :   Any      =       self.convert_ids_to_tokens(self.suffix_tokens)

         lowercase__    :   Optional[int]      =       processors.TemplateProcessing(
             single=prefix_tokens_str + ['$A'] + suffix_tokens_str						,   pair=prefix_tokens_str + ['$A', '$B'] + suffix_tokens_str						,   special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str						,   self.prefix_tokens + self.suffix_tokens))						,   )





    def       snake_case_				(      self						,   a						,   a = None):
         if not self.can_save_slow_tokenizer:
              raise ValueError(
                  'Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '
                  'tokenizer.')

         if not os.path.isdir(_lowerCAmelCase):
              logger.error(f"""Vocabulary path ({save_directory}) should be a directory.""")
              return
         lowercase__    :   Any      =       os.path.join(
             _lowerCAmelCase						,   (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'])

         if os.path.abspath(self.vocab_file) != os.path.abspath(_lowerCAmelCase):
              copyfile(self.vocab_file						,   _lowerCAmelCase)

         return (out_vocab_file,)







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       __lowerCAmelCase				:






       def __init__(      self:    List[str]				,    _lowerCAmelCase:    Dict				,    _lowerCAmelCase:    List[str]=13				,    _lowerCAmelCase:    Dict=7				,    _lowerCAmelCase:    Tuple=True				,    _lowerCAmelCase:    Dict=True				,    _lowerCAmelCase:    Optional[Any]=True				,    _lowerCAmelCase:    Union[str, Any]=True				,    _lowerCAmelCase:    str=99				,    _lowerCAmelCase:    Dict=32				,    _lowerCAmelCase:    Dict=5				,    _lowerCAmelCase:    int=4				,    _lowerCAmelCase:    Optional[Any]=4				,    _lowerCAmelCase:    Any="gelu"				,    _lowerCAmelCase:    Any=0.0				,    _lowerCAmelCase:    List[Any]=0.1				,    _lowerCAmelCase:    Optional[int]=True				,    _lowerCAmelCase:    Any=5_12				,    _lowerCAmelCase:    Optional[Any]=16				,    _lowerCAmelCase:    str=2				,    _lowerCAmelCase:    Tuple=0.02				,    _lowerCAmelCase:    str=3				,    _lowerCAmelCase:    List[str]=4				,    _lowerCAmelCase:    Optional[Any]=None				,    ):
         lowercase			:List[str]							=  parent
         lowercase			:List[str]							=  batch_size
         lowercase			:List[str]							=  seq_length
         lowercase			:Dict							=  is_training
         lowercase			:Union[str, Any]							=  use_input_mask
         lowercase			:Optional[int]							=  use_token_type_ids
         lowercase			:Dict							=  use_labels
         lowercase			:Any							=  vocab_size
         lowercase			:List[Any]							=  hidden_size
         lowercase			:Optional[int]							=  num_hidden_layers
         lowercase			:Union[str, Any]							=  num_attention_heads
         lowercase			:Any							=  intermediate_multiple_size
         lowercase			:str							=  hidden_act
         lowercase			:List[Any]							=  hidden_dropout
         lowercase			:Optional[Any]							=  attention_dropout
         lowercase			:int							=  weight_tying
         lowercase			:Tuple							=  max_position_embeddings
         lowercase			:str							=  type_vocab_size
         lowercase			:Union[str, Any]							=  type_sequence_label_size
         lowercase			:Dict							=  initializer_range
         lowercase			:Dict							=  num_labels
         lowercase			:Dict							=  num_choices
         lowercase			:Any							=  scope






       def     SCREAMING_SNAKE_CASE			(      self:    str ):
         lowercase			:List[Any]							=  ids_tensor([self.batch_size, self.seq_length]				,    self.vocab_size )

         lowercase			:Optional[Any]							=  None
         if self.use_input_mask:
           lowercase			:str							=  random_attention_mask([self.batch_size, self.seq_length] )

