'''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        A__				:





   """simple docstring"""





   def __init__(					self	:						List[Any]		,	lowerCAmelCase__	:						int		,	lowerCAmelCase__	:						Any=1_3		,	lowerCAmelCase__	:						Dict=7		,	lowerCAmelCase__	:						List[str]=True		,	lowerCAmelCase__	:						Tuple=True		,	lowerCAmelCase__	:						List[str]=True		,	lowerCAmelCase__	:						Union[str, Any]=True		,	lowerCAmelCase__	:						Optional[Any]=9_9		,	lowerCAmelCase__	:						Union[str, Any]=3_2		,	lowerCAmelCase__	:						List[str]=5		,	lowerCAmelCase__	:						Union[str, Any]=4		,	lowerCAmelCase__	:						str=4		,	lowerCAmelCase__	:						Optional[int]="gelu"		,	lowerCAmelCase__	:						List[str]=0.0		,	lowerCAmelCase__	:						List[str]=0.1		,	lowerCAmelCase__	:						Union[str, Any]=True		,	lowerCAmelCase__	:						List[str]=5_1_2		,	lowerCAmelCase__	:						int=1_6		,	lowerCAmelCase__	:						Dict=2		,	lowerCAmelCase__	:						Optional[int]=0.02		,	lowerCAmelCase__	:						Any=3		,	lowerCAmelCase__	:						str=4		,	lowerCAmelCase__	:						Union[str, Any]=None		,	)			->       List[Any]:







      """simple docstring"""




      _UpperCAmelCase				:      List[str]				=  parent
      _UpperCAmelCase				:      Optional[int]				=  batch_size
      _UpperCAmelCase				:      List[str]				=  seq_length
      _UpperCAmelCase				:      List[Any]				=  is_training
      _UpperCAmelCase				:      List[str]				=  use_input_mask
      _UpperCAmelCase				:      Optional[Any]				=  use_token_type_ids
      _UpperCAmelCase				:      Any				=  use_labels
      _UpperCAmelCase				:      str				=  vocab_size
      _UpperCAmelCase				:      Union[str, Any]				=  hidden_size
      _UpperCAmelCase				:      Tuple				=  num_hidden_layers
      _UpperCAmelCase				:      str				=  num_attention_heads
      _UpperCAmelCase				:      Any				=  intermediate_multiple_size
      _UpperCAmelCase				:      Union[str, Any]				=  hidden_act
      _UpperCAmelCase				:      Any				=  hidden_dropout
      _UpperCAmelCase				:      str				=  attention_dropout
      _UpperCAmelCase				:      Any				=  weight_tying
      _UpperCAmelCase				:      Dict				=  max_position_embeddings
      _UpperCAmelCase				:      List[Any]				=  type_vocab_size
      _UpperCAmelCase				:      List[str]				=  type_sequence_label_size
      _UpperCAmelCase				:      Union[str, Any]				=  initializer_range
      _UpperCAmelCase				:      Tuple				=  num_labels
      _UpperCAmelCase				:      List[Any]				=  num_choices
      _UpperCAmelCase				:      Optional[Any]				=  scope




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







      """simple docstring"""




      _UpperCAmelCase				:      Union[str, Any]				=  ids_tensor([self.batch_size, self.seq_length]		,	self.vocab_size     )

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

      _UpperCAmelCase				:      Any				=  None
      if self.use_labels:
         _UpperCAmelCase				:      List[str]				=  ids_tensor([self.batch_size, self.seq_length]		,	self.num_labels     )

      _UpperCAmelCase				:      Optional[Any]				=  self.get_config()

      return config, input_ids, input_mask, token_labels




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







      """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=A_		,	initializer_range=self.initializer_range		,	)




   def      _lowerCAmelCase						(					self	:						Dict     )			->       int:







      """simple docstring"""




      _UpperCAmelCase				:      int				=  self.prepare_config_and_inputs()

      _UpperCAmelCase				:      Tuple				=  True

      return config, input_ids, input_mask, token_labels




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







      """simple docstring"""




