

"""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 lowerCamelCase__   :



    '''simple docstring'''




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




    def UpperCamelCase__		( self    )    ->      Optional[int]:
          A				  =		ids_tensor([self.batch_size, self.seq_length] ,self.vocab_size    )

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

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

          A				  =		self.get_config()

          return config, input_ids, input_mask, token_labels




    def UpperCamelCase__		( 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 UpperCamelCase__		( self    )    ->      Dict:
          A  ,			A  ,			A  ,			A				  =		self.prepare_config_and_inputs()

          A				  =		True

          return config, input_ids, input_mask, token_labels




    def UpperCamelCase__		( self ,lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_    )    ->      Any:
          A				  =		GPTNeoXJapaneseModel(config=lowerCamelCase_    )
          model.to(lowerCamelCase_    )
          model.eval()
          A				  =		model(lowerCamelCase_ ,attention_mask=lowerCamelCase_    )
          A				  =		model(lowerCamelCase_    )
          self.parent.assertEqual(result.last_hidden_state.shape ,(self.batch_size, self.seq_length, self.hidden_size)    )




    def UpperCamelCase__		( self ,lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_    )    ->      Union[str, Any]:
          A				  =		True
          A				  =		GPTNeoXJapaneseModel(lowerCamelCase_    )
          model.to(lowerCamelCase_    )
          model.eval()
          A				  =		model(lowerCamelCase_ ,attention_mask=lowerCamelCase_    )
          self.parent.assertEqual(result.last_hidden_state.shape ,(self.batch_size, self.seq_length, self.hidden_size)    )




    def UpperCamelCase__		( self ,lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_    )    ->      Tuple:
          A				  =		GPTNeoXJapaneseForCausalLM(config=lowerCamelCase_    )
          model.to(lowerCamelCase_    )
          model.eval()
          A				  =		model(lowerCamelCase_ ,attention_mask=lowerCamelCase_ ,labels=lowerCamelCase_    )
          self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.seq_length, self.vocab_size)    )




    def UpperCamelCase__		( self ,lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_    )    ->      List[str]:
          A				  =		True
          A				  =		GPTNeoXJapaneseForCausalLM(config=lowerCamelCase_    )
          model.to(lowerCamelCase_    )
          model.eval()

          # first forward pass
          A				  =		model(lowerCamelCase_ ,attention_mask=lowerCamelCase_ ,use_cache=lowerCamelCase_    )
          A				  =		outputs.past_key_values

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

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

          A				  =		model(lowerCamelCase_ ,attention_mask=lowerCamelCase_ ,output_hidden_states=lowerCamelCase_    )
          A				  =		output_from_no_past["""hidden_states"""][0]
          A				  =		model(
              lowerCamelCase_ ,attention_mask=lowerCamelCase_ ,past_key_values=lowerCamelCase_ ,output_hidden_states=lowerCamelCase_ ,)["""hidden_states"""][0]

          # select random slice
          A				  =		ids_tensor((1,) ,output_from_past.shape[-1]    ).item()
          A				  =		output_from_no_past[:, -3:, random_slice_idx].detach()
          A				  =		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 UpperCamelCase__		( self    )    ->      Union[str, Any]:
          A				  =		self.prepare_config_and_inputs()
          A  ,			A  ,			A  ,			A				  =		config_and_inputs
          A				  =		{"""input_ids""": input_ids, """attention_mask""": input_mask}
          return config, inputs_dict





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



    '''simple docstring'''




    _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 UpperCamelCase__		( self    )    ->      Union[str, Any]:
          A				  =		GPTNeoXJapaneseModelTester(self    )
          A				  =		ConfigTester(self ,config_class=lowerCamelCase_ ,hidden_size=3_7    )




    def UpperCamelCase__		( self    )    ->      Any:
          self.config_tester.run_common_tests()




    def UpperCamelCase__		( self    )    ->      int:
          A  ,			A  ,			A  ,			A				  =		self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_    )




    def UpperCamelCase__		( self    )    ->      str:
          A  ,			A  ,			A  ,			A				  =		self.model_tester.prepare_config_and_inputs_for_decoder()
          self.model_tester.create_and_check_model_as_decoder(lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_    )




    def UpperCamelCase__		( self    )    ->      Any:
          # This regression test was failing with PyTorch < 1.3
          A  ,			A  ,			A  ,			A				  =		self.model_tester.prepare_config_and_inputs_for_decoder()

          A				  =		None

          self.model_tester.create_and_check_model_as_decoder(lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_    )




    def UpperCamelCase__		( self    )    ->      str:
          A  ,			A  ,			A  ,			A				  =		self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_decoder_model_past_large_inputs(lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_    )




    def UpperCamelCase__		( self    )    ->      Optional[int]:
          A				  =		self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_causal_lm(*lowerCamelCase_    )




    @slow
    def UpperCamelCase__		( self    )    ->      Dict:
          A				  =		"""abeja/gpt-neox-japanese-2.7b"""

          A				  =		["""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、"""]

          A				  =		[
              """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。""",
          ]

          A				  =		GPTNeoXJapaneseTokenizer.from_pretrained(lowerCamelCase_    )
          A				  =		GPTNeoXJapaneseForCausalLM.from_pretrained(lowerCamelCase_    )

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







"""simple docstring"""


UpperCAmelCase													=[
    [0, 16, 13, 0, 0, 0],
    [0, 0, 10, 12, 0, 0],
    [0, 4, 0, 0, 14, 0],
    [0, 0, 9, 0, 0, 20],
    [0, 0, 0, 7, 0, 4],
    [0, 0, 0, 0, 0, 0],
]



def   _A					(	_a	:						Dict     ,   _a	:						int     ,   _a	:						Dict     ,   _a	:						List[Any]				):



      """simple docstring"""






      A				  =		[False] * len(_a				)
      A				  =		[s]
      A				  =		True

      while queue:
            A				  =		queue.pop(0				)
            for ind in range(len(graph[u]				)				):
                  if visited[ind] is False and graph[u][ind] > 0:
                        queue.append(_a				)
                        A				  =		True
                        A				  =		u

      return visited[t]



def   _A					(	_a	:						str     ,   _a	:						Optional[Any]     ,   _a	:						Optional[int]				):



      """simple docstring"""






      A				  =		[-1] * (len(_a				))
      A				  =		0
      A				  =		[]
      A				  =		[i[:] for i in graph]  # Record original cut, copy.
      while bfs(_a     ,   _a     ,   _a     ,   _a				):
            A				  =		float("""Inf"""				)
            A				  =		sink

            while s != source:
                  # Find the minimum value in select path
                  A				  =		min(_a     ,   graph[parent[s]][s]				)
                  A				  =		parent[s]

            max_flow += path_flow
            A				  =		sink

            while v != source:
                  A				  =		parent[v]
                  graph[u][v] -= path_flow
                  graph[v][u] += path_flow
                  A				  =		parent[v]

      for i in range(len(_a				)				):
            for j in range(len(graph[0]				)				):
                  if graph[i][j] == 0 and temp[i][j] > 0:
                        res.append((i, j)				)

      return res


if __name__ == "__main__":
  print(mincut(test_graph, source=0, sink=5))




