


"""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__				:





   def __init__(							self      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE=13      ,   SCREAMING_SNAKE_CASE=7      ,   SCREAMING_SNAKE_CASE=True      ,   SCREAMING_SNAKE_CASE=True      ,   SCREAMING_SNAKE_CASE=True      ,   SCREAMING_SNAKE_CASE=True      ,   SCREAMING_SNAKE_CASE=99      ,   SCREAMING_SNAKE_CASE=32      ,   SCREAMING_SNAKE_CASE=5      ,   SCREAMING_SNAKE_CASE=4      ,   SCREAMING_SNAKE_CASE=4      ,   SCREAMING_SNAKE_CASE="gelu"      ,   SCREAMING_SNAKE_CASE=0.0      ,   SCREAMING_SNAKE_CASE=0.1      ,   SCREAMING_SNAKE_CASE=True      ,   SCREAMING_SNAKE_CASE=512      ,   SCREAMING_SNAKE_CASE=16      ,   SCREAMING_SNAKE_CASE=2      ,   SCREAMING_SNAKE_CASE=0.02      ,   SCREAMING_SNAKE_CASE=3      ,   SCREAMING_SNAKE_CASE=4      ,   SCREAMING_SNAKE_CASE=None      ,   )  ->  List[str]:
      _lowerCamelCase	:					Optional[Any]   =		parent
      _lowerCamelCase	:					str   =		batch_size
      _lowerCamelCase	:					Optional[int]   =		seq_length
      _lowerCamelCase	:					Optional[Any]   =		is_training
      _lowerCamelCase	:					Any   =		use_input_mask
      _lowerCamelCase	:					List[Any]   =		use_token_type_ids
      _lowerCamelCase	:					Dict   =		use_labels
      _lowerCamelCase	:					Dict   =		vocab_size
      _lowerCamelCase	:					Optional[int]   =		hidden_size
      _lowerCamelCase	:					Union[str, Any]   =		num_hidden_layers
      _lowerCamelCase	:					Tuple   =		num_attention_heads
      _lowerCamelCase	:					Tuple   =		intermediate_multiple_size
      _lowerCamelCase	:					str   =		hidden_act
      _lowerCamelCase	:					Dict   =		hidden_dropout
      _lowerCamelCase	:					str   =		attention_dropout
      _lowerCamelCase	:					Optional[int]   =		weight_tying
      _lowerCamelCase	:					Union[str, Any]   =		max_position_embeddings
      _lowerCamelCase	:					Tuple   =		type_vocab_size
      _lowerCamelCase	:					str   =		type_sequence_label_size
      _lowerCamelCase	:					int   =		initializer_range
      _lowerCamelCase	:					Union[str, Any]   =		num_labels
      _lowerCamelCase	:					List[str]   =		num_choices
      _lowerCamelCase	:					List[Any]   =		scope





   def 		UpperCamelCase_  (							self)  ->  Optional[Any]:
      _lowerCamelCase	:					int   =		ids_tensor([self.batch_size, self.seq_length]      ,   self.vocab_size)

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

      _lowerCamelCase	:					Optional[Any]   =		None
      if self.use_labels:
         _lowerCamelCase	:					Optional[int]   =		ids_tensor([self.batch_size, self.seq_length]      ,   self.num_labels)

      _lowerCamelCase	:					List[str]   =		self.get_config()

      return config, input_ids, input_mask, token_labels





   def 		UpperCamelCase_  (							self)  ->  Tuple:
      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=SCREAMING_SNAKE_CASE      ,   initializer_range=self.initializer_range      ,   )





   def 		UpperCamelCase_  (							self)  ->  int:
      _lowerCamelCase   ,    _lowerCamelCase   ,    _lowerCamelCase   ,    _lowerCamelCase	:					int   =		self.prepare_config_and_inputs()

      _lowerCamelCase	:					List[Any]   =		True

      return config, input_ids, input_mask, token_labels





   def 		UpperCamelCase_  (							self      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE)  ->  Tuple:
      _lowerCamelCase	:					Dict   =		GPTNeoXJapaneseModel(config=SCREAMING_SNAKE_CASE)
      model.to(SCREAMING_SNAKE_CASE)
      model.eval()
      _lowerCamelCase	:					str   =		model(SCREAMING_SNAKE_CASE      ,   attention_mask=SCREAMING_SNAKE_CASE)
      _lowerCamelCase	:					List[Any]   =		model(SCREAMING_SNAKE_CASE)
      self.parent.assertEqual(result.last_hidden_state.shape      ,   (self.batch_size, self.seq_length, self.hidden_size))





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





   def 		UpperCamelCase_  (							self      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE)  ->  Any:
      _lowerCamelCase	:					Optional[Any]   =		GPTNeoXJapaneseForCausalLM(config=SCREAMING_SNAKE_CASE)
      model.to(SCREAMING_SNAKE_CASE)
      model.eval()
      _lowerCamelCase	:					Optional[int]   =		model(SCREAMING_SNAKE_CASE      ,   attention_mask=SCREAMING_SNAKE_CASE      ,   labels=SCREAMING_SNAKE_CASE)
      self.parent.assertEqual(result.logits.shape      ,   (self.batch_size, self.seq_length, self.vocab_size))





   def 		UpperCamelCase_  (							self      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE)  ->  int:
      _lowerCamelCase	:					Union[str, Any]   =		True
      _lowerCamelCase	:					List[Any]   =		GPTNeoXJapaneseForCausalLM(config=SCREAMING_SNAKE_CASE)
      model.to(SCREAMING_SNAKE_CASE)
      model.eval()