      _UpperCAmelCase				:      List[Any]				=  GPTNeoXJapaneseModel(config=A_     )
      model.to(A_     )
      model.eval()
      _UpperCAmelCase				:      str				=  model(A_		,	attention_mask=A_     )
      _UpperCAmelCase				:      List[str]				=  model(A_     )
      self.parent.assertEqual(result.last_hidden_state.shape		,	(self.batch_size, self.seq_length, self.hidden_size)     )




   def      _lowerCAmelCase						(					self	:						str		,	lowerCAmelCase__	:						Tuple		,	lowerCAmelCase__	:						Tuple		,	lowerCAmelCase__	:						Any     )			->       Any:







      """simple docstring"""




      _UpperCAmelCase				:      Any				=  True
      _UpperCAmelCase				:      Optional[Any]				=  GPTNeoXJapaneseModel(A_     )
      model.to(A_     )
      model.eval()
      _UpperCAmelCase				:      List[str]				=  model(A_		,	attention_mask=A_     )
      self.parent.assertEqual(result.last_hidden_state.shape		,	(self.batch_size, self.seq_length, self.hidden_size)     )




   def      _lowerCAmelCase						(					self	:						int		,	lowerCAmelCase__	:						Dict		,	lowerCAmelCase__	:						Union[str, Any]		,	lowerCAmelCase__	:						Dict		,	lowerCAmelCase__	:						int     )			->       Union[str, Any]:







      """simple docstring"""




      _UpperCAmelCase				:      int				=  GPTNeoXJapaneseForCausalLM(config=A_     )
      model.to(A_     )
      model.eval()
      _UpperCAmelCase				:      Tuple				=  model(A_		,	attention_mask=A_		,	labels=A_     )
      self.parent.assertEqual(result.logits.shape		,	(self.batch_size, self.seq_length, self.vocab_size)     )




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







      """simple docstring"""




      _UpperCAmelCase				:      Any				=  True
      _UpperCAmelCase				:      Any				=  GPTNeoXJapaneseForCausalLM(config=A_     )
      model.to(A_     )
      model.eval()

      # first forward pass
      _UpperCAmelCase				:      Dict				=  model(A_		,	attention_mask=A_		,	use_cache=A_     )
      _UpperCAmelCase				:      Dict				=  outputs.past_key_values

      # create hypothetical multiple next token and extent to next_input_ids
      _UpperCAmelCase				:      str				=  ids_tensor((self.batch_size, 3)		,	config.vocab_size     )
      _UpperCAmelCase				:      Union[str, Any]				=  ids_tensor((self.batch_size, 3)		,	vocab_size=2     )

      # append to next input_ids and
      _UpperCAmelCase				:      Dict				=  torch.cat([input_ids, next_tokens]		,	dim=-1     )
      _UpperCAmelCase				:      Optional[Any]				=  torch.cat([input_mask, next_mask]		,	dim=-1     )

      _UpperCAmelCase				:      str				=  model(A_		,	attention_mask=A_		,	output_hidden_states=A_     )
      _UpperCAmelCase				:      Dict				=  output_from_no_past["""hidden_states"""][0]
      _UpperCAmelCase				:      Dict				=  model(
          A_		,	attention_mask=A_		,	past_key_values=A_		,	output_hidden_states=A_		,	)["""hidden_states"""][0]

      # select random slice
      _UpperCAmelCase				:      str				=  ids_tensor((1,)		,	output_from_past.shape[-1]     ).item()
      _UpperCAmelCase				:      List[Any]				=  output_from_no_past[:, -3:, random_slice_idx].detach()
      _UpperCAmelCase				:      Union[str, Any]				=  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(A_		,	A_		,	atol=1e-3     )     )




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







      """simple docstring"""




      _UpperCAmelCase				:      str				=  self.prepare_config_and_inputs()
      _UpperCAmelCase				:      Dict				=  config_and_inputs
      _UpperCAmelCase				:      List[Any]				=  {"""input_ids""": input_ids, """attention_mask""": input_mask}
      return config, inputs_dict