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

         lowercase			:Tuple							=  self.get_config()

         return config, input_ids, input_mask, token_labels






       def     SCREAMING_SNAKE_CASE			(      self:    str ):
         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=_lowerCAmelCase				,    initializer_range=self.initializer_range				,    )






       def     SCREAMING_SNAKE_CASE			(      self:    Optional[int] ):
         lowercase		,			lowercase		,			lowercase		,			lowercase			:Any							=  self.prepare_config_and_inputs()

         lowercase			:Tuple							=  True

         return config, input_ids, input_mask, token_labels






       def     SCREAMING_SNAKE_CASE			(      self:    List[Any]				,    _lowerCAmelCase:    Optional[Any]				,    _lowerCAmelCase:    Dict				,    _lowerCAmelCase:    List[str] ):
         lowercase			:List[str]							=  GPTNeoXJapaneseModel(config=_lowerCAmelCase )
         model.to(_lowerCAmelCase )
         model.eval()
         lowercase			:Optional[int]							=  model(_lowerCAmelCase				,    attention_mask=_lowerCAmelCase )
         lowercase			:Dict							=  model(_lowerCAmelCase )
         self.parent.assertEqual(result.last_hidden_state.shape				,    (self.batch_size, self.seq_length, self.hidden_size) )






       def     SCREAMING_SNAKE_CASE			(      self:    Optional[int]				,    _lowerCAmelCase:    Dict				,    _lowerCAmelCase:    List[Any]				,    _lowerCAmelCase:    int ):
         lowercase			:Dict							=  True
         lowercase			:Optional[Any]							=  GPTNeoXJapaneseModel(_lowerCAmelCase )
         model.to(_lowerCAmelCase )
         model.eval()
         lowercase			:List[str]							=  model(_lowerCAmelCase				,    attention_mask=_lowerCAmelCase )
         self.parent.assertEqual(result.last_hidden_state.shape				,    (self.batch_size, self.seq_length, self.hidden_size) )






       def     SCREAMING_SNAKE_CASE			(      self:    int				,    _lowerCAmelCase:    str				,    _lowerCAmelCase:    Dict				,    _lowerCAmelCase:    int				,    _lowerCAmelCase:    List[Any] ):
         lowercase			:str							=  GPTNeoXJapaneseForCausalLM(config=_lowerCAmelCase )
         model.to(_lowerCAmelCase )
         model.eval()
         lowercase			:Tuple							=  model(_lowerCAmelCase				,    attention_mask=_lowerCAmelCase				,    labels=_lowerCAmelCase )
         self.parent.assertEqual(result.logits.shape				,    (self.batch_size, self.seq_length, self.vocab_size) )






       def     SCREAMING_SNAKE_CASE			(      self:    Dict				,    _lowerCAmelCase:    List[Any]				,    _lowerCAmelCase:    int				,    _lowerCAmelCase:    Any ):
         lowercase			:Optional[Any]							=  True
         lowercase			:List[str]							=  GPTNeoXJapaneseForCausalLM(config=_lowerCAmelCase )
         model.to(_lowerCAmelCase )
         model.eval()

         # first forward pass
         lowercase			:str							=  model(_lowerCAmelCase				,    attention_mask=_lowerCAmelCase				,    use_cache=_lowerCAmelCase )
         lowercase			:Tuple							=  outputs.past_key_values

         # create hypothetical multiple next token and extent to next_input_ids
         lowercase			:Optional[int]							=  ids_tensor((self.batch_size, 3)				,    config.vocab_size )
         lowercase			:Tuple							=  ids_tensor((self.batch_size, 3)				,    vocab_size=2 )

         # append to next input_ids and
         lowercase			:Tuple							=  torch.cat([input_ids, next_tokens]				,    dim=-1 )
         lowercase			:List[Any]							=  torch.cat([input_mask, next_mask]				,    dim=-1 )

         lowercase			:Dict							=  model(_lowerCAmelCase				,    attention_mask=_lowerCAmelCase				,    output_hidden_states=_lowerCAmelCase )
         lowercase			:List[str]							=  output_from_no_past["hidden_states"][0]
         lowercase			:str							=  model(
             _lowerCAmelCase				,    attention_mask=_lowerCAmelCase				,    past_key_values=_lowerCAmelCase				,    output_hidden_states=_lowerCAmelCase				,    )["hidden_states"][0]