      # first forward pass
      _lowerCamelCase	:					List[Any]   =		model(SCREAMING_SNAKE_CASE      ,   attention_mask=SCREAMING_SNAKE_CASE      ,   use_cache=SCREAMING_SNAKE_CASE)
      _lowerCamelCase	:					Any   =		outputs.past_key_values

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

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

      _lowerCamelCase	:					Union[str, Any]   =		model(SCREAMING_SNAKE_CASE      ,   attention_mask=SCREAMING_SNAKE_CASE      ,   output_hidden_states=SCREAMING_SNAKE_CASE)
      _lowerCamelCase	:					int   =		output_from_no_past["""hidden_states"""][0]
      _lowerCamelCase	:					Union[str, Any]   =		model(
          SCREAMING_SNAKE_CASE      ,   attention_mask=SCREAMING_SNAKE_CASE      ,   past_key_values=SCREAMING_SNAKE_CASE      ,   output_hidden_states=SCREAMING_SNAKE_CASE      ,   )["""hidden_states"""][0]

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





   def 		UpperCamelCase_  (							self)  ->  str:
      _lowerCamelCase	:					Any   =		self.prepare_config_and_inputs()
      _lowerCamelCase   ,    _lowerCamelCase   ,    _lowerCamelCase   ,    _lowerCamelCase	:					List[Any]   =		config_and_inputs
      _lowerCamelCase	:					Optional[Any]   =		{"""input_ids""": input_ids, """attention_mask""": input_mask}
      return config, inputs_dict







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





   def 		UpperCamelCase_  (							self)  ->  Tuple:
      _lowerCamelCase	:					Optional[Any]   =		GPTNeoXJapaneseModelTester(self)
      _lowerCamelCase	:					List[str]   =		ConfigTester(self      ,   config_class=SCREAMING_SNAKE_CASE      ,   hidden_size=37)





   def 		UpperCamelCase_  (							self)  ->  List[str]:
      self.config_tester.run_common_tests()





   def 		UpperCamelCase_  (							self)  ->  str:
      _lowerCamelCase   ,    _lowerCamelCase   ,    _lowerCamelCase   ,    _lowerCamelCase	:					Union[str, Any]   =		self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE)





   def 		UpperCamelCase_  (							self)  ->  List[Any]:
      _lowerCamelCase   ,    _lowerCamelCase   ,    _lowerCamelCase   ,    _lowerCamelCase	:					int   =		self.model_tester.prepare_config_and_inputs_for_decoder()
      self.model_tester.create_and_check_model_as_decoder(SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE)





   def 		UpperCamelCase_  (							self)  ->  int:
      # This regression test was failing with PyTorch < 1.3
      _lowerCamelCase   ,    _lowerCamelCase   ,    _lowerCamelCase   ,    _lowerCamelCase	:					Dict   =		self.model_tester.prepare_config_and_inputs_for_decoder()

      _lowerCamelCase	:					str   =		None

      self.model_tester.create_and_check_model_as_decoder(SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE)





   def 		UpperCamelCase_  (							self)  ->  Any:
      _lowerCamelCase   ,    _lowerCamelCase   ,    _lowerCamelCase   ,    _lowerCamelCase	:					Tuple   =		self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_decoder_model_past_large_inputs(SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE)





   def 		UpperCamelCase_  (							self)  ->  List[Any]:
      _lowerCamelCase	:					List[Any]   =		self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_causal_lm(*SCREAMING_SNAKE_CASE)





   @slow
   def 		UpperCamelCase_  (							self)  ->  int:
      _lowerCamelCase	:					Tuple   =		"""abeja/gpt-neox-japanese-2.7b"""

      _lowerCamelCase	:					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、"""]

      _lowerCamelCase	:					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。""",
      ]

      _lowerCamelCase	:					Optional[int]   =		GPTNeoXJapaneseTokenizer.from_pretrained(SCREAMING_SNAKE_CASE)
      _lowerCamelCase	:					int   =		GPTNeoXJapaneseForCausalLM.from_pretrained(SCREAMING_SNAKE_CASE)

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





"""simple docstring"""
def 				_snake_case      (   __snake_case							:      list[list[int]]						, __snake_case							:      int						, __snake_case							:      int						, __snake_case							:      list[int]   ):




   """simple docstring"""







   if graph[path[curr_ind - 1]][next_ver] == 0:
      return False

   # 2. Validate that next vertex is not already in path
   return not any(vertex == next_ver for vertex in path   )

def 				_snake_case      (   __snake_case							:      list[list[int]]						, __snake_case							:      list[int]						, __snake_case							:      int   ):




   """simple docstring"""







   if curr_ind == len(__snake_case   ):
      # return whether path exists between current and starting vertices
      return graph[path[curr_ind - 1]][path[0]] == 1

   # Recursive Step
   for next_ver in range(0						, len(__snake_case   )   ):
      if valid_connection(__snake_case						, __snake_case						, __snake_case						, __snake_case   ):
         # Insert current vertex  into path as next transition
         _lowerCamelCase	:					List[str]   =		next_ver
         # Validate created path
         if util_hamilton_cycle(__snake_case						, __snake_case						, curr_ind + 1   ):
            return True
         # Backtrack
         _lowerCamelCase	:					Tuple   =		-1
   return False





def 				_snake_case      (   __snake_case							:      list[list[int]]						, __snake_case							:      int = 0   ):




   """simple docstring"""







   _lowerCamelCase	:					Any   =		[-1] * (len(__snake_case   ) + 1)
   # initialize start and end of path with starting index
   _lowerCamelCase	:					Optional[int]   =		start_index
   # evaluate and if we find answer return path either return empty array
   return path if util_hamilton_cycle(__snake_case						, __snake_case						, 1   ) else []