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





   """simple docstring"""





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




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







      """simple docstring"""




      _UpperCAmelCase				:      Optional[int]				=  GPTNeoXJapaneseModelTester(self     )
      _UpperCAmelCase				:      Optional[int]				=  ConfigTester(self		,	config_class=A_		,	hidden_size=3_7     )




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







      """simple docstring"""




      self.config_tester.run_common_tests()




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







      """simple docstring"""




      _UpperCAmelCase				:      Any				=  self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(A_		,	A_		,	A_     )




   def      _lowerCAmelCase						(					self	:						int     )			->       str:







      """simple docstring"""




      _UpperCAmelCase				:      int				=  self.model_tester.prepare_config_and_inputs_for_decoder()
      self.model_tester.create_and_check_model_as_decoder(A_		,	A_		,	A_     )




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







      """simple docstring"""




      _UpperCAmelCase				:      Tuple				=  self.model_tester.prepare_config_and_inputs_for_decoder()

      _UpperCAmelCase				:      Tuple				=  None

      self.model_tester.create_and_check_model_as_decoder(A_		,	A_		,	A_     )




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







      """simple docstring"""




      _UpperCAmelCase				:      List[Any]				=  self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_decoder_model_past_large_inputs(A_		,	A_		,	A_     )




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







      """simple docstring"""




      _UpperCAmelCase				:      Dict				=  self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_causal_lm(*A_     )




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







      """simple docstring"""




      _UpperCAmelCase				:      Optional[int]				=  """abeja/gpt-neox-japanese-2.7b"""

      _UpperCAmelCase				:      Union[str, Any]				=  ["""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、"""]

      _UpperCAmelCase				:      List[Any]				=  [
          """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。""",
      ]

      _UpperCAmelCase				:      str				=  GPTNeoXJapaneseTokenizer.from_pretrained(A_     )
      _UpperCAmelCase				:      Optional[Any]				=  GPTNeoXJapaneseForCausalLM.from_pretrained(A_     )

      _UpperCAmelCase				:      Optional[int]				=  []
      for prompt in prompts:
         _UpperCAmelCase				:      int				=  tokenizer(A_		,	return_tensors="pt"     ).input_ids
         _UpperCAmelCase				:      Optional[Any]				=  model.generate(A_		,	max_length=5_0     )
         _UpperCAmelCase				:      int				=  tokenizer.batch_decode(A_		,	skip_special_tokens=A_     )
         predicted_outputs += generated_string
      self.assertListEqual(A_		,	A_     )



# A Bipartite Graph is a graph whose vertices can be divided into two independent sets,
# U and V such that every edge (u, v) either connects a vertex from U to V or a vertex
# from V to U. In other words, for every edge (u, v), either u belongs to U and v to V,
# or u belongs to V and v to U. We can also say that there is no edge that connects
# vertices of same set.




def    a__    (		snake_case__					:							Dict     ):
  _UpperCAmelCase		:							str							=     [False] * len(snake_case__     )
  _UpperCAmelCase		:							str							=     [-1] * len(snake_case__     )

  def dfs(snake_case__					:							Dict						,   snake_case__					:							Optional[Any]     ):
    _UpperCAmelCase		:							str							=     True
    _UpperCAmelCase		:							Optional[Any]							=     c
    for u in graph[v]:
      if not visited[u]:
        dfs(snake_case__						,   1 - c     )

  for i in range(len(snake_case__     )     ):
    if not visited[i]:
      dfs(snake_case__						,   0     )

  for i in range(len(snake_case__     )     ):
    for j in graph[i]:
      if color[i] == color[j]:
        return False

  return True


# Adjacency list of graph
SCREAMING_SNAKE_CASE__					:     Optional[Any]       =     {0: [1, 3], 1: [0, 2], 2: [1, 3], 3: [0, 2], 4: []}





print(check_bipartite_dfs(graph))





