

'''simple docstring'''






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     lowercase_			:




  """simple docstring"""




  def __init__(   self     :  List[str]      ,lowercase__     :  Union[str, Any]      ,lowercase__     :  Optional[int]=1_3      ,lowercase__     :  Union[str, Any]=7      ,lowercase__     :  Any=True      ,lowercase__     :  Tuple=True      ,lowercase__     :  Union[str, Any]=True      ,lowercase__     :  Optional[int]=True      ,lowercase__     :  Optional[int]=9_9      ,lowercase__     :  Optional[Any]=3_2      ,lowercase__     :  Dict=5      ,lowercase__     :  Optional[Any]=4      ,lowercase__     :  List[str]=4      ,lowercase__     :  str="gelu"      ,lowercase__     :  Optional[Any]=0.0      ,lowercase__     :  int=0.1      ,lowercase__     :  Optional[Any]=True      ,lowercase__     :  Dict=5_1_2      ,lowercase__     :  Tuple=1_6      ,lowercase__     :  List[str]=2      ,lowercase__     :  Union[str, Any]=0.0_2      ,lowercase__     :  Union[str, Any]=3      ,lowercase__     :  List[str]=4      ,lowercase__     :  List[Any]=None      ,):
   __lowercase													=				parent
   __lowercase													=				batch_size
   __lowercase													=				seq_length
   __lowercase													=				is_training
   __lowercase													=				use_input_mask
   __lowercase													=				use_token_type_ids
   __lowercase													=				use_labels
   __lowercase													=				vocab_size
   __lowercase													=				hidden_size
   __lowercase													=				num_hidden_layers
   __lowercase													=				num_attention_heads
   __lowercase													=				intermediate_multiple_size
   __lowercase													=				hidden_act
   __lowercase													=				hidden_dropout
   __lowercase													=				attention_dropout
   __lowercase													=				weight_tying
   __lowercase													=				max_position_embeddings
   __lowercase													=				type_vocab_size
   __lowercase													=				type_sequence_label_size
   __lowercase													=				initializer_range
   __lowercase													=				num_labels
   __lowercase													=				num_choices
   __lowercase													=				scope






  def       SCREAMING_SNAKE_CASE   (   self     :  Optional[Any]    ):
   __lowercase													=				ids_tensor([self.batch_size, self.seq_length]      ,self.vocab_size    )

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

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

   __lowercase													=				self.get_config()

   return config, input_ids, input_mask, token_labels






  def       SCREAMING_SNAKE_CASE   (   self     :  Dict    ):
   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       SCREAMING_SNAKE_CASE   (   self     :  List[Any]    ):
   __lowercase						,			__lowercase						,			__lowercase						,			__lowercase													=				self.prepare_config_and_inputs()

   __lowercase													=				True

   return config, input_ids, input_mask, token_labels






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






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






  def       SCREAMING_SNAKE_CASE   (   self     :  Dict      ,lowercase__     :  Optional[Any]      ,lowercase__     :  Dict      ,lowercase__     :  Any      ,lowercase__     :  Optional[Any]    ):
   __lowercase													=				GPTNeoXJapaneseForCausalLM(config=lowercase__    )
   model.to(lowercase__    )
   model.eval()
   __lowercase													=				model(lowercase__      ,attention_mask=lowercase__      ,labels=lowercase__    )
   self.parent.assertEqual(result.logits.shape      ,(self.batch_size, self.seq_length, self.vocab_size)    )






  def       SCREAMING_SNAKE_CASE   (   self     :  Optional[Any]      ,lowercase__     :  List[Any]      ,lowercase__     :  Union[str, Any]      ,lowercase__     :  List[Any]    ):
   __lowercase													=				True
   __lowercase													=				GPTNeoXJapaneseForCausalLM(config=lowercase__    )
   model.to(lowercase__    )
   model.eval()

   # first forward pass
   __lowercase													=				model(lowercase__      ,attention_mask=lowercase__      ,use_cache=lowercase__    )
   __lowercase													=				outputs.past_key_values

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

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

   __lowercase													=				model(lowercase__      ,attention_mask=lowercase__      ,output_hidden_states=lowercase__    )
   __lowercase													=				output_from_no_past['''hidden_states'''][0]
   __lowercase													=				model(
       lowercase__      ,attention_mask=lowercase__      ,past_key_values=lowercase__      ,output_hidden_states=lowercase__      ,)['''hidden_states'''][0]