         # select random slice
         lowercase			:Dict							=  ids_tensor((1,)				,    output_from_past.shape[-1] ).item()
         lowercase			:str							=  output_from_no_past[:, -3:, random_slice_idx].detach()
         lowercase			:List[str]							=  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(_lowerCAmelCase				,    _lowerCAmelCase				,    atol=1e-3 ) )






       def     SCREAMING_SNAKE_CASE			(      self:    Dict ):
         lowercase			:str							=  self.prepare_config_and_inputs()
         lowercase		,			lowercase		,			lowercase		,			lowercase			:Tuple							=  config_and_inputs
         lowercase			:Union[str, Any]							=  {"input_ids": input_ids, "attention_mask": input_mask}
         return config, inputs_dict







@require_torch
class       __lowerCAmelCase				(							lowerCAmelCase					,       lowerCAmelCase					,       unittest.TestCase):
       _a           =  (GPTNeoXJapaneseModel, GPTNeoXJapaneseForCausalLM) if is_torch_available() else ()
       _a           =  (GPTNeoXJapaneseForCausalLM,) if is_torch_available() else ()
       _a           =  (
           {'''feature-extraction''': GPTNeoXJapaneseModel, '''text-generation''': GPTNeoXJapaneseForCausalLM}
           if is_torch_available()
           else {}
       )
       _a           =  False
       _a           =  False
       _a           =  False
       _a           =  False






       def     SCREAMING_SNAKE_CASE			(      self:    List[str] ):
         lowercase			:Any							=  GPTNeoXJapaneseModelTester(self )
         lowercase			:List[str]							=  ConfigTester(self				,    config_class=_lowerCAmelCase				,    hidden_size=37 )






       def     SCREAMING_SNAKE_CASE			(      self:    Tuple ):
         self.config_tester.run_common_tests()






       def     SCREAMING_SNAKE_CASE			(      self:    Dict ):
         lowercase		,			lowercase		,			lowercase		,			lowercase			:Tuple							=  self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(_lowerCAmelCase				,    _lowerCAmelCase				,    _lowerCAmelCase )






       def     SCREAMING_SNAKE_CASE			(      self:    Tuple ):
         lowercase		,			lowercase		,			lowercase		,			lowercase			:Optional[Any]							=  self.model_tester.prepare_config_and_inputs_for_decoder()
         self.model_tester.create_and_check_model_as_decoder(_lowerCAmelCase				,    _lowerCAmelCase				,    _lowerCAmelCase )






       def     SCREAMING_SNAKE_CASE			(      self:    Optional[int] ):
         # This regression test was failing with PyTorch < 1.3
         lowercase		,			lowercase		,			lowercase		,			lowercase			:Tuple							=  self.model_tester.prepare_config_and_inputs_for_decoder()

         lowercase			:Union[str, Any]							=  None

         self.model_tester.create_and_check_model_as_decoder(_lowerCAmelCase				,    _lowerCAmelCase				,    _lowerCAmelCase )






       def     SCREAMING_SNAKE_CASE			(      self:    Union[str, Any] ):
         lowercase		,			lowercase		,			lowercase		,			lowercase			:List[str]							=  self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_decoder_model_past_large_inputs(_lowerCAmelCase				,    _lowerCAmelCase				,    _lowerCAmelCase )






       def     SCREAMING_SNAKE_CASE			(      self:    str ):
         lowercase			:Optional[int]							=  self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_causal_lm(*_lowerCAmelCase )






       @slow
       def     SCREAMING_SNAKE_CASE			(      self:    str ):
         lowercase			:int							=  "abeja/gpt-neox-japanese-2.7b"

         lowercase			:Optional[int]							=  ["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、"]

         lowercase			:int							=  [
             "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。",
         ]

         lowercase			:List[str]							=  GPTNeoXJapaneseTokenizer.from_pretrained(_lowerCAmelCase )
         lowercase			:Tuple							=  GPTNeoXJapaneseForCausalLM.from_pretrained(_lowerCAmelCase )

         lowercase			:List[str]							=  []
         for prompt in prompts:
           lowercase			:Optional[int]							=  tokenizer(_lowerCAmelCase				,    return_tensors="pt" ).input_ids
           lowercase			:Union[str, Any]							=  model.generate(_lowerCAmelCase				,    max_length=50 )
           lowercase			:List[str]							=  tokenizer.batch_decode(_lowerCAmelCase				,    skip_special_tokens=_lowerCAmelCase )
           predicted_outputs += generated_string
         self.assertListEqual(_lowerCAmelCase				,    _lowerCAmelCase )