   # select random slice
   __lowercase													=				ids_tensor((1,)      ,output_from_past.shape[-1]    ).item()
   __lowercase													=				output_from_no_past[:, -3:, random_slice_idx].detach()
   __lowercase													=				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       SCREAMING_SNAKE_CASE   (   self     :  List[Any]    ):
   __lowercase													=				self.prepare_config_and_inputs()
   __lowercase						,			__lowercase						,			__lowercase						,			__lowercase													=				config_and_inputs
   __lowercase													=				{'''input_ids''': input_ids, '''attention_mask''': input_mask}
   return config, inputs_dict






@require_torch
class     lowercase_			(lowerCamelCase__       , lowerCamelCase__       , unittest.TestCase   ):




  """simple docstring"""




  SCREAMING_SNAKE_CASE					:						Union[str, Any]           =    (GPTNeoXJapaneseModel, GPTNeoXJapaneseForCausalLM) if is_torch_available() else ()
  SCREAMING_SNAKE_CASE					:						List[Any]           =    (GPTNeoXJapaneseForCausalLM,) if is_torch_available() else ()
  SCREAMING_SNAKE_CASE					:						Optional[int]           =    (
      {'feature-extraction': GPTNeoXJapaneseModel, 'text-generation': GPTNeoXJapaneseForCausalLM}
      if is_torch_available()
      else {}
  )
  SCREAMING_SNAKE_CASE					:						List[str]           =    False
  SCREAMING_SNAKE_CASE					:						int           =    False
  SCREAMING_SNAKE_CASE					:						Optional[Any]           =    False
  SCREAMING_SNAKE_CASE					:						Dict           =    False






  def       SCREAMING_SNAKE_CASE   (   self     :  List[Any]    ):
   __lowercase													=				GPTNeoXJapaneseModelTester(self    )
   __lowercase													=				ConfigTester(self      ,config_class=lowercase__      ,hidden_size=3_7    )






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






  def       SCREAMING_SNAKE_CASE   (   self     :  List[str]    ):
   __lowercase						,			__lowercase						,			__lowercase						,			__lowercase													=				self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_model(lowercase__      ,lowercase__      ,lowercase__    )






  def       SCREAMING_SNAKE_CASE   (   self     :  List[str]    ):
   __lowercase						,			__lowercase						,			__lowercase						,			__lowercase													=				self.model_tester.prepare_config_and_inputs_for_decoder()
   self.model_tester.create_and_check_model_as_decoder(lowercase__      ,lowercase__      ,lowercase__    )






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

   __lowercase													=				None

   self.model_tester.create_and_check_model_as_decoder(lowercase__      ,lowercase__      ,lowercase__    )






  def       SCREAMING_SNAKE_CASE   (   self     :  Dict    ):
   __lowercase						,			__lowercase						,			__lowercase						,			__lowercase													=				self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_decoder_model_past_large_inputs(lowercase__      ,lowercase__      ,lowercase__    )






  def       SCREAMING_SNAKE_CASE   (   self     :  List[Any]    ):
   __lowercase													=				self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_causal_lm(*lowercase__    )






  @slow
  def       SCREAMING_SNAKE_CASE   (   self     :  List[str]    ):
   __lowercase													=				'''abeja/gpt-neox-japanese-2.7b'''

   __lowercase													=				['''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													=				[
       '''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													=				GPTNeoXJapaneseTokenizer.from_pretrained(lowercase__    )
   __lowercase													=				GPTNeoXJapaneseForCausalLM.from_pretrained(lowercase__    )

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




'''simple docstring'''






def 	_A  (					A__ = 50   ):






 """simple docstring"""



 __lowercase													=				[1] * (length + 1)

 for row_length in range(3  ,       length + 1   ):
  for block_length in range(3  ,       row_length + 1   ):
   for block_start in range(row_length - block_length   ):
    ways_number[row_length] += ways_number[
        row_length - block_start - block_length - 1
    ]

   ways_number[row_length] += 1

 return ways_number[length]


if __name__ == "__main__":
 print(f'{solution() = }')

